# Final Report

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## 1 Introduction

Contributors: D. Ruffieux, A.J. Illingworth

### 1.1 General Remarks

The advent of high resolution climate models together with high resolution weather forecast models running at global, regional and 1km convection resolving scales requires an integrated composite observing sytem which builds on existing infrastructure.Such a system must be of appropriate quality to meet the requirements of the numerical weather prediction (NWP) and climate coummunity. The goal of the COST action ES0702 is to specify an optimum European network of inexpensive, unmanned ground based profiling stations, which can provide continuous profiles of winds, humidity, temperature, clouds and aerosol properties.

If the data from these networks are to be used, then several conditions must be fulfilled. Firstly a standard calibration, maintenance, and automatic data quality checking system must be developed. Secondly, the format of the data must be the same, so that it can be exchanged efficiently and rapidly in near real time. The model can then be evaluated by comparison with the observations over over a suitably long period. Ideally difficulties with the model can be identified and rectified so we can move to the third stage, and the 'O-B' statistics can be derived. That is to say the statistics of the difference between the observation and the background state. If the absence of bias can be confirmed, the standard deviations of the differences characterised and the spatial representativity of the observations established, then the observations can be considered as candidates for data assimilation and they can be used to improve the initial state of the model so that it better represents the true state of the atmosphere. The density of the network is a balance between the spatial representativity of the observations and the economic costs of deploying the instruments.

This document provides a summary of the major findings and conclusions of the EG-CLIMET action. It highlights four profiling instruments, their synergy, and NWP applications. The instruments provide profiles of aerosol and cloud backscatter, winds, temperature and humidity:

• Ceilometers
• Doppler lidars,
• Wind profilers
• Synergy and NWP applications

### 1.2 Summary of Findings and Recommendations

Ceilometers: EG-CLIMET has

• Compiled a list of hundreds of ceilometers deployed in Europe.
• Demonstrated they could supply real time backscatter profiles from clouds and aerosols.
• Demonstrated simple accurate calibration techniques using atmospheric targets
• Demonstrated they can measure the boundary layer height in unstable boundary layers.
• Compared the backscatter profiles of clouds and aerosols with NWP models predictions.
• Recommended to EUCOS that these instruments be networked to provide real time data.

Doppler Lidars: EG-CLIMET has

• Examined the performance of new Doppler lidars; 25 are now deployed in Europe.
• Demonstrated that they can provide accurate winds in the boundary layer.
• Demonstrated they can measure turbulence and vertical exchange in the boundary layer.
• Recommended to EUCOS that these instruments be networked to provide real time data.

Wind Profilers: EG-CLIMET has

• Developed algorithms, now implemented operationally, to reject spurious bird echoes.
• Improved algorithms, now implemented operationally, for rejecting spurious ground clutter.
• Demonstrated the positive impact of well-maintained wind profilers on NWP forecasts.

• Compiled a list of MWRs in Europe and developed an international network: MWRnet.
• Demonstrated the value of MWR in estimating boundary layer depth
• Demonstrated the accuracy of temp and water vapour in retrieved profiles
• Provided the first comparison of MWR retrievals with NWP model predictions.

Synergy and NWP: EG-CLIMET has shown that

• Ceilometer data may be used for evaluation of NWP models and subsequent assimilation
• Doppler Lidars, together with Wind Profilers, can provide winds throughout the troposphere
• Strategically placed wind profilers have a positive impact on NWP forecasts

Following EG-CLIMET presentations to EUCOS, the body responsible for the European observing system, E-PROFILE has been launched which will run from 2013-2017 with a kick-off meeting in April-May 20134. E-PROFILE will be responsible for Wind Profiler data quality and for coordinating real time exchange of backscatter profiles from lidars and ceilometers.

## 2 Instruments

Contributors: V. Lehmann

Contributors: Boris Thies, Ulrich Goersdorf

### 2.4 Doppler Lidar

Contributors: Laurent Sauvage, Ludovic Thobois, Alain Dabas

### 2.5 Raman Lidar

Contributors: Alexander Haefele

### 2.6 Ceilometer

Contributors: Martial Haffelin, Ewan O Connor

Contributors: Nico Cimini, Ulrich Loehnert, Bernhard Pospichal

Ground-based microwave radiometer (MWR) measurements of atmospheric thermal emission are useful to derive temperature and humidity profiles as well as information on integrated values of water vapor and liquid water. With careful design, MWR can make continuous observations (time scales of seconds to minutes) in a long-term unattended mode in nearly all weather conditions.

MWR are used for a variety of environmental and engineering applications, including meteorological observations and forecasting, communications, astronomy, radio-astronomy, geodesy and long-baseline interferometry, satellite validation, climate, air-sea interaction, and fundamental molecular physics.

## 3 Products

Contributors: Various

### 3.1 Temperature profile

In combination with profiles of humidity, accurate temperature profiles are essential for measuring atmospheric stability and thus for determining the onset of convection, precipitation and severe weather in general. State-of-the-art high-resolution NWP models are able to explicitly resolve convective processes leading to precipitation. These typically occur on time scales < 1h and spatial scales < 2 km underlining the need for temporally and spatially highly resolved observations. Continuous and accurate temperature profiles of the boundary layer may also provide valuable information for dispersion calculations of pollutants, i.e. MeteoSwiss has installed a network of 3 combined wind profiler / microwave profiler stations to continuously monitor the atmospheric conditions within the vicinities of nuclear power plants (Calpini et al. 2011).

#### 3.1.1 User requirements and benefits

In the context of high-resolution NWP, WMO Observing Requirements Database sets the uncertainty goals for temperature measurements in the lower troposphere to 0.5 K accuracy at 0.1 km vertical and 15 min temporal resolution. Note that the corresponding uncertainty thresholds are set to 3 K accuracy at 1 km vertical and 6 h temporal resolution. Current weather forecast models rely on a) radiosondes, b) polar-orbiting satellites and c) ascending/descending commercial aircrafts at major airline hubs (AMDAR: Aircraft Meteorological DAta Relay) for the lower tropospheric temperature profile. While the vertical resolution and accuracy of radiosondes are high, the temporal resolution is commonly only 12 h at a given launch site. Moreover, the already sparse global coverage of radiosonde sites will be further reduced in the comming years as a result of economic pressure. Polar orbiting satellites provide temperature information of the upper and middle troposphere with a temporal resolution of typically 3-6 h. However, lower tropospheric temperature information is extremely difficult to retrieve due to surface contamination effects. In case commercial air traffic is running operationally, AMDAR measurements provide vertically highly resolved measurements of temperature with an absolute accuracy of better than 1.0 K (Drüe et al. 2008) around major airport hubs of the word. However these measurements are more frequent during daytime and are subject to natural hazards, i.e. volcanic eruptions or extreme weather events. Within this COST action ground-based remote sensing instruments have been identified for atmospheric temperature profiling of the lower troposphere. Advantages of these instruments in comparison to the above-mentioned platforms are continuous measurements with higher temporal resolution, i.e. on the order of minutes. Also the remote sensing systems are typically very sensitive towards the boundary layer temperature profile, complementing polar-orbiting satellite measurements. Next to the temporal aspect, MWR distributed at stations without regular radiosonde launches can add additional information in space. As shown by Löhnert et al. (2007), the optimal combination of available radiosonde data with continuously measuring microwave profilers has the potential to improve the 4D temperature field within a measurement domain.

#### 3.1.2 Available techniques

Several ground-based remote sensing techniques for temperature profiling the lower troposphere are available. Due to their suitability for operational measurements and widespread European distribution (i.e. organized within the new international Microwave Radiometer Network MWRnet), microwave profilers have been in the main focus of EG-CLIMET (see Microwave radiometers for temperature profiling) and are suited for fulfilling the WMO User Requirements within the next years. Additionally, some of the wind profiler stations within E-WINPROF are also equipped with a Radio Acoustic Sounding System (RASS), which also offers the possibility of operational temperature sounding. Additionally, some of the European Atmospheric Observatories (AO) have been equipped with infrared spectrometers, which can deliver temperature profile information in clear-sky conditions. Raman lidar seems also a promising technology for future applications. The latter three measurement principles are shortly highlighted below, while the potential of microwave radiometers is discussed in detail.

Infrared spectrometer: Passive observations in the infrared can be used to obtain temperature profile information. ”Passive” in this sense means that the instrument only receives natural radiation of the atmosphere without actively emitting any radiation itself. High spectral resolution infrared observations contain information on the vertical profile of temperature due to the spectral absorption features of carbon dioxide. If homogeneous mixing of carbon dioxide is assumed, the changes of the corresponding line shapes can solely be attributed to the vertical temperature structure, e.g. in the spectral region around $14 \mu m$. Löhnert et al. (2009) demonstrated that an infrared spectrometer can provide more than twice the information on the temperature profile than a zenith-looking microwave profiler during clear-sky cases (Fig. 1). However their profiling capability during the presence of clouds is limited because measurements are easily saturated in the presence of even very low-liquid water content clouds. Also, in clear-sky conditions infrared spectrometer methods need information on aerosol loading and trace gases in order to infer thermodynamic profiles with sufficient accuracy. Additionally, their calibration requires continuous monitoring and robust all-weather operation currently still proves difficult. Currently, only a few instruments are measuring worldwide for research purposes and no sophisticated network structure has yet been conceived.

Figure 1: Profiles of temperature for a summer and winter case at Payerne, Switzerland. Shown are radiosonde measurements (black), microwave retrievals using zenith measurements only (green), and infrared spectrometer retrievals (red). Adapted from Löhnert et al. (2009).

RASS: Radio Acoustic Sounding Systems are able to measure the speed of sound waves as a function of height, from which the profile of the atmospheric virtual temperature can be derived (Wilczak et al., 1996). A RASS consists of a wind profiler radar combined with an acoustic source. The transmitted sound waves create artificial inhomogeneities in the refractive index field, which propagate with the speed of sound. Due to its sensitivity to refractive index variations, the wind profiler can measure the speed of sound. Since the speed of sound is dependent on temperature and humidity, the profile of virtual temperature can be retrieved. Depending on the configuration of the system a vertical resolution on the order of 100 to 500 m can be obtained with absolute uncertainties below 1K. The most important source of uncertainty is caused by turbulent vertical air motion. The measured speed of sound is the true speed of sounded added to the background vertical air motion. To compensate for this error most RASS systems measure the acoustic speed and the vertical air speed simultaneously. However, whether this correction is applied in real time depends on the data processing of each specific system. Due to the strong attenuation of the acoustic signals, the vertical range of RASS is generally lower than that of the wind profilers. Depending on system and the atmospheric conditions, RASS can profile virtual temperature up to heights ranging from 0.5 to 4 km. Only a few (how many?) of the 31 wind profilers within E-WINPROF are currently equipped with RASS technology. These data are processed simultaneously to the wind vector profiles, however are not further injected further into any forecasting system. Currently there are no plans for expanding the spatial coverage of RASS technology within E-WINPROF, nor for assimilating this data into NWP. However, a combination with microwave profiler at the E-WINPROF sites could help deriving an optimized temperature product throughout the full depth boundary layer.

Raman Lidar: Raman Lidar uses the scattering properties of molecules and aerosols to derive profiles of water vapor, aerosols and temperature with a high vertical and temporal resolution. Raman Lidars can cover an altitude range from a few hundreds of meters to the lower stratosphere. In contrast to classical backscatter Lidar that relies on elastic scattering, Raman Lidar makes use of the inelastic scattering where the scattering molecule changes its vibrational and/or rotational energy state and by this changes the wavelength of the scattered photon. The change in wavelength depends on the two involved energy levels and is specific for the scattering molecule. Since the population of the energy states follows a Boltzman distribution, the Raman backscatter coefficient depends on temperature, which states the physics behind the rotational Raman technique to measure atmospheric temperature (Vaughan et al., 1993). Recent progress in Lidar research has brought Raman Lidars into a nearly operational state and they are becoming suited for meteorological applications. However, the number of operational Raman Lidars is still very small and they are still in the focus of active research.

