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Liao-Fan Lin, Ardeshir M. Ebtehaj, Alejandro N. Flores, Satish Bastola, and Rafael L. Bras

into these numerical models. Although predictions of precipitation and soil moisture are intertwined ( Case et al. 2011 ; Jiménez et al. 2014 ; Feng and Houser 2015 ), modern weather data assimilation systems often do not include soil moisture as a control state variable ( Parrish and Derber 1992 ; Derber and Bouttier 1999 ; Barker et al. 2004 ; Wang et al. 2013 ). Therefore, the relative usefulness of assimilating satellite soil moisture observations into a coupled land–atmosphere model

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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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Thomas Stanley, Dalia B. Kirschbaum, George J. Huffman, and Robert F. Adler

://collection1.libraries.psu.edu/cdm/ref/collection/digitalbks2/id/48274 .] R Core Team , 2015 : R: A language and environment for statistical computing. Accessed 17 July 2015. [Available online at http://www.r-project.org/ .] Reichle , R. H. , and R. D. Koster , 2004 : Bias reduction in short records of satellite soil moisture . Geophys. Res. Lett. , 31 , L19501 , doi: 10.1029/2004GL020938 . 10.1029/2004GL020938 Turkington , T. , A. Remaître , J. Ettema , H. Hussin , and C. van

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

other precipitation datasets ( Beck et al. 2019 ). In addition, to obtain the best possible precipitation estimates at global scale, MSWEP accounted a gauge-correction scheme that minimizes timing mismatches when applying the daily gauge corrections ( Beck et al. 2019 ). Bhuiyan et al. (2017) developed a machine learning–based multisource data blending technique and have used it to evaluate the impact of land surface conditions (e.g., vegetation cover and soil moisture) on passive microwave

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

the atmospheric initial condition. Land surface initial conditions (soil moisture and skin temperature) are derived from LIS spinup ( Kumar et al. 2008 ) of the Noah land surface model (LSM) with the MERRA-Land meteorological forcing ( Reichle 2012 ). In subsequent data assimilation cycles, the analysis produced by assimilating observations is used to issue ensemble 3-h forecasts, with new perturbations derived from updated analysis error covariance. The ensemble forecasts are used to update the

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Gail Skofronick-Jackson, Walter A. Petersen, Wesley Berg, Chris Kidd, Erich F. Stocker, Dalia B. Kirschbaum, Ramesh Kakar, Scott A. Braun, George J. Huffman, Toshio Iguchi, Pierre E. Kirstetter, Christian Kummerow, Robert Meneghini, Riko Oki, William S. Olson, Yukari N. Takayabu, Kinji Furukawa, and Thomas Wilheit

2013 ; Zavodsky et al. 2013 ; Case et al. 2016 ; https://nasasport.wordpress.com/category/land-information-system-lis/ ) at NASA’s Marshall Space Flight Center ( Xia et al. 2012 ; Zhang et al. 2016 ; Vargas et al. 2015 ) to produce analyses and short-term forecasts of soil moisture and other fields. LIS is a land surface modeling and data assimilation framework designed to integrate satellite observations, including GPM and the Soil Moisture Active Passive (SMAP) satellite data ( Entekhabi et

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

assimilating satellite observations to improve hurricane track and intensity predictions ( Pu et al. 2002 , 2008 ; Pu and Zhang 2010 ; Liu et al. 2012 ; Xu et al. 2013 ; Zou et al. 2013 ; Zhang and Pu 2014 ; Yang et al. 2016 ; Xu et al. 2016 ; Wu et al. 2016 ). Specifically, these previous studies found that satellite microwave sounders is particularly useful for understanding moist processes associated with hurricanes owing to its unique capability to depict precipitation structure and moisture

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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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Stephen E. Lang and Wei-Kuo Tao

Syst. , 8 , 66 – 95 , https://doi.org/10.1002/2015MS000469 . 10.1002/2015MS000469 Chong , M. , and D. Hauser , 1990 : A tropical squall line observed during the COPT 81 experiment in West Africa. Part III: Heat and moisture budgets . Mon. Wea. Rev. , 118 , 1696 – 1706 , https://doi.org/10.1175/1520-0493(1990)118<1696:ATSLOD>2.0.CO;2 . 10.1175/1520-0493(1990)118<1696:ATSLOD>2.0.CO;2 Choudhury , A. D. , and R. Krishnan , 2011 : Dynamical response of the South Asian monsoon trough

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

-retrieved products (LH, radiation Q R , apparent moisture sink Q 2 , and eddy transport) are also shown in the table. Note that intensive rawinsonde networks can be used to obtain vertical profiles of the apparent heat source Q 1 (see Yanai et al. 1973 ), which is equivalent to the sum of LH, radiation and eddy heat transport. Further details on the CSH and SLH algorithms are presented in section 2 . Table 1. Summary of the SLH and CSH algorithms. Note the conventional relationship between Q 1 (apparent

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