The geostationary interferometric infrared sounder (GIIRS) onboard the Fengyun-4B (FY-4B) is the first operational geostationary hyperspectral infrared (IR) sounder. This study analyzes the first-year FY-4B/GIIRS on-orbit calibration performance by comparing it to the collocated IR atmospheric sounder interferometer (IASI) observations and radiative transfer (RT) simulations. The results reveal that the mid-wave IR (MWIR) channels had a slightly larger calibration bias compared to the long-wave IR (LWIR) channels. However, the operational FY-4B/GIIRS showed improved performance compared to the experimental FY-4A/GIIRS. Furthermore, this study also found that most channels exhibited negligible annual and weak diurnal variations in calibration bias. However, there was a significant degradation in the LWIR channels (<850 cm1) and the weak diurnal variation in the MWIR channels. Finally, the calibration performance of FY-4B/GIIRS demonstrates a reduced dependence on brightness temperature (BT), except for the channel at wavenumber 703.125 cm1. Overall, it concluded that FY-4B/GIIRS demonstrated consistent calibration stability and high accuracy, highlighting its capability for precise quantitative applications.

The Geostationary Interferometric Infrared Sounder (GIIRS) onboard China’s Fengyun-4A (FY-4A) geostationary (GEO) meteorological satellite provides high-spectral-resolution infrared (IR) observations for targeted observing areas with high temporal resolution. Due to the high uncertainties in radiative transfer modeling of cloudy radiances, it is challenging to take full advantage of the thermodynamic information from GIIRS in all-sky conditions. The Advanced Geostationary Radiation Imager (AGRI) onboard the same platform provides a variety of cloud products with high spatial resolution. A bias-corrected optimal cloud-clearing (BCOCC) approach is introduced to generate GIIRS cloud-cleared radiances (CCRs) with the help of AGRI-collocated clear radiances (CLRs). The bias correction (BC) scheme is based on the inter-comparisons between GIIRS and AGRI for each field-of-view (FOV) and different scene temperatures. The BC method ensures the radiometric consistency between GIIRS and AGRI. Evaluations of GIIRS CCRs show that the mean biases are 0.09, −0.06, and 0.06 K when compared with the three AGRI IR bands, B12, B13, and B14. In addition, BCOCC significantly increases the data yields of successful CCRs by three times that of the optimal cloud-clearing (OCC) approach without BC. For 15 days from September 16–30, 2021, around 37% more GIIRS partially cloudy footprints than clear sky are cloud cleared successfully. The CCRs can be assimilated as CLRs in numerical weather prediction (NWP) models without worrying about the cloud impact. This study provides evidence of the importance of placing an advanced hyperspectral IR sounder and imager onboard the same GEO platform for better quantitative applications.

Aerosol altitude is one of the key parameters for radiative forcing estimation and environment studies. In order to obtain wide coverage and multiple times Aerosol Layer Height (ALH) per day, we developed a machine learning based algorithm for retrieving ALH from the Earth Polychromatic Imaging Camera (EPIC) observations. The reflectance of all bands from EPIC, solar and view angles are used as predictors. High-accuracy ALH from Cloud-Aerosol LIDAR with Orthogonal Polarization (CALIOP) is used as response. The algorithm was applied to observations over western sea of Africa (Area 1) and north plain of China (Area 2). The data from January to November 2021 are used for the XGBoost model construction, and independent 10-fold cross-validation results show very good performance of retrieval algorithm with correlation coefficient (R) larger than 0.9, root mean square error (RMSE) less than 0.55km and the proportion falling within the error range (EE) is over 80%. The independent EPIC ALH retrievals of whole 2016 year also show consistency with CALIOP observations. The RMSE is 0.78 (0.9) km and the proportion falling within EE is 58% (45%) for Area 1 and 2, respectively. The comparisons between XGB retrievals and the operational Aerosol Optical Centroid Height (AOCH) product from EPIC for dust and haze cases show that the XGB retrievals are much more consistent with CALIOP observations than the operational EPIC AOCH. ALH retrievals from this study could capture the spatial and temporal variations both for the high aerosol layer (dust) and the low aerosol layer (haze).

