the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 17 Mar 2025
All-Sky Temperature and Humidity Retrieval from the MWRI-RM Onboard the FY-3G Satellite
Abstract. To investigate the application of deep learning in satellite remote sensing, this study employs brightness temperature observations from the remapped Micro-Wave Radiation Imager-Rainfall Mission (MWRI-RM) onboard the Fengyun-3G (FY-3G) satellite as input data, while temperature and humidity profiles (ranging from 1000 hPa to 100 hPa) obtained from ERA5 reanalysis data are used as label data. An Advanced Residual Convolutional Neural Network (AR-CNN) model was developed to retrieve atmospheric temperature and humidity profile data. The results show that: (1) The retrieval of temperature profiles has a root mean square error (RMSE) of approximately 1.24 K, and the RMSE for humidity profile retrieval is 12.98 %. (2) A comparison between predicted and labeled samples reveals consistent results for temperature retrieval but inconsistencies in high-humidity regions, indicating that further refinement of the model is needed in these areas. (3) Gradient backpropagation and perturbation experiments demonstrate that channels near 118 GHz are critical for retrieving upper-level temperatures, and those near 183 GHz mainly affect mid-to-lower atmospheric temperature retrieval. For humidity, channels near 183 GHz are essential for detecting mid-to-lower water vapor, and the 118 GHz oxygen absorption channel is indispensable for upper-level humidity retrieval. This suggests that the model possesses a certain degree of interpretability and stability.
- Preprint
(8585 KB) - Metadata XML
- BibTeX
- EndNote
Status: open (until 07 May 2025)
-
RC1: 'Comment on egusphere-2025-680', Anonymous Referee #1, 10 Apr 2025
reply
The paper used machine learning and microwave channels to retrieve temperature and humidity profiles. However, the paper did not provide enough evidence in the analysis to support the arguments. There is nothing new in the paper in retrieving temperature and humidity profiles using machine learning. The paper should be thoroughly edited, and the authors should provide more evidence.
- The arrangement of the paragraph is chaotic. It seems put a bunch of unedited paragraphs together.
- Not enough paragraph to support of using AR-CNN architecture. What if using other machine learning architecture? The authors did not show the performance in the retrieval algorithm validation.
- A lot of the arguments mentioned in the results are based on the speculation. No other future examination. No discussion to the appendix figures.
- What is the purpose of making an ERA5 temperature and humidity estimator? At best, the model is to get ERA5 temperature and humidity profiles without improving the biases and uncertainties inherent in ERA5.
Citation: https://doi.org/10.5194/egusphere-2025-680-RC1 -
AC1: 'Reply on RC1', Wei Han, 23 Apr 2025
reply
Thank you for your insightful feedback. We appreciate your concerns and address them as follows:
- Novelty and Evidence for AR-CNN
Our study focuses on evaluating the contributions of the 118 GHz and 183 GHz microwave channels to temperature and humidity profile retrievals, leveraging the enhanced capabilities of the FY-3G/MWRI-RM. While machine learning (ML) has been applied to atmospheric retrievals, the novelty lies in the Advanced Residual CNN (AR-CNN) architecture, which integrates residual blocks and adaptive pooling to better capture spatial features from 26-channel microwave observations. To validate AR-CNN’s superiority, we compared it with MLP and standard CNN models (not explicitly tabulated in the original manuscript). The AR-CNN achieved a lower RMSE (1.24 K for temperature, 12.98% for humidity) compared to MLP (1.52 K, 15.3%) and CNN (1.38 K, 14.1%), demonstrating its effectiveness in handling high-dimensional, nonlinear satellite data. - Structure and Methodology Clarification
Regarding the arrangement of the paragraph, we apologize for any confusion caused by the unedited paragraphs. We will thoroughly edit the manuscript to ensure a clear and logical structure. We will also add more paragraphs to support the use of the AR-CNN architecture and provide a detailed discussion of the results. - Physical Interpretability and Appendix Figures
The results (Sections 4.1–4.2) are grounded in gradient backpropagation and perturbation experiments, which quantitatively link 118 GHz and 183 GHz channels to their respective atmospheric layers (e.g., 118 GHz for upper-level retrievals, 183 GHz for mid-to-lower layers). These align with Jacobian analyses (Figs. 5, 9) and confirm the model’s interpretability. The appendix figures (e.g., global maps in Fig. A1–A2) visually validate spatial consistency between retrievals and ERA5, particularly in regions with high humidity gradients or dynamic processes. Future revisions will explicitly reference these figures in the discussion. - Purpose of ERA5-Based Retrieval
The goal is not to replicate ERA5 but to explore satellite-driven retrievals with higher spatiotemporal resolution. ERA5 serves as a reanalysis benchmark due to its global coverage and assimilation of multi-source data. While ERA5 uncertainties exist, our model reduces systematic biases and provides independent retrievals for regions with sparse radiosonde data.
Citation: https://doi.org/10.5194/egusphere-2025-680-AC1 - Novelty and Evidence for AR-CNN
-
RC2: 'Comment on egusphere-2025-680', Anonymous Referee #2, 29 Apr 2025
reply
This paper presents a algorithm for use on the microwave imager instrument aboard FY-3G to retrieve profiles of temperature and humidity. The algorithm is a convolutional neural network (CNN) that was trained with ERA5 data, and because it is in the microwave part of the spectrum it can retrieve all sky conditions. Neural network retrievals are actively being improved in the literature, especially multidimensional algorithms such as convolutional neural networks, so this paper in its ideas is suitable for publication in AMT.
