the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 06 Aug 2025
Enhancing nighttime cloud optical and microphysical properties retrieval using combined imager and sounder from geostationary satellite
Abstract. Accurate retrieval of cloud optical and microphysical properties (COMP) at night is important for monitoring changes in weather and climate systems. The nighttime cloud optical and microphysical properties (NCOMP) retrieval is enhanced by integrating data from hyperspectral infrared sounder and high-resolution imager on the same geostationary platform with a machine learning framework. Using geostationary satellite imager broadband thermal infrared (TIR) channels along with dozens of optimally selected hyperspectral IR (HIR) channels, we demonstrate substantial improvements over traditional TIR-channel-based methods. The HIR channels enhance sensitivity to cloud effective radius (CER) and optical thickness (COT), particularly for optically thin clouds, reducing retrieval errors to 9.73 μm and 6.09, respectively, with an approximate 10 % accuracy improvement. The ML-based model preserves strong day-night continuity in COMP retrievals and assures the diurnal information for clouds, although challenges remain for thick clouds. This work highlights the importance of GEO-satellite-based HIR sounders, which provide critical spectral information that complements imager data for cloud optical and microphysical property retrievals. Middle-wave IR (MWIR) channels significantly improve COT retrieval. The proposed fusion approach offers a flexible retrieval framework applicable to future geostationary satellite systems for enhancing the cloud property retrievals containing diurnal information.
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Status: open (until 08 Oct 2025)
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CC1: 'Comment on egusphere-2025-2928', Mengchu Tao, 06 Aug 2025
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CC2: 'Reply on CC1', Xinran Xia, 07 Aug 2025
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Thank you for your insightful question regarding the spatial and temporal matching between GIIRS and AGRI data. Given the differences in their resolutions (GIIRS China region: 12 km/2 h; AGRI fulldisk: 4 km/15 min), we adopted the following approach:
For each GIIRS pixel, we identified the nearest AGRI pixels—typically a 3×3 block (9 pixels) centered on the closest GIIRS footprint. To ensure temporal consistency, we enforced a maximum time difference of 15 minutes between matched GIIRS and AGRI observations, guaranteeing that only contemporaneous measurements were paired.
Notably, since the geolocations of GIIRS long-wave infrared (LWIR) and mid-wave infrared (MWIR) channels exhibit slight differences in their pixel center coordinates, we performed separate matching procedures: (1) GIIRS LWIR with AGRI, and (2) GIIRS MWIR with AGRI. This dual-matching approach accounts for the instrument's spectral band-dependent geolocation characteristics, ensuring higher spatial alignment accuracy for both infrared regimes.
Citation: https://doi.org/10.5194/egusphere-2025-2928-CC2
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CC2: 'Reply on CC1', Xinran Xia, 07 Aug 2025
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RC1: 'Comment on egusphere-2025-2928', Anonymous Referee #1, 30 Aug 2025
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General comments
The authors present a machine learning framework to derive cloud optical and microphysical properties (COMP) from infrared brightness temperatures measured by a geostationary satellite. In their approach not only measurements from an imager but also from a sounder are used. The method allows retrieving COMP during nighttime, which is very useful since traditional methods are only available during daytime as they are based on shortwave radiance measurements. The paper indicates that IR-based COMP retrievals are feasible and can provide consistent results during day and night, albeit with large RMSE in both cloud optical thickness (COT) and cloud effective radius (CER). However, the added value of sounder data, which is the subject of this study, is not convincingly demonstrated.
The validation results indicate that COT from the IR-only model (Fig. 5) looks better than from the combined IR+LWIR+MWIR model (Fig. 6). Specifically, the combined model is strongly skewed towards lower values and does not produce COT larger than 50, while the IR-only model COT is more balanced. By not predicting high COT the combined model reaches a slightly lower RMSE but this appears to be rather an artifact than an achievement. Therefore, the only advantage of the combined model, the claimed marginal improvement in COT (CER is not improved), is actually not an improvement. Perhaps this is not surprising, since the spectral regions of the HIR sounder channels are basically covered by the imager channels, which furthermore have a much higher spatial resolution.
To make the manuscript suitable for publication, the claims of added value of the sounder for COMP retrievals would have to be removed or firmly demonstrated, while the specific comments below would also have to be considered.
Specific comments
P3, L73-79: MODIS and SEVIRI are not ‘satellite data application centers’. Also, Thies et al. (2008) is not a representative reference for SEVIRI.
P4, L116-117: With respect to which reference have these correlations between determined?
P5, L123: The reference Charles et al. (2024) is not included in the reference list. Furthermore, I cannot find a paper by Charles et al. Could it be that this is actually White et al. (2025, https://doi.org/10.1029/2024JD042829)?
P6, L155-159: It could be noted that Europe/Eumetsat launched a GEO sounder (IRS on MTG-S) in July 2025. In addition, I believe that monitoring temperature and humidity profiles rather than cloud/wind fields is the primary application of these instruments. Finally, the Lindsey reference could already be added in line 156 to reflect the planned US GEO IR sounder.
