The atmospheric temperature profile is a key parameter for characterizing the thermodynamic state of the atmosphere, which is essential for understanding weather processes, assessing climate change, and implementing various meteorological support tasks (Zhang et al., 2024). Furthermore, it serves as a critical component of the global observing system for numerical weather prediction, significantly contributing to the accuracy of weather forecasts (Yan et al., 2020; Wang et al., 2021; Xue et al., 2022).

Among various techniques for observing the atmospheric vertical thermal structure, spaceborne passive microwave (PMW) remote sensing has the advantages of all-sky and large-scale observation, utilizing the dependence of oxygen absorption on frequency and atmospheric temperature. Near an oxygen absorption band, channels closer to the center line generally have larger absorption coefficients and optical depths, so radiative contributions from the lower atmosphere and surface are more strongly attenuated. As a result, these channels are mainly sensitive to radiation from higher atmospheric layers, leading to higher peak altitudes of their temperature weighting functions (Rodgers, 2000; Aires et al., 2015). Meanwhile, warmer atmospheric layers emit stronger microwave radiation. There are currently numerous spaceborne microwave temperature sounders equipped with the 50–60 GHz oxygen absorption band, including the Microwave Sounding Unit (MSU) deployed on the National Oceanic and Atmospheric Administration (NOAA) series satellites (Mo et al., 2001), and the Advanced Technology Microwave Sounder (ATMS) onboard the Suomi National Polar-orbiting Partnership (Suomi-NPP) and Joint Polar Satellite System (JPSS) satellites from the United States (Weng et al., 2013), as well as the Micro-Wave Temperature Sounder-2 (MWTS-2) carried by FengYun-3C (FY-3C) and FengYun-3D (FY-3D) satellites from China (Zhang et al., 2022). In addition, China has taken the lead in using the 118 GHz oxygen absorption band on instruments such as the Micro-Wave Humidity Sounder-2 (MWHS-2) onboard the FY-3 series satellites for vertical atmospheric temperature profiling (Lawrence et al., 2017). The oxygen absorption line at this frequency is nearly symmetric and isolated from other lines, so that the negligible overlapping effect allows for simplifying atmospheric transmittance modeling (Gasiewski et al., 1990; Tretyakov et al., 2001). It should be noted that due to hardware design constraints such as antenna size, these microwave sounders are exclusively deployed on polar-orbiting satellites, operating at altitudes of 400–800 km. The sampling frequency of twice per day is insufficient for continuous regional observation, which limits the application of retrieved temperature profiles in weather monitoring and data assimilation (Lambrigtsen et al., 2016).

Developing PMW payloads onboard geostationary satellite with the unique capability of large-scale and high-frequency observations is an effective way to address the aforementioned observational bottlenecks, which is of great significance for monitoring and nowcasting rapidly evolving weather systems (Christensen et al., 2007). In fact, several countries have launched development programs for PMW instruments in geostationary orbit, including the Geostationary Synthetic Thinned-Aperture Radiometer (GeoSTAR) concept proposed by the United States (Lambrigtsen et al., 2007) and the Geostationary Atmospheric Sounder (GAS) project developed in Europe (Christensen et al., 2007). In comparison, China has made the fastest progress. A prototype based on a large real-aperture antenna scheme has already been developed (Xie et al., 2018a, b), and the formal instrument will be onboard the FengYun-4M (FY-4M) satellite, which is scheduled to be launched at the end of 2026. As the world's first geostationary PMW payload used for operational purpose, it would usher in a new era of Earth observation. It is worth mentioned that this instrument is equipped with 135 channels, far exceeding the channel numbers of existing spaceborne PMW sensors. Among them, 89 hyperspectral channels located in the 52.6–57.3 GHz oxygen absorption band (Bi et al., 2024), together with several high-frequency channels in the millimeter to submillimeter wavebands, are being used for the first time worldwide in spaceborne instruments. This makes precise observations of vertical temperature structure and solid hydrometeor possible. Chen et al. (2024, 2025) conducted observing system simulation experiments for the FY-4M satellite, confirming that the assimilation of its observational data has a positive impact on typhoon forecasting due to the high temporal resolution. Specifically, measurements in the oxygen and water vapor absorption bands can respectively contribute to improvements in typhoon track and intensity predictions.