#### 3.1.3 Retrieval algorithms and errors

MicroWave Radiometers (MWR) for temperature profiling measure passively at several frequencies along the 60-GHz oxygen absorption complex from the ground as well as from space. A clear advantage of using passive microwave measurements for temperature profiling is the semi-transparency with respect to liquid water clouds. Microwave signals around 50-60 GHz do not saturate due to clouds yielding that profiles of temperature may be derived is cloudy and clear-sky cases. The spectral absorption features of oxygen in the microwave region allow for retrieving information on the vertical structure of temperature. The homogeneous mixing of oxygen within the troposphere results in the fact that changes of the corresponding line shapes can solely be attributed to the vertical temperature structure. From the ground, observations are typically taken in zenith direction at about five to ten frequency channels from 50–60 GHz. Channels in the center of the absorption band are highly opaque and the observed brightness temperature (TB) is close to the environmental temperature. For frequencies further away from the center the atmosphere is less opaque and the signal systematically originates additionally from higher atmospheric layers. For ground-based observations the weighting functions at the different frequencies all decrease continuously with height and limit the vertical resolution rather than the radiometric noise. By observing the atmosphere under different elevation angles, additional information about the temperature of the lowest kilometer can be gained. One-channel systems operating around 60 GHz have been developed (Kadygrov and Pick, 1998), which derive profile information from elevation scanning when assuming horizontal homogeneity of the atmosphere. In this case, the lower the elevation angle measurement, the lower the height from which the temperature information originates. Since these TB vary only slightly with elevation angle, the method requires a highly sensitive MWR that is typically realized by using wide bandwidths up to 2 GHz. Since the use of a single highly opaque channel limits the information content to altitudes below 600 m, combined multi-channel and multi-angle observations can be used (Crewell and Löhnert, 2007) improving the accuracy in the lowest 1500 m. Quantitative retrieval accuracies as well as typical values of vertical resolution for microwave temperature profiling of the lower troposphere are summarized in Tab. 1. Generally, profiles can be derived up to 4 km height above ground with a high vertical resolution at the surface, which rapidly decreases above 1.5 km height. This implies that close-to-the-surface inversion can be observed very well while elevated or multiple inversions are difficult to capture. Text here

Table 1: MWR characterization for temperature profiling
Temporal resolution Height range Idependent pieces of information Vertical resolution Accuracy
5-15 minutes up to 4 km ~4 with elevation scanning
• ~10m at surface
• ~150m at 500m
• ~500m at 1500m
• 0.5-1.0 K in lowest km
• 1.0-1.7 K from 1-4 km

The accuracies (Standard DEViation STDEV with respect to radiosonde) shown in Fig. 2 underline the potential of MWR for temperature profiling during clear and cloudy situations. After the an offset correction, STDEV values are within 0.4 to 1.4 K in the lowest 2 km and increase to 1.7 K at 4 km. Above this height only 5% independent information originates from the radiometer measurement itself. The high accuracies below 1 km are primarily due to the information contained in the elevation scans (Löhnert et al. 2009).

Figure 2: Temperature profile differences (BIAS and STDEV) during all-sky conditions from August 2006-December 2009 between MWR and radiosonde measurements at Payerne, Switzerland aerological station. Black lines show the retrieval results without using the systematic TB offset correction while green lines show the results applying the systematic TB offset correction (OC). A total number of 2107 matching MWR/radiosonde cases were considered; in 1816 cases the MWR measurements passed the quality control and are evaluated in the plot. Adapted from Löhnert and Maier 2012.

A further way to characterize accuracy and vertical resolution of passive remote sensing methods is to evaluate the degrees of freedom for signal that state the number of independently vertically resolved levels of temperature (or humidity or other) that can be determined from the measurements (Hewison (2007)). This number of independent pieces of information depends to some degree on the number of spectral channels of the observations, the noise levels and the spectral location of the channels, but also depends strongly on the spectral characteristics of the absorption lines observed. As shown in Tab. 1, microwave radiometers using a multi-frequency, multi-angle approach are able to give 4 independent pieces of temperature information in the lower troposphere (here up to 4 km). Note that temperature profiles measurements can be derived with temporal resolutions of 15 min and smaller. If an operation mode of permanent elevation scanning were chosen, temperature profiles could be derived every 2-3 minutes, however the simultaneous retrieval of integrated water vapor and cloud liquid water path (IWV, LWP) water requires periods of zenith observations in between.

#### 3.1.4 Operational performance and technical implementation

Rapid technological development for MWRs in the last two decades has brought forward a generation of commercially available instruments, which can, on long-term time periods, measure autonomously under varying weather conditions. Within MWRnet), which was initiated through EG-CLIMET, a large part of the European and US-American MWR users have linked together under the aspects of harmonization of measurement modes, data formats, meteorological parameter retrieval, advice for operations, etc. Table 2 gives an overview of the identified advantages, challenges and limitations of the proposed microwave temperature profiling system.

Driven by the demands stated by MWRnet, Löhnert and Maier (2011) have carried out a study to define quality control measures for operational MWR measurements. A critical point to be addressed is the automated detection/removal of liquid of frozen water on the instrument radome to guarantee uninterrupted performance also during precipitation conditions. Also, operational MWR measurements need to be monitored permanently during clear sky conditions using simple non-scattering radiative transfer models as reference for calibration stability. Such monitoring is necessary to identify possible TB offsets. TB offset corrections are essential for providing an optimized temperature profile product. Fig. 2 shows typical systematic differences that range between -0.6 and +0.3 K in the lowest 4 km if a TB offset correction is not applied. After applying the correction the overall temperature bias is in the range +-0.1 K. With respect to the WMO User Requirements for high-resolution NWP, microwave profilers can fulfill the standards for temperature profiling if operators agree on standardized calibration and operation procedures within a network such as MWRnet.

Table 2: MWR performance for temperature profiling
• Boundary layer profile comparable to radiosonde
• Operation in clear-sky, cloudy-sky and light precipitation
• Continuous measurements on the order of minutes
• Network suitable, remotely steerable
• MWRnet as an emerging international network
• calibration controll
• Harmonized data processing: from raw data to quality-controlled temperature profiles (MWRnet goal)
• Vertical resolution above 1500m
• Inversions only detectable < 1.5 km
• No information above 4 km

#### 3.1.5 Summary and recommendations

A network of microwave profilers bears potential for improving short-term weather forecasts as well as nowcasting applications. While the advantages of high temporal resolution and un-manned routine observations must be stressed, a limited vertical resolution (with respect to radiosondes) and corresponding random error inherent within the measurement principle must be kept in mind. Microwave profilers allow the addition of information on the stability development in the boundary layer between two consecutive radiosondes launched typically at 12 hourly intervals. This is particularly important during weather conditions that are triggered by the boundary layer, when timely soundings are crucial for accurate local forecasting.

Microwave profilers state current emerging technology that will be able to provide operational and quality controlled temperature profiles in the near future. Note that most microwave profilers are also capable of providing humidity and cloud liquid water content informatio (see sections 4.2 and 4.7). Within EG-CLIMET MWRnet has been established as a prototype of a worldwide microwave radiometer network setting up common calibration and operation procedures for microwave radiometers to guarantee continuous, unified and quality-controlled temperature profiles. In this respect EG-CLIMET recommends the following:

• Further consolidation of MWRnet to be able to provide near-real-time, quality controlled temperature profiles on an openly available platform in the near future.
• In an optimum future configuration, the E-WINPROF sites could be equipped additionally with microwave profilers, so that these sites could simultaneously deliver dynamic and thermodynamic information on the atmospheric state. The E-WINPROF sites equipped additionally with RASS technology could then deliver an optimized temperature profile product by merging the boundary layer information from the microwave profiler with the low-mid tropospheric temperature profile obtained from the RASS.

In order to prove the impact of additional measurements on the short-term weather forecast, EG-CLIMET recommends the following:

• Evaluation by means of Observation System Simulation Experiments (OSSE) in collaboration with national weather services: in such an experiment a first independent model run is used to simulate the atmospheric state as well as all measurements (including remote sensing), and a second model is used to calculate a forecast initiated by the "model truth" of the first model. Evaluation of forecast accuracy using the additional remote sensing instruments can then be carried out in a straight-forward manner. Of course the validity of this experiment depends on the how well the first model can characterize "reality" and its variability. Such an experiment was carried out by Otkin et al. (2011) and Hartung et al. (2011). Their aim was to characterize the impact of ground-based AERI, MWR, Doppler lidar and water vapor lidar measurements on forecast quality. Improved wind and moisture analyses obtained through assimilation of these observations contributed to more accurate forecasts of moisture flux convergence and the intensity and location of accumulated precipitation due to improved dynamical forcing and meso-scale boundary layer thermodynamic structure. However, these results must be verified during different cases in future and currently lack the inclusion of standard satellite systems.
• If real measurements are available, Observation System Experiment (OSE) should be carried out to characterize the forecast impact of different observations by comparing the results of two or more different model runs with standard observations. For ground-based water vapor lidar observations during the LAUNCH 2005 measurement campaign, Grzeschik et al. (2008) could show a downstream impact on forecasted humidity within a four-hour time window after assimilation. Similar impact studies are currently planned within MWRnet in cooperation with the international Hydrological cycle in Mediterranean EXperiment HyMeX project.

### 3.2 Humidity profile

Contributors: N. Cimini, H. Czekala, U. Löhnert, J. Güldner, O. Maier, F. Hurter, D. Leuenberger,...

Water vapor is one of the most relevant component of the atmosphere, controlling both weather and climate and playing a central role in atmospheric chemistry. Water vapor is the dominant greenhouse gas in the Earth's atmosphere, contributing for 2/3 of the whole green-house effect. The distribution of water vapour is highly variable, both in time and space, spanning more than 3 orders of magnitude (in terms of ppmv) in the vertical distribution over the troposphere. Water vapour, both at surface and in the upper-air, is indicated as an Essential Climate Variables by GCOS (Global Climate Observing System) For NWP, with the increasing resolution of NWP models from global to local, the knowledge of the 3D humidity field becomes more and more important, as the humidity acts as a trigger for microphysical processes that are usually explicitly resolved at finer scales. Due to the role of water vapor in weather and climate, precise measurements of the vertical distribution of water vapor are essential for the aims of EG-CLIMET.

#### 3.2.1 User requirements and benefits

Humidity profiles are currently available from radiosondes over populated land areas; the WMO Statements of Guidance for NWP and Climate state that the vertical resolution is adequate and the accuracy is good or acceptable, but the horizontal and temporal resolution is sometimes marginal, due to the high horizontal variability of the humidity field. Satellite passive observations provide useful information on stratospheric and upper tropospheric humidity with good horizontal resolution and acceptable accuracy. Also, satellite radio-occultation measurements provide high accuracy and high vertical resolution in the stratosphere and upper troposphere. Differently from the AMDAR system providing temperature profiles, currently very few aircraft provide humidity measurements. As a conseguence, the humidity in the lower troposphere (including the planetary boundary layer) is highly under-observed.

The WMO Observing Requirements Database sets the goal, breakthrough, threshold values for the uncertainty, observing cycle, horizontal and vertical resolution for lower tropospheric specific humidity observations, as reported in Table 1.

Table 1: WMO Observing Requirements for specific humidity profiling in the lower troposphere
CLIMATE Goal Breakthrough Threshold
Uncertainty 2% 4% 15%

Horizontal resolution

10km 15km 25km

Vertical resolution

n.a. n.a. n.a.
Observing cycle 3h 4h 6h
NWP Goal Breakthrough Threshold
Uncertainty 2% 5% 10%

Horizontal resolution

0.5km 5km 20km

Vertical resolution

0.1km 0.2km 1km
Observing cycle 15min 30min 120min

Within this COST action ground-based remote sensing instruments have been identified for atmospheric water vapor profiling of the lower troposphere. Advantages of these instruments in comparison to the above-mentioned platforms are continuous measurements with higher temporal resolution, i.e. on the order of minutes. Next to the temporal aspect, instruments distributed at stations without regular radiosonde launches can add additional information in space. Similarly for temperature (Löhnert et al. (2007)), the optimal combination of available radiosonde data with continuously measuring humidity profilers has the potential to improve the 4D humidity field within a measurement domain.

#### 3.2.2 Available Techniques

Tropospheric humidity profiles may be measured by in-situ soundings and several ground-based remote sensing techniques, including the following:

• Infrared spectrometer
• Raman lidar
• Differential Absorption Lidar (DIAL)
• GNSS tomography
• MWR humidity profilers

Some of the European Atmospheric Observatories (AO) are equipped with infrared spectrometers, which can deliver humidity profile information in clear-sky conditions. Also, most of the lidar stations belonging to the European network EARLINET deploy Raman lidars for humidity profiling, while DIAL systems have still relatively sparse distribution. Microwave radiometer (MWR) profilers have wider distribution in Europe, recently organized within the International Microwave Radiometer Network MWRnet. Water vapor tomography based on Global Navigation Satellite System (GNSS) relies on ground-based GNSS receivers, which have a much higher density with respect to the other instrumentation above, e.g. EUREF. The principles, advantages and limitations of these techniques are shortly introduced below, while the potential of microwave radiometers is discussed in more detail.

Infrared spectrometer: Passive observations in the infrared can be used to obtain humidity profile information, similarly to temperature. High spectral resolution infrared observations contain information on the vertical profile of water vapor due to its spectral absorption features in the spectral region within $8-13 \mu m$ and around $18 \mu m$. The so-called “onion-peeling” method has been used for over a decade for temperature and humidity profiling from infrared spectrometers (Smith et al., 1999; Feltz et al., 1998). This technique doesn’t yield information on the error covariance matrix of the retrieved profiles, thereby making it difficult to assimilate the data into a numerical model. More recently Optimal Estimation (OE) methods have been used. This iterative technique uses a priori information, together with the sensitivity of the forward model, to retrieve the entire profile of temperature and humidity simultaneously, providing the uncertainty covariance matrix of the retrieved temperature and humidity profiles (Feltz et al., 2005). A long history of infrared spectrometer observations exist, with nearly two dozen systems deployed world-wide, many of which are providing long-term monitoring.

