1. Applications of JPSS Imagers and Sounders to Tropical Cyclone Track and Intensity Forecasting
Team Lead: Mark DeMaria
NOAA/NESDIS/STAR Fort Collins, CO
Team Members:
Galina Chirokova, Robert DeMaria, Steve Miller, CIRA/CSU
Jack Beven , NOAA/NHC
Chris Velden, Tony Wimmers, UW/CIMSS
Briefing to the JPSS Program Office
January 25th, 2013

2. Project Overview
Two basic methods for improving tropical cyclone forecasts with S-NPP
Assimilate data in numerical forecast models
F. Weng and J. Li JPSS projects
Improve analysis and statistical post-processing forecast products
This project focusses on method 2
Multi-spectral center fix algorithm
ATMS, CrISS, VIIRS
Maximum intensity estimation method
Generalization of operational AMSU methods
Improve statistical-dynamical intensity forecasts using ATMS/CrISS retrievals

3. 1. Multi-Spectral Center Fix Algorithm
Aircraft reconnaissance only available for about 30% of Atlantic tropical storm forecasts
Center fix is usually the first step in the forecast process
Accurate center estimate impacts all downstream forecasts
Better satellite intensity estimates
Better numerical model forecasts

4. Operational Center Fix Methods
Center Location = surface center
Center of circulation
Lowest sea-level pressure
Visible and IR methods – Dvorak
Eye
Parallax
Distinct and inferred center with shear pattern and low-level clouds
Spiral bands and curved cloud lines
Wedge method
Using animation
Low-level cloud motions
Deep layer cloud motions
Ignore cirrus layer cloud motions
Mid-level centers tilted from surface center
Using microwave images
Thick cirrus clouds in visible and IR images obscure features below, used for center location
Thick cirrus clouds in microwave images are more transparent, and the microwave images may often provide better views of features, for improved center locations
Using 3.9-micrometer images at night
Proxy for visible
CIMSS developed automated ARCHER method to fit spiral patterns to microwave imagery

5. New JPSS Center Fix Algorithm
Start with ATMS (and maybe CrISS) T,q retrievals
Use hydrostatic and nonlinear balance equations to diagnose geopotential height and wind fields
Estimate center from Z and wind fields
Refine using VIIRS IR, vis and DNB imagery
Initial testing with AMSU retrievals and AVHRR data
ATMS/VIIRS dataset also being collected
MIRS ATMS retrievals from K. Garrett

6. Hydrostatic Balance Ptop Ztop R = ideal gas constant
dp/dz = -ρg p = pressure
z = height
p= ρRTv ρ = density
P Z g = gravity
 dp/p = -(g/RTv)dz Tv = virtual temperature
Ptop Ztop R = ideal gas constant
Z = Z(x,y,P)
Given T, RH retrieval, Tv can be used to provide
geopotential height (Z) on pressure levels

7. Pressure-Wind Relationships
Hydrostatic integration and ideal gas law give
gZ = Φ(x,y,P)
Approximate form of horizontal momentum equations provides horizontal wind estimates
Symmetric flow – gradient wind
V2/r + fV = ∂Φ/∂r
Asymmetric flow – Nonlinear balance equation
from soundings, u,v = horizontal components of non-divergent wind

8. Initial Test of Sounder Center Fix Algorithm
Use AMSU temperature retrievals
Atlantic Sample
2021 Cases
Hydrostatic/nonlinear balance Z, winds
Simple machine learning algorithms tested
LDA, QDA
Provides estimate of most likely center location
Provides estimate of the importance of predictors
Next step is refinement with vis, IR, DNB

9. Tropical Storm Gordon Example Aug 17 2012 16 UTC
925 hPa geopotential height hPa nonlinear balance winds
AVHRR Visible AVHRR IR Window Channel

10. Contributions to Center Fix Algorithm

11. VIIRS Imagery and Sounder Z

12. ATMS and AMSU Temperature Retrievals for Hurricane Sandy
AMSU ATMS

13. 2. Operational AMSU Tropical Cyclone Intensity Estimation Products
CIRA AMSU intensity and wind structure estimation
Hydrostatic integration of AMSU soundings to give Pmin and Vmax
Statistical bias correction
Also provides radii of 34, 50 and 64 kt winds
Transitioned to NCEP operations in 2005
CIMSS AMSU intensity estimation
Uses 4 channels sensitive to upper level warm core
Eye size parameter (IR data or ATCF) to account for resolution variations
Run in real time at CIMSS, provided to NHC in real time

14. Conversion of AMSU Algorithms to ATMS
CIRA
Supported by PSDI/NDE project
K. Garrett providing MIRS ATMS retrievals to statistically adjust CIRA AMSU algorithm
CIMSS
Supported by JPSS-PGRR project
Radiative transfer model being used to adjust warm core-intensity relationship

15. Evaluation of Min Pressure-Warm Core Relationships

16. 3. Operational Atlantic Intensity Forecast Model Errors (2008-2012)
HWRF, GFDL are regional coupled ocean/atmosphere models
DSHIPS, LGEM are statistical-dynamical models

17. Logistic Growth Equation Model (LGEM)
dV/dt = V - (V/Vmpi)nV
(A) (B)
Term A: Growth term, related to shear, structure, etc
Term B: Upper limit on growth as storm approaches
its maximum potential intensity (Vmpi)
LGEM Parameters:
(t) Growth rate (from shear, instability, etc)
 MPI relaxation rate (constant)
Vmpi(t) MPI (from SST and sounding)
n “Steepness” parameter (constant)
LGEM might be improved by estimating Vmpi(0) and instability
contribution to (0) from ATMS/CrIS soundings.

18. Maximum Potential Intensity Theory (Emanuel 1988, Bister and Emanuel 1998)
Ts, To, k*, k can be estimated from
the SST and a sounding.
Ck/CD= specified ratio of surface
exchange coefficients

19. Maximum Potential Intensity Estimation in Irene’s Environment

20. Summary
Satellite T/RH soundings used to estimate tropical cyclone wind field using hydrostatic and nonlinear balance
ATMS soundings better resolve warm core due to increased horizontal resolution and wider swath than AMSU
Multispectral center fixing algorithm combines ATMS and VIIRS input
CIMSS and CIRA AMSU intensity algorithms being transitioned to ATMS
ATMS/CrISS soundings have potential to improve statistical dynamical intensity forecast models
VIIRS imagery to be demonstrated in 2013 NHC Proving Ground