Search Results

You are looking at 1 - 6 of 6 items for :

  • Boundary conditions x
  • The 1st NOAA Workshop on Leveraging AI in the Exploitation of Satellite Earth Observations & Numerical Weather Prediction x
  • User-accessible content x
Clear All
Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

thunderstorms with abundant cloud water concentrations (e.g., Williams et al. 2005 ) that produce large anvils that obscure the convective cores in infrared imagery. While these conditions also lead to very high lightning rates, Rutledge et al. (2020) show these conditions also produce storms for which the lighting flash height is relatively low, making for large optical paths between the lightning source and the upper cloud boundary along the GLM sensor line of sight (both in general and for this

Open access
Eric D. Loken, Adam J. Clark, Amy McGovern, Montgomery Flora, and Kent Knopfmeier

are derived using a blend of Global Ensemble Forecast System (GEFS) and SREF analyses. Lateral boundary conditions (LBCs) are from the GFS and GEFS members. Convective parameterizations include the Kain–Fritsch (KF; Kain 2004 ), Grell (1993) , Betts–Miller–Janjić (BMJ; Betts 1986 ; Janjić 1994 ), and simplified Arakawa–Schubert ( Han and Pan 2011 ) schemes. Planetary boundary layer (PBL) schemes include the Yonsei University (YSU; Hong et al. 2006 ), Mellor–Yamada–Nakanishi–Niino (MYNN

Full access
Sid-Ahmed Boukabara, Vladimir Krasnopolsky, Jebb Q. Stewart, Eric S. Maddy, Narges Shahroudi, and Ross N. Hoffman

lower cost, and mass and power requirements. This will improve analyses close to the surface (in the lower atmospheric boundary layer) where existing observations are not optimal. Fig . 1. Growth in annual mean number of satellite observations (millions) per 0000 UTC cycle (a) available and (b) used by the NCEP DA system for different data types (colors). The data are grouped into the following types: atmospheric motion vector, ocean surface wind, solar backscatter ozone, radio occultation, and

Free access
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

( Krasnopolsky 2013 ), the most important being to achieve high performance within the host NWP model. Fast emulations of existing model physics parameterizations are usually developed for complex parameterizations that are computational bottlenecks, such as atmospheric radiation parameterizations and the planetary boundary layer (e.g., Wang et al. 2019 ). Krasnopolsky (2019) demonstrated that a 0.1 K day −1 RMS accuracy can be obtained for varied individual instantaneous profiles with shallow NN

Open access
Imme Ebert-Uphoff and Kyle Hilburn

. Pattern complexity is very difficult to evaluate for several reasons: 1) patterns can only be evaluated after NN training is completed; 2) techniques for discovering patterns, such as feature visualization ( Olah et al. 2017 , 2018 ), to date only provide limited answers; and 3) feature visualization is even more challenging for meteorological imagery, because it tends to have amorphous boundaries (e.g., clouds, atmospheric rivers, ocean eddies) ( Karpatne et al. 2019 ) rather than the crisp

Full access
Hanoi Medina, Di Tian, Fabio R. Marin, and Giovanni B. Chirico

to spatial inconsistencies at the boundaries between tiles ( Hamill and Whitaker 2006 ; Hamill et al. 2006 ). However, this is not an issue present in this study. Leave-one-out cross validation are carried out by excluding the current year from the list of potential analogs. For a detailed description and theoretical basis of the analog method, the readers can refer to Hamill and Whitaker (2006) . 2) Logistic regression method In the logistic regression (LR) method a nonlinear function is

Full access