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The Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE) was a year-round campaign conducted by the US Department of Energy at the Scripps Institution of Oceanography in La Jolla, CA, USA, with a focus on characterizing atmospheric processes at a coastal location. The ground-based prototype of a new Ka-, W-, and G-band (35.75, 94.88, and 238.8 GHz) profiling atmospheric radar, named CloudCube, which was developed at the Jet Propulsion Laboratory, took part in the experiment during 6 weeks in March and April 2023. This article describes the unique data sets that were obtained during the field campaign from a variety of marine clouds and light precipitation. These are, to the best of the authors' knowledge, the first observations of atmospheric clouds using simultaneous multifrequency measurements including 238.8 GHz. These data sets therefore provide an exceptional opportunity to study and analyze hydrometeors with diameters in the millimeter- and submillimeter size range that can be used to better understand cloud and precipitation structure, formation, and evolution. The data sets referenced in this article are intended to provide a complete, extensive, and high-quality collection of G-band data in the form of Doppler spectra and Doppler moments. In addition, Ka-band and W-band reflectivity and Ka-, W-, and G-band reflectivity ratio profiles are included for several cases of interest on 6 different days. The data sets can be found at https://doi.org/10.5281/zenodo.10076227 (Socuellamos et al., 2024). [ABSTRACT FROM AUTHOR]

The firn layer on the Greenland Ice Sheet (GrIS) plays a crucial role in buffering surface meltwater runoff, which is constrained by the available firn pore space and impermeable ice layers that limit deeper meltwater percolation. Understanding these firn properties is essential for predicting current and future meltwater runoff and its contribution to global sea-level rise. While very-high-frequency (VHF) radars have been extensively used for surveying the GrIS, their lower bandwidth restricts direct firn stratigraphy extraction. In this study, we use concurrent VHF airborne radar and laser altimetry data collected as part of Operation IceBridge over the 2011–2019 period to investigate our hypothesis that vertical heterogeneities in firn (i.e. ice layers) cause vertical offsets in the radar surface reflection (dz). Our results, corroborated by modelling and firn core analyses, show that a dz larger than 1 m is strongly related to the vertical heterogeneity of a firn profile and effectively delineates between vertically homogeneous and vertically heterogeneous firn profiles over a depth range of ∼ 4 m. Temporal variations in dz align with climatic events and reveal an expansion of heterogeneous firn between 2011–2013 covering an area of ∼ 350 815 km 2 , followed by firn replenishment over the years 2014–2019 spanning an area of ∼ 667 725 km 2. Our approach reveals the firn evolution of key regions on the Greenland Ice Sheet, providing valuable insights for detecting potential alterations in meltwater runoff patterns. [ABSTRACT FROM AUTHOR]

Global marine gravity anomaly models are predominantly recovered from along-track radar altimeter data. While remarkable advancements has been achieved in gravity anomaly modelling, the quality of gravity anomaly model remains constrained by the absence of across-track geoid gradients and the reduction of radar altimeter data, particularly in coastal and high-latitudes regions. ICESat-2 laser altimetry operates three-pair laser beams with a small footprint and near-polar orbit, enabling the determination of across-track geoid gradients and providing more valid observations in certain regions. The ICESat-2 altimeter data processing method is presented including the determination of across-track geoid gradients and the combination of along/across-track geoid gradients. A new global marine gravity model, SDUST2022GRA, is recovered from radar and laser altimeter data using different method for determining each altimeter data error. The accuracy and spatial resolution of SDUST2022GRA is assessed by published global gravity anomaly models (DTU17, V32.1, NSOAS22) and available shipborne gravity measurements. The accuracy of SDUST2022GRA is 4.43 mGal on a global scale, which is at least 0.22 mGal better than that of others models. Moreover, in local coastal and high-latitude regions, SDUST2022GRA achieves an accuracy improvement of 0.16–0.24 mGal compared to others models. The spatial resolution of SDUST2022GRA is approximately 20 km in a certain region, slightly better superior others models. These assessments suggests that SDUST2022GRA is a reliable global marine gravity anomaly model. By comparing SDUST2022GRA with incorporating ICESat-2 and SDUST2021GRA without ICESat-2, the percentage contribution of ICESat-2 to the improvement of gravity anomaly model accuracy is 13 % in the global ocean region, and it is increasing with an proportion of ICESat-2 altimeter data in high-latitude and coastal regions. The SDUST2022GRA are freely available at the site of https://doi.org/10.5281/zenodo.8337387 (Li et al., 2023). [ABSTRACT FROM AUTHOR]

This paper presents an open-access data set of reanalysed radar reflectivities and rainfall rates at sub-kilometre spatial and minute temporal scales. Variability at these scales is a blind spot for both operational rain gauge networks and operational radar networks. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at high temporal (30s), range (60m), and azimuthal sampling (1°) resolutions are made available for a period of more than 8 years. We describe in detail the reanalysis of the raw radar data, outline the radar performance for the years 2013 to 2021, and discuss open issues and limitations of the data set. Several sources of radar-based errors were adjusted gradually, affecting the radar reflectivity and rainfall measurements, e.g. noise, alignment, non-meteorological echoes, radar calibration, and attenuation. The deployment of additional vertically pointing micro rain radars yields drop size distributions at the radar beam height, which effectively reduces errors concerning the radar calibration and attenuation correction and monitors the radar data quality. A statistical evaluation revealed that X-band radar reflectivities and rainfall rates are in very good agreement with the micro rain radar measurements. Moreover, the analyses of rainfall patterns shown for an event and accumulated rainfall of several months prove the quality of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates themselves can be used for studies on the spatial and temporal scales of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. The radar reflectivities and rainfall rates are available at https://doi.org/10.26050/WDCC/LAWR_UHH_HHG_v2. [ABSTRACT FROM AUTHOR]

Stratiform rain and the overlying ice play crucial roles in Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles' concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a spaceborne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a large increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold-rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and the precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling. [ABSTRACT FROM AUTHOR]

Mixed-phase clouds are not well represented in climate and weather forecasting models, due to a lack of the key processes controlling their life cycle. Developing methods to study these clouds is therefore essential, despite the complexity of mixed-phase cloud processes and the difficulty of observing two cloud phases simultaneously. We propose in this paper a new method to retrieve the microphysical properties of mixed-phase clouds, ice clouds and supercooled water clouds using airborne or satellite radar and lidar measurements, called VarPy-mix. This new approach extends an existing variational method developed for ice cloud retrieval using lidar, radar and passive radiometers. We assume that the lidar attenuated backscatter β at 532 nm is more sensitive to particle concentration and is consequently mainly sensitive to the presence of supercooled water. In addition, radar reflectivity Z at 95 GHz is sensitive to the size of hydrometeors and hence more sensitive to the presence of ice particles. Consequently, in the mixed phase the supercooled droplets are retrieved with the lidar signal and the ice particles with the radar signal, meaning that the retrievals rely strongly on a priori and error values. This method retrieves simultaneously the visible extinction for ice αice and liquid αliq particles, the ice and liquid water contents IWC and LWC, the effective radius of ice re,ice and liquid re,liq particles, and the ice and liquid number concentrations Nice and Nliq. Moreover, total extinction αtot , total water content (TWC) and total number concentration Ntot can also be estimated. As the retrieval of ice and liquid is different, it is necessary to correctly identify each phase of the cloud. To this end, a cloud-phase classification is used as input to the algorithm and has been adapted for mixed-phase retrieval. The data used in this study are from DARDAR-MASK v2.23 products, based on the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Cloud Profiling Radar (CPR) observations from the CALIPSO and CloudSat satellites, respectively, belonging to the A-Train constellation launched in 2006. Airborne in situ measurements performed on 7 April 2007 during the Arctic Study of Tropospheric Aerosol, Clouds and Radiation (ASTAR) campaign and collected under the track of CloudSat–CALIPSO are compared with the retrievals of the new algorithm to validate its performance. Visible extinctions, water contents, effective radii and number concentrations derived from in situ measurements and the retrievals showed similar trends and are globally in good agreement. The mean percent error between the retrievals and in situ measurements is 39 % for αliq , 398 % for αice , 49 % for LWC and 75 % for IWC. It is also important to note that temporal and spatial collocations are not perfect, with a maximum spatial shift of 1.68 km and a maximum temporal shift of about 10 min between the two platforms. In addition, the sensitivity of remote sensing and that of in situ measurements is not the same, and in situ measurement uncertainties are between 25 % and 60 %. [ABSTRACT FROM AUTHOR]

