Multi-source climate data integration

Read CODATA Data Science Journal's new paper on 'Global Disaster Data Master Directory (GDMD)' complementing existing disaster databases like EM-DAT and other United Nations Office for Disaster Risk Reduction (UNDRR) platforms (e.g., DesInventar, GAR) to support global hub for integrating diverse disaster data resources. EM-DAT focuses on historical disaster events, and UNDRR tools specialize in localized disaster loss trends; GDMD connects over 1,400 datasets globally, enabling interoperability across scattered resources. It supports real-time, historical, and predictive data and adheres to #FAIR principles, offering human-readable and machine-accessible interfaces. GDMD's search feature allows unified access to multiple data sources to make a one-stop solution for disaster metadata aggregation. It's built on open-source technology (e.g., pycsw) and open standards, ensuring seamless collaboration with future platforms. It bridges gaps by enabling metadata exchange across databases like NASA FIRMS, DesInventar, and Munich RE. It is good disaster-related knowledge that integrates geospatial data, AI-ready resources, and climate projections for improving disaster monitoring, resilience planning, and response in line with the Sendai Framework goals. Simon Hodson, International Science Council Full paper here https://lnkd.in/g8dhhkVm Open Source Code available at GitHub: https://lnkd.in/gHzSXx4f

Global #precipitation products leave gaps and biases, especially over mountains and at high rain rates. Check this new paper in Communications Earth & Environment where we introduce an open-access framework that merges multi-source observations using regional-scale intelligent optimization and explicit #topographic factors within an end-to-end neural pipeline. The approach reconstructs missing values and corrects biases in global, time-varying precipitation fields, yielding stronger correlations and reduced errors versus standard satellite estimates. Results suggest immediate value for #storm monitoring and #flood forecasting in data-sparse, complex terrain. Paper: Communications Earth & Environment (2025), doi:10.1038/s43247-025-02624-3. https://lnkd.in/dKp6NsyY Hohai University Consiglio Nazionale delle Ricerche

🌍 Improving Climate Model Accuracy Using Machine Learning: A Multi-Model Ensemble Approach 📢 Just wrapped up an exciting project where I used Bayesian Optimization + XGBoost to compute a Multi-Model Ensemble (MME) of Global Climate Models (GCMs). 🧠 The Goal: Climate models vary widely. Instead of relying on a single GCM, I combined outputs from multiple models—CESM2-WACCM, INM-CM4-8, and EC-Earth3—to better match observed record. 🔧 The Process: ✅ Data Preprocessing ✔ Cleaned + normalized GCM & observed data ✔ Filled missing values and ensured time-consistent splits ✅ Bayesian Optimization Used scikit-optimize to find optimal hyperparameters for an XGBoost model, accelerating convergence with smart probabilistic search. ✅ Grid Search Refinement Fine-tuned the best Bayesian result using a local Grid Search for extra precision. ✅ Evaluation 📊 Metrics: R², RMSE, and NSE 📈 Visuals: Time series comparison + residual analysis 🔍 Why It Matters: MMEs are crucial for reducing uncertainty in climate predictions. By integrating machine learning with GCM outputs, we can boost reliability for real-world decision-making—from water resource management to climate adaptation strategies. 🚀 Youtube video link🌱 https://lnkd.in/d5k3wFrx #ClimateChange #MachineLearning #XGBoost #BayesianOptimization #GCM #EnvironmentalScience #AI4Climate #Hydrology #DataScience #ClimateModeling #Python #TimeSeries #MME

Scientists from San Diego State University and Woods Hole Oceanographic Institution collaboratively built a toolkit for proactive ocean management. The Fisheries and Climate Toolkit (FaCeT) brings together mulitple data sources using ArcGIS Hub to integrate satellite-based ocean data, climate models, biological data, and ecological forecasting tools. The goals are to support resource and conservation managers, policymakers, and communities understand and adapt to changes taking place in the ocean. Learn more in this article from Dr. Camrin Braun (Woods Hole Oceanographic Institution) and Dr. Rebecca Lewison (San Diego State University). https://lnkd.in/gvhq5_eS