Near real-time precipitation estimation from geostationary satellites plays an important role in flood forecasting, water resource management, and disaster prevention and reduction. Currently, near real-time precipitation products based on geostationary satellites still face great challenges in accurately detecting precipitation and monitoring small-scale precipitation. In this study, a novel Geostationary Satellite Precipitation Estimation (GSPE) algorithm for near real-time condition was developed to retrieve precipitation at a spatial resolution of 0.05°×0.05° every 10 minutes both day and night. The major highlight of the GSPE is that a new precipitation detection scheme was created by introducing a newly proposed precipitation detection index (PDI) and 24-hour continuous cloud microphysical parameters for the first time. Another highlight is that a dynamic updating scheme was proposed in building conversion models between brightness temperature of geostationary satellite and precipitation to keep the accuracy and stability of the estimated precipitation. Furthermore, the 10-minute temporal resolution of the estimated precipitation could accurately capture the evolution of a short precipitation process and improve the calculation of total precipitation amount. According to the validation using rain gauges observations from Chinese mainland, the Heidke Skill Score of the GSPE in hourly scale could reach up to 0.39 which was improved by 11.43% compared to the GSMaP_NOW. The root mean square error of precipitation from the GSPE in hourly, daily, and monthly scale are 1.66mm, 13.65mm, and 97.76mm respectively, and were improved by 15.74%, 13.17%, and 21.01% respectively compared to that of the GSMaP_NOW.

Accurate identification of cloud phase in the Arctic is critical for evaluating surface energy budgets and reducing uncertainties in climate models, as clouds exert a complex, warming influence highly sensitive to phase partitioning amidst amplified warming. The submillimeter and infrared radiation exhibit distinct sensitivities toward hydrometeors in different cloud phases. In this study, the performance of synergistic observations of submillimeter and infrared spectrum for cloud identification and phase classification in the Arctic is explored, through sensitivity analysis and classification accuracy analysis. The spectral variances between the submillimeter and infrared bands under the scenarios of clear sky, ice cloud, liquid water cloud, and mixed-phase cloud are analyzed by quantifying the disparities in the observed spectra. The sensitivity analysis reveals that the synergistic metrics constructed by synergistic channels can distinguish clouds from clear skies or identify cloud thermodynamic phases. To quantitatively estimate the classification performance of the combined spectrum, a synergistic classification algorithm is constructed based on the Random Forest framework, and then trained and tested by the simulated synergistic observation datasets in the Arctic. Results from assessment metrics revealed that the overall accuracy of the classification model reaches 91.35%. Especially for clear skies and ice clouds, the classification accuracy is 99.43% and 92.23%, respectively. While for mixed-phase clouds, the overall classification accuracy reaches 87.34%. Specifically, ice-over-water clouds demonstrate 89.40% classification accuracy, while water-over-ice clouds achieve lower accuracy of 85.52%, reflecting fundamental differences in their thermodynamic stability and radiometric signatures. Results provide a robust statistical foundation for advanced cloud detection and phase classification algorithms, demonstrating notable improvements in classification accuracy by synergistic channels. With the upcoming spaceborne submillimeter and infrared passive sensors, the results demonstrate a pressing need and the potential of combining observations to better understand cloud phase and evolution in the future.

The spatial representativeness error of in situ soil moisture (SM) is recognized as a major source of uncertainty when validating satellite SM products with a spatial resolution of tens of kilometers. Site underrepresentation is primarily caused by environmental heterogeneity, but their relationship remains poorly understood. Here, we assessed the spatial representativeness of in situ SM from 322 strictly screened stations worldwide relative to coarse-resolution (~0.25°) satellite footprint based on the extended triple collocation (ETC) method. We then evaluated the influence of the heterogeneity of four environmental factors (soil texture, land cover types, elevation, and vegetation coverage) on site representativeness. Moreover, we calculated SM variability within the satellite footprint based on 1-km SM data to explore its relationship with environmental heterogeneity. Results indicate that about 63% of the sites have relatively good spatial representativeness (ETC-derived correlation coefficient$\ge 0.7$). Soil texture and land cover exhibit greater heterogeneity across the mid and high latitudes of the Northern Hemisphere. The larger heterogeneity in elevation and vegetation coverage is primarily found in regions with significant ridges and dense vegetation, respectively. Land cover is the major factor influencing the spatial representativeness of SM sites, and the increase in the heterogeneity of land cover enhances SM variability, which negatively impacts site representativeness. The in situ SM can be more representative when the proportion of the land cover type where the site is located is higher or when there are fewer land cover types within the satellite footprint. Moreover, it is found that the newly proposed metric of the similar area ratio of sites, as a measure of land cover heterogeneity, can effectively reflect SM variability. This metric can also serve as a supplementary criterion for selecting representative sites, particularly in situations where sites are sparse and the ETC method is inapplicable. These findings provide useful references for robust evaluation of satellite SM products based on in situ measurements (e.g., in situ SM upscaling and SM site deployment).

This study introduces an innovative operational Bayesian neural network framework for high-precision joint retrieval of aerosol optical depth (AOD) and layer height (ALH) with physically-consistent uncertainty decomposition from TROPOMI hyperspectral measurements. Unlike conventional approaches, three different full-physics Bayesian neural network architectures (implemented via Bayes-by-Backprop, Dropout, and Batch Norm techniques) are developed to simultaneously estimate target parameters and their heteroscedastic aleatoric uncertainties while preserving radiative transfer constraints. Epistemic uncertainties are quantified via Monte Carlo sampling of stochastic forward propagation, enabling systematic separation of data-driven vs. model-driven uncertainties. A comprehensive validation demonstrates: (1) Synthetic experiments show epistemic uncertainties strongly correlate with retrieval errors, particularly for observing geometries outside the training data distribution, outperforming aleatoric estimates; (2) Analyses using TROPOMI measurements demonstrate that the framework delivers comparable accuracy to operational products while providing unique uncertainty diagnostics. The framework’s computational efficiency combined with its probabilistic outputs establishes a new paradigm for characterizing aerosol properties from satellite measurements, particularly valuable for climate and air quality applications.

