Abstract Accurately understanding the evolution and development of cloud physical properties (CPP) in advance is crucial for extreme weather forecasting and early warning. This study utilized the Fourier neural operator (FNO) method to develop a short‐term forecasting model of Cloud (Cloud‐FNO). Using the multi‐task learning framework and autoregression strategy, the model achieves accurate 6‐hr forecasting of cloud phase (CLP), cloud top height (CTH), cloud effective radius (CER), and cloud optical thickness (COT). Evaluation results on the independent testing data set show that the Cloud‐FNO model achieves an average CLP identification accuracy exceeding 74%, and the average root mean square errors for CTH, CER, and COT forecasts are 2.28 km, 6.52 μm, and 9.01, respectively. Importantly, the Cloud‐FNO model demonstrates strong forecasting capability and promising application potential for the CPP evolution under severe weather.

This paper introduces a comprehensive Tropical Cyclone (TC) dataset based on China’s FengYun (FY) series geostationary satellite observations in the Western North Pacific (WNP), designated as FYSATTY. This innovative dataset integrates high-resolution geostationary meteorological satellite observations with best-track data, providing a unique resource for TC monitoring and analysis in the WNP region. FYSATTY is derived from infrared, water vapor, and visible channel imagery captured by the FY-2 and FY-4 satellites, which is processed using an equal-interval latitude-longitude grid system within a 1500-km radius centered on each TC. The best-track data, sourced from the China Meteorological Administration (CMA), is interpolated into 3-h intervals to ensure temporal consistency with the satellite observations. This dataset consists of 99714 observations for 614 TCs from 2005 to 2024 and will be updated annually. It represents the most comprehensive collection of TC satellite imagery derived from the FY series of geostationary meteorological satellites of the National Satellite Meteorological Center (NSMC). It encompasses a synthesis of infrared, water vapor, and visible channel data from the FY-2 and FY-4 satellite series. The dataset has been meticulously cropped and georeferenced using the CMA best-track data, with fundamental geographic spatial calibration completed. Comparative analysis with analogous elements within other TC geostationary meteorological satellite datasets reveals a high degree of consistency. It is suitable for reanalyzing TC best-track data, assessing climatic characteristics of TC satellite observations, and enhancing forecasting capabilities. This dataset is particularly useful for TC research, forecasting, and machine learning, and is publicly available.

Clouds play a crucial role in the Earth's energy budget and the hydrological cycle. However, differences in the spatiotemporal resolution of satellite sensors and in retrieval algorithms lead to substantial heterogeneity among retrieved cloud products. Based on global geostationary satellite thermal infrared brightness temperature data from the Gridded Satellite (GridSat-B1) project, this study applied the single-layer Cloud retrieval model - Small Attention-UNet (Cloud-SmaAtUNet) algorithm in the DaYu CLoud Analysis System (DaYu-CLAS) to retrieve a global cloud product with a 3 h temporal resolution, 0.07 degrees spatial resolution, and 23 year temporal span (2000-2022). This product is referred to as the DaYu Global Cloud physical properties Products (DaYu-GCP). The DaYu-GCP includes CLoud Phase (CLP), Cloud Top Height (CTH), Cloud Optical Thickness (COT), and Cloud Effective Radius (CER), covering all regions between 70 degrees S-70 degrees N and 180 degrees W-180 degrees E. Evaluation based on the Moderate-resolution Imaging Spectroradiometer (MODIS) official cloud products shows that the annual CLP identification accuracy of DaYu-GCP remains stable at 85 % +/- 0.7 %, while the annual RMSE for CTH, COT, and CER stabilize at 1.50 +/- 0.03 km, 10.71 +/- 0.15, and 6.75 +/- 0.10 & micro;m, respectively. The multi-year variations in accuracy are within 2 %, with no evident interannual differences, and the spatiotemporal distributions are continuous. In addition, evaluation based on observations from the Cloud Profiling Radar and the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) indicates that the DaYu-GCP products show reasonable day-night consistency for optically thin cloud. Furthermore, the DaYu-GCP products are compared with other global cloud products. Taking the Northern Hemisphere as an example, the interannual variations of Cloud Cover Frequency (CCF), CTH, COT, and CER retrieved from DaYu-GCP show correlation coefficients of 0.760, 0.486, 0.764, and 0.514 with the ISCCP product, respectively, and 0.444, 0.778, 0.171, and 0.412 with the CLARA-A3 product. The DaYu-GCP dataset, which is stored in the Network Common Data Format (NetCDF), is freely available on the Science Data Bank at 10.57760/sciencedb.26292 (Zhao et al., 2026). The corresponding code can be found at https://github.com/lingxiao-zhao/DaYu-GCP (last access: 25 June 2025).

