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Radio frequency interference (RFI) has become a growing concern for weather radar, distorting radar variable estimation. By simultaneously or alternately transmitting the horizontal and vertical polarized waves, polarimetric weather radar can be referred to as SHV radar or AHV radar. The SHV radar can mimic the AHV radar by discarding either H- or V-channel measurements, which leads to an alternating scheme. In this research, the real RFI measurements from an operational C-band SHV radar are used to characterize the RFI temporal, spectral, and polarimetric features. Then, the RFI is simulated to quantify the performance of the object-orientated spectral polarimetric (OBSPol) filter in RFI mitigation. The OBSPol filter has been previously proposed by the authors to mitigate the narrowband clutter (both stationary and moving) and noise. This work extends the application of the filter to remove the RFI for SHV radar. Specifically, by taking advantage of the low copolar correlation of the RFI signal measured in AHV radar, the RFI mitigation method is designed, and its effectiveness is proven by qualitative and quantitative analyses. In particular, in the case of RFI overlapped to weather echoes in the time domain, the RFI can be mitigated, also when the duty cycle of the RFI is high. However, this work does not provide a full evaluation of the RFI mitigation performance on all radar data outputs but a proof of concept to show the effectiveness of the proposed filter for RFI mitigation.

A better understanding of aerosol–cloud interaction processes is important to quantify the role of clouds and aerosols on the climate system. There have been significant efforts to explain the ways aerosols modulate cloud properties. However, from the observational point of view, it is indeed challenging to observe and/or verify some of these processes because no single instrument or platform has been proven to be sufficient. Discrimination between aerosol and cloud is vital for the quantification of aerosol–cloud interaction. With this motivation, a set of observational field campaigns named balloon-borne aerosol–cloud interaction studies (BACIS) is proposed and conducted using balloon-borne in situ measurements in addition to the ground-based (lidar; mesosphere, stratosphere and troposphere (MST) radar; lower atmospheric wind profiler; microwave radiometer; ceilometer) and space-borne (CALIPSO) remote sensing instruments from Gadanki (13.45∘ N, 79.2∘ E), India. So far, 15 campaigns have been conducted as a part of BACIS campaigns from 2017 to 2020. This paper presents the concept of the observational approach, lists the major objectives of the campaigns, describes the instruments deployed, and discusses results from selected campaigns. Balloon-borne measurements of aerosol and cloud backscatter ratio and cloud particle count are qualitatively assessed using the range-corrected data from simultaneous observations of ground-based and space-borne lidars. Aerosol and cloud vertical profiles obtained in multi-instrumental observations are found to reasonably agree. Apart from this, balloon-borne profiling is found to provide information on clouds missed by ground-based and/or space-borne lidar. A combination of the Compact Optical Backscatter AerosoL Detector (COBALD) and Cloud Particle Sensor (CPS) sonde is employed for the first time in this study to discriminate cloud and aerosol in an in situ profile. A threshold value of the COBALD colour index (CI) for ice clouds is found to be between 18 and 20, and CI values for coarse-mode aerosol particles range between 11 and 15. Using the data from balloon measurements, the relationship between cloud and aerosol is quantified for the liquid clouds. A statistically significant slope (aerosol–cloud interaction index) of 0.77 found between aerosol backscatter and cloud particle count reveals the role of aerosol in the cloud activation process. In a nutshell, the results presented here demonstrate the observational approach to quantifying aerosol–cloud interactions., Atmospheric Measurement Techniques, 15 (16), ISSN:1867-1381, ISSN:1867-8548

The Ruisdael Observatory is a long-term initiative designed to improve the measurement and modeling of atmospheric processes under the umbrella of the National Roadmap for Large-Scale Research Facilities in the Netherlands. The observatory is run by a consortium of eight institutes and universities, each of which operate unique, state-of-the-art equipment and facilities for characterizing the state of the atmosphere, its radiative properties and the interaction with the land surface.As part of Ruisdael, TU Delft is responsible for operating a large number of in-situ and remote sensing instruments for measuring precipitation, clouds, aerosols, wind and temperature. Currently, our network consists of 3 cloud radars, 6 micro-rain radars, 8 optical disdrometers, 3 radiometers and a mobile facility. Initial deployment started in Cabauw in 2020, followed by a rapid expansion of the network to Delft, Rotterdam and Lutjewad in 2021-2022.At each site, high-resolution observations of precipitation, clouds, aerosols and wind at multiple-frequencies, polarizations are collected. A wide spectrum of parameters is covered, with special emphasis on sensor synergy and co-location. All data are free and open to the general public. Higher level data are stored in NetCDF format and distributed via the KNMI Data Platform (KDP) and ACTRIS data platform, together with all relevant information and metadata. Raw data are stored locally at TU Delft and can be accessed upon request to the authors. In addition to providing high-quality research data, the testing facilities at the Green Village in Delft also provide unique opportunities for education and training within the new MSc programs in Applied Earth Sciences and Environmental Engineering.

As of 2018 the atmospheric research community in the Netherlands, including universities, agencies and institutes, joined forces in the Ruisdael Consortium, named after the 17th century painter who depicted the skies over Holland with a realistic interplay of light, clouds, and the land surface. The consortium centres its activities around the combined use of experimental facilities and model development, aiming at better forecasts of the weather and air quality, as well as getting deeper insights into climate processes. While the driving force behind the consortium results from the urge to advance science, the societal spin-off is hard to neglect. The observational data as well as the high resolution models increasingly find their use in industrial applications such as the generation of sustainable energy, or the evaluation of air quality in urban areas. The Ruisdael Consortium acts as an agenda setting body in the atmospheric sciences, and contributes to the long term strategy of science in the Netherlands, and with its combination of academia, applied institutes and agencies it embodies a direct link between education, research, application and public outreach.Selected examples of the Ruisdael activities areCoordinated actions in Amsterdam to study the urban climate for improving climate adaptation strategies in cities, Studies in Rotterdam of the interplay between climate change and potentially enhanced virus outbreaks, Fine weather forecasts for the wind and solar energy production. With the Ruisdael stations currently confined to land, plans are being developed to install additional station on the North Sea, making use of the proliferation of wind farms in a co-creative setting of the scientific and industrial communities.

