What is the price and ability of SD-GCM?

As we know two fundamental types of downscaling exist: dynamical and statistical. Dynamical downscaling nests a regional climate model in a global climate model, and is advantageous in that it physically resolves processes that occur at scales smaller than the driving GCM. However, dynamical downscaling suffers from biases introduced by the driving GCM (e.g. Plummer et al., 2006) and computational demands. Thus, current dynamical downscaling capabilities are limited by a lack of ensembles, and have to date been used sparingly in climate impact assessment. By contrast, statistical downscaling is computationally efficient, is able to directly incorporate observations used in operational decision-making or modelling, and can be applied across multiple GCMs to develop ensembles for scenario building.

Statistical downscaling can produce site-specific climate projections, which RCMs cannot provide since they are computationally limited to a 20-50 kilometers spatial resolution. However, there are different methods in statistical downscaling approach, but the implementation of them is not easy for researchers. So, Agrimetsoft has decided to develop a software which can easy run three various methods of statistical downscaling.

In this software user can make a database for applying every CMIP5 model under every RCP scenario. SDGCM with manual data entry of input values and other details are to be provided as a separate file. All CMIP5 models have an especial format in the name of models. Every model has developed by a special company, and every company produce an individual name. Therefore, there is a challenge to read the name of files by the software for every model.

In this software, the user can easily browse the desired CMIP5 model for a specific variable. There is an option in the SDGCM software for evaluation data of models. In this step the user can assess the ability of the CMIP5 model with observation data, in a common period. The observation data would be in Excel format file and the order of data in the columns are not important, therefore user can easily load the input observation data without any concerns.

There are six efficiency criteria for evaluation phase: Pearson Correlation, Nash-Sutcliffe efficiency, Spearman Correlation, RMSE (Root Mean Squared Error), d (index of agreement), and MAE (Mean Absolute Error).There are a complete help file in this package that describes all setup steps. For the price, please visit the list of meteorological softwares.