Reflectances simulated by running RTTOV/MFASIS on input meteorological fields from ECMWF’s operational IFS short-range forecasts are compared to observations in the 0.64-μm channel of the MSG-4, Himawari-8, GOES-16, and GOES-17 geostationary satellite imagers. This work is an extension to multiple geostationary satellites of the validation focused on GOES-16, which was recently described in Lopez et al. (2022). A comparison of validation results obtained with input data from operational forecasts from IFS versions 47R3 and 47R1 is also presented.
Month-long statistics of reflectances are used to identify deficiencies in both the IFS and in the RTTOV/
MFASIS radiative transfer model. This study confirms the existence of biases resulting from simplifications in RTTOV/MFASIS, such as those made in the Rayleigh scattering computations, and from the use of a look-up table. These biases mainly affect regions with low solar elevation. As for the IFS, the two main issues relate to the representation of trade-wind low-level clouds over tropical oceans, with an underprediction of marine stratocumuli on their eastern side, and oversized cloud clusters in their central part. Other systematic discrepancies between the IFS and observations are evidenced over Africa, during both the dry season (excessive shallow convection over Ethiopian Highlands) and the rainy season (occasional poor representation of squall lines over the Sahel; excessive cloud amounts from Guinea to the Ivory Coast). In other respects, prescribing land surface reflectance properties from a monthly atlas is found to yield a reasonable agreement between simulated and observed clear-sky reflectances during the investigated period.
This work can be seen as a preliminary step towards the assimilation of visible reflectances in ECMWF’s 4-Dimensional Variational data assimilation (4D-Var) system
Reflectances simulated by running RTTOV/MFASIS on input meteorological fields from ECMWF’s operational IFS short-range forecasts are compared to observations in the 0.64-μm channel of the MSG-4, Himawari-8, GOES-16, and GOES-17 geostationary satellite imagers. This work is an extension to multiple geostationary satellites of the validation focused on GOES-16, which was recently described in Lopez et al. (2022). A comparison of validation results obtained with input data from operational forecasts from IFS versions 47R3 and 47R1 is also presented.
Month-long statistics of reflectances are used to identify deficiencies in both the IFS and in the RTTOV/
MFASIS radiative transfer model. This study confirms the existence of biases resulting from simplifications in RTTOV/MFASIS, such as those made in the Rayleigh scattering computations, and from the use of a look-up table. These biases mainly affect regions with low solar elevation. As for the IFS, the two main issues relate to the representation of trade-wind low-level clouds over tropical oceans, with an underprediction of marine stratocumuli on their eastern side, and oversized cloud clusters in their central part. Other systematic discrepancies between the IFS and observations are evidenced over Africa, during both the dry season (excessive shallow convection over Ethiopian Highlands) and the rainy season (occasional poor representation of squall lines over the Sahel; excessive cloud amounts from Guinea to the Ivory Coast). In other respects, prescribing land surface reflectance properties from a monthly atlas is found to yield a reasonable agreement between simulated and observed clear-sky reflectances during the investigated period.
This work can be seen as a preliminary step towards the assimilation of visible reflectances in ECMWF’s 4-Dimensional Variational data assimilation (4D-Var) system