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Large-scale atmospheric circulation characteristics and their relations to local daily precipitation extremes in Hesse

Anahita Amiri Department of Geography Justus Liebig University Giessen. Large-scale atmospheric circulation characteristics and their relations to local daily precipitation extremes in Hesse , central Germany. Objective. Challenges

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Large-scale atmospheric circulation characteristics and their relations to local daily precipitation extremes in Hesse

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  1. Anahita Amiri Department of Geography Justus Liebig University Giessen Large-scale atmospheric circulation characteristics and their relations to local daily precipitation extremes in Hesse, central Germany

  2. Objective Challenges Coarse resolution of global climate models (GCMs) Doubt about the reliability of some GCM output variables scale mismatch between the reliable outputs of GCM and climate change impact needs

  3. Outline Downscaling Selecting predictors Finding statistical relationship between predictors and predictand Validating the model

  4. Downscaling What is downscaling? A method for obtaining high-resolution climate or climate change information from coarse-resolution GCMs Downscaling techniques Regional climate models Weather classification and re–sampling Mixtures of stochastic processes, weather generators Linear and non–linear regression

  5. Statistical Downscaling Approaches

  6. Practical Considerations Predictors and Predictand Selecting best set of Predictors for each domain (size and location) Transform Function (or model type) Seasonal Variability Calibration and Validation

  7. Types of Predictors 1- Synoptic predictors (MSLP, 500 hPa Geopotential heights) 2- Temperature predictors (T850, Tmax, Tmin) 3- Moisture predictors (specific and relative humidity, precipitation) 4- Air flow predictors (u, v)

  8. Predictor Selection Considerations: An “ideal” Predictor should be: Strongly correlated with the Predictand Physically and/or conceptually sensible Able to preserve covariance between local variables Accurately described by the GCM Archived at the same temporal resolution as the local variable(s)

  9. Steps for selecting downscaling predictor variables 1- Calculating correlation between predictors and daily precipitation monthly /seasonal maxima 2- Calculating PCA1 and average for high correlated areas 3- Fitting GEV and finding confidence interval for location parameter 4- Finding correlation between predictors 5- Using AIC to find the best combination of predictors

  10. Correlation between specific humidity at 850hPa and maximum precipitation for winter

  11. Correlation between relative vorticity at 850hPa and maximum precipitation for winter

  12. Correlation between temperature at surface level and maximum precipitation for winter

  13. Model Selection Model Derivation for the statistical relations between selected set of predictors and the monthly/seasonal maxima of Percipitation Calculate AIC for each model Validate model by cross validation methods

  14. Summary Select predictand Station data Select predictors Screen variables ERA40 data Set model structure Calibrate model Downscale predictand ERA40 predictors GCM predictors Synthesize Observed data Generate scenario Analyse results Impact assessment

  15. Asking For your Suggestions Thank you for your suggestions

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