1 Abstract-The cloud water path (CWP) has an important influence on the radiative effects of clouds and the water cycle in the Earth’s atmospheric system, serving as a key parameter in physical cloud processes. In this study, a novel method for retrieving CWP by leveraging the advantages of multisource and multiband active and passive satellite observations is proposed. A retrieval model to retrieve CWP that using Himawari-8/AHI) thermal infrared channels is established by learning from active radar (CloudSat) measurements, the model enables continuous CWP retrieval throughout the day. Compared with all-day CloudSat-CWP, our CWP products has has a higher retrieval accuracy that that of MODIS. The distribution of the monthly average CWP product based on the Himawari-8 full-disk dataset resembles that of CloudSat observations, with the highest average CWPs in equatorial region, followed by the CWPs in midlatitude regions. This spatial pattern of CWP is possibly due to the prevalence of strong convective systems in these areas, which facilitate the formation and progression of deep clouds, leading to higher CWP values. This algorithm can offer valuable data support for atmospheric-related analyses and has been integrated into the Cloud Remote Sensing, Atmospheric Radiation, and Renewable Energy Application (CARE) platform for atmospheric remote sensing algorithms.

The uncertainty inherent in validating satellite-derived soil moisture (SM) products is significantly attributed to the spatial underrepresentation of in situ SM measurements. The main reason for this phenomenon is the varying environmental conditions (called as environmental heterogeneity) within the satellite footprint. To better understand this issue, we assessed the spatial representativeness of in situ SM from 383 strictly screened stations worldwide relative to the coarse-resolution (~0.25°) satellite footprint and analyzed the effects of four environmental factors (i.e., soil texture, land cover, elevation, and vegetation coverage) using the extended triple collocation (ETC) technique. Results show about 63% of the sites have satisfactory levels of spatial representativeness (ETC derived correlation coefficient ⩾0.7). Land cover is the foremost factor affecting the spatial representativeness of SM sites. The in situ SM can better represent the true variability of SM when the proportion of land cover types where the site is located is higher or there are fewer land cover types within the satellite pixels.

The spatial resolution of existing satellite soil moisture products is very coarse (~25 km), and thus there is usually significant spatial heterogeneity in the land surface covered by satellite footprints. However, the effects of spatial heterogeneity on satellite soil moisture products are largely under-studied previously. The study firstly evaluated seven satellite soil moisture products comprising SMAP (DCA, IB, and MTDCA), SMOS-IC, AMSR2 (LPRM and JAXA) and FY-3C at a global scale using the extended triple collocation (ETC) method. Then, the skills of these products under a wide range of surface heterogeneity including heterogeneity in vegetation coverage, terrain, land cover, and soil texture were ascertained. The results indicate: (1) SMAP-IB and SMAP DCA products generally outperform others, followed by SMOS-IC and SMAP MTDCA which also exhibit satisfactory performance; (2) heterogeneity in vegetation coverage, terrain, and land cover generally decreases the R value of satellite soil moisture products, while heterogeneity in soil texture has an insignificant effect on product skills; (3) L-band products demonstrate greater stability compared to C/X-band datasets across various surface heterogeneity.

Satellite soil moisture products have great potential for many applications, such as drought monitoring and landslide warning. However, these applications often require accurate and continuous soil moisture records, and the missing values in satellite soil moisture datasets often hamper the usefulness of these products for such applications. This study firstly proposed and compared three approaches, i.e., linear regression, linear rescaling, and random forest to fill the missing values in the SMAP soil moisture products in both temporal and spatial dimensions, based on the seamless ERA5 data from 2016 to 2019. Then, a total of twelve auxiliary data were incorporated into the training datasets of random forest to improve the accuracy of gap-filled SMAP data. Finally, the gap-filled SMAP data were compared with the original SMAP data and validated by in situ measurements from 1071 sites worldwide. The results indicate: 1) when using only the ERA5 datasets, the random forest performs better than linear regression and linear rescaling methods in the training phase, but its skill degrades noticeably in the validation phase; 2) by adding the auxiliary data, the performance of random forest improves significantly in the validation phase; 3) the gap-filled SMAP data maintain or even exceed the accuracy of the original SMAP soil moisture, demonstrating the effectiveness of the proposed gap-filling method.

Hourly Himawari-8 (H8) Advanced Himawari Imager Level 3 Ocean Color (L3 OC) products have been recently released; however, a thorough evaluation and uncertainty analysis of L3 OC data spanning full disk, as well as applicability to studies on photosynthetically active radiation (PAR) have not yet been conducted. This study evaluates the accuracy of L3 OC products, including normalized water-leaving radiance (Lwn) at 470, 510, and 640 nm, Chlorophyll-a concentration (Chlor-a), aerosol optical thickness (AOT) at 510 nm, and Ångström exponent (AE), by comparing them to ground-based measurements obtained from Ocean Color Component of the AErosol RObotic NETwork (AERONET-OC). Our results demonstrate a general agreement with the ground-based measurements, especially Lwn510. Chlor-a and AOT510 also demonstrate an overall consistency, whereas AE shows a larger discrepancy. Uncertainty analysis shows that Lwn remained accurate under different conditions, although increased uncertainties were observed in turbid water and periods of severe air pollution. Spatial analysis revealed that the distribution of L3 OC and Aqua-MODIS L2 OC products were strongly correlated. Lwn and Chlor-a in the Yellow and Bohai Seas exhibit seasonal variations, with both parameters decreasing in summer and increasing in winter. The impact of aerosols and Chlor-a on PAR calculations was investigated by developing a sophisticated algorithm for estimating PAR under clear-sky conditions using a coupled radiative transfer (RT) model. An analysis of the May 2021 dust event in the Southern Yellow Sea, which exhibited an AOT of 0.82, showed a notable increase in Chlor-a levels —one to two days later, while the average daytime PAR forcing was −42.469 W/m2 under clear-sky conditions.

