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Extreme rainfall on the Island of Madeira on 20 February 2010 triggered flash floods and mudslides with 45 casualties, 8 missing people, and 100 injured. The NE-moving frontal system originating from a low-pressure center in the Madeira Archipelago is not unusual for the area, but its consequences on the island were rather extreme. The study dwells on passive microwave sounders from polar orbiters for the retrieval of rainfall intensity and cloud classification. Heavy rainfall was generated by severe local convection and enhanced over the central mountain chain. Physical cloud classification identifies the shallow convective precipitation type lasting for a few hours around noon and the observations confirm the numerical model results. [ABSTRACT FROM PUBLISHER]

During recent decades East Africa (EA) and Southern Africa (SA) have experienced an intensification of hydrological hazards, such as floods and droughts, which have dramatically affected the population, making these areas two of the regions of the African continent most vulnerable to these hazards. Thus, precipitation monitoring and the evaluation of its variability have become fundamentally important actions through the analysis of long-term data records. In particular, satellite-based precipitation products are often used because they counterbalance the sparsity of the rain gauge networks which often characterize these areas. The aim of this work is to compare and contrast the capabilities of three daily satellite-based products in EA and SA from 1983 to 2017. The selected products are two daily rainfall datasets based on high-resolution thermal infrared observations, TAMSAT version 3 and CHIRPS, and a relatively new global product, MSWEP version 2.2, which merges satellite-based, rain gauge and re-analysis precipitation data. The datasets have been directly intercompared, avoiding the traditional rain gauge validation. This is done by means of pairwise comparison statistics at 0.25° spatial resolution and daily time scale to assess rain–detection and quantitative estimate capabilities. Monthly climatology and spatial distribution of seasonality are analyzed as well. The time evolution of the statistical indexes has been evaluated in order to analyze the stability of the rain detection and estimation performances. Considerable agreement among the precipitation products emerged from the analysis, in spite of the differences occurring in specific situations over complex terrain, such as mountainous and coastal regions and deserts. Moreover, the temporal evolution of the statistical indices has demonstrated that the agreement between the products improved over time, with more stable capabilities in identifying precipitating days and estimating daily precipitation starting in the second half of the 1990s. [ABSTRACT FROM AUTHOR]

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties' influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm's performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions. [ABSTRACT FROM AUTHOR]

The water cycle is the most essential supporting physical mechanism ensuring the existence of life on Earth. Its components encompass the atmosphere, land, and oceans. The cycle is composed of evaporation, evapotranspiration, sublimation, water vapor transport, condensation, precipitation, runoff, infiltration and percolation, groundwater flow, and plant uptake. For a correct closure of the global water cycle, observations are needed of all these processes with a global perspective. In particular, precipitation requires continuous monitoring, as it is the most important component of the cycle, especially under changing climatic conditions. Passive and active sensors on board meteorological and environmental satellites now make reasonably complete data available that allow better measurements of precipitation to be made from space, in order to improve our understanding of the cycle's acceleration/deceleration under current and projected climate conditions. The article aims to draw an up-to-date picture of the current status of observations of precipitation from space, with an outlook to the near future of the satellite constellation, modeling applications, and water resource management. [ABSTRACT FROM AUTHOR]

Abstract: The Water vapor Strong Lines at 183GHz (183-WSL) algorithm is a method for the retrieval of rain rates and precipitation type classification (convective/stratiform). It exploits the water vapor absorption line observations centered at 183.31GHz of the Advanced Microwave Sounding Unit module B (AMSU-B) and of the Microwave Humidity Sounder (MHS) flying on NOAA-15/-17 and NOAA-18-19/MetOp-A satellite series, respectively. The characteristics of this algorithm were described in Part I of this paper together with comparisons against analogous precipitation products. The focus of Part II is the analysis of the performance of the 183-WSL technique based on surface radar measurements. The “ground truth” dataset consists of 2years and 7months of rainfall intensity fields from the NIMROD radar network, which covers North-Western Europe. The investigation of the 183-WSL retrieval performance is based on a twofold approach: 1) the dichotomous statistic is used to evaluate the capabilities of the method to identify rain and no-rain clouds and 2) the accuracy statistic is applied to quantify the errors in the estimation of rain rates. The results reveal that the 183-WSL technique shows good skills in the detection of rain/no-rain areas and in the quantification of rain rate intensities. The categorical analysis shows annual values of the Probability Of Detection (POD), False Alarm Ratio (FAR) and Hanssen–Kuiper discriminant (HK) indices varying in the range 0.80–0.82, 0.33–0.36 and 0.39–0.46, respectively. The RMSE value is 2.8mmh−1 for the whole period despite an overestimation in the retrieved rain rates. Of note is the distribution of the 183-WSL monthly mean rain rate with respect to radar: the seasonal fluctuations of the average rainfalls measured by radar are reproduced by the 183-WSL. However, the retrieval method appears to suffer during winter seasonal conditions especially when the soil is partially frozen and the surface emissivity drastically changes. This is verified by the discrepancy distribution diagrams where the 183-WSL performs better during the warm months, while during the winter time the discrepancies with radar measurements tend to maximum values. The stable behavior of the 183-WSL algorithm is demonstrated over the whole study period by an overall overestimation for rain rate intensities less than 1mmh−1. This threshold is especially crucial in wintertime when the classification of low-intensity precipitation regimes is difficult. [Copyright &y& Elsevier]