Zobrazeno 1 - 10
of 55
pro vyhledávání: '"Craig S. Schwartz"'
Autor:
Tzu-Yu Chien, Shu-Ya Chen, Ching-Yuang Huang, Cheng-Peng Shih, Craig S. Schwartz, Zhiquan Liu, Jamie Bresch, Jia-Yang Lin
Publikováno v:
Atmosphere, Vol 13, Iss 9, p 1353 (2022)
Global Navigation Satellite System (GNSS) radio occultation (RO) provides plentiful sounding profiles over regions lacking conventional observations. The Gridpoint Statistical Interpolation (GSI) hybrid system for assimilating RO data is integrated i
Externí odkaz:
https://doaj.org/article/7c2d81de018a44eb977bcc5824263d3b
Autor:
Yonghan Choi, Shu‐Hua Chen, Chu‐Chun Huang, Kenneth Earl, Chih‐Ying Chen, Craig S. Schwartz, Toshihisa Matsui
Publikováno v:
Journal of Advances in Modeling Earth Systems, Vol 12, Iss 4, Pp n/a-n/a (2020)
Abstract This study evaluates the impact of assimilating moderate resolution imaging spectroradiometer (MODIS) aerosol optical depth (AOD) data using different data assimilation (DA) methods on dust analyses and forecasts over North Africa and tropic
Externí odkaz:
https://doaj.org/article/c405c887edf44f3ca72dbeca123d434f
Publikováno v:
Remote Sensing, Vol 10, Iss 4, p 642 (2018)
This paper evaluates the use of precipitation forecasts from a numerical weather prediction (NWP) model for near-real-time satellite precipitation adjustment based on 81 flood-inducing heavy precipitation events in seven mountainous regions over the
Externí odkaz:
https://doaj.org/article/01c93c099e3b4d099c5fa30080fe85ed
Publikováno v:
Weather and Forecasting. 38:401-423
Herein, 14 severe quasi-linear convective systems (QLCS) covering a wide range of geographical locations and environmental conditions are simulated for both 1- and 3-km horizontal grid resolutions, to further clarify their comparative capabilities in
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b9ba6326725089e1ca939be06da9e39
https://doi.org/10.1175/waf-d-22-0112.1
https://doi.org/10.1175/waf-d-22-0112.1
Autor:
Brett Roberts, Adam J. Clark, Israel L. Jirak, Burkely T. Gallo, Caroline Bain, David L. A. Flack, James Warner, Craig S. Schwartz, Larissa J. Reames
Publikováno v:
Weather and Forecasting. 38:99-123
As part of NOAA’s Hazardous Weather Testbed Spring Forecasting Experiment (SFE) in 2020, an international collaboration yielded a set of real-time convection-allowing model (CAM) forecasts over the contiguous United States in which the model config
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::28bad8b0e56b1da4c2f77bdecae62264
https://doi.org/10.1175/waf-d-21-0211.1
https://doi.org/10.1175/waf-d-21-0211.1
Autor:
Craig S. Schwartz, Jonathan Poterjoy, Glen S. Romine, David C. Dowell, Jacob R. Carley, Jamie Bresch
Publikováno v:
Weather and Forecasting. 37:1259-1286
Nine sets of 36-h, 10-member, convection-allowing ensemble (CAE) forecasts with 3-km horizontal grid spacing were produced over the conterminous United States for a 4-week period. These CAEs had identical configurations except for their initial condi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::07a274f013d56cde0e2fce2d0253fd06
https://doi.org/10.1175/waf-d-21-0165.1
https://doi.org/10.1175/waf-d-21-0165.1
Autor:
Ryan A. Sobash, David John Gagne, Charlie L. Becker, David Ahijevych, Gabrielle N. Gantos, Craig S. Schwartz
Publikováno v:
Monthly Weather Review.
While convective storm mode is explicitly depicted in convection-allowing model (CAM) output, subjectively diagnosing mode in large volumes of CAM forecasts can be burdensome. In this work, four machine learning (ML) models were trained to probabilis
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c4cb23253f9c7a118a4f8786cd75e619
https://doi.org/10.1175/mwr-d-22-0342.1
https://doi.org/10.1175/mwr-d-22-0342.1
Autor:
Jonathan J. Guerrette, Zhiquan Liu, Chris Snyder, Byoung-Joo Jung, Craig S. Schwartz, Junmei Ban, Steven Vahl, Yali Wu, Ivette Hernandez Banos, Yonggang G. Yu, Soyoung Ha, Yannick Tremolet, Thomas Auligne, Clementine Gas, Benjamin Menetrier, Anna Shlyaeva, Mark Miesch, Stephen Herbener, Emily Liu, Daniel Holdaway, Benjamin T. Johnson
An ensemble of three-dimensional ensemble-variational (En-3DEnVar) data assimilations is demonstrated with the Joint Effort for Data assimilation Integration (JEDI) with the Model for Prediction Across Scales – Atmosphere (MPAS-A) (i.e., JEDI-MPAS)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a9e843ed7a836ce650350923a6e0bf91
https://gmd.copernicus.org/preprints/gmd-2023-54/
https://gmd.copernicus.org/preprints/gmd-2023-54/
Publikováno v:
Weather and Forecasting. 36:379-405
Using the Weather Research and Forecasting Model, 80-member ensemble Kalman filter (EnKF) analyses with 3-km horizontal grid spacing were produced over the entire conterminous United States (CONUS) for 4 weeks using 1-h continuous cycling. For compar
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::95886bcdc57ef613254f00e7deb6e142
https://doi.org/10.1175/waf-d-20-0110.1
https://doi.org/10.1175/waf-d-20-0110.1
Publikováno v:
Weather and Forecasting. 35:1981-2000
A feed-forward neural network (NN) was trained to produce gridded probabilistic convective hazard predictions over the contiguous United States. Input fields to the NN included 174 predictors, derived from 38 variables output by 497 convection-allowi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9663142f669c2f2b382b3be1e1121743
https://doi.org/10.1175/waf-d-20-0036.1
https://doi.org/10.1175/waf-d-20-0036.1