Principal investigator: Renate Brummer research team
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- Application of Joint Polar Satellite System ( JPSS) Imagers and Sounders to Tropical Cyclone Track and Intensity Forecasting
Figure 4. Comparison of MPI estimated from the empirical formula used in NHC’ statistical models (Stat MPI), from a theoretical formula with NCEP global model input (GFS MPI) and from a theoretical formula with the ATMS MIRS retrieval input (MPI) for Hurricane Leslie from the 2012 Atlantic hurricane season. Also show are the observed maximum wind (Best Track) and SST from two different analyses, Reynolds weekly (RSST) and Navy NCODA daily (SST). 4-- Apply maximum potential intensity and stability analysis to all MIRS tropical cyclone cases from 2012 and 2013. The stability parameter described in DeMaria (2009) was successfully adapted to using ATMS retrievals as input. The stability parameter is more general than the standard CAPE calculations because it includes the effect of entrainment, the ice phase, and the weight of the condensate on the buoyancy of a lifted parcel. The stability parameter is the vertically averaged vertical velocity (VVAV) of a parcel lifted from the surface to the level where the vertical velocity first becomes negative. Both Bister and Emanuel (1998) Maximum Potential Intensity (MPI) and VVAV were calculated for all 2012 cases in the preliminary MIRS dataset. Both calculations are very sensitive to SST used as input. In order to separate the effect of the SST input from the effect of different atmospheric profiles, as calculated from GFS model and ATMS, all estimates were reprocessed using the same weekly Reynolds SST temperature that was used in the operational version of SHIPS and LGEM. Figures 5a and 5b show the scatter plots of MPI and VVAV with GFS versus ATMS sounding input. The T, q profiles and sea level pressure (SLP) for all calculations were averaged between 200 km and 800 km to represent near-storm environment. The weekly Reynolds SST, used in all calculations, is a single point value at the storm center. The ATMS MPI has positive bias relative to GFS MPI for weaker storms for both Atlantic and East Pacific Basins. For MPI greater than about 100 kt, in some cases GFS MPI is larger than ATMS MPI, and in some cases that relationship is reversed. The reasons for these differences are being investigated. The ATMS VVAV tends to have negative bias in the low VVAV range and small positive bias in the high VVAV range. The differences shown in Fig. 5 indicate that the replacement of the GFS soundings with those from ATMS will have some impact on the LGEM forecasts, since MPI is used in the operational version, and VVAV is used in a new version under development. The related Rapid Intensification Index (RII) will also be impacted by this change. The forecast impact on the RII is being evaluated. 
Figure 5: a) Left panel: Bister and Emanuel (1998) MPI(kt) calculated from ATMS and GFS profiles, with all other parameters identical. b) Right panel: average vertical velocity (m/s) from DeMaria (2009) generalized CAPE calculation. For both a) and b) blue and red dots show Atlantic Basin storms south and north of 30o N, correspondingly, and magenta dots correspond to East Pacific Basin. For both plots the atmospheric profiles, including temperature and moisture profiles, and sea level pressure (SLP) are calculated from either ATMS or GFS, while all other parameters, including SST, are kept identical. T, q, SLP are averaged between 200 and 800 km for all calculations. 5-- Begin investigation of NPP input into the Rapid Intensification Index (RII) Preliminary estimates of the RII changes from ATMS profiles have been obtained. Considerable RII forecast sensitivity to the MPI calculation was found in some cases (not shown). Figure 6 shows the RII forecast change for 2012 Atlantic Hurricane Michael (AL13). Green dots show observed RII index, which is 0 if no RI occurred, and 100% if RI occurred. Red line with stars shows RI forecast based on operational GFS model fields, and blue line with triangles shows RI forecast with MPI calculated from ATMS data. Both forecasts are somewhat late in predicting the rapid intensification period; however, the ATMS version shows improvement by the significantly faster decrease in RII probability after the RI event was completed. The bias of ATMS data is 1.67 compared to 1.87 bias from GFS. 
