http:// 2 nd acre workshop 1st - 3rd april, 2009 o’reilly’s rainforest retreat, lamington...
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2nd ACRE Workshop1st - 3rd April, 2009
O’Reilly’s Rainforest Retreat, Lamington National Park, Queensland, Australia
Downscaling the historical reanalyses
Antonio S. Cofiñoantonio.cofino@unican.es
www.meteo.unican.es
Santander Meteorology &
Data Mining Group
GCMs
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GCMGlobal
PredictionsEmission Scenarios
Why Downscaling Methods?
AEMET
Interpolated Temp (20 km)
SCALE NEEDEDFOR IMPACT STUDIES
ECHAM5/MPI-OM (200 km)
TYPICAL SCALE OFGCMs
REALWORLD
Climatology (1961-90)
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GCMGlobal
PredictionsEmission Scenarios
Downscaling Methodologies
Climatology (1961-90)
A2
Statistical Downscaling techniques
are based on empirical models fitted to data using historical records.
Y = f (X;)
Historical Records
The form and parameters of the model depend of the different tecniques used.
A2
RCMA2 B2Dynamical Downscaling
runs regional climate models in reduced domains with boundary conditions given by the GCMs.
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Climate: Multi-Model, Multi-Scenario
Partners Model Atmosphere Resolution Ocean Resol.
UKMO
METO-HC
HadGem1 HadGam1 1.25x1.875° L38 HadGom1 0.33-1° L40
IPSL IPSL-CM4 LMDZ-4 2.5x3.75° L19 OPA8.1 0.5-2° L31
MPI ECHAM5/MPI-OM
ECHAM5 T63 L31 MPI-OM 1.5° L40
FUB EGMAM ECHAM4-MA T30 L39 HOPE-G 0.5-2.8° L20
CNRM CNRM-CM3 ARPEGE V3 T63 L45 OPA8 0.5-2° L31
NERSC ARPEGE V3-MICOM-OASIS
ARPEGE V3 T63 L31 NERSC Modified MICOM2.8 1.5° L35
DMI ECHAM5/MPI-OM
ECHAM5 T63 L31 MPI-OM 1.5° L40
UiO OSLO CTM2 OSLO CTM2 T21 L60 ---
GCM Global Predictions
Emission Scenarios
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Dynamical Downscaling: RCMs
Based on numerical models solving, at “high” temporal and spatial resolution, the primitive equations of the atmosphere.
Usually the low resolution outputs from GCMs are used as Boundary Conditions and Initial Conditions for one-way nesting of a Local Area Model.
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Dynamical Downscaling: RCMs
• The results from RCMs are critically dependent on decisions about:
– Spatial ant temporal resolution– The size and the position of the domain– The parameterizations of physical process– The Boundary Conditions – The Initial Conditions -> Internal variability
Fernandez et al. (2007) J Geoph Res 112:D04101 “Sensitivity of MM5 to physical parameter ...”Jones et al. (1995) Q J R Meteorol Soc 121:1413 “Simulation of climate change over Europe ...”Vukicevic & Errico (1990) Mon Wea Rev 118:1460 “The influence of artificial and physical ...”Fernández (2004) PhD Diss. UPV/EHU “Statistical and dynamical downscaling models ...”GonzálezRouco et al. (2001) J Clim 14:964 “Quality Control and Homogeneity of Precip ...”Colle et al. (2000) Wea Forecasting 15:730 “MM5 precipitation verification over the Pacific ...”
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ENSEMBLES. The SDS Portal
http://www.meteo.unican.es/ensembles
Y = f (X;)
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Advantages Shorcomings
Linear Regression Very simpleEasy to interpret
Linear assumption
Spatially inconsistent
Selection of predictors
Neural Networks Nonlinear
“Universal” interpolator
Complex blackbox-like
Optimization required
Selection of predictors
Analogs Nonlinear
Spatial consistency
Algorithmic. No model.
