Explore @RDCEP Open Source projects

@RDCEP

RDCEP

Stars
110
Global Org. Rank 55,390 (Top 18 %)
Registered over 12 years ago
Most used languages

Python
36.0 %

R
28.0 %

JavaScript
8.0 %

CSS
8.0 %

Shell
4.0 %

GAMS 4.0 %

Fortran
4.0 %

Jupyter Notebook
4.0 %

MATLAB
4.0 %
Location 🇺🇸 United States
Country Total Rank 43,573
Country Ranking

GAMS 31

Fortran
566

R
1,443

MATLAB
6,287

Jupyter Notebook
9,322

psims

ggcmi

chicagowebdice

clouds

Jupyter Notebook

STEP

a Python package that identifies, tracks, and computes physical characteristics of rainstorms.

climate_emulator

Climate Emulator

BEAM-carbon

wth_gen

Fortran code for generating lots of weather files for crop models from NetCDF files.

sankey-timeline

PyFUND

A Python version of the FUND model [work-in-progress]

EDE

energy-maps

The US Energy Infrastructure Visualization is a data visualization web app that classifies and displays nationwide infrastructure data by infrastructure type, total asset value, and location on a map of the United States.

NBCD

Download the National Biomass and Carbon Dataset from the Woods Hole Research Center

CFS

Download data from the CFS (Climate Forecast System) archive and CFS2 rolling updates.

faoAfricaData

Convert African agriculture data from FAO CountrySTAT to GIS formats

conus-soil

Download data from the Penn State Soil Info web site for analysis

watch

transform WATCH weather archive into pSIMS format

galaxyTools-wrf

WRF Toolbox for FACE-IT Galaxy

atlas-db

Data storage and ingestion for ATLAS

Rainstorm-Object-Characteristics

Rainstorm Object Characteristics for Stage IV (2002-2011).

NLCEQ_CODE

1896

various utilities and codes developed for pSIMS applications around the 1896 project

hadisd

Convert the HasISD station data to pSIMS daily weather

face-it

nldas

A workflow to download and transform NLDAS weather data

atlas-viewer

atlas-of-variability-code-package

The Atlas of Variability Code Package allows a quick, standardized analysis of the variability characteristics of climate model output. It is a series of MATLAB functions that take NetCDF files and return results from spectral density analysis and basic distribution characteristics, and present them in several useful visualizations.