However, the high-frequency and multi-band observation characteristics of the FY-4M satellite also introduce new challenges to the subsequent retrieval of various physical parameters. The minimum observation interval of approximately 3 min imposes stringent requirements on the computational complexity of operational algorithms, especially considering that using more channels generally increases computational cost and the accumulation of observation errors. In fact, among these 135 channels, information redundancy may exist within certain channel subsets. In addition, the effectiveness of measurements from those channels being used for the first time in the retrieval of key geophysical variables is still unknown. In order to achieve a balance between computational efficiency and accuracy, therefore, channel selection tailored to specific retrieval objectives is essential for the FY-4M satellite. Generally, existing channel selection methods are primarily based on weighting functions (He et al., 2015) or information content (IC) analysis (Rodgers, 1998; Sahoo et al., 2015). The former selects channels by quantifying their sensitivity to atmospheric parameters at different altitudes, but fails to fully account for the impacts of instrument noise and background errors (Zhou et al., 2024). In comparison, as a more comprehensive technique, the IC analysis focuses on the retrieval capability of the channels with full consideration of noise effects, quantifying the independent IC of each channel or combination of channels, thereby only retaining the channels that maximize IC while discarding those with little or redundant information (Bi et al., 2024; Rodgers, 2000; Xiao and Weng, 2022).

It is noted that current few studies on channel sensitivity analysis for the FY-4M satellite generally focus on cloud-free scenarios. For instance, Bi et al. (2024) investigated the characteristics of hyperspectral channels under clear-sky condition and found that the most informative channels of temperature are mainly located near 57 GHz. In terms of non-hyperspectral channels, Xiao and Weng (2022) pointed out that high IC for atmospheric temperature is mainly obtained from 50–70 GHz, whereas that for water vapor comes from 183 and 380 GHz. However, under cloudy and precipitating conditions, hydrometeors in various phases would exert significant scattering and absorption effects on microwave radiation (Mech et al., 2020), and in turn may affect the sensitivity and response of certain channels to some atmospheric parameters at different altitudes, leading to potential discrepancies in their retrieval capabilities compared to those under clear-sky condition.

Therefore, to gain a more comprehensive understanding of the characteristics of over a hundred channels for the FY-4M satellite, and to better use them for all-sky atmospheric temperature profile retrieval, in this study optimal channel selection under various weather conditions is investigated, employing an IC analysis based on the Jacobian matrix and degrees of freedom for signal (DFS). Also, the selected channels are further evaluated through temperature retrieval with the Bayesian algorithm. This paper is organized as follows: Sect. 2 introduces the FY-4M satellite, as well as the IC analysis and retrieval methods. Section 3 presents the results of the channel optimization and the corresponding retrieval validation. Finally, Sect. 4 summarizes the main findings and outlines future perspectives.

To ensure the applicability and accuracy of the final temperature profile retrieval algorithm under all-sky conditions, the channel selection and retrieval performance evaluation will be separately conducted under different weather conditions, including clear-sky, cloudy, and precipitating. For each weather type, 1 000 000 samples were randomly selected from the aforementioned a priori database to perform IC analysis, and the corresponding mean temperature profiles are shown in Fig. 3. Although these profiles all show a similar vertical structure of decreasing with height across the three weather regimes, their degrees of dispersion differ significantly. By comparison, temperature profiles under clear-sky condition have the greatest stability (Fig. 3a), while the variation is greatest on rainy days (Fig. 3c).

Considering that the hyperspectral channels of the FY-4M satellite are planned to be used globally for the first time, their observational performance is still unknown. As a result, in this study the temperature detection capabilities of the hyperspectral channels and the remaining channels are investigated separately, in order to explore whether the former can provide incremental advantages in temperature profile detection.

