Comparison of Airborne SFMR, Best Track and Dvorak ADT Maximum Surface Wind Estimates in Atlantic Tropical Cyclones Peter G. Black (1), Stephanie Mullins (2), Chris S. Velden (3), Mark D. Powell (4), Eric W. Uhlhorn (4), Tim L. Olander (3), Andrew Burton (5), and Jack L. Beven (6) 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. Background 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 retrieve simultaneous surface wind speed and rainfall rate estimates Calibration of the SFMR system using GPS dropsonde estimates of the 10-m sustained surface wind has been maintained since 1998 (Uhlhorn and Black, 2003; Uhlhorn et al, 2007) 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. Methods Results & Discussion Under Sampling Issue SFMR maximum surface winds with ATCF minimum pressure values (black points and dotted line), Best Track winds and pressures (blue points) with Brown et al, pressure-wind relationship (red line) with Dvorak points (green) in close agreement. SFMR points show bifurcation with two clusters of points indicated by solid and dashed ellipses. SFMR data indicate that a new pressure-wind relationship is needed. Upper-Left: Best Track vs SFMR maximum surface wind speeds. The Best Track winds overestimate the SFMR winds by ~ 6 ms -1 for tropical storm to category 4 cases (10-17%), or about one Saffir-Simpson category. Upper-Right: Dvorak ADT vs SFMR maximum surface wind speeds. Lower-Left: Histogram of Best Track and SFMR case numbers vs Saffir-Simpson storm category. Best Track underestimates number of CAT 1 storms while overestimating number of CAT 4 and 5 storms. Lower-right: Histogram of ADT and SFMR case numbers vs Saffir-Simpson storm category. ADT dramatically underestimates numbers of CAT 2-3 storms, and overestimates CAT 4 numbers. Analyzed a nine-year data base of 53 cases from 17 named Atlantic tropical cyclones (TCs) for Utilized concurrent peak wind estimates from SFMR surface wind data processed and stored at HRD, ‘Best Track’ data base maintained at NHC and Automated Dvorak Technique (ADT) data base processed and stored at CIMSS. A case is defined as peak wind for a complete Fig 4 (‘Alpha’) or ‘Butterfly’ pattern. Created scatter plots, bar charts, and cumulative probability distributions to visualize any differences in data sets, including any Saffir-Simpson category discrepancies. Derived new observed minimum pressure vs SFMR maximum wind relation and compared with existing Best Track and Dvorak (DT) pressure-wind relations. Investigated two-year HRD TA Doppler radar data base ( ) for evidence of aircraft along-track peak Doppler wind sampling bias relative to off-track peak Doppler wind at 500m and 3km altitudes (John Gamache, HRD). Cumulative Distribution Function (CDF) for TC peak winds from SFMR (black solid), Best Track (blue solid), ADT (red solid), 0.9 method (blue dashed), and Powell et al 2008 method (grey-dashed). Best Track positive bias, similar to.9 method, relative to SFMR is evident. ADT underestimate at low winds and abrupt transition to overestimate at high winds is evident. This is likely due to obscuration of eye in IR at intermediate winds and Best Track tuning at high wind Two years of airborne tail Doppler radar data were examined to address maximum wind under-sampling concerns. Initial results suggest this may account for only 2-3% of observed differences between Best Track and SFMR Vmax estimates. Larger sample size is necessary to draw more definitive conclusions about this uncertainty. Build on sample size with data from AFRC WC-130J’s, which will be fully equipped with SFMR technology in (1) Naval Research Lab/SAIC, Inc.- Monterey, (2) NOAA Hollings Scholar, University of Louisiana- Monroe, (3) UW/CIMSS-Madison, (4) NOAA/AOML/HRD-Miami, (5) BoM-Australia, (6) NOAA/NWS/NCEP/NHC (TPC)-Miami