Search Results

You are looking at 11 - 17 of 17 items for :

  • Forecasting techniques x
  • The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction x
  • Refine by Access: All Content x
Clear All
Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Stephen G. Penny, Jebb Q. Stewart, Amy McGovern, David Hall, John E. Ten Hoeve, Jason Hickey, Hung-Lung Allen Huang, John K. Williams, Kayo Ide, Philippe Tissot, Sue Ellen Haupt, Kenneth S. Casey, Nikunj Oza, Alan J. Geer, Eric S. Maddy, and Ross N. Hoffman

observations and satellite applications, including the use of neural networks for NWP model parameterization ( Krasnopolsky et al. 2010 ) and using deep learning to infer missing data ( Boukabara et al. 2019a ). NCAR, a federally funded research and development center, has a long history of developing AI techniques for weather applications. Haupt et al. (2019) highlighted the Dynamic Integrated Forecast (DICast) system, a 20-year effort at NCAR that forms the “weather engine” of many applications, as

Open access
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

Full access
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 , . 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 , . 10.1016/j

Free access
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 , . 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 ,<0154:UORTTP>2.0.CO;2 . 10

Full access
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

Restricted access
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

Full access
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

Restricted access