This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 640276.
Remedy 2: Improved characterisation of error covariances in GRUAN measurements.
Uncertainty-covariance information needs to be made available and used appropriately within applications that convert from geophysical-profile data to TOA radiances. Firstly, the profile information needs to contain the uncertainty and the correlation structure in a usable format. Within GAIA-CLIM, simple parametrised versions of the vertical error covariances have been developed and tested as part of the significance testing in the GRUAN processor. Further work could refine approaches to more robustly utilising the uncertainty covariance information available.
Alternative approaches based on methods (Desroziers et al, 2005) routinely used to characterise errors in data assimilation systems should also be tested. This method requires that observations are actively assimilated. Initial estimates could be obtained from sub-selecting from the larger set of GUAN data currently assimilated in operational NWP systems, where the selection is based on those GUAN stations exhibiting gross-error characteristics similar to those of GRUAN measurements.
The solution proposed here is fully aligned with the requirement (to establish traceable uncertainties for NWP fields and radiances calculated from them).
Parametrised error covariances, developed and tested in consultation with experts from the GRUAN community.
- High
- Single institution
- Consortium
- Less than 3 years
- Low cost (< 1 million)
- Yes
- EU H2020 funding
- National funding agencies
- National Meteorological Services