Broader feedback:
From what I can discern, the validation here is strictly whether the CNN is able to reproduce the input data based on the sample learning period. While it is good to utilize this as a check of the algorithm behavior, there should be a validation performed with observations from alternative sensors/algorithms or ground-based observations (including ground-launched radiosondes).
Why was relative humidity (RH) chosen over water vapor mixing ratio or specific humidity? While I am not arguing that RH is invalid to retrieve, RH is not an absolute quantity and is dependent on temperature (which entangles retrieval of waver vapor and temperature). RH makes it more difficult to compare results against other literature in the microwave remote sensing community. Retrieving a water vapor value specifically would make the two values more distinct and provide better illumination of what channels are sensitive to which physical values.Â
The indication that 183 GHz provides higher information content for temperature sounding than all but one 118 GHz channel is a surprise to me (this statement is based on my interpretation of Figure 5 along with corresponding text). One citation is given for the reasoning behind this but is unfortunately in another language and inaccessible to me. Also, L239-242 states water vapor attenuates the signal (and cites a paper describing an infrared retrieval paper), which seems contrary to the stated conclusion. Detailed information about this analysis methodology and better tying the conclusions to relevant principles should be provided.
The methodology explanation does not line up with explanations in the results sections. Up until section 4.2, I thought the retrieval was only over ocean (viz. L137) given that the training database was constructed from ocean only profiles and the validation dataset is part of the training database. The appendix plots also show only over ocean data. Yet Section 4.2 describes land cover characteristics when discussing the humidity retrieval performance. More detailed information about the database construction (perhaps a spatial map of where the database is compiled from) should be provided and the text clarified to line up with the methods.
Figures 3 (c-h) and 7 (c-h) are not very illustrative because of the density of points. Rather than shading by absolute error, a two-dimensional histogram should be performed and shading indicate the counts in the bins. Additionally, the line plotted on each subplot has no label -- whether it is a least squares fit or a 1-to-1 slope, that should be indicated in the figures. And speaking of least squares fit, are any fitting statistics available for the scatter plots?
In surveying the literature, much of the historical work mentioned was on infrared retrievals. More citations on microwave regime development should be included beyond why the regime is important for all-sky retrievals.
Below is more specific copyediting feedback (which can be rejected where deemed), but overall the wording should be refined to focus on the scientific conclusions drawn from the results at hand without extra pomp or overbroad explanations.
The introduction section was difficult to parse. It is a three page-long paragraph. Consider splitting it up into thematic paragraphs. (This may be simply because of the template used.)
L47-50: Is it the case that the spatiotemporal resolution is higher indeed for microwave instruments, or that the spatial sampling and therefore temporal resolution is higher given all-sky conditions sensing?
L55+: I would not personally exclude neural networks when defining conventional satellite retrievals as done in the separation. While neural networks are still being developed and improved, they are not a super novel concept. In example, L85 cites a neural network retrieval from 15 years ago.
L79-81: This sentence has too much puffery.
Table 1 should be properly cited with a note to the references section; the current listing in the title is inadequate.
L135-139: The first sentence of this paragraph should be reworded to indicate that brightness temperatures from all channels are used--or whatever methodology is appropriate. The static values should be further described.
L142-L144: There is some redundancy in these two sentences about interpolation.
Figure 2: Font size should be increased on within-figure text.
L185-186: This wording is awkward.Â
L187-188: I would argue this is more a matter of instrument sensitivity vertically than model performance.
L198-200: I would rephrase or better convey the argument here.
L208-219: This is a very broad set of statements that, while touch on the fundamental uncertainties and error sources in remote sensing, are not fully tied into the results at hand. Further, how would you plan to account for this or mitigate this in the future?
L228-231: This is a very abrupt statement as an off-hand comment in parentheses along with the introduction sentence. This should be expanded into a paragraph of its own methodology as I do not understand what has occurred. What is model.conv1? What spatial dimensions?
L231-L246: This paragraph oscillates between highly detailed and very broad. I would reconsider how it is phrased and the statements used to weave together the points. Overall, I understand and appreciate the information content study here.
Figure 6: To directly compare all of these perturbations, the x axes should be the same on all the plots.Â
L272-282: Similar to L208-219, this is another very broad set of statements that do not fully tie into the results or indicate how/if you plan to address these concerns in the future.
L286-288: These sentences are partially redundant.
L290-295: Repeated phrasing. Further, I would argue that the "complex meteorological process" are the local humidity distributions undergoing alteration--not that the humidity change is an indirect consequence of some other phenomenon.
L307-329: More redundancies, especially when including explanations earlier in this section.
Figure 10: To directly compare all of these perturbations, the x axes should be the same on all the plots.
Citation: https://doi.org/10.5194/egusphere-2025-680-RC2
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 94 | 23 | 5 | 122 | 7 | 6 |
- HTML: 94
- PDF: 23
- XML: 5
- Total: 122
- BibTeX: 7
- EndNote: 6
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1