# AI met group 1 José Manuel Gutiérrez Daniel San Martín Dpto. Matemática Aplicada y Ciencias de la Computación Applied Meteorology.

## Presentation on theme: "AI met group 1 José Manuel Gutiérrez Daniel San Martín Dpto. Matemática Aplicada y Ciencias de la Computación Applied Meteorology."— Presentation transcript:

AI met group http://www.meteo.unican.es/ 1 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 group http://www.meteo.unican.es/ 1 http://www.meteo.unican.es http://www.meteo.unican.es/ensembles Statistical Downscaling Portal for ENSEMBLES (RT2B)

AI met group http://www.meteo.unican.es/ 2 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). 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).

AI met group http://www.meteo.unican.es/ 3 Precipitation Temperature ( T( 1ooo mb ),..., T( 500 mb ); Z( 1ooo mb ),..., Z( 500 mb ); H( 1ooo mb ),..., H( 500 mb ) ) X n Regres., CCA, … Y n = W T X n YnYn Features and Structure of the Downscaling Portal 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) PredictorsPredictandsDownscaling Model

AI met group http://www.meteo.unican.es/ 4 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. Cano Tellus 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.

AI met group http://www.meteo.unican.es/ 5 Current Registered Users are from ENSEMBLES partners.

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AI met group http://www.meteo.unican.es/ 10 http://www.meteo.unican.es/ensembles

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