Atmospheric motion vectors (AMVs) from satellite measurements serve as critical indicators of atmospheric dynamics, playing an essential role in enhancing the prediction precision of numerical weather prediction (NWP) models through data assimilation (DA). The implementation of finer satellite-derived vector products has the potential to significantly augment the accuracy of atmospheric flow field data in high-resolution regional NWP model simulations, thereby fulfilling the burgeoning requirements of operational weather nowcasting and forecasting. This study is focused on the development of mesoscale AMV (MAMV) products, which are distinguished by their exceptional quality and spatiotemporal resolution, leveraging data from the geostationary high-speed imager aboard the Fengyun-4B geostationary meteorological satellite (FY-4B/GHI). MAMVs of FY-4B/GHI feature an enhanced horizontal resolution of 3 km, enabling more accurate identification and monitoring of nongeostrophic flow patterns of mesoscale weather systems, as well as their fast-evolving dynamical structures and characteristics. Furthermore, a comparative analysis with radiosonde measurements highlights the precision of MAMV products, as evidenced by a speed bias (SB) of 0.37 m/s, a speed root mean square error (sRMSE) of 4.68 m/s, and a direction root mean square error (dRMSE) of 26.35°. The prospects of high-resolution satellite wind field data hold great potential for propelling scientific advancement and enriching our comprehension of atmospheric dynamics. This is particularly valuable in the context of typhoon monitoring and forecasting, where such data can lead to significant improvements in predictive capabilities.

Compared with low earth orbit satellites, the high altitude from geostationary earth orbit (GEO) satellites leads to increased diffraction effects on hyperspectral infrared sounders, which reduce the ensquared energy (EE) within the satellites’ field-of-view (FOV) and increase the pseudo noise of the measurements. To help understand how the instrument performance is affected by EE for the Geostationary Extended Observations Sounder (GXS), a point spread function (PSF) is used to simulate the contribution of each location within and outside of an FOV (4 km by 4km at nadir). The PSF is applied to the Moderate Resolution Imaging Spectroradiometer airborne simulator data with a spatial resolution of 50m, to determine an appropriate EE for GXS. Although wavenumber dependent, an EE of 70% is recommended which ensures all GXS channels have pseudo noise less than the instrument specifications. Regardless of the EE value, the pseudo noise reduces the precision of the temperature and moisture sounding retrievals in the troposphere. Even with an EE of 70%, the pseudo noise slightly increases the root mean square error (RMSE) by 3-4% for temperature and by 1-4% for relative humidity. If an EE of 70% is difficult to meet, due to cost for example, a lower EE can be a good tradeoff with only a slight degradation in the sounding retrieval quality, which may be overcome with spatial averaging using the inverted cone method. An EE of 50% would lead to an RMSE increase of about 6% for temperature, and 3-6% for the relative humidity.

Clouds play an important role in the Earth’s climate system; however, various observational methods describe clouds differently, leading to cloud products being described with different characteristics, and affecting our understanding of cloud effects. To address this problem, this study integrates different cloud products into the transfer-learning procedure of a deep learning model and determined the Cloud Effective Radius (CER), Cloud Optical Thickness (COT), and Cloud Top Height (CTH) from Himawari-8 thermal infrared measurements. The retrieval results were independently evaluated against the Moderate-resolution Imaging Spectroradiometer cloud products and further compared with Himawari-8 cloud products during the day. The Root Mean Squared Errors (RMSE) of the model for the CER, COT, and CTH were 4.490 μm, 11.198, and 1.904 km, respectively, which are lower than those of Himawari-8 cloud products (RmSe:11.172 μm, 14.755, and 2.860 km). Moreover, validation results against active sensors show that the model performs slightly better during the day than at night, and both are generally better than the Himawari-8 cloud product. Overall, the model maintains stable performance during both day and night, and its accuracy is higher than that of Himawari-8 cloud products.

