EditorialGlobal and Regional Remote Sensing Precipitation Estimation,Evaluation, and Applications

Youcun Qi ,1 Bin Yong,2 and Yalei You3

1Key Laboratory of Water Cycle and Related Land Surface Processes,Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China2State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 210098, China3Cooperative Institute for Climate and Satellites-Maryland, Earth System Science Interdisciplinary Center,University of Maryland, College Park, MD, USA

Correspondence should be addressed to Youcun Qi; youcun.qi@igsnrr.ac.cn

Received 14 June 2018; Accepted 14 June 2018; Published 24 July 2018

Copyright © 2018 Youcun Qi et al. #is is an open access article distributed under the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

In the past three decades, many precipitation products withhigh spatial-temporal resolutions have been developed.Global and regional quantitative precipitation estimations(QPEs) are very important for understanding climate var-iability and hydrometeorological cycles, improving flashflood and weather forecast, effectively managing the usage ofearth’s freshwater resources, detecting the natural disasters,and other hydrometeorological applications. However, obtain-ing accurate QPEs is a big challenge in many areas of the worlddue to sparse gauge networks and complicated terrains. Recentadvances in remote sensing have allowed us to retrieve QPEinformation, representing one unprecedented contribution to-ward the global and regional mapping of precipitation.

Current global QPEs are often derived by combining theobservations from both geostationary satellite (GEO) thermalinfrared (IR) sensors and low-earth orbiting (LEO) satellitemicrowave imagers/sounders. Especially, the Tropical RainfallMeasuring Mission (TRMM) from late 1997 to early 2015,a joint mission between NASA and the Japan Aerospace EX-ploration Agency (JAXA), significantly improved the globalQPEs. As the follow-on to TRMM, the Global PrecipitationMeasurement (GPM) Core Observatory satellite with a dualfrequency precipitation radar (DPR) and a multichannel GPMmicrowave imager (GMI) was launched in February 2014,which provides great opportunity to further improve the globalQPE development.

#e GPM satellite constellation supports the next gen-eration of unified global precipitation products with a betteraccuracy and a shorter data latency essential for researches

and applications. #e challenges faced in improving pre-cipitation products are not only in developing better retrievalalgorithms but also in improved approaches to integrateobservations from the different sensors and then to assimilatethem into various applications.

Furthermore, ground observations such as those fromradar networks and gauge networks and disdrometer ob-servations are available, providing potentially more accurateprecipitation measurements on the ground than satellites.However, these observations are not available over oceans(except overseas adjacent to land). #us, this special issuefocused on improving precipitation products not only bydeveloping newer retrieval algorithms but also through newapproaches to integrate observations from the differentsensors for assimilation into various applications.

Precipitation with high resolution and accuracy is ofgreat significance for hydrological, meteorological, and eco-logical studies. Since satellite-based precipitation measure-ment is often too coarse for practical applications, it is crucialto develop spatial downscaling algorithms. Zhan et al. (2018)investigated two downscaling algorithms based on the mul-tiple linear regression (MLR) and the geographically weightedregression (GWR), respectively. Both algorithms were appliedto downscale annual and monthly precipitation obtained fromthe Global Precipitation Measurement (GPM) Mission inHengduan Mountains, Southwestern China, from 10 km×

10 km to 1 km× 1 km. #ese results indicated that GWR isa promising method in satellite precipitation downscalingresearches and needed to be further studied. Sahlu et al. (2017)

HindawiAdvances in MeteorologyVolume 2018, Article ID 5362638, 2 pageshttps://doi.org/10.1155/2018/5362638

evaluated the performance of six satellite-based and threenewly released reanalysis rainfall estimates at the daily time-scale and the spatial grid size of 0.25 degrees during the periodof 2000 to 2013 over the Upper Blue Nile Basin, Ethiopia, withthe purpose of improving the reliability of precipitation esti-mates of the wet (June to September) and secondary rainy(March to May) seasons. #ey evaluated both adjusted andunadjusted satellite-based products of TMPA, CMORPH,PERSIANN, and ECMWF ERA-Interim reanalysis as well asMulti-Source Weighted-Ensemble Precipitation (MSWEP) es-timates. Among the six satellite-based rainfall products, adjustedCMORPH exhibits the best accuracy of the wet season rainfallestimate. Overall, all precipitation datasets need further im-provement in terms of detection during the occurrence of highrainfall intensity.

