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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]

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]

We present a comprehensive quality-controlled 15-month dataset of remote sensing observations of low-level mixedphase clouds (LLMPCs) taken at the high Arctic site of Ny-Ålesund, Svalbard, Norway. LLMPCs occur frequently in the Arctic region, and extensively affect the energy budget. However, our understanding of the ice microphysical processes taking place in these clouds is incomplete. The dual-wavelength and polarimetric Doppler cloud radar observations, which are the cornerstones of the dataset, provide valuable fingerprints of icemicrophysical processes, and the high number of cases included allows for the compiling of robust statistics for process studies. The radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data are quality controlled, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. We finally present an analysis of the temperature dependence of Doppler, dual wavelength, and polarimetric radar variables, to illustrate how the dataset can be used for cloud microphysical studies. The dataset has been published in Chellini et al. (2023) and is freely available at: www.doi.org/10.5281/zenodo.7803064. [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]

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]

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]

Accurate rainfall monitoring is critical for sustainable societies, and yet challenging in many ways. Opportunistic monitoring using commercial microwave links (CML) 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-second 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. The article presents an overview of the data, including collection approach, descriptive statistics, and a case study of a highintensity 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-minute interval. One of the most intense events was observed on 28 July 2015, during which the Torslanda gauge recorded a peak of 1.1 mm min-1. The variability of 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 strength 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 https://doi.org/10.5281/zenodo.6673751 (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]

Over the past 50 years, the British Antarctic Survey (BAS) has been one of the major acquirers of aerogeophysical data over Antarctica, providing scientists with gravity, magnetic and radar datasets that have been central to many studies of the past, present, and future evolution of the Antarctic Ice Sheet. Until recently, many of these datasets were unpublished in full, restricting further usage of the data for different applications such as gravity inversions, bed-reflectivity analysis, and englacial-layer tracking. Starting in 2020, scientists and data managers at the British Antarctic Survey have worked on standardising and releasing large swaths of aerogeophysical data acquired during the period 1994-2020, including a total of 64 datasets from 24 different surveys, amounting to ~450,000 line-km (or 5.3 million km²) of data across West Antarctica, East Antarctica, and the Antarctic Peninsula. Amongst these are the extensive surveys over the fast-changing Pine Island (BBAS 2004-05) and Thwaites (ITGC 2018-20) glacier catchments, and the first ever surveys of the Wilkes Subglacial Basin (WISE-ISODYN 2005-06) and Gamburtsev Subglacial Mountains (AGAP 2007-09). Considerable effort has been made to standardise these datasets to comply with the FAIR (Findable, Accessible, Interoperable and Re-Usable) data principles, as well as to create the Polar Airborne Geophysics Data Portal (https://www.bas.ac.uk/project/nagdp/), which serves as a user-friendly interface to interact with and download the newly published data. This paper reviews how these datasets were acquired and processed, presents the methods used to standardise them, and introduces the new data-portal infrastructure and interactive tutorials that were created to improve the accessibility of the data. Lastly, we exemplify future potential uses of the datasets by extracting information on the continuity of englacial layering from the fully processed radar data. We believe this newly released data will be a valuable asset to future geophysical and glaciological studies over Antarctica and will extend significantly the life cycle of the data. All datasets included in this data release are now fully accessible at: https://data.bas.ac.uk. [ABSTRACT FROM AUTHOR]

The global coverage of the observational network of the wind waves is still characterized by the significant gaps in in situ observations. At the same time wind waves play an important role into the Earth' climate system specifically in the air-sea interaction processes and energy exchange between the ocean and the atmosphere. In this paper we present the SeaVision system for measuring wind waves' parameters in the open ocean with navigational marine X-band radar and prime data collection from the three research cruises in the North Atlantic (2020 and 2021) and Arctic (2021). Simultaneously with SeaVision observations of the wind waves we were collecting data in the same locations and time with Spotter wave buoy and running WaveWatch III model over our domains. Measurements with SeaVision were quality controlled and validated by comparison with Spotter buoy data and WaveWatch III experiments. Observations of the wind waves with navigational Xband radar are in agreement among these three sources of data, with the best agreement for wave propagation directions. The dataset that supports this paper consists of significant wave height, wave period and wave energy frequency spectrum from both SeaVision and Spotter buoy. Currently the dataset is available through the temporary link (https://sail.ocean.ru/tilinina2021/) while supporting dataset (Tilinina et al., 2021) is in technical processing at PANGAEA repository. The dataset can be used for validation of satellite missions as well as model outputs. One of the major highlights in this study is potential of all ships navigating into the open ocean and equipped with X-band marine radar to participate into the development of another observational network for the wind waves in the open ocean once cheap and independently operating version of the SeaVision (or any other system) is available. [ABSTRACT FROM AUTHOR]