Figure 1: The distribution of DFS in zenith spectral MWR observations (MZ), elevation scanning MWR observations (ME), and Infrared spectrometer (AE) observations for profiles of temperature (left) and water vapor (right) at a mid-latitude (Payerne, top) and tropical (Darwin, bottom) site. Adapted from Löhnert et al. (2009).
Figure 2: Simulated humidity profiling performances (left: root-mean-square-error; right: bias) from Microwave radiometer zenith observations (MZ), infrared spectrometer observations (AE), two different combined microwave and infrared retrievals (MZAE and MZAE4), and a priori (APR) at a mid-latitude (Payerne, top) and tropical (Darwin, bottom) site. Adapted from Löhnert et al. (2009).

Performances: An infrared spectrometer can provide more degrees of freedom for signal (DFS), i.e. piece of independent information, on the humidity profile than a zenith-looking microwave profiler, as demonstrated by Löhnert et al. (2009) (Fig. 1), though the first depends more on the total water vapour content and it is limited to clear-sky only. Simulated results in Figure 2 (adapted from Löhnert et al., 2009) show that in clear sky, humidity profiles from infrared spectrometer (AE, in red) are 30-50% more accurate than those provided by microwave radiometer (MZ, in green) for a midlatitude site (top), while much less pronounced for a tropical site (bottom), where MZ actually outperforms AE above 2 km. Advantages: Infrared spectrometers offer relatively high information content on the vertical profile of water vapor related to other passive profilers. Due to their calibration approach, both the absolute calibration and the sensitivity of the instrument is monitored, which makes these observations particularly well suited for long-term observations that can be used to develop climatology and trend analyses (Gero and Turner, 2011). Infrared spectrometers can operate day and night. Limitations: One limitation to humidity profiling with ground-based infrared spectrometers is the presence of clouds above the instrument. Also, in clear-sky conditions infrared spectrometer methods need information on aerosol loading and trace gases in order to infer thermodynamic profiles with sufficient accuracy. Retrievals of thermodynamic profiles from ground-based IR observations in precipitating conditions are not possible. Another limitation is the low vertical resolution of the humidity profiles, which is higher than for microwave radiometers, but still much lower than for active sensors (as lidars). Infrared spectrometers need to be housed at laboratory temperatures leaving the front end in the ambient environment, which requires a proper infrastructure. These systems are typically configured to only view the atmosphere in the zenith direction.

Raman LIDAR: Raman lidar is a very powerful method to measure tropospheric water vapor profiles. The measurement principle and the inversion are described in Raman Lidar. The water vapor profile has to be calibrated with an external measurement like radiosonde or other remote sensing measurements like MWR. Only recently, new methods have removed this requirement by using a NIST traceable light source to determine the calibration with less than 3% relative uncertainty (Venable et al., 2011).

Figure 3: 2D time seris of water vapor mixing ratio above Payerne, Switzerland, measured by Raman lidar.

Performances: With a temporal resolution of typically 30 min and a vertical resolution in the order of meters, Raman lidars are able to resolve the extremely high variability of water vapor (Fig.2). The relative random error is reported in the range of 2 to 10% depending on altitude and background noise level (daylight). Advantages: The Raman lidar system gives very high vertical and temporal resolution profiles of water in the troposphere. Unlike passive remote sensing instruments that require inversion schemes, the Raman lidar technique allows for a direct method of profiling water vapor mixing ratio amounts. Furthermore, for each profile, accurate estimates of the error covariance matrix can be determined in dependence of weather conditions. Limitations: Up to now, no commercial Raman water vapor lidars exist. The exisiting systems for applications in research and operational meteorology are mostly prototypes and require an important maintenance effort to achieve a high data availability. Raman lidar water vapor measurements are only possible under non-precipitating conditions and below clouds. Because of the solar radiation interference, the daytime profiling capability is limited; for most systems this maximum daytime altitude for the water vapor mixing ratio profile is about 4-6 km. These laser systems require temperature controlled housing for optimal operation. Other protective designs from rain and/or hail need to be installed to safeguard the telescope and associated electronics. The incomplete overlap of the laser beam and the receiver telescope field of view limits the first usable range to some 100 meters, depending on the lidar design properties.

DIAL: The Differential Absorption Lidar (DIAL) is a powerful technique to measure water vapor profiles without the need of a external calibration source. In fact, water vapor profiles are retrieved by measuring the differential absorption in the backscatter signals at two close wavelengths: a water vapor DIAL transmit laser pulses at two wavelengths, one on a water vapor absorption line and the other outside the absorption line. The two wavelengths are chosen close enough to consider the scattering by molecules and particles essentially equal at the two wavelengths. Therefore, any difference in the lidar backscatter can be entirely attributed to water vapor absorption. The ratio of the backscatter measured at both wavelengths as a function of range can be directly linked to the profile of water vapor concentration. Ground based DIAL systems have been demonstrated recently for quasi-operational observations. Performances: High-power DIAL systems demonstrated the highest accuracy and resolution of all water-vapor remote sensing technologies yet. The accuracy is mainly determined by laboratory measurements of the water-vapor absorption cross section in the wavelength range of interest. Advantages: Obviously, no calibration of the DIAL system is required. The combination of spatial and temporal resolution up to the upper troposphere (few 100 m and min) fulfills the requirements for data assimilation in mesoscale models during daytime and nighttime. Low-power compact DIAL systems are interesting and affordable options for future water vapor remote sensing networks. Limitations: DIAL systems are affected by the overlap issue (same as the Raman lidar above) in the first some 100 meters. Current instruments are quasi-operational. High-power DIAL systems are expensive and typically require significant scientific technical expertise. Work is ongoing to improve low power DIAL systems to enable daytime operation in regions of high water vapor. Water vapor profiling DIAL systems are not commercially available yet.

GNSS Tomography: Remote sensing of the atmosphere with the microwave signal of the Global Navigation Satellite Systems (GNSS) has become a well-established field of research. The primary use of these signals is the positioning of receiving antennas on or within several 100 kilometers around the earth. The waves passing the atmosphere are affected by the ion concentration in the ionosphere and by the air density in the lower stratosphere and in the troposphere. These influences can be retrieved to a certain degree in the processing of the GNSS data with sophisticated software packages, yielding for example an integral measure of the water vapor content above a GNSS station. With a receiver network, the integral measure can be used to reconstruct a 3D field of wet refractivity, which depends on both atmospheric temperature and humidity (water vapor pressure). The spatial resolution of such a field depends on the number of stations that are deployed. In regions with complex orography, where the terrain allows stations to be placed at various heights above mean sea level, it is possible to retrieve vertical information on the wet refractivity field in the available vertical range and at a very limited resolution above the top station of the network (Champollion et al., 2005; Perler et al., 2011). The International Association of Geodesy has set up a Sub-Commission on Remote sensing and modeling of the atmosphere with the objectives, among others, to investigate the development and enhancement of the GNSS-based sounding techniques, e.g. neutral atmosphere/ionosphere tomography, GNSS reflectometry/scatterometry for altimetry, meteorology, and soil moisture. The new GNSS signals’ structures for GNSS based atmospheric remote sensing are also studied and additional platforms for GNSS based atmospheric remote sensing (buoys, aircrafts, balloons, more dense ground networks, Low Earth Orbiting constellations) are suggested.

Performances: The capability of the tomography to investigate the diurnal cycle of water vapor in a coastal area was shown by (Bastin et al., 2007). (Bender et al., 2011) demonstrate that near real-time processing of a large GNSS station network in Germany with dedicated tomography software is possible and show a qualitative comparison to a NWP model analysis. Another study assesses the uncertainty of the tomographic reconstruction for the wet refractivity at Payerne for a one year study period to be 10 mm km-1 at the ground and 6 mm km-1 at 4500 above m.s.l. (Perler, 2011). (Nilsson et al., 2007) arrives at 4-5 mm km-1 absolute difference to a radiosonde reference and an accuracy of 10% most of the time for the refractivity in the lower 2km of the troposphere. Recent investigations combining wet refractivities from GNSS with temperature profiles of V-band radiometers to derive humidity profiles (Hurter and Maier, 2012) show temporal resolution and a quality comparable to remote sensing with a K-band microwave radiometer, with a vertical resolution being representative of meteorological events in the boundary layer. Advantages: Rather simple deployment of passive, stable all-weather instruments, high data availability, financing being shared with other applications (GNSS reference networks for positioning) and low maintenance make GNSS an attractive source of information for the spatial distribution of water vapor in mountainous topography. Furthermore, its spatial and temporal resolution is somewhat in between the other available water vapor measurement techniques and could bridge the sampling gap between those techniques. Limitations: Derivation of humidity from GNSS requires an accurate 3D field of atmospheric temperature. Therefore, tomography retrievals are currently not judged as the best way to insert information about humidity into NWP models. Instead the integration of humidity information from GNSS data in NWP models might be more successful with the assimilation of path delays, which could be accomplished with a statistical data assimilation scheme (such as 3DVAR, 4DVAR or ensemble kalman filter) in a mathematically thorough way and using information on model and observation uncertainty. Results from assimilation tests using GPS derived slant water vapor are for example given in (de Haan and van der Marel, 2008). The forward operator to be implemented into a NWP model corresponds to the operator used in the water vapor tomography. Experience gained therein can thus help to assimilate path delays.

#### 3.2.3 Retrieval algorithms and errors

Humidity profiling by MicroWave Radiometers (MWR) relies on the passive measurement of thermal emission by atmospheric water vapor. The water vapor absorption line at 22.235 GHz is mostly used, though the higher sensitive 183.3 GHz line is also used in dry environments. A clear advantage of using passive microwave measurements with respect to the other techniques is that humidity profiles can be retrieved also in cloudy conditions, as the emission of ice clouds is negligible and the contribution of liquid water clouds can be effectively accounted for. From the ground, observations are typically taken in zenith direction at about five to ten frequency channels from 20–30 GHz. Channels closer to the line center correspond to higher absorption (i.e. higher brightness temperature). The atmospheric opacity at these channels is relatively low ($\sim 0.1$, see a typical 10-100 GHz opacity spectrum) and thus the radiation comes from throughout the atmosphere and beyond. Consequently, the weighting functions at the different frequencies are quite constant with height and do not change shape significantly with elevation angle. Therefore, water vapor retrievals from MWR are characterized by low vertical resolution; approximately 1 to 3 DFS are available, slightly depending on water content and almost independently on elevation angle (see Fig.1), as shown in Löhnert et al. (2009). However, combined multi-channel and multi-angle observations are often used to better constrain the retrieval problem and improve tha accuracy (Crewell and Löhnert, 2007). In Tab.2 reports a summary of the quantitative retrieval accuracies and typical values for vertical resolution for microwave humidity profiling.

Table 2: MWR characterization for humidity profiling
Temporal resolution Height range Idependent pieces of information Vertical resolution Accuracy
5-15 minutes up to 10 km ~1-3 depending upon total water vapor content

incresing with height from 0.5 to 3 km in the troposphere (defined as the inter-level covariance)

• 0.5-1.5 g/m3 in lowest km
• <0.5 g/m3 above 2 km

MWR water vapor retrievals are usually validated against radiosonde measurements. Statistics (mean and rms) of the difference between MWR retrievals and radiosonde measurements are used to quantify the accuracy of MWR retrievals, though these include the radiosonde representativeness error as well. Typical results are shown in Fig. 2 for a 1-year dataset collected in Lindenberg during clear and cloudy situations. Typical rms are within 1 g/m3 in the first 2 km and less than 0.5 above that. Rms difference slightly decrease when the sistematic difference is removed a posteriori.

Figure 2: Statistics of humidity profile differences (mean and rms) during all-sky conditions from September 2004-August 2005 between MWR and radiosonde measurements at the DWD Meteorological Observatory in Lindenberg, Germany. The retrieval results using a Neural Network (NN) retrieval approach are shown in blue (solid: mean; dashed: std). The results for a "a-posteriori" regression are shown in red (solid: mean; dashed: std), demonstrating the potential after the bias removal. The black line show the std (i.e. the variability) of the whole set of radiosonde water vapor profiles. A total number of 1171 matching MWR/radiosonde cases have been considered in the plot. Courtesy of J. Güldner, DWD.

#### 3.2.4 Operational performance and technical implementation

Nowadays, off-the-shelf commercial microwave radiometers are robust and unattended instruments providing real time accurate atmospheric observations under nearly all-weather conditions. Accurate MWR observations are subject to instrument integrity and proper signal calibration. Commercial MWR consists in robust hardware exhibiting long life-time (years) even in extreme conditions. However, the radome protecting the antenna aperture must be kept clean, requiring services every once in a while and replacement every few months depending upon environment conditions (presence of dirt, sand, dust, etcetera). To ensure proper calibration, commercial MWR use internal noise diodes and a combination of cryogenic external targets and tipping curve. These last two calibration methods are well know and characterized, although are sometimes impractical. In fact, the tipping curve can be applied to low-absorption channels only (as it assumes a linear relationship between atmospheric absorption and the observed air mass), requiring clear sky and horizontally stratified atmosphere. The method using a cryogenic external target requires an high emissivity target in a cryogenic bath; the cryogenic liquid (often liquid nitrogen, LN2) is not always easily available and it poses some safety issues for handling it. However, the current MWR technology is such that receivers are stable over long periods (months), thus tipping curve and cryogenic calibrations are recommended only few times a year. For avoiding long periods of miscalibration, an operational protocol (including severe quality criteria and a testing period) shall be adopted before accepting the calibration coefficient updates. Operational MWR measurements need to be monitored routinely during clear sky conditions using simple non-scattering radiative transfer models as reference for calibration stability. Such monitoring is necessary to identify the presence of possible measurement bias, which can be subsequently removed for providing an the best humidity profile product. Advantages, challenges and limitations of humidity profiling with MWR are summarised in Table 3.