Global measurements of horizontal winds in cloud and precipitation systems represent a gap in the global observation system. The Wind Velocity Radar Nephoscope (WIVERN) mission, one of the two candidates to be the ESA's Earth Explorer 11 mission, aims at filling this gap based on a conically scanning W-band Doppler radar instrument. The determination of the antenna boresight mispointing angles and the impact of their uncertainty on the line of sight Doppler velocities is critical to achieve the mission requirements. While substantial industrial efforts are on their way to achieving accurate determination of the pointing, alternative (external) calibration approaches are currently under scrutiny. The correction of the line of sight Doppler velocity error introduced by the mispointing only needs knowledge of such mispointing angles and does not need the correction of the mispointing itself. Thus, this work discusses four methods applicable to the WIVERN radar that can be used at different timescales to characterize the antenna mispointing both in the azimuthal and in the elevation directions and to correct the error in the Doppler velocity induced by such mispointing. Results show that elevation mispointing is well corrected at very short timescales by monitoring the range at which the surface peak occurs. Azimuthal mispointing is harder but can be tackled by using the expected profiles of the non-moving surface Doppler velocity. Biases in pointing at longer timescales can be monitored by using a well-established reference database (e.g. ECMWF reanalysis) or ad-hoc ground-based calibrators. Although tailored to the WIVERN mission, the proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates retrievals of liquid precipitation over snow-free and non-mountainous surfaces from GPROF V7 and multiple configurations of GPROF-NN against ground-based radar measurements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPM Microwave Imager (GMI) are validated over several years, and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable estimates of liquid precipitation over the CONUS. During all four assessed years, annual mean precipitation is within 8 % of gauge-corrected radar measurements. Although biases of up to 25 % are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reliably reproduces each region's diurnal and seasonal precipitation characteristics. The effective resolution of GPROF V7 is found to be 51 km over the CONUS and 18 km over the tropical Pacific. GPROF V7 also produces robust precipitation estimates for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPM PMW precipitation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error of instantaneous precipitation estimates by about 20 % for GPROF GMI while the effective resolution is improved to 31 km over land and 15 km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar–radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used to generate the training data for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

Measurements of radar backscatter are sensitive to snow water equivalent (SWE) across a wide range of frequencies, motivating proposals for satellite missions to measure global distributions of SWE. However, radar backscatter measurements are also sensitive to snow stratigraphy, to microstructure, and to ground surface roughness, complicating SWE retrieval. A number of recent advances have created new tools and datasets with which to address the retrieval problem, including a parameterized relationship between SWE, microstructure, and radar backscatter, and methods to characterize ground surface scattering. Although many algorithms also introduce external (prior) information on SWE or snow microstructure, the precision of the prior datasets used must be high in some cases in order to achieve accurate SWE retrieval. We hypothesize that a time series of radar measurements can be used to solve this problem and demonstrate that SWE retrieval with acceptable error characteristics is achievable by using previous retrievals as priors for subsequent retrievals. We demonstrate the accuracy of three configurations of prior information: using a global SWE model, using the previously retrieved SWE, and using a weighted average of the model and the previous retrieval. We assess the robustness of the approach by quantifying the sensitivity of the SWE retrieval accuracy to SWE biases artificially introduced in the prior. We find that the retrieval with the weighted averaged prior demonstrates SWE accuracy better than 20 % and an error increase of only 3 % relative RMSE per 10 % change in prior bias; the algorithm is thus both accurate and robust. This finding strengthens the case for future radar-based satellite missions to map SWE globally. [ABSTRACT FROM AUTHOR]

Upper-atmosphere winds from a meteor radar and a field-widened Michelson interferometer, co-located at the Polar Environment Atmospheric Research Laboratory in Eureka, Nunavut, Canada (80° N, 86° W) are compared. The two instruments implement different wind-measuring techniques at similar heights and have very different temporal and spatial observational footprints. The meteor radar provides winds averaged over a ∼ 300 km horizontal area in 3 km vertical bins between 82 and 97 km on a 1 h cadence. The E-Region Wind Interferometer II (ERWIN) provides airglow-weighted winds (averaged over volumes of ∼ 8 km in height by ∼ 5 km radius) from three nightglow emissions (O(1S) , oxygen green line, 557.7 nm , 97 km ; an O2 line, 866 nm , 94 km ; and an OH line, 843 nm , 87 km) on a ∼ 5 min cadence. ERWIN's higher precision (1–2 ms-1 for the O(1S) and OH emissions and ∼ 4 ms-1 for the O2 emissions) and higher cadence allows more substantive comparisons between winds measured by meteor radar and Doppler shifts in airglow emissions than previously possible for similar meteor radar/airglow Doppler shift comparisons using Fabry–Perot interferometers. The best correlation is achieved using Gaussian weighting of meteor radar winds with peak height and vertical width being optimally determined. Peak heights agree well with co-located SABER airglow observations. Offsets between the two instruments are ∼ 1–2 ms-1 for the O2 and O(1S) emissions and less than 0.3 ms-1 for the OH emission. Wind directions are highly correlated with a ∼ 1:1 correspondence. On average, meteor radar wind magnitudes are ∼ 40 % larger than those from ERWIN. Gravity wave airglow brightness weighting of observations is discussed. Non-quadrature phase offsets between the airglow weighting and gravity wave associated wind and temperature perturbations will result in enhanced or reduced layer-weighted wind amplitudes. [ABSTRACT FROM AUTHOR]

Rain cells are the most common units in the natural precipitation system. Enhancing the understanding of these rain cell characteristics can significantly improve the cognition of the precipitation system. Previous studies have mostly analyzed rain cells from a single radar data. In this study, we merged the precipitation parameters measured by the Tropical Rainfall Measuring Mission (TRMM) precipitation radar (PR) with the muti-channel cloud-top radiance measured by the visible and infrared scanner (VIRS) and the muti-channel brightness temperature measured by the TRMM microwave imager (TMI). The rain cells were identified within the PR orbit, and the swath truncation effect was eliminated. We used two methods for rain cell identification: the minimum bounding rectangle (MBR) method and the best fit ellipse (BFE) method, and compared the differences between these two methods in describing the rain cell characteristics. The results indicate that both methods can better reflect the geometric characteristics of rain cells. Compared with the MBR method, the BFE method can obtain a smaller rain cell area, and the filling ratio is better. However, the MBR method can simplify the data storage volume. Consequently, we employed the MBR method to analyze the precipitation structure of two typical rain cell precipitation cases. The results show that the new rain cell dataset can be used for the analysis of rain cell precipitation parameters and visible/infrared and microwave signals, which provides valuable data for comprehensive studies on the rain cell structural characteristics and furthers the understanding of precipitation mechanisms. The data which were used in this paper are freely available at https://doi.org/10.5281/zenodo.8352587 (Wu et al., 2023). [ABSTRACT FROM AUTHOR]

A physical–statistical framework to estimate snow water equivalent (SWE) and snow depth from synthetic aperture radar (SAR) measurements is presented and applied to four SnowSAR flight-line data sets collected during the SnowEx'2017 field campaign in Grand Mesa, Colorado, USA. The physical (radar) model is used to describe the relationship between snowpack conditions and volume backscatter. The statistical model is a Bayesian inference model that seeks to estimate the joint probability distribution of volume backscatter measurements, snow density and snow depth, and physical model parameters. Prior distributions are derived from multilayer snow hydrology predictions driven by downscaled numerical weather prediction (NWP) forecasts. To reduce the signal-to-noise ratio, SnowSAR measurements at 1 m resolution were upscaled by simple averaging to 30 and 90 m resolution. To reduce the number of physical parameters, the multilayer snowpack is transformed for Bayesian inference into an equivalent one- or two-layer snowpack with the same snow mass and volume backscatter. Successful retrievals meeting NASEM (2018) science requirements are defined by absolute convergence backscatter errors ≤1.2 dB and local SnowSAR incidence angles between 30 and 45 ∘ for X- and Ku-band VV-pol backscatter measurements and were achieved for 75 % to 87 % of all grassland pixels with SWE up to 0.7 m and snow depth up to 2 m. SWE retrievals compare well with snow pit observations, showing strong skill in deep snow with average absolute SWE residuals of 5 %–7 % (15 %–18 %) for the two-layer (one-layer) retrieval algorithm. Furthermore, the spatial distributions of snow depth retrievals vis-à-vis lidar estimates have Bhattacharya coefficients above 94 % (90 %) for homogeneous grassland pixels at 30 m (90 m resolution), and values up to 76 % in mixed forest and grassland areas, indicating that the retrievals closely capture snowpack spatial variability. Because NWP forecasts are available everywhere, the proposed approach could be applied to SWE and snow depth retrievals from a dedicated global snow mission. [ABSTRACT FROM AUTHOR]

Abstract. In this study, we present a new forward polarimetric radar operator called the Accurate and Efficient Radar Operator designed by ZheJiang University (ZJU-AERO). This operator was designed to interface with the numerical weather prediction NWP) model of the global forecast system/regional mesoscale model of the China Meteorology Administration (CMA- GFS/MESO). The main objective of developing this observation operator was to assimilate observations from the Precipitation Measurement Radar (PMR) and ground-based radar's polarimetric radar variables, excluding the Doppler variables. To calculate the hydrometeor optical properties of ZJU-AERO, we utilized the invariant-imbedding T-matrix (IITM) method, which can handle non-spherical and inhomogeneous hydrometeor particles in the atmosphere. The optical database of ZJUAERO was designed with a multi-layered architecture to ensure the flexibility in hydrometeor morphology and orientation specifications, while maintaining operational efficiency. In this work, we elaborate on the design concepts, physical basis, and hydrometeor specifications of ZJU-AERO. Additionally, we present a case study demonstrating the application of ZJU-AERO in simulating radar observations of Typhoon Haishen. [ABSTRACT FROM AUTHOR]

