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<p>Data assimilation (DA) of satellite visible radiance observations holds great potential to improve the forecasting skills of numerical weather prediction (NWP) models, yet several challenges remain. This review synthesizes recent progress in visible radiance DA and critically assesses its impacts on the analyses and forecasts of cloud and precipitation. As two major components of DA, observation operators [e.g., the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for TOVS (RTTOV)] and DA methods (e.g., variational and ensemble-based approaches) have both received significant research efforts. On one hand, improvements in observation operators, particularly in radiative transfer solvers and cloud optical parameterizations, have enhanced both computational accuracy and efficiency for Top-of-Atmosphere (TOA) visible radiance simulations. However, there are still challenges in generating representative visible images due to uncertainties in cloud optical parameterizations and simplifications of three-dimensional (3D) radiative effects. On the other hand, variational DA methods are constrained by the quasi-static assumption in background error covariances, whereas ensemble Kalman filter (EnKF)-based methods offer greater flexibility at the current stage. Nevertheless, both methods are limited by the nonlinear and non-Gaussian nature of moist physics processes. EnKF-based methods also suffer from limitations in vertical localization. Although particle filters are theoretically well suited to nonlinear and non-Gaussian problems, the strong nonlinearity in observation operators severely limits the representativeness of resampled particles. Future efforts should focus on incorporating 3D radiative effects, refining cloud optical parameterizations, designing DA methods specifically for nonlinear and non-Gaussian problems in visible radiance assimilation, and designing adaptive vertical localization strategies. Addressing these challenges will facilitate more effective application of visible radiance DA in cloud and precipitation forecasting.</p>

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Satellite Visible Radiance Data Assimilation: A Review

  • 期刊论文

  • 发表日期：

    2026年6月25日

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  • Chao Liu,
  • Chong Luo,
  • Yongbo Zhou,
  • Songhao Li,
  • Yumeng Ai

摘要

Data assimilation (DA) of satellite visible radiance observations holds great potential to improve the forecasting skills of numerical weather prediction (NWP) models, yet several challenges remain. This review synthesizes recent progress in visible radiance DA and critically assesses its impacts on the analyses and forecasts of cloud and precipitation. As two major components of DA, observation operators [e.g., the Community Radiative Transfer Model (CRTM) and the Radiative Transfer for TOVS (RTTOV)] and DA methods (e.g., variational and ensemble-based approaches) have both received significant research efforts. On one hand, improvements in observation operators, particularly in radiative transfer solvers and cloud optical parameterizations, have enhanced both computational accuracy and efficiency for Top-of-Atmosphere (TOA) visible radiance simulations. However, there are still challenges in generating representative visible images due to uncertainties in cloud optical parameterizations and simplifications of three-dimensional (3D) radiative effects. On the other hand, variational DA methods are constrained by the quasi-static assumption in background error covariances, whereas ensemble Kalman filter (EnKF)-based methods offer greater flexibility at the current stage. Nevertheless, both methods are limited by the nonlinear and non-Gaussian nature of moist physics processes. EnKF-based methods also suffer from limitations in vertical localization. Although particle filters are theoretically well suited to nonlinear and non-Gaussian problems, the strong nonlinearity in observation operators severely limits the representativeness of resampled particles. Future efforts should focus on incorporating 3D radiative effects, refining cloud optical parameterizations, designing DA methods specifically for nonlinear and non-Gaussian problems in visible radiance assimilation, and designing adaptive vertical localization strategies. Addressing these challenges will facilitate more effective application of visible radiance DA in cloud and precipitation forecasting.