Improving the accuracy of measuring ice cloud properties is crucial for the study of atmospheric circulation and climate models, and for understanding the radiative forcing effects of ice clouds. In this study, a novel approach to retrieve ice cloud microphysics involving synergistically analyzing the spectra of submillimeter (sub-mm) and infrared (IR) is proposed, combining the complementary information regarding ice cloud properties from each spectrum. The sensitivity of the synergistic channel pairs to ice water paths (IWPs) and mean mass diameters is thoroughly investigated by the synthetic lookup tables and sensitivity parameter analysis. A synergistic retrieval algorithm based on Quantile Regression Neural Networks is constructed toward a better evaluation of the retrieval biases and uncertainties quantitatively. The simulated retrieval results reveal that the synergistic retrievals outperform the results from individual spectra across the full range of IWP from 1 to 1000 g/m2 and mean mass diameter from 1 to$500~\mu $m. Specifically, the mean root-mean-square-error of the synergistic retrievals for IWP is 68% (95%) lower than that of the sub-mm-only (IR-only) retrievals, and a 10% (24%) lower root-mean-square-error for mean mass diameter, respectively. In addition, the synergy can improve the correlation for IWP by 3.7% (5.6%) and yields a 12.5% (17.6%) higher correlation for mean mass diameter compared to the sub-mm-only (IR-only) retrievals. With the upcoming spaceborne sub-mm and IR passive sensors, the results demonstrate a pressing need and the potential of combining observations to better understand ice cloud properties in the future.

A physics-driven machine learning (ML) algorithm that integrates radiative transfer model (RTM) simulation and ML techniques has been developed to facilitate multilayer cloud property retrievals from advanced Himawari imager (AHI) measurements based on the geostationary satellite Himawari-8. Theoretical sensitive study revealed that integrating RTM-simulated clear-sky radiances into the retrieval model holds substantial potential to enhance multilayer cloud property retrieval. Thus, a convolutional neural network (CNN) model called CNN_TL was designed for simultaneous retrieval of ice and water properties in multilayer clouds, based on the combined use of visible/near-infrared (VNIR) and thermal infrared (TIR) measurements and RTM-simulated clear-sky radiances as predictors. The CNN_TL model was initially trained using the simulated datasets generated by RTM to gain better model initiation and generalization and further tuned using the standard references from collocated active sensor products through transfer learning (TL) method. Validation on independent dataset shows CNN_TL-retrieved cloud top heights agreeing well with active sensor products for both overlaying ice and underlying water (RMSEs: 1.219 and 0.863 km), substantially outperforming AHI official products. Additionally, due to separate retrieval of ice and water microphysical properties in multilayer clouds, CNN_TL retrieved overlying ice microphysical properties exhibited a substantial correlation with active sensor products, showing Pearson coefficients above 0.78. Notably, CNN_TL model outperformed the purely ML-based model, demonstrating the advantage of integrating physical RTM simulation and ML techniques through TL method. Finally, a novel investigation into the spatial distribution of multilayer cloud properties across full disk is conducted, revealing distinct seasonal variations and notable latitudinal dependencies.

Large inaccuracies remain in the traditional convective cloud identification system over the plateau area struggles to capture mid- and low-level clouds due to the complex topographic effects influencing cloud pressure. Besides, the lack of efficient nighttime cloud-type products hinders progress in the research on the diurnal cycle and seasonal variation in convective clouds (including deep convection and cumulus clouds) over the Tibet Plateau (TP). In this study, we incorporated Shapley additive explanation (SHAP) tuning into the fundamental machine learning CatBoost Classifier technology, which was applied to a 24-h convective cloud detection algorithm utilizing cloud top temperature (CTT) and optical thickness data derived from the Himawari-8 infrared channels. This specifically tackles the problem of underestimating cumulus clouds in plateau areas. This innovative product enables capturing important processes of deep convection, especially for cumulus clouds, facilitating a comprehensive spatial-temporal analysis of the entire TP region. The results confirm that the new algorithm shows significant improvements in cumulus detection compared to the official cloud product of Himawari-8. In addition, the deep convective clouds have also improved from 35.85% to 63.05% for hit rate (HR) value. The analysis reveals a notable diurnal variation in convective cloud activity over the TP, predominantly occurring from noon to night. This finding underscores the influential heating role of the TP in convective activity.

Remote sensing of cloud properties based on multispectral or hyperspectral observations from satellites is important for earth radiation budget and climate change studies. Currently, most retrieval algorithms for the hyperspectral measurements are developed based on the O2-A band to derive cloud optical thickness (COT) and cloud top height (CTH) via the optimal estimation theory. Nevertheless, there are few studies on the retrieval of COT and CTH using the O4band, where the direct computation of slant column density and spectral information in the blue band provide a faster yet flexible inversion strategy. In this study, we develop a novel cloud retrieval algorithm based on neural networks using the O4band (CRANN-O4) for the simultaneous derivation of COT and CTH. CRANN-O4 employs a transfer learning strategy that combines the radiative transfer model (RTM) and multisource satellite data, for which the deep neural network module is pretrained based on the simulation data from RTM to enhance its adaptability and interpretability, following a fine-tuning scheme using multisource satellite data. To evaluate the CRANN-O4 performance, we apply CRANN-O4 to TROPOMI and make an intercomparison with its official products, which is generated based on the O2-A band. The results indicate that the CRANN-O4-derived spatial distributions of COT and CTH are generally similar to the official TROPOMI cloud product but are more consistent with the SNPP-VIIRS cloud product. The RMSEs of COT and CTH derived by CRANN-O4 are approximately 15.88 and 2.33 km, respectively, while those of the TROPOMI cloud product are 20.85 and 3.00 km, respectively. In addition, the validation of CRANN-O4-derived CTH using CALIOP measurements demonstrates better agreement than that of the TROPOMI official cloud product, with RMSE decreasing from 2.7 km to 2.2 km. The methodology presented in this study provides innovative insight into cloud parameter retrieval for hyperspectral instruments with O4channels, such as FY-3F/OMS.

