anahita amiri department of geography justus liebig university giessen n.
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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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anahita amiri department of geography justus liebig university giessen
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
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

outline
Outline

Downscaling

Selecting predictors

Finding statistical relationship between predictors and predictand

Validating the model

downscaling
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

practical considerations
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

types of predictors
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)

predictor selection considerations
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)

steps for selecting downscaling predictor variables
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

model selection
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

summary
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

asking for your suggestions
Asking For your Suggestions

Thank you for your suggestions