Table 3: Advantages, challenges and limitations of MWR humidity profiling
• 24/7 continuous unattended measurements on the order of minutes
• Meaningful retrievals in clear and cloudy-sky
• Capable of azimuth- and elevation-angle scanning
• Network suitable, remotely steerable
• MWRnet as an emerging international network
• Automated integrity and quality control of radome
• Calibration control
• Harmonized data processing: from raw data to quality-controlled humidity profiles (MWRnet goal)
• Low vertical resolution
• Larger errors in heavy cloud and precipitation conditions

The performances of humidity profiling by MWR are within the WMO User Requirements for climate and NWP in terms of uncertainty (threshold), observing cycle (goal), and vertical resolution in the lower levels (threshold). Concerning the horizontal resolution, this is currently limited by the density of operational ground-based MWR, which is steadily increasing. For example, nowadays in Europe the number of ground-based MWR is larger than the number of operational radiosonde launch sites.

Within EG-CLIMET, a cooperation and coordination effort was initiated under the name of (MWRnet), an International Network of Microwave Radiometers. MWRnet links the international MWR experts and users community to share knowledge and best practices on the aspects of harmonization of measurement modes, data formats, meteorological parameter retrieval, operation modes, etc. The successful achievement of the MWRnet goals, specially the standardization of operation procedures and retrieval methods, shall make the performances of MWR humidity profiling more appealing to the WMO User Requirements for climate and NWP.

#### 3.2.5 Summary and recommendations

Recommendations similar to as for temperature profiling (see section 4.1) apply for humidity profiles. Due to the suitability for 24/7 nearly all-weather operational measurements and widespread European distribution, microwave profilers have been chosen within EG-CLIMET as the most effective and network-ready instrumentation for humidity profiling, fulfilling potentially most (but not all) of the WMO User Requirements. In fact, limited vertical resolution and corresponding smoothing error are inherent in the passive measurement principle. Note that most microwave profilers are also capable of providing temperature profiles and cloud liquid water path (see sections 4.1 and 4.7) and thus provide additional information on atmospheric stability, continuously within consecutive radiosondes launched typically at 12 hourly intervals. Within EG-CLIMET, MWRnet has been established as a prototype of an international microwave radiometer network setting up common calibration and operation procedures to guarantee continuous, harmonized and quality-controlled observations and retrieved profiles, with uncertainties. In order to prove the value of MWR measurements for NWP and climate studies, EG-CLIMET recommends the following:

• Further consolidation of MWRnet to be able to provide near-real-time, quality controlled humidity (and temperature) profiles on an openly available platform in the near future.
• Consideration of microwave radiometers within the EUMETNET E-PROFILE project, as a powerful tool for providing high temporal resolution IWV, LWP, and temperature and humidity profiles.
• Evaluation of observations impact by means of Observation System Simulation Experiments (OSSE) in collaboration with national weather services (see discussion in the temperature profile summary and recommendations section).
• Large and coordinated international experiments (e.g. LAUNCH, COPS, HyMeX), should be exploited to carry out Observation System Experiment (OSE) to characterize MWR data impact into the analysis and forecast. For example, using ground-based water vapor lidar observations during the LAUNCH 2005 measurement campaign, Grzeschik et al. (2008) showed a downstream impact on forecasted humidity within a four-hour time window after assimilation. Similar impact studies are ongoing within MWRnet in cooperation with the HyMeX project.

### 3.3 Wind profile

Contributors: C. Gaffard, V. Lehmann

#### 3.3.1 Fundamentals

The wind vector field (u,v,w) as a function of altitude above some point (x0,y0) on the surface having an altitude z0 is commonly referred to as the wind profile above that site. Conventionally, it is measured via radio sounding, whereas another approach to this is to use a remote sensing method. Depending on the wavelength of the radiation used, the scattering targets of interest are aerosols (in case of an IR heterodyne Doppler Lidar), air molecules (for an UV Doppler Lidar for observations in clear air, i.e. aerosol free), precipitation (for weather radar), or Bragg Scattering on refractive index variations due to turbulence eddies(for a radar wind profiler). If the respective tracer is moving with the velocity of the surrounding air, the backscattered signal will be shifted in frequency due to the Doppler effect. If this frequency shift is measured, the component of the velocity in the direction of the beam, the radial velocity Vr, is obtained. Using such a methodology, one gets the whole wind profile instantaneously, i.e. not one point at a time like in a radiosonde descent, and with a high update rate.

Considering the relationship of the radial component of the wind Vr in the direction of observation

$V_r=u \cdot\sin\theta\cos\phi+v\cdot\cos\theta\cos\phi+w\cdot\sin\phi$     Eq. 1

with the radar or lidar beam having an elevation angle $\phi$ and an azimuth angle $\theta$ (see figure 1) , three different directions of observation that are not coplanar are the minimum necessary to determine all three wind components.

Figure 1: Sketch of volume scan strategy showing the polar coordinate system being the basis for Eq. 1

#### 3.3.2 Retrieval Algorithms and Errors

A scan strategy widely used by radar wind profilers and vertically pointing Doppler lidars is the Doppler Beam Swinging Technique (DBS), using for example four orthogonal azimuth directions at some elevation close to the vertical and a fifth measurement for the vertical itself. Another technique in this vein has been proposed by Lhermitte and Atlas for precipitation Doppler radars, but of course also applicable to every other dopplerized remote sensing technique, makes use of a full circle scan in azimuth (R.M. Lhermitte, D.A. Atlas, 1961; R.M. Lhermitte 1962). The technique is called Velocity Azimuth Display (VAD), since in the days when fast personal computers were not ubiquitous the horizontal wind and the particle fall velocity were extracted from the actual display of the range gated Doppler shift over azimuth (as an example see figure 2).

Figure 2: Example of a Velocity Azimuth Display, the corresponding radial wind field is shown in the small box

Although the trained observer may be able to extract also characteristics of inhomogeneous wind fields, the general approach would be to fit the parameters u, v and w (w being dominated by the fall velocity of rain drops in the case of weather radar) to Eq.1.

A refined version of the VAD analysis has been proposed by Browning and Wexler (K.A. Browning and R. Wexler, 1968) using more terms of a general Fourier expansion of the VAD graph.

$V_r=\frac{1}{2}a_0+\sum_{n=1}^\infty a_n\sin n\theta+b_n\cos n\theta$    Eq. 2

Assuming homogeneous fall speed of rain drops over the area of the VAD circle (or equivalently constant vertical velocity w), the following parameters can be expressed by the first Fourier coefficients (R: radius of the VAD circle). Note that Browning and Wexler used the normal mathematical definition of angles, not the meteorological, and therefore Eq. 5 differs slightly from their publication in that a1 and b1 are interchanged.

Horizontal divergence:$div(v_H)=\frac{a_0}{R\cos\phi}-\frac{2w}{r}$    Eq. 3
Horizontal wind speed:$|v_H|=\frac{\sqrt{a_1^2+b_1^2}}{\cos\phi}$    Eq. 4
Wind Direction:$\beta=\begin{cases}\frac{\pi}{2}-\arctan\frac{b_1}{a_1},a_1<0 \\ \frac{3\pi}{2}-\arctan\frac{b_1}{a_1},a_1>0\end{cases}$    Eq. 5
Resultant deformation:$def_r(v_H)=-2\frac{\sqrt{a_2^2+b_2^2}}{R\cos\phi}$    Eq. 6

Combining the concept of a linear wind field ($\vec{V}$ : 3D wind vector, $\hat{J_v}$ : Jacobian matrix)

$\vec{V}=\vec{V_0}+\hat{J_v}\cdot(\vec{r}-\vec{r_0})$    Eq. 7

with a full volume scan, Waldteufel and Corbin suggested in 1979 the Volume Velocity Processing (VVP) Method (P. Waldteufel, H. Corbin, 1979) . When Eq. 8 is transformed to polar coordinates (r: Range) and inserted into Eq. 1 setting x0 = y0 = 0 it follows:

\begin{align}V_r=u_0\cdot\sin\theta\cos\phi+v_0\cdot\cos\theta\cos\phi+w_0\cdot\sin\phi\end{align}    Eq. 8
\begin{align}+r\sin^2\theta\cos^2\phi\cdot u'_x \\ +r\cos^2\theta\cos^2\phi\cdot v'_y \\ +r\cos\theta\sin\theta\cos^2\phi\cdot (u'_y+v'_x) \\ +\sin\phi(r\sin\phi-z_0)\cdot w'_z \\ +\sin\theta\cos\phi(r\sin\phi-z_0)\cdot (u'_z+w'_x) \\ +\cos\theta\cos\phi(r\sin\phi-z_0)\cdot (v'_z+w'_y)\end{align}

Thus, there are a maximum of nine parameters that can be obtained by fitting Eq. 8 to the volume velocity data for each altitude gate. As an example for one of these parameters, a plot of a vertical profile of the horizontal wind is shown in figure 3. Particularly for the horizontal wind, it is also common to plot wind barbs for each altitude gate. This can be conviently displayed as a time series (figure 4).

Figure 3: Profile of horizontal wind, similar plots could be shown for other paramters extracted from a volume scan by fittinbg Eq. 8 to it.
Figure 4: Time series of wind barbs as a function of altitude (time: abscissa, altitude: ordinate). This plot has the advantage that the direction of the horizontal wind can be displayed as well.

It is worthwhile noting that horizontal vorticity η (the vertical component of the curl of the velocity field, Eq. 9) cannot be extracted, since u'y and v'x only appear summed together.

$\eta=(\vec{\nabla}\times\vec{V})\cdot \vec{k} = {\partial v\over\partial x}-{\partial u\over\partial y}$, $\vec{k}$: unit vector in z-direction.    Eq. 9

Since the assumption of a linear variation of the wind field is most likely to hold if the weather situation is stratified, it seems also sensible to set w'x and w'y to zero. Thus, profiles of the following meaningful properties can be obtained by the VVP method in addition to the wind profile: Divergence, vertical shear and Deformation. With regard to sensor synergies, temperature advection as a derived property might be of interest as well. Assuming pure geostrophic wind vg, i.e. the flow of air is devoid of any friction with the surface, gives rise for the following expression (see e.g. Holton 1992)

${\partial v_g\over\partial \ln p}=-\frac{R}{f}\vec{k}\cdot\vec{\nabla}_{H,p}T$    Eq. 10

where T is the thermodynamic temperature, R the gas constant, f the Coriolis factor, and $\vec{\nabla}_{H,p}$ the horizontal Nabla Operator on isobaric surfaces. Using the ideal gas law, the following relation for the temperature advection holds (Rainbow® 5 instruction manual):

$\vec{V}\cdot\vec{\nabla}T=\frac{T\cdot f}{g}(u{\partial v\over\partial z}-v{\partial u\over\partial z})$    Eq. 11

However, if derivatives of the wind field are to be extracted in addition to the horizontal wind using VVP, one has to be aware that the corresponding matrix used in the fit is generally prone to be ill-conditioned, i.e. the solution varies too strongly with a variation of the arguments with respect to round-off errors due to the finite precision of floating point numbers, or, stated more formally, the condition number C given by the ratio of the largest and the smallest singular value of the matrix XTX encountered in the least square fit procedure is too large, meaning roughly that log10(C) is greater than the machine size precision (for more information see e.g. Wolfram Mathworld). For this reason, Nan et al. have suggested an algorithm called SVVP (S for “stepped”) to overcome this problem (Nan et al., 2007).

### 3.4 Aerosol Backscatter and Extinction profile

Contributors: I. Mattis, D. Nicolae, O. Cox

The transmission of the atmosphere is highly dependent upon the wavelength of the spectral radiation, and upon the composition and speciﬁc optical properties of the constituents in the atmosphere. The prominent spectral features in the atmosphere's transmission spectrum are primarily due to absorption bands and individual absorption lines of the molecular gases in the atmosphere, while a portion of the slowly varying background transmission is due to aerosols.

Recent estimations on the possible impact of aerosols on the radiative forcing (assumed cooling effect) in a global average are of the same order of magnitude as the CO2 effect (warming effect). A full understanding of the role of aerosols is important for improving weather forecasting and understanding climate change. They scatter and absorb both incoming and outgoing radiation (direct effect), and influence cloud formation, their microphysical properties and lifetime (indirect effect). The amount of radiation that is scattered and the directions of scatter, as well as the amount of radiation absorbed, varies with aerosol composition, size, and shape. Thus, the measurements of aerosol optical properties (aerosol backscatter and extinction coefficients at various wavelengths) contribute to the quantification of the radiative forcing, but also to the estimation of particle's physical properties, by inversion of the spectral optical data.

