Satellite Observation Data Research Articles

Microcystins risk assessment in lakes from space: Implications for SDG 6.1 evaluation

Cyanobacterial blooms release a large number of algal toxins (e.g., Microcystins, MCs) and seriously threaten the safety of drinking water sources what the SDG 6.1 pursues (to provide universal access to safe drinking water by 2030, United Nations Sustainable Development Goal). Nevertheless, algal toxins in lake water have not been routinely monitored and evaluated well and frequently so far. In this study, a total of 100 large lakes (>25 km2) in densely populated eastern China were studied, and a remote sensing scheme of human health risks from MCs based on Sentinel-3 OLCI data was developed. The spatial and temporal dynamics of MCs risk in eastern China lakes since OLCI satellite observation data (2016–2021) were first mapped. The results showed that most of the large lakes in eastern China (80 out of 100) were detected with the occurrence of a high risk of more than 1 pixel (300×300 m) at least once. Fortunately, in terms of lake areas, the frequency of high human health risks in most waters (70.93% of total lake areas) was as less as 1%. This indicates that drinking water intakes can be set in most waters from the perspective of MCs, yet the management departments are required to reduce cyanobacterial blooms. This study highlights the potential of satellite in monitoring and assessing the risk of algal toxins and ensuring drinking water safety. It is also an important reference for SDG 6.1 reporting for lakes that lack routine monitoring.

Methane Emissions in Boreal Forest Fire Regions: Assessment of Five Biomass-Burning Emission Inventories Based on Carbon Sensing Satellites

Greenhouse gases such as CH4 generated by forest fires have a significant impact on atmospheric methane concentrations and terrestrial vegetation methane budgets. Verification in conjunction with “top-down” satellite remote sensing observation has become a vital way to verify biomass-burning emission inventories and accurately assess greenhouse gases while looking into the limitations in reliability and quantification of existing “bottom-up” biomass-burning emission inventories. Therefore, we considered boreal forest fire regions as an example while combining five biomass-burning emission inventories and CH4 indicators of atmospheric concentration satellite observation data. By introducing numerical comparison, correlation analysis and trend consistency analysis methods, we explained the lag effect between emissions and atmospheric concentration changes and evaluated a more reliable emission inventory using time series similarity measurement methods. The results indicated that total methane emissions from five biomass-burning emission inventories differed by a factor of 2.9 in our study area, ranging from 2.02 to 5.84 Tg for methane. The time trends of the five inventories showed good consistency, with the Quick Fire Emissions Dataset version 2.5 (QFED2.5) having a higher correlation coefficient (above 0.8) with the other four datasets. By comparing the consistency between the inventories and satellite data, a lagging effect was found to be present between the changes in atmospheric concentration and gas emissions caused by forest fires on a seasonal scale. After eliminating lagging effects and combining time series similarity measures, the QFED2.5 (Euclidean distance = 0.14) was found to have the highest similarity to satellite data. In contrast, Global Fire Emissions Database version 4.1 with small fires (GFED4.1s) and Global Fire Assimilation System version 1.2 (GFAS1.2) had larger Euclidean distances of 0.52 and 0.4, respectively, which meant that they had lower similarity to satellite data. Therefore, QFED2.5 was found to be more reliable while having higher application accuracy compared to the other four datasets in our study area. This study further provided a better understanding of the key role of forest fire emissions in atmospheric CH4 concentrations and offered reference for selecting appropriate biomass burning emission inventory datasets for bottom-up inventory estimation studies.

Satellite Optical Brightness

The apparent brightness of satellites is calculated as a function of satellite position as seen by a ground-based observer in darkness. Both direct illumination of the satellite by the Sun as well as indirect illumination due to reflection from the Earth are included. The reflecting properties of the satellite components and of the Earth must first be estimated (the Bidirectional Reflectance Distribution Function, or BRDF). The reflecting properties of the satellite components can be found directly using lab measurements or accurately inferred from multiple observations of a satellite at various solar angles. Integrating over all scattering surfaces leads to the angular pattern of flux from the satellite. Finally, the apparent brightness of the satellite as seen by an observer at a given location is calculated as a function of satellite position. We develop an improved model for reflection of light from Earth’s surface using aircraft data. We find that indirectly reflected light from Earth’s surface contributes significant increases in apparent satellite brightness. This effect is particularly strong during civil twilight. We validate our approach by comparing our calculations to multiple observations of selected Starlink satellites and show significant improvement on previous satellite brightness models. Similar methodology for predicting satellite brightness has already informed mitigation strategies for next-generation Starlink satellites. Measurements of satellite brightness over a variety of solar angles widens the effectiveness of our approach to virtually all satellites. We demonstrate that an empirical model in which reflecting functions of the chassis and the solar panels are fit to observed satellite data performs very well. This work finds application in satellite design and operations, and in planning observatory data acquisition and analysis.

