Water resources management, forecasting, and decision making require reliable estimates of precipitation. Extreme precipitation events are of particular importance because of their severe impact on the economy, the environment, and the society. In recent years, the emergence of various satellite‐retrieved precipitation products with high spatial resolutions and global coverage have resulted in new sources of uninterrupted precipitation estimates. However, satellite‐based estimates are not well integrated into operational and decision‐making applications because of a lack of information regarding the associated uncertainties and reliability of these products. In this study, four satellite‐derived precipitation products (CMORPH, PERSIANN, TMPA‐RT, and TMPA‐V6) are evaluated with respect to their performance in capturing precipitation extremes. The Stage IV (radar‐based, gauge‐adjusted) precipitation estimates are used as reference data. The results show that with respect to the probability of detecting extremes and the volume of correctly identified precipitation, CMORPH and PERSIANN data sets lead to better estimates. However, their false alarm ratio and volume are higher than those of TMPA‐RT and TMPA‐V6. Overall, no single precipitation product can be considered ideal for detecting extreme events. In fact, all precipitation products tend to miss a significant volume of rainfall. With respect to verification metrics used in this study, the performance of all satellite products tended to worsen as the choice of extreme precipitation threshold increased. The analyses suggest that extensive efforts are necessary to develop algorithms that can capture extremes more reliably.

Abstract. Time histories of the characteristics of the drop size distribution of surface disdrometer measurements collected at Kapingamarangi Atoll were partitioned for several storms using rain rate R, reflectivity factor Z, and median diameter of the distribution of water content D 0. This partitioning produced physically based systematic variations of the drop size distribution (DSD) and Z-R relations in accord with the precipitation types viewed simultaneously by a collocated radar wind profiler. These variations encompass the complete range of scatter around the mean Z-R relations previously reported by Tokay and Short [ 1996] for convective and stratiform rain and demonstrate that the scatter is not random. The systematic time or space variations are also consistent with the structure of mesoscale convective complexes with a sequence of convective, transition, and stratiforrn rain described by various authors. There is a distinct inverse relation between the coefficient A and the exponent of the Z-R relations which has been obscured in prior work because of the lack of proper discrimination of the rain types. Contrary to previous practice it is evident that there is also a distinct difference in the DSD and the Z-R relations between the initial convective and the trailing transition zones. The previously reported Z-R relation for convective rain is primarily representative of the transition rain that was included in the convective class. The failure of present algorithms to distinguish between the initial convective and the trailing transition rains causes an erroneous apportionment of the diabatic heating and cooling and defeats the primary intent of discriminating stratiform from convective rains.

Characterization of the error associated with satellite rainfall estimates is a necessary component of deterministic and probabilistic frameworks involving spaceborne passive and active microwave measurements for applications ranging from water budget studies to forecasting natural hazards related to extreme rainfall events. The authors focus here on the error structure of NASA's Tropical Rainfall Measurement Mission (TRMM) Precipitation Radar (PR) quantitative precipitation estimation (QPE) at ground. The problem is addressed by comparison of PR QPEs with reference values derived from ground-based measurements using NOAA/NSSL ground radar-based National Mosaic and QPE system (NMQ/Q2). A preliminary investigation of this subject has been carried out at the PR estimation scale (instantaneous and 5 km) using a 3-month data sample in the southern part of the United States. The primary contribution of this study is the presentation of the detailed steps required to derive a trustworthy reference rainfall dataset from Q2 at the PR pixel resolution. It relies on a bias correction and a radar quality index, both of which provide a basis to filter out the less trustworthy Q2 values. Several aspects of PR errors are revealed and quantified including sensitivity to the processing steps with the reference rainfall, comparisons of rainfall detectability and rainfallrate distributions, spatial representativeness of error, and separation of systematic biases and random errors. The methodology and framework developed herein applies more generally to rainfall-rate estimates from other sensors on board low-earth-orbiting satellites such as microwave imagers and dual-wavelength radars such as with the Global Precipitation Measurement (GPM) mission.