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The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular via a few distinct techniques: the use of radar polarimetry, of multi-frequency radar measurements, and of the radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining the latter two techniques, while relaxing some assumptions on, e.g., beam alignment and non-turbulent atmosphere. The method relies on a two-step deep-learning framework inspired from data compression techniques: an encoder model maps a high-dimensional signal to a low-dimensional latent space, while the decoder reconstructs the original signal from this latent space. Here, Doppler spectrograms at two frequencies constitute the high-dimensional input, while the latent features are constrained to represent the snowfall properties of interest. The decoder network is first trained to emulate Doppler spectra from a set of microphysical variables, using simulations from the Passive and Active Microwave radiative TRAnsfer model (PAMTRA) as training data. In a second step, the encoder network learns the inverse mapping, from real measured dual-frequency spectrograms to the microphysical latent space; in doing so, it leverages with a convolutional structure the spatial consistency of the measurements to mitigate the ill-posedness of the problem. The method was implemented on X- and W-band data from the ICE GENESIS campaign that took place in the Swiss Jura Mountains in January 2021. An in-depth assessment of the retrieval accuracy was performed through comparisons with colocated aircraft in situ measurements collected during three precipitation events. The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets. A discussion of the sensitivity and limitations of the method is also conducted. The main contribution of this work is, on the one hand, the theoretical framework itself, which can be applied to other remote-sensing retrieval applications and is thus possibly of interest to a broad audience across atmospheric sciences. On the other hand, the seven retrieved microphysical descriptors provide relevant insights into snowfall processes. [ABSTRACT FROM AUTHOR]

This paper introduces an experimental setup for retrieving horizontal wind speed and direction profiles with a high temporal and vertical resolution for process studies and validation of convection-permitting model simulations. The CMTRACE (tracing convective momentum transport in complex cloudy atmospheres) campaign used collocated wind lidar and cloud radar measurements to retrieve seamless wind profiles from near the surface up to cloud tops. It took place in Cabauw, the Netherlands, between 13 September and 3 October 2021. The intermediate processing steps for generating the level 1 and level 2 data, such as second trip echoes filtering, offset correction, wind retrieval, re-gridding, and flagging, are described. In level 1 (https://doi.org/10.5281/zenodo.6926483 ,), the data from lidar and radars are kept in the original spatial and temporal resolution, while in level 2 (https://doi.org/10.5281/zenodo.6926605 ,), they are regridded to a common spatial and temporal resolution. Statistical analyses of the lidar's and radar's wind speed and direction profiles indicate a correlation higher than 0.95 for both variables. The bias of wind direction and speed calculated between radar's and lidar's observations are 0.24 ∘ and -0.16 ms-1 , respectively. The foreseen initial application of the datasets includes the study of convective momentum transport and its validation in regional weather forecasts and large-eddy simulation hindcasts. [ABSTRACT FROM AUTHOR]

Precipitation nowcasting has important implications for urban operation and flood prevention. Radar echo extrapolation is the common method in precipitation nowcasting. Using deep learning models to extrapolate radar echo data has great potential. The increase of lead time leads to a weaker correlation between real rainfall evolution and generated images. The evolution information is easily lost during extrapolation, which is reflected as echoes attenuation. Existing models, including Generative Adversarial Network (GAN)-based models, are all difficult to reduce loss and curb attenuation, which results in insufficient rainfall prediction accuracy. Aim to the problem, a Spatiotemporal Process Intensification Network (GAN-argcPredNet v2.0) based on GAN-argcPredNet v1.0 is designed. GAN-argcPredNet v2.0 reduces the loss by intensifying the influence of the previously input evolution information. A Spatiotemporal Information Changes Prediction (STIC-Prediction) network is designed as generator. With the intensification of echo feature sequence, the generator focuses on the spatiotemporal variation and generates more accurate images. Furthermore, discriminator is a Channel-Spatial Convolution (CS-Convolution) network. The discriminator intensifies the discrimination of echoes information by strengthening spatial information of single image. Identification results are fed back to the generator, which reduces the loss of important evolutionary information. The experiments are based on the radar dataset of South China. The results show that GAN-argcPredNet v2.0 performs better than other models. In heavy rainfall prediction, compared with baseline, the Probability of Detection (POD), the Critical Success Index (CSI), and the Heidke Skill Score (HSS) increase by 24.8 %, 22.2 % and 21.5 % respectively. The False Alarm Ratio (FAR) decreases by 3.76 %. [ABSTRACT FROM AUTHOR]

This work assesses the effectiveness of sample preservation techniques for measurements of pHT (total scale), total dissolved inorganic carbon (DICT), and total alkalinity (AT) in organic-rich estuarine waters. Using HgCl2-treated and untreated water samples, measurements of these carbonate system parameters were examined over a period of three months. Over this duration, continued respiration of dissolved organic matter (DOM) in untreated samples created large discrepancies in DICT concentrations, while DICT was effectively constant in treated samples. Changes in AT were observed for both treated and untreated samples, with treated samples showing the greatest variation. In response to changing AT / DICT ratios, pHT changes were observed in both treated and untreated samples but were relatively small in treated samples. Improved accuracy of results in organic-rich estuarine waters that reflect the in situ carbonate system characteristics of the samples at the time of collection can be achieved when samples obtained for DICT and AT analysis are collected and stored separately. Accurate analyses of DICT can be obtained by filtration and preservation with HgCl2. Accuracy of AT analyses can be improved by filtration and storage in polypropylene bottles at 4 °C without adding HgCl2. Quality of pHT measurements can be improved by prompt analysis in the field and, if this cannot be accomplished, then samples can be preserved with HgCl2 and measured in the laboratory within one week. [ABSTRACT FROM AUTHOR]

Polarimetric microphysical retrievals reveal a great potential for the evaluation of numerical models and data assimilation. However, the accuracy of ice microphysical retrievals is still poorly explored. To evaluate these retrievals and assess their accuracy, polarimetric radar measurements are spatially and temporally collocated with in situ aircraft measurements obtained during the OLYMPEX campaign (Olympic Mountain Experiment). Retrievals for ice water content IWC, total number concentration Nt , and mean volume diameter Dm of ice particles are assessed by comparing an in situ dataset obtained by the University of North Dakota (UND) Citation II aircraft with X-band Doppler on Wheels (DOW) measurements. Sector averaged range height indicator (RHI) scans are used to derive vertical profiles of microphysical retrievals. The comparison of these estimates with in situ data provides insights into strengths, weaknesses, and accuracy of the different retrievals, and quantifies the improvements of polarimetry-informed retrievals compared to conventional, non-polarimetric ones. In particular, the recently introduced hybrid ice water content retrieval exploiting reflectivity Z H, differential reflectivity Z DR and specific differential phase K DP outperforms other retrievals based on either (Z H, Z DR) or (ZH, K DP) or non-polarimetric retrievals in terms of correlations with in situ measurements and the root mean square error. [ABSTRACT FROM AUTHOR]

In this research, we investigate the surface displacements in the regions where the buildings collapsed. Istanbul has a surface that is active for earthquakes. As the construction regulations have been changed after the year 2000, it is expected that the buildings built after this year, are much safer due to the strict retime series. But the majority of the buildings are at high risk since they are constructed before the year 2000. Therefore suddenly collapsing buildings are an often case in Istanbul, and we have investigated six buildings that collapsed during the six-year time. The used data are Sentinel 1 Radar images. The methodology is SBAS provided by the COMET-LiCS portal. The study area is Istanbul-Türkiye. six buildings are selected for the investigation. The velocity of the displacements and the time series of the whole displacements on the building locations have been plotted. The neighbouring areas of the collapsed buildings are also investigated and analysed if there are any surface displacement changes between them. [ABSTRACT FROM AUTHOR]

Convective wind gusts (CGs) are usually related to thunderstorms, and they may cause great structural damage and serious hazards, such as train derailment, service interruption, and building collapse. Due to the small-scale and nonstationary nature of CGs, reliable CGs nowcasting with high spatial and temporal resolutions has remained unattainable. In this study, a novel nowcasting model based on deep learning - namely, CGsNet - is developed for 0-2 h of quantitative CGs nowcasting, first achieving minute-kilometer-level forecasts. CGsNet is a physics-constrained model established by training on large corpora of average surface wind speed (ASWS) and radar observations, it can produce realistic and spatiotemporally consistent ASWS predictions in CGs events. By combining the gust factor (1.77, the ratio of the observed peak wind gust speed (PWGS) to the ASWS) with the ASWS predictions, the PWGS forecasts are estimated with a spatial resolution of 0.01° × 0.01° and a 6-minute temporal resolution. CGsNet is shown to be effective, and it has an essential advantage in learning the spatiotemporal features of CGs. In addition, quantitative evaluation experiments indicate that CGsNet exhibits higher generalization performance for CGs than the traditional nowcasting method based on numerical weather prediction models. CGs nowcasting technology can be applied to provide real-time quantitative CGs forecasts and alerts the damaging wind events in meteorological services. [ABSTRACT FROM AUTHOR]

