Yonghui Weng
RESEARCH ASSOCIATE | Pennsylvania State University
University Park, PA, UNITED STATES
Yonghui Weng's research focus on limited area data assimilation and modelling
Industry Expertise (2)
Research
Education/Learning
Areas of Expertise (2)
Extratropical and Tropical Cyclones
Data Assimilation
Biography
My research focus on limited area data assimilation and modeling. Currently I'm develping a WRF-EnKF real-time hurricane analyses and forecasting system with NOAA P-3 airborne radar observation assimilation with the corporation of HRD under the support of NOAA Hurricane Forecast Improvement Project (HFIP), the project of the PRE-Depression Investigation of Cloud-systems in the Tropics, NSF and TACC. This system had being operated in real-time since the middle of 2008 Atlantic hurricane season on TACC's supercomputer.
Education (3)
Chinese Academy of Sciences: PhD 2010
Chinese Academy of Meteorological Sciences: MS 1997
Nanjing Institute of Meteorology: BS 1994
Links (2)
Media Appearances (3)
Stampede 2 Supercomputer Drives the Frontiers of Science and Engineering Forward
UT News online
2016-06-02
Today, the National Science Foundation (NSF) announced a $30 million award to the Texas Advanced Computing Center (TACC) at The University of Texas at Austin to acquire and deploy a new large-scale supercomputing system, Stampede 2, as a strategic national resource to provide high-performance computing capabilities for thousands of researchers across the U.S.
How bad will a hurricane get? Fly in to find out
Futurity online
2016-05-26
“Hurricane hunters” can improve hurricane intensity predictions by up to 15 percent, new research finds. Prior to this study, no hurricane prediction model incorporated the vast amount of data collected by “hurricane hunters,” which are National Oceanic and Atmospheric Administration or US Air Force airborne reconnaissance missions that fly into hurricanes to collect data.
Early use of 'hurricane hunter' data improves hurricane intensity predictions
Science Daily online
2016-05-25
Data collected via airplane when a hurricane is developing can improve hurricane intensity predictions by up to 15 percent, according to researchers who have been working to put the new technique into practice.
Articles (5)
SMAP L-Band Passive Microwave Observations of Ocean Surface Wind During Severe Storms
IEEE Transactions on Geoscience and Remote Sensing
Simon H Yueh, Alexander G Fore, Wenqing Tang, Akiko Hayashi, Bryan Stiles, Nicolas Reul, Yonghui Weng, Fuqing Zhang
2016 The L-band passive microwave data from the Soil Moisture Active Passive (SMAP) observatory are investigated for remote sensing of ocean surface winds during severe storms. The surface winds of Joaquin derived from the real-time analysis of the Center for Advanced Data Assimilation and Predictability Techniques at Penn State support the linear extrapolation of the Aquarius and SMAP geophysical model functions (GMFs) to hurricane force winds. We apply the SMAP and Aquarius GMFs to the retrieval of ocean surface wind vectors from the SMAP radiometer data to take advantage of SMAP's two-look geometry. The SMAP radiometer winds are compared with the winds from other satellites and numerical weather models for validation. The root-mean-square difference (RMSD) with WindSat or Special Sensor Microwave Imager/Sounder is 1.7 m/s below 20-m/s wind speeds. The RMSD with the European Center for Medium-Range Weather Forecasts direction is 18° for wind speeds between 12 and 30 m/s. We find that the correlation is sufficiently high between the maximum wind speeds retrieved by SMAP with a 60-km resolution and the best track peak winds estimated by the National Hurricane Center and the Joint Typhoon Warning Center to allow them to be estimated by SMAP with a correlation coefficient of 0.8 and an underestimation by 8%-18% on average, which is likely due to the effects of spatial averaging. There is also a good agreement with the airborne Stepped-Frequency Radiometer wind speeds with an RMSD of 4.6 m/s for wind speeds in the range of 20-40 m/s.
