ssk351

ncl-precipi

NCL-Scripts-for-WRF

This repository includes NCL scripts that can be used to post-processing WRF outs, including but not limited to spatial plots, write WRF outputs to csv files, and time-height plots. Please feel free to contact Xia Sun (emsunxia@gmail.com) if you have any questions. I would happy to help.

container-wrf

WRF in containers, related code and data sets for release purposes.

docker-ncl

Centos7 with NCL installed for WRF post-processing

ERA5-Reanalysis_plots

Visualizing ECMWF's ERA5 Reanalysis from both Single and Pressure Levels on hourly basis (These notebooks can be used, accordingly, based on your preference).

hirsch_james_2019

hrldas

HRLDAS (High Resolution Land Data Assimilation System)

HWRF_LPI_TM

Miscellaneous

ncl-1

ncl_script

pih-ncl-scripts

NCL Scripts for WRF output

Python

Python-for-Earth-Science-Students

pyWRF

pyWRF is designed to read, process, and plot data from the Weather Research and Forecasting model.

Rainfall-prediction-for-the-state-of-Gujarat-using-deep-learning-technique

Prediction of rainfall which varies both spatially and temporally is extremely challenging. Infrared and visible spectral data from satellites have been extensively used for rainfall prediction. In this study, two deep learning methods MLP and LSTM are discussed at length for predicting precipitation at a fine spatial (10km × 10km) and temporal (hourly) resolution for the state of Gujarat. These methods are applied by using the multispectral (VIS, SWIR, MIR, WV, TIR1, TIR2) channel data such as cloud top temperature and radiance values of the INSAT-3D satellite (ISRO) as features for the model. Textural features of satellite images are incorporated by considering mean and standard deviation of each pixel’s neighbourhood. Rainfall also heavily depends on the elevation and vegetation of earth’s surface so we have used SRTM DEM and AWIFS NDVI respectively. Measurements of actual rainfall are obtained from AWS (point source stations) and TRMM (10km × 10km resolution). First dataset contains only TIR1 band temperature and AWS rainfall data for training but the second dataset includes multispectral channel data and TRMM rainfall data which brought about great improvement in results. For each data- set, a comparison between MLP and LSTM models is discussed here. We were able to classify the rainfall into nil (0mm), low ( < 2mm), medium ( > = 2mm and < 5mm) and high ( > = 5 mm) with a high accuracy. Metrics like accuracy, precision, recall and fscore have been computed to get better insights about the dataset and its corresponding outcome. Our results show that LSTM performs significantly better than MLP for any given balanced class data-sets.

ssk351

Config files for my GitHub profile.

wrf

will give the namelist files

wrf_Qatar

WRF for Qatar

wrf_shell_scripts

WRFDA_TOOLS

A repository for graphics and other scripting tools for WRFDA. These are offered AS IS and support may not be available.

wrflib_instsh

Shell scripts for installing libraries in order to compile the WRF model and additional apps.

wrfout_ncl

WRFV3

This is the release repository for the Weather Research and Forecasting Model