Various methodologies have been developed for selecting a subset of channels from a hyperspectral infrared (IR) sounder for assimilation. The information entropy iterative method was considered optimal for channel selection. However, this method only considers the decrease in uncertainty in the atmospheric state caused by measurements at a single time, without considering the dynamic effect of measurements over a period of time; therefore, it might not be optimal for hyperspectral IR sounders onboard geosynchronous satellites that mainly aim to observe rapidly changing weather events. An alternative channel selection method is developed by adding an$M$index, which reflects the Jacobian variance over time; the adjusted algorithm is ideal for the Geosynchronous Interferometric Infrared Sounder (GIIRS), which is the first high-spectral-resolution advanced IR sounder onboard a geostationary weather satellite. Comparisons between the conventional algorithm (information entropy iterative method) and the adjusted algorithm show that the channels selected from GIIRS by the adjusted algorithm will have larger brightness temperature diurnal variations and better information content than the conventional algorithm, based on the same background error covariance matrix, the observational error covariance matrix, and the channel blacklist. The adjusted algorithm is able to select the channels for monitoring atmospheric temporal variation while retaining the information content from the conventional method. The 1-D variational (1Dvar) retrieval experiment also verifies the superiority of this adjusted algorithm; it indicates that using the channel selected by the adjusted algorithm could enhance the water vapor profile retrieval accuracy, especially for the lower and middle troposphere atmosphere.

A deep-learning-based cloud detection and classification algorithm for advanced Himawari imager (AHI) measurements from the geostationary satellite Himawari-8 has been developed. It is found that a combination of observed radiances and simulated clear-sky radiances can substantially improve cloud phase discrimination, especially for optically thin clouds. Therefore, cloud detection, cloud phase classification, and multilayer cloud detection are obtained simultaneously from multispectral observed radiances and simulated clear-sky radiances using deep neural networks (DNNs). Two DNN models are established for all-day and daytime-only applications, respectively, using active Cloud Profiling Radar (CPR) and Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) merged cloud products from 2016 as reference labels. The independent dataset from 2017 is used to validate the DNN models. It is shown that both the DNN models outperform the official Moderate Resolution Imaging Spectroradiometer (MODIS) and AHI products in cloud detection and phase discrimination, and the enhancement is more significant over land than over water surface. For multilayer cloud detection, the probability of detecting multilayer clouds reaches ~60% for the all-day model and is increased to ~70% for the daytime model, which is substantially better than MODIS and AHI products. In practical cases, multilayer cloud detection by DNN models is more consistent with CPR/CALIOP than two official products. In addition, the DNN models have superior capability in detecting the optically thin cirrus, which is omitted by MODIS and AHI products. Specifically, the cases also demonstrate that the DNN models can provide effective mixed-phase cloud identification. This deep-learning-based algorithm has the potential for measurements from other similar instruments.