Figure 4 first shows the atmospheric temperature Jacobians across all non-hyperspectral channels under various weather conditions. For each weather condition, the Jacobians were calculated separately for each of the 1 000 000 samples described above and then averaged. In clear-sky scenario (Fig. 4a), the 23 GHz band and the window channels are sensitive to near-surface atmosphere. Similarly, in some typical oxygen absorption bands of 54 and 118 GHz, those channels located farther from the absorption peaks can also respond strongly to the near-surface layers, and the effective detection height gradually increases as the frequency approaches the corresponding absorption peak. Moreover, it is seen that the 424 GHz band is mainly sensitive to temperatures in the middle and upper atmosphere, which is consistent with the findings of Xiao and Weng (2022). Under cloudy and precipitating conditions (Fig. 4b and c), the distribution patterns of the Jacobians are basically similar to that in the clear-sky cases, but the response of the sounding channels near the 54 and 118 GHz bands sensitive to the lower atmosphere is notably weakened, primarily due to the absorption and scattering effects of clouds and precipitation. Interestingly, some low frequency channels, such as 23.8 and 31.4 GHz, show relatively little change in response to the lower atmosphere compared to clear-sky condition, which may be related to their great sensitivity to the emission effects of water vapor and liquid hydrometeors.

By comparison, Fig. 5 shows the spectral response of atmospheric temperature Jacobians for the hyperspectral band. Regardless of the weather conditions, the oxygen absorption coefficient generally increases with frequency across this range of 52.7–57 GHz. It is worth noting that this increase is not monotonic in detail, but exhibits some fluctuations due to the presence of secondary absorption peaks. Under clear-sky condition (Fig. 5a), the lower-frequency part of the hyperspectral band (near 52.7 GHz) with a lower absorption coefficient, can capture near-surface temperature information. As the frequency increases, the peak height gradually rises, but also experiences some fluctuations due to the influence of secondary absorption peaks. Compared with several other oxygen absorption bands in Fig. 4, hyperspectral channels have a stronger and more distinguishable capability to characterize the temperature stratification of the mid-to-upper atmosphere. Note that this general trend is consistent with the findings of Aires et al. (2015), but the peak heights are not identical, which may be attributed to differences in data employed by the two studies. In Aires et al. (2015), a total of only 1989 global atmospheric profiles in clear-sky scenes derived from the ECMWF forecasting system were used. In comparison, the data used in this study covers the whole coverage of the FY-4M satellite observations, and the larger data sample size allows for a more comprehensive display of the vertical temperature distribution characteristics of this region. Under cloudy and precipitating conditions (Fig. 5b and c), the lower-frequency channels (e.g., 53 GHz) far from the main absorption line show a weakened response near the surface due to the impact of clouds and precipitation on microwave signals, while the high-frequency channels associated with higher-altitude atmospheric temperatures are completely unaffected and show little difference from clear-sky condition. In summary, by utilizing some low-frequency channels sensitive to the lower atmosphere, combined with hyperspectral channels that can detect finer structures in the middle and upper atmosphere, it may be possible to obtain more complete temperature profile information across all vertical layers, even under cloudy and rainy conditions.

In order to make the above channel combination strategy clearer, a more detailed evaluation and screening of each channel is carried out using the previously mentioned DFS calculation results. Figure 6 shows the joint IC for the non-hyperspectral channels, where the horizontal axis represents the channel frequencies ranked by DFS, while the vertical axis denotes the cumulative joint DFS.

Under clear-sky condition (Fig. 6a), the channels ranking high include not only those directly associated with oxygen absorption in the 50–60 and 118 GHz bands that are sensitive to temperature information at various altitudes, but also water vapor sounding channels in the 23, 183, and 380 GHz bands, which can be attributed to the temperature dependence of water vapor absorption coefficient (Koshelev et al., 2021), thereby affecting the emitted radiation being detected. For cloudy and precipitating scenarios (Fig. 6b and c), the performance of various channels is generally similar to that under clear-sky condition, except for the 89 GHz channel, whose ranking drops sharply. This is related to the fact that this window channel is in the transition zone between high and low frequencies, with complex emission and scattering effects. In clear-sky condition, the low atmospheric attenuation allows the measurements of this channel to be closely linked to the sea surface emission signal (Yang et al., 2024), whose variations are primarily determined by SST. This also leads to the inclusion of information from the lower atmospheric temperature with high IC ranking, which is highly correlated with SST due to air-sea heat exchange processes. With the appearance of clouds, however, the shorter wavelength makes this channel's penetration ability insufficient, and surface information is easily blocked by hydrometeors (Weng et al., 2003), resulting in a complete loss of near-surface temperature information. In addition, the liquid water emission signal of this channel is easily saturated, and the scattering effect of ice particles is more pronounced than at lower frequencies. All of this enhances the complexity of this channel's signal, and leads to a negligible contribution (approaching zero) to the joint DFS, especially in the presence of optically thick precipitating clouds.

