• Automated Statistical Downscaling (ASD)

• Statistical Downscaling Model (SDSM)

• Stochastic Weather Generator (LARS-WG)

Getting Started with ASD Version 1.1

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). This tool has been developed by the team of the Canada Research Chair on the Estimation of Hydrometeorological variables (Pr. T. Ouarda) at INRS-ÉTÉ, in collaboration with the Adaptation and Impacts Research Division (Environment Canada, i.e. Dr. Philippe Gachon). The development team includes also Pr. M. Hessami from Shahid Bahonar University of Kerman (Iran) and A. St-Hilaire from INRS-ÉTÉ. ASD runs on all platforms that support MATLAB, i.e. you need MATLAB to run the ASD software. For a more detailed introduction to the software, see the ASD Brief Introduction Document and the User Guide (Version 1.1) for all details on this method.

ASD is a hybrid of a stochastic weather generator and regression-based downscaling methods and facilitates the rapid development of multiple, low-cost, single-site scenarios of daily surface weather variables under current and future climate forcing. ASD is designed to help the user identify those large-scale climate variables (the predictors) which explain most of the variability in the climate (the predictand) at a particular site and statistical models are then built based on this information. Statistical models are built using daily observed data – local climate data for a specific location for the predictand and larger-scale NCEP data for the predictors – and these models are then used with GCM-derived predictors to obtain daily weather data at the site in question for a future time period.

Where can I get ASD?

After a registration procedure where a passcode will be send to you via email, you can download the software, and user manual for free from this page.

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

It is best to set up a new directory for each site you wish to downscale using ASD. This directory should contain both the observed daily data (i.e. the predictand) and the observed (NCEP, i.e. National Centre for Environmental Prediction, Kalnay et al., 2006) and GCM-derived predictors. You will need to supply the predictand information, but predictor information can be obtained from the CCCSN web site (see Statistical Downscaling Input). CGCM2 predictors can be downloaded from [here] and CGCM3 predictors can be downloaded from [here].

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, i.e., 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) to be compatible with ASD. Date information should not be included. Have a look at the observed daily data included with the Blogsville example to ensure that you understand the format required. 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 will need to make sure that ASD will recognise the code used to identify missing data values in the data set (a threshold value for the identification of missing values, i.e. miss value, any values less than the threshold value will be treated as missing). This is done by entering the correct code in the Miss Value Identifier window on the Setting/Parameters screen (see ASD Introduction V1.1. document for more information).

Predictor files downloaded from the CCCSN are in zipped format and the files contained within the zip file 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.

How do I use ASD?

All details how to use ASD are given in the ASD Introduction V1.1 document and in the guidelines.

For any question or feedback concerning ASD please contact masoud.hessami@gmail.com or Philippe Gachon

Further Reading:

ASD tool

• Hessami, M., P. Gachon, T. Ouarda, and A. St-Hilaire, 2008: Automated regression-based Statistical Downscaling Tool. Environmental Modelling & Software, 23, 813-834.
• 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, 65p.
• 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. (2008): 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, in final review. [Supervised by P. Gachon].

Predictors Variables (Reanalysis)

• Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K.C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D., (1996) The NCEP/NCAR 40-year reanalysis project. Bulletin of the American Meteorological Society 77, 437-471.

Administrative contact

Dr. Philippe Gachon (Ph.D)
Research Scientist
Adaptation & Impacts Research Division (AIRD) Environment Canada @ McGill University Department of Civil Engineering and Applied Mechanics
817 Sherbrooke Street West
Montreal, Québec, Canada, H3A 2K6
Phone: 514-398-2930
Fax: 514-398-7361
E-mail: philippe.gachon@ec.gc.ca or philippe.gachon@mail.mcgill.ca

Pr. Masoud R. Hessami K.
Assistant Professor Shahid Bahonar University of Kerman Department of Civil Engineering Kerman 76169-133, Iran
Phone: +98 341 322-0054
Fax: +98 341 322-0054
E-mail: masoud.hessami@gmail.com
http://www.uk.ac.ir/hessami

Technical problems ? Click here.