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

%, meaning that all red-segmented regions have a value of at least 0.7 and indicate a higher confidence of cyclone ROI event. On the basis of the analysis concluded by IMILAST, there is a climatological pattern of more frequent labels in the Southern Hemisphere than the Northern Hemisphere. The Southern Ocean has more extratropical cyclones and the Heuristic-GFS U-Net identifies the more ambiguous cyclone events that occur in that ocean basin because of that learned trait. Since there is no distinction

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Kyle A. Hilburn, Imme Ebert-Uphoff, and Steven D. Miller

day has a mode between 20 and 50 reports per case. Each case represents a 6-h period on each day, which may span 0000 UTC. Figure 2a shows that this construction approach results in a geographic preference for the upland South and southern Great Plains. Figure 2b shows a temporal preference for mid- to late-afternoon into the early evening. We split the data using a chronological 80%–20% split for training–validation. Based on this split, the July cases were used for validation and April

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

, and number of tornadic examples (or “events”) (a),(c) by month and (b),(d) by hour. Fig . 7. As in Fig. 6 , but for MYRORSS. Figures 8 and 9 break down the testing performance of the two models by location, into 100-km grid cells. Grid cells with no tornadic examples are not shown, because this causes the scores to degenerate. Most examples and most tornadic examples occur in the southeast quadrant of the CONUS, with a secondary maximum in tornadic examples in the southern Great Plains. In

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

global NWP models ( Bauer et al. 2015 ). The representation of these processes is especially challenging over continental areas from the Southern Hemisphere where the abundant vegetation and the sparse observations for evaluation and data assimilation have limited the models’ accuracy. Recent progress in forecasting tropical convection ( Bechtold et al. 2014 ; Subramanian et al. 2017 ) and the increasing quantity and quality of global information encourage the use of NWP for tropical precipitation

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

Two cases are subjectively selected to illustrate the RFFPs’ relative performance on individual days. 1) 1200 UTC 2 October–1200 UTC 3 October 2017 The heaviest precipitation during this period occurred in a corridor extending from northeastern Minnesota into west-central Kansas ahead of a cold front. Relatively heavy precipitation also occurred in northern Montana downstream of a midlevel shortwave trough, while southern Louisiana and southern Florida experienced weakly forced tropical showers

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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

techniques in Earth science ( Reichstein et al. 2019 ). By predicting temporally varying target variables in land, ocean and atmosphere domains from temporally varying features, machine learning has been actively used to study Earth system dynamics. Particularly, compared to previous mechanistic or semiempirical modeling approaches, machine learning methods have been proven to be more powerful and flexible when inferring continental or global estimates from point observations, such as predicting carbon

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Anthony Wimmers, Christopher Velden, and Joshua H. Cossuth

Atlantic or east Pacific basins, and the Central Pacific Hurricane Center in the central Pacific basin ( Landsea et al. 2013 ); and 2) the Joint Typhoon Warning Center best tracks in the west Pacific, north Indian Ocean, and Southern Hemisphere ( Chu et al. 2002 ). Each image is assigned the best track MSW matching the image time through linear interpolation. Note that using these best track MSW estimates as “truth” is not optimal because only a minority of these values has in situ confirmation

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Imme Ebert-Uphoff and Kyle Hilburn

.3390/math7100992 Haynes , J. M. , C. Jakob , W. B. Rossow , G. Tselioudis , and J. Brown , 2011 : Major characteristics of Southern Ocean cloud regimes and their effects on the energy budget . J. Climate , 24 , 5061 – 5080 , https://doi.org/10.1175/2011JCLI4052.1 . 10.1175/2011JCLI4052.1 Hertel , L. , J. Collado , P. Sadowski , J. Ott , and P. Baldi , 2020 : Sherpa: Robust hyperparameter optimization for machine learning. arXiv, https://arxiv.org/abs/2005.04048 . 10

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