Aerosols play an important role in global climate change, which requires long-term data records. Advanced very high-resolution radiometer (AVHRR) provides continuous observations for up to 40 years since 1979, which makes it worthwhile to retrieve aerosol optical depth (AOD) from AVHRR over land. A novel algorithm for retrieving AOD from AVHRR is developed based on the machine learning (ML) method. The AVHRR observations from pathfinder atmospheres–extended (PATMOS-x) Level-2 dataset and corresponding AOD products ($0.55~\mu \mathrm {m}$) from moderate resolution imaging spectroradiometer (MODIS) in 2014 are used as training data. And AOD products in three years (2015, 2006, and 1998) named AVHRR XGB-AOD were generated for evaluation. Comparisons show that the AVHRR XGB-AOD is consistent with the MODIS AOD with correlation coefficients greater than 0.80 and RMSE less than 0.18 for most months in 2015 and 2006. The temporal and spatial characteristics from AVHRR XGB-AOD are similar to those from the MODIS AOD, but those from the previous AVHRR AOD with deep blue (DB) algorithm are significantly different. Validation with AERONET indicates that more than 68% of the matchups fall within expected error [EE, ±($0.05\,\,\pm \,\,0.25\times {\mathrm {AOD}}_{\mathrm {AERONET}}\mathrm {)] }$in 2015 and 2006, while the fraction is 66% in 1998. Compared to the DB algorithm, the ML-based algorithm performs better in high-AOD conditions over vegetated regions, such as in Southeast Asia, where the DB algorithm significantly underestimates. In low-AOD conditions, the ML-based algorithm performs better over western North America and Australia, where the aerosol composition varies greatly.

For operational weather forecasting and nowcasting, a refresh rate (RR) of 10 min and a better-than-4-km footprint size for Infrared (IR) bands is desired. Such imaging capability is only available from geostationary orbit (GEO) satellites, such as ABI onboard geostationary operational environmental satellites (GOES)-16/-17, but mainly limited to the tropics and mid-latitudes (referred to as GEO-like imaging capability). For high latitudes such as the Alaskan region, the IR footprint size from ABI/GOES-17 is worse than 6 km, limiting the application over the region. Tundra satellites, with a nonzero inclination angle and a nonzero eccentricity, have longer dwell times near the apogee than the perigee and can be used to monitor the high latitudes and polar regions. This study investigates Tundra satellites’ imaging capability by assuming an ABI-like instrument with the same IR spatial resolution of$56~\mu $rad onboard. For regional applications, a constellation of two Tundra satellites may provide GEO-like imaging capability for a large domain. This useful domain is further improved when combined with a GEO, i.e., two$Tundra + GOES-17$. For global applications, a constellation of three Tundra satellites may provide GEO-like imaging capability for high latitudes (improved capability over GEO) and polar regions (unprecedented capability) in both hemispheres. The additional capability from Tundra satellites in tropics and mid-latitudes makes the global space-based meteorological observing system more robust and resilient. While a constellation of three$Tundra + 3$GEOs may provide global GEO-like imaging capability, having more than three GEOs may be desired by agencies/countries, allowing for improved spatial resolutions over their sub-point locations.

The Suomi National Polar-orbiting Partnership Program Visible Infrared Imaging Radiometer Suite (VIIRS) thermal emissive bands (TEB) have been performing well during nominal operations since launch. However, small but persistent calibration anomalies are observed in all TEBs during the quarterly blackbody (BB) warm-up/cool-down (WUCD) events. As a result, the time series of daytime sea surface temperature (SST) (derived from bands M15-M16) show warm spikes on the order of 0.25 K. This paper suggests that VIIRS TEB WUCD biases are band dependent, with daily-averaged biases about -0.04 and 0.05 K for I4 and I5, and -0.05, -0.05, 0.11, 0.09, and 0.05 K for M12-M16, respectively. Two correction methods-Ltrace and WUCD-C-have been implemented and evaluated using colocated observations from the Cross-track Infrared Sounder (CrIS), radiative transfer simulations, and SST retrievals. Also an error in the National Oceanic and Atmospheric Administration operational processing was identified and fixed. Both correction methods effectively minimize WUCD-induced SST anomalies. The Ltrace method works well for I5, M12, and M14-M16, with residual biases about 0.01 K. The WUCD-C method, on the other hand, performs well to correct WUCD biases in all TEBs, with residual biases also about 0.01 K. However, it introduces warm biases relative to CrIS at cold scene temperatures, which requires further study. Applying nonequal BB thermistor weights improves calibration at BB temperature set points, but its impact on daily-averaged WUCD biases is small. The proposed methodologies may also be applied to the VIIRS onboard the follow-on Joint Polar Satellite System satellites.

