Lin Chen

Cloud masking is the fundamental step in accurately retrieving cloud and many other properties from satellite-based imagers. Intercomparisons among imagers are frequently conducted to assess the agreement of cloud masks at both global and granule scales, ensuring accuracy while also enabling the merging of multi-satellite products for use in NWP models and climate datasets. However, common challenges arise in scenarios with reduced spectral contrast between clouds and backgrounds, leading to generally degraded performance at polar regions and making validation through intercomparison less reliable. Building on previous synergistic observations between CALIOP and MODIS in the A-Train constellation, this study employed China's Aerosol and Carbon Detection Lidar (ACDL) as a new benchmark to evaluate cloud masks from 3 passive sensors across different operational stages. Analyzing approximately 3 million collocated samples in February and August 2024, the overall hit rates for confident cloudy/clear pixels from Aqua/MODIS, FY-3D/MERSI-II, and NOAA-21/VIIRS reached 82.9 %, 79.3 %, and 86.4 %, respectively. Detailed examinations of monthly, day/night, and latitudinal subsets revealed variation in each sensor's performance across scenarios and identified the reasons behind the discrepancies. The study highlighted the importance of active-passive validation, which confirms consistency among passive imagers while also identifying algorithmic weaknesses that need improvement for future missions.

Reliable ground-based aerosol observations are essential for evaluating satellite aerosol products across heterogeneous surfaces and aerosol regimes. This study established a six-site CW193 sun photometer network in China with quality-controlled observations from January 2023 to May 2026 and assessed CW193 AOD consistency using collocated CE318 observations at Fujin and Lijiang. A consistent processing chain including Langley calibration, preprocessing, triplet consistency screening, 440 nm daily-stability screening, temporal smoothness tests, and statistical outlier removal was applied, and the sensitivity to QC threshold perturbations was examined. The quality-controlled observations were then used to evaluate the FY-3F/MERSI-III daily AOD product and three MODIS products (MCD19A2, MOD04_L2, and MYD04_L2). Across 440–1020 nm, CW193 and CE318 showed strong agreement (R = 0.979–0.999; slopes = 0.989–1.019; RMSE = 0.007–0.024), with no evident long-term drift during the available comparison periods. At Lijiang, Hebi, and Qingdao, FY-3F/MERSI-III yielded R values of 0.220, 0.664, and 0.702 and RMSE values of 0.128, 0.157, and 0.183, respectively. MODIS performance varied strongly among sites and products; MCD19A2 yielded the lowest RMSE at Dunhuang, Kashgar, and Lijiang, whereas MYD04_L2 yielded the lowest RMSE at Hebi and Qingdao. The small MOD04_L2 and MYD04_L2 matchup samples at Fujin precluded a robust ranking. All three MODIS products substantially underestimated AOD at Kashgar. Regional CW193 observations further distinguished high-AOD, low-AE conditions at Kashgar from low AOD, high-AE conditions at Lijiang. QC threshold perturbations affected data retention more than the principal aerosol and satellite evaluation statistics. These results support CW193 as a regional ground-based reference for satellite AOD evaluation while emphasizing the importance of site conditions, collocation strategy, sampling, and the empirical nature of AOD–AE optical regimes.

Accurate information on atmospheric temperature profiles is crucial for improving numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which thereby fail to meet the high spatial resolution required for NWP. In this study, we present an integrated framework as an engineering refinement combining the variation method with an artificial neural network (Var-ANN) to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) aboard the polar-orbiting satellite FY-3C. The variation method is first applied to construct a spatially consistent reference field from available station observations, and this field is then used as the training target for a back-propagation neural network that learns the empirical relationship between satellite brightness temperatures and corrected atmospheric temperature. The calibrated temperature profiles were evaluated against independent radiosonde observations and further tested through assimilation into the Weather Research and Forecasting (WRF) model for precipitation simulation over the TP. Results indicate that the Var-ANN calibration reduces the root-mean-square error (RMSE) by approximately 60% and the mean bias from approximately −5 °C to −0.7 °C relative to radiosonde observations. In two WRF case studies, the calibrated profiles show potential for improving precipitation forecast skill, although the limited sample size precludes robust conclusions about operational forecast improvements. The Var-ANN framework provides a practical approach for enhancing the utility of FY-3C VASS temperature products for NWP applications over data-sparse complex terrain.

Latent heat release is associated with phase changes in water. Current physical‐based satellite latent heating (LH) retrieval methods mainly depend on precipitation radar observations, which detect hydrometeors only below the precipitation top height (PTH), thereby limiting the LH estimation to the inner precipitation region. However, phase transitions between the PTH and the cloud top height (CTH) also release non‐negligible LH, which accounts for up to 9.74% of the total based on statistical results. Neglecting this component can lead to systematic underestimation of LH. To address this issue, this study develops a Residual Two‐stage Convolutional Neural Network Heating (R2CH) algorithm to retrieve LH associated with non‐precipitating clouds above the PTH. The method adopts a residual learning strategy within a two‐stage framework, where a convolutional framework refines LH retrieval using CTH, PTH, air temperature, and vertical derivation of precipitation rates from satellite observation. Compared with Weather Research and Forecasting (WRF) model simulations, R2CH effectively captures LH from non‐precipitating clouds above the PTH and reproduces both horizontal and vertical structures. Relative to the Convolutional Neural Network Heating (CNNH) and two official GPM LH products, the R2CH retrieval based on GPM and Himawari‐8 observations addresses the underestimation of LH above the precipitation tops from CNNH and becomes more consistent with the two reference products at that layer. Nevertheless, the LH above PTH retrieved by R2CH is generally weaker than that from the two other products. More validation studies are needed to determine which method is more accurate.

