Geology

Meteorology

Relative humidity

Scatterometer

Computer graphics (images)

Numerical weather prediction

Interpolation (computer graphics)

Moisture

Artificial intelligence

Kalman filter

Wind speed

Geography

Extended Kalman filter

Data assimilation

Geotechnical engineering

Water content

Computer science

Animation

Environmental science

Quarterly Journal of the Royal Meteorological Society

A new land surface analysis system based on a simplified point‐wise Extended Kalman Filter (EKF) was implemented at the European Centre for Medium‐Range Weather Forecasts in the global operational Integrated Forecasting System (IFS) in November 2010. This system will allow consistent and optimal analyses of land surface parameters like soil moisture, surface temperatures, snow and vegetation properties. As part of the system implementation, the surface analysis structure has been revised to permit an independent and parallel computation with the upper‐air 4D‐Var analysis. The new analysis system is used for the soil moisture analysis, replacing the previous Optimal Interpolation (OI) scheme. Similar to the OI system, the simplified EKF uses 2 m air temperature and relative humidity observations from the SYNOP (land surface synoptic report) ground‐based networks to analyse soil moisture. This paper describes the new land surface analysis, its application for analysing soil moisture, and initial verification results which supported its operational implementation at ECMWF. The performance is evaluated based on a set of one‐year analysis experiments. The simplified EKF is compared to the OI, on soil moisture, 2 m temperature and relative humidity, showing a consistent improvement on screen‐level parameters and soil moisture forecasts. To demonstrate the potential of the new analysis scheme, soil moisture derived from ASCAT (Advanced Scatterometer) has been assimilated through the simplified EKF.

A simplified Extended Kalman Filter for the global operational soil moisture analysis at ECMWF