• The backscatter coefficient is a measure of the fraction of incident radiation that is scattered directly back toward the source.
• The extinction coefficient is a measure of attenuation of the light passing through the atmosphere due to the scattering and absorption by atmospheric components (aerosols, molecules). The extinction coefficient is the sum of the absorption coefficient and the scattering coefficient, and generally depends on wavelength and temperature.

Applications: Atmospheric correction for satellite imagery; air quality; health and environment; monitoring of volcanic eruptions and forest fire; Earth radiation budget; radiative transfer models; climate change; aviation safety; visibility.

#### 3.4.1 User requirements and benefits

User requirements and benefits of measuring the backscatter and the extinction coefficient, especially using ground-based remote sensing techniques are described in the WMO's document " Transitioning to operations: lidars and ceilometers" (W. Thomas, DWD, June 2012). Conform to this document: "Atmospheric profiling from ground-based remote sensing instruments has reached a level of maturity making it possible to gather routinely information about the atmospheric state. Recent improvements of data assimilation techniques pave the way for ingesting profile data into models for numerical weather prediction, thus supporting and fostering the operational use of such instruments and measurements. Further applications are long-term monitoring thus climate research at global and regional level and observations of long-range transport phenomena. Current networks are however heterogeneous with respect to the spatial density of instruments, remote sensing capabilities of instruments and products. Consequently, first steps have recently been undertaken to harmonize data and products and to close the gap between research-oriented operations and application-oriented operations."

User requirements refer to data accuracy and data availability. From this point of view, lidars are providing the best accuracy (relative error better than 10%) while lacking continuous operation. On the contrary, ceilometers are all-weather continuous operation instruments, but are lacking the accuracy in retrieving (especially) the extinction profile. Intermediate lidar products are now available with Nitrogen Raman detection capability and have shown a good data availability with a low maintenance thanks to the use of diode-pump lasers. According to Heese et al. (2010), a combination of different instrument types seems feasible as well as appropriate for a prototype aerosol profiling (backscatter and extinction profiles, plus subsequent parameters) near-real-time system that bridges between research-based and operational aerosol observations. The retrieval of first backscatter profiles and secondly extinction profiles (and further mass extinction coefficients) from routine ceilometer measurements is in principal possible, provided that additional measurements are available (sun photometer, advanced Lidars) and that the instrument was calibrated. Note that the conversion of ceilometer data into backscatter and extinction coefficients is associated with large error bars and limited to 4-5 km in daytime conditions. This method is described in Flentje et al. (2010). Elastic backscatter lidars equipped with nitrogen Raman channels are another way to retrieve backscatter and extinction coefficients profiles independently and without assumptions on particle type. As operational lidar products are now available with nitrogen Raman detection capability, these instruments offer an accurate way to self-calibrate the optical extinction profile without any coupling to other sensors. With respect to hazardous aerosol layers (after volcanic outbreaks, dust storms, forest fires) and thanks to continuous monitoring such network may also act as a warning and tracking system of atmospheric particles.

#### 3.4.2 Available techniques

There are several techniques to measure the backscatter coefficient and the extinction coefficient (or at least its integral over a measurement path).
Figure 1: Multiwavelength Raman lidar operating at the National Institute of R&D for Optoelectronics, Romania

Scattering instruments are used to measure a basic optical property of the air sample: the volume scattering function. Scattering instruments include integrating nephelometers, backscatter meters, forwardscatter meters, and polar nephelometers. Integrating nephelometers perform a point measurement of the light scattered over a range of angles and permit determination of the scattering component of extinction. The air sampled by the integrating nephelometer is enclosed and illuminated indirectly by an artificial light source, allowing automated continuous day and night operation. Nephelometers have been used in a variety of applications (Charlson et al., 1978). Since nephelometers involve point measurements, care must be taken to minimize the influence from local sources. Also, inhomogeneous impairment, such as plume blight, cannot be detected.

Photometers measure light intensity using a photodetector, which converts brightness into representative electric signals. By the use of combinations of lenses and filters, different optical properties may be determined. Sun photometers are photometers pointing at the Sun. Recent sun photometers are automated instruments incorporating a Sun-tracking unit, an appropriate optical system, a spectrally filtering device, a photodetector, and a data acquisition system. These instruments measure the solar radiance after the absorption / scattering from the atmosphere, at several wavelengths. The integral of the extinction coefficient (AOD), as well as other column optical and microphysical parameters can be extracted from multiwavelength sun photometer data.

Transmissometers are instruments which measure the amount of light transmitted from a specified source (artificial) to a receiver, allowing the direct calculation of the average extinction coefficient of the air along the instrument path. When transmissometers are used in very clean atmospheres, their critical sensitivity to atmospheric turbulence can introduce error. Additionally, these instruments are usually limited to a single wavelength and not very portable.

Lidar provides a method of directly measuring the optical properties of atmospheric aerosol distributions, as well as of other atmospheric parameters or components (molecules and fluorescent species, wind velocity and direction, temperature and water vapor) as profiles. Lidar makes use of a laser to excite backscattering in the atmosphere. This backscattered signal is observed using a telescope receiver, which collects the light and send it to the receiver optics. The role of the optical chain is to select specific wavelengths, split between them and direct them to photodetectors, which further convert the optical signal into electrical signals. These are recorded as a function of time by analog-to-digital converters and/or photon counting devices. Each lidar signal represents, therefore, the spatial variation of the measured parameter, i.e. the vertical, slant or horizontal profile of the variable.

Interaction of the laser beam with the atmosphere is complex. Multiple phenomena are produced simultaneously, both elastic (i.e. at the same wavelength: Mie and Rayleigh scattering) and inelastic (i.e. at a different wavelength: vibrational and rotational Raman, fluorescence). Thereby, the set-up of the lidar is application-driven. The backscatter coefficient can be measured using simple elastic backscatter lidars and several types of ceilometers, although some assumptions has to be made (see the "Retrieval algorithms and errors" sub-section). For the measurement of the extinction coefficient, inelastic (vibrational or rotational) Raman detection is generally used. The combination of the two gives independent estimation of both parameters. High Spectral Resolution Lidars take advantage of the spectral distribution of the lidar return signal to discriminate aerosol and molecular signals and thereby measure aerosol extinction and backscatter independently. Multi-wavelength detection is also recommended in order to extract supplementary information (e.g. intensive optical parameters such as Angstrom exponent, color indexes and color ratios, or microphysical properties such as size distribution, complex refractive index and single scattering albedo.

Table 1: Advantages and limitations of the various aerosol lidar types
Lidar type Principle Retrieved parameters Limitations
General
• Scattering (Mie, Rayleigh, Raman)
• Backscatter and / or extinction coefficient
• Incomplete overlap (at low range)
• Signal-to-noise ratio (at far range)
• Saturation (at high extinction values)
• Multiple scattering (fog, rain, clouds)
• Estimation of the atmospheric density
Ceilometers
• Mie and Rayleigh elastic scattering
• Backscatter coefficient
• Assumption on the lidar ratio (backscatter/extinction)
• Calibration value (backscatter coefficient at far range)
• Daytime signal-to-noise ratio
• Combination with othe sensors required for the calibration of backscatter coefficient
Elastic backscatter
• Mie and Rayleigh elastic scattering
• Backscatter coefficient
• Assumption on the lidar ratio (backscatter/extinction)
• Calibration value (backscatter coefficient at far range)
Elastic backscatter and Raman (vibrational / rotational)
• Mie and Rayleigh elastic scattering
• Raman inelastic scattering
• Extinction coefficient
• Backscatter coefficient
• Calibration value (backscatter coefficient at far range)
• Daytime signal-to-noise ratio
Multi-wavelength Raman
• Mie and Rayleigh elastic scattering
• Raman inelastic scattering
• Extinction coefficient
• Backscatter coefficient
• Angstrom exponent
• Size distribution
• Calibration value (backscatter coefficient at far range)
• Daytime signal-to-noise ratio
High Spectral Resolution
• Aerosol and molecular (spectral separation) scattering
• Extinction coefficient
• Backscatter coefficient
• Temperature stability of the ethalons
• Accurate estimation of the instrument's function

Lidars provide important advantages in the determination of the backscatter and extinction coefficients: high spatial resolution (1 value each 3 - 60 m), high temporal resolution (1 profile each 1 - 60 min), high dynamic range (0.2 ... 15 km). There are two important types of errors associated to the measurement of the backscatter and extinction coefficients from lidar: a) instrumental errors due to technological limitations (both at components' level and their integration); b) retrieval errors due to the non-determination of the lidar equation (not exhaustive, see Section "Retrieval algorithms and errors"). Nevertheless, the quality assurance program developed by the European Aerosol Research LIdar NETwork EARLINET made possible the estimation of the backscatter and extinction profiles with an uncertainty less than 10%, which is proper for microphysical inversion and climatological studies. The range in which this uncertainty threshold is kept depends on the individual lidar systems (the signal-to-noise ratio of the channels).

#### 3.4.3 Retrieval algorithms and errors

Whatever the set-up of the lidar system, the magnitude of the received lidar signal is proportional to the number density of the atmospheric diffusers (molecules or aerosols), their intrinsic properties (i.e. probability of interaction with the electromagnetic radiation at the laser wavelengths, called cross–section value) and with the laser incident energy (Measures, 1992). Therefore, obtaining information about the aerosols means to find solutions of the equation which relates the characteristics of the received and emitted signal, and the propagation medium. The form of the equation depends of the interaction type. The basic lidar equation takes into account all forms of scattering and can be used to calculate the signal strength for all types of lidar, except those that employ coherent detection. The propagation of light in an inhomogeneous medium is well described by the electromagnetic theory.

The detected light backscatter power at the wavelength λD from a distance R can be expressed as follows (Nicolae, 2010):

where RCS is the range corrected signal of the lidar and Cs is the instrument function.

The atmospheric backscatter coefficient is a key element of the lidar equation, and is proportional to the cross section of the involved physical process and to the number density of the atmospheric active diffusers (i.e. atoms, molecules, particles, clouds) in the probed volume.

The atmospheric transmittance from the transmitter to the probed volume and from the probed volume to the receiver, respectively, are expressed as the integrals of the atmospheric extinction coefficient and may be different on the two directions of the laser pulses, as is the case of the Raman backscatter radiation (λD = λR ≠ λL). The atmospheric extinction coefficient, backscatter coefficient and the backscatter cross section refer to all possible physical interactions within the atmosphere.

Both in case of elastic backscatter lidar, and Raman lidar, the solution of the equation is not unique (Kovalev,2004), since it has two unknowns: backscatter and extinction coefficients. Combination of elastic and (vibrational) Raman channels allow to retrieve these parameters with minimum assumptions:

where the “L” and “R” indexes correspond to laser and Raman detected wavelength, respectively, and k ranges between 0 for coarse mode particles (maritime, dusts) and 1.5-2 for fine mode particles (pollution forest fires). This relation is valid if the difference between the Raman and laser wavelength is small. Nitrogen molecules are used to obtain the Raman signal, due to the fact that Nitrogen is considered a gas with constant concentration over time and has a Raman spectra easy to be separated from the Rayleigh one. In this case, the second (532 nm) and third harmonics (355 nm) of Nd:YAG laser can be used as excitation, since the associated Raman lines are close to these wavelengths: 607 nm and 387 nm, respectively. With this hypothesis, the aerosol extinction coefficient at the laser wavelength can be obtained by applying the natural logarithm and the derivative to the Raman lidar equation.

Molecular parameters (indices "m") can be calculated with sufficient accuracy from ground values of pressure and temperature using an atmospheric model, or radiosounding. A simple mathematical procedure applied to the couple elastic + Raman channel leads further to the retrieval of the backscatter coefficient (Mattis, 2002). With different couples of elastic and Raman channels, extinction and backscatter coefficients at several wavelengths can be computed, and therefore more products can be obtained (Müller,1999a): lidar ratios, Angstrom exponents, color ratios and even microphysical parameters such as size distribution, effective radius, complex refractive index, single scattering albedo, volume and number concentration.
Figure 2: Backscatter, extinction and lidar ratio profiles obtained from multiwavelength Raman lidar (1064, 532 and 355 sounding wavelengths); example
An example of the aerosol backscatter coefficient profiles (1064, 532 and 355 nm) and extinction profiles (532 and 355 nm) is shown in Fig. 2.

Two kinds of errors are associated to the retrieval of the backscatter and extinction from lidar measurements: statistical errors and systematic errors.

1. Statistical errors are mainly due to the to signal detection, i.e. background of sky and dark current of detector (Theopold and Bösenberg, 1988). Directly related to this kind of error, there is the error introduced by operational procedures such as signal averaging during varying atmospheric extinction and scattering conditions (Ansmann et al., 1992; Bösenberg, 1998). Statistical errors can be assessed either by analytical propagation (Gaussian or Poison statistical distributions have to be considered), either by Monte Carlo techniques.

