Preliminary Analysis of New Operational SFMR During 2004

1. Preliminary Analysis of New Operational SFMR During 2004 Eric W. Uhlhorn University of Miami/RSMAS/CIMAS Peter G. Black NOAA/AOML/HRD Alan S. Goldstein NOAA/NMAO/AOC Validation efforts support through 2004 Hurricane Supplemental

2. Purpose: Perform an initial performance evaluation of the new operational Stepped Frequency Microwave Radiometer (SFMR) during 2004 based on independent surface wind measurements from HRD SFMR and GPS dropwindsondes • Talk Outline • Season summary • Overall SFMR wind and rain inter-comparison (HRD vs. AOC) • Surface wind comparisons with GPS dropwindsondes • Identify sources of measurement differences • Calibration corrections and re-evaluation • Real time processing issues: Hurricane Jeanne at Landfall • Conclusions and future work

3. Summary of 2004 Season • First full season with dual Stepped-Frequency Microwave Radiometers on single aircraft (NOAA-43) • New operational SFMR built by ProSensing, Inc. (AOC/SFMR-A) • Old research SFMR (HRD/SFMR-I) • Development history and performance validation of research SFMR reported in Uhlhorn and Black (2003) • SFMR winds biased high by ~2 m s-1 relative to GPS surface winds • No wind-speed dependence of bias was detected (10 -- 55 m s-1) • Overall uncertainty ~ 3.4 m s-1 • 15 NOAA-43 Flights • Frances (5), Ivan (5), Jeanne (3), Calibration (2) • 3 Landfall flights • Preliminary Analysis Data Resources • Winds, rain rate, Tb estimates from both systems • Over 200 GPS dropwindsonde wind measurements

4. AOC vs. HRD SFMR Surface Wind Comparison – All flights • N=303543 • 10 s avg. (minimum resolution of HRD SFMR) • General AOC SFMR overestimate relative to HRD • Overestimate largest at low winds, decreases at higher winds

5. AOC vs. HRD SFMR Surface Wind Comparison – Distribution of Differences • Mean difference m = +8.0 m s-1 (AOC minus HRD)

6. AOC vs. HRD SFMR rain rates • Rain rates are generally underestimated (relative to HRD SFMR estimates) • HRD SFMR rain rate estimate validation reported in Jiang, H. et al. (upcoming JAS CAMEX issue)

7. SFMR Winds vs GPS Dropwindsonde Surface Wind Estimates • AOC and HRD SFMR 60 s running avg. (for smoothing) • GPS Surface (10 m) estimated from reported MBL (lowest 500 m) • G10 = 0.798*GMBL (Uhlhorn and Black 2003)

8. Distribution of differences from GPS dropwindsonde surface estimates • Differences are defined as SFMR minus sonde

9. Why the difference in winds? • AOC SFMR Brightness temperature Tb measurements • Calibration flight (06/28) at G. of M. Buoy 42003 • Tb estimates relative to Klein-Swift model predicted values (SST=29.4 C, Sal=36.0 ppt, WS=5.5 m s-1) 2400’ Tb estimates within ~5 K of prediction 1200’

10. Why the difference in winds? • AOC and HRD SFMR Brightness temperature measurements • Hurricane Frances (08/30) low winds (< 10 m s-1) HRD SFMR Differences <5 K from model AOC SFMR Differences 0 - 15 K from model

11. Brightness Temperature behavior from low to high winds • Frances 08/30 • Tb differs at low winds, agrees better at high winds • Winds agree better at high winds

12. AOC SFMR Calibration adjustment (offset coefficient) AOC vs. HRD SFMR AOC SFMR vs. GPS Winds

13. Hurricane Jeanne Landfall (09/25) • HRD SFMR transmitting winds in real time • Questionable measurements in north eyewall (0216 – 0236 UTC)

14. Jeanne 09/25 Landfall Tb measurements • Example of RFI contamination @ 5.6 GHz • Median filter (HRD SFMR), which checks Tb measurements against each other, has problems in high rain rates • Effect is to zero-out rain rate, thus attributing all emission to wind • Result is large over-estimate of wind

15. AOC SFMR confirmed bad measurements • Examine “corrected” AOC SFMR surface winds • More intelligent filter (AOC, A. Goldstein) checks Tb measurements against model • Bad measurements are thrown out prior to solving for wind/rain (min 3 of 6 required)

16. Conclusions • New AOC operational SFMR shows good consistency in measurements (exception is issue between calibration flight and first mission) • Lower noise, less uncertainty, than HRD SFMR • Tendency for overestimating winds relative the HRD SFMR (using original calibration coefficients) • Adjusting offset coefficients based on Klein-Swift model improves comparisons, but distributions still do not quite coincide • Both instruments show tendency to underestimate winds >55 m s-1, based on GPS measurements – appears to be real • Need surface measurements in high winds to accurately quantify this behavior • UBLOX GPS dropwindsondes will be essential

17. Future work – What’s next? • Need to revisit calibration • SFMR coefficients may require “tweaking” based on low-wind Tb measurements vs. model predictions (with ProSensing) • Match distributions with HRD SFMR (winds and rain) and GPS sonde estimates • Implement improved error checking (with AOC) • Understand high-wind behavior (with NHC) • Does Tb response flatten at these wind speeds? • Is emissivity/wind model adequate? • Possible new model based on GPS surface wind estimates – need true surface (10 m) measurements • UBLOX GPS dropwindsondes will be invaluable • Examine response in abnormal conditions (“shallow” water, strong surface currents, etc.)

18. Acknowlegements • Funding support from 2004 Hurricane Supplemental • ProSensing, Inc. (Dr. I. Popstefanija) • AOC (Dr. James McFadden) • TPC/NHC (M. Mayfield) • Poster session plugs: • Ivan Popstefanija (ProSensing) • Alan Goldstein (AOC)