Major Research Topics
Data Assimilation and Ensemble forecasting
- Next-Generation Global Prediction System (NGGPS)
- Hurricane Forecast Improvement Program (HFIP)
- Tropical Cyclone Intensity Experiment (TCI)
- Joint Center for Satellite Data Assimilation (JCSDA)
- NASA Energy and Water Cycle Study (NEWS)
- Years of the Maritime Continent (YMC)
- NASA Convective Processes Experiment (CPEX)
Data assimilation research is focused on making the best use of observations in numerical weather prediction using advanced variational and ensemble data assimilation techniques. Our major research emphases include data assimilation methods, satellite and radar data assimilation and observing system simulation experiments.
Ensemble forecasts quantify uncertainties in weather prediction and estimate risks of particular weather events. Our major research emphases include ensemble-based data assimilation (to reduce the uncertainties) and evaluation of ensemble forecasting methods (to assess the performance of both initial-perturbation and stocastic physics ensemble methods).
The interaction between landfalling hurricanes and the atmospheric boundary layer
Accurate forecasts of the intensity and structure of a hurricane at landfall can save lives and mitigate social impacts. However, among recent efforts in hurricane forecast improvements, few studies have focused on landfal
ling hurricanes. This reflects the complexity of predicting hurricane landfalls and the uncertainties in repres
enting the atmospheric boundary layer conditions in numerical weather prediction (NWP) models. The aim of our research is to study the interaction between landfalling hurricanes and the atmospheric boundary layer using ensemble-based data assimilation.
We incorporate the Doppler radar observations with other available in-situ and satellite data into an ensemble data assimilation system with the community mesoscale weather research and forecasting (WRF) model to address the related science questions.
Mountain Terrain Atmospheric Modeling and Observations
This project studies the predictability of flows over mountainous terrain at mesoscale, in particular, the error growth (i.e., the sensitivity to initial conditions at various lead times), and develop meaningful measures of skill relative to appropriate conditional climatologies (i.e., the skill of capturing specific phenomena when they are supposed to appear; e.g., turbulence generation when a Richardson number criterion is satisfied).
Specifically, our research emphasizes on investigating the sensitivities of model forecasts to input properties (initial conditions and model parameters) and boundary conditions, data assimilation, andcomparison of different techniques (e.g., 4DVAR, ensemble Kalman filtering, 3DVAR) for their abilities in analyses and forecasts over the regions of complex terrain.
4-dimensional atmospheric boundary layer structure for cloud life cycles
The principal objective of our research in this topic is to create realistic estimates of high-resolution (1 km by 1 km horizontal grids) atmospheric boundary layer structure and the characteristics of precipitating convection, including updraft and downdraft cumulus mass fluxes and cold pool properties over a region the size of a GCM grid column from analyses that assimilate the surface mesonet observations of wind, temperature, and water vapor mixing ratio and available profiling data from single or multiple surface stations using advanced data assimilation methods.
Tropical cyclone formation and rapid intensification
The objective of our study is to investigate large-scale environmental conditions, mesoscale phenomena and small scale convective bursts as well as their interactions that are responsible for TC formation and intensity changes. Specific areas include 1) Characterize the intensity of convection over the western Pacific and Atlantic oceans from radar, aircraft and satellite data; 2) Derive an accurate mesoscale environment of convective systems through the assimilation of satellite, radar, lidar and in-situ data; 3) Evaluate the quality of the global forecast system (e.g., NCEP and NOGAPS ensembles) for accurate TC analyses and forecasts; 4) Understand the environmental factors that determine tropical cyclone formation and rapid intensification.
The group mainly uses the community Weather Research and Forecasting (WRF) model and its data assimilation systems as basic research tools. Major research efforts in most recent activities involve the use of various types of satellite and airborne observations from NASA, NOAA and research communities.
The research group maintains an excellent track in the areas of atmospheric data assimilation and mesoscale numerical simulations. Most recent developments are highlighted by assimilation of satellite and radar data, high-resolution numerical simulations of hurricane and frontal systems, model validation and satellite data analysis.
Our research laboratory is well-equipped by personal computers,4 workstations (each has > 16 CPUs and > 64GB memory); and >500 TB hard disk spaces, as well as our own linux cluster nodes that are available all the time for our group members. As of August 2016, our group has a 19 cluster nodes with a total of 240 CPUs to support our research needs. With the available of these computer nodes, we have recently built up a real-time forecast capability. The group members also have access to the large linux cluster (with more than 3000 CPUs) from University of Utah's Center for High Performance Computing(CHPC). In addition, the group gets external computing recourses through NOAA, NASA, NSF and other government agencies.
Publications (Click here for the full list of publications)