Remote Sensing Monitoring and Analysis of Spatiotemporal Changes in China’s Anthropogenic Carbon Emissions Based on XCO2 Data

The monitoring and analysis of the spatiotemporal distribution of anthropogenic carbon emissions is an important part of realizing China’s regional “dual carbon” goals; that is, the aim is for carbon emissions to peak in 2030 an to achieve carbon neutrality by 2060, as well as achieving sustainable development of the ecological environment. The column-averaged CO2 dry air mole fraction (XCO2) of greenhouse gas remote sensing satellites has been widely used to monitor anthropogenic carbon emissions. However, selecting a reasonable background region to eliminate the influence of uncertainty factors is still an important challenge to monitor anthropogenic carbon emissions by using XCO2. Aiming at the problems of the imprecise selection of background regions, this study proposes to enhance the anthropogenic carbon emission signal in the XCO2 by using the regional comparison method based on the idea of zoning. First, this study determines the background region based on the Open-Data Inventory for Anthropogenic Carbon dioxide (ODIAC) dataset and potential temperature data. Second, the average value of the XCO2 in the background area was extracted and taken as the XCO2 background. On this basis, the XCO2 anomaly (XCO2ano) was obtained by regional comparison method. Finally, the spatiotemporal variation characteristics and trends of XCO2ano were analyzed, and the correlations between the number of residential areas and fossil fuel emissions were calculated. The results of the satellite observation data experiments over China from 2010 to 2020 show that the XCO2ano and anthropogenic carbon emissions have similar spatial distribution patterns. The XCO2ano in China changed significantly and was in a positive growth trend as a whole. The XCO2ano values have a certain positive correlation with the number of residential areas and observations of fossil fuel emissions. The purpose of this research is to enhance the anthropogenic carbon emission signals in satellite observation XCO2 data by combining ODIAC data and potential temperature data, achieve the remote sensing monitoring and analysis of spatiotemporal changes in anthropogenic carbon emissions over China, and provide technical support for the policies and paths of regional carbon emission reductions and ecological environmental protection.

A geostationary orbit microwave multi-channel radiometer

The geostationary orbit microwave multi-channel radiometer has the advantages of high real-time performance and large coverage, which plays an important role in typhoon, strong precipitation detection, and medium-to-short-term meteorological/oceanic forecasting. However, due to the difficulty in engineering development of the payload, its application on-orbit has not yet been achieved at present. To satisfy the requirements of fine and quantitative application of satellite observation data, a geostationary orbit microwave multi-channel radiometer with a 10-m-caliber is developed, in which the spatial resolution at horizontal polarization is better than 24 km at 54 GHz. In geostationary orbit microwave multi-channel radiometer, a quasi-optical feed network covering nearly 28 frequency octave bands and ranging from 23.8 to 664 GHz is proposed to solve the technical problem of multi-frequency sharing in the system. Meanwhile, a high-precision reflector preparation method and a high-precision unfolding scheme are proposed, which are considered as a solution for the large-diameter reflector with a high maintaining surface accuracy. A high-precision antenna prototype with 0.54-m is developed, and the tests are performed to verify the key technologies, such as the preparation of high-precision grating reflectors at the micron level, high surface accuracy detection, and sub-millimeter wave antenna electrical performance testing. The results indicate that measured main beam efficiency of the 664 GHz antenna is better than 95.5%. In addition, the system sensitivity is greater than 1.5 K, and the calibration accuracy is better than 1.8 K, according to the results of an analysis of the multi-channel radiometer’s essential parameters and calibration errors.

Scalable approach for high-resolution land cover: a case study in the Mediterranean Basin

The production of land cover maps is an everyday use of image classification applications on remote sensing. However, managing Earth observation satellite data for a large region of interest is challenging in the task of creating land cover maps. Since satellite imagery is getting more precise and extensive, Big Data techniques are becoming essential to handle the rising quantity of data. Furthermore, given the complexity of managing and analysing the data, defining a methodology that reduces the complexity of the process into different smaller steps is vital to data processing. This paper presents a Big Data methodology for creating land cover maps employing artificial intelligence algorithms. Machine Learning algorithms are contemplated for remote sensing and geodata classification, supported by explainable artificial intelligence. Furthermore, the process considers aspects related to downloading data from different satellites, Copernicus and ASTER, executing the pre-processing and processing of the data in a distributed environment, and depicting the visualisation of the result. The methodology is validated in a test case for er map of the Mediterranean Basin.