The adequacy of the gamma model to describe the variability of raindrop size distributions (DSD) is studied using observations from an optical disdrometer. Model adequacy is checked using a combination of Kolmogorov–Smirnov goodness-of-fit test and Kullback–Leibler divergence and the sensitivity of the results to the sampling resolution is investigated. A new adaptive DSD sampling technique capable of determining the highest possible temporal sampling resolution at which the gamma model provides an adequate representation of sampled DSDs is proposed. The results show that most DSDs at 30 s are not strictly distributed according to a gamma model, while at the same time they are not far away from it either. According to the adaptive DSD sampling algorithm, the gamma model proves to be an adequate choice for the majority (85.81%) of the DSD spectra at resolutions up to 300 s. At the same time, it also reveals a considerable number of DSD spectra (5.55%) that do not follow a gamma distribution at any resolution (up to 1800 s). These are attributed to transitional periods during which the DSD is not stationary and exhibits a bimodal shape that cannot be modeled by a gamma distribution. The proposed resampling procedure is capable of automatically identifying and flagging these periods, providing new valuable quality control mechanisms for DSD retrievals in disdrometers and weather radars.

Better understanding of aerosol-cloud interaction processes is an important aspect to quantify the role of clouds and aerosols in the climate system. There have been significant efforts to explain the ways aerosols modulate cloud properties. However, from the observational point of view, it is indeed challenging to observe and/or verify some of these processes because no single instrument or platform is proven sufficient. With this motivation, a unique set of observational field campaigns named Balloon borne Aerosol Cloud Interaction Studies (BACIS) is proposed and conducted using balloon borne in-situ measurements in addition to the ground-based (Lidars, MST radar, LAWP, MWR, Ceilometer) and space borne (CALIPSO) remote sensing instruments from Gadanki (13.45° N, 79.2° E). So far, 15 campaigns have been conducted as a part of BACIS campaigns from 2017 to 2020. This paper presents the concept of observational approach, lists the major objectives of the campaigns, describes the instruments deployed, and discusses results from selected campaigns. Consistency in balloon borne measurements is assessed using the data from simultaneous observations of ground-based, space borne remote sensing instruments. A good agreement is found among multi-instrumental observations. Balloon borne in-situ profiling is found to complement the information provided by ground-based and/or space borne measurements. A combination of the Compact Optical Backscatter AerosoL Detector (COBALD) and Cloud Particle Sensor (CPS) sonde is employed for the first time to discriminate cloud and aerosol in an in-situ profile. A threshold value of COBALD color index (CI) for ice clouds is found to be between 18 and 20 and CI values for coarse mode aerosol particle range between 11 and 15. Using the data from balloon measurements, the relationship between cloud and aerosol is quantified for the liquid clouds. A statistically significant slope (aerosol-cloud interaction index) of 0.77 (0.86) found between aerosol back scatter from 300 m (400 m) below the cloud base and cloud particle count within the cloud indicates the role of aerosol in the cloud activation process. In a nutshell, the results presented here demonstrate the observational approach to quantify aerosol-cloud interactions and paves the way for further investigations using the approach.

In the near future, wind and solar generation are projected to play an increasingly important role in Europe's energy sector. With such fast-growing renewable energy development, the presence of simultaneous calm wind and overcast conditions could cause significant shortfalls in production with potentially serious consequences for system operators. Such events are sometimes dubbed “Dunkelflaute” events and have occurred several times in recent history. The capabilities of contemporary mesoscale models to reliably simulate and/or forecast a Dunkelflaute event are not known in the literature. In this paper, a Dunkelflaute event near the coast of Belgium is simulated utilizing the Weather Research and Forecasting (WRF) model. Comprehensive validation using measured power production data and diverse sets of meteorological data (e.g., floating lidars, radiosondes, and weather stations) indicates the potential of WRF to reproduce and forecast the boundary layer evolution during the event. Extensive sensitivity experiments with respect to grid-size, wind farm parameterization, and forcing datasets provide further insights on the reliability of the WRF model in capturing the Dunkelflaute event.

Accurate lidar-based measurements of cloud optical extinction, even though perhaps limited to the cloud base region, are useful. Arguably, more advanced lidar techniques (e.g. Raman) should be applied for this purpose. However, simpler polarisation and backscatter lidars offer a number of practical advantages (e.g. better resolution and more continuous and numerous time series). In this paper, we present a backscatter lidar signal inversion method for the retrieval of the cloud optical extinction in the cloud base region. Though a numerically stable method for inverting lidar signals using a far-end boundary value solution has been demonstrated earlier and may be considered as being well established (i.e. the Klett inversion), the application to high-extinction clouds remains problematic. This is due to the inhomogeneous nature of real clouds, the finite range resolution of many practical lidar systems, and multiple scattering effects. We use an inversion scheme, where a backscatter lidar signal is inverted based on the estimated value of cloud extinction at the far end of the cloud, and apply a correction for multiple scattering within the cloud and a range resolution correction. By applying our technique to the inversion of synthetic lidar data, we show that, for a retrieval of up to 90 m from the cloud base, it is possible to obtain the cloud optical extinction within the cloud with an error better than 5 %. In relative terms, the accuracy of the method is smaller at the cloud base but improves with the range within the cloud until 45 m and deteriorates slightly until reaching 90 m from the cloud base.

In the coming decades, both wind and solar power production will be playing increasingly important roles in Europe’s energy economy. It is absolutely essential that power grids are resilient against any unusual weather phenomena. One such meteorological phenomenon, “Dunkelflaute”, is causing serious concern to the renewable energy industry, which is primarily characterized by calm winds and overcast conditions. For example, a Dunkelflaute event happened in the Netherlands on 30th April 2018 leading to a significant shortfall in renewable energy generation requiring emergency intervention by the system operator. By analyzing this case, this paper investigates the performance of a state-of-the-art mesoscale model, Weather Research and Forecasting (WRF), in forecasting a Dunkelflaute event. Multiple WRF simulations are driven using real-time Global Forecast System (GFS) operational data over a range of prediction horizons. For comparison, a benchmark run is carried out using ERA5 reanalysis data as boundary conditions. Through validation using a variety of measured data covering onshore and offshore areas, wind speed is shown to be more predictable than cloud-cover in this particular case study.