The Cloud Profiling Radar (CPR) of the Earth Cloud, Aerosol and Radiation Explorer (EarthCARE) has a new capability to observe the Doppler velocity related to the vertical air motion of the terminal velocity of hydrometeors. The new observation from space will be used to evaluate and improve the model. Before the launch of EarthCARE, we need to develop a methodology for using the CPR data for model evaluations. In this study, we evaluated simulated data by a stretched version of the global non-hydrostatic model over Japan with a ground-based CPR using an instrument design similar to the EarthCARE CPR. We chose two cases with different precipitation events in September 2019 using two cloud microphysics schemes. We introduced the categorization method for evaluating microphysics using Doppler velocity. The results show that the liquid and solid phases of hydrometeors are divided in Doppler velocity, and the model's terminal velocities of rain, snow, and graupel categories can be evaluated with the observation. The results also show that the choice of microphysics scheme has a more significant impact than the dependence on precipitation cases. We discussed the application of the EarthCARE-like simulation results using a satellite simulator. [ABSTRACT FROM AUTHOR]

Cloud radar Doppler spectra are of particular interest for investigating cloud microphysical processes, such as ice formation, riming and ice multiplication. When hydrometeor types within a cloud radar observation volume have sufficiently different terminal fall velocities, they produce individual Doppler spectrum peaks, convoluted by dynamical effects. If these (sub-)peaks can be separated, properties of the underlying hydrometeor populations can potentially be estimated, such as their fall velocity, number, size and to some extent their shape. However, this task is complex and dependent on the cloud radar operation settings, atmospheric dynamics and hydrometeor characteristics. As a consequence, there is a need for adjustable tools that are able to detect peaks in cloud radar Doppler spectra to extract the valuable information contained in them. This paper presents the synergistic use of two cloud radar Doppler spectra peak analysis algorithms, PEAKO and peakTree. PEAKO is a supervised machine learning tool that can be trained to obtain the optimal parameters for peak detection in cloud radar Doppler spectra for specific cloud radar instrument settings. The learned Doppler spectrum peak detection parameters can then be applied by peakTree, which is used to detect, structure and interpret Doppler spectrum peaks. The application of the improved PEAKO-peakTree toolkit is demonstrated in two case studies. The interpretation is supported by forward simulated cloud radar Doppler spectra by the Passive and Active Microwave TRAnsfer tool (PAMTRA), which are also used to explore the limitations of the algorithm toolkit posed by turbulence and the number of spectral averages chosen in the radar settings. From the PAMTRA simulations, we can conclude that a minimum number of 20–40 spectral averages is desirable for Doppler spectrum peak discrimination. Furthermore, liquid peaks can only be reliably separated for turbulence eddy dissipation rate values up to approximately 0.0002 m2 s−3. The first case study demonstrates that the methods work for different radar systems and settings by comparing the results for two cloud radar systems which were operated simultaneously at a site in Punta Arenas, Chile. Detected peaks which can be attributed to liquid droplets agree well between the two systems, as well as with an independent liquid-predicting neural network. The second case study compares PEAKO-peakTree-detected cloud radar Doppler spectra peaks to in situ observations collected by a balloon-based holographic imager during a campaign in Ny-Ålesund, Svalbard. This case showcases the Doppler spectrum peak detection algorithms' ability to identify different hydrometeor types, but also reveals the limitations of the algorithm toolkit posed by strong turbulence and a low number of spectral averages. Despite these challenges, the algorithm toolkit offers a powerful means of extracting comprehensive information from cloud radar observations. In the future, we envision PEAKO-peakTree application on the one hand for interpreting cloud microphysics in case studies. The identification of liquid cloud peaks emerges as a valuable asset e.g. in studies on cloud radiative effects, seeder-feeder processes, or for tracing vertical air motions. Furthermore, the computation of the moments for each sub-peak enables the tracking of hydrometeor populations and the observation of growth processes along fall streaks. On the other hand, PEAKO-peakTree application could be extended to statistical evaluations of longer data sets. Both algorithms are openly available on GitHub, offering accessibility for the scientific community. [ABSTRACT FROM AUTHOR]

We present the geophysical data set acquired in the summer of 2022 close to Ny-Ålesund ((Western Svalbard, Broggerhalvoya peninsula - Norway) as part of the project ICEtoFLUX. The aim of the investigation is to characterize the role of groundwater flow in correspondence of the active layer as well as through and/or below the permafrost. The data set 20 is composed of Electrical Resistivity Tomography (ERT) and Ground Penetrating Radar (GPR) surveys, which are well-known geophysical techniques for the characterization of glacial and hydrological processes and features. 18 ERT profiles and 10 GPR lines were acquired, for a total surveyed length of 9.3 km. The data have been organized in a consistent repository that includes both raw and processed (filtered) data. Some representative examples of 2D models of the subsurface are provided, that is, 2D sections of electrical resistivity (from ERT) and 2D radargrams (from GPR). These 25 examples can support the identification of the active layer. The data set is of major relevance because there is little geophysical data published about the Ny-Ålesund area. Moreover, these geophysical data can foster multidisciplinary scientific collaborations in the fields of hydrology, glaciology, climate, geology, geomorphology, etc. The geophysical data are provided in a free repository and can be accessed at the repository under data doi (Pace et al., 2023, https://zenodo.org/doi/10.5281/zenodo.10260056). [ABSTRACT FROM AUTHOR]

Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications. [ABSTRACT FROM AUTHOR]

A new dual-frequency stratospheric–tropospheric (ST) and meteor radar has been built and installed at the Langfang Observatory in northern China. It utilizes a new two-frequency system design that allows interleaved operation at 53.8 MHz for ST mode and at 35.0 MHz for meteor mode, thus optimizing performance for both ST wind retrieval and meteor trail detection. In dedicated meteor mode, the daily meteor count rate reaches over 40 000 and allows wind estimation at finer time resolutions than the 1 h typical of most meteor radars. The root mean square uncertainty of the ST wind measurements is better than 2 m s -1 when estimating the line of best fit with radiosonde winds. Preliminary observation results for 1 month of winter gravity wave (GW) momentum fluxes in the mesosphere, lower stratosphere and troposphere are also presented. A case of waves generated by the passage of a cold front is found. [ABSTRACT FROM AUTHOR]

Weather radar is a crucial tool in rainfall estimation, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and the drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods, imposing path-integrated attenuation effects, a stochastic drop-size distribution field, along with sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field, and the corrected 'best guess' rainfall field which would be obtained if they were observed in the real-world case. The structure of these errors is then investigated, with a focus on frequency and behaviour of 'rainfall shadows'. Half of simulated weather radar images have at least 3 % of significant rainfall rates shadowed and 25 % had at least 45 km2 containing rainfall shadows, resulting in underestimation of potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well at generating realistic weather radar images visually, for a large range of event types. [ABSTRACT FROM AUTHOR]

Ground-based radar precipitation products typically need adjustment with rain gauge accumulations to achieve a reasonable accuracy. This is certainly the case for the pan-European radar precipitation products. The density of (near) real-time rain gauge accumulations from official networks is often relatively low. Crowdsourced rain gauge networks have a much higher density than conventional ones and are a potentially interesting (complementary) source to merge with radar precipitation accumulations. Here, a 1-year personal weather station (PWS) rain gauge dataset of ∼ 5 min accumulations is obtained from the private company Netatmo over the period 1 September 2019–31 August 2020, which is subjected to quality control using neighbouring PWSs and, after aggregating to 1 h accumulations, using unadjusted radar data. The PWS 1 h gauge accumulations are employed to spatially adjust OPERA radar accumulations, covering 78 % of geographical Europe. The performance of the merged dataset is evaluated against daily and disaggregated 1 h gauge accumulations from weather stations in the European Climate Assessment & Dataset (ECA&D). Results are contrasted to those from an unadjusted OPERA-based radar dataset and from EURADCLIM. The severe average underestimation for daily precipitation of ∼ 28 % from the unadjusted radar dataset diminishes to ∼ 3 % for the merged radar–PWS dataset. A station-based spatial verification shows that the relative bias in 1 h precipitation is still quite variable and suggests stronger underestimations for colder climates. A dedicated evaluation with scatter density plots reveals that the performance is indeed less good for lower temperatures, which points to limitations in observing solid precipitation by PWS gauges. The outcome of this study confirms the potential of crowdsourcing to improve radar precipitation products in (near) real time. [ABSTRACT FROM AUTHOR]

