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Giuseppe Mascaro

and Wallis 1997 ). As a notable example, the regionalization technique proposed by Hosking and Wallis (1997) was adopted in the National Oceanic and Atmospheric Administration (NOAA) Atlas 14 to generate IDF curves for most of the conterminous United States (CONUS; Bonnin et al. 2004 ). The accuracy and uncertainty of IDF analyses are dependent on quality and availability of historical records. In developed countries, rainfall data are in general available; however, data availability is higher

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Phu Nguyen, Mohammed Ombadi, Vesta Afzali Gorooh, Eric J. Shearer, Mojtaba Sadeghi, Soroosh Sorooshian, Kuolin Hsu, David Bolvin, and Martin F. Ralph

to perform evaluations of precipitation data because of its high spatiotemporal resolution, multidecadal extent, and proven accuracy from manual quality control ( Beck et al. 2019 ). In the present study, Stage IV (hereafter referred to as ST4) is used as a baseline over the contiguous United States (CONUS) against which the performance of PDIR-Now and PERSIANN-CCS is benchmarked. 2) IMERG final run IMERG is a half-hourly 0.1° × 0.1° precipitation dataset that uses both PMW and IR data. The

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Shruti A. Upadhyaya, Pierre-Emmanuel Kirstetter, Jonathan J. Gourley, and Robert J. Kuligowski

from PMW precipitation estimates to VIS/IR is of critical significance for precipitation estimation from space, this topic has not been extensively studied in the literature. The overall objective of the present work is to carry out an error budget analysis of the new improved SCaMPR algorithm and its calibrator data MWCOMB. A quality-controlled radar and gauge-based precipitation dataset across the CONUS from the Multi-Radar Multi-Sensor (MRMS) system is used as reference to evaluate both MWCOMB

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Chandra Rupa Rajulapati, Simon Michael Papalexiou, Martyn P. Clark, Saman Razavi, Guoqiang Tang, and John W. Pomeroy

offered for operational use (since their estimates are based on small samples); yet, these estimates highlight how strongly the return levels can be affected by the choice of the probability model. Though the InSitu data are considered based on quality flags provided by GHCN-Daily, the gridded products are considered to be quality controlled by the data providers. The comparison between the products and the InSitu data is simplified by averaging the tail index of stations within a coarser grid

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Zhe Li, Daniel B. Wright, Sara Q. Zhang, Dalia B. Kirschbaum, and Samantha H. Hartke

-A), along with precipitation-sensitive radiances from GMI, SSMIS, and AMSR2. Currently, data assimilation cycles of EDAS consists of an ensemble model simulation and an analysis at each 3-h interval. Conventional and satellite observations obtained around the analysis time pass the quality control and an online bias correction ( Chambon et al. 2014 ) and enter into the optimization solver. For the assimilation of NU-WRF in this study, satellite swath observations such as PMW data within ±30 min of the

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Efi Foufoula-Georgiou, Clement Guilloteau, Phu Nguyen, Amir Aghakouchak, Kuo-Lin Hsu, Antonio Busalacchi, F. Joseph Turk, Christa Peters-Lidard, Taikan Oki, Qingyun Duan, Witold Krajewski, Remko Uijlenhoet, Ana Barros, Pierre Kirstetter, William Logan, Terri Hogue, Hoshin Gupta, and Vincenzo Levizzani

) forecasting (the gap between weather forecasts and seasonal climate predictions) using models and observations and assessment of uncertainty propagation to impact studies such as floods, droughts and ecological changes. IPC12 also aimed to provide a forum to explore new data analytic and machine learning (ML) methodologies, taking advantage of the unprecedented explosion of Earth observations from space and climate model outputs, for improved estimation and prediction. It also brought together scientists

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Samantha H. Hartke, Daniel B. Wright, Dalia B. Kirschbaum, Thomas A. Stanley, and Zhe Li

phenomena in locations and at scales not previously possible. SMPPs use algorithms that merge passive microwave and infrared sensing data from multiple satellites (e.g., Kidd and Levizzani 2011 ; Kidd and Huffman 2011 ; Tapiador et al. 2012 ; Wright 2018 ). Commonly used SMPPs include the TRMM Multisatellite Precipitation Analysis (TMPA; Huffman et al. 2007 ), the Climate Prediction Center (CPC) morphing technique (CMORPH; Joyce et al. 2004 ), and the Precipitation Estimation from Remotely Sensed

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Stephen E. Lang and Wei-Kuo Tao

, 2000 ) uses the vertical derivative of retrieved hydrometeor profiles to estimate LH. Its derivation was also based on CRM simulations. The precipitation radar heating (PRH) algorithm ( Satoh and Noda 2001 ; Satoh 2004 ; Kodama et al. 2009 ) does not require CRM data to estimate heating but must estimate cloud drafts and thermodynamic structures instead. Please see Tao et al. (2006) for an additional overview of these TRMM-related heating algorithms. Several studies have taken advantage of the

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