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SDMs for artic refugia study

Codes for the CHELSA-W5E5 paper

StreamFlow is an extension to the spatially-distributed snow model Alpine3D which allows the user to perform hydrological simulations. On top of discharge and water height, StreamFlow can also compute stream temperature at any point along the stream.

INIshell is a graphical user interface for numerical simulation software. It dynamically builds GUIs from XML files containing semantic descriptions of the models' parameters and allows for easy rapid deployment of new software features in the GUI.

The goal of niViz is to interactively generate a variety of plots from either measured or simulated snow profiles including auxiliary data (such as meteorological time series). This works in a web browser (for example, go to https://run.niviz.org).

Codes for the CHELSA_ISIMIP3b_BA_1km project.

Codes related to CHELSA EarthEnv project. Karger, D.N., Wilson, A.M., Mahony, C., Zimmermann, N.E., Jetz, W. (in review) 'Global daily 1km land surface precipitation based on cloud cover-informed downscaling', Scientific Data

Large Eddy Simulation flow solver coupled with Lagrangian Stochastic Model

Codes for very long chelsa climatologies

Containerised CKAN, Postgres, Solr using Docker

snowpackBuoyantPimpleFoam is a two-phase solver implemented to model convection of water vapor with phase change in snowpacks. This new solver is based on the standard solver of buoyantPimpleFoam in OpenFOAM 5.

Implements support for resource storage against multiple popular providers via apache-libcloud (S3, Azure Storage, etc...)

GC-Net

Django API and data import/export Python software package for long-term environmental monitoring data.

Backend database and API for MastWeb.

Microservice cron to make EnviDat data accessible via NASA Earthdata scrapers.

This python code uses the Finite Element library FENICS (via docker) to solve the one dimensional partial differential equations for heat and mass transfer in snow. The results are written in vtk format from which the paper figure is reproduced.

Wind-Topo is a statistical downscaling model for near surface wind fields especially suited for highly complex terrain. It is based on deep learning and was trained with data from 261 stations. Dujardin and Lehning 2022 "Wind-Topo: Downscaling.."