Volumetric measurements of the ionosphere are important for investigating spatial variations of ionospheric features, like auroral arcs and energy deposition in the ionosphere. In addition, such measurements make it possible to distinguish between variations in space and time. While spatial variations in scalar quantities such as electron density or temperature have been investigated with incoherent scatter radar (ISR) before, spatial variation in the ion velocity, which is a vector quantity, has been hard to measure. The upcoming EISCAT3D radar will be able to do volumetric measurements of ion velocity regularly for the first time. In this paper, we present a technique for relating volumetric measurements of ion velocity to neutral wind and electric field. To regularize the estimates, we use Maxwell's equations and fluid-dynamic constraints. The study shows that accurate volumetric estimates of electric field can be achieved. Electric fields can be resolved at altitudes above 120 km, which is the altitude range where auroral current closure occurs. Neutral wind can be resolved at altitudes below 120 km. [ABSTRACT FROM AUTHOR]

The Hunga Tonga–Hunga Ha′apai volcano eruption was a unique event that caused many atmospheric phenomena around the globe. In this study, we investigate the atmospheric gravity waves in the mesosphere/lower-thermosphere (MLT) launched by the volcanic explosion in the Pacific, leveraging multistatic meteor radar observations from the Chilean Observation Network De Meteor Radars (CONDOR) and the Nordic Meteor Radar Cluster in Fennoscandia. MLT winds are computed using a recently developed 3DVAR+DIV algorithm. We found eastward- and westward-traveling gravity waves in the CONDOR zonal and meridional wind measurements, which arrived 12 and 48 h after the eruption, and we found one in the Nordic Meteor Radar Cluster that arrived 27.5 h after the volcanic detonation. We obtained observed phase speeds for the eastward great circle path at both locations of about 250 m s -1 , and they were 170–150 m s -1 for the opposite propagation direction. The intrinsic phase speed was estimated to be 200–212 m s -1. Furthermore, we identified a potential lamb wave signature in the MLT winds using 5 min resolved 3DVAR+DIV retrievals. [ABSTRACT FROM AUTHOR]

During the concluding phase of the NASA Operation IceBridge (OIB), we successfully completed two airborne measurement campaigns (in 2018 and 2021, respectively) using a compact S and C band radar installed on a Single Otter aircraft and collected data over Alaskan mountains, ice fields, and glaciers. This paper reports seasonal snow depths derived from radar data. We found large variations in seasonal radar-inferred depths with multi-modal distributions assuming a constant relative permittivity for snow equal to 1.89. About 34 % of the snow depths observed in 2018 were between 3.2 and 4.2 m, and close to 30 % of the snow depths observed in 2021 were between 2.5 and 3.5 m. We observed snow strata in ice facies, combined percolation and wet-snow facies, and dry-snow facies from radar data and identified the transition areas from wet-snow facies to ice facies for multiple glaciers based on the snow strata and radar backscattering characteristics. Our analysis focuses on the measured strata of multiple years at the caldera of Mount Wrangell (K'elt'aeni) to estimate the local snow accumulation rate. We developed a method for using our radar readings of multi-year strata to constrain the uncertain parameters of interpretation models with the assumption that most of the snow layers detected by the radar at the caldera are annual accumulation layers. At a 2004 ice core and 2005 temperature sensor tower site, the locally estimated average snow accumulation rate is ∼2.89 m w.e. a -1 between the years 2003 and 2021. Our estimate of the snow accumulation rate between 2005 and 2006 is 2.82 m w.e. a -1 , which matches closely to the 2.75 m w.e. a -1 inferred from independent ground-truth measurements made the same year. The snow accumulation rate between the years 2003 and 2021 also showed a linear increasing trend of 0.011 m w.e. a -2. This trend is corroborated by comparisons with the surface mass balance (SMB) derived for the same period from the regional atmospheric climate model MAR (Modèle Atmosphérique Régional). According to MAR data, which show an increase of 0.86 ∘ C in this area for the period of 2003–2021, the linear upward trend is associated with the increase in snowfall and rainfall events, which may be attributed to elevated global temperatures. The findings of this study confirmed the viability of our methodology, as well as its underlying assumptions and interpretation models. [ABSTRACT FROM AUTHOR]

This study explores the potential of using Doppler (power) spectra from vertically pointing C-band radar birdbath scans to investigate precipitating clouds above the radar. First, the new birdbath scan strategy for the network of dual-polarization C-band radars operated by the German Meteorological Service (Deutscher Wetterdienst, DWD) is outlined, and a novel spectral postprocessing and analysis method is presented. The postprocessing algorithm isolates the weather signal from non-meteorological contributions in the radar output based on polarimetric attributes, identifies the statistically significant precipitation modes contained in each Doppler spectrum, and calculates characteristics of every precipitation mode as well as multimodal properties that describe the relation among different modes when more than a single mode is identified. To achieve a high degree of automation and flexibility, the postprocessing chain combines classical signal processing with clustering algorithms. Uncertainties in the calculated modal and multimodal properties are estimated from the small variations associated with smoothing the measured radar signal. The analysis of five birdbath scans recorded at different radar sites and for various precipitation conditions delivers reliable profiles of the derived modal and multimodal properties for two snowfall cases and for stratiform precipitation above and below the melting layer. To help identify the dominant precipitation growth mechanism, Doppler spectra from DWD's birdbath scans can be used to retrieve the typical degree of riming for individual snow modes. Here, the automatically identified snow modes span a wide range of riming conditions with estimated rime mass fractions (RMFs) of up to RMF>0.5. The evaluation of Doppler spectra inside the melting layer and for an intense frontal shower, with observed radar reflectivities of up to about 40 dBZ, occasionally shows erroneously identified precipitation modes and spurious results for the calculated higher-order Doppler moments of skewness and kurtosis. Nonetheless, the Doppler spectra from DWD's operational C-band radar birdbath scan provide a detailed view into the precipitating clouds and allow for calculating a high-resolution profile of radar reflectivity, mean Doppler velocity, and spectral width even in intense frontal precipitation. [ABSTRACT FROM AUTHOR]

Studying precipitation at very high latitudes is difficult because of the harsh environmental conditions that limit the external activity of humans and instruments, especially in the polar winter. The direct monitoring of ice crystal habits and size distribution in Antarctic precipitation is important for the validation of the algorithms used for retrieving precipitation from ground-based and satellite-borne radar instruments and for the improvement of the climatological modelling of polar areas. This paper describes an automated device (ICE-CAMERA) specifically developed for the imaging, measurement, and classification of ice precipitation on the Antarctic high plateau. The instrument gives detailed information on precipitation on an hourly basis. The article provides a description of the device and its image processing software. Starting in 2014, the instrument has operated almost unattended all year round at Concordia station, Antarctica (75 ∘ S, 123 ∘ E, 3220 m altitude). [ABSTRACT FROM AUTHOR]

Dual-wavelength (3.2 and 0.32 cm , i.e., X- and W-radar bands) radar ratio (DWR) measurements in ice clouds and precipitation using Canada's National Research Council Institute for Aerospace Research airborne radar are compared to closely collocated particle microphysical in situ sampling data in order to develop relations between DWR and characteristic hydrometeor size. This study uses the radar and in situ data sets collected during the In-Cloud ICing and Large-drop Experiment (ICICLE) campaign in midlatitude frontal clouds. Since atmospheric particle scattering at X band is predominantly in the Rayleigh regime and the W-band frequency is the highest frequency usually used for hydrometeor remote sensing, the X–W-band combination provides a relatively strong dual-wavelength reflectivity difference. This study considers radar and in situ measurements conducted in relatively homogeneous cloud and precipitation conditions. Measurements show that under these conditions, the difference between the X-band radar reflectivities observed with vertical and horizontal pointing of the radar beam are generally small and often negligible. However, W-band reflectivities at vertical beam pointing are, on average, larger than those for horizontal beam pointing by about 4 dB , which is a non-Rayleigh scattering effect from preferentially oriented non-spherical particles. A horizontal radar beam DWR–mean volume particle size relation, Dv , provides robust estimates of this characteristic size for populations of particles with different habits. Uncertainties of Dv retrievals using DWR are around 0.6 mm when Dv is greater than approximately 1 mm. Size estimates using vertical radar beam DWRs have larger uncertainties due to smaller dual-wavelength signals and stronger influences of hydrometeor habits and orientations at this geometry of beam pointing. Mean relations among different characteristic sizes, which describe the entire particle size distribution (PSD), such as Dv , and other sizes used in various applications (e.g., the mean, effective, and median sizes) are derived, so the results of this study can be used for estimating different PSD characteristic sizes. [ABSTRACT FROM AUTHOR]