A long-term historical aerosol optical depth (AOD) data set from 1982 to 2011 over China (15-45° N; 75-135° E) with 0.1 spatial resolution has been produced from Advanced Very High Resolution Radiometer (AVHRR) Pathfinder Atmospheres-Extended level-2B data. The spatial distribution pattern shows that high AOD values are found in central and eastern China over the entire period with AODs larger in summer and spring than in autumn and winter. As the high-quality products from AERONET were absent for this period over mainland China, AOD data obtained using the broadband extinction method from solar radiation stations have been used to verify the quality of the AVHRR AOD data set over China. The intercomparison results show that the interannual variation of AOD has been well captured in the variation curve of the AOD monthly mean and the variation trend is also consistent over the whole period. The correlation coefficient of the monthly mean is mostly larger than 0.55, the agreement index is larger than 0.57, and the relative error is less than 21%. Both AVHRR and visibility data sets show high values in regions with rapid economic development. Using Moderate Resolution Imaging Spectroradiometer AOD data as references, it is found that AVHRR AOD from this paper has better accuracy in general than that from Deep Blue (DB) algorithm over China, especially over eastern and southern China, while DB provides more coverage especially over bright surface such as northwest China. This long-term historic AOD data set can be used together with other AOD data sets to study the climate and environmental changes, especially in the 1980s and 1990s.

Random forests (RFs), an advanced machine learning (ML) method, was used here to develop a robust and rapid quantitative precipitation estimates (QPEs) algorithm for the new-generation geostationary satellite of Himawari-8. In this algorithm, the global precipitation measurement (GPM) product has been employed to train QPE prediction model. The real-time multiband infrared brightness temperature from Himawari-8, combined with the spatiotemporally matched numerical weather prediction (NWP) data from the global forecast system, have been used as predictor variables for QPE. Among the variables used in RF learning model, total precipitable water and$K$-index from NWP data have the highest rankings, indicating the importance of atmospheric environment for QPE. To enhance the accuracy of RF models or to optimize model training, a sample-balance technique has been utilized to adjust the ratios of samples in nonprecipitation/precipitation classification and quantitative precipitation regression data sets. Further sensitivity and validation analyses help determine the optimal RF classification and regression models for predicting nonprecipitation/precipitation pixel and rain rate. The selected RF classification model is found to predict precipitation area with an accuracy of 0.87. For predicted QPE product, the mean-absolute-error and root-mean-square error of RF regression model are 0.51 and 2.0 mm/h, respectively. Overall, the RF ML algorithm has a higher detection rate over homogenous ocean surface as compared with over land. Meanwhile, this RF algorithm tends to underestimate rain rate, especially in the presence of heavy rainfall. Despite this, it still produces a reasonable pattern of rainfall area and intensity, which are highly consistent with GPM observations.

As Advanced Very High Resolution Radiometer (AVHRR) lacks a 2.1-μm band, the widely used “dark target” algorithm cannot be used to retrieve aerosol optical depth (AOD) from AVHRR over land. Instead, a multiple regression algorithm has been developed to process a time series of AVHRR Level_1b measurements over China (15°-45° N, 75°-135° E) for AOD retrieval. As the apparent reflectance of AVHRR is closely related to AOD, which can be provided by Moderate Resolution Imaging Spectroradiometer (MODIS), spatially and temporally collocated Aqua/MODIS AOD and AVHRR Level_1b measurements from four years (January 2008-December 2011) were used to generate the regression coefficients. Angle information, normalized difference vegetation index, water vapor, and surface elevation are chosen in addition to the apparent reflectance as predictors for different surface types. By applying the regression coefficients to AVHRR, the AOD product from independent AVHRR Level_1b measurements (May 2003-December 2007) was generated. Validation with AErosol RObotic NETwork (AERONET) AOD and comparison with MODIS AOD products have been conducted to evaluate the uncertainty of the AVHRR AOD from May 2003 to December 2007. The distribution pattern of the seasonal mean AOD from AVHRR is consistent with that of the MODIS AOD. Taking regions with rapid economic development, for example, the regional monthly mean AOD for these two data sets agrees well, with a consistent tendency and high correlation coefficients. When compared with AERONET from four sites in China, the results are also encouraging. Results show that the multiple regression method offers the potential to generate an AOD climatology data record from a long-term AVHRR Level_1b data set over land.

