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.
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Status: open (until 07 May 2025)
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RC1: 'Comment on egusphere-2025-680', Anonymous Referee #1, 10 Apr 2025
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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
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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
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