Data assimilation for the land surface and for atmospheric reanalysis.
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 since 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. This aims to provide reliable information about the state of the climate in Europe. For the follow-on project, UERRA, the group will develop a European regional reanalysis for a period covering more than thirty years. This will use an ensemble of analyses to give an estimate of uncertainty in the reanalysis fields.
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.
Last updated: 9 December 2015