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146
Dynamical downscaling of ECMWF experimental seasonal forecasts:
Probabilistic verification
Mirta Patarčić and Čedo Branković
Croatian Meteorological and Hydrological Service, Gric 3, 10000 Zagreb, Croatia, patarcic@cirus.dhz.hr
1. Introduction
Predictability of the atmosphere on seasonal time scales
mostly depends on lower boundary forcings such as sea
surface temperature, surface albedo, soil moisture and snow
cover. They are defined on global scales and have influence
on distant regions. Therefore, seasonal forecasts are made
with global coupled atmosphere, ocean and land surface
models. One way of increasing spatial and temporal scales
of a global model's results is dynamical downscaling by a
regional climate model. In this study we used the 50-km
Regional Climate Model (RegCM, Pal et al. 2007) to
dynamically downscale ECMWF experimental seasonal
integrations from the EU ENSEMBLES project.
Due to uncertainties in initial conditions and uncertainties in
representation of physical processes, predictions on seasonal
time scales are inherently probabilistic. Reliable seasonal
forecasts can be made by using ensembles of integrations
that enable probabilistic approach to a particular event. In
order to assess the quality of probability forecasts it is
necessary to perform forecast verification. Verification of
probabilistic forecasts of summer 2m temperature has been
done for both models.
2. Experiments
Dynamical downscaling has been done for nine-member
ensembles for summer (July-September, JAS) season for the
11-year period (1991-2001). RegCM domain covered central
and southern Europe and the Mediterranean.
The data from Climatic Research Unit (CRU) from
University of East Anglia (New et al. 2002) were used for
verification.
3. Methods of analysis
Probabilistic verification of seasonal ensemble integrations
for global and regional model is made with emphasis on
Brier score, reliability and resolution.
Brier score is the mean squared error of the probability
forecasts (Palmer et al. 2000 and Wilks, 2006):
N
1
2
BS = ∑(
pi − vi
) , 0 ≤ pi ≤ 1,
v
N
i ∈ { 0,1}
i=
1
where p i is probability, v i is observation and N is the number
of forecast-event pairs over all ensemble members and grid
points in 11 years. Probabilities are calculated as a fraction
of ensemble members predicting particular event forming 10
probability bins.
Reliability diagrams are constructed by plotting hit rate
(HR) for each probability bin against corresponding forecast
probability. HR for each probability bin is defined as:
On
HRn
=
On
+ NOn
where n is the number of n th bin, O n is number of observed
occurrences and NO n number of observed non-occurrences
in each probability bin.
Relative operating characteristic (ROC) diagrams are
constructed by plotting hit rate against false alarm rate
(FAR) for accumulated probability bins (i.e. for each
probability threshold P n ).
For each probability threshold P n HR and FAR are defined
as:
⎛
N
⎞ ⎛
N
⎞
HR ⎜ ⎟ ⎜ ⎟
n = ∑Oi
∑Oi
⎝ i=
n ⎠ ⎝ i=
1 ⎠
⎛
N
⎞ ⎛
N
⎞
FAR ⎜ ⎟ ⎜ ⎟
n = ∑ NOi
∑ NOi
⎝ i=
n ⎠ ⎝ i=
1 ⎠
where N is total number of probability bins.
For verification purposes global and regional model
outputs were interpolated to CRU grid (0.5deg). Since the
CRU data are defined over land, verification was
performed for land points only. 2m temperature anomalies
were calculated as differences of seasonal means and
model/verification climatology.
4. Results
Skill measures have been calculated for 2m temperature
anomalies for the summer season for the whole regional
model domain and southern part of the domain (south of
48°N). Brier score, reliability diagram and relative
operating characteristic were determined for three events:
JAS 2m temperature anomaly above normal (0.0 K),
above 0.1 K and 0.5 K.
According to all skill measures for temperature anomalies
above normal, both models have better results for the
southern part of the domain than for the entire domain
indicating an increased potential seasonal predictability for
south Europe.
Brier score, which is negatively oriented, for global model
in the southern part is 0.20, and for the whole domain
0.25.
Forecasts are more reliable in the southern part of the
domain, which is indicated by the proximity of the curve
to the diagonal line in reliability diagram in Fig. 1.
Observed frequency
Observed frequency
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
ECMWF model
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
RegCM
Forecast probability
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Forecast probability
Observed frequency
Observed frequency
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
1
0.9
0.8
0.7
0.6
0.5
0.4
0.3
0.2
0.1
0
ECMWF model
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
RegCM
Forecast probability
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
Forecast probability
Figure 1. Reliability diagram for global (upper
panels) and regional model (lower panels), for entire
(left) and southern part of the domain (right) for JAS
2m temperature above normal.