the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Evaluation of FY-4A/AGRI visible reflectance using the equivalents derived from the forecasts of CMA-MESO using RTTOV
Abstract. The Advanced Geostationary Radiation Imager (AGRI) onboard the FY-4A geostationary satellite provides high spatiotemporal resolution visible reflectance data since 12 March 2018. Data assimilation experiments under the framework of observing system simulation experiments have shown great potential of these data to improve the forecasting skills of numerical weather prediction (NWP) models. To assimilate the AGRI visible reflectance observations, it is important to evaluate the data quality and to correct the biases contained in these data. In this study, the FY-4A/AGRI channel 2 (0.55 μm - 0.75 μm) reflectance data were evaluated by the equivalents derived from the short-term forecasts of the China Meteorological Administration Mesoscale Model (CMA-MESO) using the Radiative Transfer for TOVS (RTTOV, v 12.3). It is shown that the observation minus background (O – B) statistics could be used to reveal the abrupt changes related to the measurement calibration processes. The mean differences of O - B statistics are negatively biased. Potential causes include the NWP model errors, the unresolved aerosol processes, the forward-operator errors, etc. The relative biases of O-B computed for cloud-free and cloudy pixels were used to correct the systematic differences in different conditions. After applying the bias correction method, the biases and standard deviations of O-B departure were reduced. The bias correction based on ensemble forecasts is more robust than deterministic forecasts due to the advantages of the former in dealing with cloud simulation errors. The findings demonstrate that analyzing the O-B departure is effective to monitor the performance of FY-4A/AGRI visible measurements and to correct the associated biases, which facilitates the assimilation of these data in conventional data assimilation applications.
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RC1: 'Comment on amt-2024-12', Anonymous Referee #1, 05 May 2024
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This paper compares the FY-4A/AGRI 0.65-um visible reflectance (O) with the model simulations generated from CMA-MESO forecasts using the RTTOV (B). The potential sources contributing to the differences between O and B, such as the unresolved aerosol processes, the ice scattering models, are analyzed.
The paper is relevant to the cloud remote sensing field, as the growing international fleet of next-generation geostationary imagers can be expected to aid in our understanding of the diurnal cycles of clouds and aerosols. Well understood and characterized the biases of their observations will therefore be well received by the community. However, the authors make what I think are several unsubstantiated assertions (see my detailed comments). I recommend major revisions before reconsidering for publication. My general and specific comments are below.
General Comments:
- A comparison with the model simulations cannot be called an “evaluation”, especially when the model simulations are not as accurate as expected. Currently, the RTTOV forward-operator for clouds and/or within the visible and shortwave infrared spectral ranges is still questionable, and the forecasts from CMA-MESO also lack adequate evaluations.
- As (1), if the authors persist in characterizing the biases of AGRI reflectance observations by comparing with the model simulations, the performances of RTTOV forward-operator and the forecasts from CMA-MESO should be evaluated first.
- The bias characteristics are not well analyzed. How about the spatial distributions or seasonal variations of AGRI biases? Do they have differences before and after the FY-4A satellite’s U–turn at the vernal and autumnal equinoxes?
Specific Comments:
- Lines 16, 22, 33 and 72: The abbreviations (FY, TOVS, and so on) should be given full name when first appeared in the abstract and text.
- Line 85: Himawari-8 satellite should be introduced because not all readers know it is the first one of the Japanese next-generation geostationary satellite.
- Line 82: How about the spatial coverage of CMA-MESO, or the region of interest in this study?
- Lines 96 and 117? Here, the authors give two cloud mask definitions. Which one will be used for Tables 1 and 2?
- Lines 201-203: I can’t understand this sentence. Aren’t the “microphysical properties therein” “cloud variables”?
- Figure 6: Readers can hardly identify the differences between observed and model simulated reflectance. The authors are suggested using a different colormap or adding figures to show their differences.
Citation: https://doi.org/10.5194/amt-2024-12-RC1 -
AC1: 'Reply on RC1', Yongbo Zhou, 30 May 2024
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Please find our reply in the supplement file.
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