Today, large model discrepancies exist in estimated cloud radiative effects (CREs) and irradiances across 1‐D radiative transfer schemes aimed for climate models. The primary purpose of this study is to understand physical causes of such model discrepancies, especially in CREs under partly cloudy sky. To achieve this goal, the unique Cloud‐Aerosol‐Radiation (CAR) ensemble modeling system was employed, offline driven by the ERA‐Interim global data for July 2004 with no feedback considered. For evaluating each individual contribution from the existing scheme diversity of cloud horizontal inhomogeneity, cloud optical properties, cloud vertical overlap, and gas absorptions, several sets of numerical experiments were conducted. It is the first time to explicitly demonstrate that after removing most of the disagreement in cloud fields, model spreads of CREs among the CAR's seven major radiation schemes, as well as those of radiative fluxes, dramatically diminish. Taking global mean CREs for example, their current model ranges can decrease to <4 W m⁻² from about 10 W m⁻² for shortwave and also to <4 W m⁻² from 5–8 W m⁻² for longwave. Dominant roles of subgrid‐scale cloud structures (including vertical overlap and horizontal variability) were proven in general, explaining about 40–75% of the total model spreads. We have also found that model spreads of CREs are very sensitive to cloud cover fractions. Such nonlinear sensitivity can be largely reduced after removing the model difference in the treatments of cloud vertical overlap.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12–M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites (S-NPP, NOAA-20, and NOAA-21) over eight months spanning from 18 March to 30 November 2023. The field of interest is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.102 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily VIIRS operational data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference observed in overlapping TEBs between VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. It is also found that the remnant scene temperature dependence of NOAA-21 versus NOAA-20/S-NPP M13 O-O BT difference after accounting for SRF difference is ~0.0033 K/K, an order of magnitude smaller than the corresponding rates in the direct BT comparisons between NOAA-21 and NOAA-20/S-NPP. Our study confirms the versatility and effectiveness of the RTM-based TEB quality evaluation method in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing a robust mechanism for assessing inter-sensor consistency. Moreover, for M12 operating as a shortwave infrared channel, it is found that the daytime O-B BT differences of S-NPP M12 exhibit greater seasonal variability compared to the nighttime data, which can be attributed to the idea that M12 radiance is affected by the reflected solar radiation during the daytime. Furthermore, in this study, we’ve also characterized the spatial distributions of inter-VIIRS BT differences, identifying variations among VIIRS M TEBs, as well as spatial discrepancies between the daytime and nighttime data.

This study investigates the long-term stability of the Suomi National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) moderate-resolution Thermal Emissive Bands (M TEBs; M12 – M16) covering a period from February 2012 to August 2020. It also assesses inter-sensor consistency of the VIIRS M TEBs among three satellites-S-NPP, NOAA-20, and NOAA-21 over eight months spanning from March 18 to November 30, 2023. The field of interesting is limited to the ocean surface between 60°S and 60°N, specifically under clear-sky conditions. Taking radiative transfer modeling (RTM) as the transfer reference, we employed the Community Radiative Transfer Model (CRTM) to simulate VIIRS TEB brightness temperature (BTs), incorporating European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis data as inputs. Our results reveal two key findings. Firstly, the reprocessed S-NPP VIIRS TEBs exhibit a robust long-term stability, as demonstrated through analyses of the observation minus background BT differences (O-B ∆BTs) between VIIRS measurements (O) and CRTM simulations (B). The drifts of the O-B BT differences are consistently less than 0.105 K/Decade across all S-NPP VIIRS M TEB bands. Notably, observations from VIIRS M14 and M16 stand out with drifts well within 0.04 K/Decade, reinforcing their exceptional reliability for climate change studies. Secondly, excellent inter-sensor consistency among these three VIIRS instruments is confirmed through the double-difference analysis method (O-O). This method relies on the O-B BT differences obtained from daily data. The mean inter-VIIRS O-O BT differences remain within 0.08 K for all M TEBs, except for M13. Even in the case of M13, the O-O BT differences between NOAA-21 and NOAA-20/S-NPP have values of 0.312 K and 0.234 K, respectively, which are comparable to the 0.2 K difference between some TEBs among VIIRS and MODIS. These disparities are primarily attributed to the significant differences in the Spectral Response Function (SRF) of NOAA-21 compared to NOAA-20 and S-NPP. Our study confirms the RTM-based TEB quality evaluation method’s versatility and effectiveness in assessing long-term sensor stability and inter-sensor consistency. The double-difference approach effectively mitigates uncertainties and biases inherent to CRTM simulations, establishing itself a robust mechanism for assessing inter-sensor consistency. In addition, we’ve observed that, except for M13, M12 always exhibits larger spatial variations of O-O BT differences with greater uncertainties compared to the other M TEBs. The influence of solar contribution through sea surface reflection on the Top of Atmosphere (TOA) radiance measurements during daytime in M12 should not be underestimated.

Physics-based fast radiative transfer models (RTMs), such as the Community Radiative Transfer Model (CRTM), face increasing computational demands from growing satellite data volumes and model complexity. We developed a physics-informed Liquid Neural Network emulator for CRTM (CRTM-LNN) that draws on continuous dynamical-system concepts while retaining physical interpretability. The framework combines an Ordinary Differential Equation (ODE)-inspired Liquid Neural Network for layer-by-layer optical-depth modeling with an analytic, differentiable radiative-transfer solver. This design preserves the explicit optical-depth-to-radiance pathway and incorporates physical constraints directly into the forward calculation. In the implementation evaluated in this study, the LNN uses discrete hidden-state updates on the fixed European Centre for Medium-Range Weather 91-layer (ECMWF91L) grid under clear-sky, absorption-only assumptions. Evaluation using Infrared Atmospheric Sounding Interferometer (IASI) observations and ECMWF91L forecast profiles shows that CRTM–LNN closely reproduces the reference CRTM simulations, with global, channel-mean brightness-temperature biases below 0.1 K in magnitude. Regional bias magnitudes nevertheless reach approximately 0.2–0.4 K for selected channels and latitude bands. Correlations with CRTM exceed 0.97 for cumulative optical depth, transmittance, and weighting functions in regimes with cumulative optical depth below five. It accelerates forward calculations by up to 18-fold and efficiently generates Jacobians through automatic differentiation. Compared with a conventional multilayer perceptron CRTM emulator, CRTM-LNN produces smoother and more physically consistent Jacobians, with improved vertical localization and fewer spurious oscillations. Absolute centroid-pressure errors for temperature Jacobians are reduced across all evaluated channels, ranging from 1.77 to 5.01 hPa for CRTM-LNN versus 5.88–8.17 hPa for CRTM–MLP, although the magnitude of improvement varies by channel. These results highlight the potential of the integrated CRTM–LNN framework, which combines ODE-driven neural emulation with physics-based radiative-transfer modeling, to provide a scalable foundation for atmospheric retrievals, satellite data assimilation, and near-real-time radiative-transfer applications. Current limitations and opportunities for extension to more complex radiative processes are also discussed.