Dynamic hurricane season prediction experiment with the ncep cfs
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Dynamic Hurricane Season Prediction Experiment with the NCEP CFS. Jae-Kyung E. Schemm January 21, 2009 COLA CTB Seminar Acknowledgements: Lindsey Long Suru Saha Shrinivas Moorthi. Outline. Description of the CFS experiment Datasets Used IRI Detection and Tracking Method

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Dynamic Hurricane Season Prediction Experiment with the NCEP CFS

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Dynamic hurricane season prediction experiment with the ncep cfs

Dynamic Hurricane Season Prediction Experiment with the NCEP CFS

Jae-Kyung E. Schemm

January 21, 2009

COLA CTB Seminar

Acknowledgements: Lindsey Long

Suru Saha

Shrinivas Moorthi


Outline

Outline

  • Description of the CFS experiment

  • Datasets Used

  • IRI Detection and Tracking Method

  • Analysis of storm activity statistics

  • Evaluation of CFS performance on environmental variables related to storm activity

  • A look at CFS-based predictions for 2008 season

  • Summary and future plan


Cfs t382 hurricane season experiments

CFS T382 hurricane season experiments

  • One of the FY07/08 CTB internal projects

  • AGCM - 2007 operational NCEP GFS in T382/L64 resolution

    LSM - Noah LSM

    OGCM - GFDL MOM3

    3. All runs initialized with NCEP/DOE R2 and NCEP GODAS.

    Initial conditions at 0Z, Apr. 19, 20, 21 and May 15(FY07)

    for 1981-2007. Forecasts extended to December 1.

    4.For May 15 cases, runs in T62 and T126 resolutions also performed.


Datasets

Datasets

  • CFS hindcasts at T382

    • May 15th Initial Condition

      • Output at every 3 hours

      • 1981-2007, 27 years

    • April 19th, 20th and 21st Initial Condition

      • Output at every 6 hours

      • 1981-2008, 28 years

      • More appropriate ICs for CPC Operational Hurricane Season Outlook

  • Observations from the HURDAT and JTWC Best Track Dataset

    • Tropical depressions and subtropical storms are not included in storm counts.


Tropical cyclone detection method

Tropical Cyclone Detection Method

  • Based on method devised by Camargo and Zebiak (2002) at IRI

  • Detection algorithm uses basin-dependent threshold criteria for three variables

    • Vorticity, xthresh

      xthresh = 2sx

    • 10-m Wind Speed, uthresh

      uthresh = ugl + suugl= wind speed averaged over

      all global basins

    • Vertically integrated temp anomaly, Tthresh

      • Tthresh = sT/2

      • Calculated using only warm-core systems


Table of basin thresholds

Table of Basin Thresholds

CFS T382 thresholds for vorticity, surface wind speed, and vertically integrated temperature anomaly for each ocean basin.


Dynamic hurricane season prediction experiment with the ncep cfs

Eight conditions must be met for a point to be considered a tropical cyclone

1.850-hPa relative vorticity > xthresh

2.Maximum 10-m wind speed in a 7x7 box > uthresh

3.SLP is the minimum in the centered 7x7 box

4.Temp anomaly averaged over the 7x7 box and three pressure levels (300, 500, & 700 mb) > Tthresh

5. Temperature anomaly averaged over the 7x7 box is positive at all levels (300, 500, & 700 mb) *

6.Temperature anomaly averaged over the 7x7 box at 300mb > 850mb *

7.Wind speed averaged over the 7x7 box at 850mb > 300mb

8.The storm must last for at least 6 hours.

* Criteria 5 & 6 define a warm core system.


Storm tracking

Storm Tracking

  • Once a point is designated as a tropical cyclone, the cyclone is tracked forward and backward in time to create a full storm track.

  • The maximum vorticity in a 5x5 grid around the cyclone is found and a new 3x3 box is formed around it.

  • At the next time step, if the maximum vorticity in this new box is greater than 3.5 x 10-5 s-1, the procedure is repeated.

  • This point has become part of the storm track.

  • If two storm tracks are the same, they are considered the same cyclone and counted as one.