Shortwave net radiation (SWNR) plays an important role in the surface radiation balance and serves as the primary driving force for the exchange of surface and atmospheric materials. Although numerous algorithms exist for estimating SWNR, most of them tend to ignore the influence of aerosols and digital elevation model (DEM) on SWNR. Specifically, the impact of different aerosol types on SWNR can vary significantly, and the SWNR also exhibits considerable variations at different altitudes. It is true that many algorithms demonstrate higher accuracy in low-altitude regions with less polluted rural aerosol (nonabsorbent aerosol) areas. However, their accuracy tends to decrease when applied to high-altitude areas and heavily polluted urban aerosol (absorbent aerosol) regions. In this study, an improved all-sky parameterized algorithm is proposed to estimate SWNR by fully considering solar zenith angle (SZA), DEM, and different aerosol types, and rural and urban aerosol types are distinguished by a random forest (RF) method. The new algorithm is verified versus surface radiation budget network (SURFRAD) and baseline surface radiation network (BSRN) observations and compared with the traditional algorithms (Tang-2006 and Li-1993) and Clouds and the Earth’s Radiant Energy System (CERES) products. The results reveal that the new algorithm exhibits excellent accuracy at both instantaneous and hourly scales. For rural and urban aerosol types under all-sky conditions, the bias and root mean square error (RMSE) of the new algorithm are both less than 3.5 and 106.5 W/m on the instantaneous scale and less than 12 and 77 W/$\text{m}^{2}$on hourly scale, respectively. However, the existing algorithms show a significant overestimation (bias > 50 W/$\text{m}^{2}$) for the urban aerosol type under various atmospheres conditions. For the CERES single scanner footprint (SSF) (instantaneous) and CERES SYNergy (CERES SYN, 1-hourly) products, the overestimation phenomenon is also detected under urban aerosol type, with bias greater than 40 and 15 W/$\text{m}^{2}$, respectively. Compared with the existing algorithms, the new algorithm demonstrates superior applicability under larger SZA conditions. When the SZA exceeds 70°, the rate of estimated effective value can be increased by up to 14%. In addition, the new algorithm can effectively solve the problem of underestimation in high-altitude areas, which frequently occurs in most existing algorithms (bias$< -16$W/$\text{m}^{2}$). The improved accuracy and applicability of the new algorithm, along with its strategy of distinguishing aerosol types, can provide valuable insights for the SWNR estimation from space.

The derivation of ice water path (IWP) from microwave radiometer measurements is challenging. This study presents a deep learning framework for global retrieval of IWP using observations from the Microwave Humidity Sounder-II (MWHS-II) aboard the FengYun-3D (FY-3D) satellites. Two deep learning models, Deep Forest (DF21) and Quantile Regression Neural Network (QRNN) are constructed to detect ice cloud flags and retrieve IWP. By collocating MWHS-II observations with 2C-ICE, a joint product of CloudSat and CALIPSO, deep learning models learn the characteristics of IWP from MWHS-II brightness temperatures. The test results show that the MWHS-II channels provide more information on IWP than the MWHS channels, particularly the 89 GHz channel and the 118 GHz channels with an offset of ≥ 0.8 GHz. Combining the QRNN and DF21 models, the IWP retrieval results in an RMSE of 707.346 g/m2, MAPE of 65.122%, MBE of -104 g/m2, determination coefficient (R2) of 0.683, and Pearson correlation coefficient (PCC) of 0.831. Application of the models to MWHS-II observations of Tropical Cyclone CILIDA shows better agreement with 2C-ICE. All datasets exhibit a similar feature on the monthly mean scale, but the magnitudes of IWP differ. Compared to GMI-GPROF, MODIS, and ERA5 IWP products, MWHS-II results are closest to 2C-ICE. Similar results are also shown for the zonal mean data. These results show that deep learning methods efficiently and probabilistically retrieve IWP from long-term observation data of MWHS/MWHS-II.

Accurate cloud detection via satellites is important for cloud radiative forcing estimation and disaster weather monitoring. Current polar-orbiting satellite cloud observation are limited during early morning orbit and contain notable uncertainty due to dimness measurements in visible bands. FY-3E\MERSI-LL is the first early morning orbit satellite worldwide and can realize global cloud observation under early morning scenarios. In this study, a dynamic threshold cloud detection algorithm is proposed based on the FY-3E\MERSI-LL infrared channel, combined with auxiliary data such as sea surface temperature, land surface temperature, snow cover mask and terrain elevation. The algorithm can detect clouds against complex land surface background, but faces classification difficulties over some plateau, high-latitude and snow surface regions, especially during early morning observation periods. Compared to coincident Himawari-8 and GOES-16 cloud measurements in the Eastern and Western Hemispheres, respectively, our algorithm recognizes reasonable cloud distributions. Furthermore, Himawari-8 and GOES-16 cloud products are used for quantitative cloud algorithm evaluation. The results show that at low-middle latitudes (60°N-60°S), the average cloud and clear hit rates during the various seasons are 73.24% and 76.46%, respectively, the cloud leakage and false alarm rates are 14.46% and 8.15%, respectively, and the total accuracy (cloud and clear) is 77.33%. The algorithm performance is better over the ocean than over land. Ground site MPLCMASK products are also used to verify the FY-3E cloud results in middle- and high-latitude areas. This algorithm provides a cloud detection reference during early morning orbit based on infrared channels.

