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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]

Improving the quality of atmospheric precipitation measurements is crucial in the view of minimizing the uncertainty in weather forecasting, climate change impact assessment, water resource assessment and management, and drought and flood prediction. Remote sensing technology has considerably improved the spatio-temporal assessment of precipitation. Despite the advancement in the remote sensing technology, there is a need to investigate the robust approach towards integrating ground-based-measured and satellite-product precipitation to better understand the hydrologic process of any basin. Several data-merging methods have been proposed; however, the application of merged precipitation products for hydrological simulation has rarely been investigated. Thus, in this review, technical characteristics including basic assumptions, along with their procedures, are discussed. Moreover, the limitations of eight commonly used merging approaches, (1) Multiple Linear Regression, (2) Residual Inverse Distance Weighting, (3) Linearized Weighting, (4) Inverse Root-Mean-Square Error Weighting, (5) Optimal Interpolation, (6) Random-Forest-Based Merging Procedure, (7) Bayesian Model Averaging, and (8) the Kriging Method, and their advances with respect to hydrological simulation are discussed. Finally, future research directions towards improving data merging approaches are recommended. [ABSTRACT FROM AUTHOR]

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]

Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual- Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5-15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for bothDPRKu and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products. [ABSTRACT FROM AUTHOR]

The diurnal variation of rainfall over China associated with landfalling tropical cyclones (TCs) is investigated using hourly rain gauge observations obtained from 2425 conventional meteorological stations in China. Records between 12 h prior to landfall and 12 h after landfall of 450 landfalling TCs in China from 1957 to 2014 are selected as samples. The harmonic analysis shows an obvious diurnal signal in TC rainfall with a rain-rate peak in the early morning and a minimum in the afternoon. The diurnal cycle in the outer region (between 400- and 900-km radii from the storm center) is found to be larger than in the core region (within 400 km of the storm center). This could be attributed to the effect of land on the inner core of the storms as the diurnal cycle is distinct in the core region well before landfall. As the result of this diurnal cycle, TCs making landfall at night tend to have cumulative precipitation, defined as the precipitation cumulated from the time at landfall to 12 h after landfall, about 30% larger than those making landfall around noon or afternoon. Moreover, the radial propagation of the diurnal cycle in TC rain rate, which has been a controversial phenomenon in some previous studies with remote sensing observations, was not present in this study that is based on rain gauge observations. Results also show that the diurnal signal has little dependence on the storm intensity 12 h prior to landfall. [ABSTRACT FROM AUTHOR]

Surface emissivities computed at 89 GHz from AMSU-A, AMSU-B, and SSMI/S instruments are used to detect rain events and to estimate a daily precipitation rate over land surfaces. This new retrieval algorithm, called the emissivity rainfall retrieval (EMIRR) algorithm, is evaluated over France and compared with several other precipitation products. The precipitation detection is performed using temporal changes in daily surface emissivities. A statistical fit, derived from a rainfall analysis product using rain gauge and radar data, is devised to estimate a daily precipitation rate from surface emissivities. Rain retrievals are evaluated over a 1-yr period in 2010 against other precipitation products, including rain gauge measurements. The EMIRR algorithm allows a reasonable detection of rainy events from daily surface emissivities. The number of rainy days and the daily rainfall rates compare well to estimates from other precipitation products. However, the algorithm tends to overestimate low precipitation amounts and to underestimate higher ones, with reduced performances in the presence of snow. Despite such limitations, this new method is very promising and provides a demonstration of the potential use of the 89-GHz surface emissivities to infer relevant information (occurrence and amounts) related to daily precipitation over land surfaces. [ABSTRACT FROM AUTHOR]