Subsequently, a similar approach is adopted, but the entire set of hyperspectral channels is treated as a predefined “super-channel” with the highest priority, and the sorting results of all channels for the FY-4M satellite are shown in Fig. 7. Similar to the results shown in Fig. 6, the growth rate of the joint DFS decreases as the number of channels increases and gradually levels off. However, a key difference is that once the hyperspectral channels are included, the highly-ranked non-hyperspectral channels are mainly window and water vapor absorption channels, rather than the oxygen absorption bands in the 50 and 118 GHz. This suggests that hyperspectral channels are already capable of adequately capturing the primary information of the vertical distribution of atmospheric temperature, so that the complementary detection information needed for temperature retrieval is more focused on further constraints of humidity, near-surface states, and hydrometeors. For instance, water vapor channels such as 23 and 183 GHz always rank among the top regardless of weather conditions. For window channels, the 89 GHz channel shows strong importance under clear-sky condition (Fig. 7a), similar to that shown in Fig. 6a, whereas the 31 GHz channel provides valuable hydrometeor information during precipitation (Fig. 7c), due to its high sensitivity to liquid water.

To ensure that the retrieval algorithm can maintain consistently high accuracy under all weather conditions after channel optimization, we separately extracted channel subsets that contribute 90 % (or 95 %) of the total joint DFS for different weather scenarios, and then took their union to construct channel configuration schemes adaptable to all-weather conditions, which are detailed in Table 2.

To evaluate the retrieval performance of different channel configuration schemes in the Bayesian algorithm, 100 000 samples each for clear-sky, cloudy, and precipitating conditions were randomly selected from the a priori database to form an independent test dataset, while the remaining samples were used as the training dataset. The corresponding atmospheric temperature profiles obtained from ERA5 in the a priori database for these test samples were used as the reference truth and compared with the Bayesian retrieval results for validation. To simulate instrumental noise, Gaussian random noise with a mean of 0 K and a standard deviation of 0.5 K was added to the simulated brightness temperatures of all FY-4M channels. In addition, retrieval results using the full set of channels of the FY-4M satellite are also provided as a reference.

The statistical distributions of temperature profile retrieval errors relative to the “true values” in the a priori database for various channel configurations are shown in Fig. 8. For the non-hyperspectral observation system (Fig. 8a–c), regardless of weather conditions, the temperature bias profiles for different channel configuration schemes are generally distributed around the zero line, with root mean square error (RMSE) below 1 K. This indicates that the Bayesian algorithm can be effectively used for temperature profile retrieval, supported by a sufficiently comprehensive a priori database describing the variations of multiple geophysical parameters. In fact, by using a similar database and the Bayesian technique, multiple hydrometeor profiles for MWRI-RM of FY-3G satellite have also been accurately retrieved (Wu et al., 2026). On the other hand, it is reasonable that the use of all non-hyperspectral channels (NHC) with more abundant surface and atmospheric observation information leads to smaller uncertainties in retrieval results, compared to using only optimal channel subsets such as NHC90 and NHC95. For clear-sky scenes (Fig. 8a), the RMSE values of NHC95 and NHC90 increase by approximately 19 % and 47 % relative to the full NHC set, respectively. Especially as the height increases, the RMSEs of the two optimized channel configurations become larger compared to that of NHC. This is mainly because the differences among the three channel configurations are concentrated in several high-frequency channels above 300 GHz (Table 2), such as the 380 GHz water vapor channel and the 424 GHz oxygen band, which are primarily sensitive to the middle and upper atmosphere. In addition, NHC90 and NHC95 also have greater uncertainty in the low atmosphere, due to their lack of the 50 GHz wing-channels. In particular, the absence of the 31.4 GHz, which is highly sensitive to the near-surface layer (Fig. 4), further exacerbates the bias of NHC90.