2. Systematic errors generally arise from:

a) the estimation of temperature and pressure profiles (Ansmann et al., 1992);

b) the estimation of the ozone profiles in the UV (Ansmann et al., 1992) and in other spectral ranges;

c) the wavelength dependence parameter k (Ansmann et al., 1992; Whiteman, 2000);

d) the multiple scattering (Ansmann et al., 1992; Wandinger, 1998; Whiteman, 2000);

e) the overlap function (Wandinger and Ansmann, 2002).

Systematic errors are more difficult to estimate and this is still an open scientific issue.

#### 3.4.4 Operational performance and technical implementation

Although lidar technique has been used for many years, recent technological developments, coupled with the implementation of modern mathematical procedures have led to a more operative use of lidar systems. Ceilometers are nowadays more and more complex and sensitive, some new products being able to detect aerosols and therefore to deliver the backscatter coefficient with the coupling of other sensors and with a large error (more than 50%). On the other hand, powerful lidars are more and more automatic and suitable for a 24/7 unmanned operation. Elastic lidars equipped with nitrogen Raman detection channel are intermediate solutions for an accurate self-calibration and an independent retrieval of backscatter and extinction coefficient profiles without any coupling to other sensors. New industrial and operational nitrogen raman lidars based on diode-pump lasers have shown a low maintenance and a high data availability. The main progresses recorded in the last years are referring to the improvement of the instruments and retrieval algorithms for:

1. the automation of the operation

From an operational point of view, alignment of the laser beam into the receiver FOV is critical. Depending on the configuration, the overlap control needs to be very precise (order of 0.01 mrad) and stable with temperature. Short term fluctuations in the laser energy are usually averaged out during the integration time of the measurement. However, long term drift in the laser energy is important if the lidar is to be kept running for longer periods of time. Therefore, laser energy should be at least monitored and eventually stabilized. In case harmonics of the fundamental laser frequency are used, conversion crystals are used (SHG, THG) that alter the direction of the outgoing laser beam when they are tuned. This has to be compensated for in order to keep the overlap properly aligned. If the (generally non-eye safe) lidar is to be kept running for longer periods of time, in particular if the operator is not present from time to time, safety precautions have to be taken according to proper use of lasers outdoors. Remote control of the lidar operation (e.g. via web tools) is an option.

2. the improvement of the dynamic range

Telescopes with large apertures for high troposphere and stratosphere exploration tend to have large focal lengths, which tends to reduce field of view and increase the distance at which field full overlap is reached. To overcome this problem, two receiver sets, using different telescopes (one for far range, another, with smaller aperture, shorter focal length and nearer full overlap distance, for close range), can be used. Other solutions based on specific optical components are also available in order to reduce the overlap and ensure the highest range as possible.

Measurements can also be corrected under the full overlap height if the overlap function of the system has been measured.

Increased dynamic range can be achieved also with combined analog and photoncounting techniques.

3. the improvement of the temporal coverage

Daytime performance of (vibrational) aerosol Raman lidar can be achieved by sufficiently suppressing the daytime background and/or by increasing the emitter power. Narrowing down interference filter passband has consequences for the optical design of the receiver (angular dependence of the position of the passband transmission peak). Other solutions exist, e.g. grating/monochromators, tilt slit diaphragm. The limited acceptance angle of small bandwidth interference filters IFF determines the whole lidar optics design.

Ceilometer detection and inversion capabilities are also limited to the lowest part of the troposphere (up to 4-5 km) in daytime conditions.

4. reducing the uncertainties of the final products

The minimum recommendation for an optimized product is to add a nitrogen Raman channel to a backscatter lidar, so that both the backscatter and the extinction coefficient can be derived independently. The same can be achieved by using a HSRL system and / or a scanning lidar (multiple zenith-angle measurements). This latter technic requires a strong assumption on horizontal and temporal stability of the atmosphere. Moreover their use in operational networks has to be demonstrated. The implementation of state-of-the-art algorithms and tools for the correction of the signal as well as for the inversion of the corrected signal contributes both to reducing the uncertainties and to increasing the availability of the data. This includes:

• signal pre-processing: cloud screening, pile-up correction, estimation of the statistical error, background subtraction, range correction, handling of signals measured at angle different from zenith, correction for depolarization-dependent receiver transmission, calculation of the profile of the Rayleigh-scattering coefficient, correction for Rayleigh-transmission, temporal averaging to create fixed time intervals, vertical smoothing up to a fixed height resolution
• calculation of the extinction coefficient from the Raman signal: calculation of the derivative, estimation of the uncertainty of the derived extinction, determination of the overlap function
• calculation of the backscatter coefficient from combined elastic - Raman signals: detection of the reference height, estimation of the reference value

Significant improvements can be achieved by the automation of the lidar inversion, to avoid human error and operator subjectivity.

#### 3.4.5 Summary and recommendations

Although satellite imagery (with its increasing temporal and spatial resolution) and satellite remote sensing will ensure in the future the growing need for knowledge of global optical properties of the atmosphere, accurate estimation of the aerosol backscatter and extinction coefficients, especially near ground and in the first atmospheric layers, can be only provided by ground-based remote sensors. Laser-based techniques have the main advantage of high spatial and temporal resolution. Two solutions can be retained for improving short-term weather forecasts and climate change trends analysis, as well as warning and tracking system of atmospheric particles:

• Firstly, a network of operational lidars equipped with depolarization and nitrogen Raman detection channels are the solution for an accurate self-calibration and an independent retrieval of backscatter and extinction coefficient profiles, as well as the ability to classify the aerosols.
• Secondly, a network of ceilometers, backed-up by multiwavelength Raman lidars and sun photometers, can be an alternative but where the operational use (24/7) of multi wavelength lidars has not been widely demonstrated and where the real capacity of ceilometers to measure above 5 km is not ensured.

Nevertheless, current networks are however heterogeneous with respect to the spatial density of instruments, remote sensing capabilities of instruments and products.

In this respect EG-CLIMET recommends the following:

1. European and national funding agencies should invest more in the technological development of:

• Nitrogen Raman lidars with depolarisation - continuous (i.e. daytime), unattended operation
• ceilometers - multiwavelength capabilities, increasing dynamic range, increasing the signal-to-noise ratio
• network infrastructure, storage capacity and software development, in order to build up the capacity for enhanced usage of profiling instruments

2. European research (EARLINET/ACTRIS) and operational networks (NMHSs, UK Met. Office, DMI, DWD, FMI, KNMI, Meteo-France, SMHI etc.) should develop an optimized and fully automatically working method for a network of different instruments. They should set up working groups to:

• encourage and support the set-up of measurement stations with parallel operations of ceilometers, lidars and sun photometers
• harmonize lidar / ceilometer observations and data products/data formats
• organize and support intercomparison campaigns for ceilometers and Lidars (involvement of EARLINET[/ACTRIS recommended)
• define best practices for ceilometer calibration (self-calibration technique, cross-calibration with Lidars and sun photometers), quality assurance, and long-term operations
• define a common data format for data exchange/data storage at scientific level and at operational (national weather services) level, including data policy
• take advantage of existing algorithms and implement homogeneous automatic data processing procedures in order to increase the near-real-time capability of the networks
• integrate European national ceilometer networks and lidar networks into GAW Aerosol Lidar Observation Network GALION, for global coverage

3. GMES “Atmosphere Core Service” initiatives (e.g. MACC II) together with European lidar and ceilometer networks should push for the use of ground-based remote sensing data for:

• validating aerosol parameters of the global integrated (chemistry + dynamics) C-IFS model operated at ECMWF
• contributing to regional models at higher spatial resolution
• assimilating aerosol profiles into models and validation strategies

4. European lidar and ceilometer networks together with WMO, various expert groups (e.g. EG-CLIMET, CIMO, CBS, WEZARD) and national and international aviation control institutions (e.g. EASA, Eurocontrol, ICAO) should set up working groups to:

• support long-term monitoring of aerosol parameters/aerosol profiles
• set up tools to make the current instrumental network available to users
• organize data exchange procedures and data policy
• close the gap between research-oriented operations and application-oriented operations (weather and climate, aviation safety, air quality)

### 3.5 Target classification

Contributors: E. O’Connor, M. Haeffelin, U. Görsdorf, JC. Dupont

### 3.6 Mixing height

Contributors: M. Haeffelin, F. Angelini, R. Lehtninen, M. Piringer

### 3.7 Liquid clouds

Contributors: U. Löhnert, G. Martucci, C. Brandau, K. Ebell, E. O'connor, D. Donovan

Low-level liquid clouds are prevalent in all seasons and on the global scale. They can be described through cloud cover, vertical distribution (macro-physics), total path integrated liquid water (LWP) as well as droplet size distribution (micro-physics), which can be expressed in terms of liquid water content (LWC), cloud droplet number concentration (N) and effective radius (Reff). Lifetime, as well as macro-physical and microphysical properties of liquid clouds are determined by various factors such as large scale meteorological forcing, physical and chemical variations of cloud condensation nuclei (CCN) population, radiation processes, turbulent surface fluxes, etc... Especially frequently occuring thin mid-level liquid water clouds (alto-cumulus) play a crucial role in the interaction with solar radiation and thus impact atmospheric radiative forcing due to clouds (Turner et al. 2007). Also, condensation and evaporation of cloud water induces vertical and horizontal transports of energy from smallest to global scales. Liquid clouds are also relevant factors in boundary layer processes leading to deep convection and govern the formation of snow and ice in supercooled environments with respect to liquid water. Recent findings (Cesana et al., 2012) even show that also in Arctic regions liquid cloud occurence dominates ice cloud occurrence.

#### 3.7.1 User requirement and benefits

Thus, the prediction of climate and weather is extremely dependent on the correct description of clouds on all spatial and temporal scales. This requirement is extremely difficult to meet since clouds since are highly variable in space and time, difficult to measure, interact in a physically complex way with their surrounding and must be correctly described by means of their phase, full particle size spectrum, and shape spectrum. In the context of high-resolution NWP, WMO Observing Requirements Database sets the uncertainty goals for cloud liquid water measurements for high-resolution NWP (global climate models) in the lower troposphere to 5 % (20 %) relative accuracy at 0.1 km (0.2 km) vertical and 15 min (60 min) temporal resolution. Note that the corresponding uncertainty thresholds are set to 20 % (100 %) relative accuracy at 0.5 km (2 km) vertical and 3 h (12 h) temporal resolution.

Satellites can only partially provide such information due to limits in vertical, horizontal and temporal resolution as well as in the information content of passive remote sensors. In this respect Cloudsat (launched in 2006), the first cloud radar in space, has revolutionized the measurement capabilities of clouds from space concerning vertical resolution. However, Cloudsat's life time is limited and is (so far) a singular instrument if its type. In situ measurements are usually more accurate however, require in-cloud flights or radiosoundings which have high costs and can only be organized occasionally or with poor temporal resolution.

It has been shown in the last two decades that a combination of active and passive ground-based remote sensors such as Doppler cloud radar, elastic (or inelastic) lidar and multi-channel microwave radiometer (together wit a limited number of assumptions) have the potential to retrieve these cloud microphysical parameters for different types of liquid clouds. National and international projects and research initiatives (FP5 Cloudnet project, US DOE ARM Programm, FP7 ACTRIS infrastucture, German Research Ministry HD(CP)2 initiative,...) have lead to a loose network of cloud profiling stations with such suitable instrumentation throughout Europe and the US. In the last years, EG-CLIMET has gathered scienstists from these reserach projects to enable a common and more accurate development of cloud profiling retrieval approaches. Retrieval approaches as well as instrument accuracy and sensitivity have improved constantly throughout the years and this, along with the improved instrumental capabilities for measuring aerosols optical properties, have contributed to significantly to reduce the uncertainty of the retrieved cloud microphysics and the related radiative forcing.

Measurements of clouds using ground-based remote sensing networks can lead to an improvement of models by providing an evaluation basis. Measurements can help to obtain insight into cloud parametrizations, i.e. methods to describe sub-scale processes such as convection or small scale processes like droplet formation and turbulence, by means of surrounding meteorological parameters of temperature, pressure, humidity and wind. Methods for determining integrated and vertically resolved cloud liquid water microphysical proeprties are presented and their accuracies are assessed. This is why it is essential to continue developing new methods with highest possible accuracy.

#### 3.7.2 Available techniques

The following ground-based remote sensing instruments are sensitive to cloud liquid water and the available techniques are summarized in the following. However, in order to gain essential information on the vertical profile of liquid water cloud microphysical properties an integration of the different measurements and methods is necessary. This is described in [[ | ]].

Figure 1: Histogram of MWP-derived LWP at Lindenberg observatory during 2010

Microwave radiometer: Due to their suitability for operational measurements and widespread European distribution (i.e. organized within the new international Microwave Radiometer Network MWRnet), microwave radiometers (MWR) have been in the main focus of EG-CLIMET. They are the most reliable technique for deriving the path-integrated liquid water amount (LWP), however provide no information on the vertical distribution of cloud liquid water (Crewell et al. 2009). The estimation of LWP by ground-based microwave radiometry has a long tradition. Westwater (1978) proposed a dual-frequency radiometer with channels at the wing of the water vapor line at 22.235 GHz and in the window region at 31.65 GHz to derive the Integrated water vapor (IWV) and the LWP of the atmosphere. First tests start in 1980. Details of the physical background are described in the MWR Fundamentals and in various reviews as e.g. Askne and Westwater (1986).

