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Earth System Digital Twin Development of NASA's Unified WRF for Assessing the Impact of Land-Cover and Land-Use Changes on Regional Weather
Project Start Date
05/05/2025
Project End Date
05/05/2028

Team Members:

Person Name Person role on project Affiliation
Toshihisa Matsui Principal Investigator University of Maryland, College Park , College Park ,
Takamichi Iguchi Co-Investigator University of Maryland, Greenbelt, USA
Mark Caroll Co-Investigator NASA GSFC, Greenbelt , USA
Carlos A. Cruz Co-Investigator Science Systems And Applications, Inc., Greenbelt, USA
Jian Li Co-Investigator INUTEQ, LLC, Greenbelt   , USA
Abstract

Land Cover and Land Use Change (LCLUC) has profoundly affected large parts of the Earth's surface, driven by the need to provide food, fiber, water, and shelter for human civilization. Agricultural expansion and intensification are the primary drivers, impacting billions of hectares worldwide. Cropland now covers nearly 11% of the total land area, while urban areas occupy less than 5%. Other contributors to LCLUC include irrigated areas and wildfires, which rapidly convert forests to agriculture or shrubland. These changes are not uniformly distributed, leading to significant regional landscape perturbations. LCLUC influences weather and climate through alterations in surface albedo, heat fluxes, and temperatures, affecting the planetary boundary layer, local wind circulation, and broader meteorological patterns. The feedback between LCLUC and the atmosphere is complex and varies with surface flux changes and synoptic weather conditions.

This project aims to develop a cloud-based Land Earth System Digital Twin (L-ESDT) through Google Earth Engine (GEE) and NASA's Science Managed Cloud Environment (SMCE). GEE will facilitate the identification, processing, and transfer of LCLUC data, while SMCE will utilize the NASA Unified Weather Research and Forecasting (NU-WRF) model and Machine Learning (ML) and Artificial Intelligence (AI) to assess the impact of LCLUC on regional weather. The goal is to create an agile, generalized L-ESDT applicable globally where satellite remote sensing is available, focusing on specific regions. Existing LCLUC satellite datasets will be utilized through the GEE cloud system to assess the impact on regional weather using the NU-WRF and AI/ML platforms within NASA SMCE. This cloud platform will allow various stakeholders to utilize a high-end ESDT with minimal computational resources and knowledge.

The project aligns with LCLUC objectives by leveraging AI/ML, Big Data Analytics, and cloud computing for L-ESDT development. It will combine real-time LCLUC and biophysical data analysis within GEE with regional Earth-system model simulations and innovative AI/ML change-detection methods within SMCE. Concurrent visualization of L-ESDT flows will enable users to conduct impact assessments without significant computational resources. The project will provide near real-time, high-resolution mapping of LCLUC and biogeophysical data for regional weather forecasting. This platform will enable stakeholders to explore scenarios, test hypotheses, and simulate outcomes without requiring significant computational resources. The proposed cloud-based L-ESDT will enhance our ability to monitor and manage land cover changes, contributing to improved environmental and socio-economic outcomes.