Developing methods to detect, quantify and allow for bias and errors in observations, and to estimate their value for weather prediction.
Data assimilation works by using the 'innovations' between observations and their prior forecast from the NWP system. As this becomes more accurate and detailed, it is increasingly important to estimate and allow for expected bias and errors.
We are frequently asked questions about the value of proposed observing systems to future NWP systems - a reasonable question from those responsible for running and funding them. To answer them requires experience on a broad range of measured current impacts of observations, from our own experiments and others worldwide, interpreted using judgement and theoretical insight.
This work is coordinated closely with Satellite Applications.
Methods for identifying and correcting bias in observations and the model.
Methods for detecting gross errors in observations and for monitoring their overall accuracy.
Methods for objectively measuring the impact and value of observations.
Research into the use of observations not simply related to the main forecast variables, such as cloud and precipitation.
Development of a system to measure adjoint-based observation sensitivities and impacts from our operational 4D-Var system.
Development of a system to improve the bias correction of radiosondes.
Development of a system to estimate observing system bias correction coefficients within the variational data assimilation.
Monitoring and determining the cause of occasional poor forecasts, particularly when due to the use of observations.