ESPHome version of Elektor weather station v2

An elegant probability model for the joint distribution of wind speed and direction.

list of papers, code, and other resources

A convolutional neural network that estimates the wind speed of a hurricane based on its satellite image

Manually submit weather station information to the APRS-IS network.

A quick and simple Raspberry Pi touchscreen clock with Philips hue, Tado, Dark Sky and Philips TV JointSpace API controls/data

MakeCode package for the SparkFun weather:bit board - beta

WindSR Dataset contains more than 22,000 pairs of HR/LR wind speed images, which are processed using the NASA's GEOS-5 Nature Run dataset. This dataset is useful for studying super-resolution for data collected using satellites rather natural RGB images.

Powered by solar, an ESP32-C3-Mini collects data from the rain, wind (speed & direction), humidity, pressure, and temperature sensors then sends it to the Wunderground PWS site and a Telegraf listener via a Wi-Fi connection to my LAN. ☁️ 🌀 ⚡ ☀️ ☔

HY-2A/B/C/D & CFOSAT & FY-3E (WindRAD) & METOP (ASCAT) Satellite Wind Product Data Plotter (Based on HDF5 / netCDF4)

Dynamical Three Dimensional Wind Turbine Power Curve Model and IEC Corrections

Statistical analysis and forecasting of wind speed for 4 states and comparison against machine learning results

Wind Energy : A Practical Power Analysis Approach - Open Sourced Code for the Research Paper published in IEEE.

Repository of python-based Jupyter Notebooks for learning about using the Copernicus Sentinel-3 SRAL sensor for marine applications.

Helsinki Testbed Weather 2.0 - Android application

My weatherstation

Professional quality 3D printed automatic weather station.

The coupled flutter velocity of a single-span suspension bridge is computed in the frequency domain

ML-based wind speed forecasting using a 4-layer MLP neural network. Achieves 0.99474 R² score with comprehensive data preprocessing and visualization tools. Built with TensorFlow and scikit-learn.

Supplementary information about the paper "Exploring Quantum Machine Learning for Weather Forecasting". Authors: Maria Heloísa F. da Silva, Gleydson F. de Jesus, Christiano M. S. Nascimento, Valéria L. da Silva, Clebson S. Cruz. Code based on Ogur (2023)¹ for QNNs and Ekman (2021)² for RNNs, with Datasets from NASA POWER platform.