Under cloudy condition (Fig. 8b), the uncertainties of all three channel configurations increase only slightly, except for the upper atmosphere, indicating that the effect of microwave emission from clouds on temperature profile retrieval can be effectively eliminated, even when only a subset of channels of the FY-4M satellite is used. Additionally, due to the shielding effect of clouds on the signals from the underlying surface, the extra 31.4 GHz channels in NHC95 compared to NHC90 at this time mainly reflect the sensitivity to cloud water, providing better constraint ability, and in turn resulting in RMSE being closer to NHC. In comparison, with the occurrence of precipitation, the retrieval errors in various channel configuration schemes increase more noticeably, which may be due to the more complex scattering effects of precipitation particles and ice particles generated by convection, making it difficult to completely remove them through channel combination.

For observation system incorporating hyperspectral channels (Fig. 8d–f), it is unsurprisingly that the all-channel (AC) configuration exhibits the best overall performance, with mean temperature bias close to 0 and minimal RMSE on all layers. Compared with the NHC, the mean RMSE values under clear, cloudy, and precipitating conditions are reduced by 19 %, 18 %, and 11 %, respectively. These results demonstrate that the design of hyperspectral channels for the FY-4M satellite makes it possible to obtain atmospheric temperature profiles more accurately under all-sky conditions. In clear-sky scenes (Fig. 8d), the RMSE values of optimal channel subsets of AC95 and AC90 increase by 17 % and 45 % compared to AC, respectively. With the appearance of clouds and precipitation, the differences between the subsets and the full set are reduced. Especially for precipitation cases, the retrieval performance of the three configurations is almost identical. This is because their differences are mainly concentrated in some typical oxygen absorption bands at 50–60, 118, and 424 GHz, whose contribution to temperature retrieval across the entire atmospheric layer cannot surpass, and may even be less than the hyperspectral channels used. Notably, the AC95 RMSE stays below 0.5 K in both clear-sky and cloudy scenes, and it is within 0.8 K even on rainy days, significantly outperforming the NHC configuration. These results further highlight the advantage of hyperspectral observations in improving the accuracy of atmospheric temperature sounding. Liu et al. (2026) reported temperature RMSEs of 3.07, 3.56, and 3.26 K for the FengYun-3E (FY-3E) vertical atmospheric sounding system (VASS) product under clear-sky, cloudy, and all-sky conditions, respectively, based on validation against radiosonde observations. The lower RMSEs obtained in the present study demonstrate the potential of the proposed algorithm. Considering the used plane-parallel MWRT model cannot fully represent three-dimensional scattering effects, however, the retrieval errors for real FY-4M observations may be larger than those indicated by the present simulation experiments. Therefore, the actual retrieval performance should be further evaluated using real on-orbit FY-4M observations in the future.

To further evaluate the retrieval performance under extreme weather conditions, moreover, the simulation results of Typhoon Doksuri at 25 July 2023 were used for a representative case study. The typhoon atmospheric fields were first simulated using the Weather Research and Forecasting (WRF) model, and treated as the reference truth. The corresponding FY-4M channel brightness temperatures were then calculated using the MWRT model and used as the input observations for the retrieval evaluation. As in the preceding simulation experiments, Gaussian random noise with a mean of 0 K and a standard deviation of 0.5 K was also added to represent instrumental noise. Fig. 9 shows the spatial distributions of the reference and retrieved temperature fields at 850, 500, and 250 hPa. Overall, the retrieved temperature fields reproduce the main spatial patterns of the reference fields at the three pressure levels. The warm-core structure near the typhoon center is clearly captured and the surrounding horizontal temperature gradients are also reasonably represented.

Figure 10 further shows vertical profiles of temperature retrieval errors for different channel configuration schemes in the typhoon case. Except for the NHC90 scheme, the biases of the other schemes are still generally distributed around 0 K, with RMSE values below 1 K at most vertical levels. This indicates that the proposed retrieval algorithm can maintain good performance even under strong convective weather conditions such as typhoons. Consistent with the results discussed above, the AC schemes generally show higher retrieval accuracy than the NHC schemes, with smaller RMSE values. This further highlights the important contribution of the hyperspectral channels to atmospheric temperature profile retrievals.