In summary, due to the nearly linear relation between TB and IWV as well as vs LWP, the integrated values can be derived by linear equations as follows:

IWV = a0 + a * TB

LWP = b0 + b * TB

where a and b denote the coefficient vector and TB the brightness temperatures of the radiometer. Regression coefficients are calculated mainly on the basis of training data sets consisting of concurrent TB and IWV/LWP values. Both TB and LWP are results of radiative transfer and cloud model calculations basing on a representative dataset of radiosonde observations. The dimension of TB increases from two to higher numbers at the end of last century.

Figure 2: Model and MWR LWP (top) and solar radiation balance (bottom) observed at Lindenberg observatory in January 2010

First multichannel radiometers were developed to retrieve vertical structures of thermodynamic variables. Closely related was an accuracy improvement of LWP retrievals achieved by multiple regression techniques or neural networks. Löhnert and Crewell (2003) have shown by numerical experiments that the use of additional channels at about 50 GHz and 90 GHz result in better retrievals. Compared to the classical two-channel algorithm the rms-error could be reduced from about 35 gm-2 to 15-20 gm-2. Therefore systematic errors due to different cloud model statistics became more important. Nevertheless, in spite of the technological progress uncertainties in LWP estimations remain unsettled. In a study (Turner et al., 2007) an identical data set of brightness temperatures was used to retrieve LWP by various methods and absorption models. The results are sensitive to the absorption models used as well as to the retrieval methods and cause biases of up to 40%. Hence, the combination of different active and passive sounding instruments is needed to meet user requirements.

Cloud radar: In the last two decades sensitive microwave radars have been developed, which can detect the much smaller cloud particles up to distances of a few kilometers. High spectral, spatial and temporal resolution allow the development of new techniques, which can help to quantify cloud microphysics at finer scales than before. Typically, higher frequencies are used for cloud radars than for weather radars, since the backscattered power is higher due to the λ0−4 dependency of σv. However, due to the increasing attenuation with frequency, the range of cloud radars is limited to a few kilometers. Consequently most cloud radars measure with the antenna pointing vertically upwards. Typically the radar reflectivity of clouds ranges from –50 to 0 dBZ, whereby –50 dBZ represents the delectability limit of most cloud radars and values above 0 dBZ increasingly imply rain. Cloud radar measurement have been used to relate Z directly to LWC with a power law relationship of the form:

$Z\,=\,aLWC^b$

with Z is in [mm6 m-3] (e.g. Atlas 1954, Sauvageot and Omar 1986). This relation is, however, extremely dependent on the DSD. For example, if a volume of 1 m3 contains 0.1 g of liquid water and only droplets 5 µm in radius, the reflectivity will be 8 times smaller than if the volume consists only of droplets 10 µm in radius. The coefficients a and b are either derived empirically from in-situ aircraft measurements by measuring the DSD, calculating Z and LWC according to equations (2.9) and (1.1) and finally using a least squares to determine a and b. Instead of aircraft measurements other attempts have been made using microphysical cloud models, which resolve the drop size spectrum (Liao and Sassen 1994) and thus allow the calculation of Z and LWC. Danne (1996) has analysed ~10 different Z-LWC relations from literature and found that the mean differences between the different relations vary between 30 and 90 % in LWC with regard to Z. Fox and Illingworth (1997a) show scatter plots of LWC and Z calculated from DSD-aircraft measurements, which show maximum uncertainties of up to one order of magnitude in LWC for a single measurement if only droplets smaller than ~50 µm in radius are considered. They claim relative LWC accuracies of ~50 % for typical marine stratocumulus. The results obtained in section 5.2.3 show that the relative LWC error of derived Z-LWC relations for continental all-liquid water clouds can be on the order of 100 % and more.

Since LWC is proportional to D^3 and Z to D^6, the occurrence of just a few larger precipitating (drizzle) drops, which do not contribute significantly to LWC, will dominate the radar signal. Many low level stratocumulus clouds contain drizzle drops and consequently no applicable relations exist between Z and LWC. Drizzle detection may help to minimize errors, which can be done by using arbitrary Z thresholds as algorithm application limit.

Most modern cloud radars have Doppler capabilities and thus the possibility of measuring the Doppler velocity spectrum. Here, the possibility is given to determine if a DSD is uni-, bi-, or multi-modal. However, it is very difficult to determine LWC by Doppler spectrum measurements, since cloud droplets do not have significant fall velocities and are dominated by turbulence within the cloud. An approach to deconvolve the Doppler spectrum of turbulence has been undertaken by Gossard et al. (1997).

As shown by Hogan et al. (1999), another approach towards retrieving LWC from radar measurements is to use two radars at different wavelengths. Since attenuation is proportional to LWC within the Rayleigh backscattering regime, the differences between the Z measurements at a certain height give information on attenuation and thus on LWC.

Infrared spectrometer: Next to being sensitive to temperature and traces gases (i.e. water vapor, carbon dioxide, methane, nitrus oxide) highly resolved spectral measurements as provided by an AERI are also sensitive to liquid and ice clouds. Thus, together with reasonable a priori assumptions for temperature and humidity, an AERI is able retrieve the cloud properties cloud effective radius (Reff) and cloud optical depth (COD). An AERI spectrum is sensitive to the liquid cloud properties Reff and COD, whereas MWR observations of liquid clouds are generally sensitive to the integrated liquid water path (LWP). However, assuming a mono-modal size distribution of a cloud drop size distribution of 10 μm the approximation τc=3LWP/(ρlReff) directly relates MWR and AERI observations. At approximately LWP=60gm-2, the signal of the infrared spectrometer is fully satured. This implies, as mentioned by Turner et al., that an AERI can be used to retrieve the microphyical properties of thin liquid water clouds, which can however, effectively regulate the solar radiation balance.

#### 3.7.3 Synergetic profling retrieval algorithms

Within the scope of the EG-CLIMET four different retrieval methods for profiling LWC, Reff and N all based on active and passive ground-based microwave remote sensing measurements have been thouroughly assessed. The combination of passive MWR and active cloud radar together with a backscatter lidar are currently the most robust way to profile liquid cloud microphysical properties, concerning both 24/7 instrument performance and well as algorithm applicability. All methods use the same information on cloud phase & type, cloud boundaries, radar reflectivity, ceilometer-backscatter and MWR-derived LWP, respectively MWR brightness temperature. Additionally these measurements are available at all atmospheric profiling observatories (LINK).

In order to have a "truth" to validate against, all retrieval methods have been applied in a sythetic model environment. Measurement simulations of the ground-based remote sensing instruments using microphysical variables from cloud resolving model output provided this reference. The simulations were carried out within the framework of ECSIM (EarthCare Simulator). After applying the microphysical retrievals to the measurement simulations, an independent evaluation of performance was carried out in order to identify strengths and weaknesses and to improve the retrieval algorithm quality in general. Additionally, the methods are applied to real measurements and evaluated through a short-wave radiative closure using simultaneous broad-band pyranometer measurements.

File:Retrieval methods.jpg
Table x1: :EG-CLIMET state-of-the-art techniques to retrieve the microphysics from liquid clouds

Table x1 shows: (2nd column) the name or acronym of the method and the main publication to which the method refers; (3rd column) the principles the methodology is based on; (4th column) the main assumptions and limitations/advantages of each method; (5th column) the availability to operate 24/7. The fourth column details the main advantages of the each method and its major sources of uncertainty, thus providing indications on where each technique can improve the accuracy of the retrievals. In brown colour are shown the methods involved in the "blind test" already in act within the scope of the COST action EG-CLIMET.

IPT - Integrated Profiling Technique: The IPT (Löhnert et al., 2004, Löhnert et al. 2008) is a variational scheme for retrieving profiles of temperature, humidity and cloud microphysical properties. The core version of this technique includes the physically consistent combination of microwave radiometer, cloud radar and lidar/ ceilometer together with information on the background state (a priori information). It is a variational technique that, similarly to data assimilation techniques, requires uncertainty estimates of the measurements, forward model and background state, which need to be carefully specified. If assuming a Gaussian distribution of atmospheric variables, measurements and their corresponding error, the solution will bring forth not only the most probable solution, but also the error covariance matrix for each derived variable. The retrievable parameters include profiles of temperature, humidity and LWC, as well as Reff. By adapting an empirical relation between Z and LWC for cloud, respectively precipitation, it can retrieve LWC also in drizzling cases.

SYRSOC - Synergistic Remote Sensing of Cloud: SYRSOC (SYnergistic Remote Sensing Of Cloud) is a multi-module technique developed at the National University of Ireland Galway and retrieving the three primary microphysical parameters from liquid clouds (Martucci and O’Dowd, 2011, Martucci et al., 2012, Ovadnevaite et al., 2011), i.e. the cloud droplet number concentration (CDNC), the effective radius ($R_{eff}$) and the cloud liquid water content (LWC). In addition to the three main microphysical variables, SYRSOC provides a number of parameters describing the cloud droplet spectral properties (relative dispersion), the degree cloud of subadiabaticity, the Doppler spectrum of droplets, the cloud optical depth and the cloud albedo. Extinction from standard 355-1500 nm inverted backscatter LIDAR signal or directly from Raman signal is used as input data for SYRSOC. Other input data are the temperature and humidity profiles from co-located operational microwave-radiometer and the reflectivity/signal-to-noise-ratio and depolarization ratio from co-located Ka-band Doppler cloud RADAR. Data from the 1064-nm and 15-km vertical range Jenoptik CHM15K LIDAR ceilometer, the RPG-HATPRO water vapour and oxygen multi-channel microwave profiler and the MIRA36, 35 GHz Ka-band Doppler cloud RADAR are currently used at the GAW Atmospheric Station of Mace Head (Ireland) to supply the necessary input to SYRSOC.

#### 3.7.4 Technical implementation and performance

LWC A comparison between the standard Cloudnet scheme and the method according to Brandau shows an improvement in LWC retrieval skill using the latter method (Fig. 1). In contrast to Cloudnet, which relies on a linearly scaled adiabatic assumption, the Brandau method uses radar reflectivity profiles from a cloud radar assuming a relationship between the 2nd and 3rd moment of the DSD based on aircraft measurement (Brenguier et al. 2011). This assumption holds only in non-precipitating clouds. Note, three of the four assessed methods require the LWP derived by independent measurements of an MWR. In non-precipitating cases, the accuracy of LWP is the most crucial factor for retrieving both LWC and Reff. In case the LWP is accurately know, random and systematic error can both be on the order of ~10% for the Brandau method. The IPT method was shown to be very sensitive to the a priori assumption, is however, independent of LWP, whereas the SYRSOC method is very sensitive towards an accurate retrieval of lidar extinction from lidar backscatter. All retrieval methods were shown to be very sensitive towards a correct description of cloud base and cloud top and the corresponding distinction between cloud droplets and precipitation.

Reff & N For non-precipitating cases, the Brandau retrieval method delivers the most satisfactory results for Reff (Fig. 2, left). Within the cloud boundaries, Reff can be derived with overall accuracies of ~15%. The Brandau method as well as the IPT assumes constant values of N throughout the cloud. Although this is actually the case within the clouds analysed (Fig 2, right), systematic errors of more than 50% occur. Improvements are only possible if a more situation-specific relation between the 2nd and 3rd moment of the droplet size distribution is used instead of the one proposed by Brenguier et al. 2011.

Precipitation Frequently, liquid clouds contain precipitation size drops (drizzle). In these cases the assumptions on the relations between 2nd and 3rd moment of the droplet size distribution is no longer valid, leading to large overestimation (underestimation) of Reff (N) because a small number of larger droplets dominate the radar reflectivity factor signal. However, both IPT and Brandau method still deliver fairly robust results in LWC with overall errors in the range of 20-50%.

Next steps Currently, research focus is on methods that allow the discrimination of microphysical properties of the non-precipitating and precipitating part of the droplet size distribution. This can only be achieved by using additional information from the full Doppler spectrum of the cloud radar. Furthermore, physically consistent a priori data (i.e. long-term statistics) of cloud profiles is needed from in-situ measurement to be able to better constrain the retrieval methods. These methods should be ideally developed within a variational framework, which is flexible concerning the used measurements and retrieval assumption and additionally allowing an inherent error approximation.

#### 3.7.5 Summary and Outlook

Due to their capability to provide continuous observations, ground-based remote sensing observations of clouds are widely used and well suited for:

• Long-term observations (Validation of new methods, climate applications). E.g. Figure 1 gives an impression about the amount of liquid water at Lindenberg during one year. It shows a histogram of non-rejected retrievals indicating that no liquid water was observed at about 24% of the cases. For only for 8% the LWP was greater than 200 gm-2.
• NWP model validation. For validation studies LWC, N and Reff measurements are needed as component of a variety of continuously observed parameters. Investigations, which aim at the revealing of possible deficiencies in the model parameterization, have to rely on the long-term availability of corresponding parameters. The periods may cover time intervals ranging from months up to several years. LWP from MWP can be provided for such long periods of time. In the frame of a validation study (Vogel et al., 2011, personal communication) of operational weather forecast models (IFS, COSMO-EU) a systematic prediction error of the 2 m temperature during winter months was found. The overestimation of the noon temperature results from an error of the radiation balance at surface of about 20-40 W m-2. In order to find reasons for the overestimation the radiation balance was split into solar and thermal components. In fact, model radiation peaks appear in low stratiform clouds because their cloud water content is considerably to low compared to the MWP-derived LWP. Furthermore, if the model produces a realistic LWP then the radiance balance simulated by the IFS agrees with the observation (Figure 2).

