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The paper is focused on the problem of weather radar precipitation compositing and aims to produce the data for the whole measurement domain in a way that ensures the quality of the composite is as high as possible. This is achieved by employing information about the data quality in the form of a quality index (QI) map. Different techniques that take the QI values as the compositing criterion are investigated; they apply the maximum or the weighted mean from particular radars. The proposed method employs a combination of the QI value and the distance from the radar site as a criterion in a weighted mean approach. Different versions of the quality‐based techniques are compared with standard techniques that are based on the maximum of estimated precipitation and distance from the nearest radar site. Verification of the techniques was performed employing statistical metrics by comparison against rain gauge observations. Moreover qualitative verification with assessment of seamlessness and the presence of contamination was carried out on long‐term accumulations of the composites generated by means of different methods. The proposed method based on QI value and distance from the radar site was found to be the most suitable for radar precipitation compositing. [ABSTRACT FROM AUTHOR]

The use of radar measurements for the space time estimation of precipitation has for many decades been a central topic in hydro-meteorology. In this paper we are interested specifically in daily and sub-daily extreme values of precipitation at gauged or ungauged locations which are important for design. The purpose of the paper is to develop a methodology to combine daily precipitation observations and radar measurements to estimate sub-daily extremes at point locations. Radar data corrected using precipitation-reflectivity relationships lead to biased estimations of extremes. Different possibilities of correcting systematic errors using the daily observations are investigated. Observed gauged daily amounts are interpolated to unsampled points and subsequently disaggregated using the sub-daily values obtained by the radar. Different corrections based on the spatial variability and the subdaily entropy of scaled rainfall distributions are used to provide unbiased corrections of short duration extremes. Additionally a statistical procedure not based on a matching day by day correction is tested. In this last procedure as we are only interested in rare extremes, low to medium values of rainfall depth were neglected leaving a small number of L days of ranked daily maxima in each set per year, whose sum typically comprises about 50% of each annual rainfall total. The sum of these L day maxima is first iterpolated using a Kriging procedure. Subsequently this sum is disaggregated to daily values using a nearest neighbour procedure. The daily sums are then disaggregated by using the relative values of the biggest L radar based days. Of course, the timings of radar and gauge maxima can be different, so the method presented here uses radar for disaggregating daily gauge totals down to 15 min intervals in order to extract the maxima of sub-hourly through to daily rainfall. The methodologies were tested in South Africa, where an S-band radar operated relatively continuously at Bethlehem from 1998 to 2003, whose scan at 1.5 km above ground [CAPPI] overlapped a dense (10 km spacing) set of 45 pluviometers recording in the same 6-year period. This valuable set of data was obtained from each of 37 selected radar pixels [1 km square in plan] which contained a pluviometer not masked out by the radar foot-print. The pluviometer data were also aggregated to daily totals, for the same purpose. The extremes obtained using disaggregation methods were compared to the observed extremes in a cross validation procedure. The unusual and novel goal was not to obtain the reproduction of the precipitation matching in space and time, but to obtain frequency distributions of the point extremes, which we found to be stable. [ABSTRACT FROM AUTHOR]

The recent intensification of extreme rainfall events has the potential to aggravate the frequency and intensity of floods in urban areas and signals the urgent need for accurate and high spatiotemporal resolution precipitation data for flood monitoring and warning to alleviate the associated damages. In this context, this paper aims to statistically assess the latest Integrated Multisatellite Retrievals from Global Precipitation Measurement (IMERG) multisatellite constellation and radar quantitative precipitation estimation (QPE) products against dense rain gauge network observations under multiple temporal scales (hourly and 3-hourly) and three spatial scales (grid cumulative, regional, and grid) over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. The results indicate that (1) at the grid cumulative scale, QPE significantly outperforms IMERG products. The correlation coefficients of QPE (0.83 ≤ CORR ≤ 0.85) are much higher than those of IMERG products (0.35 ≤ CORR ≤ 0.56), the estimation of QPE is generally significantly underestimated (−0.23 ≤ BIAS ≤ -0.16), and the BIAS of IMERG products is larger than that of QPE (−0.24 ≤ BIAS ≤ 0.23). (2) At the regional scale, the accuracy of IMERG products has been significantly improved, with relatively high correlation coefficients (CORR≥0.8) and decreased root mean squared error (RMSE) values (1.18 mm ≤ RMSE ≤ 0.22 mm), and Taylor diagrams visually show that QPE is closer to the gauge observations than are IMERG products. (3) The evaluation results at the grid scale show that both IMERG products and QPE can accurately reflect the spatial distribution of precipitation. Among these results, the spatial distribution of QPE is the most consistent with the actual rainfall, although an obvious underestimation of rainfall occurs in areas with strong rainfall. (4) In terms of detection indicators, the three IMERG products are superior to QPE at capturing rainfall events and have more advantages over QPE in the estimation of instantaneous precipitation, especially for light rain events. However, QPE has better detection effects for rainfall events under rainstorm and heavy rainstorm events, indicating the hydrologic utility of this product. Among the three IMERG products, the IMERG Early Run (ER) product has the advantages of a short lag time and high accuracy, and it has the potential to be applied in flood forecasting. Future studies should improve IMERG algorithms and promote the application of QPE in urban rainstorm and flood research. • The quality of the IMERG products and radar quantitative precipitation estimation (QPE) is compared over the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. • The dense rain gauge network observations are used for evaluation in the GBA. • The evaluation of multisource precipitation products under different temporal and spatial scales has different results and effects. [ABSTRACT FROM AUTHOR]

In the French Alps the quality of the radar Quantitative Precipitation Estimation (QPE) is limited by the topography and the vertical structure of precipitation. A previous study realized in all the French Alps, has shown a general bias between values of the national radar QPE composite and the rain gauge measurements: a radar QPE over-estimation at low altitude (+20% at 200 m a.s.l.), and an increasing underestimation at high altitudes (until −40% at 2100 m a.s.l.). This trend has been linked to altitudinal gradients of precipitation observed at ground level. This paper analyzes relative altitudinal gradients of precipitation estimated with rain gauges measurements in 2016 for three massifs around Grenoble, and for different temporal accumulations (yearly, seasonal, monthly, daily). Comparisons of radar and rain gauge accumulations confirm the bias previously observed. The parts of the current radar data processing affecting the bias value are pointed out. The analysis shows a coherency between the relative gradient values estimated at the different temporal accumulations. Vertical profiles of precipitation detected by a research radar installed at the bottom of the valley also show how the wide horizontal variability of precipitation inside the valley can affect the gradient estimation. [ABSTRACT FROM AUTHOR]