## 6 Experimental OMPS Radiance Assimilation  
Infrared and microwave radiances (actually brightness temperatures) have been operationally assimilated at NWP centers in support of weather forecasting. The infrared and microwave radiance assimilation has greatly improved the analysis of temperature, water vapor, and ozone in both the troposphere and stratosphere. Visible and ultraviolet radiances have not been assimilated because of a number of challenges, for example, the availability of a fast and accurate radiative transfer model and an appropriate bias correction scheme. The CRTM has been successfully supporting operational infrared and microwave radiance assimilation at NWP centers. Recently, the CRTM has been extended with the capability for ultraviolet radiance simulations and assimilations [30]. The extended CRTM can be run for fully polarized radiative transfer calculations or for scalar (i.e., intensity) calculations. However, in radiance assimilation, the fully polarized CRTM is 20 times slower than the scalar CRTM because Rayleigh scattering is present in all 91 layers. A method is needed to significantly reduce the computation time for pure Rayleigh scattering through a look-up table technique or a machine learning approach in the future. An artificial intelligence (AI) based radiative transfer model can be 100 to 1000 times faster than a traditional RT model [42]. We found that the large difference between the fully polarized and scalar CRTM calculations can be mitigated using corresponding surface reflectance. In this study, we have used the scalar CRTM for OMPS radiance simulation and assimilation. To further minimize the computational demand, we selected nine OMPS NM channels, similar to the channels used in ozone retrieval. The NDEV8 ozone product was retrieved from the Version 8 total ozone algorithm [12]. The algorithm uses a look-up table technique by matching radiances. A radiative transfer lookup table was generated using standard ozone profiles offline. For radiance assimilation, the selection of observations, bias correction strategy, error covariance matrix, and the minimization technique are critical. The background error covariance matrices were derived from one day of global analysis data. The observation error covariance matrix was based on experiments at several analysis times. We used the 1 DVAR technique in the minimization. We followed the steps of operational 3 DVAR radiance assimilation. The error covariance matrices remained unchanged, and the background was updated during the outer loop iteration. The increments from the minimization were then propagated back to the model grids at the desired analysis time. OMPS ultraviolet radiance assimilation is very different from assimilating infrared and microwave brightness temperatures. There are two key differences between microwave/infrared and ultraviolet/visible radiance assimilations. The first key difference is the radiation source. The radiation source within microwave and infrared spectrum is the thermal emission of the atmosphere and Earth's surface. The radiation over visible and UV spectral ranges is the solar radiation of sunlight. The short-wave infrared radiation can be affected by solar radiation, but they are not assimilated at numerical prediction centers. The second key difference is emissions vs. scatterings. Microwave and infrared radiation are dominated by emissions/absorptions, while UV and visible radiation are dominated by scatterings. For this reason, this is an initial experiment to assimilate OMPS radiance for atmospheric ozone in troposphere and stratosphere. Normalized radiance, a ratio between OMPS radiance and OMPS measured solar irradiance, is better for assimilation than radiance itself. The UV solar spectrum can change by a few percent [2], while the solar irradiance used in the CRTM is static. As one can see from Figure 4, the normalized radiance can vary by a factor of more than one hundred, which would be extremely challenging for the minimization procedure.