Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting
Round 1
Reviewer 1 Report
Comments and Suggestions for Authorsv
Comments for author File: 
Comments.pdf
I have made all my comments and suggestions on a separate documents. Nevertheless, quality of written English language is good.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for Authors
The main goal of the reviewed manuscript is to show, using the example of two cases, that the use of meteorological data from the Fengyun-4B geostationary satellite can improve the results of predicting typhoon behavior. In addition, the work compares satellite weather sounding results with radio sounding and NCEP reanalysis data. Of course, the two cases considered cannot objectively show the advantage of using satellite data, but they allow us to make an optimistic forecast. Thus, the work is of some interest and useful.
General remarks
Section 2 compares weather data from the FY-4B satellite with radiosonde data and NCEP analysis. The average and root-mean-square differences between the data of two measurements (the authors call the difference an error, see below) of temperature and absolute humidity are given. But there is no data on the natural variability of these quantities, which does not allow us to judge the increase in information about meteorological parameters relative to a priori. A comparison of relative humidity profiles and their a priori variability should also be provided.
Sections 3, 4 analyze the impact of using FY-4B weather information on forecasting the situation associated with typhoons. These sections represent the main content of the work and there are no substantive comments. Let us note that we would like to see a more representative ensemble of comparisons and we hope that in the future, when new data appears, the authors will expand it.
Minor Notes
We draw the attention of the authors to the fact that it is incorrect to call the difference between the results of two measurements, each of which has limited accuracy and precision, the word “error”. Although we have already seen this usage in other articles, it still remains incorrect. The temperature error of radiosondes is 0.5-1K, which is comparable to the resulting differences, and it also contributes to the differences. In addition, the vertical and horizontal resolution of radiosondes and remote sensing data differs significantly, which the authors ignore. The remote sensing averaging kernels should at least be shown and analyzed. These considerations also apply to humidity.
When comparing with analysis data, calling the differences “error” is also incorrect. While useful generalizations and interpolations of measurement results, analysis data cannot in any way be recognized as an absolute standard, so we should talk about differences, not errors.
Lines 208-211. The phrase is unclear. What does the word “innovation” mean here? please phrase it differently.
There are undeciphered abbreviations in the text. Although, for example, the meaning of the abbreviations CTRL & GIIRS_TQ is explained, formally their decoding should be given.
Please accompany at least the first occurrences of the word “experiment” with the adjective “numerical”.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 3 Report
Comments and Suggestions for Authors
This paper describes both a validation study of temperature and humidity retrievals from the hyperspectral IR sounder on FY-4B geostationary satellite and the impact of these retrievals on forecasts of two typhoons. The paper is clearly organized, clearly written and complete. I find the impact on typhoon forecasts very encouraging! The paper should be published after some minor corrections (detailed below) and editing to remove some non-standard English usage.
Line 35-42. “Data” is sometimes plural, and sometimes singular. I am “old school” and think that “data” is plural, but this seems to be changing. But it should at least be consistent in one manuscript.
Line 93. “…fast piecewise-defined newly developed….” This doesn’t make sense to me – please rewrite.
Line 105. Suggest “ratio” to “proportion” or “fraction”.
Line 104-108. Any explanation for the decreasing fraction of high-quality data?
Line 108. “…which increased rapidly subsequently” is confusing, please rewrite.
Line 117-118. I am confused – I thought that the quality marker refers to the satellite retrieval, not the radiosonde observation. Also, I would think that collocations that do NOT meet quality standards would be filtered out.
Line 123. Suggest mentioning that FY-4B/GIIRS data are *not* assimilated by ERA5, so ERA5 and FY-4B/GIIR are independent of each other. (Unless the retrieval algorithm is initialized by ERA5)
Line 127-128. The RMSE error for humidity is small higher in the atmosphere where the absolute values of humidity are low. This suggests a constant fractional error. What does RMSE(Q)/Q look like?
Figure 8. The plots need y-axis labels.
Line 260-262. This statement is too strong – only two typhoons were analyzed, so while the results indicated the possibility of improvement for all cyclones, it is not yet proven. Probably a statement about the small number typhoons analyzed should be included in the Summary and discussion section.
