CADRE research is broad in scope, covering 12 distinct research thrusts that ultimately aim at revolutionizing forecasting on time/space scales ranging from convective weather to subseasonal-to-seasonal. The individual research thrusts focus on both advanced assimilation of existing and new observations and on advanced DA methods and capabilities, such as novel data assimilation algorithm development, novel use of ML and AI in data assimilation, and addressing data assimilation issues at the earth system component interfaces. CADRE will perform innovative research, fostering an environment of organic collaboration among the research thrusts on cross-disciplinary issues.
Advanced assimilation of existing and new observations for UFS short-range and medium-range weather, S2S, aerosol and ocean predictions.
Assimilation of ground-based, remotely-sensed planetary boundary layer (PBL) and all-sky satellite radiance observations to improve warm-season convective-scale prediction
- CADRE Advisor(s): David Stensrud, Yunji Zhang
- NOAA Collaborator(s): Dave Turner
Satellite data assimilation and observation gaps for tropical cyclogenesis prediction
- CADRE Advisor(s): Xingchao Chen, Sen Chiao
- NOAA Collaborator(s): Zhan Zhang, Xuejin Zhang, Gopal Sundararaman, Jason Sippel, Bin Liu
Enhancing satellite and radar data assimilation for winter weather prediction
- CADRE Advisor(s): Steven Greybush
- NOAA Collaborator(s): Anders Jensen, Haidao Lin
Assimilation of aerosol backscatter profiles to improve UFS GFS aerosol forecast
- CADRE Advisor(s): Sen Chiao, Sarah Lu
- NOAA Collaborator(s): Cory Martin
Implementation and testing of multigrid-Beta filter for radar DA within RRFS JEDI variational framework
- CADRE Advisor(s): Nathan Snook
- NOAA Collaborator(s): Shun Liu
Making use of existing observations and improving ocean predictions through direct assimilation of Lagrangian trajectory data
- CADRE Advisor(s): Kayo Ide
- NOAA Collaborator(s): Guillaume Vernieres
Advanced data assimilation methods and capabilities for UFS short-range, medium-range and S2S predictions.
Attributing model error and improving S2S coupled ocean-atmosphere predictions by weak constraint DA and ML
- CADRE Advisor(s): Peter Jan van Leeuwen
- NOAA Collaborator(s): Sergey Frolov
Non-parametric likelihood estimation for non-Gaussian/state-dependent observation errors in UFS GFS/SFS ice and ocean data assimilation
- CADRE Advisor(s): Jonathan Poterjoy
- NOAA Collaborator(s): Neil Barton
Strongly coupled land-atmosphere DA for S2S prediction
- CADRE Advisor(s): Zhaoxia Pu
- NOAA Collaborator(s): Clara Draper, Daryl Kleist
ML and AI based cost-efficient methods to improve RRFS multiscale ensemble DA
- CADRE Advisor(s): Xuguang Wang
- NOAA Collaborator(s): Sergey Frolov, David Dowell
Improving land-atmosphere coupled covariance for RRFS strongly coupled data assimilation to improve CONUS weakly forced convection initiation prediction
- CADRE Advisor(s): Xuguang Wang
- NOAA Collaborator(s): Clara Draper, Terra Ladwig, Mike Barlage
Non-Gaussian satellite radiance observation error estimation and implementation in existing GFS and RRFS DA
- CADRE Advisor(s): Peter Jan van Leeuwen
- NOAA Collaborator(s): Amanda Back, Ming Hu, Liaofan Lin