Impact of precipitation on the prevalence of schistosomiasis mekongi in Lao PDR: Structural equation modelling using Earth observation satellite data

Increasing attention is being given to the effect of climate change on schistosomiasis, but the impact is currently unknown. As the intermediate snail host (Neotricula aperta) of Schistosoma mekongi inhabits the Mekong River, it is thought that environmental factors affecting the area of water will have an impact on the occurrence of schistosomiasis mekongi. The aim of the present study was to assess the impact of precipitation on the prevalence of human schistosomiasis mekongi using epidemiological data and Earth observation satellite data in Khong district, Champasak province, Lao PDR. Structural equation modelling (SEM) using epidemiological data and Earth observation satellite data was conducted to determine the factors associated with the number of schistosomiasis mekongi patients. As a result, SEM identified 3 significant factors independently associated with schistosomiasis mekongi: (1) a negative association with mass drug administration (MDA); (2) negative association with total precipitation per year; and (3) positive association with precipitation during the dry season. Precisely, regardless of MDA, the increase in total yearly precipitation was suggested to decrease the number of schistosomiasis patients, whereas an increase in precipitation in the dry season increased the number of schistosomiasis patients. This is probably because when total precipitation increases, the water level of the Mekong River rises, thus decreasing the density of infected larvae, cercaria, in the water, and the frequency of humans entering the river would also decrease. In contrast, when precipitation in the dry season is higher, the water level of the Mekong River also rises, which expands the snail habitant, and thus water contact between humans and the snails would also increase. The present study results suggest that increasing precipitation would impact the prevalence of schistosomiasis both positively and negatively, and precipitation should also be considered in the policy to eliminate schistosomiasis mekongi in Lao PDR.

Divergent sensitivity of vegetation to aridity between drylands and humid regions

The land surface has been drying over recent decades, which is inconsistent with the greening of the Earth. The extent and spatial variation in the sensitivity of vegetation to aridity changes in drylands and humid regions remain unclear. In this study, satellite observation and reanalysis data were used to analyze the relationship between vegetation growth and atmospheric aridity changes in different climatological regions on a global scale. Our results showed that the leaf area index (LAI) increased at a rate of 0.032/decade from 1982 to 2014, while the aridity index (AI) increased slightly at a rate of 0.005/decade. Over the past three decades, the sensitivity of the LAI to AI has decreased in drylands and increased in humid regions. Thus, the LAI and AI were decoupled in drylands, whereas the effect of aridity on vegetation was enhanced in humid regions during the study period. The physical and physiological effects of increasing CO2 concentration are responsible for the divergent responses of vegetation sensitivity to aridity in drylands and humid regions. The results of the structural equation models showed that the effect of increasing CO2 concentration via LAI and temperature, with respect to decreasing AI, enhanced the negative relationship between LAI and AI in humid regions. The greenhouse effect of increasing CO2 concentration resulted in an increase in temperature and a reduction in aridity, whereas the fertilization effect of CO2 increased LAI, thus creating an inconsistent trend with LAI and AI in drylands.

Projected Changes in the Snow Cover Extent in the 21st Century in Eurasia According to Simulations with the Ensemble of Climate Models CMIP6

The analysis of changes in the snow cover extent S in Eurasia was performed using the results of simulations with the ensemble of global climate models of the international project CMIP6 under the scenario of anthropogenic impacts SSP2-4.5 for the 21st century. Features of S variability in relation to changes in surface air temperature T in different seasons are revealed by comparison of ensemble model calculations for the “historical” scenario to CDR satellite data against the background of a general decrease in the snow cover extent in Eurasia during the contemporary warming. It is noted that the multimodel ensemble mean estimates of the sensitivity parameter dS/dT for the transitional seasons in spring and autumn can be significantly lower in absolute values than those for the individual models and those derived from the satellite data. Comparison of model estimates with satellite observation data showed that the models generally reproduce the observed variations in the snow cover area in Eurasia, while variations in the area may be underestimated for individual models. According to ensemble model calculations, the rate of snow cover reduction in Eurasia in the second half of the XXI century decreases compared to the first half of the XXI century for all seasons. At the same time, the maximum values of snow cover extent reduction rate in Eurasia are inherent in transitional seasons – autumn and spring.

