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Optimizing radar observation strategies is one of the most important considerations in pre-field campaign periods. This is especially true for isolated convective clouds that typically evolve faster than the observations captured by operational radar networks. This study investigates uncertainties in radar observations of the evolution of the microphysical and dynamical properties of isolated deep convective clouds developing in clean and polluted environments and aims to optimize the radar observation strategy for deep convection through the use of cloud-resolving model simulations coupled with a radar simulator and a cell tracking algorithm. Our analysis results include the following four outcomes. First, a 5-7 m s-1 median difference in maximum updrafts of tracked cells was shown between the clean and polluted simulations in the early stages of the cloud lifetimes. This demonstrates the importance of obtaining accurate estimates of vertical velocity from observations if aerosol impacts are to be properly resolved. Second, tracking of individual cells and using vertical cross section scanning every minute captures the evolution of precipitation particle number concentration and size represented by polarimetric observables better than the operational radar observations that update the volume scan every 5 min. This approach also improves the multi-Doppler radar updraft retrievals above 5 km AGL for regions with updraft velocities greater than 10 m s-1. Third, we propose an optimized strategy which is composed of cell tracking by quick (1-2 min) vertical cross section scans from more than one radar in addition to the operational volume scans. We also propose the use of a single range-height indicator updraft retrieval technique for cells close to the radars, where the multi-Doppler radar retrievals are still challenging. Finally, increasing the number of deep convective cells sampled by such observations better represents the median maximum updraft evolution with sample sizes of more than 10 deep cells, which decreases the error associated with sampling the true population to less than 3 m s-1. [ABSTRACT FROM AUTHOR]

The WIVERN (WInd VElocity Radar Nephoscope) mission, soon entering in Phase-0 of the ESA Earth Explorer program, promises to complement Doppler wind lidar by globally observing, for the first time, vertical profiles of winds in cloudy areas. This work describes an end to end simulator of the WIVERN conically scanning 94 GHz Doppler radar, the only payload of the mission. Specific features of the simulator are: the conically scanning geometry; the inclusion of cross-polarization effects and of the simulation of a radiometric mode; the applicability to global cloud model outputs via an orbital model; the incorporation of a mispointing model accounting for thermo-elastic distortions, microvibrations, star-trackers uncertainties, etc.; the inclusion of the surface clutter. Some of the simulator capabilities are showcased for a case study involving a full rotational scan of the instrument. The simulator represents a very useful tool for studying the performances of the WIVERN concept and possible trade-offs for the different configurations (e.g. different antenna sizes, pulse lengths, antenna patterns, .....). Thanks to its modular structure the simulator can be easily adapted to different orbits, different scanning geometries and different frequencies. [ABSTRACT FROM AUTHOR]

The Micro Rain Radar (MRR) PRO is a K-band Doppler weather radar, using frequency modulated continuous wave (FMCW) signals, developed by Metek Meteorologische Messtechnik GmbH (Metek) as successor to the MRR-2. Benefiting from four datasets collected during two field campaigns in Antarctica and Switzerland, we developed a processing library for snowfall measurements, named ERUO (Enhancement and Reconstruction of the spectrUm for the MRR-PRO), with a two-fold objective. Firstly, the proposed method addresses a series of issues plaguing the radar variables, which include interference lines, power drops at the extremes of the Doppler spectrum and abrupt cutoff of the transfer function. Secondly, the algorithm aims to improve the quality of the final variables, by lowering the minimum detectable equivalent attenuated reflectivity factor and extending the valid Doppler velocity range through antialiasing. The performance of the algorithm has been tested against the measurements of a co-locatedW-band Doppler radar. Information from a close-by X-Band Doppler dual-polarization radar has been used to exclude unsuitable radar volumes from the comparison. Particular attention has been dedicated to verify the estimation of the meteorological signal in the spectra covered by interferences. [ABSTRACT FROM AUTHOR]