As climate change intensifies the frequency and severity of extreme precipitation and flooding events across the globe, the associated risks to public safety and economic assets continue to grow. In this context, accurate, real-time satellite-based precipitation estimation has become essential for operational large-scale hydrometeorological analysis and effective disaster monitoring. NASA’s Integrated Multi-satellitE Retrievals for GPM (IMERG Final Run) combines information from ”all” satellite microwave observations with gauge correction and climatological adjustment to produce precipitation estimates at 0.1° spatial and 30-min temporal resolution. However, despite its superior performance over mainstream satellite precipitation datasets in capturing rainfall patterns and variability, its latency of approximately 3.5 months significantly limits its applicability for real-time operational use. We proposed Huayu, a novel deep learning-based real-time satellite precipitation retrieval system that relies solely on infrared observations from the FengYun-4B geostationary satellite to provide an accurate precipitation estimate at a finer spatiotemporal resolution (15 min, 0.05°) over a 120° × 120° domain. Experimental validations demonstrate that Huayu achieves strong consistency with rain gauge observations, yielding a Critical Success Index (CSI) of 0.693 - 3.3% improvement over IMERG Final Run (CSI: 0.671).

Satellite observations play a crucial role in quantifying ammonia sources by capturing large-scale variations of atmospheric NH3 concentrations. As the world's first geostationary hyperspectral infrared sounder, the Geostationary Interferometric Infrared Sounder (GIIRS) on board China's FengYun-4 satellite series provides a unique opportunity to monitor the diurnal cycle of NH3. Using NH3 retrievals from July 2022 to June 2025, this study investigates the spatio-temporal variability of NH3 columns over East Asia, with a focus on daytime variations (07:00-19:00 LT - local time) in major agricultural regions. Inter-comparison with polar-orbiting IASI and CrIS data shows that GIIRS NH3 retrievals are consistent in capturing spatial patterns and temporal dynamics. The NH3 peaks occur between March and July, with peak timing earlier in the south and later in the north, reflecting regional differences primarily driven by agricultural activities. Validation with ground-based FTIR measurements at Hefei in eastern China demonstrates the accuracy of GIIRS NH3, with a correlation coefficient of 0.77 and an RMSE of 9.67 & times;1015 moleccm-2, while reproducing daytime variations observed by FTIR. For major agricultural areas, the NH3 columns generally increase from early morning to late afternoon, reaching 1.10-1.56 times morning levels in summer and spring. Compared with GEOS-CF model simulations, the results reveal pronounced discrepancies in spatial distributions over the Sichuan Basin in southwestern China and daytime variations over northern India. These findings highlight the valuable capability of FY-4B/GIIRS in identifying and tracking daytime dynamics of NH3 sources over East Asia, offering new insights beyond current low-Earth orbit (LEO) instruments.