Geostationary imagers such as the Advanced Baseline Imager (ABI) proposed for the next generation of Geostationary Operational Environmental Satellites (GOESs), i.e., GOES Series R and beyond, and the spinning enhanced visible and InfraRed Imager (SEVIRI) onboard METEOSAT 8 provide atmospheric total column ozone (TCO) with high temporal (better than 15 min) and spatial (better than 5 km) resolutions. The purpose of this paper is to present a method that evolved from the current GOES sounder TCO retrieval that can be applied to ABI, with SEVIRI serving as a proxy for ABI. Although ABI and SEVIRI have fewer infrared spectral bands than the current series of GOES (-8 to -P) sounders, ABI and SEVIRI can provide TCO with an accuracy that is comparable to the current GOES sounder by using forecast temperature profiles as additional a priori information. Despite the need for additional data, the greatest advantage of ABI and SEVIRI is their fast 15-min coverage of the full disk. The SEVIRI TCO demonstrates how well geostationary imagers can capture ozone transport and change at high temporal and spatial resolutions. The estimated TCO has very good agreement (R = 0.92 and root-mean-square error = 3.7%) with the total ozone measurements from the Ozone Monitoring Instrument (OMI) onboard the Earth Observing System aura platform.

The blurring from diffraction for the infrared (IR) radiances on a geostationary satellite (GEO) e.g., the next generation of Geostationary Operational Environmental Satellite (GOES-R) was simulated by using Moderate Resolution Imaging Spectroradiometer Airborne Simulator data and the point spread function (PSF) model for an unobscured telescope. The portion of the total radiance contributed from each nearby geometrical field of view (GFOV) was calculated. For 90% ensquared energy (EE) (equivalent to 10% of the energy coming from outside the footprint), the closest GFOVs contribute 7%; the contribution from the closest GFOVs increases to 22% for 70% EE. The increased portion from the nearby GFOVs causes larger blurring and degrades the pixel-based retrieval product accuracy. Radiance contamination from the nearby field for the GEO IR radiances with 90%, 80%, and 70% EE causes 0.2-, 0.3-, and 0.4-K blurring errors, respectively, in the 12-mum IR longwave window band in clear 300-K scenes. The blurring error is doubled in cloudy 230-K scenes. For the 13.8-mum absorption band, the blurring error will be smaller than that of the 12-mum band because the atmospheric layer where the temperature sensitivity peaks for the 13.8 mum is more uniform than the surface where the 12 mum is most sensitive. This indicates that the PSF has a greater impact on a heterogeneous surface. Similar blurring errors occur at both 4- and 10-km spatial resolution IR sensors. The blurring error is not random, and it varies spectrally. These conclusions are very relevant to the design of a cost-effective GEO IR sounder that meets the science requirements