Some of the highest specific mass change rates in Antarctica are reported for the Antarctic Peninsula. However, the existing estimates for the northern Antarctic Peninsula (<70 ∘ S) are either spatially limited or are affected by considerable uncertainties. The complex topography, frequent cloud cover, limitations in ice thickness information, boundary effects, and uncertain glacial–isostatic adjustment estimates affect the ice sheet mass change estimates using altimetry, gravimetry, or the input-output method. Within this study, the first assessment of the geodetic mass balance throughout the ice sheet of the northern Antarctic Peninsula is carried out employing bi-static synthetic aperture radar (SAR) data from the TanDEM-X satellite mission. Repeat coverages from the austral winters of 2013 and 2017 are employed. Overall, coverage of 96.4 % of the study area by surface elevation change measurements and a total mass budget of -24.1±2.8 Gt a -1 are revealed. The spatial distribution of the surface elevation and mass changes points out that the former ice shelf tributary glaciers of the Prince Gustav Channel, Larsen A and B, and Wordie ice shelves are the hotspots of ice loss in the study area and highlights the long-lasting dynamic glacier adjustments after the ice shelf break-up events. The highest mass change rate is revealed for the Airy–Seller–Fleming glacier system at -4.9±0.6 Gt a -1 , and the highest average surface elevation change rate of -2.30±0.03 m a -1 is observed at Drygalski Glacier. The comparison of the ice mass budget with anomalies in the climatic mass balance indicates, that for wide parts of the southern section of the study area, the mass changes can be partly attributed to changes in the climatic mass balance. However, imbalanced high ice discharge drives the overall ice loss. The previously reported connection between mid-ocean warming along the southern section of the west coast and increased frontal glacier recession does not repeat in the pattern of the observed glacier mass losses, excluding in Wordie Bay. The obtained results provide information on ice surface elevation and mass changes for the entire northern Antarctic Peninsula on unprecedented spatially detailed scales and with high precision and will be beneficial for subsequent analysis and modeling. [ABSTRACT FROM AUTHOR]

Operational rain gauge networks provide reliable local precipitation measurements, but are unable to represent rainfall variability for large domains at small temporal scales. In the urban area of Hamburg, precipitation measurements of a single-polarized X-band weather radar operating at a high temporal (30s), range (60m), and azimuthal sampling (1°) resolution are available for more than eight years. These measurements refine observations of the German nationwide C-band radars within the 20km scan radius. Studies on short time periods (several months) prove the performance of this low-cost local area weather radar. A case study on a tornado in a rain event demonstrates its refined resolution compared to the German nationwide C-band radars. The deployment of additional vertically pointing micro rain radars yields drop size distributions at relevant heights, which reduces errors effectively concerning the radar calibration and required statistical relations (k-Z and Z-R relation), and monitors the radar data quality. This paper describes an open-access data set covering reanalysed radar reflectivities and rainfall estimates measured by an X-band radar at high spatio-temporal resolution in the urban environment of Hamburg between 2013 and 2021. Additionally, this paper outlines the performance of this X-band radar for long time periods, and discuss open issues and limitations of the data set. The provided radar reflectivities facilitate studies on attenuation correction and the derivation of further weather radar products, like an improved rainfall rate. The rainfall rates will be used for studies on the spatial and temporal scale of precipitation and hydrological research, e.g. input data for high-resolution modelling, in an urban area. [ABSTRACT FROM AUTHOR]

WindRAD (Wind Radar) is a dual-frequency rotating fan-beam scatterometer instrument on the FY-3E (FengYun-3E) satellite. Scatterometers are generally calibrated using the linear NOC (NWP Ocean Calibration) method to control the main gain factor of the radar. While WindRAD is stable, the complex geometry, the design of the instrument, and the rotating antenna make the backscatter (σ∘) distributions persistently non-linear; hence NOC is insufficient. Therefore, a higher-order calibration (HOC) method is proposed. The CDF (cumulative distribution function) matching technique is employed to match the CDF of measured σ∘ instances to simulated σ∘ instances. HOC removes the non-linearities for each incidence angle. However, it is not constructed to remove the anomalous harmonic azimuth dependencies caused by the antenna rotation. These azimuth dependencies are reduced by NOCant (NOC as a function of incidence angle and relative antenna azimuth angle). Therefore, the combination of HOC and NOCant is implemented to correct both anomalous σ∘ amplitude and azimuth variations. The wind retrieval performance is evaluated with NOCant, HOC, and HOC and NOCant combined. The wind statistics and the cone distance metric both show that HOC&NOCant achieves the optimal winds for C-band and Ku-band. The calibrations have been tested on two operational input data versions; HOC works well on both data versions and HOC&NOCant can achieve the optimal wind performance for both data versions. This confirms the usefulness of HOC in the case of non-linear instrument gain anomalies. [ABSTRACT FROM AUTHOR]

The Dome Fuji (DF) region in Antarctica is a potential site for an ice core with a record of over 1 Myr. Here, we combine large-scale internal airborne radar stratigraphy with a 1-D model to estimate the age of basal ice in the DF region. The radar data used in the study were collected in a survey during the 2016–2017 Antarctic season. We transfer the latest age–depth scales from the DF ice core to isochrones traced in radargrams in the surrounding 500 km × 550 km region. At each point of the survey the 1-D model uses the ages of isochrones to construct the age–depth scale at depths where dated isochrones do not exist, the surface accumulation rate and the basal thermal condition, including melt rate and the thickness of stagnant ice. Our resulting age distribution and age density suggest that several promising sites with ice older than 1.5 Myr in the DF region might exist. The deduced melt rates and presence of stagnant ice provide more constraints for locating sites with a cold base. The accumulation rates range from 0.015 to 0.038 m a -1 ice equivalent. Based on sensitivity studies we find that the number and depth of picked isochrones and the timescale of the ice core severely affect the model results. Our study demonstrates that constraints from deep radar isochrones and a trustworthy timescale could improve the model estimation to find old ice in the DF region. [ABSTRACT FROM AUTHOR]

Stratiform rain and the overlying ice play crucial roles in the Earth's climate system. From a microphysics standpoint, water mass flux primarily depends on two variables: particles concentration and their mass. The Dual-frequency Precipitation Radar (DPR) on the Global Precipitation Measurement mission core satellite is a space-borne instrument capable of estimating these two quantities through dual-wavelength measurements. In this study, we evaluate bulk statistics on the ice particle properties derived from dual-wavelength radar data in relation to the properties of rain underneath. Specifically, we focus on DPR observations over stratiform precipitation, characterized by columns exhibiting a prominent bright band, where the melting layer can be easily detected. Our analysis reveals a significant increase in the retrieved mass flux as we transition from the ice to the rain phase in the official DPR product. This observation is in disagreement with our expectation that mass flux should remain relatively stable across the bright band in cold rain conditions. To address these discrepancies, we propose an alternative retrieval algorithm that ensures a gradual transition of Dm (mean mass-weighted particle melted-equivalent diameter) and precipitation rate across the melting zone. This approach also helps in estimating bulk ice density above the melting level. These findings demonstrate that DPR observations can not only quantify ice particle content and their size above stratiform rain regions but also estimate bulk density, provided uniform conditions that minimize uncertainties related to partial beam filling. [ABSTRACT FROM AUTHOR]

Most Arctic clouds occur below 2 km altitude, as revealed by CloudSat satellite observations. However, recent studies suggest that the relatively coarse spatial resolution, low sensitivity, and blind zone of the radar installed on CloudSat may not enable it to comprehensively document low-level clouds. We investigate the impact of these limitations on the Arctic low-level cloud fraction, which is the number of cloudy points with respect to all points as a function of height, derived from CloudSat radar observations. For this purpose, we leverage highly resolved vertical profiles of low-level cloud fraction derived from down-looking Microwave Radar/radiometer for Arctic Clouds (MiRAC) radar reflectivity measurements. MiRAC was operated during four aircraft campaigns that took place in the vicinity of Svalbard during different times of the year, covering more than 25 000 km. This allows us to study the dependence of CloudSat limitations on different synoptic and surface conditions. A forward simulator converts MiRAC measurements to synthetic CloudSat radar reflectivities. These forward simulations are compared with the original CloudSat observations for four satellite underflights to prove the suitability of our forward-simulation approach. Above CloudSat's blind zone of 1 km and below 2.5 km , the forward simulations reveal that CloudSat would overestimate the MiRAC cloud fraction over all campaigns by about 6 percentage points (pp) due to its horizontal resolution and by 12 pp due to its range resolution and underestimate it by 10 pp due to its sensitivity. Especially during cold-air outbreaks over open water, high-reflectivity clouds appear below 1.5 km , which are stretched by CloudSat's pulse length causing the forward-simulated cloud fraction to be 16 pp higher than that observed by MiRAC. The pulse length merges multilayer clouds, whereas thin low-reflectivity clouds remain undetected. Consequently, 48 % of clouds observed by MiRAC belong to multilayer clouds, which reduces by a factor of 4 for the forward-simulated CloudSat counterpart. Despite the overestimation between 1 and 2.5 km , the overall low-level cloud fraction is strongly reduced due to CloudSat's blind zone that misses a cloud fraction of 32 % and half of the total (mainly light) precipitation amount. [ABSTRACT FROM AUTHOR]