Despite the importance of radar reflectivity (Z) measurements in the retrieval of liquid water cloud properties, it remains nontrivial to interpret Z due to the possible presence of drizzle droplets within the clouds. So far, there has been no published work that utilizes Z to identify the presence of drizzle above the cloud base in an optimized and a physically consistent manner. In this work, we develop a retrieval technique that exploits the synergy of different remote sensing systems to carry out this task and to subsequently profile the microphysical properties of the cloud and drizzle in a unified framework. This is accomplished by using ground-based measurements of Z, lidar attenuated backscatter below as well as above the cloud base, and microwave brightness temperatures. Fast physical forward models coupled to cloud and drizzle structure parameterization are used in an optimal-estimation-type framework in order to retrieve the best estimate for the cloud and drizzle property profiles. The cloud retrieval is first evaluated using synthetic signals generated from large-eddy simulation (LES) output to verify the forward models used in the retrieval procedure and the vertical parameterization of the liquid water content (LWC). From this exercise it is found that, on average, the cloud properties can be retrieved within 5 % of the mean truth. The full cloud–drizzle retrieval method is then applied to a selected ACCEPT (Analysis of the Composition of Clouds with Extended Polarization Techniques) campaign dataset collected in Cabauw, the Netherlands. An assessment of the retrieval products is performed using three independent methods from the literature; each was specifically developed to retrieve only the cloud properties, the drizzle properties below the cloud base, or the drizzle fraction within the cloud. One-to-one comparisons, taking into account the uncertainties or limitations of each retrieval, show that our results are consistent with what is derived using the three independent methods.

The representation of aerosol–cloud interaction (ACI) processes in climate models, although long studied, still remains the source of high uncertainty. Very often there is a mismatch between the scale of observations used for ACI quantification and the ACI process itself. This can be mitigated by using the observations from ground-based remote sensing instruments. In this paper we presented a direct application of the aerosol–cloud interaction monitoring technique (ACI monitoring). ACI monitoring is based on the standardised Cloudnet data stream, which provides measurements from ground-based remote sensing instruments working in synergy. For the data set collected at the CESAR Observatory in the Netherlands we calculate ACI metrics. We specifically use attenuated backscatter coefficient (ATB) for the characterisation of the aerosol properties and cloud droplet effective radius (re) and number concentration (Nd) for the characterisation of the cloud properties. We calculate two metrics: ACIr = ln(re)/ln(ATB) and ACIN = ln(Nd)/ln(ATB). The calculated values of ACIr range from 0.001 to 0.085, which correspond to the values reported in previous studies. We also evaluated the impact of the vertical Doppler velocity and liquid water path (LWP) on ACI metrics. The values of ACIr were highest for LWP values between 60 and 105 g m−2. For higher LWP other processes, such as collision and coalescence, seem to be dominant and obscure the ACI processes. We also saw that the values of ACIr are higher when only data points located in the updraught regime are considered. The method presented in this study allow for monitoring ACI daily and further aggregating daily data into bigger data sets.

Remote visibility (Vis) estimation by radar is of interest to aviation, road traffic, and other fields. Millimeter-wave radars are suitable candidates because of such advantages as high spatial resolution and sensitivity to small droplets in reflection and attenuation. To investigate remote Vis estimation and to develop physics-based models and algorithms, a 35 GHz cloud radar at the Cabauw Experimental Site for Atmospheric Research (CESAR) in the western part of the Netherlands has acquired data during fog periods in “fog mode.” The advantage in using millimeter-waves for remote sensing of fog is that they interact strong enough with fog for sensing it, but not too strong so that they can penetrate the fog allowing to sense fog top. Simultaneously, fog drop size distribution (DSD) and Vis are continuously automatically measured by the in situ optical sensors at CESAR. Radar reflectivity (Z) and Vis can be linked theoretically since they are related to the sixth and the second moments of an assumed Gamma-shaped DSD. However, in reality the fog DSD is not always Gamma-shaped, leading to errors in the Vis estimation. A further development of the Vis-Z model includes the attenuation factor (La), which is proportional to the liquid water content at a given radar wavelength. This improves the estimated accuracy of Vis, in the theoretical moments-based model. Finally, we were able to arrive at a higher accuracy by introducing an empirical exponential model, estimating Vis from Z and La. A test based on DSD data sets for various fog types in the literature showed robust performance of the Vis-Z-La model for large variations in DSD. The Vis-Z-La model is also validated with the actual fog data sets that were collected by the in situ and remote sensing instruments synergy at CESAR.

Weather radar is well recognized as an effective sensor for obtaining the microphysical and dynamical properties of precipitation at high spatial and temporal resolution. Radar calibration is one of the most important prerequisites for achieving accurate observations. In this article, a portable, cost-effective and repeatable radar calibration technique, namely, unmanned aerial vehicle (UAV)-aided radar calibration, is proposed. A UAV serves as the stable aerial platform carrying a metal sphere, flying over the radar illumination areas to complete the calibration process. The flying routine of the UAV can be pre-programmed, and thus, the antenna pattern regarding different elevation and azimuth angles can be retrieved. To obtain the position of the sphere, the real-time single-frequency precise point positioning-type global navigation satellite system solution is developed. In addition, the radar constant is calculated in the range-Doppler domain, and only the data where the metal sphere separates from clutter and other objects are selected. The S-band polarimetric Doppler transportable atmospheric radar (TARA) is used in the calibration campaign. The experiments demonstrate the following results: 1) antenna pointing calibration can be completed and 2) antenna pattern can be retrieved and weather radar constant can be accurately calculated.

In this article, five velocity-based energy dissipation rate (EDR) retrieval techniques are assessed. The EDR retrieval techniques are applied to Doppler measurements from Transportable Atmospheric Radar (TARA)—a precipitation profiling radar—operating in the vertically fixed-pointing mode. A generalized formula for the Kolmogorov constant is derived, which gives potential for the application of the EDR retrieval techniques to any radar line of sight (LOS). Two case studies are discussed that contain rain events of about 2 and 18 h, respectively. The EDR values retrieved from the radar are compared to in situ EDR values from collocated sonic anemometers. For the two case studies, a correlation coefficient of 0.79 was found for the wind speed variance (WSV) EDR retrieval technique, which uses 3D wind vectors as input and has a total sampling time of 10 min. From this comparison it is concluded that the radar is able to measure EDR with a reasonable accuracy. Almost no correlation was found for the vertical wind velocity variance (VWVV) EDR retrieval technique, as it was not possible to sufficiently separate the turbulence dynamics contribution to the radar Doppler mean velocities from the velocity contribution of falling raindrops. An important cause of the discrepancies between radar and in situ EDR values is thus due to insufficient accurate estimation of vertical air velocities.

