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

1. P6 Machine Learning Techniques for Tropical Cyclone Center Fixing using S-NPP Robert DeMaria1, Charles Anderson2 1NOAA/NESDIS Regional and Mesoscale Meteorology Branch (RAMMB), Ft. Collins, CO2Colorado State University (CSU), Ft. Collins, CO • Center Fix Method • Features from AMSU (ATMS) fields input to machine learning to provide micowave-only center estimate • AVHRR (VIIRS) data will refine microwave center estimate using image processing and machine learning • Microwave Center Method • Select area around first guess position • Area size based on average error between the first guess position and the true center • Used ~0.5 degrees in either direction from first guess point • 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 LDA, QDA • Problem: Even using the smallest reasonable search area, “no storm center” class outnumbers “storm center” class 24 to 1 • “no storm center” discriminant function always evaluates to be more probable than “storm center” • Grid cell most probable to be storm center found for each satellite image • Difference calculated between the “storm center” discriminant function and the “no storm center” discriminant function • The grid cell with the maximum difference is found for each satellite image • The distances between these grid cells and the true storm center positions are measured • Preliminary Results • Mean Error Using First Guess: • ~0.45 degrees • Mean Error Using LDA: • ~0.45 degrees • Mean Error Using QDA: • ~0.40 degrees • 11% improvement • Introduction • Only west Atlantic has routine hurricane hunter aircraft for finding storm centers • Satellite data used subjectively to find centers across the globe • Object center fixingin 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 • Future Plans • Refine Machine Learning: • Investigate optimal altitude to use as input • Investigate use of neural network • Add additional input parameters from retrieved wind field • Investigate use of ATMS instead of AMSU • 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 find sub-set of VIIRS data • Find center estimate based on imagegradient features in VIIRS data • Spiral patterns fitted to Harris corner/edge detection locations (find Harris detection on line reference) • IR image processing method from Ritchie et al. (2011) • Feed estimates into machine learning algorithm to determine final storm center estimate • Show picture of matching VIIRS and wind field from MIRS from Galina 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 First guess 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 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.   Figure 1. Hurricane Katia 04 Sept 2011 1210 UTC 21.7 N, 58.8 W