As part of the EUREC4A field campaign, the research vessel Maria S. Merian probed an oceanic region between 6° N and 13.8° N and 51° W to 60° W for approximately 32 days. Trade wind cumulus clouds were sampled in the trade-wind alley region east of Barbados as well as in the transition region between the trades and the intertropical convergence zone, where the ship crossed some mesoscale oceanic eddies. We collected continuous observations of cloud and precipitation profiles at unprecedented vertical resolution (7-10 m in the first 3000 m) and high temporal resolution (1-3 s) using a W-band radar and micro-rain radar (MRR-PRO), installed on an active stabilization platform to reduce the impact of ship motions on the observations. The paper describes the ship motion correction algorithm applied to the Doppler observations to extract corrected hydrometeors vertical velocities and the algorithm created to filter interference patterns in the MRR-PRO observations. Radar reflectivity, mean Doppler velocity, spectral width and skewness for W-band and attenuated reflectivity, mean Doppler velocity and rain rate for MRR-PRO are shown for a case study to demonstrate the potential of the high resolution adopted. As non-standard analysis, we also retrieved and provided liquid water path (LWP) from the 89 GHz passive channel available on the W-band radar system. All datasets and hourly and daily quicklooks are publically available. Data can be accessed and basic variables can be plotted online via the intake catalog of the online book "How to EUREC4A". [ABSTRACT FROM AUTHOR]

We present a high-resolution airborne radar data set (EGRIP-NOR-2018) for the onset region of the Northeast Greenland Ice Stream (NEGIS). The radar data were acquired in May 2018 with Alfred Wegener Institute's multichannel ultra- wideband (UWB) radar mounted on the Polar6 aircraft. Radar profiles cover an area of ~24000 km2 and extend over the well- defined shear margins of the NEGIS. The survey area is centred at the location of the drill site of the East Greenland Ice-Core Project (EastGRIP) and several radar lines intersect at this location. The survey layout was designed to: (i) map the stratigraphic signature of the shear margins with radar profiles aligned perpendicular to ice flow, (ii) trace the radar stratigraphy along several flow lines and (iii) provide spatial coverage of ice thickness and basal properties. While we are able to resolve radar reflections in the deep stratigraphy, we can not fully resolve the steeply inclined reflections at the tightly folded shear margins in the lower part of the ice column. The NEGIS is causing the most significant discrepancies between numerically modelled and observed ice surface velocities. Given the high likelihood of future climate and ocean warming, this extensive data set of new high-resolution radar data in combination with the EastGRIP ice core will be a key contribution to understand the past and future dynamics of the NEGIS. The EGRIP-NOR-2018 radar data products can be obtained at the PANGAEA Data Publisher (https://doi.pangaea.de/10.1594/PANGAEA.928569; Franke et al. 2021a). [ABSTRACT FROM AUTHOR]

A better understanding of the hydrological functioning of irrigated crops using remote sensing observations is of prime importance in the semi-arid areas where the water resources are limited. Radar observations, available at high resolution and revisit time since the launch of Sentinel-1 in 2014, have shown great potential for the monitoring of the water content of the upper soil and of the canopy. In this paper, a complete set of data for radar signal analysis is shared to the scientific community for the first time to our knowledge. The data set is composed of Sentinel-1 products and in situ measurements of soil and vegetation variables collected during three agricultural seasons over drip-irrigated winter wheat in the Haouz plain in Morocco. The in situ data gathers soil measurements (time series of half-hourly surface soil moisture, surface roughness and agricultural practices) and vegetation measurements collected every week/two weeks including above-ground fresh and dry biomasses, vegetation water content based on destructive measurements, cover fraction, leaf area index and plant height. Radar data are the backscattering coefficient and the interferometric coherence derived from Sentinel-1 GRDH (Ground Range Detected High resolution) and SLC (Single Look Complex) products, respectively. The normalized difference vegetation index derived from Sentinel-2 data based on Level-2A (surface reflectance and cloud mask) atmospheric effects-corrected products is also provided. This database, which is the first of its kind made available in open access, is described here comprehensively in order to help the scientific community to evaluate and to develop new or existing remote sensing algorithms for monitoring wheat canopy under semi-arid conditions. The data set is particularly relevant for the development of radar applications including surface soil moisture and vegetation parameters retrieval using either physically based or empirical approaches such as machine and deep learning algorithms. The database is archived in the DataSuds repository and is freely-accessible via the DOI: https://doi.org/10.23708/8D6WQC (Ouaadi et al., 2020a). [ABSTRACT FROM AUTHOR]