Cloud radars are widely used in observing clouds and precipitation. However, the raw data products of cloud radars are usually affected by multiple factors, which may lead to misinterpretation of cloud and precipitation processes. In this study, we present a Doppler-spectra-based data processing framework to improve the data quality of a multi-mode pulse-compressed Ka–Ku radar system. Firstly, non-meteorological signal close to the ground was identified with enhanced Doppler spectral ratios between different observing modes. Then, for the Doppler spectrum affected by the range sidelobe due to the implementation of the pulse compression technique, the characteristics of the probability density distribution of the spectral power were used to identify the sidelobe artifacts. Finally, the Doppler spectra observations from different modes were merged via the shift-then-average approach. The new radar moment products were generated based on the merged Doppler spectrum data. The presented spectral processing framework was applied to radar observations of a stratiform precipitation event, and the quantitative evaluation shows good performance of clutter or sidelobe suppression and spectral merging. [ABSTRACT FROM AUTHOR]

Meteor radars have become widely used instruments to study atmospheric dynamics, particularly in the 70 to 110 km altitude region. These systems have been proven to provide reliable and continuous measurements of horizontal winds in the mesosphere and lower thermosphere. Recently, there have been many attempts to utilize specular and/or transverse scatter meteor measurements to estimate vertical winds and vertical wind variability. In this study we investigate potential biases in vertical wind estimation that are intrinsic to the meteor radar observation geometry and scattering mechanism, and we introduce a mathematical debiasing process to mitigate them. This process makes use of a spatiotemporal Laplace filter, which is based on a generalized Tikhonov regularization. Vertical winds obtained from this retrieval algorithm are compared to UA-ICON model data. This comparison reveals good agreement in the statistical moments of the vertical velocity distributions. Furthermore, we present the first observational indications of a forward scatter wind bias. It appears to be caused by the scattering center's apparent motion along the meteor trajectory when the meteoric plasma column is drifted by the wind. The hypothesis is tested by a radiant mapping of two meteor showers. Finally, we introduce a new retrieval algorithm providing a physically and mathematically sound solution to derive vertical winds and wind variability from multistatic meteor radar networks such as the Nordic Meteor Radar Cluster (NORDIC) and the Chilean Observation Network De meteOr Radars (CONDOR). The new retrieval is called 3DVAR+DIV and includes additional diagnostics such as the horizontal divergence and relative vorticity to ensure a physically consistent solution for all 3D winds in spatially resolved domains. Based on this new algorithm we obtained vertical velocities in the range of w = ± 1–2 ms-1 for most of the analyzed data during 2 years of collection, which is consistent with the values reported from general circulation models (GCMs) for this timescale and spatial resolution. [ABSTRACT FROM AUTHOR]

In winter storms, enhanced radar reflectivity is often associated with heavy snow. However, some higher reflectivities are the result of mixed precipitation including melting snow. The correlation coefficient (a dual-polarization radar variable) can identify regions of mixed precipitation, but this information is usually presented separately from reflectivity. Especially under time pressure, radar data users can mistake regions of mixed precipitation for heavy snow because of the high cognitive load associated with comparing data in two fields while simultaneously attempting to discount a portion of the high reflectivity values. We developed an image muting method for regional radar maps that visually de-emphasizes the high reflectivity values associated with mixed precipitation. These image muted depictions of winter storm precipitation structures are useful for analyzing regions of heavy snow and monitoring real-time weather conditions. [ABSTRACT FROM AUTHOR]

Information about precipitation extremes is of vital importance for many hydrological planning and design purposes. However, due to various sources of error, some of the observed extremes may be inaccurate or false. The purpose of this investigation is to present quality control of observed extremes using space–time statistical methods. To cope with the highly skewed rainfall distribution, a Box–Cox transformation with a suitable parameter was used. The value at the location of a potential outlier is estimated using the surrounding stations and the calculated spatial variogram and compared to the suspicious observation. If the difference exceeds the threshold of the test, the value is flagged as a possible outlier. The same procedure is repeated for different temporal aggregations in order to avoid singularities caused by convection. Detected outliers are subsequently compared to the corresponding radar and discharge observations, and finally, implausible extremes are removed. The procedure is demonstrated using observations of sub-daily and daily temporal resolution in Germany. [ABSTRACT FROM AUTHOR]

Lidar at 1064 nm and Ka-band millimetre-wave cloud radar (MMCR) are powerful tools for detecting the height distribution of cloud boundaries and can monitor the entire life cycle of cloud layers. In this study, lidar and MMCR are employed to jointly detect cloud boundaries under different conditions. By enhancing the echo signal of lidar at 1064 nm and combining its signal-to-noise ratio (SNR), the cloud signal can be accurately extracted from the aerosol signals and background noise. The interference signal is eliminated from Doppler spectra of the MMCR by using the noise ratio of the smallest measurable cloud signal (SNR min⁡) and the spectral point continuous threshold (Nts). Moreover, the quality control of the reflectivity factor of MMCR obtained by the inversion is conducted, which improves the detection accuracy of the cloud signal. We analysed three typical cases studies; case one presents two interesting phenomena: (a) at 19:00–20:00 CST (China standard time), the ice crystal particles at the cloud top boundary are too small to be detected by MMCR, but they are well detected by lidar. (b) At 19:00–00:00 CST, the cirrus cloud changes to altostratus where the cloud particles eventually grow into large sizes, producing precipitation. Further, MMCR has more advantages than lidar in detection of the cloud top boundary within this period. Considering the advantages of the two devices, the change characteristics of the cloud boundary in Xi'an from December 2020 to November 2021 were analysed, with MMCR detection data as the main data and lidar data as the assistant data. The seasonal variation characteristics of clouds show that, in most cases, high clouds often occur in summer and autumn, and the low clouds are usually in winter. The normalized cloud cover shows that the maximum and minimum cloud cover occur in summer and winter, respectively. Furthermore, the cloud boundary frequency distribution results for the whole of the observation period show that the cloud bottom boundary below 1.5 km is more than 1 %, the frequency within the height range of 3.06–3.6 km is approximately 0.38 %, and the frequency above 8 km is less than 0.2 %. The cloud top boundary frequency distribution exhibits the characteristics of a bimodal distribution. The first narrow peak lies at approximately 1.0–3.1 km, and the second peak appears at 6.4–9.8 km. [ABSTRACT FROM AUTHOR]

Raindrop size distributions (DSDs) play a crucial role in quantitative rainfall estimation using weather radar. Thanks to dual polarization capabilities, crucial information about the DSD in a given volume of air can be retrieved. One popular retrieval method assumes that the DSD can be modeled by a constrained gamma distribution in which the shape (μ) and rate (Λ) parameters are linked together by a deterministic relationship. In the literature, μ – Λ relationships are often taken for granted and applied without much critical discussion. In this study, we take another look at this important issue by conducting a detailed analysis of μ – Λ relations in stratiform rain and quantifying the accuracy of the associated DSD retrievals. Crucial aspects of our research include the sensitivity of μ – Λ relations to the temporal aggregation scale, drop concentration, inter-event variability, and adequacy of the gamma distribution model. Our results show that μ – Λ relationships in stratiform rain are surprisingly robust to the choice of the sampling resolution, sample size, and adequacy of the gamma model. Overall, the retrieved DSDs are in a rather decent agreement with ground observations (correlation coefficient of 0.57 and 0.74 for μ and Dm). The main sources of errors and uncertainty during the retrievals are calibration offsets in reflectivity (Zhh) and differential reflectivity (Zdr). Measurement noise and differences in scale between radars and disdrometers also play a minor role. The raindrop concentration (NT) remains the most difficult parameter to retrieve, which can be off by several orders of magnitude. After careful data filtering and removal of problematic Zhh/Zdr pairs, the correlation coefficient for the retrieved NT values remained low, only slightly increasing from 0.12 into 0.24. [ABSTRACT FROM AUTHOR]

