Prediction and Predictability of Tropical Cyclones over Oceanic and Coastal Regions and Advanced Assimilation of Radar and Satellite Data for the Navy Coupled Ocean-Atmosphere Mesoscale Prediction System
Ming Xue1,2, Guifu Zhang1,2, Keith Brewster1 and Fanyou Kong1
1Center for Analysis and Prediction of Storms, University of Oklahoma
2School of Meteorology, University of Oklahoma
Funded by Office of Navy Research via Defense EPSCoR Program
10/1/2009 - 7/2012.
Tropical cyclones (TCs), including typhoons and hurricanes, are among the most costly forms of natural disaster. "Tropical cyclones ... continue to be the most disruptive and devastating peacetime threat affecting operations within the USPACOM AOR (U.S. Pacific Command Area of Operations)", quoting Captain John O'Hara of U.S. Navy. Despite their huge impact, the prediction of hurricane intensity has seen little improvement over the past two decades. Studies have shown that resolutions on the order of 1 km are required to capture inner-core structure and rapid intensity changes. Such high-resolution predictions require comparably high-resolution observations to initialize, while at the same time, the growth of convective scale error on the hurricane predictability requires systematic study. Recent studies have also indicated that the rapid growth of small amplitude errors in convective unstable regions can seriously limit the predictability even at the larger scales, and the understanding of error growth dynamics in TCs are essential for designing an effective ensemble prediction system. Among all observation platforms, radar is about the only observing technology that can provide measurements on the inner core structures of TCs. Satellite observations provide much greater coverage over the oceans but their data, especially those that can observe through cloud and precipitation regions, are generally under-utilized in numerical weather prediction (NWP) models.
In this proposal, a group of world-leading scientists with unique experience and expertise in cutting-edge very-high-resolution modeling, advanced assimilation of non-conventional observations including those of routine and specially deployed radars, convective-ensemble prediction and related process studies, and in radar meteorology and theory, will work as a team to address TC structure and intensity prediction improvement problem by (a) developing and testing advanced data assimilation (DA) capabilities for use by the Navy's COAMPS model and other community mesoscale prediction systems; and (b) by studying the effects of DA and initial condition and model errors at the convective scales on the predictability of TCs, which will in turn provide guidance to optimal ensemble prediction system design and DA improvement.
The ensemble Kalman filter (EnKF) DA system developed by this team will be significantly enhanced to assimilate additional airborne, shipboard, as well as other types of ground-based Doppler weather radar data. Available dropsonde, driftsonde, satellite track wind, sea surface QuikScat wind, Doppler lidar wind, and routine observational data will be assimilated together. The effort will also involve the development of new observation operators for radar and satellite data. Cases from major field experiments, the Tropical Cyclone Structure-2008 (TCS-08) over western Pacific, CBLAST and RAINEX over the western Atlantic, for which rich sets of special observations are available will be used as the test cases. We will perform systematic predictability studies that emphasize the understanding of convective-scale errors and uncertainties in both the initial conditions and the prediction models themselves on the predictability of hurricane structure and intensity. Initial condition uncertainties derived from both idealized perturbations and realistic perturbations provided by DA systems will be examined. The error growth and error propagation across scales, in the presence of model errors, will be studied in controlled settings.
The proposal project helps fill some gaps of the Navy, DoD and NOAA's weather forecasting research and development. The research will accelerate our nation's capability to accurately predict hurricane intensity, thereby potentially reducing hurricane-related losses through better preparedness and response. Reduction in the uncertainty in track and intensity forecasting can directly translate into huge economic savings. The project will directly contribute to Navy's goal of reducing TC structure and intensity prediction error by 50% within a decade. The software developed has a direct path of transition to Navy's operations.