Four nh ocean basins

Four NH Ocean Basins


Examples of storm tracks for 4 nh basins

Atlantic

Eastern North Pacific

Western North Pacific

North Indian

Examples of Storm Tracks for 4 NH Basins


Dynamic hurricane season prediction experiment with the ncep cfs

Atlantic

Basin

Atlantic Tropical Storms


Dynamic hurricane season prediction experiment with the ncep cfs

Eastern

Pacific

Basin

Eastern Pacific Tropical Storms


Dynamic hurricane season prediction experiment with the ncep cfs

Western

Pacific

Basin

Western Pacific Tropical Storms


Atlantic basin correlations

Atlantic Basin Correlations

Anomalies of Atlantic Storms

N=27 N=13 N=13

Red= Statistically Significant at 0.95


Enp correlations

ENP Correlations

Anomalies of Eastern North Pacific Storms

N=27 N=13 N=13

Red= Statistically Significant at 0.95


Wnp correlations

WNP Correlations

Anomalies of Western North Pacific Storms

N=27 N=13 N=13

Red= Statistically Significant at 0.95


Dynamic hurricane season prediction experiment with the ncep cfs

Atlantic Basin ACE Index

% of Normal

N=27 N=13 N=13

Red= Statistically Significant at 0.95


Atl detrended correlations

Correlations

Total

81-93

94-06

IC=0419

0.24

0.19

0.01

IC=0420

0.34

0.32

0.40

IC=0421

0.25

0.38

0.24

April Ensm

0.35

0.16

0.34

IC=0515

0.22

-0.13

0.42

ATL Detrended Correlations

N=27 N=13 N=13

Red= Statistically Significant at 0.95


Atl detrended step function correlations

Correlations

Total

81-93

94-06

IC=0419

0.46

0.23

0.15

IC=0420

0.38

0.36

0.45

IC=0421

0.29

0.25

0.10

April Ensm

0.49

0.22

0.30

IC=0515

0.35

0.11

0.49

ATL Detrended, Step Function Correlations

N=27 N=13 N=13

Climatology used for detrending is broken into two 13-year periods, the inactive period of 1981-1993 and the active period 1994-2006

Red= Statistically Significant at 0.95


Evaluation of sst and wind shear prediction for storm season

Evaluation of SST and Wind Shear prediction for storm season


Jja nino 3 4 sst index

JJA Nino 3.4 SST Index

Red= Statistically Significant at 0.95


Aso nino 3 4 sst index

ASO Nino 3.4 SST Index

Red= Statistically Significant at 0.95


Jja atlantic mdr sst index

JJA Atlantic MDR SST Index

Red= Statistically Significant at 0.95


Aso atlantic mdr sst index

ASO Atlantic MDR SST Index

Red= Statistically Significant at 0.95


Jja atlantic mdr shear index

JJA Atlantic MDR Shear Index

Wind Shear:

U200 – U850

Red= Statistically Significant at 0.95


Aso atlantic mdr shear index

ASO Atlantic MDR Shear Index

Wind Shear:

U200 – U850

Red= Statistically Significant at 0.95


Tna non local index jun nov

TNA Non-Local Index, Jun-Nov

[60W-30W; 5N-20N] - [0-360, 0-15N] SSTA

Red= Statistically Significant at 0.95


2008 atlantic season summary

2008 Atlantic Season Summary


Atlantic basin prediction for 2008

Atlantic Basin Prediction for 2008

  • Two additional runs were made using July 15th and 16th initial conditions for 2008 only

  • Used as a trial run for the CPC Hurricane Outlook Update


Atlantic basin cfs 2008 monthly storm count

2008

May

Jun

Jul

Aug

Sep

Oct

Nov

Total

0419

1

1

5

3

2

1

1

14

0421

1

3

3

2

4

2

15

0422

1

3

3

3

1

11

ENSM

1

1.33

3.67

2.33

3

1

0.67

13.33

0715

2

4

3

9

0716

1

4

8

2

1

16

Obs

1

3

4

4

1

16

Atlantic BasinCFS 2008 Monthly Storm Count

3


Dynamic hurricane season prediction experiment with the ncep cfs

2008 Atlantic Storm Tracks

(Courtesy of Unisys)

Large number of storms over the Gulf of Mexico this year


Dynamic hurricane season prediction experiment with the ncep cfs

Resemble Bertha and Cristobal


Dynamic hurricane season prediction experiment with the ncep cfs

.Summary

  • CFS in T382 resolution exhibits robust climatological seasonal cycle of tropical cyclone over four NH basins.

  • Warming trend and intensification of hurricane activity in the Atlantic basin captured in the CFS hindcasts.

  • Fair level of skill in predicting interannual variability of seasonal storm activities based on the limited number of forecast runs.

  • Model SST and wind shear bias explain part of less number of TCs in Atlantic and EN Pac. basins, in particular over the Gulf of Mexico.

  • Continue to increase number of ensemble members for better climatology and storm statistics. Hope to provide input for 2009 Hurricane Season Outlook with real time prediction runs.

  • Multi-model ensemble approach for dynamical hurricane season prediction is being explored.


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