A retrieval methodology based on the Bayesian neural network (BNN) is presented that inverts the ice water path (IWP), mean mass-weighted diameter (Dme), and cloud height of ice clouds from sub-millimeter radiometer observations. The training dataset was created using collecting cloud profiles from the DARDAR (raDAR/liDAR) database and running simulations by the Atmospheric Radiative Transfer Simulator (ARTS) model. Since the effective radius (re) is the size descriptor of ice particles in the DARDAR database, a look-up table of ice water content (IWC), Dme, and rewas constructed to convert reprofiles into Dmeprofiles. In addition, random noises corresponding to the measurement uncertainties of the Compact Scanning Submillimeter-wave Imaging Radiometer (CoSSIR) during the TC4 experiment were added to the simulated brightness temperatures before training the BNN. The proposed retrieval method was first applied to the simulated testing database, and then to the observations of CoSSIR. Moreover, the retrieved IWP and Dmewere compared to the retrievals of the Bayesian Monte Carlo Integration (BMCI) method. The retrieved cloud height was assessed by cloud height extracted from the reflectivity data of the Cloud Radar System (CPS) flow on the same aircraft with CoSSIR. The comparison showed that the correlation coefficients of the retrieved IWP and Dmefrom the two methods are above 0.8, and the retrieved cloud height also showed good agreement with that extracted from the CPS.

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.

Clouds play an important role in the radiative energy balance of the Earth–atmosphere system. Compared with traditional optical satellite sensors, polarimetric sensors combine multi-angle, multi-polarization, and multispectral information, displaying the advantages of high spatial and temporal resolutions and global coverage. Such remote sensing measurements improve the accuracy of cloud properties retrieval. Due to the observation characteristics of passive satellites, even a tiny variation in position will result in a great change in the observation geometry. A large number of studies have shown that the scattering angle is very crucial for the polarization characteristics retrieval of reflected light. In this study, we analyze the dependence of the remote sensing retrieval implement of different cloud characteristics on the observed scattering angle coverage, considering both ice and water clouds. Three satellite sensors – POLarization and Directionality of the Earth’s Reflectance-3/Polarization and Anisotropy of Reflectance for Atmospheric Sciences coupled with Observations from a Lidar (POLDER-3/PARASOL), Directional Polarimetric Camera/ GaoFen-5 spacecraft (DPC/GF-5), and DPC/GF-5(02) – were selected to compare their scattering angle coverages and the number of angular measurements at equatorial, middle, and high latitudes. The requirements for angular polarized and nonpolarized observations varied depending on the retrieval of cloud properties. The impact of orbital characteristics and viewing settings was investigated for cloud detection, cloud phase classification, and cloud microphysical properties retrieval. Finally, an analytical model to comprehensively evaluate the effective angular measurements according to the orbital characteristics and viewing settings was developed to facilitate the future design of similar sensors for cloud remote sensing.

Longwave downward radiation (LWDR) is an important parameter that modulates the earth’s radiation and energy balance, and is also a key variable that affects the global warming. Currently, although many reanalysis LWDR products and satellite-based algorithms are available, their coarse spatio-temporal resolutions as well as the difficulties in organizing the corresponding driving parameters seriously limit their applications. As China’s new generation geostationary satellite, Fengyun-4A (FY-4A) provides higher spatial and temporal resolutions (4 km @nadir, 15min at full disk mode) at longwave infrared channels which can routinely monitor the changes of the earth’s radiation in near real-time and therefore provide great potentials in generating various high-accuracy radiation products. Unfortunately, the existing official LWDR products of FY-4A can only provide estimates under clear skies, and its accuracy still has much room for improvement. For above-mentioned points, an improved general all-sky parameterization algorithm is proposed based on readily available input variables, such as land surface temperature (LST), column water vapor (CWV) and cloud-top temperature (CTT). Then the new algorithm is applied to FY-4A aiming to derive believable all-sky LWDR. The validation results show that, the new algorithm does show a noticeable improvement over the original one by reducing the relatively large errors in LWDR under conditions of extremely cold and dry (flux range <150 W/m²), as well as the large bias in the polar and high altitude regions. Moreover, the new method can generate more reliable LWDR than that of FY-4A official product in terms of both spatio-temporal continuity and accuracy, with RMSE less than 22 W/m² and bias less than 0.5 W/m² under all-sky conditions. The easy-to-use and believable performance of the new algorithm provide an opportunity to accurately derive all-sky LWDR from FY-4A and similar satellite missions with high resolutions.

In the above article[1], the following corrections to text citations should be noted. In Sections II “DATA” and IV “RESULTS AND ANALYSIS,” the citation [13] is changed to [24] and all text citations for [24] through [45] link to the latter citation.Table Iprovides the incorrect citation as shown in the published article along with the reference to which it should direct. In addition, the citation [27] in the above article[1]is revised to[4]in this Errata.

