Transcript Document

4th General Assembly
http://www.meteo.unican.es/ensembles
Prague 12-16 November 2007
Hands-on Demonstration of the
Statistical Downscaling Portal for
Regional Climate Change Projection
José M. Gutiérrez
Daniel San-Martín, Antonio S. Cofiño, Carmen Sordo,
Jesús Fernández, Dolores Frías, Miguel A. Rodríguez, S.
Herrera, Rafael Ancell, M.R. Pons, B. Orfila, E. Díez
Clare Goodess (CRU), Francisco Doblas-Reyes (ECMWF)
Applied Meteorology
Research Group,
Santander, Spain
Motivation
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There are many projects around the world producing global (GCM) and
regional (RCM) simulations of climate change.
Many of these projects involve end-uses from impact sectors ...
However, it is still difficult for end-users to access the stored simulations and
to post-process them to be suitable for their own models: daily resolution,
interpolation to prescribed locations, etc.
There is a need of friendly interactive tools so users can easily
run interpolation/downscaling jobs on their own data using the
existing downscaling techniques and simulation datasets (AR4,
Prudence, ENSEMBLES, ...).
Collaboration with End-Users
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Two ongoing research collaborations with s2d users.
Fabio Micale
Iacopo Cerrani
Giampiero Genovese
Downscale DEMETER and ENSEMBLES s2d hindcasts to get daily
precip, radiation, wind speed, and maximum/minimum
temperatures to make crop yield modeling. The goal is to
compare the downscaled data to GCM outputs and to estimate
seasonal predictability.
ELECTRICITÉ DE FRANCE
Laurent Dubus
Marta Nogaj
Downscale DEMETER and ENSEMBLES s2d hindcasts to get daily
maximum and minimum temperatures to make electricity demand
forecasts. The goal is to compare the downscaled data to GCM
outputs.
Local precipitation forecasts for hydropower production
capacities.
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Data Access Portal
60,Potential Vorticity,PV
129,Geopotential,Z
130,Temperature,T
131,U velocity,U
132,V velocity,V
133,Specific humidity,Q
136,Total Column Water,TCW
137,Total Column Water Vapour,TCW
138,Relative vorticity,VO
142,Large Scale Precipitation,LSP
143,Convective Precipitation,CP
151,MSLP,MSL
155,Divergence,D
157,Relative humidity,R
165,10m E-Wind Component,10U
166,10m N-Wind Component,10V
167,2m Temperature,2T
168,2m Dew Point,2D
1000, 925, 850, 700, 500, 300 mb
00, 06, 12, 18 , 24 UTC
1.125ºx1.125º resolution
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Data Access: s2d & acc
Statistical Downscaling Portal
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Problem: Local climate change prediction for
Madrid (Spain): maximum temperature
Goal: Provide daily local values for the summer
season june-august 2010-2040 in a suitable format
(e.g., text file, or Excel file).
Predictors
(T(1ooo mb),..., T(500 mb);
Global zone
Local zone
Z(1ooo mb),..., Z(500 mb);
H(1ooo mb),..., H(500 mb))
Xn
Downscaling
Model
Regres, CCA, …
Yn = WT Xn
Predictands
Precipitation
Temperature
Yn
This is the structure followed in the portal’s design:
predictors + predictand + downscaling method.
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Demo... My Home
The “My Home”
tab allows the
user to explore:
1.
The zones
(pre-defined
regions).
2.
The profile
with the
account
information.
3.
The status
of the jobs:
queued,
running,
finished.
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Demo... Predictors
A simple zone with
a single predictor
parameter:
T850mb
was created.
New zones can be
easily defined by
clicking in the “new
zone” button.
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Demo... predictand
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Demo... Downscaling Method
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Demo... Validation
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Demo... Regional Projection
Demo... Computing Time
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Scheduling the job
Five minutes later ...
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Distributed Data Access
PRESENT
FUTURE
Typical
Application
Distributed-data
Application
Downs. Portal
Downs. Portal
netCDF lib
OpenDAP Client
Local
access
to data
Data
(local)
s2d
RCM ACC
GCM ACC
Paco Doblas-Reyes
Antje Weisheimer
Philippe Gachon
OpenDAP
Via
http
OpenDAP Servers
Data
(ECMWF)
Data
(DMI)
Data
(remote)
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Big Projects ... Using Restricted Data
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Summary
• Nowadays, Statistical Downscaling (SD) is a
mature field and there is a huge amount of
data (observations, reanalysis and
simulations) to apply SD techniques in a
variety of problems.
• Web-based interactive tools (such as the
statistical downscaling portal) can help endusers to explore and use this information.
• These tools should be part of the
different climate change projects in order
to maximize the analysis and explotation
of the results.
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Future Plans
• Support end-users using the portal
(complete documentation).
• Link the portal to the DMI database of
regional models.
• Connect with existing E-Science EU
initiatives (EGEE Earth Science VO).
Santander is one of the nodes of
the National Supercomputing
Center and has also great
experience in GRID computing.
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Final Remark
Without data we are nothing and
our work is useless !!!
so we highly encourage
CERA and GCM providers to
work as much as they can to
put their data in CERA as
quick as possible.