P6, L160-163: Here two research questions are formulated. However, the first one ‘What is the advantage of a GEO HIR sounder over a GEO IR imager for NCOMP retrieval?’ is not addressed. Only the added value of a HIR sounder (question 2) is studied. There are no experiments comparing sounder-based and imager-based retrievals.
P7, L194-199: The relocation of the satellite did presumably not take place on one day. It appears sub-optimal to use measurements from a month (March 2024) during which the satellite was drifting. In addition, more details on which data (days, time slots, ..) was used for training and validation are welcome? Was there also a test dataset? And were these sets independent?
P7, Section 2.2: The procedure for collocation of GIIRS and AGRI data should be better explained (how is resampling done, what is ‘top to bottom’, ..?).
P9, L256: What does ‘are softmax to CLP’ mean?
P10, L298-299: Please check RTTOV credentials. I believe it is developed by the NWP SAF.
P11, L339-341: This looks like a duplication of earlier information in lines 333-336, while the sentence in between is about a different topic.
P12, L349-350: Should this be COT (rather than COMP) sensitivity? A sensitivity of 0.2 is nowhere reached for CER.
P12, L356-359: How does sensitivity to water vapour and temperature provide a theoretical basis for COMP retrieval?
P13, L384-389: Isn’t this a counterintuitive result? Adding the LWIR channels, which have relatively high sensitivity to COT, increases the retrieval error. In contrast, the MWIR channels, with much lower sensitivity to COT, reduce the retrieval error. How can this be explained?
P13, L403-405: What does it mean that COT shows better agreement than CER? How can the errors be compared?
P14, L410-412: An improvement in liquid cloud detection is mentioned with specific numbers, while the deterioration in ice cloud detection is disguised by stating that ‘it remains > 94%’. That is not balanced reporting.
P15, L442: The term droplets suggests that only liquid clouds were analysed here. However, the droplets appear to be much larger than what is commonly retrieved (CER<40 micron). That raises the question whether ice particles are also included. And – if not – how are the results for ice clouds?
P15, L468: Are all these clouds cirrus? COT appears to reach values of 50 and higher, which is not compatible with cirrus. Also on later occasions, it looks like cirrus has been used as a synonym for ice clouds.
P15, L469-470: Compared to which other models/retrievals is the performance ‘exceptional’?
P18, L547: The number 9.73 seems to refer to the CER RMSE in Figure 6. However, the reference RMSE in Figure 5 was 9.72. How is this a reduction?
Table 2: These numbers differ from those in Figures 5 and 6. Is that because they are based on a different dataset (validation versus test?) or because the figures are for a restricted range of COT and CER? If the latter restriction is thought to be more relevant, why was the model choice not based on that restricted dataset?
Figure 3: Is the RTTOV simulation, which is presumably based on many uncertain inputs, a proper reference? IASI seems better suited to serve as a reference, and biases with respect to IASI actually appear to be rather small.
Figure 6: The LWIR+MWIR model clearly has a problem with predicting high COT (panel d) and the histogram in panel e also deviates from AGRI-L2. Both these aspects are clearly better for the IR-only model in Figure 5. The RMSE of the combined model appears to be artificially lowered as a result of the LWIR+MWIR model not predicting high values (which blow up the RMSE). Hence, IR-only actually appears to be the better model for COT. This undermines the main conclusion of the paper.
Figure 9: Usually IR channels are inverted to give a more natural appearance of clouds in brighter colours.
Figure 9: How is COMP retrieved for mixed-phase clouds? From the images it looks like they are treated as liquid water clouds. Also, there are regions (e.g., western side of the area) where COT>0 and CER>0 but neither LWP nor IWP have a value. How can that be explained?
Technical comments
P2, L41: What is ML?
P2, L44: Explain GEO, also in main text at first occurrence.
P2, L46: Middle-wave -> Mid-wave
P3, L70: Introduce VIS/NIR abbreviations
P3, L80-82: Write full name followed by acronym/abbreviation in brackets.
P4, L97: Insert ‘are’ after that; no capital in Daytime; makes -> making
P4, L103: intelligent -> intelligence
P4, L112-117: Split up in three sentences.
P5, L130: offers -> offer
P8, L232: Start new sentence at ‘The detailed ..’.
P11, L334 and L336: Are the minus signs typos? The figures present sensitivities in absolute sense.
P13, L379-381: Check sentence.
P17, L510: Figure 10 should be Figure 11.
P18, L566-567: (e.g., MTG and successors of GOES-R).
P18, L570: Capitalize GFS.
P20, L605: Andi, W. should be Walther, A.
Citation: https://doi.org/10.5194/egusphere-2025-2928-RC1
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Thank you for your valuable work on Enhancing nighttime cloud optical and microphysical properties retrieval using combined imager and sounder from geostationary satellite.
I have a question regarding your methodology:
How do you achieve the spatial matching between the satellite sounder and imager data, especially considering differences in their spatial resolutions and observation geometries? Could you elaborate on the procedures or algorithms you use to ensure accurate co-location of measurements from these two instruments?
Thank you very much in advance for your clarification.