why downscaling over the se usa?

Young-Kwon Lim, D.W. Shin, S. Cocke, T. E. LaRow, J. J. O’Brien, and E. P. Chassignet

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

Regional Climate Simulation of Surface Air Temperature (Tmax) and Precipitation by

Downscaling over the Southeast US

Why downscaling over the SE USA?

Extremely high temperature and heavy rainfall with severe storms during summer, resulting in potential property damage and injuries.

The largest areas of agricultural farms in the nation.

An accurate forecast with higher spatial resolution is essential to adapt management, increase profits, reduce production risks, and mitigate damages.

Simulation of regional climate by FSU

FSU/COAPS Global Spectral Model (FSU/COAPS GSM) has been downscaled to the 20km grid resolution by FSU/COAPS nested regional spectral model (FSU/COAPS NRSM) over the southeast US. Dynamical Downscaling

FSU/COAPS NRSM : 1) Same physics as the GSM, 2) 3 or 6 hr nesting interval, and 3) Output : Surface T, prcp., and radiative variables.

Statistical downscaling model has been also developed. (CSEOF, multiple regression, and stochastic PC generation are used.)

Training Predictor : model output

Predictand : observation

&

Regressed eigenfunctions of GSM runs over training period used

0.2° 0.2° (~20km res.) 1.8° 1.8° (~180km res.)

Eigenfunctions of the Obs. over training period and the Generated CSEOF PC used

Prediction period

Withholding different Withholding different year for year for Cross-Cross-validationvalidation

Data (Obs. & Model) and period

Variables : Daily Tmax, Tmin, and precipitation

Period : 1994 ~ 2002 (March ~ September each year (daily))

Observed data source :

National Weather Service Cooperative Observing Program surface data over the southeast US : ~20km×20km

Large-scale model data :

FSU/COAPS GSM : ~1.8°×1.8° (T63), initial condition centered on Mar. 1 each year, seasonally integrated.

Results

2-d monthly mean field (Obs. GSM, NRSM, and Statistical Down.)

Time series of monthly Tmax anomaly over the selected local grids

(Tallahassee, Jacksonville, Orlando, Miami, Atlanta, Tifton,

Birmingham, and Huntsville)

Time series of seasonal T anomaly and correlations

Categorical Predictability (%) for above/below seasonal T

climatology

Predictability (e.g., rainy/dry, false alarm, HSS) for precipitation

Correlation and 3-category predictability for summer monthly

prcp.

Monthly mean field (1994)

Spring Summer

Monthly anomaly time series

Peaks seen in the observation are reasonably captured by both downscaling methods.

Both methods appear to have comparable skill in reproducing the observed fluctuations.

Poor coincidence in peaks between the downscaled and the observed time series are found at a few time steps (e.g., e, g, and h in 96 and 97).

Black solid : ObservationRed solid : statistical downscaling

Blue solid : FSU/COAPS NRSM


Blue solid : FSU/COAPS NRSMGreen dashed : GSM

Both downscaled time series tend to undulate in accordance with the observed time series

Incorrect predictions : 94 summer, 95 spring, and 97 spring

The relatively poor downscaling at these periods arises from poor simulation of the GSM anomaly.

Seasonal anomaly Time series

Anomaly Correlation

Top : Statistical downscalingMiddle : FSU/COAPS NRSM

Bottom : Difference

Correlation ranges from 0.3 to 0.8 over most of grids (seasonal).

Florida region tends to be highly correlated with observation.

Differences do not exceed the magnitude 0.1, indicating any of these methods is not significantly better than the other.

seasonal, monthly

PbaPba Pbb

PabPaa Pab

Paa Pbb

Categorical evaluation

Left : Correct forecast (%), second column : (+) forecast but (-) obs.(%), third : (-) forecast but (+) obs. (%), right : Heidke skill score

SD

NRSM


Bottom : Difference

Correlations exceed 0.4 except for N. Georgia and Alabama, and SW tip of Florida.

Corr. : Statistical downscaling shows higher correlations.

MAE : Statistical downscaling shows greater MAE than dynamical downscaling. (significant overestimation / underestimation should be improved specifically in the statistical downscaling method.)

MAE and Correlation for frequency of daily extreme

event

Monthly anomaly time series (Prcp.)

Categorical evaluation for rainfall event

Left : Correct forecast (%), second column : False alarm ratio (%), third : Prcp. missed (%), right : Heidke skill score

SD

NRSM

Monthly anomaly correlation & Categorical predictability

(summer)

Concluding remarks

Daily Tmax and Prcp. obtained from FSU/COAPS GSM (~1.8°lon.-lat., T63, seasonal integration) run have been downscaled to local spatial scale of ~20km for the southeast US region, covering Florida, Georgia, and Alabama.

