1. Defining Uncertainty in Hurricane Maximum Surface Wind Estimation
Stephanie Mullins, NOAA Hollings Scholar, University of Louisiana –Monroe; and P. G. Black, C. S. Velden, M. D. Powell, E. W. Uhlhorn, T. L. Olander, A. Burton, and J. L. Beven
Background
Results & Discussion
There have been major advancements in the past 50 years in peak hurricane surface wind speed estimation techniques.
Stepped Frequency Microwave Radiometer (SFMR): new airborne technology measuring sea surface microwave emissions to retrieves simultaneous surface wind speed and rainfall estimates
Calibration of the SFMR system using GPS dropsonde estimates of the 10-m sustained surface wind has been maintained since This calibration has produced consistent estimates of maximum surface winds over a nine year period, providing a consistent hurricane maximum surface wind data base for the first time.
Left: Best Track to SFMR maximum wind speed values. Statistical analysis shows that the Best Track data set tends to overestimate the SFMR maximum surface wind speeds by about 6 ms-1 for tropical storm to category 4 cases, or about one Saffir-Simpson category, while category 5 cases displayed little difference.
Right: SFRM and Dvorak Saffir-Simpson category distribution. Discrepancies in categories 2-4 may be due to eye wall recognition.
SFMR maximum surface winds with ATCF minimum pressure values (black points and dashed line), Best Track winds and pressures (blue points), Dvorak winds and pressures (green points), pressure-wind relationship (red line). P-W relation over estimates SFMR by about 6 ms-1 on average, similar to Best Track estimates.
Can possibly reduce uncertainty in estimates to level of scatter in SFMR vs dropsonde by tuning future Best Track and satellite Dvorak maximum wind estimates to those obtained with the SFMR
Methods
Evaluated maximum wind values in the NHC Best Track archive, derived via the satellite-based Dvorak Technique (DT), the .9 method, and the new SFMR-based method of estimating maximum surface winds from maximum flight level winds to find how closely these estimates match the SFMR maximum surface wind speeds in 53 cases in 17 named storms in
Created scatter plots, bar charts, and cumulative probability distributions to visualize any differences in data sets, including any Saffir-Simpson category discrepancies
Calculated bias, RMSE, percent differences, and p-values for each method relative to the SFMR measurements
Also compared maximum winds computed from observed minimum pressure by the pressure-wind relationship recently fit to Best Track data to Best Track, Dvorak (DT), Powell et al, and SFMR maximum winds
Further Research
Two years of airborne tail Doppler radar data examined to address maximum wind under-sampling concerns; initial results suggest this may only account for 2-3% observed differences.
Investigation into Dvorak-SFMR differences, especially at category 2-4 intensity levels
Much larger sample size necessary to draw more accurate conclusions about this uncertainty, with data from many sources including the AFRC fleet, which will be fully equipped with SFMR technology in 2008
Evaluate intensity change, azimuth, storm motion and asymmetries to find if these effect discrepancies and “clusters” of SFMR points noted here
Consider other pressure-wind relationships
Left: SFMR and Powell et al Saffir-Simpson category discrepancies. Statistical analysis shows Powell et al estimates SFMR wind speeds within ±3 ms-1 (same random error as SFMR to dropsondes).
Right: Cumulative probability functions show distributions of each method relative to the SFMR (black solid): Best Track (blue solid), Dvorak (red solid), 0.9 (blue dashed), and Powell et al (grey dashed).
Photo: Sim Aberson/HRD This research was performed under an appointment to the NOAA Ernest F. Hollings Undergraduate Scholarship Program administered by Oak Ridge Institute for Science and Education for The U.S. Department of Commerce.