31 skills found · Page 1 of 2
nasa / NASAaccessNASAaccess is R package that can generate gridded ascii tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys, ..etc)
amidaware / Trmm AwesomeLinked awesomeness from the open source community about Tactical RMM
ghiggi / Gpm ApiA python package to download and analyze the Global Precipitation Measurement Mission (GPM) data archive
dinger1986 / TRMM GrafanaGrafana Dashboards setup and preconfigured to work with Tactical RMM
adokter / RslTRMM Radar Software Library (RSL)
urmilkadakia / Rainfall Prediction For The State Of Gujarat Using Deep Learning TechniquePrediction 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.
matlat8 / TRMM RustDesk IntegrationNo description available
imohamme / NASAaccessNASAaccess is R package that can generate gridded ascii tables of climate (CIMP5) and weather data (GPM, TRMM, GLDAS) needed to drive various hydrological models (e.g., SWAT, VIC, RHESSys, ..etc). The package assumes that users have already set up a registration account(s) with Earthdata login as well as authorizing NASA GESDISC data access. Please refer to https://disc.gsfc.nasa.gov/data-access for further details. The package relies on 'curl' (https://curl.haxx.se/) commands and library to access and download data from NASA remote sensing servers. Since Mac users have curl as part of macOS, Windows users should make sure that their local machines have 'curl' installed properly. Creating the ".netrc" file at the user machine 'Home' directory and storing the user NASA GESDISC logging information in it is needed to execute the package commands. Instructions on creating the ".netrc" and ".urs_cookies" files can be accessed at https://wiki.earthdata.nasa.gov/display/EL/How+To+Access+Data+With+cURL+And+Wget.
amidaware / Trmm DocsDocumentation for the Tactical RMM software
sahg / PytrmmPyTRMM is a BSD licensed Python library containing tools for reading TRMM data files
lapig-ufg / PPTsPrecipitation Processing Tools (PPTs) it's an open source code developed by Vinícius Vieira Mesquita to download and process satellite precipitation data from NASA Tropical Rainfall Measuring Mission (TRMM) and Global Precipitation Measurement (GPM)
vlouf / Matchproj PythonScript for matching space-borne radar and ground radar (cross-validation code).
vlouf / Gpmmatch🛰️ Ground-radar vs TRMM/GPM volume-matching. 🛰️
environmentalinformatics-marburg / HeavyRainDownload and pre-process (partly) satellite-based CHIRPS and TRMM rainfall data sets in R.
bernardolankheet / Trmm TelegramNo description available
jaypotnis / JP 01 Real Time Alignment And Distribution Of Weather Radar Data With Rain Gauge Data For Deep LearnQuantitative Precipitation Estimation (QPE) based on weather radar observations plays a significant role in the understanding of weather events, especially in real-time, where fast evolving phenomena like convective storm cells can be dangerous. We wish to demonstrate QPE using deep learning as an alternative approach to empirical relationship equations between rainfall rate and reflectivity which were developed in the past. QPE using radar reflectivity is one of the possible applications of deep learning in the weather radar field. Preprocessing this data and saving it in real-time on cloud would let the users skip the time-consuming preprocessing step and assist them to directly get to the deep learning phase. To train and test deep learning models with radar data, we must align rain gauge data in space and time. This data preprocessing requires time and resource consuming processes that involve downloading, extracting, gridding, aligning the radar data with respect to every gauge in the region. If this preprocessed dataset was readily available in real-time, deep learning can be easily performed on it by anyone without going through the heavy computations required in the process. EarthCube’s CHORDS tool is a real-time data service that can be used to store preprocessed data on cloud so that it can be accessed whenever and wherever required. In this work, we demonstrate the steps involved in preprocessing such as accessing WSR-88D radar and NASA-TRMM rain gauge data, Cartesian gridding of radar data, aligning the radar data with gauge data in real-time. This aligned data is stored on cloud using CHORDS, so that it can be readily available to users who wish to use it for deep learning. The notebook will also demonstrate the procedure for storing and retrieving the dataset from CHORDS server and an example of the deep learning process on the downloaded dataset.
khalidmeister / Trmm DownscalingNo description available
ssteeltm / Trmm Sync ItflowNo description available
bashclub / Trmm ScriptsNo description available
derekwtian / TRMMAEfficient Methods for Accurate Sparse Trajectory Recovery and Map Matching (ICDE'25)