Dynamics and Predictability of the Intensification of Hurricane Edouard (2014)
Journal of the Atmospheric Sciences
Erin B Munsell, Fuqing Zhang, Jason A Sippel, Scott A Braun, Yonghui Weng
2017 The dynamics and predictability of the intensification of Hurricane Edouard (2014) are explored through a 60-member convection-permitting ensemble initialized with an ensemble Kalman filter that assimilates dropsondes collected during NASA’s Hurricane and Severe Storm Sentinel (HS3) investigation. The 126-h forecasts are initialized when Edouard was designated as a tropical depression and include Edouard’s near–rapid intensification (RI) from a tropical storm to a strong category-2 hurricane. Although the deterministic forecast was very successful and many members correctly forecasted Edouard’s intensification, there was significant spread in the timing of intensification among the members of the ensemble.
A Multiple-Model Convection-Permitting Ensemble Examination of the Probabilistic Prediction of Tropical Cyclones: Hurricanes Sandy (2012) and Edouard (2014)
Weather and Forecasting
Christopher Melhauser, Fuqing Zhang, Yonghui Weng, Yi Jin, Hao Jin, Qingyun Zhao
2017 This study examines a multimodel comparison of regional-scale convection-permitting ensembles including both physics and initial condition uncertainties for the probabilistic prediction of Hurricanes Sandy (2012) and Edouard (2014). The model cores examined include COAMPS-TC, HWRF, and WRF-ARW. Two stochastic physics schemes were also applied using the WRF-ARW model. Each ensemble was initialized with the same initial condition uncertainties represented by the analysis perturbations from a WRF-ARW-based real-time cycling ensemble Kalman filter. It is found that single-core ensembles were capable of producing similar ensemble statistics for track and intensity for the first 36–48 h of model integration, with biases in the ensemble mean evident at longer forecast lead times along with increased variability in spread. The ensemble spread of a multicore ensemble with members sampled from single-core ensembles was generally as large or larger than any constituent model, especially at longer lead times. Systematically varying the physic parameterizations in the WRF-ARW ensemble can alter both the forecast ensemble mean and spread to resemble the ensemble performance using a different forecast model. Compared to the control WRF-ARW experiment, the application of the stochastic kinetic energy backscattering scheme had minimal impact on the ensemble spread of track and intensity for both cases, while the use of stochastic perturbed physics tendencies increased the ensemble spread in track for Sandy and in intensity for both cases. This case study suggests that it is important to include model physics uncertainties for probabilistic TC prediction. A single-core multiphysics ensemble can capture the ensemble mean and spread forecasted by a multicore ensemble for the presented case studies.
Assimilating Airborne Doppler Radar Observations with an Ensemble Kalman Filter for Cloud-resolving Hurricane Initialization and Prediction: Katrina
Monthly Weather Review
Weng, Y. and F. Zhang
2012 Through a Weather Research and Forecasting model (WRF)-based ensemble Kalman filter (EnKF) data assimilation system, the impact of assimilating airborne radar observations for the convection-permitting analysis and prediction of Hurricane Katrina (2005) is examined in this study. A forecast initialized from EnKF analyses of airborne radar observations had substantially smaller hurricane track forecast errors than NOAA’s operational forecasts and a control forecast initialized from NCEP analysis data for lead times up to 120 h. Verifications against independent in situ and remotely sensed observations show that EnKF analyses successfully depict the inner-core structure of the hurricane vortex in terms of both dynamic (wind) and thermodynamic (temperature and moisture) fields. In addition to the improved analyses and deterministic forecast, an ensemble of forecasts initiated from the EnKF analyses also provided forecast uncertainty estimates for the hurricane track and intensity.
Advanced data assimilation for cloud-resolving hurricane initialization and prediction
Computing in Science and Engineering
Weng, Y., M. Zhang, and F. Zhang
2011 Data assimilation aims to decrease errors in initial conditions of numerical weather prediction models, which are a primary source of uncertainty in hurricane prediction. This study examines the performance of three advanced techniques that assimilate inner-core, high-resolution Doppler radar observations for cloud-resolving hurricane initialization and forecasting for Hurricane Katrina.