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

Regional Climate Simulation of Surface Air
Temperature (Tmax) and Precipitation by
Downscaling over the Southeast US
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
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.)
0.2° 0.2° (~20km res.)
Training
1.8° 1.8° (~180km res.)
&
Predictor : model output
Predictand : observation
Regressed eigenfunctions
of GSM runs over training
period used
Prediction
period
Eigenfunctions of the Obs.
over training period and
the Generated CSEOF PC
used
Withholding different
year for Cross-validation
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
Black solid : Observation
Red solid : statistical downscaling
Blue solid : FSU/COAPS NRSM
 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).
Seasonal anomaly Time series
Black solid : Observation
Red solid : statistical downscaling
Blue solid : FSU/COAPS NRSM
Green 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.
Anomaly Correlation
seasonal, monthly
Top : Statistical downscaling
Middle : 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.
Categorical evaluation
Left : Correct forecast (%), second column : (+) forecast but (-) obs.(%),
third : (-) forecast but (+) obs. (%), right : Heidke skill score
SD
NRSM
Paa  Pbb
Pab
Paa  Pab
Pba
Pba  Pbb
MAE and Correlation for
frequency of daily extreme event
Top : Statistical downscaling
Middle : FSU/COAPS 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.)
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.
Statistical downscaling procedure (2)
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
?
(training period)
Eigenfunction (from Observation)
forecast period
Regressed Eigenfunction (model)
Both are physically consistent.
Result of multiple regression
Eigenfunction (from Observation)
Regressed Eigenfunction (model)
Statistical downscaling procedure (3)
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
Statistical downscaling procedure (4)
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
0.2° 0.2° (~20km res.)
Training
1.8° 1.8° (~180km res.)
&
Predictor : model output
Predictand : observation
Regressed eigenfunctions
of GSM runs over training
period used
Prediction
period
Eigenfunctions of the Obs.
over training period and
the Generated CSEOF PC
used
Withholding different
year for Cross-validation
Monthly time series
(Tmax)
Black solid : Observation
Red solid : statistical downscaling
Blue solid : FSU/COAPS NRSM
Green 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.
The number of extreme Tmax events
Black solid : Observation
Red solid : statistical downscaling
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.
Mean absolute error
Top : Statistical downscaling
Middle : FSU/COAPS NRSM
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.
Categorical evaluation
 Two categories : above average and below average
 Correct forecast : the same sign of anomalies between
observation and the downscaled forecast (Paa, Pbb)  Pc
 Paa  Pbb
 Incorrect forecast : opposite anomalies between observation
and downscaled forecast (Pab, Pba) 
,
P
P
ab
Paa  Pab
P  PE
HSS  C
1 PE
PE : probability of a
random forecast
(F and P are independent)


PE  P P  P P
F
a
P
b
Pba  Pbb

 Heidke skill score :
P
a
ba
F
b
Verifying
 analysis 
Forecast
above below
normal normal
above
Paa
Pba
PaP
below
Pab
Pbb
PbP
PaF
PbF
1
Obs.