1. (WCRP Seasonal Prediction Workshop) Applied Meteorology Group
ENSEMBLES Web Portal for Seasonal Statistical Downscaling. Description and Demo
(WCRP Seasonal Prediction Workshop)
Antonio S. Cofiño
Daniel San-Martín
José Manuel Gutiérrez
Dept. of Applied Mathematics and Computational Sciences
University of Cantabria, Spain
Univ. Cantabria – INM
Applied Meteorology Group AI
met
group

2. Xn Yn Downscaling Portal. Local Predictions for End-Users
There is a gap between the global low-resolution simulations and the high-resolution needed for end-users’ applications. One of the solutions is statistical downscaling.
Example: Forecast models for electricity demand require local predictions of daily temperatures in a number of cities (local stations). (See Laurent Dubus’ Poster)
Goal: Provide predictions of daily local values for individual members for a season JJA 2007
in a suitable format (e.g., text file, or Excel file).
Temperature Yn
Predictands
The portal helps with the Downscaling Workflow:
Predictors
T 1000mb Q 1000mb
Z 500mb Xn
SLP
Yn = F(Xn)
Downscaling
Model
This is the structure that we followed in the portal’s design.

3. Datasets uploaded by users (private). e.g. EDF dataset.
Data Available in the Portal (so far!!)
Reanalysis:
ERA40 ( )
NCEP ( )
JRA25 ( )
Seasonal Hindcasts:
DEMETER ( , 7 models, 9 members)
ENSEMBLES S1 ( , 3 model, 9 members)
INM/RCA 0.5 ( , 1 model, 9 members)
ECMWF System2 ( , 5-40 members)
MARS-Stat Data
ECA Data
Observations:
0.5x0.5 grid over Europe provided by JRC
1000 local points provided by ECA.
Datasets uploaded by users (private). e.g. EDF dataset.

4. Testbed: ENSEMBLES s2d data providers.
Distributed Data Access. OpenDAP
Local Data
OpenDAP Client
Downs. Portal
OpenDAP
Via
https
OpenDAP Servers
INTERNET
Distributed-data
Data
(ECMWF)
(NCEP)
(others)
Data Providers
Downs. Portal
netCDF lib
Local
access
to data
Testbed: ENSEMBLES s2d data providers.
(F. Doblas-Reyes)
Work smarter, Generalized distributed data access protocols – “give me exactly and only what I need”
Data
(local)

5. Downscaling Methods
One of the main goals of the portal is including AS MANY downscaling methods AS POSSIBLE.
Weather Typing
Analogues
K-Means
Self Org. maps
Regression Methods
Linear
Neural Nets
CCA
Weather Generators
Markov Chains
Stochastic Models
Bayesian Nets
Perfect-Prog validation using reanalysis data.
(Error estimation associated with the downscaling method)

6. LIVE DEMO !!!

7. Near Future
A first operational version of the portal would be released in September 2007.
Open to the research community (preserving restrictions from data providers).
Look for users and data providers to extend the portal to other world regions.
Calibration of Models.
The GRID technology will provide access to distributed resources allowing to share compute and storage resources.
E-infrastructure shared Between Europe and Latin America