In this study, a thermal infrared (TIR) based convolutional neural network (TIR-CNN), originally designed for retrieving cloud optical properties from TIR radiometry, is further developed to obtain global cloud base height (CBH) and cloud thermodynamic phase during both daytime and nighttime. It employs TIR radiances, cloud optical properties retrieved by TIR-CNN, altitude, landcover, and lifting condensation level as inputs to estimate global CBH and cloud phase for both single- and multi-layer clouds. This new model is trained, validated, and tested using radar-lidar products from CloudSat/CALIPSO. It provides global CBH with root-mean-square errors of 1.19 km and 1.91 km for single- and multi-layer clouds, respectively. A cloud layer classifier is trained to provide information on the quality of retrieved CBH, with total accuracies of 82% and 85% for single- and multi-layer clouds, respectively. In addition, the new model has remarkable accuracy in identifying the cloud phase within each pixel’s vertical column, particularly in differentiating mixed-phase clouds with an ice cloud top.

Shortwave downward radiation (SWDR) plays a major role in the material and energy balance of the Earth’s climate system. However, most of existing SWDR research and products assume that the surface is flat, ignoring the effect of topography. This approach introduces significant uncertainties in the calculated fluxes and smooths the spatial distribution of SWDR. This paper proposes a uniform shortwave topographic radiation model (USWTRM) based on the principle of energy conservation. To evaluate the USWTRM, we compared it with the large-scale remote sensing data and image simulation framework (LESS). The USWTRM performed better than the traditional method in most conditions. For clear-sky, when the SZA=0°, the relative root-mean-square error (rRMSE), relative bias (rbias), and R2of the USWTRM at 1-km were 0.1 %, 0.0 %, and 1.000, respectively. At SZAs of 20°, 40°, and 60°, the USWTRM also showed better results than the traditional method. Moreover, the USWTRM performed similarly at 3-km and 5-km as that of 1-km. For cloudy-sky, the rRMSE and rbias of the USWTRM at fine-scale were 3.5%, and 0.0%, respectively. At 1-km, the rRMSE and rbias of the USWTRM were 0.9%, and 0.5%, respectively. In particular, the USWTRM outperformed previous studies in accurately quantifying the SWDR over rugged areas, under both clear and cloudy skies. Overall, the analysis reveals that the USWTRM works well over mountainous regions in terms of reliable accuracy, applicability, and generalization. It provides a new perspective for accurately deriving topographic SWDR at various scales and significantly reduces radiation uncertainties over rugged terrain.

Like many other countries, China is still facing severe air pollution issues after extensive efforts. The difficulties in deriving near-surface concentrations from satellite measurements restrict the application of remote sensing of large-scale surface air quality. Aiming at providing daily accurate near-surface ail pollution estimates (PM2.5, PM10, O3, NO2, SO2, and CO), we propose a robust estimation framework called learning air pollutants from satellite observations (LAPSO). The principle of LAPSO is to derive a nonlinear relationship between surface pollutant concentrations of interest and satellite observations with the aid of meteorological reanalyzes based on deep learning techniques. The LAPSO framework is superior to other algorithms due to its robust retrieval performance, independence from chemical transport models (CTMs), lower hardware requirements, and a user-friendly interface. The retrieval results of LAPSO were in good agreement with ground-level measurements according to extensive cross-validation at 1628 sites ($\text{R}^{2}>$0.8 in polluted areas and uncertainty$\ll 5~\mu \text{g}/\text{m}^{3}$for most pollutants) in China. The framework also showed a strong capability to capture the temporal variability of different air pollutants. By comparing with the estimation results from different satellite platforms, TROPOspheric monitoring instrument (TROPOMI) onboard the Sentinel-5P demonstrated marginally better performance for estimating PM2.5. Although the selection of satellite observations did not significantly affect the results of O3 estimation, the number and spatial sampling density of in situ sites imposed large impacts on O3 estimation performance. The success of LAPSO for estimating near-surface concentrations from satellite remote sensing at an enhanced spatiotemporal resolution is expected to serve the continuous and dynamical monitoring of regional and global air pollution.

Investigations of the effects of clouds on Earth’s radiation budget demand accurate representations of cloud top parameters, which can be efficiently obtained by large-scale satellite remote sensing approaches. However, the insufficient utilization of multiband information is one of the major sources of uncertainty in cloud top products derived from geostationary satellites. In this study, we developed a new algorithm to estimate Cloud, Atmospheric Radiation and renewal Energy application (CARE) version 1.0 cloud top properties (cloud top height (CTH), cloud top pressure (CTP), and cloud top temperature (CTT)). The algorithm is constructed from ten thermal spectral measurements in Himawari-8 observations by using the random forests method to comprehensively consider the contribution of each band to the cloud top parameters. We chose the highly accurate Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) products in 2018 as the true values. The sensitivity analysis demonstrated that the products can be fully reproduced by using multiple Himawari-8 channels with the addition of the digital elevation model (DEM) data. The validation results of the 2019 CALIOP data confirm that the new algorithm shows an effective performance, with correlation coefficients (R) of 0.89, 0.89, and 0.90 for CTH, CTP, and CTT, respectively. Moreover, a significant improvement in the ice cloud estimation is achieved, wherein the CTT R value increased from 0.46 to 0.70, as well as an improvement in the sea area, where the CTT R value increased from 0.71 to 0.84 compared with the Himawari-8 products of the Japan Aerospace Exploration Agency (JAXA) P-tree system. The further analyses performed herein capture the diurnal cycle of cloud top parameters well in different temporal scales over the Asia-Pacific region.

