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

被引:2
作者
Coopmann, O. [1 ]
Guidard, V. [1 ]
Fourrie, N. [1 ]
Josse, B. [1 ]
机构
[1] Univ Toulouse, CNRS, CNRM, Meteo France, Toulouse, France
关键词
4D-Var data assimilation; chemistry transport model; numerical weather prediction; ozone; radiative transfer model; DATA ASSIMILATION; CHEMISTRY; TRANSPORT; IMPACT; IMPLEMENTATION; SIMULATIONS; RADIANCES; AEROSOLS; GASES; FLUX;
D O I
10.1002/qj.3869
中图分类号
P4 [大气科学(气象学)];
学科分类号
0706 ; 070601 ;
摘要
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 Meteo-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.
引用
收藏
页码:3729 / 3746
页数:18
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