In dry southwestern South Tyrol, Italy, rock glaciers are dominant landforms of the high-mountain cryosphere. Their spatial distribution and degree of activity hold critical information on the current state of discontinuous permafrost and consequently on the response potential to climate warming. Traditional geomorphologic mapping, however, owing to the qualitative expert-based nature, typically displays a high degree of uncertainty and variability among operators with respect to the dynamic classification of intact (permafrost-bearing) and relict (permafrost-devoid) rock glaciers. This limits the reliability of geomorphologic rock glacier inventories for basic and applied purposes. To address this limitation, (i) we conduct a systematic evaluation of the improvements that synthetic aperture radar interferometry (InSAR) can afford to the detection and dynamic classification of rock glaciers and (ii) build an integrated inventory that combines the strengths of geomorphologic- and InSAR-based approaches. To exploit fully InSAR-based information towards a better understanding of the topo-climatic conditions that sustain creeping permafrost, we further explore how velocity and the spatial distribution of moving areas (MAs) within rock glaciers may vary as a function of simple topographic variables known to exert first-order controls on incoming solar radiation, such as elevation and aspect. Starting from a geomorphologic inventory (n=789), we characterize the kinematics of InSAR-based MAs and the relevant hosting rock glaciers on 36 Sentinel-1 interferograms in the 2018–2019 period. With respect to the original inventory, InSAR analysis allowed us to identify 14 previously undetected rock glaciers. Further, it confirmed that 246 (76 %) landforms, originally interpreted as intact, do exhibit detectable movement (i.e., ≥1 cm yr -1) and that 270 (60 %) of the relict labeled counterparts do not, whereas 144 (18 %) were kinematically undefined due to decorrelation. Most importantly, InSAR proved critical for reclassifying 121 (15 %) rock glaciers, clarifying that 41 (13 %) of those interpreted as being intact do not exhibit detectable movement and that 80 (17 %) of the original relict ones do move. Reclassification (i) allowed us to identify a cluster of intact rock glaciers below 2000 m a.s.l. associated with positive mean annual air temperature (MAAT), and (ii) by increasing the altitudinal overlap between intact and relict rock glaciers, it depicts a broad transition belt in the aspect–elevation space, which varies from 50 m on west-facing slopes to 500 m on easterly ones. This finding deteriorates the significance of elevation and aspect as topographic proxies for modeling permafrost occurrence and highlights the importance of using InSAR to inform such models. From a process-oriented standpoint, InSAR information proves fundamental for imaging how this altitudinal transition manifests through changing rates and styles of rock glacier surface deformation. Specifically, we find that, as rock glaciers move faster, an increasingly larger proportion of their surface becomes kinematically involved (i.e., percent MA cover) and that this proportion increases with elevation up to 2600–2800 m, beyond which an inflection occurs and consistent average values are attained. Considering that the inflection falls between the -1 and -2 °C MAAT – the lower boundary for discontinuous permafrost – and is independent of slope gradient, we conclude that this altitudinal pattern represents a geomorphic signature: the dynamic expression of increasing permafrost distribution, from sporadic to discontinuous. [ABSTRACT FROM AUTHOR]

This paper introduces a machine-learning-driven approach for automated Nocturnal Low-Level Jet (NLLJ) identification using observations of wind profiles from a Radar Wind Profiler (RWP). The work discussed here is an effort to lay the groundwork for a systematic study of the Mid-Atlantic NLLJ's formation mechanisms and their influence on nocturnal and diurnal air quality in major urban regions by establishing a general framework of NLLJ features and characteristics with an identification algorithm. Leveraging a comprehensive wind profile dataset maintained by the Maryland Department of Environment's RWP network, our methodology employs supervised machine learning techniques to isolate the features of the south-westerly NLLJ. This methodology was developed to illuminate spatiotemporal patterns and nuanced characteristics of NLLJ events, unveiling their significant role in shaping the planetary boundary layer. This paper discusses the construction of this methodology, its performance against known NLLJs in the current literature, intended usage, and a preliminary statistical analysis. First light results from this analysis have identified a total of 90 south-westerly NLLJs from May–September of 2017–2021 as captured by the RWP stationed in Beltsville, MD (39.05°, -76.87°, 135 m ASL). A composite of these 90 jets is presented to better illustrate many of the bulk parameters, such as core height, duration, and maximum wind speed, associated with the onset and decay of the Mid-Atlantic NLLJ. We hope our study equips researchers and policymakers with further means to monitor, predict, and address these nocturnal dynamics phenomena that frequently influence boundary layer composition and air quality in the U.S. Mid-Atlantic and Northeastern regions. [ABSTRACT FROM AUTHOR]

Hailstorms, while fascinating from a meteorological perspective, pose significant risks to communities, agriculture, and infrastructure. In regions such as the Central Andes of Peru, the characteristics and frequency of these extreme weather events remain largely uncharted. This study fills this gap by investigating the historical frequency and vertical structure of hailstorms in this region. We analyzed historical hailstorm records dating back to 1958 alongside 4 years of observations (2017–2021) from the Parsivel2 disdrometer and a cloud-profiling radar MIRA35c. Our findings indicate a trend of decreasing hail frequency (- 0.5 events per decade). However, the p value of 0.07 suggests the need for further investigation, particularly in relation to environmental changes and reporting methods. The results show that hailstorms predominantly occur during the austral summer months, with peak frequency in December, and are most common during the afternoon and early evening hours. The analysis of radar variables such as reflectivity, radial velocity, spectral width, and linear depolarization ratio (LDR) reveals distinct vertical profiles for hail events. Two case studies highlight the diversity in the radar measurements of hailstorms, underscoring the complexity of accurate hail detection. This study suggests the need for refining the Parsivel2 algorithm and further understanding its classification of hydrometeors. Additionally, the limitations of conventional radar variables for hail detection are discussed, recommending the use of LDR and Doppler spectrum analysis for future research. Our findings lay the groundwork for the development of more efficient hail detection algorithms and improved understanding of hailstorms in the Central Andes of Peru. [ABSTRACT FROM AUTHOR]

Reliable estimation of precipitation fields at high resolution is a key issue for snow cover modelling in mountainous areas, where the density of precipitation networks is far too low to capture their complex variability with topography. Adequate quantification of the remaining uncertainty in precipitation estimates is also necessary for further assimilation of complementary snow observations in snow models. Radar observations provide spatialised estimates of precipitation with high spatial and temporal resolution, and are often combined with rain gauge observations to improve the accuracy of the estimate. However, radar measurements suffer from significant shortcomings in mountainous areas (in particular, unrealistic spatial patterns due to ground clutter). Precipitation fields simulated by high-resolution numerical weather prediction (NWP) models provide an alternative estimate, but suffer from systematic biases and positioning errors. Even though these uncertainties can be partially described by ensemble NWP systems and systematic errors can be reduced by statistical post-processing, NWP precipitation estimates are still not reliable enough for the requirements of high resolution snow cover modelling. In this study, better precipitation estimates are obtained through a specific analysis based on a combination of all these available products. First, a pre-processing step is proposed to mitigate the main deficiencies of radar and gauges precipitation estimation products, focusing on reducing unrealistic spatial patterns. This method also provides a spatialised estimate of the associated error in mountainous areas, based on a climatological analysis of both radar and NWP-estimated precipitation. Three ensemble daily precipitation analysis methods are then proposed, first using only the modified precipitation estimates and associated errors, then combining them with ensemble NWP simulations based on the Particle Filter and Ensemble Kalman Filter data assimilation algorithms. The performance of the different precipitation analysis methods is evaluated at a local scale using independent ski resort precipitation observations. The evaluation of the pre-processing step shows its ability to remove the main spatial artefacts coming from the radar measurements and to improve the precipitation estimates at the local scale. The local scale evaluations of the ensemble analyses do not demonstrate an additional benefit of ensemble NWP forecasts, but their contrasted spatial patterns are challenging to evaluate with the available data. [ABSTRACT FROM AUTHOR]

Remote hail detection and hail size estimation using weather radar observations has the advantage of wide spatial coverage and high spatial and temporal resolution. Switzerland National Weather Service (MeteoSwiss) uses two radar-based hail metrics: the probability of hail at the ground (POH) to assess the presence of hail, and the maximum expected severe hailstone size (MESHS) to estimate the largest hailstone diameter. However, radar-based metrics are not direct measurements of hail and have to be calibrated with and verified against ground-based observations of hail, such as crowdsourced hail reports. Switzerland benefits from a particularly rich and dense dataset of crowdsourced hail reports from the MeteoSwiss app. We combine a new spatiotemporal clustering method (ST-DBSCAN) with radar reflectivity to filter the reports and use the filtered reports to verify POH and MESHS in terms of the Hit Rate, False Alarms Ratio (FAR), Critical Success Index (CSI), and Heidke Skill Score (HSS). Using a 4 km × 4 km maximum upscaling approach, we find FAR values between 0.3 and 0.7 for POH and FAR > 0.6 for MESHS. For POH, the highest CSI (0.37) and HSS (0.52) are obtained for a 60 % threshold, while for MESHS the highest CSI (0.25) and HSS (0.4) are obtained for a 2 cm threshold. We find that the current calibration of POH does not correspond to a probability and suggest a recalibration based on the filtered reports. [ABSTRACT FROM AUTHOR]