Table 3 shows the computational time required for retrieval under various channel configuration schemes. As more channels are used, the three configurations that include hyperspectral channels take longer, while achieving higher accuracy. Among them, the computational time for the AC95 is reduced by 14 % compared with the AC, while maintaining a high level of retrieval accuracy. It should be noted that this reduction in computational time mainly comes from the retrieval phase. The AC95 scheme uses fewer channels in brightness temperature matching and probability calculation, thereby reducing the computational cost of each retrieval while preserving retrieval accuracy. Therefore, AC95 is recommended as the top priority choice of channel configuration in operational system.

As the world's first geostationary satellite carrying a passive microwave payload, the FY-4M satellite would open a new era of continuous three-dimensional observations of various key geophysical parameters associated with regional weather processes, featuring an unprecedented number of channels including hyperspectral channels of temperature sounding and multiple high-frequency channels being used for the first time. For the future operational system of this satellite, it is necessary to choose the appropriate channels from a total of 135 channels to achieve retrieval for different variables, in order to improve computational efficiency to cope with the maximum observation frequency of up to 3 min and to reduce observation errors or interference from redundant channels to enhance accuracy. In this study, channel optimization for temperature profile retrieval over ocean was performed based on information entropy theory, and the retrieval performance was evaluated using a Bayesian algorithm, thereby revealing the unique potential of the FY-4M satellite in all-sky and high-accuracy atmospheric temperature profile detection.

Based on the analysis of the Jacobian matrix, using degrees of freedom for signal (DFS) as the evaluation metric, channel optimization was performed via a sequential forward selection method based on information contribution ranking. The results show that in the absence of hyperspectral channels, the oxygen absorption bands at 50 and 118 GHz are essential in atmospheric temperature sounding. However, once hyperspectral channels are introduced, the importance of these two bands would be significantly reduced. Instead, some water vapor and window channels (e.g., 23, 31, 89, and 183 GHz) become more important by providing information on water vapor, near-surface conditions, and hydrometeors, thereby playing a crucial role in constraining the atmospheric thermodynamic state. Based on the channel selection results, this study proposes an optimal channel configuration for atmospheric temperature profile retrieval, namely the AC95 scheme. It includes the hyperspectral channels (i.e., 52.7–57.3 GHz), along with selected channels mainly from water vapor absorption bands (i.e., 23, 183, and 380 GHz), and window channels (i.e., 31, 165, and 229 GHz).

To validate the accuracy and effectiveness of this optimized channel configuration scheme, atmospheric temperature profiles are retrieved based on the constructed a priori database. The validation results demonstrate that the inclusion of hyperspectral channels significantly improves the accuracy of temperature profile retrievals. For the AC95 configuration, the RMSE remains below 0.5 K under clear-sky and cloudy conditions, and is within 0.8 K during precipitating, which is clearly better than the results using all non-hyperspectral channels. In addition, the computational time is reduced by 14 % relative to the full-channel configuration. Therefore, the proposed algorithm using AC95 configuration achieves a favorable balance between retrieval accuracy and computational efficiency, making it a preferred choice for future operational retrieval system.

Since the Bayesian framework can also be applied to the retrieval of other geophysical variables, future work will adopt this technique with a similar channel optimization strategy to obtain atmospheric humidity and various hydrometeor profiles, thereby providing more comprehensive three-dimensional information about the physical state of the atmosphere. In addition, to address the challenges of retrieving multiple parameters over land, efforts will be made to resolve the representation of complex land surface emissivity and then to construct a more accurate a priori database for channel optimization. On this basis, a complete operational retrieval system will be developed, enabling the FY-4M satellite to play a more important role in monitoring and forecasting typhoons, heavy precipitation, and other extreme weather events. Also, it should be noted that treating the hyperspectral band as a single superchannel effectively demonstrates the irreplaceable value of this band for temperature sounding, but the differences in information contribution among individual hyperspectral channels haven't been further quantified here. Future work will focus on channel selection within the hyperspectral band, with the aim of identifying the most critical sounding channels without substantially degrading retrieval accuracy.