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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]

In a series of two papers, rain-rate retrievals based on specific attenuation A at radar X-band wavelength using the R( A) method presented by Ryzhkov et al. are thoroughly investigated. Continuous time series of overlapping measurements from two polarimetric X-band weather radars in Germany during the summers of 2011-13 are used to analyze various aspects of the method, like miscalibration correction, ground clutter contamination, partial beam blockage (PBB), sensitivity to precipitation characteristics, and sensitivity to temperature assumptions in the retrievals. In Part I of the series, the relations inherent to the R( A) method were used to calculate radar reflectivity Z from specific attenuation and it was compared with measured reflectivity to estimate PBB and calibration errors for both radars. In this paper, R( A) rain estimates are compared to R( Z) and R( KDP) retrievals using specific phase shift KDP. PBB and calibration corrections derived in Part I made the R( Z) rainfall estimates almost perfectly consistent. Accumulated over five summer months, rainfall maps showed strong effects of clutter contamination if R( KDP) is used and weaker impact on R( A). These effects could be reduced by processing the phase shift measurements with more resilience toward ground clutter contamination and by substituting problematic R( KDP) or R( A) estimates with R( Z). Hourly and daily accumulations from rain estimators are compared with rain gauge measurements; the results show that R( A) complemented by R( Z) in segments with low total differential phase shift correlates best with gauges and has the lowest bias and RMSE, followed by R( KDP) substituted with R( Z) at rain rates below 8 mm h−1. [ABSTRACT FROM AUTHOR]

Abstract: The availability of precipitation data with high spatial resolution is of fundamental importance in several applications such as hydrology, meteorology and ecology. At present, there are mainly two sources of precipitation estimates: raingauge stations and remote sensing technology. However, a large number of studies demonstrated that traditional point measurements based on raingauge stations cannot reflect the spatial variation of precipitation effectively, especially in ungauged basins. The technology of remote sensing has greatly improved the quality of precipitation observations and produced reasonably high resolution gridded precipitation fields. These products, derived from satellites, have been widely used in various parts of the world. However, when applied to local basins and regions, the spatial resolution of these products is too coarse. In this paper, we present a statistical downscaling algorithm based on the relationships between precipitation and other environmental factors in the Qaidam Basin such as topography and vegetation, which was developed for downscaling the spatial precipitation fields of these remote sensing products. This algorithm is demonstrated with the Tropical Rainfall Measuring Mission (TRMM) 3B43 dataset, the Digital Elevation Model (DEM) from the Shuttle Radar Topography Mission (SRTM) and SPOT VEGETATION. The statistical relationship among precipitation, DEM and Normalized Difference Vegetation Index (NDVI), which is a proxy for vegetation, is variable at different scales; therefore, a multiple linear regression model was established under four different scales (0.25°, 0.50°, 0.75° and 1.00°, respectively). By applying a downscaling methodology, TRMM 3B43 0.25°×0.25° precipitation fields were downscaled to 1×1km pixel precipitation for each year from 1999 to 2009. On the basis of three criteria, these four downscaled results were compared with each other and the regression model established at the resolution of 0.50° was selected as the final downscaling algorithm in this study. The final downscaled results were validated by applying the observations for a duration of 11years obtained from six raingauge stations in the Qaidam Basin. These results indicated that the downscaled result effectively captured the trends in inter-annual variability and the magnitude of annual precipitation with the coefficient of determination r 2 ranging from 0.72 to 0.96 at six different raingauge stations. [Copyright &y& Elsevier]

Convective precipitation is the main cause of extreme rainfall events in small areas. Its primary characteristics are both large spatial and temporal variability. For this reason, the monitoring of accumulated precipitation fields (liquid and solid components) at the surface is difficult to carry out through the use of rain gauge networks or remote sensing observations. Alternatively, numerical models may be a useful tool to simulate convective precipitation for various analyses and predictions. This paper focuses on improving quantitative convective precipitation estimates that are obtained with a cloud-resolving model. This aim is attained by using the appropriate cloud drop size distribution and modified single sounding data. The authors perform comparisons between observations and three model samples of the areal-accumulated convective precipitation for a 15-yr period over mountainous and flat land areas with 45 and 29 convective events, respectively. They compare the results from a numerical cloud model that uses 2 different microphysical schemes--the unified Khrgian--Mazin size distribution of cloud drops--and an alternative scheme that is a combination of a monodispersed cloud droplet spectrum and the Marshall--Palmer size distribution for raindrops. The authors'' statistical analysis shows that the model version with the Khrgian--Mazin size distribution and the new initial conditions better simulates the observed areal-accumulated convective precipitation than the alternative microphysical approach for both study areas. The model simulations with the Khrgian--Mazin size distribution most closely match observations for the flat land area with a correlation coefficient of 0.94, while it is somewhat lower (0.89) for the mountainous area. Use of the alternative microphysical approach, on the other hand, underestimates the observed precipitation, and has the lowest correlation coefficient among the methods, 0.82 for the mountainous area and 0.85 for the flat land. [ABSTRACT FROM AUTHOR]

Abstract: This review paper deals with four aspects of precipitation: measurement, remote sensing, climatology and modeling. The measurement of precipitation is summarized in terms of the instruments that count and measure drop sizes (defined as disdrometers) and the instruments that measure an average quantity proportional to the integrated volume of an ensemble of raindrops (these instruments are normally called rain gauges). Remote sensing of precipitation is accomplished with ground based radar and from satellite retrievals and these two approaches are separately discussed. The climatology of precipitation has evolved through the years from the traditional rain gauge data analyses to the more sophisticated data bases that result from a coalescence of data and information on precipitation that is available from several sources into amalgamated products. Recently, rain observations from both ground and space have been assimilated into regional and global numerical weather prediction models aiming at improved moisture analysis and better forecasts of extreme weather events. The current status and the main outstanding issues related to precipitation forecasting are discussed, providing a basic structure for research coordination aimed at the improvement of modeling, observation and data assimilation applicable to global and regional scales. [Copyright &y& Elsevier]

This Special Issue hosts papers on all aspects of remote sensing of precipitation, including applications that embrace the use of remote-sensing techniques of precipitation in tackling issues, such as precipitation estimations and retrievals, along with their methodologies and corresponding error assessment; precipitation modelling including validation, instrument comparison, and calibration; understanding of cloud and precipitation microphysical properties; precipitation downscaling; precipitation droplet size distribution; assimilation of remotely sensed precipitation into numerical weather prediction models; and measurement of precipitable water vapor. [ABSTRACT FROM AUTHOR]