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Statistical Downscaling Portal for ENSEMBLES (RT2B). José Manuel Gutiérrez Daniel San Martín Dpto. Matemática Aplicada y Ciencias de la Computación. Applied Meteorology Group Univ. Cantabria – INM (Santander, Spain). Bartolomé Orfila Antonio S. Cofiño Instituto Nacional de Meteorología.
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Statistical Downscaling Portal for ENSEMBLES (RT2B) José Manuel Gutiérrez Daniel San Martín Dpto. Matemática Aplicada y Ciencias de la Computación Applied Meteorology Group Univ. Cantabria – INM (Santander, Spain) Bartolomé Orfila Antonio S. Cofiño Instituto Nacional de Meteorología AI met http://www.meteo.unican.es http://www.meteo.unican.es/ensembles group http://www.meteo.unican.es/ 1
This downscaling process can be performed in 4 steps (Perfect Prog): 1. Select a set of predictors from a reanalysis database (ERA40,…) 2. Select simultaneous observations in the desired grid (e.g., JRC gridded observations). 3. Fit a downscaling model (e.g., a weather clustering scheme, or analogs) 4. Apply it to the output of some seasonal GCM (e.g., DEMETER, STREAM1, EURO-SIP). Use of the Portal by Example: Crop Yield in Italy Example: To input a crop-yield model, the Joint Research Center (JRC) needs to obtain seasonal forecasts of several surface variables: maximum and minimum temperature, mean daily rainfall, daily global radiation, evapotranspiration..... in a high-resolution 50kmx50km grid over Italy. GOAL: Daily values for june-august 2006 in a suitable format (e.g., text file, or Excel file).
ERA40 and NCEP reanalysis (fields over Europe). Observations in a 0.5x0.5 grid over Europe provided by JRC Observations in 1000 local points provided by ECA. S2d outputs from DEMETER models (seven models from 1958-2001) and ENSEMBLES STREAM1 (three models from 1991-2001) Features and Structure of the Downscaling Portal Predictors Downscaling Model Predictands (T(1ooo mb),..., T(500 mb); Z(1ooo mb),..., Z(500 mb);H(1ooo mb),..., H(500 mb))Xn PrecipitationTemperature Regres., CCA, … Yn = WTXn Yn
S2D Algorithms Implemented in the Downscaling Portal 1. Weather-Clustering Analog Method based on Self-Organizing Maps A generalization of the analog method which uses a pre-classification of the reanalysis patterns to obtain “weather classes” from where probabilistic forecasts according to the observed climatolgies within groups are obtained. The clustering method used is a self-organizing map (SOM) which provides a lattice of “weather classes” which is the support of the resulting PDF. Clustering methods for statistical downscaling in short-range weather forecast J.M. Gutiérrez , R. Cano, A.S. Cofiño, and M.A. Rodríguez Monthly Weather Review, 132(9), 2169 - 2183 (2004). Analysis and downscaling multi-model seasonal forecasts in Perú using self-organizing maps A.S. Cofiño, J.M. Gutiérrez, and R. CanoTellus A, 57, 435-447 (2005). 2. Weather Generator Stochastic generation of daily precipitation conditioned on predictions of the probability of a wet day in the season and daily persistence. The method uses SVD of model output and observations to obtain ensemble-mean seasonal means to feed the stochastic model. A method for statistical downscaling of seasonal ensemble predictions H. Feddersen and U. Andersen Tellus A, 57, 398 - 405 (2005). Soon available Other methods from other partners to be include.