Trend and spatial pattern of stable cropland productivity in China based on satellite observations (2001−2020)

Sustained productivity growth of China's stable cropland is crucial for meeting the food and nutritional needs of the 1.4 billion population amid global food market volatility, limited uncultivated land, and urban and ecological land squeezing agricultural space. Despite this, research on trend tracking and spatial pattern identifying in productivity of China's stable croplands at a national scale is currently lacking. Here, we attempted to fill this gap based on satellite observation data and cloud computing platform, using the crop growth index, quadratic regression model, and indicator-based spatial overlay analysis. Results show the productivity of China's stable cropland rose by ∼31.07% from 2001 to 2020 but will fall to ∼78.89% to ∼85.78% of the 2015 level (baseline year of SDGs) by 2030. The declining trends can be attributed to three main factors: (1) 69.15% of cropland that previously exhibited significant productivity growth has started to decline; (2) the productivity of cropland with the highest agricultural suitability (Level 4–5) has been long-term declining, showing no signs of reversing; (3) 44.12% of stable cropland's productivity was volatile over the research period, leading to high uncertainty in productivity growth. The large-scale spatial overlap between high agricultural suitability and human activity intensity determined the spatial pattern of stable cropland productivity. Therefore, comprehending and mitigating urbanization's indirect and off-site detrimental impacts on cropland productivity are critical to ensuring China's future food security.

Scalable Data Processing Platform for Earth Observation Data Repositories

Earth observation (EO) satellite data is essential to environmental monitoring. At a national and regional level, the open data cubes harness the power of satellite data by providing application programming interfaces and services to the end-users. The volume and the complexity of satellite observations are increasing, demanding novel approaches for data storing, managing, and processing. High-performance computing (HPC) and cloud platforms may improve Big EO data processing performance. However, it is necessary to consider several vital aspects for efficient and flexible EO data processing, such as the interoperability from cloud-HPC and EO data repositories, automatic provisioning and scaling of cloud-HPC resources, cost-effectiveness, support of new EO data formats and open-source packages, or linkage with data cube platforms. The article proposes a scalable EO data processing platform interoperable from cloud-HPC and EO data repositories. The platform enables linking any data repository supporting web coverage service or SpatioTemporal Asset Catalog Application Programming Interfaces (STAC-API), and any cloud or HPC resource supporting scheduling system API for providing access to the cluster backends.

ГИДРОЛОГО-ГИДРОХИМИЧЕСКАЯ СТРУКТУРА ВОД В ЗОНЕ АПВЕЛЛИНГА У ЮГО-ЗАПАДНОГО ПОБЕРЕЖЬЯ О. САХАЛИН

Based on the spatial distributions of sea surface temperature according to satellite observations and observational data on standard oceanographic sections of the southwestern shelf of the Sakhalin Island in summer (August) and autumn (November) shows the existence of seasonal upwelling, which is formed under the influence of northerly and northwesterly winds, typical of the winter monsoon. In this area, it manifests to a lesser extent than in the northern part of the Tatar Strait (Shevchenko et al., 2011) due to the features of the bottom topography (short deep shelf) and the large thickness of the upper heated layer, but quite clearly. At the same time, deep water rises, rich in biogenic elements and mineral phosphorus, which is important for the development of young growth of kelp. In the warm period of the year, under the conditions of the prevailing southerly wind, upwelling is observed in this area sporadically (4 ̶ 5 times per season) in the form of sharp, up to 12 ̶ 15 °C, water temperature drops in the coastal area lasting from 3 to 12 days. Most often they occur during the passage of cyclones over the southern part of Sakhalin or Hokkaido Island, when they enter the southern part of the Sea of Okhotsk or the South Kuril Islands and formed east and northeast blowing winds over the study area.

Study of winter haze pollution events in Jinan (China)

A comprehensive study of the formation process of winter haze pollution in Jinan, China, was conducted using ground and satellite observation data from January 2 to January 13, 2022. According to the variations in the pollutant concentrations, three haze pollution events occurred in the Jinan area during this period. Compared to haze pollution event 1, event 2 lasted longer and exhibited a cumulative growth state with hourly PM2.5 values peaking at 250 μg/m3. This was primarily caused by a mixture of local and nearby pollutants emitted in Jinan. Haze pollution event 3 was shorter and showed a similar trend to event 2, but the event dissipated rapidly after it was generated. Moreover, unfavorable weather factors, such as lower wind speeds, a stable inversion layer, and higher relative humidity, contributed to pollution accumulation and haze event formations. In addition, important photochemical processes also influenced haze pollution formation. The results of this study revealed the haze pollution formation process in Jinan. These results are helpful for promoting the study of regional climate changes and provide a reference for the government to prevent and control haze pollution in eastern China.

