1. Validation of CIRA Tropical Cyclone Algorithms
Julie Demuth, Mark DeMaria, John Knaff,
Kotaro Bessho, Kimberly Mueller, and Ray Zehr
CoRP Satellite Calibration and Validation Symposium
14 July 2005

2. Outline AMSU intensity and wind radii estimation
M. DeMaria, J. Demuth, J. Knaff
new datasets
different methods
new estimation models
AMSU 2-D surface wind retrieval
K. Bessho, M. DeMaria, J. Knaff
IR wind structure estimation
M. DeMaria, K. Mueller, J. Knaff

3. AMSU Intensity and Wind Radii
In general…
derive ~20 parameters from AMSU data
statistically relate them to dependent data (from extended best track) using MLR
develop algorithms to estimate TC intensity (MSW, MSLP) and axisymmetric 34-, 50-, 64-kt wind radii
use axisymmetric wind estimates with modified Rankine vortex model to estimate winds in NE, SE, SW, NW quadrants relative to TC center

4. Int. & Winds - Data Data global dataset for intensity estimation
n > 2600 cases … 5x more than before
&
45 cases at Cat-5 level
Data
global dataset for intensity estimation
for AL, EP; for SH, WP; for CP, IO
for wind radii, used only cases with recon 12 hrs prior
Indian Ocean
1.4%
Southern
Hemisphere
7.2%
Central Pacific
0.2%
East Pacific
25.4%
West Pacific
33.7%
Atlantic
32.1%
2x as many cases as before…
34: n=255
50: n=170
64: n=120

5. Int. & Winds - Methods Added 4 variables to pool
tmax2, clwave2, tmax*clwave, p600
Using “best subsets” MLR technique
tests all possible models with up to some N number of independent variables…we chose N=15
Cross-validation
every model tested with 80/20 scheme run 1000 times
no longer using backward stepwise regression for model selection and jackknife for validation
Model selection
minimize MAE of developmental and cross-validated datasets
 = 0.01 for intensity models,  = 0.05 for radii models

6. Intensity - Results MSW MSLP: NEW: R2=78.7%, MAE=10.8 kt
OLD: R2=76.4%, MAE=11.5 kt
MSLP:
NEW: R2 = 80.2%, MAE = 7.8 hPa
OLD: R2 = 76.4%, MAE = 8.9 hPa

7. Intensity Results – Ivan Example
Hurricane Ivan Example (n=25)
New MAE = 15.4 kt
New RMSE = 18.0 kt
Old MAE = 18.7 kt
Old RMSE = 21.3 kt

8. AMSU Wind Radii Results

9. AMSU 2-D Surface Winds Quick summary…
use nonlinear balance equation (Charney, 1955) to estimate 3-D wind field from AMSU data
compare AMSU-derived nonlinear balance winds at 850 hPa with QuikSCAT and H*Wind surface wind analyses
AMSU wind speeds at 850 hPa linearly related to surface wind speeds
characteristic biases of wind direction between AMSU and Quik SCAT or H*Wind  
develop algorithm to convert 850 hPa to surface winds

10. IR Wind Structure Quick summary…
Use IR data to develop algorithms that estimate RMAX and V182 via MLR
Use these estimates with modified Rankine vortex model to estimate symmetric tangential wind profile
Add storm motion-derived wind asymmetry to reconstruct entire 2-D wind field

11. Sources of More Info Demuth et al. 2004 (JAM)
Demuth et al. (follow-up note submitted to JAM)
Bessho et al. (submitted to JAM)
Mueller et al. (submitted to Wea. Forecasting)