A new model is proposed on how to account for the inertia of scatterers in radar-based turbulence intensity retrieval techniques. Rain drop inertial parameters are derived from fundamental physical laws, which are gravity, the buoyancy force, and the drag force. The inertial distance is introduced, which is a typical distance at which a particle obtains the same wind velocity as its surroundings throughout its trajectory. For the measurement of turbulence intensity, either the Doppler spectral width or the variance of Doppler mean velocities is used. The relative scales of the inertial distance and the radar resolution volume determine whether the variance of velocities is increased or decreased for the same turbulence intensity. A decrease can be attributed to the effect that inertial particles are less responsive to the variations of wind velocities. An increase can be attributed to inertial particles that have wind velocities corresponding to an average of wind velocities over their backward trajectories, which extend outside the radar resolution volume. Simulations are done for the calculation of measured radar velocity variance, given a 3-D homogeneous isotropic turbulence field, which provides valuable insight in the correct tuning of parameters for the new model.

In radar polarimetry, the differential phase consists of the propagation differential phase and the backscatter differential phase . While is commonly used for attenuation correction (i.e., estimation of the specific attenuation A and specific differential phase ), recent studies have demonstrated that can provide information concerning the dominant size of raindrops. However, the estimation of and is not straightforward given their coupled nature and the noisy behavior of , especially over short paths. In this work, the impacts of estimating on the estimation of A over short paths, using the extended version of the ZPHI method, are examined. Special attention is given to the optimization of the parameter α that connects and A. In addition, an improved technique is proposed to compute from and in rain. For these purposes, diverse storm events observed by a polarimetric X-band radar in the Netherlands are used. Statistical analysis based on the minimum errors associated with the optimization of α and the consistency between and A showed that more accurate and stable α and A are obtained if is estimated at range resolution, which is not possible by conventional range filtering techniques. Accurate estimates were able to depict the spatial variability of dominant raindrop size in the observed storms. By following the presented study, the ZPHI method and its variations can be employed without the need for considering long paths, leading to localized and accurate estimation of A and .

The cloud droplet number concentration (N d) is of central interest to improve the understanding of cloud physics and for quantifying the effective radiative forcing by aerosol‐cloud interactions. Current standard satellite retrievals do not operationally provide N d, but it can be inferred from retrievals of cloud optical depth (τ c) cloud droplet effective radius (r e) and cloud top temperature. This review summarizes issues with this approach and quantifies uncertainties. A total relative uncertainty of 78% is inferred for pixel‐level retrievals for relatively homogeneous, optically thick and unobscured stratiform clouds with favorable viewing geometry. The uncertainty is even greater if these conditions are not met. For averages over 1° ×1° regions the uncertainty is reduced to 54% assuming random errors for instrument uncertainties. In contrast, the few evaluation studies against reference in situ observations suggest much better accuracy with little variability in the bias. More such studies are required for a better error characterization. N d uncertainty is dominated by errors in r e, and therefore, improvements in r e retrievals would greatly improve the quality of the N d retrievals. Recommendations are made for how this might be achieved. Some existing N d data sets are compared and discussed, and best practices for the use of N d data from current passive instruments (e.g., filtering criteria) are recommended. Emerging alternative N d estimates are also considered. First, new ideas to use additional information from existing and upcoming spaceborne instruments are discussed, and second, approaches using high‐quality ground‐based observations are examined., Key Points Satellite cloud droplet concentration uncertainties of 78% for pixel‐level retrievals and 54% for 1 by 1 degree retrievals are estimatedThe effective radius retrieval is the most important aspect for improvement, and more in situ evaluation is neededPotential improvements using passive and active satellite, and ground‐based instruments are discussed

In 2007, IRCTR (Delft University of Technology) installed a new polarimetric X-band LFMCW radar (IDRA) at the meteorological observation site of Cabauw, The Netherlands. It provides plan position indicators (PPI) at a fixed elevation with a high range resolution of either 3 m or 30 m at a maximum observation range of 1.5 km and 15 km, respectively. IDRA aims to monitor precipitation events for the long-term analysis of the hydrological cycle. Due to the specifications of IDRA, the spatial and temporal variability of a large range of rainfall intensities (from drizzle to heavy convective rain) can be studied. Even though the usual observation range of IDRA is limited to 15 km, attenuation due to precipitation can be large enough to seriously affect the measurements. In this contribution we evaluate the application of a combined method to correct for the specific and the differential attenuation, and in the same vein estimate the parameters of the raindrop-size distribution. The estimated attenuations are compared to a phase constraint attenuation correction method.

One of the most beneficial polarimetric variables may be the specific differential phase KDP because of its independence from power attenuation and radar miscalibration. However, conventional KDP estimation requires a substantial amount of range smoothing as a result of the noisy characteristic of the measured differential phase ΨDP. In addition, the backscatter differential phase δhv component of ΨDP, significant at C- and X-band frequency, may lead to inaccurate KDP estimates. In this work, an adaptive approach is proposed to obtain accurate KDP estimates in rain from noisy ΨDP, whose δhv is of significance, at range resolution scales. This approach uses existing relations between polarimetric variables in rain to filter δhv from ΨDP while maintaining its spatial variability. In addition, the standard deviation of the proposed KDP estimator is mathematically formulated for quality control. The adaptive approach is assessed using four storm events, associated with light and heavy rain, observed by a polarimetric X-band weather radar in the Netherlands. It is shown that this approach is able to retain the spatial variability of the storms at scales of the range resolution. Moreover, the performance of the proposed approach is compared with two different methods. The results of this comparison show that the proposed approach outperforms the other two methods in terms of the correlation between KDP and reflectivity, and KDP standard deviation reduction.

In-situ measurements using Unmanned Aerial Vehicles (UAVs) and remote sensing observations can independently provide dense vertically-resolved measurements of atmospheric aerosols; information which is highly required in climate models. In both cases, inverting the recorded signals to useful information requires assumptions and constraints, and this can make the comparison of the results difficult. Here we compare, for the first time, vertical profiles of the aerosol mass concentration derived from Light Detection And Ranging (lidar) observations and in-situ measurements using an Optical Particle Counter (OPC) onboard a UAV during moderate and weak Saharan dust episodes. Agreement between the two measurement methods was within experimental uncertainty for the coarse-mode (i.e., particles having radii > 0.5 μm) where the properties of dust particles can be assumed with good accuracy. This result proves that the two techniques can be used interchangeably for determining the vertical profiles of the aerosol concentrations, bringing them a step closer towards their systematic exploitation in climate models.

