Overview of HFIP FY10 activities and results Bob Gall HFIP Annual - - PowerPoint PPT Presentation

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Overview of HFIP FY10 activities and results Bob Gall HFIP Annual - - PowerPoint PPT Presentation

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Overview of HFIP FY10 activities and results Bob Gall HFIP Annual Review Meeting Miami Nov 9, 2010 Outline In this presentation I will show a few preliminary results from the summer program. More detail should come out in the team

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Overview of HFIP FY10 activities and results

Bob Gall HFIP Annual Review Meeting Miami Nov 9, 2010

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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
  • utline changes to the management of

Stream 1.5

  • On Wed we will discuss next steps for the

HFIP program

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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

  • prational 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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Global statistics, GFS/EnKF vs. ECMWF

(ensemble statistics, 5 June to 21 Sep 2010; all basins together)

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GFS/EnKF competitive despite lower resolution (T254 vs. ECMWF’s T639). GFS/EnKF has less spread than error this year, more similar last year. Is this due to this year’s T254 vs. last year’s T382?

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FORECASTED HURRICANE COUNT

FORECASTED TROPICAL STORM COUNT

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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

The GFDL Ensemble

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Paula

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Paula

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200 400 600 800 1000 1200 12 24 36 48 60 72 84 96 108 120 Error (nm) Forecast Hour

Ike (2008) Track Errors

ARFS GFDL HWRF COTC AHW1 GFD5 ENSM CBC 5 10 15 20 25 30 35 40 45 12 24 36 48 60 72 84 96 108 120 Error (kt) Forecast Hour

Ike (2008) Intensity Errors

ARFS GFDL HWRF COTC AHW1 GFD5 ENSM CBC

Hurricane Ike (2008)

Sep 1-14

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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

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HFIP Intensity Baseline

VT (h) N OFCL PRCL BASE 820 1.9 2.2 2.2 12 745 7.2 8.3 7.7 24 667 10.4 11.5 10.1 36 590 12.6 14.2 11.7 48 522 14.6 16.1 13.7 72 415 17.0 17.8 16.0 96 316 17.5 19.3 16.6 120 250 19.0 19.3 17.0

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End

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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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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

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Model Descriptions for Mesoscale Models for ensemble forecasts

Models Nesting Horizontal resolution (km) Vertical levels Cumulus Parameterizati

  • n

Microphysic s PBL Land Surface Radiation Initial and boundary conditions Initialization HWRF HWRF 2 27/9 43 Simplified Arakawa Schubert Ferrier GFS Non- Local PBL GFDL Slab Model Schwarzkopf and Fels (1991) (longwave) / Lacis and Hansen (1974) (shortwave) GFS Advanced vortex initialization that uses GSI 3D-var assimilation of Doppler radar data to run in development parallel. HWRF 2 13.5/4.5 42 Simplified Arakawa Schubert Ferrier GFS Non- Local PBL GFDL Slab Model GFS HWRF-X HRD version

  • f HWRF

HWRF-x 2 9/3 42 Simplified Arakawa Schubert Ferrier GFS scheme NCEP LSM RRTM (longwave) / Dudhia (shortwave) GFS HWRF WRF ARW (NCAR) AHW1 2 12/4 36 New Kain Fritsch (12 km

  • nly)

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

  • rder closure

Force and restore slab land surface model Harshvardardet et al. (1987) NOGAPS 3D-Var data assimilation with synthetic

  • bservations

GFDL GFDL GFD5 3 30/15/7.5 42 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

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Observed increment values (Lat, Lon, Int) for each lead time 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

Utilize the above coefficients during the forecast phase and construct a new forecast Correlation based model ensembles 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

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Paula

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Global statistics, GFS/EnKF vs. ECMWF

(ensemble statistics, 5 June to 21 Sep 2010; all basins together)

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GFS/EnKF competitive despite lower resolution (T254 vs. ECMWF’s T639). GFS/EnKF has less spread than error this year, more similar last year. Is this due to this year’s T254 vs. last year’s T382?