Permafrost-affected ecosystems of the Arctic–boreal zone in northwestern North America are undergoing profound transformation due to rapid climate change. NASA's Arctic Boreal Vulnerability Experiment (ABoVE) is investigating characteristics that make these ecosystems vulnerable or resilient to this change. ABoVE employs airborne synthetic aperture radar (SAR) as a powerful tool to characterize tundra, taiga, peatlands, and fens. Here, we present an annotated guide to the L-band and P-band airborne SAR data acquired during the 2017, 2018, 2019, and 2022 ABoVE airborne campaigns. We summarize the ∼80 SAR flight lines and how they fit into the ABoVE experimental design (Miller et al., 2023; https://doi.org/10.3334/ORNLDAAC/2150). The Supplement provides hyperlinks to extensive maps, tables, and every flight plan as well as individual flight lines. We illustrate the interdisciplinary nature of airborne SAR data with examples of preliminary results from ABoVE studies including boreal forest canopy structure from TomoSAR data over Delta Junction, AK, and the Boreal Ecosystem Research and Monitoring Sites (BERMS) area in northern Saskatchewan and active layer thickness and soil moisture data product validation. This paper is presented as a guide to enable interested readers to fully explore the ABoVE L- and P-band airborne SAR data (https://uavsar.jpl.nasa.gov/cgi-bin/data.pl). [ABSTRACT FROM AUTHOR]

To better understand the interactions between C-band radar waves and snow, a tower-based experiment was set up in the Idaho Rocky Mountains for the period of 2021–2023. The experiment objective was to improve understanding of the sensitivity of Sentinel-1 C-band backscatter radar signals to snow. The data were collected in the time domain to measure the backscatter profile from the various snowpack and ground surface layers. The data show that scattering is present throughout the snow volume, although it is limited for low snow densities. Contrasting layer interfaces, ice features and metamorphic snow can have considerable impact on the backscatter signal. During snow melt periods, wet snow absorbs the signal and the soil backscatter becomes negligible. A comparison of the vertically integrated tower radar data with Sentinel-1 data shows that both systems have a similar temporal behavior, and both feature an increase in backscatter during the dry snow period in 2021–2022, even during weeks of nearly constant snow depth, likely due to morphological changes in the snowpack. The results demonstrate that C-band radar is sensitive to the dominant seasonal patterns in snow accumulation, but that changes in microstructure, stratigraphy, melt-freeze cycles, and snow wetness may complicate satellite-based snow depth retrievals. [ABSTRACT FROM AUTHOR]

Archives of composite weather radar images represent an invaluable resource to study the predictability of precipitation. In this paper, we compare two distinct approaches to construct empirical low-dimensional attractors from radar precipitation fields. In the first approach, the phase space variables of the attractor are defined using the domain-scale statistics of precipitation fields, such as the mean precipitation, fraction of rain, and spatial and temporal correlations. The second type of attractor considers the spatial distribution of precipitation and is built by principal component analysis (PCA). For both attractors, we investigate the density of trajectories in phase space, growth of errors from analogue states, and fractal properties. To represent different scales and climatic and orographic conditions, the analyses are done using multi-year radar archives over the continental United States (≈4000×4000 km 2 , 21 years) and the Swiss Alpine region (≈500×500 km 2 , 6 years). [ABSTRACT FROM AUTHOR]

The structure and distribution of sub-glacial water directly influences Antarctic ice mass loss by reducing or enhancing basal shear stress and accelerating grounding line retreat. A common technique for detecting sub-glacial water involves analyzing the spatial variation in reflectivity from an airborne radar echo sounding (RES) survey. Basic RES analysis exploits the high dielectric contrast between water and most other substrate materials, where a reflectivity increase ≥ 15 dB is frequently correlated with the presence of sub-glacial water. There are surprisingly few additional tools to further characterize the size, shape, or extent of hydrological systems beneath large ice masses. We adapted an existing radar backscattering simulator to model RES reflections from sub-glacial water structures using the University of Texas Institute for Geophysics (UTIG) Multifrequency Airborne Radar Sounder with Full-phase Assessment (MARFA) instrument. Our series of hypothetical simulation cases modeled water structures from 5 to 50 m wide, surrounded by bed materials of varying roughness. We compared the relative reflectivity from rounded Röthlisberger channels and specular flat canals, showing both types of channels exhibit a positive correlation between size and reflectivity. Large (> 20 m), flat canals can increase reflectivity by more than 20 dB , while equivalent Röthlisberger channels show only modest reflectivity gains of 8–13 dB. Changes in substrate roughness may also alter observed reflectivity by 3–6 dB. All of these results indicate that a sophisticated approach to RES interpretation can be useful in constraining the size and shape of sub-glacial water features. However, a highly nuanced treatment of the geometric context is necessary. Finally, we compared simulated outputs to actual reflectivity from a single RES flight line collected over Thwaites Glacier in 2022. The flight line crosses a previously proposed Röthlisberger channel route, with an obvious bright bed reflection in the radargram. Through multiple simulations comparing various water system geometries, such as canals and sub-glacial lakes, we demonstrated the important role that topography and water geometry can play in observed RES reflectivity. From the scenarios that we tested, we concluded the bright reflector from our RES flight line cannot be a Röthlisberger channel but could be consistent with a series of flat canals or a sub-glacial lake. However, we note our simulations were not exhaustive of all possible sub-glacial water configurations. The approach outlined here has broad applicability for studying the basal environment of large glaciers. We expect to apply this technique when constraining the geometry and extent of many sub-glacial hydrologic structures in the future. Further research may also include comprehensive investigations of the impact of sub-glacial roughness, substrate heterogeneity, and computational efficiencies enabling more complex and complete simulations. [ABSTRACT FROM AUTHOR]

Severe hailstorms result in substantial damage to buildings and vehicles, necessitating the quantification of associated risks. Here, we present a novel open-source hail damage model for buildings and cars based on single-polarization radar data and 250 000 geolocated hail damage reports in Switzerland from 2002 to 2021. To this end, we conduct a detailed evaluation of different radar-based hail intensity measures at 1 km resolution and find that the maximum expected severe hail size (MESHS) outperforms the other measures, despite a considerable false-alarm ratio. Asset-specific hail damage impact functions for buildings and cars are calibrated based on MESHS and incorporated into the open-source risk modelling platform CLIMADA. The model successfully estimates the correct order of magnitude for the number of damaged building in 91 %, their total cost in 77 %, the number of damaged vehicles in 74 %, and their total cost in 60 % of over 100 considered large hail events. We found considerable uncertainties in hail damage estimates, which are largely attributable to limitations of radar-based hail detection. Therefore, we explore the usage of crowdsourced hail reports and find substantially improved spatial representation of severe hail for individual events. By highlighting the potential and limitations of radar-based hail size estimates, particularly MESHS, and the utilization of an open-source risk modelling platform, this study represents a significant step towards addressing the gap in risk quantification associated with severe hail events in Switzerland. [ABSTRACT FROM AUTHOR]

To solve the current problem of insufficient exploration of three-dimensional spatial information detected by 3D ground penetrating radar (3D GPR) and the data processing mainly based on the analysis and interpretation of two-dimensional slice images, a method is proposed to extract underground target based on the local entropy feature of discrete point clouds after the voxelization of 3D GPR data. First, the acquired 3D GPR data was voxelized into discrete three-dimensional point clouds. Then the local entropy feature of the voxelized point clouds over the entire region were calculated. The soil background and underground targets were distinguished by classifying them from multiple dimensions through Support Vector Machine (SVM). Finally, the urban road underground environment was taken as the research object, and this method was used for experimental analysis using measured data. The experimental results show that the accuracy of this method in extracting underground targets is as high as 90.1%, and the missing detection rate of missing underground targets is as low as 7.8%. The proposed method is accurate and effective, providing a new approach for 3D GPR to extract underground target. [ABSTRACT FROM AUTHOR]

The Goddard Profiling Algorithm (GPROF) is used operationally for the retrieval of surface precipitation and hydrometeor profiles from the passive microwave (PMW) observations of the Global Precipitation Measurement (GPM) mission. Recent updates have led to GPROF V7, which has entered operational use in May 2022. In parallel, development is underway to improve the retrieval by transitioning to a neural-network-based algorithm called GPROF-NN. This study validates GPROF V7 and multiple configurations of the GPROF-NN retrieval against ground-based radar mea- surements over the conterminous United States (CONUS) and the tropical Pacific. GPROF retrievals from the GPMMicrowave Imager (GMI) are validated over several years and their ability to reproduce regional precipitation characteristics and effective resolution is assessed. Moreover, the retrieval accuracy for several other sensors of the constellation is evaluated. The validation of GPROF V7 indicates that the retrieval produces reliable precipitation estimates over CONUS. During all four assessed years, annual mean precipitation is within 8% of gauge-corrected radar measurements. Although biases of up to 25% are observed over sub-regions of the CONUS and the tropical Pacific, the retrieval reproduces the principal precipitation characteristics of each region. The effective resolution of GPROF V7 is found to be 51km over CONUS and 18km over the tropical Pacific. GPROF V7 produces robust precipitation estimates also for the other sensors of the GPM constellation. The evaluation further shows that the GPROF-NN retrievals have the potential to significantly improve the GPROF precipi- tation retrievals. GPROF-NN 1D, the most basic neural network implementation of GPROF, improves the mean-squared error, mean absolute error, correlation and symmetric mean absolute percentage error by about twenty percent for GPROF GMI while the effective resolution is improved to 31km over land and 15km over oceans. The two GPROF-NN retrievals that are based on convolutional neural networks can further improve the accuracy up to the level of the combined radar/radiometer retrievals from the GPM core observatory. However, these retrievals are found to overfit on the viewing geometry at the center of the swath, reducing their overall accuracy to that of GPROF-NN 1D. For the other sensors of the constellation, the GPROF-NN retrievals produce larger biases than GPROF V7 and only GPROF-NN 3D achieves consistent improvements compared to GPROF V7 in terms of the other assessed error metrics. This points to shortcomings in the hydrometeor profiles or radiative transfer simulations used in the training of the retrievals for the other sensors of the GPM constellation as a critical limitation for improving GPM PMW retrievals. [ABSTRACT FROM AUTHOR]