Surface longwave downward radiation (LWDR) plays an important role in modulating greenhouse effect and climate change. Constructing a global longtime series LWDR dataset is greatly necessary to systematically and in-depth study the LWDR effect on the climate. However, the current multi-source LWDR products (satellite and reanalysis) show large differences in terms of both spatio-temporal resolutions and accuracy in various regions. Therefore, it is necessary to fuse multi-source datasets to generate more accurate LWDR with high spatio-temporal resolution on a global scale. To this end, a downscaling strategy is firstly proposed to generate LWDR dataset with 0.25° resolution from CERES-SYN data with 1° scale, by incorporating the Land Surface Temperature (LST), Total Column Water Vapor (TCWV) and Elevation. Then a machine learning-based fusion method is provided to generate a global hourly LWDR dataset with spatial resolution of 0.25° by combing three products (CERES-SYN, ERA5 and GLDAS). Compared with ground measurements, the performance of generated LWDR product reveals that the correlation coefficient (R), mean bias error (BIAS), and root mean square error (RMSE) were 0.97, -0.95 W/m2 and 22.38 W/m2 respectively over the land, and 0.99, -0.88 W/m2 and 10.96 W/m2 over the ocean. Specially, it shows improved accuracy in the low and middle latitude regions compared with other LWDR products. Considering its better accuracy and higher spatio-temporal resolution, the new LWDR product can provide essential data for deeply understanding the global energy balance and even the global warming. Moreover, the proposed fusion strategy can be enlightening for readers in the fields of multi-source data combination and big data analysis.

In this article, we developed a hybrid method to estimate surface shortwave radiation (SSR) for the new-generation Himawari-8 geostationary satellite. This hybrid method combines the advantages of a deep neural network (DNN) with high speed and radiative transfer model (RTM) to achieve high accuracy: the RTM provides training data for the DNN under various cloud and aerosol conditions (including heavy aerosol loadings). Moreover, our hybrid method can simultaneously output the byproducts of photosynthetically active radiation (PAR), ultraviolet A (UVA), and Ultraviolet B (UVB), the direct and diffuse components at the surface, and the upward solar radiation at the top-of-atmosphere (TOA). The trained DNN was applied to the Himawari-8 satellite atmospheric products for 2016 and comprehensively validated using a total of 118 stations from four networks located in the full-disk regions of Himawari-8. The results showed an RMSE of 125.9 Wm-2for instantaneous SSR, 105.4 Wm-2for hourly SSR, 31.9 Wm-2for daily SSR, and respective mean bias error (MBE) scores of 8.1, 27.6, and 12.3 Wm-2. The hybrid method developed in this study performed well, achieving high accuracy and high speed, and it is capable of providing near-real-time SSR estimates for many applied energy fields.

The Japan Meteorological Agency (JMA) successfully launched the Himawari-8 (H-8) new-generation geostationary meteorological satellite with the Advanced Himawari Imager (AHI) sensor on October 7, 2014. The H-8/AHI level-2 (L2) operational cloud property products were released by the Japan Aerospace Exploration Agency during September 2016. The Voronoi light scattering model, which is a fractal ice particle habit, was utilized to develop the retrieval algorithm called “Comprehensive Analysis Program for Cloud Optical Measurement” (CAPCOM-INV)-ice for the AHI ice cloud product. In this paper, we describe the CAPCOM-INV-ice algorithm for ice cloud products from AHI data. To investigate its retrieval performance, retrieval results were compared with 2000 samples of the ice cloud optical thickness and effective particle radius values. Furthermore, AHI ice cloud products are evaluated by comparing them with the MODIS collection-6 (C6) products. As an experiment, cloud property retrievals from AHI measurements, with an observation interval time of 2.5 min and ground-based rainfall observation radar data (the latter of which is supplied by the JMA, with a 1-km grid mesh), are used to investigate the generation processes of deep convective (DC) cloud in the vicinity of the Kyushu island, Japan. It revealed that AHI measurements have the capability of monitoring the growth processes, including variation of the cloud properties and the precipitation in the DC cloud.

Near-infrared (NIR) channels, such as the 1.6- and 2.13-μm channels of Moderate Resolution Imaging Spectroradiometer (MODIS), play an important role in inferring cloud properties because of their sensitivity to cloud amount and particle size. Instead of the 2.13-μm channel, which has shown great success on MODIS, the central wavelength of the Visible Infrared Imaging Radiometer Suite (VIIRS) is shifted to 2.25 μm. This paper investigates the influences of NIR channels (i.e., 2.13 and $2.25 μm) on cloud optical and microphysical property retrievals and reveals the potential applications of the 2.25-μm channel to cloud thermodynamic phase and multilayer cloud detections by combining with the 1.6-μm channel. Rigorous radiative transfer simulations are performed to provide theoretical reflectance at the channels of interest, and MODIS and VIIRS observations are used for case studies. Our results indicate a minor influence of the 2.25-μm channel on cloud optical depth and effective particle size retrievals. In combination with the 1.6-μm channel, the 2.25-μm channel provides additional information indicating cloud phases. However, the 1.6- and 2.13-μm channels do not show any sensitivity to cloud phase. Furthermore, by considering the infrared-based cloud phase results, the 1.6- and 2.25-μm channel combination becomes possible to infer multilayer clouds. Case studies based on simultaneous MODIS and VIIRS observations demonstrate the capability of the 1.6-2.25-μm channel combination for determining cloud phase and multilayer clouds. Collocated satellite-based active lidar observations further validate these advantages of the 2.25-μmu channel over the original 2.13-μm channel.

Land surface longwave (LW) radiation (or longwave radiative flux), including longwave upwelling (LWUR), downward (LWDR) and net radiation (LWNR), are key components of the total energy that drives the surface energy balance at the interface between the earth's surface and the atmosphere. The importance of LW radiation in regulating air temperature and balancing surface energy is enlarged especially under cloudy-sky conditions. Unfortunately, to date, a tremendous attempts have been made to derive LW radiation from space only valid under clear-sky conditions leading to difficulty of utility of remote sensing-based LW radiation products in most land models due to their spatial discontinuity. Although few studies focused on LW radiation estimation under cloudy-sky conditions, while their global application are still problematic. In this paper, novel strategies are proposed aiming to derive high resolution cloudy-sky LWDR and LWUR by fusing collocated optical and microwave satellite data. The results reveal that the new approaches work rather well, thus, more importantly, providing unprecedented possibilities for generating high resolution global LW radiation datasets.

