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.
台灣環境開放資料策展 — 海洋・氣候・水文・能源・空品・地震,每個來源附實測存取方法與踩過的坑,外加四個真實混淆稽核案例。Curated Taiwan environmental open data (ocean/climate/energy/air/seismic) with verified access recipes, gotchas & worked confound-audit case studies.
☀️ Analyze NASA POWER solar irradiance data with a professional Python toolkit for accurate assessments in climate research and renewable energy.
Instantly estimate soil water loss worldwide!
SAR-based crop yield forecasting for 966 Sokhda farm plots using six Capella X-band HH SLC passes, canopy-based scaling, external validation, and a reproducible Python pipeline.
Solar Radiation Prediction from NASA POWER data Ver.2
Reproducible code, data and notebooks for a twelve-model benchmark of statistical, ML and deep learning approaches to frost prediction in the Peruvian Altiplano (IJACSA 2025, DOI 10.14569/IJACSA.2025.0160992)
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.
🌾 End-to-end ML system: 50K-row dataset → XGBoost training → NASA POWER weather enrichment → Open-Meteo forecasts → Streamlit dashboard + FastAPI. Full SHAP explainability, farmer-editable overrides, weather risk alerts. Production-deployed on Streamlit Cloud.
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
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).
Professional Python toolkit for analyzing NASA POWER satellite-derived solar irradiance data with multi-language support, document export capabilities, and comprehensive statistical analysis features
Interactive global wildfire intelligence with historic incident replay, source-backed perimeters, meteorology, and exposure analysis.
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
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