Aiming at removing stationary and moving clutter while retaining precipitation for dual-polarization weather radar, a new clutter suppression method, named the object-orientated spectral polarimetric (OBSpol) filter, is put forward in this paper. Based on the spectral polarimetric feature and the range-Doppler continuity of precipitation, the OBSpol filter generates a filtering mask implemented on the raw range-Doppler spectrogram to mitigate the clutter and noise. The procedures are as follows. After the spectral polarimetric filtering and the mathematical morphology method, one binary mask where “1” indicates the precipitation is obtained. Then, the contiguous bins of the same value “1” in the range-Doppler domain are grouped in areas termed as objects. Whether the produced objects are precipitation or not will be further judged based on appropriate weather radar observable. Thus, combining all the filtered separate objects, a filtering mask can be obtained. In this paper, data collected by the polarimetric Doppler IRCTR drizzle radar are used to demonstrate and assess the performance of the proposed technique in the case of ground clutter, narrowband moving clutter, and noise. Two precipitation cases are examined: 1) moderate precipitation with large scale and 2) light precipitation with severe clutter contamination. In the range-Doppler spectrogram, both stationary and narrowband moving clutters are mitigated, while maintaining nonoverlapping precipitation signal. This helps to solve the problem when clutter and precipitation overlap in the time domain. In addition, the OBSpol filter is proven to be effective with different Doppler velocity resolutions. This technique can be applied in real-time due to its low computation complexity. Moreover, the spectral polarimetric filtering can be designed using the measurements of dual-polarization radar systems which do not have cross-polar measurements. Hence, the proposed clutter mitigation technique can be implemented for operational dual-polarization weather radar.

A novel algorithm is put forward to separate radar target and moving clutter based on a combination of the low-rank matrix optimization (LRMO) and the decision tree. Similar to the moving object detection in the field of automated video analysis, the proposed separation method, which is carried out in the range-Doppler domain, makes use of different motion variations of radar target and clutter in the spectrogram sequence. The technique is very general, but the focus of this paper is on narrowband moving clutter suppression in a weather radar. The first step in implementing this method is the generation of a range-Doppler spectrogram sequence. Then, the LRMO is applied to the obtained sequence to divide target and moving clutter into foreground and background. From the foreground sequence which is obtained by solving the LRMO, foreground frequency and spectral width are combined in a decision tree to obtain a filtering mask to mitigate the narrowband moving clutter and noise. Data collected by a polarimetric Doppler weather radar known as the IRCTR Drizzle Radar are used to validate the performance of the proposed algorithm. Moreover, its effectiveness in removing narrowband moving clutter is quantitatively assessed through comparisons with another clutter mitigation method.

Ground-based remote sensing of water clouds, using radar and lidar, has proven to be very succesfull in determining the microphysical properties of clouds. We discuss the methodologies and underlying assumptions. We conclude with a case study and show how ground-based sky radiation measurements can be used for validation.

Due to the contradiction between the high sensitivity requirement and low transmission power of weather radars with solid-state transmitters, a pulse compression technique is necessary. For the purpose of range sidelobe suppression, methods based on amplitude modulation and a mismatched filter are commonly used for target detection radars, which are not applicable for weather observations because of its drawbacks such as main lobe expansion and power loss. This letter presents a nonlinear frequency modulation pulse compression waveform to achieve a very low range sidelobe level. A mathematical model for waveform design is established, in which the time–frequency relation is expressed as the combination of a linear function and a sine series. Thus, a set of parameters can fully characterize the time–frequency curve. By using a simulated annealing algorithm, the optimal parameter set can be obtained, which shows that a peak sidelobe level under −60 dB is achievable. Finally, this kind of waveform is implemented on hardware, and its performance is verified.

In this paper, a new clutter suppression method, named the moving double spectral linear depolarization ratio (MDsLDR) filter, is put forward to mitigate narrow-band clutter in weather radars. The narrow-band clutter observed in the Doppler domain includes: 1) stationary clutter such as ground clutter and 2) nonstationary clutter such as artifacts caused by the radar system itself or external sources. These artifacts are difficult to remove, because they are not confined to specific azimuth and range bins. Based on the difference of the spectral-polarization feature and the spectral continuity of precipitation and clutter, the MDsLDR filter can remove ground clutter, artifacts, and noise. The performance of the newly proposed filter is assessed by data collected by the Doppler-polarimetric IRCTR Drizzle Radar. Three precipitation cases are considered in this paper: moderate/light precipitation, convective precipitation with hook-echo signature, and light precipitation with severe artifact contamination. Furthermore, the implementation of the MDsDLR filter requires relatively low computation complexity, so that the MDsLDR filter can be operated in real time.

The interaction of ice crystals with supercooled liquid droplets in mixed-phase clouds leads to an enhanced growth of ice particles. However, such processes are still not clearly understood although they are important processes for precipitation formation in midlatitudes. To better understand how ice particles grow within such clouds, changes in the microphysical parameters of a particle population falling through the cloud have to be analyzed. The Transportable Atmospheric Radar (TARA) can retrieve the full 3D Doppler velocity vector based on a unique three-beam configuration. Using the derived wind information, a new fall streak retrieval technique is proposed so that microphysical changes along those streaks can be studied. The method is based on Doppler measurements only. The shown examples measured during the Analysis of the Composition of Clouds with Extended Polarization Techniques (ACCEPT) campaign demonstrate that the retrieval is able to capture the fall streaks within different cloud systems. These fall streaks can be used to study changes in a single particle population from its generation (at cloud top) until its disintegration. In this study fall streaks are analyzed using radar moments or Doppler spectra. Synergetic measurements with other instruments during ACCEPT allow the detection of liquid layers within the clouds. The estimated microphysical information is used here to get a better understanding of the influence of supercooled liquid layers on ice crystal growth. This technique offers a new perspective for cloud microphysical studies.

