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
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
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
Int. & Winds - Data
•
n > 2600 cases … 5x
more than before
Data
&
– global dataset for intensity estimation
 1999-2004 for AL, EP; 2002-04 for SH, WP; 2003- 45 cases at Cat-5
level
04 for CP, IO
– for wind radii, used only cases with recon
12 hrs prior
Atlantic
32.1%
West Pacific
33.7%
Central Pacific
0.2%
Southern
Hemisphere
7.2%
Indian Ocean
1.4%
East Pacific
25.4%
2x as many cases
as before…
34: n=255
50: n=170
64: n=120
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
• Model selection
– minimize MAE of developmental and cross-validated
datasets
–  = 0.01 for intensity models,  = 0.05 for radii models
Intensity - Results
• MSW
– 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
Intensity Results – Ivan Example
160
140
Old AMSU Est
Hurricane Ivan Example (n=25)
New AMSU Est
MSW (kt)
120
Best Track
100
80
60
40
20
0
090317 090409 090505 090610 090707 090810 090923 091011 091200 091223 091409 091500 091600
Date and Time
New MAE = 15.4 kt
New RMSE = 18.0 kt
Old MAE = 18.7 kt
Old RMSE = 21.3 kt
AMSU Wind Radii Results
35
34 MAE - new
30
NW
25
NE
34 MAE - old
20
50 MAE - new
15
10
50 MAE - old
5
64 MAE - new
0
64 MAE - old
SW
SE
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
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
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)