API Client for NASA POWER Global Meteorology, Surface Solar Energy and Climatology in R

Download meteorological data from NASA POWER using a simple Python API client (https://power.larc.nasa.gov/).

Simple Python script to download historical meteorological data records from 1981 to today for any place on Earth from Nasa Power: https://power.larc.nasa.gov/data-access-viewer/

Official code for arXiv:2604.11807 - Physics-Informed State Space Models for Off-Grid Solar Forecasting

Bot for downloading Solar irradiance data at target locations and region surrounded it.

Instantly estimate soil water loss worldwide!

☀️ Analyze NASA POWER solar irradiance data with a professional Python toolkit for accurate assessments in climate research and renewable energy.

Solar Radiation Prediction from NASA POWER data Ver.2

Professional Python toolkit for analyzing NASA POWER satellite-derived solar irradiance data with multi-language support, document export capabilities, and comprehensive statistical analysis features

Machine learning-based flood and flash flood prediction across 8 Malaysian cities using Decision Tree, Random Forest, and XGBoost. 16-year NASA POWER MERRA-2 dataset (2010–2026).

Bayesian Long Short-Term Memory (LSTM) neural network to demonstrate uncertainty-aware forecasting of solar irradiance. The model predicts daily Global Horizontal Irradiance (GHI) and provides confidence intervals for predictions, allowing us to understand both the forecast and its associated uncertainty.

Wind vs. Solar LCOE feasibility analysis at a real Ankara site (METU K1) using live PVGIS & NASA POWER APIs, DCF modelling, and 2026 Turkish market data

Automate the downloading and merging process from NASA POWER dataset

Official code: Physics-Informed Cross-Attention Networks for Solar Irradiance Forecasting with Dual Self+Cross Attention

AI-based Smart Farming Decision Support System using multi-temporal NASA climate data | Decision Tree, Random Forest & Gradient Boosting | ACLI Index | 92.61% Accuracy | IEEE WAMS 2026 Accepted Paper

This program aims to develop a solar potential map of India.

Computational analysis of direct solar radiation (DNI) and diffuse solar radiation (DIF) across multiple climatic regions using long-term satellite data from the NASA POWER dataset. The study examines how geographic location, atmospheric conditions, and seasonal patterns influence solar radiation distribution.

Project analyzing the relationship between El Niño weather patterns and cocoa futures market volatility using NASA satellite data and financial market analysis.

Official code for arXiv:2604.13455 - Physics-Guided CNN-BiLSTM for Solar Irradiance Forecasting

Web-based drought monitoring and SPI drought analysis platform by AgriMetSoft.