Wind turbine clutter has gradually become a concern for the radar community for its increasing size and quantity worldwide. Based on the S-band polarimetric Doppler PARSAX radar measurements, this paper demonstrates the micro-Doppler features and spectral-polarimetric characteristic of wind turbine clutter, the probability distribution functions of different spectral-polarimetric variables. Finally, a simple thresholding method to remove wind turbine clutter is put forward, and its effectiveness can be verified by the measured data. This work is expected to contribute to developing effective algorithms for this dynamic clutter suppression for operational weather radar.

In 2007, the horizontally scanning polarimetric X-band radar IDRA (IRCTR Drizzle Radar) was installed on top of the 213 m high mast at the Dutch meteorological observatory Cabauw Experimental Site for Atmospheric Research (CESAR) at Netherlands. This radar complements a large variety of measurement instruments at CESAR by providing information on the horizontally spatial distribution and the temporal evolution of precipitation around the site. IDRA is a frequency-modulated continuous-wave radar developed at TU Delft's International Research Centre for Telecommunications and Radar (IRCTR). IDRA is designed to provide a high spatial resolution (down to 3 m in range) at a temporal resolution of 1 min. Its central frequency of 9.475 GHz, sensitive receivers with a large dynamic range, and the possibility to adjust the power of the transmitted signal permit IDRA to measure the whole spectrum of meteorological echoes from low-level clouds and drizzle to heavy convective rain. Similarly to most data collected at CESAR, also the data collected by IDRA are freely available for scientific purposes. IDRA data are stored at the Dutch 3TU.Datacentrum in order to make it easily accessible for everyone. In this article, we outline the IDRA dataset, including details on the data acquisition, processing, and possible applications.

The implementation of state-of-the-art retrieval techniques of wind vectors and turbulence intensity (EDR) will be presented. They are applied to measurements from the fast scanning X-band radar during the UFO trials at Toulouse airport. It will be demonstrated which retrievals, both for wind vectors and EDR, give the most reliable results when applied in the most challenging cases, that is when there are clouds or when it is raining.

The climate research community aims to better characterize climate forcings such as aerosols, reactive gases, and greenhouse gases, and to better understand the responses of the climate system to these forcings. Such investigations rely in part on monitoring, studying, and understanding essential climate variables such as temperature, water vapor, clouds, radiation, and perturbations of aerosols and reactive gases. According to Dufresne and Bony (2008), the parameters that play a predominant role in radiative feedbacks of the climate system are atmospheric humidity, adiabatic thermal gradients, clouds, and surface albedo. Interactions between humidity, clouds, aerosols, and radiation make climate predictions more complex. The climate research community has long recognized the link between climate prediction uncertainty and atmospheric process complexity. For more than 20 years, it has demonstrated the necessity to perform collocated long-term observations of thermodynamic parameters (temperature, humidity, wind) and atmospheric constituents (gases, aerosols, clouds) distributed along the entire atmospheric column (surface to stratosphere) and associated radiative components. As a result, the U.S. Department of Energy (DOE) launched the Atmospheric Radiation Measurement (ARM) Program in the 1990s (Ackerman and Stokes 2003; Stokes 2016, chapter 2). Four atmospheric profiling observation facilities were developed to gather in situ and remote sensing instruments to monitor physical processes in the atmospheric column. A large research community of observation experts and climate modelers was funded to exploit the observation data. Similar atmospheric profiling observation facilities associated with large scientific communities emerged in Europe at the end of the 1990s. Several European initiatives were triggered or encouraged through bilateral collaborations between U.S. and European Union (EU) scientists or through participation of EU scientists in ARM projects (e.g., Cabauw observatory in the Netherlands; Palaiseau observatory in France; Jülich observatory in Germany). Atmospheric profiling observatories provide scientists with the most resolved description of the atmospheric column. In Europe, as in the United States, these observatories have been collecting data every minute daily for more than a decade, allowing links to be established between processes occurring at diurnal or finer temporal scales and phenomenon occurring at climate scales. The limitation of an atmospheric profiling observatory is that it can only document one location of the globe with its specific atmospheric properties. The aerosol distributions, meteorological anomalies, and cloud properties observed at that location are representative of a limited spatial domain. Hence, atmospheric profiling observatories are needed at many locations around the globe to cover climatically diverse areas: near coasts, in continental plains, mountains, and urban environments. The U.S. ARM Program was designed initially to cover three distinct climatic regions (Cress and Sisterson 2016, chapter 5): the Arctic (Alaska), midlatitudes [U.S. southern Great Plains (SGP)], and the tropics [tropical western Pacific (TWP) Ocean]. Atmospheric profiling observatories in Europe were developed primarily over the European continent, extending from locations around the Mediterranean Basin to the Arctic, and including coastal, continental, urban, and mountain sites. The European Commission established several funding mechanisms to develop collaborations between researchers in Europe, to promote development of harmonized research infrastructures, and to reduce fragmentation in European research investments. As a result, in the past 10 years Europe was able to build an infrastructure essential to a large community of users by harmonizing aerosol, cloud, and trace gas observations across Europe. As infrastructures, measurement techniques, data interpretation algorithms, and scientific expertise developed on both sides of the Atlantic, scientists became interested in the added benefits of collaboration and cross-fertilization between the U.S. ARM Program and EU atmospheric profiling research observatories. To expand investigations beyond existing atmospheric observatories, U.S. ARM scientists and ARM Mobile Facility (AMF) infrastructures participated in field experiments initiated by EU programs. EU and U.S. ARM scientists developed collaborations to harmonize data interpretation algorithms and to exploit jointly U.S. and EU observation datasets. Further development of formal collaboration between U.S. ARM and EU programs would enhance the ability of scientists worldwide to take on science challenges about climate change. This chapter presents several European atmospheric profiling research observatories, development of European networking, and the current European research infrastructure (section 2). Section 3 presents EU program initiatives of interest for future collaboration with the ARM Program. Section 4 highlights collaborations that were developed subsequently between the U.S. ARM Program and its European counterparts. In section 5, we present an outlook toward future U.S.-EU collaborations around climate change challenges and observations.