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

Based on the quality-controlled observational spectral width data of the Beijing mesosphere–stratosphere–troposphere (MST) radar in the altitudinal range of 3–19.8 km from 2012 to 2014, this paper analyses the relationship between the proportion of negative turbulent kinetic energy (N-TKE) and the horizontal wind speed and the vertical shear of horizontal wind domain and gives the distributional characteristics of atmospheric turbulence parameters obtained by using different calculation models. Three calculation models of the spectral width method were used in this study – namely the H model (Hocking, 1985), N-2D model (Nastrom, 1997) and D–H model (Dehghan and Hocking, 2011). The results showed that the proportion of N-TKE in the H model, N-2D model and D–H model increases with the horizontal wind speed u and/or the vertical shear of horizontal wind speed ∂u∂z , and the maximum values are 60 %, 45 % and 35 %, respectively. When the ∂u∂z is greater than 0.006 s -1 , the N-TKE of the H model increases sharply with ∂u∂z ; the increase rate is about 20%0.002s-1. For these three models, the results are similar except that the vertical shear of the horizontal wind speed is greater than 0.006 s -1. When ∂u∂z>0.006 s -1 , the proportion of N-TKE in the N-2D and H models increases with ∂u∂z , while the proportion in the D–H model is less than 10 % and has slight variation. However, it is still necessary to consider the applicability of the N-2D model and D–H model in some weather processes with strong winds. The distributional characteristics with height of the turbulent kinetic energy dissipation rate ε and the vertical eddy diffusion coefficient Kz derived by the three models are consistent with previous studies. Still, there are differences in the values of turbulence parameters. Also, the range resolution of the radar has little effect on the differences in the range of turbulence parameters' values. The median values of ε in the H model, N-2D model and D–H model is 10 -3.2 –10 -2.7 , 10 -3.0 –10 -2.6 and 10 -3.3 –10 -2.8 m 2 s -3 , respectively. The median values of Kz in these three models are 10 0.3 –10 0.7 , 10 0.4 –10 0.7 and 10 0.1 –10 0.5 m 2 s -1. [ABSTRACT FROM AUTHOR]

We demonstrate that bistatic reception of high-frequency oceanographic radars can be used as single-frequency oblique ionospheric sounders. We develop methods that are agnostic of the software-defined radio system to estimate the group range from the bistatic observations. The group range observations are used to estimate the virtual height and equivalent vertical frequency at the midpoint of the oblique propagation path. Uncertainty estimates of the virtual height and equivalent vertical frequency are presented. We apply this analysis to observations collected from two experiments run at two locations in different years, but utilizing similar software-defined radio data collection systems. In the first experiment, 10 d of data were collected in March 2016 at a site located in Maryland, USA, while the second experiment collected 20 d of data in October 2020 at a site located in South Carolina, USA. In both experiments, three Coastal Oceanographic Dynamics and Applications Radars (CODARs) located along the Virginia and North Carolina coast of the US were bistatically observed at 4.53718 MHz. The virtual height and equivalent virtual frequency were estimated in both experiments and compared with contemporaneous observations from a vertical incident digisonde–ionosonde at Wallops Island, VA, USA. We find good agreement between the oblique CODAR-derived and WP937 digisonde virtual heights. Variations in the virtual height from the CODAR observations and the digisonde are found to be nearly in phase with each other. We conclude from this investigation that observations of oceanographic radar can be used as single-frequency oblique incidence sounders. We discuss applications with respect to investigations of traveling ionospheric disturbances, studies of day-to-day ionospheric variability, and using these observations in data assimilation. [ABSTRACT FROM AUTHOR]

All-sky meteor radars have become a reliable and widely used tool to observe horizontal winds in the mesosphere and lower thermosphere (MLT) region. The horizontal winds estimated by conventional single-station radars are obtained after averaging all meteor detections based on the assumption of the homogeneity of the horizontal wind in the meteor detection area (approximately 200-300 km radius). In this study, to improve the horizontal winds, we apply a multistatic meteor radar system consisting of a monostatic meteor radar in Mengcheng (33.36 °N, 116.49 °E) and a bistatic remote receiver in Changfeng (31.98 °N, 117.22 °E), separated by approximately 167 km to increase the number of meteors by at least 70% and provide two different viewing angles of the meteor echoes. The accuracy of the horizontal wind measurement depends on the meteor number in time and altitude intervals. Compared to typical monostatic meteor radar, our approach shows the feasibility of estimating the two-dimensional horizontal wind field. The technique allows us to estimate the mean horizontal wind and the gradient terms of the horizontal wind, moreover, the horizontal divergence, relative vorticity, stretching and shearing deformation of the wind field. We are confident that the improved horizontal wind parameters will contribute to improving the understanding of the dynamics in the MLT region. [ABSTRACT FROM AUTHOR]

A still wind turbine (WT) observed with a fixed pointing radar antenna shows peculiar polarimetric signatures: during two minutes (from 17:08 to 17:10 UTC) of the first day (March 4, 2020) of the WT MeteoSwiss X-band radar campaign in Schaffhausen, the copolar correlation coefficient between the two orthogonal polarization states was persistently equal to 1. The reflectivity at vertical polarization was bounded between 38.5 and 41.5 dBz; however, the changes between two consecutive 64 ms values (retrieved by means of 128 transmitted pulses) were either 0 dBz or ±0.5 dBz. The 2-min median (mean) value was 40.0 (39.9) dBz over the 1875 echoes of this interval. The reflectivity at horizontal polarization was persistently equal to 56.5 dBz, which means no change exceeding ±0.25 dBz. The standard deviation (1874 degrees of freedom) of the differential phase shift, which in the absence of precipitation was, in fact, coincident with the dispersion of the differential backscattering phase shift, was as small as 3.0°. During two 10-min intervals (17:10-17:20 UTC and 17:30-17:40 UTC) the rotor has moved less than 1 revolution; however, this slow movement together with a change in blade angles and nacelle orientation was sufficient to cause large changes and significant variability in the polarimetric signatures, with two pairs of ZH consecutive values reaching the extreme of 78.5 dBz, which is the absolute reflectivity maximum reached in the whole campaign (March 4-21, 2020). Between 17:20 and 17:30 UTC, the rotor has accomplished 22.5 revolutions: the variability becomes smoother and softer in the central part of the interval (probably thanks to uniform rotor speed and "frozen" blade angles and nacelle orientation). It is desirable and recommended to extend this preliminary (32-minute) analysis (based on thirty thousand polarimetric measurables) to several other 10-min intervals with zero rotor speed. [ABSTRACT FROM AUTHOR]

A set of Doppler power spectrum processing methods for TJ-II, a vertical sensing 94 GHz millimeter-wave cloud radar(MMCR), is proposed to distinguish clouds and enhance the data quality. The noise level is estimated by a modified segment method with 2-D segments. A two-step cloud signal mask method is proposed to distinguish clouds and noise. A Gaussian filter with adaptive standard variance is used to improve detection performance at the boundary of clouds and noise. Square signal blocks were constructed to test our method. Velocity dealiasing is carried out combined with a pre-dealiasing processing and the dual Pulse Repetition Frequency (PRF) technique to address a particular phenomenon called half-folded in MMCRs, which can not be fixed by post-processing at the base datum stage. Some observations of TJ-II are used to demonstrate our method. A comparison between our method as pre-processing and the method as post-processing proposed by Key Laboratory for Semi-Arid Climate Change of the Ministry of Education and College of Atmospheric Sciences, Lanzhou University, was carried out using one-day observation. It was found that our method shows some advantages in cloud base detection. [ABSTRACT FROM AUTHOR]

Carrying a laser Doppler instrument, the Aeolus satellite was launched in 2018, becoming the first mission for atmospheric wind profile measurements from space. Before utilizing the Aeolus winds for different applications, evaluating their data quality is essential. With the help of ground-based wind profiling radar measurements and the European Centre for Medium-Range Weather Forecasts (ECMWF) model equivalents, this study quantifies the error characteristics of Aeolus L2B (baseline-11) near-real-time horizontal line-of-sight winds across Australia during October 2020–March 2021 by using both inter-comparison and triple collocation analysis. The results of the inter-comparison analysis indicate that both Rayleigh-clear winds and Mie-cloudy winds are in good agreement with the ground-based radar measurements with overall absolute mean biases smaller than 0.7 m s -1 and correlation coefficients larger than or equal to 0.9. Moreover, assuming the radar measurements as the reference data set, Mie-cloudy winds are shown to be more precise than Rayleigh-clear winds with an overall random error of 4.14 and 5.81 m s -1 , respectively. Similar results were also found from triple collocation analysis, with error standard deviations of 5.61 and 3.50 m s -1 for Rayleigh-clear winds and Mie-cloudy winds. In addition, the Mie channel is shown to be more capable of capturing the wind in the planetary boundary layer (< 1500 m). The findings of this study demonstrate the good performance of space-borne Doppler lidar for wind profiling and provide valuable information for data assimilation in numerical weather prediction. [ABSTRACT FROM AUTHOR]

