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Future Plans  Refine Machine Learning:  Investigate optimal pressure level to use as input  Investigate use of neural network  Add additional input.

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Presentation on theme: "Future Plans  Refine Machine Learning:  Investigate optimal pressure level to use as input  Investigate use of neural network  Add additional input."— Presentation transcript:

1 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  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  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 References 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, Bessho, 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, 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, Collins, Robert. “Harris Corner Detector.” Penn State University. Lecture 06. 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 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 micowave-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)  ???? 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  The distance between the selected grid cell and the true storm center position is measured for verification of the algorithm Machine Learning Techniques for Tropical Cyclone Center Fixing using S-NPP Robert DeMaria 1, Charles Anderson 2 1 NOAA/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 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 retrievals and VIIRS visible image for Hurricane Leslie 09 Sept UTC Figure 5. MIRS wind and geopotential retrievals and VIIRS visible image for Hurricane Leslie 02 Sept UTC


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