Overview of HFIP FY10 activities and results

Transcription

1 Overview of HFIP FY10 activities and results Bob Gall HFIP Annual Review Meeting Miami Nov 9, 2010

2 Outline In this presentation I will show a few preliminary results from the summer program. More detail should come out in the team reports The second talk later this morning will outline changes to the management of Stream 1.5 On Wed we will discuss next steps for the HFIP program

3 Recent HFIP Activities/Results A 20 member low resolution GFS (T256~60 km) ensemble using an EnKF DA system showed a 20% improvement over the higher resolution operational GFS using GSI DA at the longer lead times The GFS ensemble appears to be providing good predictions of genesis at lead times of several days. This statistic needs to be verified Mike may have some results A multi model ensemble has been run twice per day on each storm this season (Oper. HWRF, Oper. GFDL, TC-COAMPS, AHW, FSU ARW, and experimental GFDL). Ensemble mean is bias corrected using retro resulkts from the strream 1.5 runs and the oprational archieve. In addition there is preliminary results from the Correlation Based Consensus proposed by Krish Initial assessment is very good Overall statistics will be available at end of season Stream 1.5 runs being made available to forecasters in real-time (consists of AHW at 1 km and the experimental GFDL at 7.5 km) Several experiments are being conducted on advanced data assimilation (using all available aircraft data) and alternate initialization systems by HRD Some statistics presented in the team reports?

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6 Global statistics, GFS/EnKF vs. ECMWF (ensemble statistics, 5 June to 21 Sep 2010; all basins together) GFS/EnKF competitive despite lower resolution (T254 vs. ECMWF s T639). GFS/EnKF h spread than error this year, more similar last year. Is this due to this year s T254 vs. 6 last year s T382?

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9 FORECASTED HURRICANE COUNT FORECASTED TROPICAL STORM COUNT

10 The GFDL Ensemble An Ensemble has been constructed from the GFDL Operational model with initial conditions defined as follows: 1. GPA - Unbogussed run 2. GPB - GFD5 with no asymmetries 3. GPC - GFD5 with old environmental filter 4. GPD - Increase storm size (ROCI-based) by 25% 5. GPE - Decrease storm size (ROCI-based) by 25% 6. GPF - All wind radii increased by 25% 7. GPG - All wind radii decreased by 25% 8. GPH - This combines the filter and size criteria of GPC and GPF 9. GPJ - This combines the filter and size criteria of GPC and GPG 10.GPK - For small storms sets the min RMAX to 45 km (in GFD5 it is 25km) 11. GP0 - Control run.--essentially the operational GFDL GFMN - The ensemble mean of the 10 perturbed members. This model was run for most of the 2010 season. Results will be presented later in the meeting

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15 Error (kt) Error (nm) 1200 Ike (2008) Track Errors ARFS Hurricane Ike (2008) Sep GFDL HWRF COTC AHW1 GFD5 ENSM Forecast Hour CBC Ike (2008) Intensity Errors 45 ARFS GFDL HWRF COTC AHW GFD5 ENSM CBC Forecast Hour

16 PSU ARW-EnKF Assimilating Airborne Radar OBS Mean Absolute Error and Ensemble Spread for all 56 cases from 2008 A1PS: PSU 1.5km single forecast initialized with EnKF analyses A4PS: PSU 4.5km single forecast initialized with EnKF analyses P400: ensemble forecast mean of 30 members in 4.5km resolution PSTD: averaged ensemble spread of P400

17 HFIP Intensity Baseline VT (h) N OFCL PRCL BASE

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19 PSU ARW-EnKF Assimilating Airborne Radar OBS Mean Error and Ensemble Spread for all 56 cases from 2008 A1PS: PSU 1.5km single forecast initialized with EnKF analyses A4PS: PSU 4.5km single forecast initialized with EnKF analyses P400: ensemble forecast mean of 30 members in 4.5km resolution PSTD: averaged ensemble spread of P400 number on the top: sample number of cases for HWRF and EnKF

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23 For earlier forecasts the ensemble predicted for 1200Z September the following probabilities: 17/23 36 hour lead time 15/23 60 hour lead time 10/23 84 hour lead time

24 Model Descriptions for Mesoscale Models for ensemble forecasts Models Nesting Horizontal resolution (km) Vertical levels Cumulus Parameterizati on Microphysic s PBL Land Surface Radiation Initial and boundary conditions Initialization HWRF HWRF 2 27/9 HWRF /4.5 HWRF-X HRD version of HWRF HWRF-x 2 9/3 43 Simplified Arakawa Schubert 42 Simplified Arakawa Schubert 42 Simplified Arakawa Schubert Ferrier Ferrier GFS Non- Local PBL GFS Non- Local PBL GFDL Slab Model GFDL Slab Model Schwarzkopf and Fels (1991) (longwave) / Lacis and Hansen (1974) (shortwave) Ferrier GFS scheme NCEP LSM RRTM (longwave) / Dudhia (shortwave) GFS GFS GFS Advanced vortex initialization that uses GSI 3D-var assimilation of Doppler radar data to run in development parallel. HWRF WRF ARW (NCAR) AHW1 2 12/4 36 New Kain Fritsch (12 km only) WSM5 YSU 5-layer thermal diffusion soil model RRTM (longwave) / Dudhia (shortwave) GFS EnKF method in a 6-hour cycling mode COAMPS-TC COTC 3 45/15/5 (15/5 km following the storm) 40 Kain Fritsch Explicit microphysics (5 class bulk scheme) Navy 1.5 order closure Force and restore slab land surface model Harshvardardet et al. (1987) NOGAPS 3D-Var data assimilation with synthetic observations GFDL GFDL GFD5 3 30/15/ Arakawa Schubert Ferrier GFS Non- Local PBL Slab Model Schwarz-kopf- Fels scheme GFS GFDL synthetic bogus vortex WRF ARW AFRS 2 12/4 27 Simplified Arakawa Schubert WSM5 YSU 5-layer thermal diffusion soil model RRTM (longwave) / Dudhia (shortwave) GFS (initial and boundary condition) GFS

25 Observed increment values (Lat, Lon, Int) for each lead time Correlation based model ensembles Model increment forecasts (Lat, Lon, Int) for each lead time Correlation coefficients for each model for Lat, Lon, Int at each lead time Normalize the coefficients using available member models for Lat, Lon, Int at each lead time Training phase 2008 and 2009 storm cases (Total 164 cases) The storm to be forecasted is taken out (if it is in the training period) to calculate the correlation coefficients Utilize the above coefficients during the forecast phase and construct a new forecast

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34 Global statistics, GFS/EnKF vs. ECMWF (ensemble statistics, 5 June to 21 Sep 2010; all basins together) GFS/EnKF competitive despite lower resolution (T254 vs. ECMWF s T639). GFS/EnKF h spread than error this year, more similar last year. Is this due to this year s T254 vs. 34 last year s T382?