Both downscalings better reproduce the regional-scale features of temperature and precipitation than the GSM.

A series of evaluations reveal that both downscaling methods reasonably produces the local climate scenario from large-scale simulations. Skills for T is greater than precipitation. Skills of both methods are comparable to each other.

FSU COAPS is the leading institution for regional climate simulation (downscaling) for seasonal forecast and crop model application over the southeast US.

Still remains a room for the improvement in predictive skill.

Statistical downscaling procedure (1)

1. Cyclostationary EOF analysis for the model output and the observation :

CSEOF (Kim and North 1997) : analysis technique for extracting the spatio-temporal evolution of physical modes (e.g., seasonal cycle, ENSO, ISOs, etc.) and their long-term amplitude variations.

P(r,t)=∑n Sn(t) Bn(r,t)

Bn(r,t) : time-dependent eigenfunctions, Sn(t) : PC time series. In this study, CSEOF is conducted on both observation and

FSUGSM runs over the training period.


2. Multiple regression between the model output and the observation :

CSFOF PC time series of the first significant modes of a predictor variable (FSUGSM data) are regressed onto a certain PC time series of the target variable (observation) in the training period.

PCTn(t)=∑iαni·PCPi(t)+ε(t) i=1,2,…10

PCTn(t): target PC time series, αni: regression coefficient

PCPi(t): predictor PC time series

Relationship between model output and the observation is extracted from CSEOF and multiple regression.

Result of multiple regression

PC time series

Eigenfunction (from Observation) Regressed Eigenfunction (model)

Both are physically consistent.

(training period)

? forecast period

Result of multiple regression

Eigenfunction (from Observation) Regressed Eigenfunction (model)


3. Generating CSEOF PC of the model data over the forecast period from the regressed fields in the training :

CSFOF PC time series of the model data are generated for the prediction period. Modeled data and the regressed eigenfunctions identified from training are used.

PCn(t)=∑gP(g,t)·Bn+(g,t)

PCn(t): the nth mode PC time series for the prediction period g : large-scale grid point

Bn+(g,t) : regressed CSEOF eigenfunctions

P(g,t): global model anomaly over the prediction period


4. Downscaled data construction from the eigenfunctions of the observation and the generated CSEOF PC time series :

D(s,t)=∑nPCn(t)·Bno(s,t)

PCn(t) : generated PC time series from the previous step

Bno(s,t): CSEOF eigenfunctions of the observation (training

period)

D(s,t) : downscaled output

5. Generating downscaled output for the entire period (9yrs) by cross-validation framework

Training Predictor : model output

Predictand : observation

&

Regressed eigenfunctions of GSM runs over training period used

0.2° 0.2° (~20km res.) 1.8° 1.8° (~180km res.)

Eigenfunctions of the Obs. over training period and the Generated CSEOF PC used

Prediction period

Withholding different Withholding different year for year for Cross-Cross-validationvalidation

Monthly time series(Tmax)

Black solid : Observation

Red solid : statistical downscalingBlue solid : FSU/COAPS NRSMGreen dashed : FSU/COAPS GSM

Downscaled results are closer to observation than FSU/COAPS GSM.

Warm or cold biases unveiled from GSM have been corrected by downscaling.

Seasonal mean field (example: 97-98 summer)

Interannual temperature difference between the two years.

Higher (lower) T in 98 (97) with detailed spatial structure is simulated by the two downscaling methods.

The GSM fields have limited capability to realize the regional temperature fields over the domain.


Blue solid : FSU/COAPS NRSM

Extreme T events : exceed the one standard deviation plus climatology.

Interannual change in the occurrences of extreme Tmax (warmer T) events are fairly captured at individual grids by both downscalings.

The number of extreme Tmax events


Bottom : FSU/COAPS GSM (interpolated)

MAE : 0.8 ~ 2°C (GA, AL).

MAE : 0.4 ~ 1.5°C (FL).

FSU/COAPS NRSM (dynamical downscaling) has the smallest biases.

Mean absolute error

Categorical evaluation

Two categories : above average and below average

Correct forecast : the same sign of anomalies between observation and the downscaled forecast (Paa, Pbb)

Incorrect forecast : opposite anomalies between observation and downscaled forecast (Pab, Pba) ,

Heidke skill score :

PE : probability of a random forecast (F and P are independent)

Verifying analysis

Forecast

above normal

below normal

Obs.above Paa Pba PaP

below Pab Pbb PbP

PaF PbF 1

HSS PC PE1 PE

PE PaPPa

F PbPPb

F

PabPaa Pab

PbaPba Pbb

Pc Paa Pbb

why downscaling over the se usa?

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