Radar-based retrievals are often employed to characterize the microphysical properties of cloud hydrometeors, i.e., their phases, habits, densities as well as their respective size and orientation distributions. These techniques are based on a synergetic use of different cloud observation sensor(s) and microphysical algorithm(s) where the information extracted from both sensors and models are combined and converted into microphysical cloud properties. However, the amount of available information is often limited, which forces current microphysical retrieval techniques to base their algorithms on several microphysical assumptions which affect the retrieval accuracy. By simultaneously combining Doppler and polarimetric measurements obtained from fully Doppler polarimetric radar, it is possible to create spectral polarimetric parameters. Although these parameters are easily contaminated with unwanted echoes, this work shows that, from a correct radar signal processing based on filtering and averaging techniques, spectral polarimetric parameters can be correlated to microphysical cloud properties. In particular, preliminary results suggest that particle orientations and habits can be easily determined from the solely use of such spectral polarimetric parameters. Therefore, such additional microphysical information offers a great opportunity to improve current microphysical models by reducing their amount of microphysical assumptions.

The estimation of the specific differential phase particularly in the presence of the differential backscatter phase and nonuniform propagation paths is a long-standing goal in weather radar polarimetry. Furthermore, a reliable estimator of the differential backscatter phase for precipitation measurements has not been proposed yet, although it contains valuable information about the presence of non-Rayleigh scattering and the microphysics of precipitation. In this letter, we introduce a novel method to estimate the specific differential phase and the differential backscatter phase from polarimetric weather radar measurements of rain. This estimation does pay off particularly at the X-band where the scattering regime is non-Rayleigh already in moderate rain. In this case, the differential backscatter phase is useful as an additional weather radar observable with manifold applications such as rain rate estimation, microphysical retrieval, and operational radar calibration. The novel method also provides an improved range resolution of the specific differential phase compared to conventional estimators. We illustrate the estimation of the differential phases with data from the Delft University of Technology's polarimetric X-band International Research Centre for Telecommunications and Radar drizzle radar.

A simple method for estimating parameters of the Doppler spectrum of atmospheric signals is described. The method is based on an adaptive filter processing that has been widely used in telecommunications but rarely applied in measurements using atmospheric radars. The method has been tested using both synthetic and real data obtained by the radar profiler-Transportable Atmospheric Radar (TARA)-and the weather radar-International Research Centre for Telecommunications and Radar (IRCTR) Drizzle Radar (IDRA)-both developed by IRCTR. The method is compared with the traditional pulse-pair method and fast Fourier transform (FFT)-based methods, as well as with the IRCTR FFT-based method implemented in the TARA and IDRA processing including an additional noise clipping stage. The tests have demonstrated high efficiency of the adaptive filtering estimator under low signal-to-noise ratio conditions. The applicability of adaptive estimations in radar meteorology and the limitations associated with a sample length are discussed.

Within the framework of the international field campaign COPS (Convective and Orographically induced Precipitation Study), a large suite of state-of-the-art meteorological instrumentation was operated, partially combined for the first time. This includes networks of in situ and remote-sensing systems such as the Global Positioning System as well as a synergy of multi-wavelength passive and active remote-sensing instruments such as advanced radar and lidar systems. The COPS field phase was performed from 01 June to 31 August 2007 in a low-mountain area in southwestern Germany/eastern France covering the Vosges mountains, the Rhine valley and the Black Forest mountains. The collected dataset covers the entire evolution of convective precipitation events in complex terrain from their initiation, to their development and mature phase until their decay. Eighteen Intensive Observations Periods with 37 operation days and eight additional Special Observations Periods were performed, providing a comprehensive dataset covering different forcing conditions. In this article, an overview of the COPS scientific strategy, the field phase, and its first accomplishments is given. Highlights of the campaign are illustrated with several measurement examples. It is demonstrated that COPS research provides new insight into key processes leading to convection initiation and to the modification of precipitation by orography, in the improvement of quantitative precipitation forecasting by the assimilation of new observations, and in the performance of ensembles of convection-permitting models in complex terrain.

A new method for retrieving air velocity fluctuations in the cloud-capped boundary layer (BL) using radar reflectivity and the Doppler velocity fields is proposed. The method was developed on the basis of data obtained by the Transportable Atmospheric Radar (TARA) located in Cabauw, Netherlands, at 0500–0812 UTC 8 May 2004, and tested using a detailed trajectory ensemble model of the cloud-capped BL. During the observations, the BL depth was 1200 m, and the cloud base (measured by a lidar) was at 500–550 m. No preliminary assumptions concerning the shapes of drop size distributions were made. On the basis of the TARA radar data, vertical profiles of the vertical air velocity standard deviation, of turbulent dissipation rate, etc. were estimated. The correlation functions indicate the existence of large eddies in the BL with a characteristic horizontal scale of about 600 m. Analysis of the slope (the scaling parameter) of the structure functions indicates that turbulence above 400 m can be considered to be isotropic. Below this level, the turbulence becomes anisotropic. The rate of anisotropy increases with the decrease of the height above the surface. The averaged values of the dissipation rate were evaluated as 1–2 cm2 s−3. The importance of using the cloud-capped BL model as a link between different types of observed data (radar, lidar, aircraft, etc.) is discussed. More data should be analyzed to understand the changes in the turbulent structure of the BL during its growth, as well as during cloud and drizzle formation.

Water cloud optical and microphysical properties are required for a better understanding of the impact of aerosols on the solar radiation feedback. The introduced retrieval technique provides droplet concentration, effective radius and optical thickness on a basis of ground-based remote sensing observations and a vertical cloud model. Mainly cloud radar, microwave radiometer and ceilometer observations are used in this approach. The model assumptions are related to a sub-adiabatic approach, in which cloud mixing processes are predefined to be homogeneous. A gamma droplet size distribution with a fixed shape parameter is considered to relate the observations with the retrieval products. The technique is applied on a water cloud case and the uncertainties in relation to the model assumptions and errors in the measurements are determined. The greatest uncertainty in the retrieval of droplet concentration is related to the fixed shape parameter, which requires in-situ data for validation processes to quantify the results. The optical parameters are less sensitive to the model assumptions and they are evaluated with a radiative closure experiment. They are used as input for radiative transfer calculations in order to compare the simulations with radiation measurements at the ground. There is a relatively good agreement between the simulated and measured radiation considering the horizontal cloud inhomogeneity, although a bias of around 5% still exists. Therefore the technique might be a suitable approach of retrieving quantitative cloud optical properties independently from radiation observations.

