We are steadily working towards a new version of PCRaster that is better capable of using the current generation of hardware resources. The goal is to make future versions of PCRaster execute models faster. More information about this work can be found on the new High-performance modelling parent project page.
We are glad to announce the final release of PCRaster-4.1.0! We fixed several bugs in the software and documentation, added support for pickling PCRaster Python types, and changed the location of shared libraries on Windows. For more information please visit the download page.
We released a beta version of the upcoming PCRaster-4.1.0 release. The (user visible) changes are listed here:
PCRaster packages are available for:
- Linux 64bit
- Windows 64bit
We dropped support for Window 32 bit. If 32 bit Windows is still important for you, you can stick with the previous version of PCRaster (4.0.2).
Please give this beta a spin if you have the chance. Note that since PCRaster 4, multiple versions of PCRaster can be installed side by side. You never have to uninstall a previous version if you prefer not to. Just install each new version next to the older one(s) and update the PATH and PYTHONPATH environment variables. If things don’t work for you, then please let us know, and revert the environment variables to their previous settings.
In case no show-stopper bugs are found, we plan to release the final version of PCRaster-4.1.0 about two weeks from now.
Merijn de Bakker joined our team as a PhD student. In his project he aims at the development of concepts for a domain specific language for integrated modelling of fields and agents. Ultimately we hope this will lead to a completely new modelling language in which models can be built containing both decision making agents and fields. Merijn will apply his concepts to an air pollution exposure case study in cooperation with our colleagues from the Healthy Urban Living research group at Utrecht University.
In the context of the Healthy Urban Living programme we implemented a set of Land Use Regression models to determine the spatial distribution of several air pollution concentrations (e.g. NO2, NOx). These models cover the entire Netherlands at 5m resolution (the figure below shows the PM10 concentration).
We combine these field-based concentration maps with human activity patterns, for instance to calculate the total exposure of individuals during their home-work travel. For more information join our presentation in the Air Pollution Modelling session next month at the EGU or contact us at email@example.com.
We have added e-Lectures to our online courses on dynamic modelling. The lectures provide a short introduction to the material. Subscribe now at the Courses link above to learn how to use our software!
We are glad to announce the final release of PCRaster-4.0.2! We fixed several bugs and added some functional enhancements for the Modflow extension. For more information please read the changes document. Packages are available for 64-bit Linux as well as 64-bit and 32-bit Windows systems.
For more information, visit the PCRaster 4.0.2 download page:
The PCRaster team, in particular Koko Alberti who recently joined our team, has developed prototype software to run PCRaster models as web simulations, at a very high (~90 m) resolution, for almost any location on earth! The current facility includes prototype models for sea level change, snow cover, and water erosion. The web simulations are available here (login required). For information and to request a login, please email firstname.lastname@example.org.
In a previous post we mentioned Canopy as a Python distribution that can be used to develop PCRaster Python models. The PCRaster Python package is compatible with other Python distributions though. One user informed us that he liked using Anaconda, and at our institute we installed WinPython for our students.