The appearance of second trip echoes generated by mirror images over the ocean and by multiple scattering tails in correspondence of deep convective cores has been investigated for space-borne nadir-looking W-band cloud radar observations. Examples extracted from the CloudSat radar are used to demonstrate the mechanisms of formation and to validate the modeling of such returns. A statistical analysis shows that, for CloudSat observations, second trip echoes are rare and appear only above 20 km (thus easy to remove). CloudSat climatology is then used to estimate the occurrence of second trip echoes in the different configurations envisaged for the operations of the EarthCARE radar, which will adopt pulse repetition frequencies much higher than the one used by the CloudSat radar in order to improve its Doppler capabilities. Our findings predict that the presence of such echoes in EarthCARE observations cannot be neglected: in particular, over the ocean, mirror images will tend to populate the EarthCARE sampling window with a maximum frequency at its upper boundary. This will create an additional fake cloud cover in the upper troposphere (of the order of 3 % at the top of the sampling window and steadily decreasing moving downwards) and, in much less frequent instances, it will cause an amplification of signals in areas where clouds are already present. Multiple scattering tails will produce also second trip echoes but with much lower frequencies: less than one profile out of 1000 in the Tropics and practically no effects at high latitudes. At the moment Level-2 algorithms of the EarthCARE radar do not account for such occurrences. We recommend to properly remove these second trip echoes and to correct for reflectivity enhancements, where needed. More generally this work is relevant for the design of future space-borne Doppler W-band radar missions. [ABSTRACT FROM AUTHOR]

Assimilation of weather radar measurements including radar reflectivity and radial wind data has been operational at the Deutscher Wetterdienst, with a diagonal observation error (OE) covariance matrix. For an implementation of a full OE covariance matrix, the statistics of the OE have to be a priori estimated, for which the Desroziers method has been often used. However, the resulted statistics consists of contributions from different error sources and are difficult to interpret. In this work, we use an approach that is based on samples for truncation error in radar observation space to approximate the representation error due to unresolved scales and processes (RE) and compare its statistics with the OE statistics estimated by the Desroziers method. It is found that the statistics of the RE help the understanding of several important features in the variances and correlation length scales of the OE for both reflectivity and radial wind data and the other error sources from the microphysical scheme, radar observation operator and the superobbing technique may also contribute, for instance, to differences among different elevations and observation types. The statistics presented here can serve as a guideline for selecting which observations to assimilate and for assignment of the OE covariance matrix that can be diagonal or full and correlated. [ABSTRACT FROM AUTHOR]

Polarimetric radar systems are commonly used to study the microphysics of precipitation. While they offer continuous measurements with a large spatial coverage, retrieving information about the microphysical processes that govern the evolution of snowfall from the polarimetric signal is challenging. The present study develops a new method, called Process Identification based on Vertical gradient Signs (PIVS), to spatially identify the occurrence of the main microphysical processes (aggregation and riming, crystal growth by vapor deposition, and sublimation) in snowfall from dual-polarization Doppler radar scans. We first derive an analytical framework to assess in which meteorological conditions the local vertical gradients of radar variables reliably inform about microphysical processes. In such conditions, we then identify regions dominated by (i) vapor deposition, (ii) aggregation and riming and (iii) snowflake sublimation and possibly snowflake breakup based on the sign of the local vertical gradients of the reflectivity ZH and the differential reflectivity ZDR. The method is then applied to data from two frontal snowfall events: one in coastal Adélie Land, Antarctica and one in the Taebaeck mountains in South Korea. The validity of the method is assessed by comparing its outcome with snowflake observations using a Multi-Angle Snowflake Camera and with the output of a hydrometeor classification based on polarimetric radar signal. The application of the method further makes it possible to better characterize and understand how snowfall forms, grows and decays in two different geographical and meteorological contexts. For the Antarctic case study, we show that crystal growth by vapor deposition dominates above 2500 m a.g.l., aggregation and riming prevail between 1500 and 2500 m a.g.l. and snowflake sublimation by low-level katabatic winds occurs below 1500 m a.g.l.. For the event in South Korea, aggregation and riming dominate between 4000 and 4800 m a.g.l., with local sublimation below and vapor deposition above. We infer some microphysical characteristics in terms of radar variables from statistical analysis of the method output (e.g. ZH and ZDR distribution for each process). We finally highlight the potential for extensive application to cold precipitation events in different meteorological contexts. [ABSTRACT FROM AUTHOR]

