Other application and Met Pathfinder studies
Read other application and Met Pathfinder studies.
30 January 2023
22 June 2020
Two precursor studies have been conducted with ECMWF and Météo-France to assimilate forecast-independent temperature and humidity retrievals (i.e. Level 2 products (L2)) from satellite hyperspectral sounders in numerical weather prediction (NWP) models. These studies build on the regional service EARS-IASI L2, which provides atmospheric sounding in clear and cloudy pixels to users within less than 30 minutes from sensing. The two studies also contribute to MTG-IRS preparatory activities.
Positive impact on forecasts have been obtained in both data assimilation (DA) systems with the L2 products, comparable to the impact of assimilating radiances.
The studies aimed at evaluating the impact and assessing the practical aspects of assimilating L2 temperature and humidity profiles in an operational NWP context, relative to the assimilation of radiances.
While there are no immediate operational plans to assimilate L2 products in NWP centres, where the assimilation of satellite hyperspectral radiances is the paradigm, there is a renewed scientific interest for geophysical parameters in numerical models (Coniglio et al. 2019, Hu et al. 2019). They may represent an interesting alternative to maximise the information ingested e.g. in regional NWP contexts with strong timeliness constraints with respect to the computational requirements associated with the assimilation of radiances.
Both studies followed the following steps:
The IASI L2 data showed good quality as evaluated against independent observations routinely assimilated and monitored in the models, e.g. radiosoundings and airborne (AIREP) data, as well as against the two models themselves.
The preliminary assimilation experiments showed that the IASI L2 products are suitable for assimilation in NWP models.
The fact that positive results have been obtained despite the simplified assimilation configuration is very encouraging. Using averaging kernels of retrievals as observation operators would be beneficial both in the product evaluation and assimilation processes. In this perspective too, the utilisation of scene-dependent retrieval error would help exploit more retrievals with appropriate weighting in the vertical. This is the scope of a new study with ECMWF, where EUMETSAT is also looking into ways of mitigating the data volume issue this may represent.
The characterisation of horizontal error correlation would be needed to increase the density of IASI profiles assimilated in the system.