Figure 6. RII for 25 knots for Hurricane Michael, AL13 2012. Green bars show observed RII index, which is 0 if no RI occurred, and 100% if RI occurred. Red line with stars shows RI forecast based on operational GFS model fields, and blue line with triangles shows RI forecast with MPI calculated from ATMS data. The bias of ATMS data is 1.67 compared to 1.87 bias from GFS. PUBLICATIONS refereed: Application of Joint Polar Satellite System (JPSS) Imagers and Sounders to Tropical Cyclone Track and Intensity Forecasting None - PRESENTATIONS: Application of Joint Polar Satellite System (JPSS) Imagers and Sounders to Tropical Cyclone Track and Intensity Forecasting 1-- Extended Abstracts: Chirokova G., M. DeMaria, R. T. DeMaria, and J.F. Dostalek: Applications of JPSS Imagers and Sounders to tropical cyclone track and intensity forecasting. 2013 EUMETSAT Meteorological Satellite Conference, 19th AMS Satellite Meteorology, Oceanography, and Climatology Conference, September 16-20 2013, Vienna, Austria. 2-- Presentations: Chirokova G., M. DeMaria, R. T. DeMaria, and J. F. Dostalek: Tropical Cyclones thermodynamic analysis using satellite microwave soundings. NCAR/NOAA/CSU TC Workshop, May 16, 2013, Fort Collins, CO. Chirokova G., M. DeMaria, R. T. DeMaria and J.F. Dostalek: Applications of JPSS Imagers and Sounders to tropical cyclone track and intensity forecasting. 2013 EUMETSAT Meteorological Satellite Conference, 19th AMS Satellite Meteorology, Oceanography, and Climatology Conference, September 16-20 2013, Vienna, Austria. Chirokova G, M. DeMaria, and J. Dostalek: Rapid Intensification Index Estimates with ATMS profiles. NCAR/NOAA/CSU Tropical Cyclone Workshop, Jan 8, 2014, Boulder, CO. DeMaria M, J. Knaff, F, Weng, C. Velden, J. Li, C. Rozoff, G, Chirokova, R. DeMaria, J. Beven, and M. Brennan: NOAA Satellite Science Week Tropical Storms/Hurricanes Science Achievements. NOAA Satellite Science Week, 18-22 March 2013. DeMaria M. and R.T. DeMaria: Application of the Computer Vision Hough Transform for Automated Tropical Cyclone Center-Fixing from Satellite data. NCAR-CSU Tropical Cyclone Workshop, Jan 8, 2014, Boulder, CO. DeMaria R. T. and C. W. Anderson: Machine Learning Techniques for Tropical Cyclone Center Fixing using S-NPP”. CoRP Science Symposium, 23-24 July 2013, Madison, WI. Slocum, C., K. D. Musgrave, L. D. Grasso, G. Chirokova, M. DeMaria, and J. Knaff: Satellite applications to hurricane intensity forecasting." CoRP Science Symposium, 23-24 July 2013, Madison, WI. Directory: ftp -> Borger -> CIRA-RAMMB-AnnualReports March2014 ftp -> Federal Communications Commission fcc 12-81 Before the Federal Communications Commission ftp -> Tr-41. 4-03-05-024 Telecommunications ftp -> Message Race Detection for Web Services by an smt-based Analysis ftp -> A proposal submitted to the ftp -> National reports on data buoy activities ftp -> Louisiana barrier island comprehensive monitoring program (bicm) Volume 1: Barrier Shoreline Post-Storm Assessment ftp -> Jcomm expert team on CIRA-RAMMB-AnnualReports March2014 -> Project title: Feature-based Validation of mirs soundings for Tropical Cyclone Analysis and Forecasting CIRA-RAMMB-AnnualReports March2014 -> Project title: Hurricane Forecast Improvement Project (hfip) – cira support to Tropical Cyclone Model Diagnostics and Product Development Download 1.49 Mb. Share with your friends:
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