Ice growth processes within clouds affect the type and amount of precipitation. Hence, the importance of an accurate representation of ice microphysics in numerical weather and numerical climate models has been confirmed by several studies. To better constrain ice processes in models, we need to study ice cloud regions before and during monitored precipitation events. For this purpose, two radar instruments facing each other were used to collect complementary measurements. The C-band POLDIRAD weather radar from the German Aerospace Center (DLR) in Oberpfaffenhofen and the Ka-band MIRA-35 cloud radar from the Ludwig Maximilians University of Munich (LMU) were used to monitor stratiform precipitation in the vertical cross-sectional area between the two instruments. The logarithmic difference of radar reflectivities at two different wavelengths (54.5 and 8.5 mm), known as the dual-wavelength ratio, was exploited to provide information about the size of the detected ice hydrometeors, taking advantage of the different scattering behavior in the Rayleigh and Mie regime. Along with the dual-wavelength ratio, differential radar reflectivity measurements from POLDIRAD provided information about the apparent shape of the detected ice hydrometeors. Scattering simulations using the T-matrix method were performed for oblate and horizontally aligned prolate ice spheroids of varying shape and size using a realistic particle size distribution and a well-established mass–size relationship. The combination of dual-wavelength ratio, radar reflectivity, and differential radar reflectivity measurements as well as scattering simulations was used for the development of a novel retrieval for ice cloud microphysics. The development of the retrieval scheme also comprised a method to estimate the hydrometeor attenuation in both radar bands. To demonstrate this approach, a feasibility study was conducted on three stratiform snow events which were monitored over Munich in January 2019. The ice retrieval can provide ice particle shape, size, and mass information which is in line with differential radar reflectivity, dual-wavelength ratio, and radar reflectivity observations, respectively, when the ice spheroids are assumed to be oblates and to follow the mass–size relation of aggregates. When combining two spatially separated radars to retrieve ice microphysics, the beam width mismatch can locally lead to significant uncertainties. However, the calibration uncertainty is found to cause the largest bias for the averaged retrieved size and mass. Moreover, the shape assumption is found to be equally important to the calibration uncertainty for the retrieved size, while it is less important than the calibration uncertainty for the retrieval of ice mass. A further finding is the importance of the differential radar reflectivity for the particle size retrieval directly above the MIRA-35 cloud radar. Especially for that observation geometry, the simultaneous slantwise observation from the polarimetric weather radar POLDIRAD can reduce ambiguities in retrieval of the ice particle size by constraining the ice particle shape. [ABSTRACT FROM AUTHOR]

Accurate rainfall monitoring is critical for sustainable societies and yet challenging in many ways. Opportunistic monitoring using commercial microwave links (CMLs) in telecommunication networks is emerging as a powerful complement to conventional gauges and weather radar. However, CML data are often inaccessible or incomplete, which limits research and application. Here, we aim to reduce this barrier by openly sharing data at 10 s resolution with true coordinates from a pilot study involving 364 bi-directional CMLs in Gothenburg, Sweden. To enable further comparative analyses, we also share high-resolution data from 11 precipitation gauges and the Swedish operational weather radar composite in the area. This article presents an overview of the data, including the collection approach, descriptive statistics, and a case study of a high-intensity event. The results show that the data collection was very successful, providing near-complete time series for the CMLs (99.99 %), gauges (100 %), and radar (99.6 %) in the study period (June–August 2015). The bandwidth consumed during CML data collection was small, and hence, the telecommunication traffic was not significantly affected by the collection. The gauge records indicate that total rainfall was approximately 260 mm in the study period, with rainfall occurring in 6 % of each 15 min interval. One of the most intense events was observed on 28 July 2015, during which the Torslanda gauge recorded a peak of 1.1 mmmin-1. The variability in the CML data generally followed the gauge dynamics very well. Here we illustrate this for 28 July, where a nearby CML recorded a drop in received signal level of about 27 dB at the time of the peak. The radar data showed a good distribution of reflectivities for mostly stratiform precipitation but also contained some values above 40 dBZ , which is commonly seen as an approximate threshold for convective precipitation. Clutter was also found and was mostly prevalent around low reflectivities of - 15 dBZ. The data are accessible at 10.5281/zenodo.7107689 (Andersson et al., 2022). We believe this Open sharing of high-resolution data from Microwave links, Radar, and Gauges (OpenMRG) will facilitate research on microwave-based environmental monitoring using CMLs and support the development of multi-sensor merging algorithms. [ABSTRACT FROM AUTHOR]

We propose a novel method for deconvolving incoherent scatter radar data to recover accurate reconstructions of backscattered powers. The problem is modelled as a hierarchical noise-perturbed deconvolution problem, where the lower hierarchy consists of an adaptive length-scale function that allows for a non-stationary prior and as such enables adaptive recovery of smooth and narrow layers in the profiles. The estimation is done in a Bayesian statistical inversion framework as a two-step procedure, where hyperparameters are first estimated by optimisation and followed by an analytical closed-form solution of the deconvolved signal. The proposed optimisation-based method is compared to a fully probabilistic approach using Markov chain Monte Carlo techniques enabling additional uncertainty quantification. In this paper we examine the potential of the hierarchical deconvolution approach using two different prior models for the length-scale function. We apply the developed methodology to compute the backscattered powers of measured polar mesospheric winter echoes, as well as summer echoes, from the EISCAT VHF radar in Tromsø, Norway. Computational accuracy and performance are tested using a simulated signal corresponding to a typical background ionosphere and a sporadic E layer with known ground truth. The results suggest that the proposed hierarchical deconvolution approach can recover accurate and clean reconstructions of profiles, and the potential to be successfully applied to similar problems. [ABSTRACT FROM AUTHOR]

This study elaborates on how aircraft-based horizontal geometries of trade wind cumulus clouds differ whether a one-dimensional (1D) profiler or a two-dimensional (2D) imager is used. While nadir profiling devices are limited to a 1D realization of the cloud transect size, with limited representativeness of horizontal cloud extension, 2D imagers enhance our perspectives by mapping the horizontal cloud field. Both require high resolutions to detect the lower end of the cloud size spectrum. In this regard, the payload aboard the HALO (High Altitude and LOng Range Research Aircraft) achieves a comparison and also a synergy of both measurement systems. Using the NARVAL II (Next-Generation Aircraft Remote-Sensing for Validation Studies) campaign, we combine HALO observations from a 35.2 GHz cloud and precipitation radar (1D) and from the hyperspectral 2D imager specMACS (Munich Aerosol Cloud Scanner), with a 30 times higher along-track resolution, and compare their cloud masks. We examine cloud size distributions in terms of sensitivity to sample size, resolution and the considered field of view (2D or 1D). This specifies impacts on horizontal cloud sizes derived from the across-track perspective of the high-resolution imager in comparison to the radar curtain. We assess whether and how the trade wind field amplifies uncertainties in cloud geometry observations along 1D transects through directional cloud elongation. Our findings reveal that each additional dimension, no matter of the device, causes a significant increase in observed clouds. The across-track field yields the highest increase in the cloud sample. The radar encounters difficulties in characterizing the trade wind cumuli size distribution. More than 60 % of clouds are subgrid scale for the radar. The radar has issues in the representation of clouds shorter than 200 m, as they are either unresolved or are incorrectly displayed as single grid points. Very shallow clouds can also remain unresolved due to too low radar sensitivity. Both facts deteriorate the cloud size distribution significantly at this scale. Double power law characteristics in the imager-based cloud size distribution do not occur in radar observations. Along-track measurements do not necessarily cover the predominant cloud extent and inferred geometries' lack of representativeness. Trade wind cumuli show horizontal patterns similar to ellipses, with a mean aspect ratio of 3:2 and having tendencies of stronger elongation with increasing cloud size. Instead of circular cloud shape estimations based on the 1D transect, elliptic fits maintain the cloud area size distribution. Increasing wind speed tends to stretch clouds more and tilts them into the wind field, which makes transect measurements more representative along this axis. [ABSTRACT FROM AUTHOR]

Ocean currents are of interest to all who deal with marine affairs. Since they can be predicted, as a result, they are essential as renewable energy for us. We can use it in many applications like navigation, generating electricity, and port constructions. So, studying them and discovering the factors that create and influence them is significant. Using the instrumentation that we can use continuously and do not have many problems is preferred. Therefore, using new technologies instead of old traditional methods is crucial. In this study, we discuss ocean currents in the Oman Sea, suggest high-frequency radars (HFR) as new technology, and represent its advantages instead of ADCP. We use Mike 21 to get tidal current constituents and compare them with other data. The results show that values of two components, Mm and MSF, are more respective to astronomical-driven currents. Therefore, the amount of tidal current on the coast of Mokran is low, and other factors cause the current, which short-term observations in this region cannot be achieved. Thus, we need data in a more extended period to find present generating forces and predict them. HFR can be an efficient and valuable tool in such areas. [ABSTRACT FROM AUTHOR]

Radio waves propagating in the atmosphere are affected by the prevailing atmospheric state. The state of the atmosphere can cause radio waves to refract more or less towards the ground. When the refractive index of the atmosphere differs from standard atmospheric conditions the propagation is called anomalous. Radars which are affected by anomalous propagation can receive ground clutter far beyond the radar horizon. In this work 4.5 years of data from five operational Swedish C-band dual polarization weather radars are presented. Analyses of the data reveal a strong seasonal cycle and weaker diurnal cycle in ground clutter from coastal regions across nearby waters. The impact of anomalous propagation on ground clutter, measured with horizontal and vertical polarization, was compared but no clear difference was found. [ABSTRACT FROM AUTHOR]

