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W.-K. Tao, T. Iguchi, and S. Lang

1. Introduction Rainfall production is a fundamental process within Earth’s hydrological cycle because it represents a principal forcing term in surface water budgets, and its energetics corollary, latent heating (LH), is also one of the principal sources of atmospheric diabatic heating. Latent heating (or cooling) results from phase changes of water; it consists of condensation/evaporation (vapor–liquid phases), deposition/sublimation (vapor–solid phases), and freezing/melting (solid

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Md. Abul Ehsan Bhuiyan, Efthymios I. Nikolopoulos, and Emmanouil N. Anagnostou

Global Precipitation Climatology Center (GPCC) dataset. The reanalysis precipitation dataset (EI_GPCC) that we used in this study was further downscaled from 0.5° to 0.25° based on the Climate Hazards Group’s Precipitation Climatology (CHPclim). The WATCH (Water and Global Change FP7 project) Forcing Dataset ERA-Interim (hereafter WFDEI; Weedon et al. 2014 ) is based on ERA-Interim with a geographical resolution of 0.5° and bias corrections using gridded rain gauge datasets. We chose specific

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Sara Q. Zhang, T. Matsui, S. Cheung, M. Zupanski, and C. Peters-Lidard

modeling system that represents cloud, precipitation, aerosol, and land process ( Peters-Lidard et al. 2015 ). It is based on the Advanced Research WRF ( Skamarock et al. 2008 ) with additional coupling to advanced Goddard physics packages, satellite simulators, and high-resolution satellite/reanalysis data to initialize boundary conditions. In this work the NU-WRF model simulations are carried out at storm scale with lateral boundary forcing from the Modern-Era Retrospective Analysis for Research and

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Yonghe Liu, Jinming Feng, Zongliang Yang, Yonghong Hu, and Jianlin Li

datasets, such as Princeton Global Meteorological Forcing data, version 2 (PGF; Sheffield et al. 2006 ), the Climate Prediction Center morphing global precipitation analysis (CMORPH; Joyce et al. 2004 ), and ERA-Interim/Land ( Balsamo et al. 2015 ), were acquired for comparison. Fig . 1. Study domain and the gauge stations. Eight stations marked by yellow dots were analyzed in more detail than the stations marked with other dots. Red dots represent the stations for calibration, and the green dots

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Stephen E. Lang and Wei-Kuo Tao

added to improve sampling. Figure 2 shows results from the new DYNAMO and GOAmazon simulations, which form the basis for the CSH algorithm’s new LUTs. On average, the model responds well to the forcing, be it fewer large events over ocean (e.g., DYNAMO) or more numerous weaker events over land (e.g., GOAmazon). Including the three new cases, a total of 10 cases (six oceanic and four continental), which are listed in Table 2 , were simulated to provide data for the new LUTs. The simulations all

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Veljko Petković, Christian D. Kummerow, David L. Randel, Jeffrey R. Pierce, and John K. Kodros

Scientific, 238 pp. 10.1142/3171 Romanov , P. , G. Gutman , and I. Csiszar , 2000 : Automated monitoring of snow cover over North America with multispectral satellite data . J. Appl. Meteor. , 39 , 1866 – 1880 , doi: 10.1175/1520-0450(2000)039<1866:AMOSCO>2.0.CO;2 . 10.1175/1520-0450(2000)039<1866:AMOSCO>2.0.CO;2 Rosenfeld , D. , R. Wood , L. J. Donner , and S. C. Sherwood , 2013 : Aerosol cloud-mediated radiative forcing: Highly uncertain and opposite effects from shallow and deep clouds

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M. Petracca, L. P. D’Adderio, F. Porcù, G. Vulpiani, S. Sebastianelli, and S. Puca

during late summer and early autumn (September–November) when such intense systems hit Italy, causing flooding, landslides, and other damage ( Panegrossi et al. 2016 ; Marra et al. 2017 ; Silvestro et al. 2016 ). The complex orography that characterizes the Italian Peninsula adds local forcing to the precipitation formation and enhancement processes from one side, and from the other side makes it difficult to measure precipitation from ground-based instruments. Moreover, even the remote sensing

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E. F. Stocker, F. Alquaied, S. Bilanow, Y. Ji, and L. Jones

is often 0.05°. A few orbits have larger uncertainties as a result of data gaps and other issues forcing fallback to the onboard attitudes. Slowly changing systematic errors, especially in pitch, are sensitive to solar beta angle and gyro bias drift uncertainties, but these effects are expected to be less than a few hundredths of a degree. c. Sensor model and instrument alignment Geolocation is also affected by the accuracy with which the instrument field-of-view directions are modeled, and each

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Dalia B. Kirschbaum, George J. Huffman, Robert F. Adler, Scott Braun, Kevin Garrett, Erin Jones, Amy McNally, Gail Skofronick-Jackson, Erich Stocker, Huan Wu, and Benjamin F. Zaitchik

and social processes. Precipitation variability can influence the dynamics of disease risk in a number of ways: heavy rain can lead to floods that alter vector habitats and also interfere with human access to healthcare, droughts can force vectors and reservoir species into closer contact around scarce water sources, and even moderate precipitation variability influences soil moisture conditions and the formation of puddles and ephemeral ponds that can provide breeding sites for disease vectors

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Jiaying Zhang, Liao-Fan Lin, and Rafael L. Bras

Precipitation Measurement Integrated Multi-Satellite Retrievals for GPM (IMERG). Algorithm Theoretical Basis Doc., version 4.5, 30 pp., . Iacono , M. J. , J. S. Delamere , E. J. Mlawer , M. W. Shephard , S. A. Clough , and W. D. Collins , 2008 : Radiative forcing by long-lived greenhouse gases: Calculations with the AER radiative transfer models . J. Geophys. Res. , 113 , D13103 ,

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