1. Simulation of the Impact of New Aircraft- and Satellite-Based Ocean Surface Wind Measurements on Estimates of Hurricane Intensity Eric Uhlhorn (NOAA/AOML) R. Atlas (NOAA/AOML), P. Black (SAIC/NRL), C. Buckley (NASA/MSFC), S. Chen (UMiami/RSMAS), S. El-Nimri (UCF), R. Hood (NOAA), J. Johnson (UCF), L. Jones (UCF), T. Miller (NASA/MSFC), C. Ruf (UMich.)

2. Objective: Improving Surface Wind Measurement Accuracy A TC’s intensity is determined by the peak sustained surface wind anywhere in the storm. Current observational practices limit the probability of directly measuring this quantity. –Radial resolution is very high (in situ flight-level and SFMR). –Azimuthal resolution above BL is moderate (Doppler radar). –Azimuthal resolution at surface is poor. If we fill this data gap, can intensity estimates be significantly improved?

3. Hurricane Imaging Radiometer (HIRad): A multi-agency partnership to extend SFMR capability to wide-swath (±60°) imaging From SFMR......to HIRad ±60°

4. Measuring hurricane winds at large incidence angles SFMR has proven the capability at nadir (i.e. zero) incidence angle, but what about at large off nadir angles? Excess Brightness Temperature (K) Current SFMR model function for nadir incidence SFMR measurements in Hurricane Gustav (2008)

5. “Nature” Model Run MM5 1.67 km grid Frances (2004) Provides synthetic “observations” Aircraft flies a typical “alpha” pattern and samples the simulated wind field based on instrument characteristics “Full-up” experiment (OSSE) considers all potentially available data Here, we concentrate on the extension of SFMR to HIRad 70 km wide HIRad swath 20 km alt. SFMR Surface (10 m) Wind Speed (m/s)

6. Radial structure is captured well. Azimuthal structure is unknown, since there are few continuous surface observations available at high wavenumber. A limitation of current sampling strategy (“alpha” pattern). Model Wind Field “Representativeness” How well does the model capture surface wind structure? Vast data void

7. Error Modeling Consider two sources of error –Instrument noise (1 K) –Model function accuracy (5 K) Express errors over a range of expected wind speeds and rain rates Extend SFMR model function error to large incidence angles Consider a four-channel system (4, 5, 6, 6.6 GHz) Instrument NoiseSFMR Model Function Accuracy 0 30 60 90 120 150

8. HIRad Surface Wind Speed Errors (knots) Instrument noise- induced error Model function - induced error

9. HIRad Surface Footprint (20 km alt.) SFMR resolution is maintained to angles ±40° off nadir. Variability in footprint size is taken into account when sampling model wind field.

10. Improving Peak Wind “Observation” Spatial Variability Find peak wind using standard alpha (Fig.4) pattern Initiate pattern at same time (17Z 08/31/2004), but at different initial points (IP) Compare “observed” peak wind to actual (model) peak wind over time of flight pattern 0 45 90 135 180 225 270 315 R M W IP Increasing spatial coverage may decrease the magnitude of underestimate, as well as limit variations.

11. Improving Peak Wind “Observation” Temporal Variability Find peak wind using alpha pattern Initiate pattern at same location (105 nm 225°/SW) hourly over six-hour period Compare observed peak wind with model peak wind over flight period Increasing spatial coverage may improve the observation of intensity change.

12. Summary Hurricane surface winds can be retrieved from brightness temperature measurements at large off-nadir incidence angles. High-resolution simulations of a hurricane can provide adequate synthetic “observations” for testing new instrument capabilities and sampling strategies. HIRad extends SFMR surface wind measurement capability to a wide swath. By increasing azimuthal resolution of the surface wind field, HIRad could potentially: –Reduce the uncertainty in the TC’s intensity estimate –Capture fine-scale details, including Rapid Intensification, with greater accuracy. See poster this evening by Miller et al. for more details/discussion/questions!