Yunheng Wang

Skip Navigation

Yunheng Wang

White silhouette icon on a crimson background.

Research
Associate III

yunheng@ou.edu
yunheng.wang@noaa.gov
National Weather Center 4374

Education

PhD, Computer Science, University of Oklahoma
MS, Atmospheric Science, University of Maryland
BS, Meteorology, Nanjing Institute of Meteorology

Affiliated CIWRO Team

CIWRO/FRDD Data Assimilation and Modeling Team

About

Dr. Wang is a Research Associate III at the Cooperative Institute for Severe and High-Impact Weather Research and Operations (CIWRO) and NOAA National Severe Storms Laboratory (NSSL). His research interest is high performance computing for numerical weather prediction and data assimilation, especially for high-impact hazardous weather events. He is now mainly involved in the NOAA’s Warn-on-Forecast project (WoF). The purpose of WoF is to increase the lead time and accuracy for hazardous weather and water warnings and forecasts. He developed and is also actively improving a variational and EnKF hybrid data assimilation system for the WoF project. He designed the NSSL finite-volume cubed sphere model (FV3) and Model for Prediction Across Scales (MPAS) experiments at convection-allowing scales for the NOAA Hazardous Weather Testbed (HWT). Dr. Wang is also one of main developers for the MPAS-based Warn-on-Forecast system (MPAS-WoFS).

Research Focus

High performance computing of numerical weather prediction and data assimilation
Radar data application in numerical weather prediction model for severe weather events

CIWRO Research Themes

Mesoscale and Stormscale Modeling R&D
Forecast Applications Improvements R&D

Publications

Chen H, J. Gao, T. Sun, Y. Chen, Y. Wang and J. Carlin, 2024: Assimilation of Water Vapor Retrievals from ZDR Columns Using the 3DVar Method for Improving the Short-Term Prediction of Convective Storms. Mon. Wea. Rev., 152, 1077–1095, https://doi.org/10.1175/MWR-D-23-0196.1.
Qin X, K. Nai, W. Li, N. Snook, Y. Wang and M. Xue, 2024: Improving Tornado Intensity Prediction by Assimilating Radar-Retrieved Vortex Winds after Vortex Relocation. Remote Sensing Data Application, Data Reanalysis and Advances for Mesoscale Numerical Weather Models.
Kerr C, P. Skinner, D. Stratman, B. Matilla, Y. Wang and N. Yussouf, 2024: Limitations of short-term thunderstorm forecasts from convection-allowing models with 3-km horizontal grid spacing. Weather and Forecasting.

Conference presentations

Wang, Y, L. Reames, T. Jones, N. Yussouf and L. Wicker, 2024: The Development of an Experimental Warn-on-Forecast System Using the MPAS dynamic Core and the DART system. 28 January to 1 February, 2024. The 104th AMS Annual Meeting. Baltimore, Maryland USA.
Yussouf N., Y. Wang, L. Reams, T. Jones and C. Schwartz, 2024: Preliminary Results from an Experimental Warn-on-Forecast System Using MPAS Model Core at the NOAA National Severe Storms Laboratory. 28 January to 1 February, 2024. The 104th AMS Annual Meeting. Baltimore, Maryland USA.
Clark A and coauthors, 2024: Activities and preliminary results from the 1st Hybrid NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. 28 January to 1 February, 2024. The 104th AMS Annual Meeting. Baltimore, Maryland USA.
Pan S., J. Gao, Y. Wang and X. Wang, 2024: Development of Weather-Dependent Background Error Structures within a Convective-Scale Variational Radar Data Assimilation System. 28 January to 1 February, 2024. The 104th AMS Annual Meeting. Baltimore, Maryland USA.
Potvin C. and coauthors, 2024: Verification and Comparison of Storm and Storm-Environment Fields in the HRRR, RRFS, and NSSL MPAS Models. The 104th AMS Annual Meeting. Baltimore, Maryland USA.
Clark, A and coauthors, 2024: Evaluations of deterministic and ensemble regional MPAS configurations for severe weather forecasting during the 2024 NOAA/Hazardous Weather Testbed Spring Forecasting Experiment. A. Joint WRF and MPAS Users’ Workshop Program 25 – 28 June 2024, Boulder Colorado USA
Corey P and coauthors, 2024: Storm-Based Verification and Intercomparison of Warm-Season Forecasts from the HRRR, RRFS, C-SHiELD, and NSSL MPAS models. UIFCW.