Correct, timely and meaningful interpretation of polarimetric weather radar observations requires an accurate understanding of hydrometeors and their associated microphysical processes along with well-developed techniques that automatize their recognition in both the spatial and temporal dimensions of the data. This study presents a novel technique for identifying different types of hydrometeors from Quasi-Vertical Profiles (QVP). In this new technique, the hydrometeor types are identified as clusters belonging to a hierarchical structure. The number of different hydrometeor types in the data is not predefined and the method obtains the optimal number of clusters through a recursive process. The optimal clustering is then used to label the original data. Initial results using observations from the NCAS X-band dual-polarization Doppler weather radar (NXPol) show that the technique provides stable and consistent results. Comparison with available airborne in situ measurements also indicates the value of this novel method for providing a physical delineation of radar observations. Although this demonstration uses NXPol data, the technique is generally applicable to similar multivariate data from other radar observations. [ABSTRACT FROM AUTHOR]

Vertical wind profiles are the foundation for numerical weather prediction systems research. Large-scale vertical wind data have been previously documented from network observations in several countries, but the nationwide vertical wind observations are poorly understood in China. In this study, the salient characteristics and performance of vertical winds as observed by the radar wind profiler network of China was investigated, which consists of more than 100 stations instrumented with 1290-MHz Doppler radar designed primarily for measuring vertical-resolved winds. This network has good spatial coverage, with denser sites in coastal areas. The vertical wind profiles observed by this network can provide the horizontal wind direction, horizontal wind speed, and vertical wind speed for every 120 m interval within the height of 0 to 3 km. The availability of the radar wind profiler network has been investigated in terms of effective detection height, data acquisition rate, data confidence, and data accuracy. Further comparison analysis with reanalysis data indicated that the observation data at 89 stations are recommended, and 17 stations are unrecommended. The vertical wind profiles can serve as important input dataset assimilated into numerical weather prediction system at both regional and global scales. [ABSTRACT FROM AUTHOR]

In this study, a shipborne 95 GHz Doppler cloud radar mounted on a stabilized platform is used to retrieve vertical profiles of three-dimensional (3D) winds by sequentially pointing the stabilized platform in different directions. A specific challenge is that the maximum angle off zenith is 8°, which implies that the projection of the horizontal wind components onto the radar beam directions is a small component of Doppler velocity in most cases. A variational 3D wind retrieval technique is then described, allowing for 1-minute resolution 3D wind profiles to be retrieved. Statistical comparisons with 3-hourly radiosonde launches and qualitative comparisons with ship-level horizontal winds demonstrate that accurate 3D wind profiles can be obtained from such cloud radar observations at small off-zenith angles. [ABSTRACT FROM AUTHOR]

The Wuhan MST radar is a 53.8 MHz monostatic Doppler radar, located in Chongyang, Hubei Province, China, which has the capability to observe the dynamics of the mesosphere-stratosphere-troposphere region in the subtropical latitudes. The system is composed of 576 Yagi antennas with square distribution, and the maximum peak power is 192 kW. The Wuhan MST radar is efficient and cheap, which applies simplifier and more flexible architecture. It includes 24 big TR modules, and the row/column data port of each big TR module connects 24 small TR modules via the corresponding row/column feeding network. Each antenna is driven by a small TR module with peak output power of 300 W. The arrangement of the antenna field, the functions of the timing signals, the structure of the TR modules, and the clutter suppression procedure are described in detail in this manuscript. We compared the MST radar observation results with other instruments and related models in the whole MST region for validation. Firstly, we made a comparison of the Wuhan MST radar observed horizontal winds in the troposphere and low stratosphere with the radiosonde in the short term, as well as the ERA-interim data sets (2016 and 2017) in the long term. Then, we made a comparison of the observed horizontal winds in the mesosphere with the meteor radar and the HWM-07 model in the same way. In general, good agreements can be obtained, and it indicates that the Wuhan MST is an effective tool to measure the three-dimensional wind fields of the MST region in the short-term and long-term. [ABSTRACT FROM AUTHOR]

