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Anthony Wimmers
,
Christopher Velden
, and
Joshua H. Cossuth

. Furthermore, a new generation of infrared imagers ( Schmit et al. 2005 , 2017 ) and emerging cubesat sensors ( Blackwell et al. 2012 ; Cahoy et al. 2015 ; Reising et al. 2016 ) require new, robust techniques for TC nowcasting and forecasting that can assimilate a spectrally diverse variety of images all at once. It is well known that the microwave imaging frequencies available on polar-orbiting meteorological satellites since the late 1980s offer unique perspectives for observing TCs. Uses of the

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Ryan Lagerquist
,
Amy McGovern
,
Cameron R. Homeyer
,
David John Gagne II
, and
Travis Smith

. Kingfield , K. Ortega , and T. Smith , 2019 : Estimates of gradients in radar moments using a linear least squares derivative technique . Wea. Forecasting , 34 , 415 – 434 , https://doi.org/10.1175/WAF-D-18-0095.1 . 10.1175/WAF-D-18-0095.1 Markowski , P. , and Y. Richardson , 2009 : Tornadogenesis: Our current understanding, forecasting considerations, and questions to guide future research . Atmos. Res. , 93 , 3 – 10 , https://doi.org/10.1016/j.atmosres.2008.09.015 . 10.1016/j

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Amy McGovern
,
Ryan Lagerquist
,
David John Gagne II
,
G. Eli Jergensen
,
Kimberly L. Elmore
,
Cameron R. Homeyer
, and
Travis Smith

-0242.1 . 10.1175/MWR-D-15-0242.1 Bergthórsson , P. , and B. Döös , 1955 : Numerical weather map analysis . Tellus , 7 , 329 – 340 , https://doi.org/10.3402/tellusa.v7i3.8902 . 10.3402/tellusa.v7i3.8902 Billet , J. , M. DeLisi , B. Smith , and C. Gates , 1997 : Use of regression techniques to predict hail size and the probability of large hail . Wea. Forecasting , 12 , 154 – 164 , https://doi.org/10.1175/1520-0434(1997)012<0154:UORTTP>2.0.CO;2 . 10

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Christina Kumler-Bonfanti
,
Jebb Stewart
,
David Hall
, and
Mark Govett

is not a tropical cyclone ( American Meteorological Society 2019 ). Heuristic-based models typically require a specific set of meteorological variables provided by weather model outputs and often cannot be run directly with observations from sources such as satellites without further information or routines. Machine-learning (ML) techniques can be used effectively to identify a range of different meteorological features that may be broadly classified as regions of interest (ROI). For instance, ML

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Dan Lu
,
Goutam Konapala
,
Scott L. Painter
,
Shih-Chieh Kao
, and
Sudershan Gangrade

observations and in absence of detailed and reliable catchment attributes. Hydrologic models allow simulating and forecasting of streamflow based on meteorological observations ( Beven 2001 ). The classical physics-based hydrologic approach is to first develop conceptual models having fixed structures and parameterizations that reflect our physical understanding of internal catchment structure and functioning such as rainfall–runoff processes and their interactions, and then to apply these prespecified

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John L. Cintineo
,
Michael J. Pavolonis
,
Justin M. Sieglaff
,
Anthony Wimmers
,
Jason Brunner
, and
Willard Bellon

1. Introduction Since the advent of weather satellites, researchers have been investigating signatures of intense convection from satellite images (e.g., Purdom 1976 ; Adler and Fenn 1979 ; Menzel and Purdom 1994 ; Schmit et al. 2005 , 2015 ). Forecasters frequently scrutinize satellite imagery to help infer storm dynamics and diagnose and forecast the intensity of thunderstorms, which can generate a variety of hazards. Intense convective updrafts frequently penetrate the tropopause

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Imme Ebert-Uphoff
and
Kyle Hilburn

. In contrast the image-to-image translation models ( Figs. 1b,c ) generate as output an image, typically of the same dimension (but not necessarily the same number of channels) as the input image. Image-to-image translation models can be used to enhance remote sensing images ( Tsagkatakis et al. 2019 ), to detect changes in satellite imagery ( Peng et al. 2019 ), for precipitation forecasting ( Sønderby et al. 2020 ), for weather forecasting ( Weyn et al. 2020 ), to detect tropical and

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Yaling Liu
,
Dongdong Chen
,
Soukayna Mouatadid
,
Xiaoliang Lu
,
Min Chen
,
Yu Cheng
,
Zhenghui Xie
,
Binghao Jia
,
Huan Wu
, and
Pierre Gentine

; Miralles et al. 2014 ), and they may also affect the intensity, frequency, and distribution of precipitation ( Findell et al. 2011 ; Guillod et al. 2015 ; Taylor et al. 2012 ). Further, a shortage or excess of SM could trigger the occurrence of droughts ( Wang et al. 2011 ) or floods ( Koster et al. 2010 ). As such, SM is crucial for weather prediction, climate forecasting and ecosystem dynamics assessment. Moreover, SM is vital for agricultural production since it is the only direct source for crop

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