Multi-GNSS Combination Multipath Reflectometry Based on IVMD Method for Sea Level Retrieval

Sea level monitoring is particularly important in coastal areas that are vulnerable to marine disasters. It was recently demonstrated that the global navigation satellite system multipath reflectometry (GNSS-MR) technique, which uses multipath signals reflected from the sea, can be applied to determine the sea level. However, this approach does not provide sufficient accuracy or equally spaced sampling to meet the actual sea level monitoring requirements for certain stations. To solve the deficiency of the traditional GNSS-MR technique, the least squares method, which is based on sliding time windows, was applied. Using the sliding windows to combine the quad-constellation multi-GNSS retrieval can effectively improve the accuracy and time resolution of sea level retrieval, but insufficient data or a lack of data in some time ranges and missing overflights in some timeframes can lead to the calculation of faults in these time windows, causing the estimated loss of corresponding sampling points. In this study, we used a robust regression solution strategy based on multi-GNSS sea level retrieval and an improved variational mode decomposition (IVMD) algorithm to process sea level retrieval after robust regression. BRST and HKQT stations are located on the western coast of France and the northern coast of Hong Kong. The two stations can both receive satellite observation data from the four satellite systems. Through the experiment, using data retrieved from the BRST and HKQT stations, the results of this study demonstrate that the IVMD method based on multi-GNSS sea level retrieval can further improve the accuracy to <10 cm and can achieve 10 min equal interval sampling. This is significant for using GNSS-MR technology to detect sea level height and monitor sea level change and could be applied to other sites.

Interannual variation in winter thermal front to the east of the Shandong Peninsula in the Yellow Sea

Oceanic fronts play an important role in ocean dynamics and ecology systems. The ear-shape front (ESF), first detected in the1980s, is a winter thermal front to the east of the Shandong Peninsula in the Yellow Sea. Using high-resolution satellite observation and ocean reanalysis data, we investigated the interannual variation in ESF and underlying dynamics during 2006–2020. The results indicate that ESF shows a strengthening trend of approximately 0.1–0.5 °C/100 km/year, accompanying the temperature on its eastern side. This was caused by the westward shift and enhancement of the Yellow Sea Warm Current (YSWC) through transporting more warm water to the eastern side of the ESF. Further analyses indicate that changes in the YSWC resulted from the decrease in wind stress divergence. Decreasing wind stress divergence induced increasing sea surface height (SSH) anomalies in the central Yellow Sea. The resultant positive SSH anomalies generated an anti-cyclonic gyre anomaly via geostrophic effects, which led to the westward shift and enhancement of the YSWC on the eastern side of the ESF.

Comparative Analysis of Gas-Geochemical Data from Ground-Based and Satellite Observations of the Sakhalin Island and Its Shelf (Northeast Russia): Tectonic Consequences

The transitional zone of the marginal seas of the Asia‒Pacific region is a significant object for geological research, the large deposits of hydrocarbons (oil, gas, gas condensate, gas hydrates) have been discovered in the regions of the transition zone. The article gives an interpretation of the main gas-geochemical and geological-tectonic regularities of the occurrence and distribution of methane and carbon dioxide fields of different types on the shelf of Sakhalin Island. A study of the gas-geochemical parameters of the emitted gases in the “continent‒shelf” transit zone was carried out based on comparison of field studies and satellite observation data. The data of satellite observation of Sakhalin Island and its shelf are presented to identify the relationship between seismic events and changes in the level of methane concentration in the atmospheric air (in particular, on the example of the methane discharge regime of the Yuzhno-Sakhalinsky mud volcano). Remote sensing data are compared with the results of the field studies, and an assessment is made of the potential using satellite observation methods for studying the gas geochemical composition of the methane and carbon dioxide field. The results obtained can make it possible to refine the assessment of the emission of climatically active gases, and also bring the significant incentive to the development of the Russian Federation project “Far Eastern Marine Carbon Monitoring Plots Consortium”.