Global soil moisture (SM) observation using spaceborne global navigation satellite system reflectometry (GNSS-R) is becoming an effective supplement and enhancement to traditional microwave remote sensing observations. The state-of-the-art SM retrieval frameworks for spaceborne GNSS-R are based on empirical or semiempirical modeling, which relies on reference SM data from other sources (e.g., microwave radiometer or in situ SM products) to eliminate the effects of land surface errors (e.g., surface roughness and vegetation). This study, for the first time, defines a generic framework for physics-based spaceborne GNSS-R SM retrieval, namely, PHYSER, and proposes initial strategies to realize the framework. The framework concept is devoted to deriving accurate soil reflectivity and retrieving SM by estimating soil permittivity from Fresnel reflection coefficients, thus wholly independent of external SM products. It assumes that GNSS-R surface reflectivity and its related soil reflectivity are affected by observed system errors and land surface errors. The framework is initially realized by deriving accurate soil reflectivity from empirical corrections to avoid the grand challenge of building a forward-scattering model under complex land surface conditions. Accurate soil reflectivity is derived through 2 steps: (a) Surface Reflectivity CALibrating (SuR-CAL), aiming to calibrate the system errors by using the reflectivity of inland water bodies, and (b) Soil Reflectivity CORrecting (SoR-COR), aiming to correct the errors mainly from surface roughness and vegetation using the zeroth-order radiative transfer (τ–ω) model. The framework is validated using 1-year data from BuFeng-1 A/B (BF-1) twin satellites. The findings and conclusions mainly include the following: (a) PHYSER reveals that independent spaceborne GNSS-R SM retrieval without reference SM products is achievable through the derivation of accurate soil reflectivity. (b) Land surface errors play a more significant role in influencing soil reflectivity than system errors. The SuR-CAL and SoR-COR steps improve the correlation coefficient (R) between BF-1 reflectivity and the Soil Moisture Active Passive (SMAP) SM up to ~7% and ~36%, respectively. (c) The BF-1 SM estimates agree well with the SMAP SM and the fifth-generation ECMWF atmospheric reanalysis (ERA5) SM, with an unbiased root-mean-square difference (ubRMSD) of 0.067 m3·m−3 and a mean absolute error (MAE) of 0.073 m3·m−3 against SMAP and a ubRMSD of 0.079 m3·m−3 and an MAE of 0.088 m3·m−3 against ERA5. The BF-1 SM also agrees well with the in situ measurements with mean unbiased root-mean-square error = 0.055 m3 m−3 and MAE = 0.066 m3 m−3. The proposed framework provides a promising physics-based concept to independently retrieve SM for the GNSS-R community, which is expected to considerably support the in-orbit and next-generation GNSS-R missions to promote operational SM retrieval and applications.

The subtropical high (hereinafter “STH”) is a fundamental large-scale weather system that governs tropical and subtropical regions. Its structural variations are closely related to regional heavy rainfall, summer heatwaves, and other extreme weather events. Accurately characterizing both the intensity and spatial features of the subtropical high is crucial for weather forecasting and short-term climate prediction. Currently, traditional weather observations and analyses are inadequate for the real-time, refined identification of STH structures. In this study, we introduce an STH identification model based on the Fengyun-4 (FY-4) satellites (FY_STH), utilizing a machine learning model that synthesizes brightness temperature data from the Advanced Geosynchronous Radiance Imager (AGRI) onboard FY-4 with satellite-retrieved outgoing longwave radiation (OLR) measurements. The model utilizes a piecewise eXtreme Gradient Boosting (XGBoost) classification decision tree ensemble to identify the subtropical high, and Bayesian optimization to refine its structure and parameters for optimal performance. The ERA5 reanalysis data serve as the benchmark for defining the subtropical high, and systematic evaluations of the FY_STH model are conducted, with sounding data used for validation. The results demonstrate that FY_STH consistently delineates the STH influence area in different seasons, achieving an average accuracy of 0.8, a false alarm rate of 0.3, a critical success index (CSI) of approximately 0.6, a dice coefficient of about 0.7, and an area under the curve (AUC) of the ROC (Receiver Operating Characteristic) around 0.9. In comparison with the CMA-GFS forecasts, FY_STH exhibits superior overall performance, with all evaluation metrics showing that its results are accurate and reliable. FY_STH is then employed to capture the relationship between Typhoon Gaemi and the STH over the western Pacific during 19–28 July 2024. It is revealed that variations in the trajectory of Typhoon Gaemi are strongly associated with shifts in the STH position, underscoring that real-time satellite monitoring facilitates a timely and precise understanding of typhoon trajectory variations and enhances the forecast accuracy based on the interaction between the STH and the typhoon.