When the radar beam goes through the rain drops, theradar reflectivity usually suffers attenuation effects. Zhanget al. (2017) proposed a new method for attenuation cor-rection of the radar reflectivity using the arbitrary orientedmicrowave link (referred henceforth to as ACML). Referringto the measurement of the arbitrary oriented microwavelink, the ACML method optimizes the ratio of specific at-tenuation to specific differential phase which is a key pa-rameter in attenuation correction schemes. #e proposedmethod was evaluated using real data of a dual-polarizationX-band radar and measurements of two microwave linksduring two rainstorm events. #e results showed that thevariation range of the optimized ratio was overall consistentwith the results of the previous studies. After attenuationcorrection with the optimal ratios, the radar reflectivity wassignificantly compensated, especially at the long distances.#e corrected reflectivity was more intense than thereflectivity corrected by the “self-consistent” (SC) methodand closer to the reflectivity of a nearby S-band radar. #eeffectiveness of the method was also verified by comparingthe rain rates estimated by the X-band radar with thosederived by rain gauges. It is demonstrated that the arbitraryoriented microwave link can be adopted to optimize theattenuation correction of radar reflectivity.

In order to improve the measurement of the pre-cipitation microphysical characteristics sensor (PMCS), Liuet al. (2018) simulated the sampling process of raindrops byPMCS based on a particle-by-particleMonte Carlomodel anddiscussed the effect of different bin sizes on DSD measure-ment. He also proposed the optimum sampling bin sizes forPMCS based on the simulation results. #e simulation resultsof five sampling schemes of bin sizes in four rain-rate cate-gories show that the raw capture DSD has a significantfluctuation variation influenced by the capture probability,whereas the appropriate sampling bin size and width canreduce the impact of variation of raindrop number on theDSD shape.#eDSD obtained by PMCS and OTT has a goodagreement.

Ice nuclei are a very important factor as they significantlyaffect the development and evolvement of convective cloudssuch as the hail clouds. Liu et al. (2017) conducted numericalsimulations of hail processes in Zhejiang Province, China,using a mesoscale numerical model (WRF v3.4). #eeffects of six ice nuclei parameterization schemes on the

macroscopic and microscopic structures of hail clouds werecompared. #e effect of the ice nuclei concentration onground hailfall is stronger than on ground rainfall. #erewere significant spatiotemporal, intensity, and distributiondifferences in hailfall. Liu et al. (2018) run a numerical sim-ulation of a southwest vortex rainstorm process. #e resultsshow that the low-pressure system originating from the TibetanPlateau affects the southwest vortex mainly at the middle level,causing the strength increase of the southwest vortex (SWV),and acts as a connection between the positive vorticity centersat the upper and lower layers. As the ice crystals grow up, snowand graupel particles form, which substantially enhances theprecipitation. #is effect leads to the rapid development ofSWV rainstorm clouds and the occurrence of precipitation. Inaddition to the effect of the plateau vortex, the subsequentmerging of the convective clouds is another important factorfor heavy rainfall because it also leads to development ofconvective clouds, causing heavy rainfall.

Yang et al. (2017) investigated the terrestrial waterstorage anomalies (TWSAs) in the Tarim River Basin (TRB)and analyzed the related factors of water variations in themountain areas based on Gravity Recovery and ClimateExperiment (GRACE) data with in situ river discharge andgauge observations during the period of 2002–2015. #eresults showed that three obvious flood events in 2005, 2006,and 2010 resulted in significant water surplus, althoughTWSA decreased in the TRB during 2002–2015. #e sig-nificant water deficits were not well consistent with thenegative anomalies of precipitation. While the river dis-charge behaved with low correlations with TWSA, linearrelationships between TWSA and climate indices were in-significant in the TRB from 2002 to 2015.

Youcun QiBin YongYalei You

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