young-kwon lim collaborators: climate modeling group (drs. l. stefanova, d.w. shin, s. cocke, and t....

Young-Kwon Lim

Collaborators: Climate modeling group (Drs. L. Stefanova, D.W. Shin, S. Cocke, and T. E. LaRow) at

FSU/COAPS, Dr. G. Baigorria (Univ. of Florida),

Dr. K. H. Seo (Pusan Nat’l Univ., Korea), Dr. S. Schubert (NASA/GSFC), Dr. H. Juang (NOAA)

Center for Ocean-Atmospheric Prediction Studies, Florida State University, Tallahassee, FL, USA

Development of Multi-model High Resolution Seasonal Forecasting System:

An Application to SE US

Current high-resolution seasonal forecast in FSU/COAPS

Current high-resolution seasonal forecast in FSU/COAPS

One dynamical model (FSU/COAPS RSM) and one statistical downscaling model at 20km resolution (Cocke et al. 2007; Lim et al. 2007, 2009).

Real-time seasonal forecasts (up to 6 month ahead) are updated four times a year through CMO web (http://coaps.fsu.edu/cmo).

Realtime high-resolution forecast by FSU/COAPS

(example: Nov. 2009, 20km resolution) Realtime high-resolution forecast by FSU/COAPS

(example: Nov. 2009, 20km resolution)

T mean

T ano.

Rainfallmean

Rainfallanomaly

Corr. skill of the current FSU/COAPS downscaling system:

1) Winter rainfall: Corr. > 0.5 (FL,GA) (Cocke et al. 2007)

2) Crop growing seasons (spring and summer) :

Sfc. air T. (Lim et al. 2007): Corr.=0.3~0.8,

Rainfall: Improvement of correlation over the large scale CFS. Statistical significance problem (Lim et al. 2009).

Skill (correlation and categorical predictability) tends to be model dependent (e.g., summer rainfall: higher skill over inland by FSU model, while higher skill over Florida peninsula by NCEP model)

Question: Can we improve the skill over the entire SE US with statistical signifcance via MM downscaling system?

Capability of the current FSU/COAPS downscaling system, and MotivationCapability of the current FSU/COAPS downscaling system, and Motivation

Error variance and Seasonal Anomaly Correlation (current downscaling system) (Lim et al. 2009)

Downscaled seasonal forecast with an improvement of Corr.

Reduction in Relative error variance (REV) (≈ 2 0.6~1.4)

Downscaled seasonal forecast with an improvement of Corr.

Reduction in Relative error variance (REV) (≈ 2 0.6~1.4)

REV Corr.

Corr. (0.~0.2)

Corr. (0.4~0.6)

REV > 6.0

REV < 1

Down. from FSU model

Categorical predictability (HSS) for the frequency of rainfall extremes (Lim et al. 2009)

Downscaling

Difference (Down. - Rescaling)

Rescaling (OA) from the CFS

Downscaling:

Florida and S. Georgia : > 0.1, Alabama and C. Georgia : -0.1 ~ 0.2,

Rescaling: -0.2 ~ 0.2

1 std. + climatology

0.1~0.5

-0.2 ~ 0.1

≥0.1

Dynamical models

1. FSU/COAPS NRSM

2. RSM (NCEP, ECPC)

3. RegCM3 (ICTP)

Statistical models

1. CRT (CSEOF + Regression + Time series generation)

2. NLCCA (Neural network based CCA) (Hsieh et al. 2006)

3. Geo-spatial weather generator (Baigorria et al. 2007)

Dynamical and statistical models involved in the multi-model downscaling study

Dynamical and statistical models involved in the multi-model downscaling study

Difference between FSU/COAPS downscaling works and other downscaling projects

Difference between FSU/COAPS downscaling works and other downscaling projects

NARCAPP MRED CORDEX FSU/COAPS

AimClimate change

projection

Seasonal forecast (winter)

Climate change

projection

All seasonal forecast, Climate

change proj.

DownscalingModels

RegCM3, CRCM, RSM,

HadRM3, WRF, MM5

MM5, WRF, RSM

RCMs……

FSU,RegCM3,RSM,

+ 3 Statistical models

Global Models

HadCM3,CGCM3,CCSM,

GFDL

CFS, GEOS-5

CMIP5 models

FSU, CCSM,GEOS-5,CFS

Resolution 50km 32km 50km 20km

Domain N. America US World SE US

ProceduresProcedures

Downscaling large-scale reanalysis using dynamical

models for model validation (bias, reliable distribution)

Downscaling large-scale retrospective forecasts

High-resolution seasonal forecasts on real-time basis

Probabilistic forecasts and application of the MME for

the improved deterministic forecasts

Expansion to high-resolution climate change

projection

Downscaling large-scale reanalysis using dynamical

models for model validation (bias, reliable distribution)

Downscaling large-scale retrospective forecasts

High-resolution seasonal forecasts on real-time basis

Probabilistic forecasts and application of the MME for

the improved deterministic forecasts

Expansion to high-resolution climate change

projection

Preliminary result: RCM response to

downscaling (2.5˚ → 20km) Preliminary result: RCM response to

downscaling (2.5˚ → 20km) 2m T. (JJA/2004) 2m T. (JJA/2005)

Preliminary result: RCM response to

downscaling (2.5˚ → 20km) Preliminary result: RCM response to

downscaling (2.5˚ → 20km) Prcp. (JJA/2004) Prcp. (JJA/2005)

Preliminary result:

Statistical downscaling models Preliminary result:

Statistical downscaling models 2m T. (JJA/2004) 2m T. (JJA/2005)

Preliminary result:

Statistical downscaling models Preliminary result:

Statistical downscaling models Prcp. (JJA/2004) Prcp. (JJA/2005)

SummarySummary

Multi-model high-resolution seasonal forecasting system study at FSU/COAPS was begun in September.

Three dynamical and three statistical models have been involved in this study.

We aim at the spatial resolution as fine as 20km for the southeastern US (FL, GA, AL, NC, and SC).

Improvement of the skill over our existing downscaling system (one dynamical and one statistical model) is expected.