Improving Tropical Cyclone Track and Intensity Forecasting with JPSS imager and Sounder Data

Suomi National Polar-Orbiting Partnership satellite (SNPP) launched in October, 2011, is part of the Joint Polar Satellite System (JPSS), the next generation polar-orbiting operational environmental satellite system. SNPP carries five instruments, including Visible Infrared Imaging Radiometer Suite (VIIRS) and Advanced Technology Microwave Sounder (ATMS). The time scale of tropical cyclone track and intensity changes is on the order of 12 hours, which makes JPSS instruments well suited for the forecasting of these parameters. Two basic methods exist for improving tropical cyclone forecasts with SNPP. First is to assimilate data in numerical forecast models, and second is to improve analysis and statistical post-processing forecast products. Our group is developing two applications focusing on the second approach.

The first application, the Maximum Potential Intensity (MPI) estimate algorithm, is using temperature and moisture retrievals from ATMS in the near storm environment to improve intensity analysis and forecasting. While tropical cyclone track errors have improved dramatically over the past few decades, the ability to forecast intensity changes has improved much more slowly. An especially difficult but very important forecast problem is predicting rapid changes in tropical cyclone intensity. Improving these forecasts is one of the highest priorities within NOAA. Accuracy of both, the Logistic Growth Equation Model (LGEM), the most accurate of the statistical models over the past few years, and the Rapid Intensification Index (RII) tool critically depends on the accuracy of the MPI estimate. Operational versions of LGEM and RII use statistical MPI calculated from Sea Surface Temperature (SST) only. We investigate the use of ATMS-MIRS retrievals as input into the MPI algorithm to improve RII and LGEM forecasts. The MPI algorithm was adapted for use with ATMS temperature and moisture profiles. Preliminary estimates for the Atlantic basin show up to 3.1% Brier Skill Score increase in RII estimates. Newly available data are being incorporated into existing intensity estimation techniques and into LGEM to improve their performance. Results of improved MPI estimates for the Atlantic, as well as the East and West Pacific Basins, will be presented together with a discussion of implications for LGEM forecast performance.

The second application, the multi-spectral center fix algorithm, uses ATMS and VIIRS data for improving center location estimates of tropical cyclones. Center fix is usually the first step in the forecast process. The accurate center estimate impacts all downstream forecasts, providing better satellite intensity estimates and better numerical model forecasts. Currently, most existing operational center fix methods are subjective, and little investigation has been made into the use of objective techniques. The goal of this project is to improve real-time estimates of tropical cyclone centers using machine learning methods in conjunction with newly available satellite data. Preliminary results from using pressure estimates only from AMSU statistical temperature retrievals for the Atlantic, for the years 2006-2011 (total of 2012 cases), showed 10% improvement in accuracy in comparison to the best storm center estimates available in real time. The analysis is being improved by using ATMS data together with multi-spectral imagery from VIIRS, including the low-light imager. Results of the improved analysis will be presented together with the discussion of the possible forecast improvements.

Disclaimer: The views, opinions, and findings contained in this article are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration (NOAA) or U.S. Government position, policy, or decision.