Anomalous variations in stratospheric circulation, together with stratosphere-troposphere coupling processes, exert profound modulating effects on tropospheric circulation patterns and extreme weather events. Stratospheric temperature serves as a critical indicator of the Earth system’s response to climate change. Satellite-borne stratospheric temperature sounders offer extensive spatiotemporal coverage and high measurement consistency, and spectral band selection is a core issue in instrument design. This study proposes an optimal spectral band selection method for stratospheric temperature profile retrieval, and compares the sounding performance of the proposed narrow-band scheme (NBS) with that of mainstream spaceborne hyperspectral infrared sounders. Using a sounding model constructed with space heterodyne spectrometer (SHS), optimal narrow bands are selected within the 15 μm CO₂ absorption band. Accounting for the interference effects of O₃ and H₂O, the application performance of NBS in atmospheric sounding is comprehensively evaluated. Comparative analyses are conducted between NBS and typical hyperspectral infrared sounders including IASI-NG, AIRS, IRS and GIIRS. The results demonstrate that NBS exhibits excellent sensitivity to stratospheric temperature profiles, and the errors in temperature retrieval caused by O₃ and H₂O remain controllable. Under idealized conditions, when the noise-equivalent temperature differential (NEdT) of NBS is matched with each reference sensor, the retrieval accuracy (average RMSE) of the 0.7–200 hPa is improved by 0.30 K, 0.84 K, 2.61 K and 2.54 K, and the vertical resolution is enhanced by 0.76 km, 2.12 km, 8.85 km and 8.39 km, respectively.

Fengyun-4 (FY-4) is a series of Chinese operational geostationary meteorological satellites, providing crucial data for weather forecasting, climate prediction, and environmental monitoring. Advanced geostationary radiation imagers (AGRIs) onboard the FY-4A and FY-4B satellites play a key role in observing the Earth's surface, oceans, and atmosphere. However, their calibration stability is still uncertain, which clearly limits corresponding downstream applications. This study evaluates the long-term radiometric stability of AGRI reflective solar bands (RSBs) using a general cloud target (CT) calibration method, covering the periods from March 2018 to December 2024 for FY-4A/AGRI and from June 2022 to December 2024 for FY-4B/AGRI. By utilizing MODIS cloud products as references for cloud properties, we simulate the top-of-atmosphere (TOA) reflectances of CTs through the Discrete Ordinates Radiative Transfer (DISORT) model and compare results with observed reflectances to infer the instrumental calibration stability. Our results indicate that the radiometric responses of AGRI exhibit significant degradation in visible (VIS) bands, while showing relatively smaller degradation rates in near-and shortwave-infrared bands. Specifically, the annual degradation rates for band 1 (0.47 mu m) of FY-4A/AGRI and FY-4B/AGRI are 4.3% and 8.8%, respectively. Both AGRIs demonstrate comparable degradation rates of approximately 3.5% for band 2 (0.65 mu m). In contrast, bands 3 (0.83 mu m), 5 (1.61 mu m), and 6 (2.25 mu m) show annual degradation rates around-1.0%, despite they exhibit notable fluctuations. The operational calibration of FY-4B/AGRI is more accurate than that of FY-4A/AGRI and with smaller fluctuations. By fitting the time series of relative errors (REs) between simulated and current calibrated reflectances, we calculate daily recalibration coefficients and effectively recalibrate the long-term data, with a calibration accuracy within +/- 3%. This study demonstrates that the CT-based calibration method can successfully track the radiometric stability of AGRI and provide a robust calibration solution to ensure data stability and accuracy.

