Dr. Yunheng Wang is a Research Scientist at the Cooperative Institute for Mesoscale Meteorological Studies (CIMMS) 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) experiments at convection-allowing scales for the NOAA Hazardous Weather Testbed (HWT) in 2018 and 2019 respectively. Dr. Wang is also actively involved in the development of the standalone regional FV3 model and the Joint Effort for Data assimilation Integration (JEDI) system.

Professional History

Dr. Wang received his B.Sc. in Meteorology from the Nanjing Institute of Meteorology (Nanjing University of Information Science & Technology, Now). After received his M.S. (2000) in Atmospheric and Oceanic Science from the The University of Maryland, he changed his field of study to Computer Science and received his M.S. (2003) and Ph.D. (2007) from the University of Oklahoma, specializing in scientific computing and digital filters. Dr. Wang joined the Center for Analysis and Prediction of Storms (CAPS) at the University of Oklahoma in 2002 as a scientific programmer. While pursuing a doctorate degree at the University Oklahoma, he continued working at CAPS as a software engineer. He then joined the Warn-on-Forecast team at CIMMS/NSSL since September 2015.

While working at CAPS, Dr. Wang was one of the main developers for the Advanced Regional Prediction System (ARPS) and the ARPS code manager. He designed and implemented the parallel algorithms for the 3DVAR system and the EnKF system in the ARPS system, which made them possible to perform high resolution (1 to 3 km) data assimilation and prediction experiments over the CONUS domain using about 150 NEXRAD (Next Generation Weather Radar) observations for severe weather forecasting. He also developed software interfaces that connected these advanced storm-scale data assimilation systems to other forecasting models, including the Weather Research and Forecasting (WRF) model, the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS),  and the NOAA Nonhydrostatic Multiscale Model on the B-grid (NMMB). So that the CAPS scientists could conduct real-time experiments within a multiple-model ensemble forecast framework for high-impact weather events during the NOAA HWT spring experiments.

After joined the WoF teams at CIMMS/NSSL, one of Dr. Wang’s main research priorities is to develop and improve a variational and EnKF hybrid Data assimilation (DA) system for the Warn-on-Forecast System (WoFS). The hybrid WoFS has been enhanced with several research works, including the direct assimilation of radar reflectivity, the assimilation of satellite derived cloud water path, satellite lightning observations and radar-derived pseudo water vapor as well as studying of dual-pol radar observations. Besides paper publishing with these studies, the hybrid DA system has been used to conduct near real-time experiments during the HWT spring periods since 2017.

Dr. Wang is one member of the NOAA UFS release team. He designed the HWT experiments locally at NSSL to evaluate the finite-volume cubed sphere model (FV3) for severe weather forecasting. The NSSL FV3 experiments included global nesting FV3 forecasts and standalone regional forecasts respectively. Both the NSSL-FV3 systems (global nesting and standalone regional version) performed the best according to subjective ratings and quantitative evaluations among all of the FV3 members in 2018 and 2019 HWT experiments respectively. Dr. Wang is also one developer for the regional FV3 workflow and the JEDI system.