The NCEP reanalysis project has been evolved from NCEP/NCAR Reanalysis (R1, 1996), NCEP/DOE Reanalysis (R2, 2002) to NCEP Climate Forecast System Reanalysis (CFSRR, 2010) with continuous improvement in observation and data assimilation systems. The next generation NCEP climate-quality reanalysis under planning will be built on a unified global coupled analysis and forecast system. The system subcomponents, i.e. atmosphere, land, ocean, sea ice, wave, chemistry and ionosphere, are coupled; and there will be a continuous process of making the coupled Reanalysis (and Reforecasts) for every implementation (weather, sub-seasonal and seasonal). Moreover, predictions for all spatial and temporal scales will be ensemble based. Since the resolution of all parts of the system is usually increased with every new implementation (in proportion to the increased computing power currently available), more efficient cloud computing and storage options can be explored. This concept is going to be proved by following ongoing developments.

• Atmosphere: Hybrid 4D-EnVAR approach using a 80- member coupled forecast and analysis ensemble, with Semi-Lagrangian dynamics, and 128 levels in the vertical hybrid sigma/pressure coordinates.

•  Land: Inline Noah Land Model for land coupling.

• Ocean/Sea ice: GFDL MOM5.1/MOM6 and/or HYCOM for the ocean and CICE/KISS for sea-ice coupling, using the NEMS coupler.

•  Aerosols: Inline GOCART for aerosol coupling.

•  Waves: Inline WAVEWATCH III for wave coupling.

•  Ionosphere: Inline Whole Atmosphere Model (WAM) –up to 600km.

New Paradigm Improvement of Analysis Coupling Meeting Requirements

The operational CFSR uses a coupled atmosphere-ocean-land-sea ice forecast for the analysis background but the analysis is done separately for each of the domains. In the next reanalysis, the goal is to increase the coupling; so that, e.g., the ocean analysis influences the atmospheric analysis (and vice versa). This will be achieved mainly by using a coupled ensemble system to provide the background and the EnKF to generate structure functions that extend across the sea-atmosphere interface. The same can be done for the atmosphere and land, because assimilation of land data will be improved for soil temperature and soil moisture content. Since the local ensemble transform Kalman filter (LETKF) performs assimilation locally at each point, each grid point is solved independently. By passing observation departures (O-F) and model sub-state (land, ocean, atmosphere), one can solve LETKF equations independently (on different grids if desired) and decide (spatial/variable localization) how “strongly” to couple (sharing departures). For example, coupled LETKF can pass atmospheric O-F to ocean LETKF, which is equivalent to “strong coupling”. NOAA’s climate reanalysis products are extensively used for monitoring, attribution and forecasting/hindcasting, and have a global user base. It is required by monitoring to place real-time climate anomalies in a historical context, and by attribution studies to access physically consistent data sets to make explanations for extreme climate events. For forecasting/hindcasting, climate reanalysis data sets provide initial conditions and the base climatology, relative to which forecasts are issued. It is also needed for calibration, bias correction as well as verification, providing skill information to the users. Climate reanalysis data sets are required in a wide array of societal applications, e.g., decision making in the context of infrastructure development.

Climate-Quality Reanalysis

Accelerate the transition of data assimilation schemes and methodologies to improve climate quality reanalysis for climate monitoring and predictions.

Next Generation of Unified Global Coupled System




Sea Ice




Scientific Challenges

Reanalysis uses NWP like technology. Is there any room for a climate reanalysis? How would this differ?

Understanding reasons for discontinuities when new observational platforms come in.

Connecting climate reanalysis efforts and analysis efforts for initializing forecasting