WOFOST-EW v1: Enhanced WOFOST for Extreme Weather WOFOST-EW v1 is an improved version of the WOFOST (World Food Studies Simulation Model) that improves crop growth simulations under extreme weather conditions. The model incorporates an Extreme Weather Function that integrates Extreme Weather Indices with an LSTM deep learning algorithm to improve prediction accuracy. In addition, the SCE-UA optimization algorithm is applied to achieve more efficient parameter calibration. --- Resources WOFOST-EW v1 Source Code- GitHub repository: https://github.com/zheng-jinhui/WOFOST-EW WOFOST ModelThe base model is part of the Python Crop Simulation Environment (PCSE):- PCSE Documentation- PCSE GitHub repository by Allard de Wit SCE-UA AlgorithmFor parameter calibration, the SCE-UA algorithm implementation is referenced in Spotpy:- Spotpy Documentation LSTM ImplementationThe LSTM algorithm is implemented using the Keras library:- Keras Documentation --- Publications This version of the WOFOST model has been used in the publication: > Zheng, J., Yu, L., Du, Z., Xiao, L., and Huang, X.: Modeling wheat development under extreme weather with WOFOST-EW v1, Geosci. Model Dev., 18, 8379–8400, https://doi.org/10.5194/gmd-18-8379-2025, 2025.