Use of variable ozone in a radiative transfer model for the global Météo‐France 4D‐Var system

Abstract

Nowadays, the assimilation of satellite observations, particularly radiances from infrared sounders, into numerical weather prediction (NWP) models plays a dominant role in improving weather forecasts. One of the keys to make optimal use of radiances is to simulate them with a radiative transfer model (RTM). At Météo-France, the RTTOV RTM is used for NWP models. Currently, simulations are carried out taking into account single chemical profiles. However, neglecting the spatial and temporal variability of these gases can affect the accuracy of the simulations and thus the quality of the subsequent analyses and forecasts. To reduce the impact of this assumption on weather forecasts, we use a variational bias correction but it would be more appropriate to correct the bias directly at the source. Ozone is one of the atmospheric constituents with significant impacts on spectral radiances measured by hyperspectral infrared sounders. Thus, the objective of this paper is to replace the use of a single ozone profile with a realistic and variable ozone field in RTTOV for the simulation of infrared observation. The results show that the use of a variable ozone allows us to further reduce biases in simulation of ozone-sensitive channels but also of channels sensitive mainly to other parameters such as CO 2 for example. This has positive effects on the analyses and improves the fit of the short-range forecasts (or analyses) to other observations such as radiosondes, microwave radiances, GNSS-RO bending angles, etc. All of these positive impacts allow us to significantly improve weather forecasts. K E Y W O R D S 4D-Var data assimilation, chemistry transport model, numerical weather prediction, ozone, radiative transfer model. This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes.

Analysis of MTG‐IRS observations and general channel selection for numerical weather prediction models

“…In addition, several NWP centres have included O$$$ {}_3 $$$ as a variable in the models because it improves the assimilation of specific channels in the infrared, which are sensitive to O$$$ {}_3 $$$, temperature, and water vapour, and it allows better accounting of radiative feedback, which improves weather forecasts (Coopmann et al . 2020a; 2018; Derber and Wu, 1998; Dragani et al ., 2018; Ivanova et al ., 2017; John and Buehler, 2004; Lahoz et al ., 2007). Thus, we have selected the most informative channels in temperature, water vapour, skin temperature, and O$$$ {}_3 $$$.…”

“…Finally, we extracted atmospheric profiles (temperature, humidity, O$$$ {}_3 $$$, cloud fraction, cloud liquid water, cloud solid water) and surface parameters (surface pressure, skin temperature, temperature at 2 m, humidity at 2 m, zonal and meridional wind at 10 m) for the 7,458 case studies. The profiles are extracted from a research configuration of the ARPEGE NWP global model at Météo‐France (Coopmann et al ., 2020a). This database will be used for the construction of the background profiles used as a priori and the synthetic observations.…”