Difficult to interpret
Weather Typing Nonlinear
Easy to interpret
Spatial consistency
Adaptations for EPS
Algorithmic & Generative
Loss of variance
Problem with borders (for deterministic forecasts)
Statistical Downscaling: Methods
• Transfer-Function Approaches (generative)
• Non-Generative Algorithmic Methods
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Gridded Atmospheric Patterns for day n
Predictands: precip., etc.for day n
Yn(T(1ooo mb),..., T(500 mb); Z(1ooo mb),..., Z(500 mb); .......;
H(1ooo mb),..., H(500 mb)) = Xn
Linear Regression:
Yn= a Xn+ b
a
x
y
x1 x2 x3 x4 x5
Linear Regression Logistic Regression
Suitable for probabilistic forecast with a simple modification:
Logistic Regression
P(precip>10mm)
Yn= F(a Xn+ b)
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Linear & Logistic Regression
Wind Speed [0,)
P(Wind Speed > 50km/h) [0,1]
Observations from 1977- 2002.
ERA40 over 27 grid points for the same period
(60% for trainning and 40% for validation)
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Artificial Neural Networks
Artificial Neural Networks are inspired in the structure and functioning of the brain, which is a collection of interconnected neurons (the simplest computing elements performing information processing):
Each neuron consists of a cell body, that contains a cell nucleus. There are number of fibers, called dendrites, and a single long fiber called axon branching out from the cell body.The axon connects one neuron to others (through the dendrites). The connecting junction is called synapse.
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Functioning of a “Neuron”
• The synapses releases chemical transmitter substances.• The chemical substances enter the dendrite, raising or lowering the
electrical potential of the cell body.• When the potential reaches a threshold, an electric pulse or action
potential is sent down to the axon affecting other neurons.(Therefore, there is a nonlinear activation).
• Excitatory and inhibitory synapses.
nonlinear activation function
neuron potential: mixed input of
neighboring neurons
weights (+ or -, excitatory or inhibitory)
(threshold)
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cxe1
1)x(f
The neural activity (output) is given by a no linear function.
Gradient descent
InputsOutputs
1. Init the neural weight with random values2. Select the input and output data and train it3. Compute the error associate with the output 4. Compute the error associate with the hidden neurons
5. Compute
and update the neural weight according to these values
Multilayer Perceptron (Feed-forward)
x h y
hi
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Regression vs. Neural Networks
Wind Speed [0,)Observations from 1977- 2002.
ERA40 over 27 grid points for the same period
60% for trainning and 40% for validation
•Model 1: Regression
•Model 2: Neural Network
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PC1
PC
2
The probabilistic local prediction is obtained from the relative frequency of snow occurrence (binary variable) in the analog set or cluster.
Analog set
WeatherType
(cluster)
Analogs & Weather Typing
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• Logistic Regression
• Neural Network 10 PCs:5:1
• Analogs, k-NN (k=50)
Model 1
P(12 )
Model 1
Model 2
P=(P(06),P(12),P(18),P(24),P(30))
Model 2
Comparison of Techniques: Wind
P(Wind Speed > 50km/h) [0,1]
Observations from 1977- 2002.
ERA40 over 27 grid points for the same period
60% for trainning and 40% for validation
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Probabilistic Weather Typing
Pforecast (precip > u) = Ck P(precip > u | Ck) Pforecast(Ck)
The application to an EPS requires applying the method to each of the ensemble members:
x1x2x3x4x5...
Prob(x)Mean(x)
Aggregation of results
WeatherType
(cluster)
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Precipitation
Perú
Do
wn
scal
ing
nee
ded
!
Seasonal precip. during DJF 1997/98 at Morropón: 1300 mm, Sausal: 360 mm.
Observations:
Predictions (DEMETER):
50 Km
Analysis and Downscaling Multi-Model Seasonal Forecasts in Peru using Self-Organizing Maps by J. M. Gutiérrez, R. Cano, A. S. Cofiño, and C. Sordo, Tellus 57A, 435-447
(2005).
Validation of Regional Projections
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U = Percentile 80
U= Percentile 90
Probabilistic: P(precip>u)=* Numeric Forecast: Precip = *
Skill of the Downscaling Method
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Conclusions
• End-User applications require downscaled data: spatial, temporal and parameters
• 2 approaches:– Dynamical: parameterization tuning, high costs in terms of computer
resources, can provide downscaled data where no observations are available,…..
– Statistical: based on “past” observations, difficult to give a physical meaning, predictor selection issues, calibration of GCM, can provide non-linear relationships between predictors and predictands,…
• Used for Climate change scenarios, Seasonal forecasting, Weather forecasting…and of course re-analysis applications
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