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Precipitation is arguably one of the most relevant surface variables impacting human lives on the planet, but global-coverage, high-resolution and good-quality observations are not readily available. In particular, gridded observational datasets are much needed for model development and forecast quality assessment. Here we compare the quality of four types of gridded precipitation products over Europe, namely: a rain-gauge interpolation; a satellite-derived product; a radar composite; and a reanalysis. Each product has its own strengths and weaknesses, and since each precipitation estimate uses different measuring techniques, we can employ a triangulation method to estimate the error variance of each product with respect to the unknown true values. Results show that: a) the satellite product has limited quality over Europe and may be problematic to use in quantitative forecast evaluation and diagnostics; b) the radar composite has spurious features that need to be considered when used in verification; c) all products struggle in topographically complex areas; d) the rain-gauge interpolation is not free of errors, despite rain gauges being often treated as ground truth in the literature; and e) the reanalysis dataset produces in some cases the best available estimates, in particular over the European near-coastal waters. [ABSTRACT FROM AUTHOR]

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

The recent release of European Ground Motion Service (EGMS) products implemented under the responsibility of the Copernicus Land Monitoring Service (CLMS) guarantees free and accessible Europe-wide ground motion data for ground deformation analysis at the local and regional scales. The need for value-adding services and tools for optimal dissemination of radar data from the Copernicus Sentinel-1 satellite mission urges the scientific community to find efficient solutions. A desktop R-based application with a user-friendly interface capable of automatically downloading and transforming EGMS products delivered as large.csv tiles, equivalent to a radar burst into geospatial databases, is presented here. EGMStream is a self-contained desktop app that enables users to systematically store, customize, and convert ground movement data into geospatial databases, burst per burst or for an area of interest directly selectable on the app interface. [ABSTRACT FROM AUTHOR]

• A review of track detection algorithms across different research fields. • Technical description of the initiation, maintenance and termination of a radar track in the BIRALES radar. • Presentation of a new track detection strategy that is used for the detection of orbital debris using a bi-static continuous-wave radar. • Performance evaluation of this new track detection method on synthetic data. The world's economy has become heavily dependent on the services provided by satellites. With the exponential increase in satellite launches, the population of defunct or inactive hardware in space has grown substantially. This is especially true in sensitive orbits such as the Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) regimes. These objects, collectively known as orbital debris, can reach speeds of up to 28 000km h−1 in LEO. At these orbital speeds, even the smallest of objects can pose a considerable threat to operational satellites or astronauts. This makes the monitoring, and detection, of these objects of the utmost importance. This work describes the latest detection strategy used in one of Europe's largest Space Situational Awareness (SSA) installation; the BIstatic RAdar for LEo Survey (BIRALES) space debris radar. We present a novel bottom-up approach that makes use of single-linkage clustering to identify faint radar streaks in spectrogram data. Tests on synthetic data have shown that the detection strategy presented in this study obtains a higher detection rate when it is compared against existing methods. Unlike other approaches, this detection strategy, using the Multi-beam streak detection strategy (MSDS) algorithm, was still able to recall 90% of the track information at an Signal-to-Noise Ratio (SNR) of 2dB. [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]

• Burn severity decreases in WUIs proportionally to nearby building aggregation. • The combination of radar and multispectral variables is useful to predict burn severity. • Variables related to the amount of living vegetation are the most important to predict burn severity. • Burn severity is more predictable outside WUIs than inside them. • Knowledge of burn severity drivers serves to improve landscape planning in WUIs. Fire danger analysis is crucial for landscape planning, which is particularly relevant in vulnerable areas such as the wildland-urban interface (WUI). The aim of our work is to investigate the capacity of fuel characteristics to predict burn severity in different WUI typologies classified according to nearby building density criteria. To achieve this goal, we selected 23 wildfires across Southern Europe, in which we differentiated non WUI areas, isolated, scattered, dense and very dense WUIs. Moreover, we spatialized burn severity and different fuel metrics using multispectral and radar satellite imagery. This information was used to analyze burn severity, and to identify its drivers in the different WUI typologies through analysis of variance, correlation analysis and machine learning models. Our results indicate that burn severity is lower in WUIs of clustered buildings than in non-WUI areas, which were also the most homogeneous in terms of vegetation cover. Moreover, we found that vegetation biophysical properties related to the amount of living fuel (fraction of vegetation cover and fraction of absorbed photosynthetically active radiation) showed the highest influence on burn severity in all WUI typologies. We also found that burn severity is less predictable in dense and very dense WUIs than in the rest, which can be attributed to their greater landscape complexity, presence of artificial structures and efficiency of extinction efforts. Our results serve to guide landscape management strategies, and impulse next-generation fire danger models, which should be based not only on synoptic fire weather indices but also on landscape-scale susceptibility to severe fires. [ABSTRACT FROM AUTHOR]

Clouds cover substantial parts of the Earth's surface and they are one of the most essential components of the global climate system impacting the Earth's radiation balance as well as the water cycle redistributing water around the globe as precipitation. Therefore, continuous observation of clouds is of primary interest in climate and hydrological studies. This work documents the first efforts in Italy in remote sensing clouds and precipitation using a combination of K- and W-band (24 and 94 GHz, respectively) radar profilers. Such a dual-frequency radar configuration has not been widely used yet, but it could catch on in the near future given its lower initial cost and ease of deployment for commercially available systems at 24 GHz, with respect to more established configurations. A field campaign running at the Casale Calore observatory at the University of L'Aquila, Italy, nestled in the Apennine mountain range is described. The campaign features are preceded by a review of the literature and the underpinning theoretical background that might help newcomers, especially in the Italian community, to approach cloud and precipitation remote sensing. This activity takes place in interesting time for radar sensing clouds and precipitation, stimulated both by the launch of the ESA/JAXA EarthCARE satellite missions scheduled in 2024, which will have on-board, among other instruments, a W-band Doppler cloud radar and the proposal of new missions using cloud radars currently undergoing their feasibility studies (e.g., WIVERN and AOS in Europe and Canada, and U.S., respectively).