Next steps Currently, research focus is on methods that allow the discrimination of microphysical properties of the non-precipitating and precipitating part of the droplet size distribution. This can only be achieved by using additional information from the full Doppler spectrum of the cloud radar. Furthermore, physically consistent a priori data (i.e. long-term statistics) of cloud profiles is needed from in-situ measurement to be able to better constrain the retrieval methods. These methods should be ideally developed within a variational framework, which is flexible concerning the used measurements and retrieval assumption and additionally allowing an inherent error approximation.

## 4 Networks

Contributors: C. Gaffard, M. Haeffelin, N. Cimini

### 4.1 Operational networks

#### 4.1.1 The European network of wind profilers CWINDE

Contributors: D.Ruffieux

The EUMETNETE-WINPROF programme is providing vertical profiles of wind measurements from wind profilers and weather radars from a network of stations across Europe. Its main goal is to improve the overall usability of wind profiler data for operational meteorology and to provide support and expertise to both profiler operators and end users. Since 2002, this programme is operating the network CWINDE which displays time series of realtime quality controlled wind profiles from more than 25 sites in Europe.

Monthly statistics on availability and data quality are sent to the data owners. A list of wind profilers and weather radars whose winds are operationally assimilated into the main Numerical and Weather Prediction (NWP) models in Europe is also updated on a regular basis.

#### 4.1.2 The Swiss nuclear powerplant meteorological surveillance tool CN-MET

Contributors: Dominique Ruffieux, Alexander Haefele

The main purpose of CN-MET (Centrale Nucléaire et METéorologie) is to bring up to date the system delivering weather information necessary to the population safety in case of a nuclear hazard. CN-MET represents the coupling of a specifically adapted measurement network (mainly ground-based remote sensing) and a predictive tool in the form of a fine grid numerical weather prediction model currently operated at MeteoSwiss (COSMO-2). This new tool, calling upon modern measurement and modeling techniques of the atmosphere, represents a solution which will keep all its relevance for the next decades.

In case of a nuclear accident, the necessary atmospheric data used to calculate the diffusion of a contaminated air mass will be provided by a fine grid numerical model, covering the whole Swiss territory. The measurement network within CN-MET combines in-situ measurements and ground-nased remote sensing systems (wind profilers and microwave radiometers). It is directly adapted to provide the best information (initial and boundary conditions, as well as test measurements) for this model. CN-MET not only ensures the emergency preparedness for the concerned population on a local scale, but also enhances it on a regional scale corresponding to the Swiss Plateau.

### 4.2 Candidate networks

Contributors:

#### 4.2.1 Ceilometer network

Contributors:Martial Haeffelin, Owen cox

#### 4.2.2 MWRnet

Contributors: N. Cimini, U. Löhnert

MWR can provide timely and enough accurate atmospheric temperature and humidity data, especially good in the planetary boundary layer (PBL), which remains the single most important under-sampled part of the atmosphere, outside the reach of surface sensors and with poor satellite coverage. At the EG-CLIMET MC Meeting in Oslo (March 2009), some MC/WG members concurred on that the use of ground-based MWR in NWP and climate studies was hampered by unsufficient communication between users, manufacturers, and experts, lack of coordination for networking, unknown number of operational MWR. Thus, a long-time overdue cooperation and coordination effort started under the auspices of EG-CLIMET: MWRnet, an International network of MicroWave Radiometers. MWRnet aims to address the lack of coordination between the MWR operations and increase the utilization of quality controlled MWR data. The long-term mission of MWRnet is the set up of an operational network sharing good practices (in terms of procedures, formats, quality control, protocols, software, etc...) and life cycle of MWR data, facilitating the access of well documented and quality controlled MWR observations and retrievals (with errors).

MWRnet currently links about 61 members, operating more than 94 MWR world wide (including dual-channel units, water vapor profilers, single-channel temperature profilers, multi-channel temperature profilers, and temperature and water vapor profilers), of which about 30 in Europe (temperature and water vapor profilers, for the most). More information on MWRnet and the network map are available at: MWRnet website

MWRnet started within EG-CLIMET and most of the activities were carried out within this COST action. In particular, 3 SWG meetings and 3 STSM were performed:

• SWG 00: From raw data to meteorological products
• SWG 02: MWR data processing
• SWG 17: Towards operational use of MWR data
• STSM 06: Assessment of Microwave Radiometer Temperature Profiling
• STSM 11: Estimate of PBL Mixing Height from MWR data
• STSM 13: Development of a ground-based Radiative Transfer Model (RTM) for NWP

The major achievements of MWRnet within EG-CLIMET were:

• Development of calibration control methods
• Advances in retrieval algorithm development
• MWR Data Assimilation experiment
• Assessment of O-B statistics
• Initial efforts towards a ground-based MWR Forward Model suited for NWP
• EU FP7 proposal EMERGE (unfunded)

In particular, the proposal EMERGE (European MicrowavE Radiometer network within GEo) grouped eight members (CETEMPS-Univ. of L’Aquila (IT), Univ. of Köln (D), KNMI (NL), IMAA (IT), FMI (FI), DWD (D), Meteoswiss (CH), MetOffice (UK)), five of which from National Weather Services. EMERGE passed all quality criteria but unfortunately did not reach the funding level.

MWRnet pursued building connections with NWP data assimilation (DA), climate, and radiopropagation communities:

• NWP DA:
• Contribution to the US Tropospheric Profiling Technology Workshop (organized by the US NSF and NWS)
• Invitation to chair a session on the German DA Workshop
• DA experiment in collaboration with Meteo France (HyMeX)
• Mention in the EUCOS roadmap for 2013-2017 as an interesting network for EUCOS expansion
• CLIMATE:
• Invitation to join the GRUAN (GCOS Reference Upper Air Network) Task Team 5 focusing on ancillary measurement (w.r.t. radiosondes)
• Contribution to the MWR handbook for GRUAN
• Contribution to the final report of COST Action IC0802 – “Propagation tools and data for integrated Telecommunication, Navigation and Earth Observation systems” with a MWR handbook focusing on MWR estimates of atmospheric attenuation

At its present stage, MWRnet faces both technical and bureaucratic challenges:

• Implement common data life-cycle (data format, quality control, retrieval)
• Establish a trusted ground-based MWR Forward Model suited for NWP
• Data sharing policy

As the outcome of the MWRnet meetings, the following recommendations were collected for good practices with MWR operations.

Table N: MWRnet recommendations for MWR operations
# Type Recommendation Note
MM1 Measurement mode Perform zenith viewing alternating with elevation scans regularly, possibly as frequent as 5 min. Store observations at all channels. If possible, perform 2-side scans.
MM2 Measurement mode Perform frequent observations of the calibration load (5min intervals). Use integration time ~10 sec (as calibrations need to have longer integration times than the observations for a safe reduction of rms noise).
MM3 Measurement mode Ideally, all raw voltages of receivers and temperatures in the radiometer system should be recorded continuously in order to make a post-calibration possible.
MM4 Measurement mode Level 0 data should always be stored. Always store data even if quality flags are on. Avoid discarding data.
CC1 Calibration control Carefully follow instructions for cryogenic calibration. If possible check Tb after cryogenic calibration against a reference (e.g. clear sky radiosonde simulations).
CC2 Calibration control Before each cryogenic calibration: observe the cold load for ~2min to characterize the instrument drifts since the last calibration. Note that this need a dedicated featured software since the observed TB will NOT be the LN2 temperature. In fact, the interface reflection on the LN2-surface, residual mirror emission, overspill-termination and other correction factors need to be applied.
CC3 Calibration control Be careful when using calibration coefficients obtained by a single sky dip (tipping curve). Make sure the threshold for a horizontally homogeneous sky are set very tight, Averaged time series of sky dip calibration coefficients may be used to avoid jumps in the data. Perform full sky-scans to assess the validity of the “homogeneous sky” assumption.
CC4 Calibration control Inspection by manufacturer every 1.5-2 years is recommended.
CC5 Calibration control Re-processing of MWR observations and retrievals may be possible if a comparable set of collocated radiosonde profiles is available. Alternatively model analyses could be used.
CC6 Calibration control Climate application should rely on careful calibration monitoring (including radiative transfer comparison and close maintenance).
CC7 Calibration control Gain calibration should be performed once every 3-5 minutes for some 5-10 sec integration time.
QC1 Quality control Use sanity checks to monitor the reliability of the instrument hardware and thus of observed Tb. Use flags provided by manufacturers as well as developed by users.
QC2 Quality control Use quality control checks to estimate the value of retrievals in opaque (rainy) situations. Use flags provided by manufacturers as well as developed by users.
QC3 Quality control Rain flag is necessary, especially for humidity, but is may overkill acceptable retrievals. Check the quality of retrievals during rain flagged periods.
RA1 Retrieval algorithm Uniform multi-linear regression (or NN) retrievals based on radiative transfer calculations should be implemented. These are robust to handle and their accuracy is mostly optimized. Alternatively, direct regression retrievals based on the relation between measurements and model output should be considered.
RA2 Retrieval algorithm Ideally, a variational approach should be adopted for all the MWR. However, future testing is required – specifically concerning the handling of liquid clouds
RA3 Retrieval algorithm The estimate of the retrieval error should be provided.
RA4 Retrieval algorithm The estimate of in-depth retrieval characteristics should be provided (averaging kernels, degrees of freedom)
RA5 Retrieval algorithm Avoid RH profiles computed from T and WV retrieved profiles.
DF1 Data format Produce data in a easy-to-share format with metadata.
DF2 Data format netCDF format complying to the Climate-Forecast (CF) convention is preferable.
DF3 Data format Common data and metadata format should be decided building on the experience of ARM, LUAMI, COPS.
DF4 Data format Data should be processed and stored in a reliable and centralized server.

## 5 NWP Applications

Contributors: Gaffard, Cimini

### 5.1 Quantifying the impact of wind profiler observations

Contributors: Gaffard

### 5.2 MWR data assimilation test

Contributors: Cimini

A first trial of assimilating microwave radiometers (MWR) measurements into numerical weather prediction was carried out in preparation to the Hydrological cycle in Mediterranean EXperiment (HyMeX) Special Observing Period 1, held in September-November 2012 in the Western Mediterranean (WMed) arget area.

The NWP system used for this study is Arome-WMed, a particular version of the Arome system covering the western part of the Mediterranean Sea (see figure). Arome-WMed has a horizontal resolution of 2.5 km, a non-hydrostatic dynamical core, detailed physics inherited from the research Meso-NH model, and is coupled with the global Arpege NWP system. It has a three-dimensional variational (3DVar) data assimilation system [10] with background covariances specially computed for the WMed domain. 3DVar analyses are performed every three hours and provide new initial states for subsequent forecasts. Data assimilation tools developed for the Météo-France Arome-WMed NWP system were used to assimilate temperature and humidity retrievals from a list of 13 MWR members of the international network of ground-based microwave radiometers MWRnet.

Table X: MWR units participating to the data assimilation test

into HyMeX Arome-WMed NWP

STATION INSTITUTION Lat Lon m a.s.l. Products
Bern IAP 46.88 7.46 905 H
Cagliari INAF/OAC 39.5 9.24 623 T, H
Granada CEAMA-UGR 37.16 -3.6 683 T, H
Kloten MeteoSwiss 47.48 8.53 436 T
Lampedusa ENEA 35.51 12.34 50 T, H
Madrid UniLeon 40.49 -3.46 620 T, H
Padova ARPAV 45.4 11.89 30 T
Payerne MeteoSwiss 46.82 6.95 491 T, H
Potenza IMAA/CNR 40.6 15.72 760 T, H
Rovigo ARPAV 45.07 11.78 23 T
Schaffhausen MeteoSwiss 47.68 6.62 437 T
Schneefernerhaus UniCologne 47.42 10.98 2650 T, H
Toulouse ONERA 43.38 1.29 144 T, H

Preliminary results are summarized in Cimini et al., 2012. As a first step, observation-minus-background (O-B) statistics have been computed for temperature and relative humidity to check the consistency between MWR products and 3-h Arome forecasts. For all MWRs, the O-B standard deviations are generally consistent with those of radiosondes, but tend to increase with height. O-B biases are generally much larger than those found for radiosondes (except for Lampedusa where the temperature bias is particularly small). This is likely due to the difference between the climatological mean assumed in the retrieval and the mean atmospheric state during the period under analysis. Ongoing work includes deeper investigation of automatic quality control and O-B biases, and a quantitative evaluation of the impact Arome-WMed on analyses and forecasts.

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