Accurate, spatiotemporally continuous total precipitable water (TPW) data under all‐weather conditions are crucial for understanding water and energy cycles. This study introduces TPWDiff‐CB, a novel deep learning‐based TPW retrieval model that employs a generative diffusion model. TPWDiff‐CB effectively estimates TPW under all‐weather conditions by leveraging thermal infrared observations from the Advanced Himawari Imager aboard Himawari‐8. Specifically, when compared to radiosonde TPW, TPWDiff‐CB yields a correlation coefficient ( R ) of 0.98 and a root mean square error of 4.51 mm under all‐weather condtions. The model demonstrates exceptional performance, maintaining near‐identical accuracy under both cloudy and clear‐sky conditions. Its robust capability in learning the TPW distribution and performing spatiotemporal retrievals surpasses that of traditional machine learning model. These findings highlight TPWDiff‐CB's high accuracy and its promising potential for applications in weather and climate research.

The Fengyun-4B satellite is the first operational satellite of China's new-generation geostationary meteorological satellite series. It is equipped with an advanced geostationary radiation imager (AGRI) that can detect aerosols. This method includes two aspects: 1) we constructed an hourly surface reflectance (SR) database and selected appropriate aerosol models based on seasonal variations. This approach enables the quantitative retrieval of aerosol properties over a variety of land surfaces, including dark pixels, urban areas, and bright desert surfaces, and 2) based on the spatiotemporal variability characteristics of aerosols in the past hour and within a 12-km radius, a quality control scheme was designed. This scheme was used to produce two datasets: the quality-controlled AOD(pure) dataset and the original uncontrolled AOD(orig) dataset. The aerosol products in 2023 were evaluated using Aerosol Robotic Network (AERONET) data. The results showed that the performance of AOD(pure) was optimal during the summer and fall seasons, with the root-mean-square error (RMSE) less than 0.1, and more than 75% of the samples falling within the expected error (EE). However, due to the overestimation of low values in winter and underestimation of high values in spring, only 64.06% of the samples fell within the EE throughout the year. For the EE criterion, the number of samples of AOD(orig )against AERONET was 10% lower than that of AOD(pure) against AERONET. The diurnal variations of AOD(pure) show more consistency with AERONET, proving the necessity of hourly SR. Both aerosol optical depth (AOD) datasets are capable of capturing dust events in spring and haze events in winter. Considering the cross-comparison, the quantitative ability of FY-4B/AGRI enhanced by this work is currently superior to that of Himawari-9/AHI.

Ozone is an important atmospheric constituent, exerting a pivotal influence on atmospheric chemistry, air quality, and climate change. The monitoring of its distribution and variation is crucial for advancing our understanding of ozone development and related processes. This study presents the first spatial and temporal distributions of total ozone columns (TOC) retrieved from the Geostationary Interferometric Infrared Sounder (GIIRS), on board China's FengYun‐4B satellite (FY‐4B/GIIRS) launched in 2021. Particularly, we focus on the variations of TOCs in East Asia from diurnal to seasonal time scales. Retrievals are implemented using spectra from March, June, September, and December, representing different seasons. The results show that the degree of freedom for the signal (DOFS) typically exhibited a range of 0.8–1.4, with the vertical detection sensitivity of GIIRS peaking in the upper troposphere/lower stratosphere (UTLS) region, where the ozone variability is the highest. Collocation comparisons with the Infrared Atmospheric Sounding Interferometer (IASI) retrievals, the Ozone Monitoring Instrument (OMI) measurements, the European Center for Medium‐Range Weather Forecasts (ECMWF) Reanalysis v5 (ERA5) simulations, and in situ ozone observations show good agreement. The comparisons of TOCs between GIIRS, Pandora and ERA5 at different latitudes and different time scales demonstrate the ability of FY‐4B/GIIRS in capturing the temporal and latitudinal ozone variations, particularly at middle and high latitudes. Our work demonstrates that FY‐4B/GIIRS has good capability to track ozone variations from diurnal to seasonal in East Asia, which will contribute to the understanding of regional and global ozone variations.

