The future state of a dynamical system is typically calculated by integrating a numerical model, and depends on parameters such as initial conditions, model errors, empirical parameters of the model, and possibly on lateral boundary conditions. Observations add new information that can be combined with model prediction to produce optimal values of dynamical system parameters and reduce their uncertainty. A mathematical methodology that can accomplish this is called data assimilation.

Data assimilation is fundamentally probabilistic since the uncertainty of dynamical system parameters can be described by a probability density function. Since dynamical models add valuable prior information, data assimilation is commonly based on Bayes theorem and thus represents a Bayesian inference. Data assimilation is also nonlinear since dynamical prediction models and observation operators can be highly nonlinear.

CIRA data assimilation research has the following goals:

1. develop new and improved data assimilation methodologies,
2. apply data assimilation to high-dimensional problems in geosciences and engineering, including carbon cycle, weather, climate, and hydrology.

Since we are primarily interested in geosciences applications to high-dimensional dynamical systems, the high-performance computational component of data assimilation is also of great importance to our research.

Typical data assimilation methodologies developed and improved at CIRA are variational, ensemble, and hybrid ensemble-variational methodologies, but we are also exploring other avenues. Our research is encompassing a wide range of applications including carbon cycle, hydrology, climate, and cloud-resolving processes. We are also actively supporting NOAA research and development by conducting data assimilation research using NOAA operational systems and observations.

RELATED PROJECTS

• Earth system research Lab/Global Monitoring Division Carbon Tracker Modeler and Software Developer
• Fine resolution CO2 Flux Estimates from AIRS and GOSAT CO2 Retrievals: Data Validation and Assimilation
• GEOS-CARB: A framework for monitoring carbon concentrations and fluxes
• Impact Assessment and Data Assimilation of NOAA NPP/JPSS Sounding products and Quality Control Parameters
• Improvements to background error covariances and moisture representation in the Navy's data assimilation system
• POES-GOES Blended Hydrometeorological Products
• A Multisensor 4-D Blended Water Vapor Product for Weather Forecasting
• Downscaling NCEP Global Climate Forecast System (CFS) Seasonal Predictions for Hydrologic Applications Using Regional Atmospheric Modeling System (RAMS)
• Sensitivity of regional climate due to land-cover changes in the eastern United States since 1650
• NESDIS Environmental Applications Team
• North american regional-scale flux estimation and observing system design for the nasa carbon monitoring system
• Ensemble Data Assimilation for Hurricane Forecasting
• Ensemble Data Assimilation for Nonlinear and Nondifferentiable Problems in Geosciences
• Ensemble-based Assimilation and Downscaling of the GPM-like Satellite Precipitation Information
• Utility of GOES-R Instruments for Hurricane Data Assimilation and Forecasting
• AFWA Coupled Assimilation and Prediction System Development at CIRA
• Five Year Cooperative Agreement for Center for GeoSciences/Atmospheric Research, Technology Transition and Interactions

RESEARCH HIGHLIGHTS

• Ensemble Data Assimilation and Prediction
  Our research is developing a general methodology for uncertainty estimation of dynamical systems. At present, our focus is the development of the Maximum Likelihood Ensemble Filter (MLEF) with applications to weather, climate, and carbon cycle.