Accurate retrieval of aerosol properties from geostationary satellites is critical for capturing rapid atmospheric variability, yet remains difficult due to uncertain aerosol-model assumptions, clear-pixel misclassification over bright surfaces, and limited physical interpretability of purely data-driven approaches. We present a Physics-Informed Transformer Algorithm (PITA) for aerosol retrieval that couples radiative transfer (RT) physics with a Transformer architecture to enable near – real-time synergistic retrieval of aerosol optical depth (AOD) at 550 nm and the Ångström exponent (AE; 440–675 nm) from Fengyun-4B (FY-4B) Advanced Geosynchronous Radiation Imager (AGRI) observations. PITA integrates: 1) six regionally optimized seasonal aerosol models derived via deep clustering of 11-year ground-based aerosol observations, reducing the mean absolute error (MAE) of forward-simulated apparent reflectance by 57.6% relative to MODIS Collection 6 models; 2) a critical-reflectance clear-pixel identification scheme to improve screening over bright surfaces; and 3) an RT-constrained Transformer network with RT-derived features, with SHAP attribution indicating these physics features contribute > 70% of model importance. Sample-based ten-fold cross-validation yields AOD performance of R = 0.94 and MAE = 0.07 (76% within the expected error, EE) and AE performance of R = 0.77 and MAE = 0.16 (93% within EE). Compared with the operational FY-4B land aerosol product, PITA improves AOD correlation by 52.5% and increases AE correlation from − 0.12 to 0.73. Sensitivity analyses show negligible bias at low aerosol loading (AOD < 0.23) and weak dependence on particle size. Without external reanalysis inputs, PITA offers a physically interpretable framework for near – real-time, high-accuracy geostationary aerosol monitoring.

TanSat-2 is the successor to TanSat, China's first dedicated carbon monitoring satellite. Compared to TanSat, TanSat-2 features improved designs with enhanced spatial and temporal coverage, a wider range of measurements, and higher measuring accuracy. TanSat-2 achieves near-daily global coverage by using a medium/medium-low earth orbit (M/MLEO) and large swath design, allowing it to monitor most land areas daily. This M/MLEO elliptical orbit intersects with the Van Allen radiation belts, requiring special radiation analysis and protection designs. TanSat-2 also features higher retrieval accu racy and carbon-pollution collaborative monitoring to identify anthropogenic emissions accurately. TanSat-2 carries three key spectrometers: (1) the Ultra-wide-field Carbon Pollution collab orative monitoring Instrument (UCPI), which provides a 2900 km swath and retrieval accuracy for XCO₂ (1 ppm), XCH₄ (8 ppb), NO2 (1.3×10¹⁵ molec cm⁻²) and SIF (0.3 mW m⁻² Sr⁻¹ nm⁻¹); (2) the Hotspot Greenhouse gas Emission Tracker, with a 500-m nadir resolution, designed for tracking carbon emission hotspots; and (3) the Cloud Aerosol Polarization Imager (CAPI). Additionally, TanSat-2 is also equipped with the On board Intelligent Hotspot Extraction and Distribution Instrument (OHEDI), providing high-performance computing capabilities and Beidou short-message capabilities for near-real-time hotspot detection and distribution. The high-precision global observation from TanSat-2 is expected to provide essential data for global assimilation systems to assess global anthropogenic emissions for global carbon stock and other scientific purposes. The near-real time hotspot detection and distribution capability is also expected to be combined with other space-ground observation programs.

Soil salinization is a major ecological barrier to sustainable agricultural development. Global Navigation Satellite System Reflectometry (GNSS-R), as an emerging passive microwave remote sensing technology, has shown considerable potential for soil parameter monitoring due to its low cost, short revisit cycle, and strong penetrability. However, the significant heterogeneity between Delay–Doppler Maps (DDMs) and auxiliary features, together with the scarcity of in situ salinity observations, poses challenges for GNSS-R-based soil salinity retrieval. To address these issues, this study proposes a Cross-modal Adaptive Fusion Network (CAF-Net) that integrates GNSS-R DDM observations with multi-source auxiliary features through a cross-modal fusion framework to enhance feature representation and model generalization. In addition, a physical reference dataset is constructed by combining SMAP soil moisture products with dielectric models to alleviate the lack of ground salinity observations. Experiments conducted in the Yellow River Delta using Tianmu-1 GNSS-R data demonstrate that the proposed method achieves a correlation coefficient (R) of 0.9546 and a root mean square error (RMSE) of 0.5925 g/kg, outperforming several mainstream benchmark models. Moreover, the retrieved soil salinity shows good agreement with independent datasets in both spatial distribution and statistical characteristics. These results indicate that integrating GNSS-R observations with deep learning provides a promising approach for large-scale soil salinity monitoring.

