Future Plans

Refine Machine Learning: Investigate optimal pressure level to use as input Investigate use of neural network Add additional input parameters from retrieved wind field Investigate use of ATMS instead of AMSU (Fig. 4 and 5) Using MIRS processing system from ATMS for T and q

retrievals instead of statistical retrievals

Add VIIRS Image Processing: Use microwave storm center location to sub-sect VIIRS data Find center estimate based on image gradient features in

VIIRS data (Fig. 4 and 5) Spiral patterns fitted to Harris corner/edge detection

locations (Collins) IR image processing method from Ritchie et al. (2011)

Feed estimates into machine learning algorithm to determine final storm center estimate

ReferencesBessho, K. , M. DeMaria, and J. A. Knaff, 2006: Tropical Cyclone Wind Retrievals from the Advanced Microwave Sounding Unit:Application to Surface Wind Analysis. Journal of Applied Meteorology and Climatology, 45, 399-415.

Collins, Robert. “Harris Corner Detector.” Penn State University. Lecture 06. http://www.cse.psu.edu/~rcollins/CSE486/lecture06.pdf

Ritchie, E. A. , G. Valliere-Kelley, M. F. Piñeros, and J. S. Tyo, 2011: Improved tropical cyclone intensity estimation using infrared imagery and best track data. Wea. Forecasting, 27, 1264-1277.

Wimmers, A.J., C.S. Velden, 2010: Objectively Determining the Rotational Center of Tropical Cyclones in Passive Microwave Satellite Imagery. Journal of Applied Meteorology and Climatology , 49, 2013-2034.

Introduction

Only west Atlantic has routine hurricane hunter aircraft for finding storm centers

Satellite data used subjectively to find centers across the globe

Objective center fixing in real-time highly desirable CIMSS ARCHER method (Wimmers et al. 2010)

estimates center from microwave imagery by fitting spiral patterns

Alternate objective method will use machine learning techniques with multi-spectral data from S-NPP

ATMS and VIIRS

Center Fix Method

Features from AMSU (ATMS) fields input to machine learning to provide microwave-only center estimate

AVHRR (VIIRS) data will refine microwave center estimate using image processing and machine learning

Microwave Center Method

Select area around extrapolated position Area size based on average error between the

extrapolated position and the true center Used ~0.5 degrees in either direction from the

extrapolated position Each grid cell in the selected area represents a single

row of data: Pressure Laplacian of pressure Distance from min pressure in selected area Value indicating if the cell contains the true storm

center Perform Linear Discriminant Analysis (LDA), and

Quadratic Discriminant Analysis (QDA) 40,125 grid cells selected for training and divided

into two classes Class1: No Storm Center Present Class 2: Storm Center is Present LDA and QDA, after training, provide a function for

each class indicating how probable a new grid cell is to belong to each class

Grid cell most probable to be the storm center is found for each satellite image (Fig. 2)

The distance between the selected grid cell and the true storm center position is measured for verification of the algorithm (Fig. 3)

Machine Learning Techniques for Tropical Cyclone Center Fixing using S-NPPRobert DeMaria

1, Charles Anderson

2

1NOAA/NESDIS Regional and Mesoscale Meteorology Branch (RAMMB), Ft. Collins, CO 2

Department of Computer Science, Colorado State University (CSU), Ft. Collins, CO

Data

Initial development with AMSU from POES as ATMS proxy

AMSU statistical retrievals used to provide T and moisture profiles

Hydrostatic and nonlinear balance constraints provide geopotential height (Z) and wind field (Bessho et al. 2006)

Standard levels 1000 to 100 hPa Data includes 2,021 Atlantic TC cases from 2006 to

2011 Extrapolated position available at time of satellite

image creation “Best track” data used as truth for training/testing Fig. 1 shows example 700 hPa Z field from AMSU Visible and IR window AVHRR data collected for

AMSU cases VIIRS Day/Night and IR I05 bands collected for

ATMS cases

Figure 1. Hurricane Katia 04 Sept 2011 1210 UTC

Preliminary Results

Mean Error Using LDA: ~0.45 degrees Mean Error Using QDA: ~0.40 degrees

11% improvement using QDA

Figure 2. Sample Probability Field With Maximum Probability Marked

Figure 3. Distance errors of the center estimates for a testing dataset

Figure 4. MIRS wind and geopotential height retrievals and VIIRS visible image for Hurricane Leslie 09 Sept 2012 1723 UTC

Figure 5. MIRS wind and geopotential height retrievals and VIIRS visible image for Hurricane Leslie 02 Sept 2012 1657 UTC