Multiyear measurements from Joss-Waldvogel disdrometer (5 years) and X-band dual-polarization radar (2 years) made at Gadanki (13.5 N, 79.18 E), a low latitude station, are used to i) retrieve appropriate raindrop size distribution (DSD) relations for monsoonal rain, ii) understand their dependency on temperature, raindrop size-shape model and season and iii) assess polarimetric radar DSD retrievals by various popular techniques (Exponential-Exp, Constrained Gamma - CG, Normalized Gamma - N-Gamma and ß methods). The coefficients obtained for different DSD relations for monsoonal rain are found to be different from that of existing relations elsewhere. The seasonal variation in DSD is quite large and significant and as a result the coefficients also vary considerably between the seasons. The slope of the drop size - shape relation, assumed to be constant in several studies, vary considerably between the seasons with warmer seasons showing smaller slope value than cold season. It is found that the constant (0.062) used in linear drop shape models is valid only for cold season. The derived coefficients for CG method for different seasons coupled with those available in the literature reveals that the warm seasons/regions typically have larger curvature and slope values than in cold seasons/regions. The coefficients of mass weighted mean diameter (Dm) - differential reflectivity (ZDR) exhibit strong dependency on drop shape model, while those for the derivation intercept parameter exhibit strong seasonal dependency. Using the retrieved relations and X-band polarimetric radar at Gadanki, four popular DSD methods are evaluated against disdrometer measurements collected over 12 events. All the methods estimated Dm reasonably well with small root mean square error, however failed to estimate intercept parameter accurately. Only N-gamma method estimated the normalized intercept parameter reasonably. Problems associated with specific differential phase (KDP)-based estimates close to the radar location, particularly during overhead convection, are also discussed. [ABSTRACT FROM AUTHOR]

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

Ice shelves surrounding the Antarctic perimeter moderate ice discharge towards the ocean through buttressing. Ice-shelf evolution and integrity depend on the local surface accumulation, basal melting and on the spatially variable ice-shelf viscosity. These components of ice-shelf mass balance are often poorly constrained by observations and introduce uncertainties in ice-sheet projections. Isochronal radar stratigraphy is an observational archive for the atmospheric, oceanographic and ice-flow history of ice shelves. Here, we predict the stratigraphy of locally accumulated ice on ice shelves with a kinematic forward model for a given atmospheric and oceanographic scenario. This delineates the boundary between local meteoric ice (LMI) and continental meteoric ice (CMI). A large LMI to CMI ratio hereby marks ice shelves whose buttressing strength is more sensitive to changes in atmospheric precipitation patterns. A mismatch between the steady-state predictions of the kinematic forward model and observations from radar can highlight inconsistencies in the atmospheric and oceanographic input data or be an indicator for a transient ice-shelf history not accounted for in the model. We discuss pitfalls in numerical diffusion when calculating the age field and validate the kinematic model with the full Stokes ice-flow model Elmer/Ice. The Roi Baudouin Ice Shelf (East Antarctica) serves as a test case for this approach. There, we find a significant east–west gradient in the LMI / CMI ratio. The steady-state predictions concur with observations on larger spatial scales (>10 km), but deviations on smaller scales are significant, e.g., because local surface accumulation patterns near the grounding zone are underestimated in Antarctic-wide estimates. Future studies can use these mismatches to optimize the input data or to pinpoint transient signatures in the ice-shelf history using the ever growing archive of radar observations of internal ice stratigraphy. [ABSTRACT FROM AUTHOR]

The European climatological high-resolution gauge-adjusted radar precipitation dataset, EURADCLIM, addresses the need for an accurate (sub-)daily precipitation product covering 78% of Europe at high spatial resolution. A climatological dataset of 1-h and 24-h precipitation accumulations on a 2-km grid is derived for the period 2013 through 2020. The starting point is the European Meteorological Network (EUMETNET) Operational Program on the Exchange of weather RAdar Information (OPERA) gridded radar dataset of 15-min instantaneous surface rain rates, which is based on data from, on average, 138 ground-based weather radars. First, methods are applied to further remove non-meteorological echoes from these composites by applying two statistical methods and a satellite-based cloud type mask. Second, the radar composites are merged with the European Climate Assessment & Dataset (ECA&D) with potentially ~7700 rain gauges from National Meteorological and Hydrological Services (NMHS) in order to substantially improve its quality. Characteristics of the radar, rain gauge and satellite datasets are presented, as well as a detailed account of the applied algorithms. The clutter removal algorithms are effective, while removing few precipitation echoes. The usefulness of EURADCLIM for quantitative precipitation estimation (QPE) is confirmed by comparing against rain gauge accumulations employing scatter density plots, statistical metrics, and a spatial verification. These show a strong improvement with respect to the original OPERA product. The potential of EURADCLIM to derive pan-European precipitation climatologies and to evaluate extreme precipitation events is shown. Specific attention is given to remaining artefacts in and limitations of EURADCLIM. Finally, it is recommended to further improve EURADCLIM by applying algorithms to 3D instead of 2D radar data, and by obtaining more rain gauge data for the radar-gauge merging. The EURADCLIM 1-h and 24-h precipitation datasets are publicly available at https://doi.org/10.21944/7ypj-wn68 (Overeem et al., 2022a) and https://doi.org/10.21944/1a54-gg96 (Overeem et al., 2022b). [ABSTRACT FROM AUTHOR]

Several data assimilation (DA) approaches exist to generate consistent and continuous precipitation fields valuable for hydrometeorological applications and land data assimilation. Usually, DA is based on either static or dynamic approaches. Static methods rely on deterministic forecasts to estimate background error covariance matrices, whereas dynamic approaches use ensemble forecasts. Associating the two methods is known as hybrid DA, and it has proven beneficial for different applications as it combines the advantages of both approaches. The present study intends to explore hybrid DA for the 6 h Canadian Precipitation Analysis (CaPA). Based on optimal interpolation (OI), CaPA blends forecasts and observations from surface stations and ground-based radar datasets to provide precipitation fields over the North American domain. The application of hybrid DA to CaPA consisted of finding the optimal linear combination between (i) an OI based on the Regional Deterministic Prediction System (RDPS) and (ii) an ensemble Kalman filter (EnKF) based on the 20-member Regional Ensemble Prediction System (REPS). The results confirmed the known effectiveness of the hybrid approach when low-density observation networks are assimilated. Indeed, the experiments conducted for the summer without radar datasets and for the winter (characterized by very few observations in CaPA) showed that attributing a relatively high weight to the EnKF (50 % and 70 % for summer and winter, respectively) resulted in better analysis skill and a reduction in false alarms compared with the OI method. A deterioration in the moderate- to high-intensity precipitation bias was, however, observed during summer. Reducing the weight attributed to the EnKF to 30 % alleviated the bias deterioration while improving skill compared with the OI-based CaPA. [ABSTRACT FROM AUTHOR]

This study presents a simulation framework for cloud and precipitation measurements via spaceborne millimeter wave radar composed of nine sub modules. To demonstrate the influence of the assumed physical parameters and optimizing the microphysical modeling of the hydrometeors, we first conducted a sensitivity analysis. The results indicated that the radar reflectivity was highly sensitive to the particle size distribution (PSD) parameter of the median volume diameter and particle density parameter, which can cause reflectivity variations of several to more than 10 dB. The variation in the prefactor of the mass-power relations that related to riming degree may result in an uncertainty of approximately 30-45 %. The particle shape and orientation also had a significant impact on the radar reflectivity. The spherical assumption may result in an average overestimation of the reflectivity by approximately 4-8 %, dependent on the particle shape and orientation modeling. Typical weather cases were simulated using optimal physical modeling accounting for the particle shapes, typical PSD parameters corresponding to the cloud precipitation types, mass-power relations for snow and graupel, and melting modeling. We present and validate the simulation results for a cold front stratiform cloud and a deep convective process with observations from W-band cloud profiling radar (CPR) on the CloudSat satellite. The simulated brightness band features, echo structure, and intensity showed good agreement with the CloudSat observations; the average relative error in the vertical profile was within 20 %. Our results quantify the uncertainty in the millimeter wave radar echo simulation that may be caused by the physical model parameters and provide a scientific basis for optimal forward modeling. They also provide suggestions for prior physical parameter constraints for the retrieval of the microphysical properties of clouds and precipitation. [ABSTRACT FROM AUTHOR]

