Automated Statistical Downscaling (ASD)

What is ASD?

The Automated Statistical Downscaling (ASD) tool is an easy to use graphical user interface for the statistical downscaling of GCM outputs to regional or local variables (Hessami et al., 2008). The ASD tool is designed to help users identify those large-scale climate variables (the predictors) which explain most of the variability in the climate (the predictand) at a particular site. Statistical models are built based on this information (local climate data for a specific location for the predictand and larger-scale NCEP data for the predictors) and then used with GCM-derived predictors to obtain daily weather data at the site in question for a future time period. MATLAB is needed to run the ASD software.

This tool has been developed by the Canada Research Chair on the Estimation of Hydrometeorological variables team (leaded by Pr. Taha Ouarda) at INRS-ÉTÉ, in collaboration with the Adaptation and Impacts Research Division of Environment Canada (Dr. Philippe Gachon). The development team includes also Pr. Masoud Hessami from Shahid Bahonar University of Kerman (Iran) and André St-Hilaire from INRS-ÉTÉ.

For a more detailed introduction to the software, see the ASD Brief Introduction Document and the User Guide for all details on this method.

How do I prepare my own data for use in ASD Version 1.1?

It is best to create a new directory for each site you wish to downscale using the ASD tool. This directory should contain the observed daily data (i.e. the predictand) and daily observed (NCEP data; See Kalnay et al., 2006) and the GCM-derived predictors.

You will need to supply the predictand information, but CGCM2 and HADCM3 predictors can be downloaded from the CCCSN website and CGCM3 predictors can be downloaded from here. Predictor files downloaded from the CCCSN website (in zipped format) follow the correct naming convention and format used in ASD (same as for SDSM). This zip file contains both the observed and GCM-derived predictor variables. Unzip these files into the appropriate site directory making sure that you preserve the sub-directory structure. You will have downloaded these zip files based on which GCM you wish to use. Currently, GCM-derived predictors are available for experiments undertaken with CGCM2, CGCM3 and HadCM3. There is a very limited data set available since daily GCM data are required for the construction of predictors, and not all climate modelling centres archive daily data from their climate change experiments. The observed predictors, derived from the NCEP reanalyses, are contained within each zip file along with the GCM predictors. The observed predictors are interpolated to the GCM grid in question and thus are slightly different from GCM to GCM. If you are using more than one GCM for downscaling at each site, you should probably set up a separate directory for each GCM to make sure that you do not confuse the observed predictor data sets. You will have to go through the calibration process with ASD for each GCM in using NCEP predictors, since the observed predictors, and hence, the statistical relationships, will be slightly different from GCM to GCM.

Observed daily data for Canada from the Historical Adjusted Climate Database for Canada may be requested from Lucie Vincent (lucie.vincent@ec.gc.ca) for temperature and Eva Mekis (eva.mekis@ec.gc.ca) for precipitation. These data files are in row format (each row contains daily data for one month for each year). These data need to be converted to single column format (i.e. the data values only; Date information should not be included) to be compatible with the ASD tool. Reformatting can be done using a programming language, such as FORTRAN, or in a spreadsheet package, such as Microsoft Excel. Once you have correctly formatted the data, you have to make sure that ASD will recognise the code used to identify missing data values in the data set. This is done by entering the correct code in the Miss Value Identifier window on the Setting/Parameters screen (see ASD Brief Introduction Document for more information).

For any question or feedback concerning the ASD tool, please contact Masoud Hessami (masoud.hessami@gmail.com) or Philippe Gachon (philippe.gachon@ec.gc.ca).

For technical problems, please contact Patrice Constanza (constanza.patrice@ouranos.ca)

Further Reading

• Barrow, E. (2007). Assessing the Performance of the Automated Statistical Downscaling (ASD) Model in the Prairie Provinces. Environment Canada, Adaptation and Impacts Research Division, internal Report, 65pp.
• Gachon, P., St-Hilaire A., Ouarda T.B.M.J., Nguyen V.T.V., Lin C., Milton J., Chaumont D., Goldstein J., Hessami M., Nguyen T.D., Selva F., Nadeau M., Roy P., Parishkura D., Major N., Choux M. and Bourque A. (2005). A first evaluation of the strength and weaknesses of statistical downscaling methods for simulating extremes over various regions of eastern Canada. Sub-component, Climate Change Action Fund (CCAF), Environment Canada, Final report, Montréal, Québec, Canada, 209 pp. (available from the 1st author).
• Parishkura, D. (2009). Évaluation de méthodes de mise à l'échelle statistique: reconstruction des extrêmes et de la variabilité du régime de mousson au Sahel. Master Thesis, Dept of Earth and Atmospheric Sciences, UQÀM, Montréal. [Supervised by P. Gachon].