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Sybille Y. Schoger, Dmitri Moisseev, Annakaisa von Lerber, Susanne Crewell, and Kerstin Ebell

.5281/zenodo.4017348 ( Ebell 2020 ). REFERENCES Battaglia , A. , E. Rustemeier , A. Tokay , U. Blahak , and C. Simmer , 2010 : PARSIVEL snow observations: A critical assessment . J. Atmos. Oceanic Technol. , 27 , 333 – 344 , . 10.1175/2009JTECHA1332.1 Boggs , P. T. , and J. E. Rogers , 1990 : Orthogonal distance regression. Statistical Analysis of Measurement Error Models and Applications: Proc. AMS-IMS-SIAM Joint Summer Research Conf

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Randy J. Chase, Stephen W. Nesbitt, and Greg M. McFarquhar

Union 2013;!!PDF-E.pdf ) and a thermodynamic sounding collected near the time of the radar data collection. The mean values for two-way correction from gaseous attenuation is 0.15 and 0.6 dB for Ku and Ka band, respectively at the surface. Since the focus of this analysis is on only solid-phase hydrometeors, liquid-phase echoes, melting echoes (i.e., the bright band), surface echoes, and the radar returns from the in situ aircraft

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Zhaoxia Pu, Chaulam Yu, Vijay Tallapragada, Jianjun Jin, and Will McCarty

hurricane (e.g., Joaquin), at least four 6-hourly data assimilation analysis–forecast cycles (each of which has at least one GMI overpass near the hurricane region) are performed. Then, using the BC coefficients obtained from linear regression as an initial guess, GSI analysis is performed sequentially at each of these 6-hourly analysis–forecast cycles. At each GSI analysis, the BC coefficients are adjusted accordingly and then passed to the next analysis–forecast cycle. After the coefficients go

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Clément Guilloteau, Efi Foufoula-Georgiou, Christian D. Kummerow, and Veljko Petković

, as the scattering of microwaves by ice particles leads to depression in the observed TBs above 40 GHz. In fact, the early-developed algorithms used only TBs at frequencies higher than 80 GHz for the retrieval over land ( Gopalan et al. 2010 ); this was the case in particular for the TRMM-era versions of the GPROF algorithm. This paper presents a joint statistical analysis of GMI TBs at 89 GHz and DPR reflectivities over the southeastern continental United States. The objective is to evaluate how

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

), Bayesian regression ( Todini 2001 ; Mazzetti and Todini 2004 ), and wavelet analysis ( Heidinger et al. 2012 ), to name a few. Past studies have shown that merging precipitation products with rain gauge measurements has considerable advantages ( Li and Shao 2010 ; Duque-Gardeazábal et al. 2018 ). The Integrated Multisatellite Retrievals for GPM (IMERG) is a blended high-resolution precipitation product developed by NASA, which merges multiple satellite precipitation estimates and ground

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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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Hooman Ayat, Jason P. Evans, Steven Sherwood, and Ali Behrangi

Prediction Center morphing technique (CMORPH), and Tropical Rainfall Measurement Mission (TRMM) Multisatellite Precipitation Analysis (TMPA) 3B42RT with NCEP Stage IV radar data in the warm season of 2008 and cold season of 2010, and they found that PERSIANN performed best in capturing the orientation of the objects, 3B42RT depict the location of the storms better than the other products and in terms of the object size, CMORPH is the best. However, the objects were not tracked in this research to capture

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Clément Guilloteau and Efi Foufoula-Georgiou

cloud type and precipitation type. For example, Prabhakara et al. (2000) , and later Gopalan et al. (2010) , used the minimum value and the standard deviation of the TB at 85 GHz within a 40 km neighborhood to estimate the convection fraction at the pixel of interest. However, these indices have only been used for unispectral or bispectral algorithms within a linear regression framework so far. What is proposed here is to exploit the information contained in the observed fields of TB by analyzing

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Yalei You, S. Joseph Munchak, Christa Peters-Lidard, and Sarah Ringerud

moisture datasets derived from spaceborne microwave sensors. They concluded that the retrieved 5-day rainfall accumulation from the soil moisture datasets agree reasonably well with a ground gauge analysis dataset, indicated by the correlation being as large as 0.54. The ability to retrieve rainfall from the soil moisture is further demonstrated by Koster et al. (2016) , which showed that satellite missions designed for soil moisture observations indeed contain valuable rainfall information. In fact

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

.g., IMERG ( Huffman et al. 2015 ), CMORPH ( Joyce et al. 2004 ), TMPA ( Huffman et al. 2007 ), PERSIANN-CCS ( Hong et al. 2004 ), and GSMaP ( Kubota et al. 2007 ; Ushio et al. 2009 )] that are commonly used in applications requiring precipitation at high spatial and temporal resolutions. Continuous work on finding physical relations between the observed [i.e., brightness temperatures (Tbs)] and state (i.e., rainfall) vectors led to PMW retrieval improvements from fairly simple regression models (e

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