We present an ice-penetrating radar data set which consists of 26 internal reflecting horizons (IRHs) that cover the entire Dome C area of the East Antarctic plateau, the most extensive to date in the region. This data set uses radar surveys collected over the space of 10 years, starting with an airborne international collaboration in 2008 to explore the region, up to the detailed ground based surveys in support of the Beyond EPICA - Oldest Ice (BE-OI) European Consortium. Through direct correlation with the EPICA-DC ice core, we date 19 IRHs that span the past four glacial cycles, from the beginning of the Holocene to over 350 ka. We indirectly date and provide stratigraphic information for seven older IRHs using an 1-D ice flow inverse model, going back to an estimated 700 ka. Depth and age uncertainties are quantified for all IRHs and provided as part of the data set. The IRH data set presented in this study is available at the U.S. Antarctic Program Data Center (USAP-DC) (https://doi.org/10.15784/601411, Cavitte et al., 2020) and represents a contribution to the SCAR AntArchitecture program. [ABSTRACT FROM AUTHOR]

The East Antarctic Ice Sheet (EAIS) is the largest source of potential sea-level rise, containing approximately 52 m of sea-level equivalent. To constrain estimates of sea-level rise into the future requires knowledge of ice-sheet properties and geometry and ice-penetrating radar offers a means to estimate these properties (e.g. ice thickness, englacial temperatures). One of the regions that have been extensively surveyed using ice-penetrating radar from the Investigating the Cryospheric Evolution of the Central Antarctic Plate (ICECAP) project in East Antarctica is Law Dome, a small independent ice cap situated to the west of Totten Ice Shelf. The ice cap is slow-moving, has a low melt-rate at the surface and moderate wind speeds, making it a useful study site for estimating the radar attenuation. A new method is proposed for the estimation of attenuation rate from radar data which is mathematically modelled as a constrained regularised l2 minimisation problem. In the proposed method, only radar data is required and the englacial reflectors are automatically detected from the radar data itself. To validate our methodology, attenuation differences at flight crossover points are calculated and statistical analyses performed to assess the accuracy of the results. For spatial analyses, the errors are of the order 22.6 %, 15.2 %, and 32.8 % for mean absolute error, median absolute error, and root mean square error respectively. Also, for the depth analyses, up to the depth of 800 m, the errors are under 29.9 %, 24.2 %, and 38.8 % for mean absolute error, median absolute error, and root mean square error respectively. A final product of 3D attenuation rates and uncertainty estimates is provided. The generated dataset is publicly available at https://doi.org/10.25959/5e6851e266f4a (Abdul Salam, 2020). [ABSTRACT FROM AUTHOR]

This article describes a four-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.pangaea.de/10.1594/PANGAEA.918315, Gehring et al. (2020a)). Classifications of hydrometeor types derived from dual-polarisation measurements and snowflake photographs are presented. The dataset covers the period from 15 November 2017 to 18 March 2018 and features nine precipitation events with a total accumulation of 195 mm of equivalent liquid precipitation. This represents 85% of the climatological accumulation over this period. To illustrate the available data, measurements corresponding to the four precipitation events with the largest accumulation are presented. The synoptic situations of these events were contrasted and influenced the precipitation type and accumulation. The hydrometeor classifications reveal that aggregate snowflakes were dominant and that some events featured significant riming. The combination of dual-polarisation variables and high-resolution Doppler spectra with ground-level snowflake images makes this dataset particularly suited to study snowfall microphysics in a region where such measurements were not available before. [ABSTRACT FROM AUTHOR]