July 10 2026
Theme: Satellite and radar data assimilation
1) Xingchao Chen (CADRE)
Title: All-Sky Radiance Data Assimilation for Tropical Cyclogenesis Forecasting: Current Successes and a Path Toward Better Performance
2) Zhuoya Ni (University of Reading)
Title: Construction of Observation Errors for HIRAS Water Vapor Channels in All-Sky Assimilation Using the CMA-MESO Model
3) Nathan Snook (CADRE)
Title: Implementation and Optimization of the Multi-Grid Beta Filter for Radar DA for the 3DEnVar MPAS-JEDI Framework
May 15 2026
Theme: Land-atmosphere coupled data assimilation
Aaron Johnson (CADRE)
Title: Toward improved land-atmosphere strongly coupled data assimilation at convective scales: Sampling error characteristics and land surface parameter uncertainty.
Feb. 13 2026
Theme: Radar and satellite data assimilation
1) Miranti Indri Hastuti (U. Reading)
Title: Impact of model resolution on all-sky infrared radiance statistics and cloud-dependent observation error modelling over Indonesia
2) Yunji Zhang (CADRE)
Title: Utilizing PBL Observations from Ground-Based Remote-Sensing Platforms
3) Steven Greybush (CADRE)
Title: Opportunities for Enhancing Data Assimilation for Winter Weather Prediction
Dec. 12 2025
Theme: Data assimilation and machine learning
1) Peter Jan Van Leeuwen (CADRE)
Title: Identifying missing physics using machine learning
2) Eviatar Bach (U. Reading)
Title: Learning enhanced ensemble filters
3) Xuguang Wang (CADRE)
Title: Toward advancing multiscale data assimilation through machine learning
Oct. 24 2025
Theme: Non-Gaussian Observation Errors in Data Assimilation
1) Jonathan Poterjoy (CADRE)
Title: Assimilating Earth system measurements with unknown or error-prone measurement operators
2) Rishabh Bhatt (DARC, Reading)
Title: Estimating spatial observation error correlations for all-sky AMSU-A at ECMWF and UK Met Office
3) Peter Jan Van Leeuwen (CADRE)
Title: Estimation and implementation of non-Gaussian observation errors in existing DA schemes
4) Yumeng Chen (DARC, Reading)
Title: Ensemble Kalman filter in latent space using a variational autoencoder pair