Optimizing cloud optical parameterizations in RTTOV for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images

Zhou, Yongbo; Cao, Tianrui; Zhu, Lijian

doi:https://doi.org/10.5194/egusphere-2025-242

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https://doi.org/10.5194/egusphere-2025-242

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19 Feb 2025

| 19 Feb 2025

Status: this preprint is open for discussion and under review for Atmospheric Measurement Techniques (AMT).

Optimizing cloud optical parameterizations in RTTOV for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images

Yongbo Zhou, Tianrui Cao, and Lijian Zhu

Abstract. The Radiative Transfer for TOVS (RTTOV) is a commonly used forward operator software package for the Data Assimilation (DA) of satellite visible reflectance data. However, a wide choice of Cloud Optical Parameterizations (COPs) in RTTOV poses challenges in discerning the optimal configuration. In this study, the performance of different COPs was evaluated by comparing the observed and synthetic visible satellite images. Observed images (O) were provided by Fengyun (FY)-4B and Himawari-9, two operational geostationary meteorological satellites covering East Asia. Synthetic images (B) were generated by RTTOV with the Discrete Ordinate Method (DOM) and the Method for FAst Satellite Image Simulation (MFASIS). The inputs to RTTOV were provided by the 3-h forecasts of the China Meteorological Administration Mesoscale (CMA-MESO) model and the fifth generation European Centre for Medium-Range Weather Forecasts reanalysis (ERA5) data. On the domain average, B was smaller than O, especially in cloudy situations. The minimum O-B bias was revealed for the COP of liquid water clouds in terms of effective diameter (Deff) in combination with the COP of ice clouds developed by the Space Science and Engineering Center (SSEC), with the Deff for ice clouds parameterized in terms of ice water content and temperature. Compared with the O-B biases, the standard deviations of the O-B departure were less sensitive to COPs. In addition, histogram analysis of reflectance indicated that the synthetic images with the minimum O-B bias resembled best with the observed images. Therefore, the optimal cloud optical parameterization was proposed to be the “Deff” + “SSEC” suite.

Received: 20 Jan 2025 – Discussion started: 19 Feb 2025

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Yongbo Zhou, Tianrui Cao, and Lijian Zhu

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RC1: 'Comment on egusphere-2025-242', Anonymous Referee #1, 12 Mar 2025 reply

Review of the manuscript titled "Optimizing cloud optical parameterizations in RTTOV for data assimilation of satellite visible reflectance data: an assessment using observed and synthetic images" by Yongbo Zhou, Tianrui Cao, and Lijian Zhu.
This paper evaluates parametrizations of visible reflectance simulated from CMA-MESO and ERA5 using RTTOV with observations from instruments onboard the satellites FY-4B and Himawari-9.
Results show that the observed reflectance is higher than modelled reflectance, on average, and that the choice of an optimal configuration yields the lowest bias while only slightly increasing unbiased error (standard deviation of O-B). In clear-sky pixels, the atmospheric contribution to visible reflectance is near zero. Thus, the choice of parametrization had no effect. The bias is induced by the land surface representation in the NWP model. In cloudy sky, bias is more relevant than in clear sky cases, because the bias is responsible for a larger fraction of error, as shown in Figures 10, 11, and 12. In clear sky, bias was mostly lower than unbiased error, except of experiment CM-FY-DM in April (Figure 3).
The authors already nicely addressed my comments to an earlier version of the manuscript in review for another journal. For example, the authors extended the analysis by the standard deviation of O-B. The manuscript is now an extensive evaluation and in a very good condition. I recommend publication after minor comments are addressed.

Minor comments
L243-244: "the increased B for C213 would amplify the O-B differences, leading to the increased standard deviations". The standard deviation (SD) is not influenced by the bias (average O-B). Thus, an change in average B does not change the SD. The explanation for an increased SD must be larger O-B differences, due to B being too high and too low (increase in |O-B|).
Figure 4: The caption seems to have an error, as the x-axis label is "reflectance" not "O-B departure".
Equation 6 seems to be incorrect, not all terms are in the exponent, compared to Equation 2 of McFarquar et al. (2003).
L237, L275: Language: Instead of "was expected", you probably mean "was found"? Or was there an expectation before seeing the results?
L243: You state "opposite circumstance" but the same sign (O-B>0) as in the line above. Probably you mean O-B < 0?
L248: "at the low- or high-reflectance ends": Maybe you can quantify this, e.g. "at low reflectance (<0.1)" or similar.
L271: "The results in Figure 4 suggested that the Baran 2018 ice scheme should be used with caution ...".

I am confused, because Figure 4 shows results for ice_scheme=1 (SSEC) and not for "Baran" parametrization. Maybe you meant Figure 5?
Figure 7 caption (c) missing.
L367: Language, "B was consistently underestimated compared with O", I think this should be "B consistently underestimated O".
L393: "The smallest O-B bias". Good, but can you quantify it? For example "the optimal configuration reduced the bias by 0.04 on average, while standard deviation increased by less than 0.005.

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Citation: https://doi.org/10.5194/egusphere-2025-242-RC1

Yongbo Zhou, Tianrui Cao, and Lijian Zhu

Data sets

The processed data for evaluating cloud optical parameterizations in RTTOV Yongbo Zhou, Tianrui Cao, and Lijian Zhu https://doi.org/10.5281/zenodo.14642334

Yongbo Zhou, Tianrui Cao, and Lijian Zhu

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Short summary

Different Cloud Optical Parameterizations (COPs) in RTTOV are evaluated by the comparing the observed and synthetic satellite images. The optimal COP for liquid water cloud is parameterized in terms of effective diameter (D_eff) in combination with the COP for ice cloud developed by the Space Science and Engineering Center (SSEC), with the D_eff for ice cloud parameterized in terms of ice water content and temperature. The findings will benefit the remote sensing and data assimilation community.


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