The Atmospheric Infrared Sounder (AIRS) onboard the National Aeronautics and Space Administration's Earth Observing System's (EOS) Aqua spacecraft, with its high spectral resolution and radiometric accuracy, provides atmospheric vertical temperature and moisture sounding information with high vertical resolution and accuracy for numerical weather prediction (NWP). Due to its relatively coarse spatial resolution (13.5 km at nadir), the chance for an AIRS footprint to be completely cloud free is small. However, the Moderate Resolution Imaging Spectroradiometer (MODIS), also on the Aqua satellite, provides colocated clear radiances at several spectrally broad infrared (IR) bands with 1-km spatial resolution; many AIRS cloudy footprints contain clear MODIS pixels. An optimal cloud-correction or cloud-clearing (CC) algorithm, an extension of the traditional single-band N/sup */ technique, is developed. The technique retrieves the hyperspectral infrared sounder clear column radiances from the combined multiband imager IR clear radiance observations with high spatial resolution and the hyperspectral IR sounder cloudy radiances on a single-footprint basis. The concurrent AIRS and MODIS data are used to verify the algorithm. The AIRS cloud-removed or cloud-cleared radiance spectrum is convolved to all the possible MODIS IR spectral bands with spectral response functions (SRFs). The convoluted cloud-cleared brightness temperatures (BTs) are compared with MODIS clear BT observations within AIRS cloud-cleared footprints passing our quality tests. The bias and the standard deviation between the convoluted BTs and MODIS clear BT observations is less than 0.25 and 0.5 K, respectively, over both water and land for most MODIS IR spectral bands. The AIRS cloud-cleared BT spectrum is also compared with its nearby clear BT spectrum, the difference, accounting the effects due to scene nonuniformity, is reasonable according to the analysis. The multiband optimal cloud-clearing is also compared with the traditional single-band N/sup */ cloud-clearing; the performance enhancement of the optimal cloud-clearing over the single-band traditional N/sup */ cloud-clearing is demonstrated and discussed. It is found that more than 30% of the AIRS cloudy (partly and overcast) footprints in this study have been successfully cloud-cleared using the optimal cloud-clearing method, revealing the potential application of this method to the operational processing of hyperspectral IR sounder cloudy radiance measurements when the collocated imager IR data are available. The use of a high spatial resolution imager, along with information from a high spectral resolution sounder for cloud-clearing, is analogous to instruments planned for the next-generation Geostationary Operational Environmental Satellite (GOES-R) instruments-the Advanced Baseline Imager and the Hyperspectral Environmental Suite. Since no microwave instruments are being planned for GOES-R, the cloud-clearing methodology demonstrated in this paper will become the most practical approach for obtaining the reliable clear-column radiances.

An approach is developed to infer the optical thickness of semitransparent ice clouds (when optical thickness is less than 5) from Atmospheric Infrared Sounder (AIRS) high spectral resolution radiances. A fast cloud radiance model is developed and coupled with an AIRS clear-sky radiative transfer model for simulating AIRS radiances when ice clouds are present. Compared with more accurate calculations based on the discrete ordinates radiative transfer model, the accuracy of the fast cloud radiance model is within 0.5 K (root mean square) in terms of brightness temperature (BT) and runs three orders of magnitude faster. We investigate the sensitivity of AIRS spectral BTs and brightness temperature difference (BTD) values between pairs of wavenumbers to the cloud optical thickness. The spectral BTs for the atmospheric window channels within the region 1070-1135 cm/sup -1/ are sensitive to the ice cloud optical thickness, as is the BTD between 900.562 cm/sup -1/ (located in an atmospheric window) and 1558.692 cm/sup -1/ (located in a strong water vapor absorption band). Similarly, the BTD between a moderate absorption channel (1587.495 cm/sup -1/) and the strong water absorption channel (1558.692 cm/sup -1/) is sensitive to ice cloud optical thickness. Neither of the aforementioned BTDs is sensitive to the effective particle size. Thus, the optical thickness of semitransparent ice clouds can be retrieved reliably. We have developed a spectrum-based approach and a BTD-based method to retrieve the optical thickness of semitransparent ice clouds. The present retrieval methods are applied to a granule of AIRS data. The ice cloud optical thicknesses derived from the AIRS measurements are compared with those retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS) 1.38and 0.645-/spl mu/m bands. The optical thicknesses inferred from the MODIS measurements are collocated and degraded to the AIRS spatial resolution. Results from the MODIS and AIRS retrievals are in reasonable agreement over a wide range of optical thicknesses.