This paper introduces an experimental setup for retrieving horizontal wind speed and direction profiles by combining wind lidar and cloud radars and the 2 level datasets produced. The experiment took place in Cabauw, the Netherlands, between September 13th and October 3rd 2021. The intermediate processing steps for generating the Level 1 and Level 2 data, such as second trip echos filtering, offset correction, wind retrieval, re-gridding and flagging, are described. In Level 1 (https://doi.org/10.5281/zenodo.6926483, Dias Neto (2022a)), the data from lidar and radars are kept in the original spatial and temporal resolution, while in Level 2 (https://doi.org/10.5281/zenodo.6926605, Dias Neto (2022b)), they are re-grided to a common spatial and temporal resolution. Statistical analyses of the lidar's and radar's wind speed and direction profiles indicate a correlation higher than 0.95 for both variables, and the bias of wind direction and speed are 0.24° and -0.16 ms-1, respectively. Applications of this dataset include numerical model validation, momentum transport studies and spectral analysis for different cloud regimes. [ABSTRACT FROM AUTHOR]

Riming of ice crystals by supercooled water droplets is an efficient ice growth process, but its basic properties are still poorly known. While it has been shown to contribute significantly to surface precipitation at mid-latitudes, little is known about its occurrence at high latitudes. In Antarctica, two competing effects can influence the occurrence of riming: (i) the scarcity of supercooled liquid water clouds due to the extremely low tropospheric temperatures and (ii) the low aerosol concentration, which may lead to the formation of fewer and larger supercooled drops potentially resulting in an enhanced riming efficiency. In this work, by exploiting the deployment of an unprecedented number of multiwavelength remote sensing systems (including triple-frequency radar measurements) in West Antarctica, during the Atmospheric Radiation Measurements West Antarctic Radiation Experiment (AWARE) field campaign, we evaluate the riming incidence at McMurdo Station and find that riming occurs at much lower temperatures when compared to previous results in the mid-latitudes. This suggests the possible occurrence of a common atmospheric state over Antarctica that includes a rather stable atmosphere inhibiting turbulent mixing, and a high riming efficiency driven by large cloud droplets. We then focus on a peculiar case study featuring a persistent layer with a particularly pronounced riming signature in triple-frequency radar data but only a relatively modest amount of supercooled liquid water. In-depth analysis of the radar observations suggests that such signatures can only be explained by the combined effects of moderately rimed aggregates or similarly shaped florid polycrystals and a narrow particle size distribution (PSD). Simulations of this case study performed with a one-dimensional bin model indicate that similar triple-frequency radar observations can be reproduced when narrow PSDs are simulated. Such narrow PSDs can in turn be explained by two key factors: (i) the presence of a shallow homogeneous droplet or humidified aerosol freezing layer aloft seeding an underlying supercooled liquid layer, and (ii) the absence of turbulent mixing throughout a stable polar atmosphere that sustains narrow PSDs, as hydrometeors grow from the nucleation region aloft to ice particles of several millimeters in size, by vapor deposition and then riming. [ABSTRACT FROM AUTHOR]

We investigate the response of the mid-latitude thermospheric neutral winds to a sub-auroral polarization stream (SAPS) event. Using red line (F region) airglow data from two Fabry–Pérot interferometers (FPIs), and F-region ionospheric flow velocities from four Super Dual Auroral Radar Network (SuperDARN) radars, the drivers behind changes seen in the neutral winds are explored within the context of the larger SAPS structure. Different, although strong, neutral wind responses to the SAPS are seen at the two FPI sites, even though they are relatively close geographically. We attribute the wind differences to the varying balance of pressure gradient, ion drag, and Coriolis forces, which ultimately depend on proximity to the SAPS. At the FPI site equatorward of the SAPS, pressure gradient and Coriolis forces drive the winds equatorward and then westward. At the FPI site co-located with the SAPS, the ion drag is strong and results in the winds surging westward before turning eastward when becoming influenced by dawnside sunward plasma convection drifts. [ABSTRACT FROM AUTHOR]

The goal of this work is to study the time and altitude echo characteristics under different solar and seasonality conditions using the VHF radar range–time–intensity (RTI) images. The occurrence of equatorial spread F depends on the existence of conditions that can seed the Rayleigh–Taylor instability, and these conditions can change with solar flux, seasonality, longitude distributions, and day-to-day variability. So, the equatorial spread F is observed as its time and altitude occurrence. The VHF radar of Christmas Island (2.0 ∘ N, 157.4 ∘ W, 2.9 ∘ N dip latitude) has been operational in the equatorial region for some time, allowing long-term observations. The occurrence of echoes during solar minimum conditions is observed throughout the night since the post-reversal westward electric field is weaker than the solar maximum and the possibilities of the vertical plasma drift becoming positive are larger. On the other hand, echoes during solar maximum will be controlled by dynamics near the time of the pre-reversal peak (PRE). Our results indicate that the peak time occurrence of echoes along this period shows a well-defined pattern, with echoes distributed as closer to local sunset during solar maximum and around/closer to midnight during solar minimum conditions; meanwhile, the peak altitude occurrence of echoes shows a slightly regular pattern with higher-altitude occurrences during solar maxima and lower altitudes during solar minimum conditions. [ABSTRACT FROM AUTHOR]

The WIVERN (WInd VElocity Radar Nephoscope) mission, now in Phase 0 of the ESA Earth Explorer program, promises to complement Doppler wind lidar by globally observing, for the first time, the vertical profiles of winds in cloudy areas. This work describes an initial assessment of the performances of the WIVERN conically scanning 94 GHz Doppler radar, the only payload of the mission. The analysis is based on an end-to-end simulator characterized by the following novel features tailored to the WIVERN radar: the conically scanning geometry, the inclusion of cross-polarization effects and the simulation of a radiometric mode, the applicability to global cloud model outputs via an orbital model, the incorporation of a mispointing model accounting for thermoelastic distortions, microvibrations, star-tracker uncertainties, etc., and the inclusion of the surface clutter. Some of the simulator capabilities are showcased for a case study involving a full rotational scan of the instrument. Preliminary findings show that mispointing errors associated with the antenna's azimuthal mispointing are expected to be lower than 0.3 m s -1 (and strongly dependent on the antenna's azimuthal scanning angle), wind shear and non-uniform beam-filling errors have generally negligible biases when full antenna revolutions are considered, non-uniform beam filling causes random errors strongly dependent on the antenna azimuthal scanning angle, but typically lower than 1 m s -1 , and cross-talk effects are easily predictable so that areas affected by strong cross-talk noise can be flagged. Overall, the quality of the Doppler velocities appears to strongly depend on several factors, such as the strength of the cloud reflectivity, the antenna-pointing direction relative to the satellite motion, the presence of strong reflectivity and/or wind gradients, and the strength of the surface clutter. The end-to-end simulations suggest that total wind errors meet the mission requirements in a good portion of the clouds detected by the WIVERN radar. The simulator will be used for studying tradeoffs for the different WIVERN configurations under consideration during Phase 0 (e.g., different antenna sizes, pulse lengths, and antenna patterns). Thanks to its modular structure, the simulator can be easily adapted to different orbits, different scanning geometries, and different frequencies. [ABSTRACT FROM AUTHOR]

The national German weather radar network operates in C-band between 5.6 and 5.65 GHZ. In a radar network, individual transmit frequencies have to be chosen such that radar-radar induced interferences are avoided. In an unique experiment the Hohenpeissenberg research radar and five operational systems from the radar network were used to characterize radar-radar induced interferences as a function of the radar frequency. The results allow to assess the possibility to add additional C-band radars with magnetron transmitters into the existing network. Based on the experiment, at least a 15 MHz separation of the nominal radar frequency is needed to avoid a radar-radar interference. The most efficient mitigation of radar-radar interference is achieved by the 'Radar Tango'. Latter refers to the synchronized scanning of all radar systems in the network. Based on those results, additional C-band radar systems can be added to the German weather radar network if a further improvement of the radar coverage is needed. [ABSTRACT FROM AUTHOR]

The datasets presented in this article were collected during a four-months measurement campaign at the Belgian research base Princess Elisabeth Antarctica (PEA). The campaign, named PEA Orographic Precipitation Experiment (POPE), was conducted by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne, with the logistical support of the International Polar Foundation, between the end of November 2019 and the beginning of February 2020. The datasets have been collected at five different sites. A W-band Doppler cloud profilers and a Multi-Angle Snowflake Camera (MASC) have been deployed in the immediate proximity of the main building of the station. An X-band dual-polarization Doppler scanning weather radar was installed 1.9 km south-east of PEA. Information on the various hydrometeor types have been derived from its measurements, as well as from the images collected by the MASC. The remaining three sites were located in a transect across the mountain chain south of the base, between 7 and 17 km apart from each other. At each site, a K-band Doppler profiler and an automated weather station have been deployed. A pyrgeometer and a pyranometer accompanied the instruments at the site in the middle of the transect. A case study, covering the precipitation event recorded on 23 December 2019, is presented to illustrate the various datasets. Overall, the availability of radar measurements over a complex terrain, relatively far from a scientific base, is extremely rare in the Antarctic context, and opens a wide range of possibilities for precipitation studies over the region. [ABSTRACT FROM AUTHOR]

