Sylvain Schmitt - Jul 29, 2024
Define the project.
knitr::include_graphics("dag/dag.svg")
This workflow is built on:
- Python ≥3.5
- Mambaforge
conda activate base
mamba create -c conda-forge -c bioconda -n snakemake snakemake
conda activate snakemake
snakemake --helpOnce installed simply clone the workflow:
git clone git@github.com:sylvainschmitt/climfor.git
cd climfor
snakemake -np module load bioinfo/Snakemake/7.20.0 # for test on nod depending on your HPC
snakemake -np # dry run
snakemake --dag | dot -Tsvg > dag/dag.svg # dag
snakemake -j 1 --resources mem_mb=10000 # local run (test)
sbatch job_muse.sh # HPC run with slurmDifferent configuration parameters to set in
config/config.yml
ordered by steps:
- Environment:
dev-climfor.yml
Description.
CHIRPS 2.0: Rainfall Estimates from Rain Gauge and Satellite Observations
Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 35+ year quasi-global rainfall data set. Spanning 50°S-50°N (and all longitudes) and ranging from 1981 to near-present, CHIRPS incorporates our in-house climatology, CHPclim, 0.05° resolution satellite imagery, and in-situ station data to create gridded rainfall time series for trend analysis and seasonal drought monitoring.
TMF V2.1.1: Tracking long-term (1990-2022) deforestation and degradation in tropical moist forests
The European Commission’s Joint Research Centre developed this new dataset on forest cover change in tropical moist forests (TMF) using 41 years of Landsat time series. The wall-to-wall maps at 0.09 ha resolution (30m) depict the TMF extent and the related disturbances (deforestation and degradation), and post-deforestation recovery (or forest regrowth) through two complementary thematic layers: a transition map and an annual change collection over the period 1990-2022. Each disturbance (deforestation or degradation) is characterized by its timing and intensity. Deforestation refers to a change in land cover (from forest to non-forested land) when degradation refers to a temporary disturbance in a forest remaining forested such as selective logging, fires and unusual weather events (hurricanes, droughts, blowdown).
MODIS: Moderate Resolution Imaging Spectroradiometer
MODIS (or Moderate Resolution Imaging Spectroradiometer) is a key instrument aboard the Terra (originally known as EOS AM-1) and Aqua (originally known as EOS PM-1) satellites. Terra’s orbit around the Earth is timed so that it passes from north to south across the equator in the morning, while Aqua passes south to north over the equator in the afternoon. Terra MODIS and Aqua MODIS are viewing the entire Earth’s surface every 1 to 2 days, acquiring data in 36 spectral bands, or groups of wavelengths (see MODIS Technical Specifications). These data will improve our understanding of global dynamics and processes occurring on the land, in the oceans, and in the lower atmosphere. MODIS is playing a vital role in the development of validated, global, interactive Earth system models able to predict global change accurately enough to assist policy makers in making sound decisions concerning the protection of our environment.
ERA5-Land is a reanalysis dataset providing a consistent view of the evolution of land variables over several decades at an enhanced resolution compared to ERA5. ERA5-Land has been produced by replaying the land component of the ECMWF ERA5 climate reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. Reanalysis produces data that goes several decades back in time, providing an accurate description of the climate of the past.
First create or update the dev-ideas mamba environment with required
libraries:
mamba env create -f envs/dev-climfor.yml # init
mamba env update -f envs/dev-climfor.yml --prune # update
mamab activate dev-climfor