Earth system data assimilation


Reanalysis is the process of applying modern data assimilation techniques to historical periods. A consistent scheme is applied to periods of decades (for climate monitoring and associated products) or longer (for climate change studies). Reanalyses are important as they provide weather and climate information across the region, not just where there are observations. They give a more complete and coherent picture of the weather than could be obtained from observation data alone. Several institutes have produced high quality reanalyses for the whole globe (e.g. ECMWF ERA-40 and ERA-Interim, NASA MERRA), but the potential of high resolution regional reanalyses is just beginning to be exploited. The ESDA group has demonstrated a two year regional reanalysis over Europe as part of the EURO4M project. For the follow-on project, UERRA, the group is extending this to a period covering more than thirty years. It also uses an ensemble of lower-resolution analyses to give an estimate of uncertainty in the reanalysis fields. The aim is to provide reliable information about the state of the climate in Europe. The group is also collaborating with NCMRWF and IMD in developing a reanalysis over the Indian region. This is through the IMDAA project (Indian Monsoon Data Assimilation and Analysis), funded by the Indian Ministry of Earth Sciences through the National Monsoon Mission.

Land surface

A number of studies have shown that there can be strong coupling between soil moisture and atmospheric variables. The Met Office uses an Extended Kalman Filter (EKF) scheme to provide global analyses of soil moisture and temperature for the Unified Model. Satellite data is a critical component of this scheme, providing observations across the globe. The EKF scheme uses data from the ASCAT instrument which gives information on moisture in the top layer of the soil. Research is ongoing to increase the use of satellite data over land, such as the use of satellite-derived surface skin temperature. It is also possible to make use of screen-level observations of temperature and humidity. The scheme operates on the principle that model errors in soil moisture lead to model errors in near surface temperature and humidity. Therefore, model errors in screen-level temperature and humidity can be used to diagnose model errors in soil moisture. Where the model boundary layer is too cool and moist, it is likely that the model soil is too moist. Where the model boundary layer is too warm and dry, it is likely that the model soil is too dry. The JULES surface exchange model provides the exact relationship for each place and time.

Air quality

Air quality models represent the many chemical processes in the atmosphere to give estimates and forecasts of the concentrations of various constituents, for instance ozone, sulphur dioxide, particulate matter. The Met Office has been running such a model for many years. Recently it has joined a CAMS project (Copernicus Atmosphere Monitoring Service) as one of several partners that will produce a long period reanalysis of air quality over Europe. This requires the ability to assimilate air quality observations (near-surface reports of O3, NO2, SO2, PM10, PM2.5).  Work is underway to adapt our atmosphere data assimilation systems for this purpose.

Key Aims

  • To provide high resolution atmosphere reanalyses and uncertainty estimates for Europe.
  • Work with NCMRWF to provide high resolution atmosphere reanalyses for India.
  • To provide high resolution air quality reanalyses for Europe.
  • To provide initial land surface conditions for the Unified Model.

Current/Future Projects

  • Develop and trial a scheme for 4D-Var assimilation of precipitation accumulations.
  • Develop and run an ensemble-based European region reanalysis system to provide uncertainty estimates (UERRA), covering the satellite era 1979 to 2016.
  • Support and evaluate IMDAA regional reanalysis over India.
  • Develop and trial the capability to assimilate observations of air quality.
  • Extend the EKF scheme for the land surface, increasing and improving the use of observation data.
  • Adapt the EKF for use in high-resolution models over the UK.
  • Look to adopt LIS (NASA Land Information System) as our land surface DA system.