In this paper, the implementation and validation of the torus mapping, a method to dealias radial wind observations by meteorological radars is presented. We apply the procedure to data from the Operational program for exchange of weather radar information (OPERA) over central part of Europe for the whole year 2021. The quality of the resulting radar winds is assessed by a comparison to colocated radiosonde and aircraft observations and also through analysis of first guess departures using a regional numerical weather prediction model. Performance of quality control on dealiased radial wind data is also evaluated.We show that the torus mapping method is a robust procedure on data with a sufficiently low amount of measurement noise. It produces datasets of comparable quality for a wide range of Nyquist velocity values and has potential for operational applications. [ABSTRACT FROM AUTHOR]

Sea ice volume's significant interannual variability requires long-term series of observations to identify trends in its evolution. Despite improvements in sea ice thickness estimations from altimetry during the past few years thanks to CryoSat-2 and ICESat-2, former ESA radar altimetry missions such as the Environmental Satellite (Envisat) and especially the European Remote-Sensing Satellite (ERS-1 and ERS-2) have remained under-exploited so far. Although solutions have already been proposed to ensure continuity of measurements between CryoSat-2 and Envisat, there is no time series integrating ERS. The purpose of this study is to extend the Arctic radar freeboard time series back to 1995. The difficulty in handling ERS measurements comes from a technical issue known as the pulse blurring effect, altering the radar echoes over sea ice and the resulting surface height estimates. Here we present and apply a correction for this pulse blurring effect. To ensure consistency of the CryoSat-2, Envisat and ERS-2 time series, a multiparameter neural-network-based method to calibrate Envisat against CryoSat-2 and ERS-2 against Envisat is presented. The calibration is trained on the discrepancies observed between the altimeter measurements during the mission-overlap periods and a set of parameters characterizing the sea ice state. Monthly radar freeboards are provided with uncertainty estimations based on a Monte Carlo approach to propagate the uncertainties all along the processing chain, including the neural network. Comparisons of corrected radar freeboards during overlap periods reveal good agreement between the missions, with a mean bias of 0.30 cm and a standard deviation of 9.7 cm for Envisat and CryoSat-2 and a 0.20 cm bias and a standard deviation of 3.8 cm for ERS-2 and Envisat. The monthly corrected radar freeboards obtained from Envisat and ERS-2 are then validated by comparison with several independent datasets such as airborne, mooring, direct-measurement and other altimeter products. Except for two datasets, comparisons lead to correlations ranging from 0.41 to 0.94 for Envisat and from 0.60 to 0.74 for ERS-2. The study finally provides radar freeboard estimation for winters from 1995 to 2021 (from the ERS-2 mission to CryoSat-2). [ABSTRACT FROM AUTHOR]

The cloud-profiling radar (CPR) on the Earth Clouds, Aerosol, and Radiation Explorer (EarthCARE) satellite (EC-CPR) is the first satellite-borne Doppler radar. In a previous study, we examined the effects of horizontal (along-track) integration and simple unfolding methods on the reduction of Doppler errors in the EC-CPR observations, and those effects were evaluated using two limited scenes in limited-latitude and low-pulse-repetition-frequency (PRF) settings. In this study, the amount of data used was significantly increased, and the area of the data used was extended globally. Not only low-PRF but also high-PRF settings were examined. We calculated the EC-CPR-observed Doppler velocity from pulse-pair covariances using the radar reflectivity factor and Doppler velocity obtained from a satellite data simulator and a global storm-resolving simulation. The global data were divided into five latitudinal zones, and each standard deviation of Doppler errors for 5 dB Ze after 10 km integration was calculated. In the case of the low-PRF setting, the error without unfolding correction for the tropics reached a maximum of 2.2 m s -1 and then decreased toward the poles (0.43 m s -1). The error with unfolding correction for the tropics became much smaller at 0.63 m s -1. In the case of the high-PRF setting, the error without unfolding correction for the tropics reached a maximum of 0.78 m s -1 and then decreased toward the poles (0.19 m s -1). The error with unfolding correction for the tropics was 0.29 m s -1 , less than half the value without the correction. The results of the analyses of the simulated data indicated that the zonal mean frequency of precipitation echoes was highest in the tropics and decreased toward the poles. Considering a limitation of the unfolding correction for discrimination between large upward velocity and large precipitation falling velocity, the latitudinal variation in the standard deviation of Doppler error can be explained by the precipitation echo distribution. [ABSTRACT FROM AUTHOR]

Evaluating extreme rainfall for a certain location is commonly considered when designing stormwater management systems. Rain gauge data are widely used to estimate rainfall intensities for a given return period. However, the poor spatial and temporal resolution of operational gauges is the main limiting factor. Several studies have used rainfall estimates based on weather radar horizontal reflectivity (Zh), but they come with a great caveat: while proven reliable for low or moderate rainfall rates, they are subject to major errors in extreme rainfall and convective cases. It is widely known that C-band weather radar can underestimate precipitation intensity due to signal attenuation or overestimate it due to hail and clutter contamination. From the late 1990s, dual-polarization weather radar started to become operational in the national surveillance radar network in Europe, providing innovative quantitative precipitation estimation (QPE) based on polarimetric variables. This study circumvents Zh shortcomings by using specific differential-phase (Kdp) data from operational dual-polarization C-band weather radars. The rain intensity estimates based on a specific differential-phase data are immune to attenuation and less affected by hail contamination. In this study, for the first time, QPEs based on polarimetric observations by operational C-band weather radars and without any rain gauge adjustments are analyzed. The purpose is to estimate return periods for 1 h rainfall total computed from polarimetric weather radar data using non-adjusted QPEs based on R(Zh,Kdp) data and to compare the results with those derived using R(Zh) and rain gauge data. Only the warm period during the year is considered here, as most of the extreme precipitation events for such a duration occur for both places studied (Italy and Estonia) at this time. Limiting the dataset to warm periods also allows us to use the radar-based rainfall quantitative precipitation estimations, which are more reliable than the snowfall ones. Data from operational dual polarimetric C-band weather radar sites are used from both Italy and Estonia. Given climatologically homogeneous regions, this study demonstrates that polarimetric weather radar observations can provide reliable QPEs compared to single-polarization estimates with respect to rain gauges and that they can provide a reliable estimation of return periods of 1 h rainfall total, even for relatively short time series. [ABSTRACT FROM AUTHOR]

Wind-driven redistribution of snow on sea ice alters its topography and microstructure, yet the impact of these processes on radar signatures is poorly understood. Here, we examine the effects of snow redistribution over Arctic sea ice on radar waveforms and backscatter signatures obtained from a surface-based, fully polarimetric Ka- and Ku-band radar at incidence angles between 0 ∘ (nadir) and 50 ∘. Two wind events in November 2019 during the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC) expedition are evaluated. During both events, changes in Ka- and Ku-band radar waveforms and backscatter coefficients at nadir are observed, coincident with surface topography changes measured by a terrestrial laser scanner. At both frequencies, redistribution caused snow densification at the surface and the uppermost layers, increasing the scattering at the air–snow interface at nadir and its prevalence as the dominant radar scattering surface. The waveform data also detected the presence of previous air–snow interfaces, buried beneath newly deposited snow. The additional scattering from previous air–snow interfaces could therefore affect the range retrieved from Ka- and Ku-band satellite altimeters. With increasing incidence angles, the relative scattering contribution of the air–snow interface decreases, and the snow–sea ice interface scattering increases. Relative to pre-wind event conditions, azimuthally averaged backscatter at nadir during the wind events increases by up to 8 dB (Ka-band) and 5 dB (Ku-band). Results show substantial backscatter variability within the scan area at all incidence angles and polarizations, in response to increasing wind speed and changes in wind direction. Our results show that snow redistribution and wind compaction need to be accounted for to interpret airborne and satellite radar measurements of snow-covered sea ice. [ABSTRACT FROM AUTHOR]