Human activity is known to have an important impact both in the radiation budget and the hydrological cycle. One particular aspect is the increase of anthropogenic aerosols due to changes in land use and industrial activity. However the relation between aerosols and precipitation is still far from being fully understood due to the complexity of the mechanisms involved and the lack of sound data. A multi-sensor approach, combining instruments meant to study aerosols and others meant at the study of precipitation, is required to get a better insight on this phenomenon. The International Research Centre for Telecommunications and Radar (IRCTR) has developed a meteorological radar called IDRA (IRCTR Drizzle Radar) aimed at obtaining high resolution temporal and spatial measurements of all sorts of precipitation including those non-detectable by standard meteorological radar like drizzle. The measurements obtained by the system, coupled with data obtained by other sensors placed in the Cabauw Experimental Site for Atmospheric Research (CESAR) observatory (Cabauw, The Netherlands), are expected to provide crucial information for a better characterization of these mechanisms. The information obtained can be used for a better spatial planning in order to reduce the impact of human activity in the climate. This paper describes the relevant characteristics of the system, its signal processing and the products obtained.

This paper describes advances in ground-based thermodynamic profiling of the lower troposphere through sensor synergy. The well-documented integrated profiling technique (IPT), which uses a microwave profiler, a cloud radar, and a ceilometer to simultaneously retrieve vertical profiles of temperature, humidity, and liquid water content (LWC) of nonprecipitating clouds, is further developed toward an enhanced performance in the boundary layer and lower troposphere. For a more accurate temperature profile, this is accomplished by including an elevation scanning measurement modus of the microwave profiler. Height-dependent RMS accuracies of temperature (humidity) ranging from ∼0.3 to 0.9 K (0.5–0.8 g m−3) in the boundary layer are derived from retrieval simulations and confirmed experimentally with measurements at distinct heights taken during the 2005 International Lindenberg Campaign for Assessment of Humidity and Cloud Profiling Systems and its Impact on High-Resolution Modeling (LAUNCH) of the German Weather Service. Temperature inversions, especially of the lower boundary layer, are captured in a very satisfactory way by using the elevation scanning mode. To improve the quality of liquid water content measurements in clouds the authors incorporate a sophisticated target classification scheme developed within the European cloud observing network CloudNet. It allows the detailed discrimination between different types of backscatterers detected by cloud radar and ceilometer. Finally, to allow IPT application also to drizzling cases, an LWC profiling method is integrated. This technique classifies the detected hydrometeors into three different size classes using certain thresholds determined by radar reflectivity and/or ceilometer extinction profiles. By inclusion into IPT, the retrieved profiles are made consistent with the measurements of the microwave profiler and an LWC a priori profile. Results of IPT application to 13 days of the LAUNCH campaign are analyzed, and the importance of integrated profiling for model evaluation is underlined.

In this study, a dual-polarization spectral analysis for retrieval of microphysical properties of ice hydrometeors is developed. It is shown that, by using simultaneous Doppler polarimetric observations taken at a 45° elevation angle, it is possible to discriminate between different types of ice particles. Particle size distribution parameters for maximally two dominating types of ice particles (aggregates and plates) observed above the melting layer are retrieved. Prior to the retrieval algorithm, a selection of possible types of ice particles based on environmental conditions is carried out. The retrieval procedure is based on a least squares optimization that simultaneously minimizes fit residuals in a Doppler power spectrum and spectral differential reflectivity. The proposed method is illustrated on transportable atmospheric radar (TARA) observations of stratiform rain collected on 19 September 2001 at Cabauw, Netherlands.

In situ measurements indicate the complexity and nonunique character of radar reflectivity–liquid water content (Z–LWC) relationships in stratocumulus and cumulus clouds. Parameters of empirical (statistical) Z–LWC dependences vary within a wide range. Respectively, the accuracy of retrieval algorithms remains low. This situation is partially related to the fact that empirical algorithms and parameters are often derived without a corresponding understanding of physical mechanisms responsible for the formation of the Z–LWC diagrams. In this study, the authors investigate the processes of formation of the Z–LWC relationships using a new trajectory ensemble model of the cloud-topped boundary layer (BL). In the model, the entire volume of the BL is covered by Lagrangian parcels advected by a turbulent-like velocity field. The time-dependent velocity field is generated by a turbulent model and obeys the correlation turbulent laws. Each Lagrangian parcel represents the “cloud parcel model” with an accurate description of processes of diffusion growth–evaporation of aerosols and droplets and droplet collisions. The fact that parcels are adjacent to each other allows one to calculate sedimentation of droplets and precipitation (drizzle) formation. The characteristic parcel size is 50 m; the number of parcels is 1840. The model calculates droplet size distributions (DSDs), as well as their moments (e.g., aerosol and drop concentration, mass content, radar reflectivity) in each parcel. In the course of the model integration, Z–LWC relationships are calculated for each parcel, as well as the scattering diagram including all parcels. The model reproduces in situ observed types of the Z–LWC relationships. It is shown that different regimes represent different stages of cloud evolution: diffusion growth, beginning of drizzle formation, and stage of heavy drizzle, respectively. The large scattering of the Z–LWC relationships is found to be an inherent property of any drizzling cloud. Different zones on the Z–LWC diagram are related to cloud volumes located at different levels within a cloud and having different DSD. This finding allows for improvement of retrieval algorithms.

The Cloudnet project aims to provide a systematic evaluation of clouds in forecast and climate models by comparing the model output with continuous ground-based observations of the vertical profiles of cloud properties. In the models, the properties of clouds are simplified and expressed in terms of the fraction of the model grid box, which is filled with cloud, together with the liquid and ice water content of the clouds. These models must get the clouds right if they are to correctly represent both their radiative properties and their key role in the production of precipitation, but there are few observations of the vertical profiles of the cloud properties that show whether or not they are successful. Cloud profiles derived from cloud radars, ceilometers, and dual-frequency microwave radiometers operated at three sites in France, Netherlands, and the United Kingdom for several years have been compared with the clouds in seven European models. The advantage of this continuous appraisal is that the feedback on how new versions of models are performing is provided in quasi-real time, as opposed to the much longer time scale needed for in-depth analysis of complex field studies. Here, two occasions are identified when the introduction of new versions of the ECMWF and Météo-France models leads to an immediate improvement in the representation of the clouds and also provides statistics on the performance of the seven models. The Cloudnet analysis scheme is currently being expanded to include sites outside Europe and further operational forecasting and climate models.