High‐intensity precipitation represents a threat for several regions of the world because of the related risk of natural disasters (e.g. floods and landslides). This work focuses on low‐level precipitation enhancement that occurs in the cloud warm layer and has been observed in relation to collision‐coalescence (CC) leading to flash floods and extreme rainfall events in tropical and temperate latitudes. Specifically, signatures of precipitation enhancement (referred to as CC‐dominant precipitation) are investigated in the observations from the Global Precipitation Measurement (GPM) core mission Dual‐frequency Precipitation Radar (DPR) over the central/eastern Contiguous United States (CONUS) during June 2014–May 2018. A classification scheme for CC‐dominant precipitation, developed for dual‐polarization S‐band radar measurements and applied in a previous work to X‐band radar observations in complex terrain, is used as a benchmark. The scheme is here applied to the GPM ground validation dataset that matches ground‐based radar observations across CONUS to space‐borne DPR retrievals. The occurrence of CC‐dominant precipitation is documented and the corresponding signatures of CC‐dominant precipitation at Ku‐ and Ka‐band are studied. CC‐dominant profiles show distinguishing features when compared to profiles not dominated by CC, e.g. characteristic vertical slopes of reflectivity at Ku‐ and Ka‐band in the liquid layer, lower freezing‐level height, and shallower ice layer, which are linked to environmental conditions driving the peculiar CC microphysics. This work aims at improving satellite quantitative precipitation estimation, particularly GPM retrievals, by targeting CC development in precipitation columns.

Abstract:This study aims to assess the characteristics and uncertainty of Integrated Multisatellite Retrievals for Global Precipitation Measurement (GPM) (IMERG) Level 3 rainfall estimates and to improve those estimates using an error model over the central Amazon region. The S-band Amazon Protection National System (SIPAM) radar is used as reference and the Precipitation Uncertainties for Satellite Hydrology (PUSH) framework is adopted to characterize uncertainties associated with the satellite precipitation product. PUSH is calibrated and validated for the study region and takes into account factors like seasonality and surface type (i.e., land and river). Results demonstrated that the PUSH model is suitable for characterizing errors in the IMERG algorithm when compared with S-band SIPAM radar estimates. PUSH could efficiently predict the satellite rainfall error distribution in terms of spatial and intensity distribution. However, an underestimation (overestimation) of light satellite rain rates was observed during the dry (wet) period, mainly over rivers. Although the estimated error showed a lower standard deviation than the observed error, the correlation between satellite and radar rainfall was high and the systematic error was well captured along the Negro, Solimões, and Amazon rivers, especially during the wet season.

Accurate quantitative precipitation estimation over mountainous basins is of great importance because of their susceptibility to natural hazards. It is generally difficult to obtain reliable precipitation information over complex areas because of the scarce coverage of ground observations, the limited coverage from operational radar networks, and the high elevation of the study sites. Warm-rain processes have been observed in several flash flood events in complex terrain regions. While they lead to high rainfall rates from precipitation growth due to collision–coalescence of droplets in the cloud liquid layer, their characteristics are often difficult to identify. X-band mobile dual-polarization radars located in complex terrain areas provide fundamental information at high-resolution and at low atmospheric levels. This study analyzes a dataset collected in North Carolina during the 2014 Integrated Precipitation and Hydrology Experiment (IPHEx) field campaign over a mountainous basin where the NOAA/National Severe Storm Laboratory’s X-band polarimetric radar (NOXP) was deployed. Polarimetric variables are used to isolate collision–coalescence microphysical processes. This work lays the basis for classification algorithms able to identify coalescence-dominant precipitation by merging the information coming from polarimetric radar measurements. The sensitivity of the proposed classification scheme is tested with different rainfall-rate retrieval algorithms and compared to rain gauge observations. Results show the inadequacy of rainfall estimates when coalescence identification is not taken into account. This work highlights the necessity of a correct classification of collision–coalescence processes, which can lead to improvements in quantitative precipitation estimation. Future studies will aim at generalizing this scheme by making use of spaceborne radar data.

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