The potential ability of VHF or UHF Doppler radars to measure turbulence kinetic energy (TKE) dissipation rate ε in the atmosphere is a major asset of these instruments because of the possibility to continuously monitor turbulence in the atmospheric column above the radars. Several models have been proposed over the past decades to relate ε to half the Doppler spectral width σ , corrected for non-turbulent contributions, but their relevance remains unclear. Recently, Luce et al. (2023) tested the performance of a new model expected to be valid for weakly stratified or strongly sheared conditions, i.e., for low Richardson (Ri) numbers. Its simplest expression is εS=CSσ2S , where CS∼0.64 and S=|dV/dz| is the vertical shear of the horizontal wind V. We assessed the relevance of this model with a UHF (1.357 GHz) wind profiler called WPR LQ-7, which is routinely operated at the Shigaraki Middle and Upper Atmosphere (MU) observatory (34.85 ∘ N, 136.10 ∘ E) in Japan. For this purpose, we selected turbulence events associated with Kelvin–Helmholtz (KH) billows, because their formation necessarily requires Ri<0.25 somewhere in the flow, a condition a priori favorable to the application of the model. Eleven years of WPR LQ-7 data were used for this objective. The assessment of εS was first based on its consistency with an empirical model εLout=σ3/Lout , where Lout has the dimension of an outer scale of turbulence. It was found to compare well in a KH layer with direct estimates of ε from in situ measurements for Lout≈70 m. Some degree of equivalence between εS and εLout was confirmed by statistical analysis of 192 KH layers found in the height range 0.3–5.0 km indicating that Lout≈LH/0.64 , where LH=σ/S is the Hunt scale defined for neutral turbulence. The degree of equivalence is even significantly improved if Lout is not treated as a constant but depends on the depth D of the layer. We found Lout≈0.0875D or equivalently LH∼0.056D , which also means that σ is proportional to the apparent variation in the horizontal velocity (S×D) over the depth of the KH layer. Consequently, εS=0.64σ2S and εLout=σ3/0.0875D would express the same model for KH layers when Ri remains low. For such a condition, we provide a physical interpretation of εLout , which would be qualitatively identical to that for neutral boundary layers. [ABSTRACT FROM AUTHOR]

Radar based microphysical retrievals of cloud and droplet properties are vital for informing model parameterisations of clouds and precipitation but these retrievals often do not capture the details of small droplets in light rain or drizzle. A state-of-the-art G-band radar is used here to demonstrate improvements to microphysical retrievals in a case study featuring light rain. Improvements are seen, as compared to W-band radar, in the retrieval of vertical wind speed, due to the location of Mie minima at smaller droplet sizes with the G-band radar. This, in turn, has an impact on the retrieval of the drop size distribution, allowing for better accuracy in the retrieval of the characteristic drop diameter and improvements in the retrieval of the number of concentration of small droplet sizes. The differential Doppler velocity between Ka- and G-bands shows increased dynamic range compared to the Ka-W pairing, particularly for instances presenting small characteristic drop diameters. The increased attenuation experienced at G-band enables improved retrievals of liquid water content and precipitation rate when paired with W-band or Ka-band as compared to the W-band and Ka- band pairing. This is particularly noticeable in periods of light rain where the W-band and Ka-band radars receive negligible attenuation while the attenuation at G-band is much greater. [ABSTRACT FROM AUTHOR]

Large hail events are typically infrequent, with significant time gaps between occurrences at specific locations. However, when these events do happen, they can cause rapid and substantial economic losses within a matter of minutes. Therefore, it is crucial to have the ability to accurately observe and understand hail phenomena to improve the mitigation of this impact. While in situ observations are accurate, they are limited in number for an individual storm. Weather radars, on the other hand, provide a larger observation footprint, but current radar-derived hail size estimates exhibit low accuracy due to horizontal advection of hailstones as they fall, the variability of hail size distributions (HSDs), complex scattering and attenuation, and mixed hydrometeor types. In this paper, we propose a new radar-derived hail product developed using a large dataset of hail damage insurance claims and radar observations. We use these datasets coupled with environmental information to calculate a hail damage estimate (HDE) using a deep neural network approach aiming to quantify hail impact, with a critical success index of 0.88 and a coefficient of determination against observed damage of 0.79. Furthermore, we compared HDE to a popular hail size product (MESH), allowing us to identify meteorological conditions that are associated with biases on MESH. Environments with relatively low specific humidity, high CAPE and CIN, low wind speeds aloft, and southerly winds at the ground are associated with a negative MESH bias, potentially due to differences in HSD, hail hardness, or mixed hydrometeors. In contrast, environments with low CAPE, high CIN, and relatively high specific humidity aloft are associated with a positive MESH bias. [ABSTRACT FROM AUTHOR]

This work presents the importance of polarimetric variables as an additional data source for nowcasting thunderstorm hazards using an existing neural network architecture with recurrent-convolutional layers. The model can be trained to predict different target variables, which enables nowcasting of hail, lightning, and heavy rainfall for lead times up to 60 min with a 5 min resolution, in particular. The exceedance probabilities of Swiss thunderstorm warning thresholds are predicted. This study is based on observations from the Swiss operational radar network, which consists of five operational polarimetric C-band radars. The study area of the Alpine region is topographically complex and has a comparatively very high thunderstorm activity. Different model runs using combinations of single- and dual-polarimetric radar observations and radar quality indices are compared to the reference run using only single-polarimetric observations. Two case studies illustrate the performance difference when using all predictors compared to the reference model. The importance of the predictors is quantified by investigating the final training loss of the model, with skill scores such as critical success index (CSI), precision, recall, precision–recall area under the curve, and the Shapley value. Results indicate that single-polarization radar data are the most important data source. Adding polarimetric observations improves the model performance compared to reference model in term of the training loss for all three target variables. Adding quality indices does so, too. Including both polarimetric variables and quality indices at the same time improves the accuracy of nowcasting heavy precipitation and lightning, with the largest improvement found for heavy precipitation. No improvement could be achieved for nowcasting of the probability of hail in this way. [ABSTRACT FROM AUTHOR]

Solid precipitation measurements in Antarctica are crucial as snowfall represents the main water input term for the polar cap, and its probable increase in the coming century can mitigate sea-level rise caused by global warming. This paper presents 7 years of Micro Rain Radar (Metek MRR-2) data at the Dumont d'Urville station in coastal Adélie Land, East Antarctica. Statistics are calculated on 3 radar variables (equivalent reflectivity, mean Doppler velocity and signal-to-noise ratio) to outline the main characteristics of the radar dataset. Seasonal and interannual variabilities are also investigated, but no significant temporal trends are detected except for the seasonal mean Doppler velocity which is higher in summer and lower in winter. We then use the snowfall rate (S) data from a colocated snow-gauge to estimate the MRR precipitation profile from the radar equivalent reflectivity (Ze) through a locally derived Ze-S relation. We find the relation Ze = 43.3 S0.88. The processing method used to obtain this relation, data quality and uncertainty considerations are discussed in the manuscript. In order to give an example of application of the dataset, a brief statistical comparison of the MRR precipitation rate along the vertical with model data from the ERA5 reanalysis and the LMDZ climate model is performed, and notably shows that models underestimate heavy precipitation events. [ABSTRACT FROM AUTHOR]

Radar Doppler spectra observations provide a wealth of information about cloud and precipitation microphysics and dynamics. The interpretation of these measurements depends on our ability to simulate these observations accurately using a forward model. The effect of small-scale turbulence on the radar Doppler spectra shape has been traditionally treated by implementing the convolution process on the hydrometeor reflectivity spectrum and environmental turbulence. This approach assumes that all the particles in the radar sampling volume respond the same to turbulent-scale velocity fluctuations and neglects the particle inertial effect. Here, we investigate the inertial effects of liquid-phase particles on the forward modeled radar Doppler spectra. A physics-based simulation (PBS) is developed to demonstrate that big droplets, with large inertia, are unable to follow the rapid change of the velocity field in a turbulent environment. These findings are incorporated into a new radar Doppler spectra simulator. Comparison between the traditional and newly formulated radar Doppler spectra simulators indicates that the conventional simulator leads to an unrealistic broadening of the spectrum, especially in a strong turbulent environment. This study provides clear evidence to illustrate the droplet inertial effect on radar Doppler spectrum and develops a physics-based simulator framework to accurately emulate the Doppler spectrum for a given droplet size distribution (DSD) in a turbulence field. The proposed simulator has various potential applications for the cloud and precipitation studies, and it provides a valuable tool to decode the cloud microphysical and dynamical properties from Doppler radar observation. [ABSTRACT FROM AUTHOR]

Global measurements of horizontal winds in cloud and precipitating systems represent a gap in the global observing system. The WIVERN mission, one of the four candidates to be the ESA's Earth Explorer 11 mission, aims at filling this gap based on a conically scanning W-band Doppler radar instrument. The determination of the antenna boresight mispointing angles and the impact of their uncertainty on the line of sight velocities is critical to achieve the mission requirements. While substantial industrial efforts are on their ways for achieving accurate determination of the pointing, alternative (external) calibration approaches are currently under scrutiny. This work discusses four methods applicable to the WIVERN radar that can be used to correct antenna mispointing both in the azimuthal and in the elevation directions at different time scales. Results show that elevation mispointing is well corrected at very short time scales by monitoring the range at which the surface peak occurs. Azimuthal mispointing is harder but can be tackled by using the expected profiles of the non-moving surface Doppler. Biases in pointing at longer time scales can be monitored by using well established reference database (e.g. ECMWF reanalysis) or ad-hoc ground based calibrators. Although tailored to the WIVERN mission, the proposed methodologies can be extended to other Doppler concepts featuring conically scanning or slant viewing Doppler systems. [ABSTRACT FROM AUTHOR]