This document discusses statistical downscaling using the Statistical Downscaling Model (SDSM). It describes SDSM as a tool for assessing local climate change impacts that facilitates the development of climate scenarios through empirical relationships between regional predictors from global climate models and local predictands. The document outlines the 7 main steps in SDSM: quality control and data transformation, screening predictor variables, model calibration, weather generation, statistical analysis, scenario generation, and graphing monthly statistics.

1. STATISTICAL DOWNSCALING
USING SDSM
Department of Geophysical and Meteorology
Bogor Agricultural University
Indonesia
1
I Putu Santikayasa, PhD
psantika@gmail.com
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2. Outlines
1. Introduction
2. Global Circulation Model
3. Downscaling
4. SDSM
5. Case study
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3. Introduction
Global Circulation
Model
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4. Future climate projection
Scenario
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5. GCM output resolution
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6. statistical donwscaling - I Putu Santikayasa 6

7. Downscaling
Method to obtain local-
scale weather and climate,
particularly at the surface level,
from regional-scale
atmospheric variables that are
provided by GCMs
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8. Downscaling technique
1. Dynamical climate modelling
2. Synoptic weather typing
3. Stochastic weather generation
4. Transfer-function approaches
Statistical
downscaling
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9. Dynamic downscaling
• Dynamical downscaling involves the nesting of a higher
resolution Regional Climate Model (RCM) within a coarser
resolution GCM.
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10. Limitations of RCM
• Computationally demanding (Costly)
• The scenarios produced by RCMs are also sensitive to
the boundary conditions used to initiate experiments
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11. Weather typing
• Weather typing approaches involve grouping local
meteorological data in relation to prevailing patterns of
atmospheric circulation.
• E.g. cluster analysis, self-organising map, and extreme
value distribution
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12. Stochastic weather generators
• Modifying the parameters of conventional weather
generators.
• E.g Markov-type procedures, conditional probability
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13. Transfer Function
• Transfer-function downscaling methods rely on empirical
relationships between local scale predictands and
regional scale predictor(s)
• E.g. Regression, canonical correlation analysis, and
artificial neural networks
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14. statistical donwscaling - I Putu Santikayasa 14

15. SDSM
Statistical downscaling model
• SDSM is a decision support tool for assessing local
climate change impacts
• SDSM facilitates the rapid development of multiple, low-
cost, single-site scenarios of daily surface weather
variables under current and future regional climate
forcing.
• The software performs the additional tasks
• predictor variable pre-screening
• model calibration
• basic diagnostic testing
• statistical analyses and
• graphing of climate data.
http://co-public.lboro.ac.uk/cocwd/SDSM/
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16. 7 steps
to be performed in SDSM
1. Quality control and data transformation
2. Screening of predictor variables
3. Model calibration
4. Weather generation
5. Statistical analyses
6. Graphing model output
7. Scenario generation
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17. 1
2
3
4 7
5
6
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18. Getting started
• Installation
• Data preparation
• Start>Program>SDSM
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19. Application setting
• Year length
• Standard start/end date
• Allow Negative Values
• Event Threshold
• Missing Data Identifier
• Random Number Seed
• Default File Directory
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20. Advanced settings
• Model Transformation
• Variance Inflation
• Bias Correction
• Conditional Selection
• Optimisation Algorithm
• Settings File
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21. 1. Quality Control and Data Transformation
• Quality control : To check an input file for missing data
and/or suspect values
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22. Quality control and data transformation
• Data transformation
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23. 2. Screening Predictors Variables
• Identifying empirical relationships between gridded
predictors (such as mean sea level pressure) and single
site predictands (such as station precipitation)
• Central to all statistical downscaling methods
• The most time consuming step in the process
• As the guidance on selecting the appropriate predictor
variables for model calibration
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25. Screening predictors setup
• Select predictand file
• Select predictor variables
• Data: start/end date, analysis period
• Process: unconditional/conditional
• Significant level
• Autoregressive
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26. Screening variables
• investigate the percentage of variance explained by
specific predictand–predictor pairs. The strength of
individual predictors often varies markedly on a month by
month basis
Tmin :
temp,rhum,r500
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27. Correlation matrix
• investigate inter–variable
correlations for specified sub–
periods (annual, seasonal or
monthly).
• partial correlations between
the selected predictors and
predictand.
• help to identify the amount of
explanatory power that is
unique to each predictor.
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28. Scatterplot
• Used for visual inspections of
inter–variable behaviour for
specified sub–periods (annual,
seasonal or monthly).
• Indicate the nature of the
association (linear, non–linear,
etc.), whether or not data
transformation(s) may be
needed, and the importance of
outliers.
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29. 3. Model Calibration
• Constructs downscaling models based on multiple
regression equations, given daily weather data (the
predictand) and regional–scale, atmospheric (predictor)
variables
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30. Model Calibration
• Select Predictand File
• Output PAR file
• Data Period
• Model type
• Process
• Autoregression
• Residual analysis
• Chow test
• Histogram catagories
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32. 4. Weather Generator
• Produces ensembles of synthetic daily weather series
given observed (or NCEP re–analysis) atmospheric
predictor variables and regression model weights
produced by the Calibrate Model operation
• Enables the verification of calibrated models (assuming
the availability of independent data) as well as the
synthesis of artificial time series representative of present
climate conditions
• Used to reconstruct predictands or to infill missing data
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33. Weather generator
• Select Parameter File
• Select Predictor Directory
• Save To .OUT File
• View Details
• Synthesis Start/Length
• Ensemble Size
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34. 5. Statistical Analysis
• Summary statistic
• Observed
• modeled
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35. Statistical Analysis
• Data sources
• Select input/output
• Analysis period
• Ensemble size
• Select required analysis
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36. Statistical Analysis
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37. Frequency Analysis
• allows the User to plot various distribution diagnostics for
both modelled (ensemble members) and observed data
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38. Frequency Analysis
• Select Observed Data and/or modelled data
• Analysis Period
• Data Period
• Apply threshold
• PDF Categories
• Save result
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39. Quantile-Quantile (Q-Q) Plot
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40. PDF Plot
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41. Line Plot
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42. Extreme value analysis
Empirical
Generalised
Extreme
Value
Gumbel Streched
Exponential
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43. 6. Scenario Generation
• Produces ensembles of synthetic daily weather series
given daily atmospheric predictor variables supplied by a
GCM (either under present or future greenhouse gas
forcing). The GCM predictor variables must be normalised
with respect to a reference period (or control run) and
available for all variables used in model calibration
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44. Scenario Generation
• Check settings: Year Length Standard Start/End Date
• Select Parameter File
• GCM Directory
• Select Output File
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45. 7. Graphing Monthly Statistics
• Enables the User to plot monthly statistics produced by
the Summary Statistics
• Graphing options allow the comparison of two sets of
results and hence rapid assessment of downscaled
versus observed, or present versus future climate
scenarios
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46. Graphing Monthly Statistics
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47. Graphing Monthly Statistics
Observed
Simulated from
NCEP
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48. Graphing Monthly Statistics
Observed
Simulated from
NCEP
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49. Thank you
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