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.
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
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)
🌾 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.
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.
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).
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
Professional Python toolkit for analyzing NASA POWER satellite-derived solar irradiance data with multi-language support, document export capabilities, and comprehensive statistical analysis features
Official code: Physics-Informed Cross-Attention Networks for Solar Irradiance Forecasting with Dual Self+Cross Attention
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
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.
Official code for arXiv:2604.13455 - Physics-Guided CNN-BiLSTM for Solar Irradiance Forecasting
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