Accurate information on atmospheric temperature profiles is crucial for improving numerical weather forecasting and short-term numerical weather prediction (NWP). However, the harsh environment of the Tibetan Plateau (TP) limits the availability of station observations, which fails to meet the high spatial resolution required for NWP. In this study, we present a method to calibrate temperature profiles obtained from the Vertical Atmosphere Sounding System (VASS) using data from the polar-orbiting satellite FY-3C. The aim is to provide high-resolution atmospheric structure for NWP in the TP. The temperature profile in VASS exhibits temporal and spatial heterogeneity due to the significant impact of clouds on the radiative transfer mode (RTM). To address this, we employ a combination of variation and artificial neural network (Var-ANN) methods to calibrate the satellite product and improve its compatibility with the model. To confirm the feasibility of our method, we compare the calibrated results with the observed data from 121 radiosonde soundings and 2400 meteorological stations in China, both of which represent conditions closest to the real atmospheric states. The calibrated temperature shows improvements over the original temperature, with a root mean square error, bias, and agreement with radiosonde soundings of 2.11, -0.72, and 0.998, respectively. We also select two classical cases involving the eastward movement of the plateau vortex (PV) and the formation of precipitation to verify the applicability of the calibration in NWP. The results demonstrate that the performance of NWP improves after assimilating the calibrated data, with the Var-ANN data assimilation scheme achieving the highest threat score of 66.9 and 66.7 for case 1 and case 2, respectively. These findings suggest that the Var-ANN method is suitable for calibrating satellite temperature profiles, and the calibrated data holds potential for precipitation forecasting. Furthermore, the novel method can also be applied in global temperature profile correction and satellite cross-calibration.

Satellite precipitation retrieval accuracy assessment requires reliable ground validation, yet conventional approaches using rain gauges as “truth” neglect representativeness errors inherent in point‐to‐area approximations. This study uses 7,253 rain gauges (2020–2024) over the Jianghuai monsoon region to quantify these errors and reassess Integrated Multi‐satellite Retrievals for GPM (IMERG) performance. We show that at least 16 gauges per 0.2° grid are required for reliable area‐mean precipitation estimates. Analysis reveals dual dependence of gauge representativeness errors on gauge density (n, number of gauges per grid cell) and rainfall intensity (RR): (a) errors decay exponentially with increasing n, following root mean square error (RMSE) = ae ^−bn , where a and b are fitted coefficients; (b) errors increase with RR when n is held constant. Parameterized relationships enable error quantification across density gradients. Direct IMERG‐gauge comparisons show that seasonal mean differences are negatively correlated with gauge density (Pearson's r = −0.33, p < 0.01), indicating that sparse gauge networks are a primary driver of apparent discrepancies. Error decomposition using gauge uncertainties yielded bounded IMERG retrieval errors (RMSE ^B _min/max). Applying the same framework to Kling‐Gupta efficiency (KGE) revealed similarly improved IMERG performance after removing gauge‐induced uncertainties, reinforcing the internal consistency of our analysis. Crucially, incorporating gauge errors reduced significant discrepancy frequency by 16%/6%/16%/17% across seasons, proving that traditional methods overestimate IMERG‐gauge deviation occurrence by 6%–17%. This establishes gauge density as critical accuracy determinant, provides robust error‐quantification framework, and reveals that terrain‐complexity misinterpretations arise when disregarding representativeness errors, with implications for global satellite precipitation validation.

The FengYun (FY) meteorological satellites, equipped with combined microwave and infrared sensors, provide temperature and humidity profiles under both clear and cloudy conditions, supporting global atmospheric monitoring and weather forecasting. This study evaluates the FY-3E Vertical Atmospheric Sounding System (VASS) using collocated radiosonde observations, reanalysis data, and satellite measurements including the Cross-track Infrared and Microwave Sounder Suite (CrIMSS) onboard JPSS-1. Under all-sky conditions, VASS temperature profiles exhibit a correlation of 0.99 with radiosonde data, and humidity profiles reach 0.80. The temperature biases (RMSEs) are 0.63K (3.07 K) under clear-sky, 0.82K (3.56 K) under cloudy-sky, and 0.67K (3.26 K) under all-sky conditions. The corresponding humidity biases (RMSEs) are 4.63% (18.36%), 6.21% (21.38%), and 4.76% (19.36%). As compared with CrIMSS products using coincident radiosonde data, the VASS retrievals show larger errors in both temperature and humidity, yet provide consistent and reliable atmospheric profiles under diverse conditions. These results confirm the current utility of FY-3E VASS in operational weather and climate services, while highlighting the need for continued algorithm development to further reduce retrieval uncertainties.