Firstly, this study discusses long-term observations of aerosol chararacteristics over four typical SKYNET sites (Chiba, Fukuejima, Miyakojima, and Hedo) within Japan to clarify the seasonal dependent characteristics of aerosols of different origins and their impacts on atmospheric heat budget. Optically thicker aerosols with significant amount of coarse-mode aerosols are found in the spring season. The aerosol radiative forcings at the surface and top of the atmosphere in the spring season can be roughly two times of the values in the winter season. Secondly, we discuss the optical characteristics of clouds obtained from the sky radiometer of SKYNET and Moderate Resolution Imaging Spectroradiometer (MODIS). Our analysis suggests that MODIS cloud optical depth (COD) may be underestimated, which in turn may lead to overestimate calculated shortwave flux.

The cloudbow structure is directly related to the retrieval of cloud droplet size distribution (droplet effective radius and effective variance). This study investigated the effect of the cloud optical thickness, ground surface albedo and the above-cloud absorbing dust layer on the cloudbow structure based on the modeled airborne directional polarimetric camera (DPC) measurements, which are simulated in 670 nm using Mie scattering theory and the vector radiative transfer mode. It is found that the polarized reflectance increase as the increase of the cloud optical thickness (COT) and saturate when COT=10. The absorbing dust layer's signal would cover the signal from the cloud layer as the aerosol optical thickness increased to 1. Additionally, the surface albedo has negligible effect on the cloudbow structure.

Several studies have clarified that electric power consumption can be estimated from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery. As digital numbers (DNs) of stable light images are often saturated in the center of city areas, we developed a saturated light correction method for the DMSP/OLS stable light image using the nighttime radiance calibration image of the DMSP/OLS. The comparison between the nonsaturated part of the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation (R2= 92.73) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To evaluate the new saturated light correction method, a regression analysis is performed between statistic data of electric power consumption from lighting and the cumulative DNs of the stable light image before and after correcting for the saturation effects by the new method, in comparison to the conventional method, which is, the cubic regression equation method. The results show a stronger improvement in the determination coefficient with the new saturated light correction method (R2= 0.91,P= 1.7 ·10-6R2= 0.81,P= 2.6 ·10-6R2= 0.70,P= 4.5 · 10-6<; 0.05). The new method proves therefore to be very efficient for saturated light correction.

Calculation of scattering and absorption properties of ice crystals are important for understanding of radiative transfer in cirrus clouds and the radiation budget of the earth atmospheric system. In this study, parameterization of the single-scattering properties for individual ice crystal of cirrus clouds are investigated using Geometrical-Optics Approximation (GOA) and Surface Integral Equations Method of Muller-type (SIEM/M) methods for developing ice crystal scattering database of Global Change Observation Mission (GCOM-C)/Second generation Global Imager (SGLI) satellite data. Wavelength origin design was used for the light scattering database. 19 channels of the SGLI are selected as 111 calculating point in the band width×1.5 area. Number of size parameter was 73 ranged from 0.1-1000. As a result, transmissivity in the inside of the band width×1.5 region is larger than 99.7%. Finally, phase function of GOA used in this study and conventional improved Geometrical-Optics Method (GOM2) methods were compared and confirmed that phase functions of both data are similar in general.

In this study, we attempted to estimate the electric power consumption using the Defense Meteorological Satellite Program (DMSP)/Operational Linescan System (OLS) stable light imagery after correction for saturation effected. Digital numbers (DNs) of a stable light image are saturated in the center of city areas. Thus, we developed a saturated light correction method for the DMSP/OLS stable light image using the radiance calibration image of DMSP/OLS nighttime imagery. The comparison between the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation (Ysta= 17Xrad102) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To estimate the electric power consumption, regression analysis was performed between statistic data of electric power consumption from lighting and cumulative DNs of the stable light image before and after correcting for the saturation effects using the new and the conventional methods of the cubic regression equation (CRE). There is a stronger improvement of the determination coefficient with the new saturated light correction method (R =0.91, P=4.5·1062=0.81, P=2.6-1062=0.70, P=1.7-106<;0.05).

This study is an attempt that is made to interpret multi- wavelength-retrieved cloud droplet effective radii (CDR) and the differences among R16, R21 and R37 in term of cloud vertical droplet distribution. For this purpose, a spectral-bin microphysical cloud model and radiative transfer computation are used to generate the realistic numerical cloud field and to simulate the CDR retrieval in remote sensing. The result indicates that the cloud vertical inhomogeneous structure causes the differences among R16, R21 and R37 similar to the differences as shown in the satellite observations and the values of R16/R21 and R37/R21 are related to the stage of warm water cloud.

The Global Land Cover by National Mapping Organizations (GLCNMO) is the product of the Global Mapping Project organized by International Steering Committee for Global Mapping (ISCGM). The GLCNMO is a global 1km land cover data set produced by 16-day composite MODIS data observed in 2003. The accuracy assessment of the map has been completed by a stratified random sampling method. The overall accuracy is 76.5%. The accuracy assessment result also shows that the needleleaf evergreen forest and the needleleaf deciduous forest were misclassified into each other. With this problem in mind, Needleleaf evergreen forest and Needleleaf deciduous forest in the GLCNMO were selected for reclassification. In the new classification process, the NDVI patterns were used. Finally, the needleleaf evergreen forest and the needleleaf deciduous forest in the GLCNMO were compared with the same land cover types in other two global land cover products IGBP DISCover and GLC2000 from total pixel number, per-pixel and the ground truth data points of view.