The accurate representation of ice particles is essential for both remotely-sensed estimates of cloud and precipitation and numerical models of the atmosphere. As it is typical in radar retrievals to assume that all snow is composed of unrimed aggregate snowflakes, both denser rimed snow and the mixed-phase cloud in which riming occurs may be under-diagnosed in retrievals, and therefore difficult to evaluate in weather and climate models. Recent experimental and numerical studies have yielded methods for using triple-frequency radar measurements to distinguish fractal aggregate snowflakes from more dense and homogeneous rimed particles. In this study we investigate which parameters of the particle size distribution (PSD) and morphology of ice particles are most important to the triple-frequency radar signature of snow, in order to carry out an optimal estimation retrieval using triple-frequency Doppler radar observations. We represent a range of ice particle morphologies using a fractal model for aggregate snowflakes and homogeneous spheroids to represent rimed graupel-like particles, and modulate the prefactor and exponent of the particles' mass-size relations with a density factor. We find that for both fractal particles and homogeneous spheroids the PSD shape has a greater influence on the triple-frequency radar signature than the density factor, and show that the PSD shape must be allowed to vary to adequately constrain a triple-frequency radar retrieval of snow. We then demonstrate a novel triple-frequency Doppler radar retrieval of three parameters of the PSD as well as particle density, and show that the estimated snow rate, PSD and bulk density compare well against in situ observations at the surface. In a case study of compact rimed snow, we find that triple-frequency radar measurements provide a strong constraint on the estimation of PSD shape, but a relatively weak constraint on particle density, which we find can be more directly estimated from the Doppler velocity due to the relation between particle density and fallspeed. Including variations in PSD shape as well as particle morphology allows for a better representation of the triple-frequency radar signatures of rimed and unrimed snow, and suggests the potential for making new insights into the interaction between particles during aggregation and riming mechanisms. However, we find that improved representation of the PSD shape has a limited impact on improved estimates of snow rate from radar. The importance of the PSD shape to triple-frequency radar retrievals of snow suggests that further work is needed to account for variations in PSD shape before triple-frequency radar measurements can be used to better constrain particle morphology. [ABSTRACT FROM AUTHOR]

Cloud radars are capable of providing continuous high-resolution observations of the cloud. These observations are related to the microphysical properties of clouds. Power law relations in the form of Z = a · LWCb are generally used to estimate liquid water content (LWC) profiles. The constants a and b from the power-law relation vary with the cloud type and cloud characteristics. Due to the variety of such parameterizations, selecting the most appropriate Z-LWC relation for a continuous cloud system is complicated. Additional information such as Liquid water path (LWP) from a co-located microwave radiometer is used to scale the LWC of the cloud profile. An algorithm for estimating the LWC of warm clouds using radar-microwave radiometer synergy in a variational framework is presented. This method also accounts for attenuation due to cloud droplets and retrieves a suitable scaling factor (lna) of the profile in addition to the LWC. The optimal estimation techniques incorporate a priori information of desired variables, and the forward model converts these variables into observation parameters. In this algorithm formulation, the measure of uncertainty in observations, forward model and, a priori acts as weights in the retrieved quantities. These uncertainties in the retrieval are analyzed in the sensitivity analysis of the algorithm. The retrieval algorithm is first tested on a synthetic profile for different perturbations in sensitivity parameters. The sensitivity study has shown that this method is susceptible to LWP information. The algorithm is then implemented to various cloud and fog cases at SIRTA observatory to estimate LWC and the scaling factor. The scaling factor changes for each cloud profile, and the range of lna are consistent with suggested values in literature. The validation of such an algorithm is challenging, as we need reference measurements of LWC co-located with the retrieved values. During the SOFOG-3D campaign (South-West of France, October 2019 to March 2020), in-situ measurements of LWC were collected in the vicinity of a cloud radar and a microwave radiometer, allowing comparison of retrieved and measured LWC. The comparison demonstrated that the cloud-fog heterogeneity was playing a key role in the assessment. The proposed synergistic retrieval algorithm is applied to 39 cloud and fog cases at SIRTA, and the behavior of the scaling factor is studied. This statistical analysis of scaling is carried out to develop a radar-only retrieval method. The climatology revealed that the scaling factor can be linked to the maximum reflectivity of the profile. From climatology, the statistical relations for scaling factor are proposed for fog and cloud. Thanks to the variational framework, a stand-alone radar version of the algorithm is adapted from the synergistic retrieval algorithm, which incorporates the climatology of scaling factor as a priori information to estimate the LWC of warm cloud. [ABSTRACT FROM AUTHOR]