Satellite radar backscatter contains unique information on land surface moisture, vegetation features, and surface roughness, and can be acquired in all weather conditions, thus has been used in a range of earth science disciplines. However, there is no single global radar data set that spans more than two decades. This has limited the use of radar data for trend analysis over extended time intervals. We here provide the first long-term (since 1992), high resolution (~8.9 km) satellite radar backscatter data set over global land areas, the C-band Scatterometer (CScat) data set, by fusing signals from European Remote Sensing satellite (ERS, 1992-2001, C-band, 5.3 GHz), Quick Scatterometer (QSCAT, 1999-2009, Ku-band, 13.4 GHz), and the Advanced Scatterometer (ASCAT, since 2007, C-band, 5.255 GHz). The six-year data gap between C-band ERS and ASCAT was filled out by modelling an equivalent C-band signal during 1999-2009 from Ku-band QSCAT signals and climatic information. Towards this purpose, we first rescaled the signals from different sensors, pixel by pixel, using a new signal rescaling method that is robust to limited overlapping observations among sensors. We then corrected the monthly signal differences between the C-band and the scaled Ku-band signals, by modelling the signal differences from climatic variables (i.e., monthly precipitation, skin temperature, and snow depth) using decision tree regression. The quality of the merged radar signal was assessed by computing the Pearson r, Root Mean Square Error (RMSE), and relative RMSE (rRMSE) between the C-band and the corrected Ku-band signals in the overlapping years (1999-2001 and 2007-2009). We obtained high Pearson r values and low RMSE values at both the regional (r ≥ 0.93, RMSE ≤ 0.16, rRMSE ≤0.37) and pixel levels (median r across pixels ≥ 0.80, median RMSE ≤ 0.38, median rRMSE ≤ 0.64), suggesting high accuracy for the data merging procedure. The merged radar signal was then validated with a continuous ERS-2 data set available between 1995 and 2011. ERS-stopped working in full mode after 2001 but observations are occasionally available for a subset of the pixels until 2011. Because the period of 1995-2011 fully overlaps with the working period of QSCAT (1999-2009), comparing the merged radar signal against the ERS-2 data in 1995-2011 is the most direct validation available. We found concordant monthly dynamics between the merged radar signals and the ERS-2 signals during 1995-2011, with Pearson r value ranging from 0.79 to 0.98 across regions. These results evidenced that our merged radar data have a consistent C-band signal dynamic. The CScat data set (https://doi.org/10.6084/m9.figshare.20407857, Tao et al. 2022a) is expected to advance our understanding of the long-term changes in, e.g., global vegetation and soil moisture. The data set will be updated on a regular basis. [ABSTRACT FROM AUTHOR]

In studying the mass balance of polar ice sheets, the fluctuation of the firn density near the surface is a major uncertainty. In this paper, we explore these variations at locations in the Greenland Ice Sheet and at the Dome C location in Antarctica. Borehole in situ measurements, snow radar echoes, microwave brightness temperatures, and modelling results from the Community firn model (CFM) are used. It is shown that firn density profiles can be represented using 3 processes: "long" and "short" length scale density variations and "refrozen layers". Consistency with this description is observed in the dynamic range of airborne 0.5–2 GHz brightness temperatures and snow radar echo peaks in measurements performed in Greenland in 2017. Based on these insights, a new analytical partially coherent model is implemented to explain the microwave brightness temperatures using the three scale description of the firn. Short and long scale firn processes are modelled as a 3D continuous random medium with finite vertical and horizontal correlation lengths as opposed to past 1D random layered medium descriptions. Refrozen layers are described as deterministic sheets with planar interfaces, with the number of refrozen layer interfaces determined by radar observations. Firn density and correlation length parameters used in forward modelling to match measured 0.5–2 GHz brightness temperatures in Greenland show consistency with similar parameters in CFM predictions. Model predictions also are in good agreement with multi-angle 1.4 GHz vertically and horizontally polarized brightness temperature measured by the SMOS satellite at DOME C, Antarctica. This work shows that co-located active and passive microwave measurements can be used to infer polar firn properties that can be compared with predictions of the CFM. In particular, 0.5–2 GHz brightness temperature measurements are shown to be sensitive to long scale firn density fluctuations with density standard deviations in the range 0.01–0.06 g/cm3 and vertical correlation lengths of 6–20 cm. [ABSTRACT FROM AUTHOR]

Wind waves play an important role in the climate system, modulating the energy exchange between the ocean and the atmosphere and effecting ocean mixing. However, existing ship-based observational networks of wind waves are still sparse, limiting therefore the possibilities of validating satellite missions and model simulations. In this paper we present data collected on three research cruises in the North Atlantic and Arctic in 2020 and 2021 and the SeaVision system for measuring wind wave characteristics over the open ocean with a standard marine navigation X-band radar. Simultaneously with the SeaVision wind wave characteristic measurements, we also collected data from the Spotter wave buoy at the same locations, and we ran the WaveWatch III model in a very high-resolution configuration over the observational domain. SeaVision measurements were validated against co-located Spotter wave buoy data and intercompared with the output of WaveWatch III simulations. Observations of the wind waves with the navigation X-band radar were found to be in good agreement with buoy data and model simulations with the best match for the wave propagation directions. Supporting datasets consist of significant wave heights, wave directions, wave periods and wave energy frequency spectra derived from both SeaVision and the Spotter buoy. All supporting data are available through the PANGAEA repository – 10.1594/PANGAEA.939620 (Gavrikov et al., 2021). The dataset can be further used for validation of satellite missions and regional wave model experiments. Our study shows the potential of ship navigation X-band radars (when assembled with SeaVision or similar systems) for the development of a new near-global observational network providing a much larger number of wind wave observations compared to e.g. Voluntary Observing Ship (VOS) data and research vessel campaigns. [ABSTRACT FROM AUTHOR]

Heavy-rainfall events in mountainous areas trigger destructive landslides, which pose a risk to people and infrastructure and significantly affect the landscape. Landslide locations are commonly mapped using optical satellite imagery, but in some regions their timings are often poorly constrained due to persistent cloud cover. Physical and empirical models that provide insights into the processes behind the triggered landsliding require information on both the spatial extent and the timing of landslides. Here we demonstrate that Sentinel-1 synthetic aperture radar amplitude time series can be used to constrain landslide timing to within a few days and present four techniques to accomplish this based on time series of (i) the difference in amplitude between the landslide and its surroundings, (ii) the spatial variability in amplitude between pixels within the landslide, and (iii) geometric shadows and (iv) geometric bright spots cast within the landslide. We test these techniques on three inventories of landslides of known timing, covering various settings and triggers, and demonstrate that a method combining them allows 20 %–30 % of landslides to be timed with an accuracy of 80 %. Application of this method could provide an insight into landslide timings throughout events such as the Indian summer monsoon, which triggers large numbers of landslides every year and has until now been limited to annual-scale analysis. [ABSTRACT FROM AUTHOR]

The use of meteorological radars to study snowfall microphysical properties and processes is well established, in particular through two techniques: the use of multi-frequency radar measurements and the analysis of radar Doppler spectra. We propose a novel approach to retrieve snowfall properties by combining both techniques, while relaxing some assumptions on e.g. beam matching and non-turbulent atmosphere. The method relies on a two-step deep-learning framework inspired from data compression techniques: an encoder model maps a high-dimensional signal to a lower-dimensional "latent" space, while the decoder reconstructs the original signal from this latent space. Here, Doppler spectrograms at two frequencies constitute the high-dimensional input, while the latent features are constrained to represent the snowfall properties of interest. The decoder network is first trained to emulate Doppler spectra from a set of microphysical variables, using simulations from the radiative transfer model PAMTRA as training data. In a second step, the encoder network learns the inverse mapping, from real measured dual-frequency spectrograms to the microphysical latent space; doing so, it leverages the spatial consistency of the measurements to mitigate the problem's ill-posedness. The method was implemented on X- and W-band data from the ICE GENESIS campaign that took place in the Swiss Jura in January 2021. An in-depth assessment of the retrieval's accuracy was performed through comparisons with colocated aircraft in-situ measurements collected during 3 precipitation events. The agreement is overall good and opens up possibilities for acute characterization of snowfall microphysics on larger datasets. A discussion of the method's sensitivity and limitations is also conducted. The main contribution of this work is on the one hand the theoretical framework itself, which can be applied to other remote sensing retrieval applications and is thus possibly of interest to a broad audience across atmospheric sciences. On the other hand, the retrieved seven microphysical descriptors provide relevant insights into snowfall processes. [ABSTRACT FROM AUTHOR]