Algorithm and Application of Foundation Displacement Monitoring of Railway Cable Bridges Based on Satellite Observation Data

In order to realize real time monitoring of foundation displacement of railway cable bridge based on satellite observation system, reasonable data sources of satellite monitoring and data analysis duration are necessary, and the influence of various errors should be eliminated in the process of satellite signal acquisition. In this paper, the validity of foundation settlement monitoring of bridge tower by satellite is verified through accuracy and stability tests. For eliminating the multipath error and random noise of GNSS signal, a new method is presented by combining stationary wavelet transform and empirical mode decomposition in this study. For improving the observation accuracy of the satellite monitoring system, combing the measured data, the GNSS data are corrected by establishing non-linear mapping between the GNSS data and precise leveling data by BP neural network. Based on that, the accuracy of the presented method is verified by the foundation settlement data of a new railway cable bridge tower.

Effects of precipitation changes and land-use alteration on streamflow: A comparative analysis from two adjacent catchments in the Qilian Mountains, arid northwestern China

Comparative analysis of the impacts of precipitation and land use on streamflow from adjacent catchments is critical to exploring pathways toward water security and sustainable development. In this work, two adjacent catchments (the Mayinghe and Xidahe, abbreviated as MYC and XDC, respectively) in northwestern China were selected to compare the impacts of precipitation change and land use alteration on streamflow change during 1956–2019 using field observation streamflow data and satellite data. An opposite trend of streamflow was found for the two catchments from 1956 to 2019: the streamflow decreased significantly in the MYC (-0.63 × 107m3/10a, p < 0.05), while it increased in the XDC (0.71 × 107m3/10a, p < 0.05). Land conversion dominated the streamflow reduction in the MYC, with a contribution of about 68.3%. In the XDC, precipitation was confirmed to be the major factor driving the increase in streamflow. The streamflow for farmland irrigation reached 27.97 × 104 m3/km2 in the MYC, which was the leading factor of streamflow reduction in the basin. The findings obtained from this work can shed light on the quantitative understanding of streamflow changes in small catchments and offer a scientific basis for sustainable water management in other inland river basins.

Tropical cyclone size estimation based on deep learning using infrared and microwave satellite data

Tropical cyclone (TC) size is an important parameter for estimating TC risks such as wind damage, rainfall distribution, and storm surge. Satellite observation data are the primary data used to estimate TC size. Traditional methods of TC size estimation rely on a priori knowledge of the meteorological domain and emerging deep learning-based methods do not consider the considerable blurring and background noise in TC cloud systems and the application of multisource observation data. In this paper, we propose TC-Resnet, a deep learning-based model that estimates 34-kt wind radii (R34, commonly used as a measure of TC size) objectively by combining infrared and microwave satellite data. We regarded the resnet-50 model as the basic framework and embedded a convolution layer with a 5 × 5 convolution kernel on the shortcut branch in its residual block for downsampling to avoid the information loss problem of the original model. We also introduced a combined channel-spatial dual attention mechanism to suppress the background noise of TC cloud systems. In an R34 estimation experiment based on a global TC dataset containing 2003–2017 data, TC-Resnet outperformed existing methods of TC size estimation, obtaining a mean absolute error of 11.287 nmi and a Pearson correlation coefficient of 0.907.

A Microphysics-Scheme-Consistent Snow Optical Parameterization for the Community Radiative Transfer Model

Abstract The satellite observational data assimilation community requires consistent hydrometer descriptions—including mass–size relation and particle size distribution—to be used in both the forecast model and observation operator. We develop a microphysics-scheme-consistent snow and graupel single-scattering property database to meet this requirement. In this database, snowflakes are modeled as a mixture of small column and large aggregated ice particles, the mixing ratios of which may be adjusted to satisfy a given mass–size relation. Snow single-scattering properties are computed for four different mass–size relations. Subsequently, the snow description in the Thompson microphysics scheme is used as an example to demonstrate how microphysics-scheme-consistent snow bulk optical properties are derived. The Thompson-scheme-consistent snow bulk optical properties are added to the Community Radiative Transfer Model (CRTM), version 2.4.0. With CloudSat Cloud Profiling Radar (CPR) snow and liquid precipitation retrievals as the inputs, CRTM simulations are performed over global oceans and compared with four collocated Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channel observations. The CRTM simulated brightness temperatures show agreement with the GMI observed brightness temperatures in cases of light-to-moderate precipitation over extratropical and polar ice-free oceans, with root-mean-square errors of 4.3, 13.0, 1.8, and 3.3 K in the 166-GHz (vertical polarization), 166-GHz (horizontal polarization), 183 ± 3-GHz (vertical polarization), and 183 ± 7-GHz (vertical polarization) channels, respectively. The result demonstrates the potential of using the newly developed microphysics-scheme-consistent snow optical parameterization in data assimilation applications.