Comments on the Quality of English LanguageThere are a lot of instances of what I would call non standard usage, but it rarely detracts from being able to understand the paper.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
Reviewer 4 Report
Comments and Suggestions for AuthorsReview of Evaluation of Temperature and Humidity Profiles Retrieved from Fengyun-4B and Implications for Typhoon Assimilation and Forecasting By Yang etc.
The paper studies the retrieved temperature and humidity profiles from GIIRS mounted on the FY-4B satellite. It also studies the application of the retrieved profiles with data assimilation for improving typhoon forecasts. The content is well-organized and it can be published in Remote Sensing after considering the following minor revision suggestions.
First, the paper does not give clear information about the spatial and temporal resolutions for the GIIRS data anywhere. In section 1, it mentions the HIS provides higher-resolution observations, but does not give what is the exact resolution. It also mentions that FY-4A & FY-5B enable high temporal and spatial resolution detections. But what will be the exact numbers? It even describes that one advantage of FY-4B over FY-4A is higher spatial resolution. Without the exact numbers, it is hard for readers to understand what the improvement will be. Furthermore, the resolutions of the retrieved temperature and humidity profiles are important for evaluating the data assimilation impacts.
Second, a few figure captions have problems. Why does Figure 2 use different lengths for the abscissa (a vs. c and b vs. d)? It had better use the same axis length for easy comparison. For Figure 4, in “Green dots plot the distribution of retrieval data for 00-02 UTC July 1, 2022”, the “green dots” means the shaded green areas, right? Please note that the typhoon tracks also contain green dots. Please find a way to distinguish them, especially in the caption. What will be the white rectangles in Figure 4, cloudy areas or missing observations? Figure 6 actually displays the PDFs of OMB/OMA but not the OMB/OMA itself. So the first paragraph in Section 4.1 and the figure caption should be improved to reveal this fact. Further, could you provide more details about how the PDFs of OMB/OMA are computed in the content? The bottom part of Figure 7 is cut too much. Could the authors provide a description of what the empty areas will be in Figure 7? Figure 8 should mention that the RMSE is calculated against the ERA5 datasets explicitly. About Figure 11(b), the GIIRS_TQ (at least to me) deviates from the reference (BMA_BST) further than CTRL after 30 hours. I wonder why it gives a smaller error in Figure 11(d).
Third, please provide clear information about the resolution for the NCEP GFS datasets used in this study. As I know, NCEP provides several resolutions for the GFS datasets, 1 degree, 0.5 degree, and 0.25 degrees, etc.
Fourth, it had better provide a flow chart about the event time configuration following the description near the end of section 3 (Lines 199-204). The current description is pretty hard to understand.
Fifth, could the authors give some descriptions of how the track error percentage is computed in Section 4.2.2?
Finally, the paper mentions that the assimilation of satellite-derived temperature and humidity profiles can reduce forecast errors effectively. Based on my examination of Figure 9, Figure 13/14, etc, the assimilation of satellite-derived profiles indeed improves the typhoon forecasts about intensity, track, precipitation, etc a little but not significantly. I agree with the authors for the last paragraph in Section 5. There is still a lot of research to do.
Minor issues:
- Line 26: “substantially” should be dropped.
- Line 29: “, particularly in forecasting high-magnitude rainfall events” Can be “, particularly with high-magnitude rainfall events.”
- Line 84: “forecast in typhoon cases,” can be “forecast with typhoon cases,”
- Line 194: “are statistically given according to the previous section” can be
“are statistically given following the previous section” - Line 220: “UTC August 24 2022 in the case of Ma-on” can be “UTC August 24 2022 for the Ma-on case”
- Line 232: “at 0600 UTC August 24 2022 in the Ma-on case.” can be “at 0600 UTC August 24 2022 for the Ma-on case.”
- Line 308: I have difficulty to understand this sentence. Please try to improve the sentence if possible.
- Line 311: What is the meaning of “more straightforward”?
Comments on the Quality of English Language
Minor editing is required for the English language. See the minor issues section in my review report above.
Author Response
Please see the attachment.
Author Response File: Author Response.pdf
