Current Research Areas
Major Research Topics
Data Assimilation and Ensemble forecasting
- Numerical weather prediction
- Ensemble Kalman filtering (EnKF) data assimilation
- Four-dimensional variational data assimilation
- Satellite and radar data assimilation
- Predictability of mesoscale systems,including tropical cyclones
- Observing system simulation experiments
- Mountain terrain atmospheric modeling
- Mesoscale convective systems and precipitation
- Atmospheric boundary layer
- Interactions between mesoscale convective systems and MJO
- Big data and machine leaning in NWP
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 primary 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 stochastic physics ensemble methods).
Big Data and Machine Learning in NWP
Along with advancements in observing system, computer power, and high-resolution NWP model simulations and ensemble forecasting, NWP becomes a big data problem. Our research is expanding to explore the applications of artificial intelligence, especially machine learning in data assimilation studies and ensemble weather forecasting.
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
Taking advantage of Utah's location in Intermountain West, our study focuses 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, and comparison of different techniques (e.g., 4DVAR, ensemble Kalman filtering, 3DVAR) for their abilities in analyses and forecasts over the regions of complex terrain.
In addition, we explore better understanding, observing, and prediction of cold fog over 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.
In addition, we also use high-resolution numerical simulations with data assimilation to study the interaction between mesoscale convective system and MJO.
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 uses the community Weather Research and Forecasting (WRF) model and its data assimilation systems (e.g., 3DVAR, 4DVAR, EnKF, DART, GSI) 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 group also uses the research version of NCEP operational models such as HWRF, GFS, FV3GFS and 3DEnVAR and 4DEnVAR data assimilation system as research tools.
Publications (Click here for the full list of publications)
The research group maintains an excellent track in the areas of atmospheric data assimilation and mesoscale numerical simulations. Recent developments are highlighted by assimilation of satellite and radar data, high-resolution numerical simulations of the hurricane and frontal systems, model validation and satellite data analysis. Most recent research efforts also included couple land-atmospheric data assimilation, atmospheric boundary layer, numerical simulation of high-impact weather systems (e.g., landfalling hurricanes, mesoscale convective systems, fog and winter storms), as well as interactions between tropical convection and MJO. We have the broad spectrum of research areas with continuous growth.
Our research laboratory is well-equipped by personal computers, four 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 fall 2018, our group has 24 cluster nodes with a total of 512 CPUs to support our research needs. With the availability 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 the University of Utah's Center for High-Performance Computing(CHPC). Also, the group gets external computing recourses through NOAA, NASA, NSF, and other government agencies.