The Geostationary Interferometric Infrared Sounder (GIIRS) on board China’s FengYun-4 satellite series provides a unique opportunity to monitor the tropospheric composition over Asia using hyperspectral infrared observations from a geostationary orbit. In this study, we retrieve atmospheric carbon monoxide (CO), ammonia (NH3), formic acid (HCOOH) and ozone (O3) for the first two years from July 2022 to June 2024 using the FengYun Geostationary satellite Atmospheric Infrared Retrieval (FY-GeoAIR) algorithm. GIIRS measures these atmospheric compounds both day and night with a temporal resolution of 2 hours and a spatial resolution of 12km at nadir. The spatial patterns, seasonal and diurnal variations of these atmospheric compounds over Asia are analyzed using the FY-4B/GIIRS retrievals. In particular, we focus on a case study of the strong emissions from forest fires over the Indochina Peninsula (ICP) in spring due to the agricultural practice of slash-and-burn. The results show that the spatial and temporal variations of wildfire enhancements of CO, NH3 and HCOOH from Southeast Asia are well captured by the FY-4B/GIIRS. In addition, the FY-4B/GIIRS retrievals are validated with ground-based observations of CO and NH3 and compared with model simulations. Our study demonstrates the potential of GIIRS data to improve our understanding of the spatial and temporal variations of important trace gas pollutants over Asia.

Cloud effective radius(CER)is a fundamental microphysical property of clouds,critical for understanding cloud formation and radiative effects.Satellite spectral imagers,widely utilized in passive remote sensing,facilitate the monitoring of cloud characteristics,including CER,over extended time periods and spatial scales.Various observa-tional methods have been employed to evaluate satellite cloud property products;however,in situ measurement eval-uations of CER remain limited,particularly for products over China.This study utilized aircraft observations provided by the China Meteorological Administration Weather Modification Centre to evaluate the CER retrieved by Fengyun-4A Advanced Geosynchronous Radiation Imager(AGRI)and Himawari-8 Advanced Himawari Imager(AHI).Three flights were selected from the aircraft dataset for evaluation,involving flights through non-precipitat-ing stratiform clouds with stable,high-quality measurements.Rigorous data selection and collocation procedures were employed to ensure a comprehensive comparison.Satellite retrievals from heterogeneous cloud fields were ex-cluded,and representative in-cloud aircraft measurements were identified through multi-parameter filtering.The flight trajectory was adjusted to account for horizontal cloud movement corresponding to time differences between observations from different platforms.Additionally,in situ measurements from different vertical layers were adjus-ted to a comparable position near the cloud top.Results indicate that CER retrieved from satellites is generally over-estimated compared to in situ measurements.For AGRI,the average difference(AD)is 2.90 μm,with a root mean square difference(RMSD)of 3.53 μm.For AHI,the AD is 2.92 μm,and the RMSD is 3.59 μm.To enhance future validation and evaluation of remote sensing results,factors such as instrument calibration,flight patterns,and cloud conditions will be carefully considered.Increasing the number of cases should further reduce errors associated with individual instances,enabling more precise assessments.

Determining instrument specifications and analyzing methods for atmospheric retrieval (DISAMAR) is a radiative-transfer (RT) model developed to simulate retrievals of atmospheric components and surface properties. This article evaluates the performance of the DISAMAR retrieval algorithm using Tropospheric Monitoring Instrument (TROPOMI) spectral measurements of the O-2-A (757-770 nm) and O-2-B (685-691 nm) bands for cloud-free scenes and fully cloudy scenes. For fully cloudy scenes, cloud pressure and cloud optical thickness (COT) are retrieved and compared with Fast Retrieval Scheme for Clouds from the O-2-A band-Sentinel (FRESCO-S), Retrieval of Cloud Information Using Neural Network (ROCINN), and Visible Infrared Imager Radiometer Suite (VIIRS) cloud products. For cloud-free scenes, the surface pressure and surface albedo are fit; the results are compared with ERA5 surface pressure and TROPOMI DLER datasets, respectively. Several parameters in DISAMAR retrieval settings are tested, including the stray-light settings, the fitting wavelength windows, the a priori values of the fitting parameters, and the performance of oxygen cross-section datasets (JPL2008, HITRAN 2008/2012/2020). As expected, DISAMAR retrieves cloud mid-level pressure (CLP) values closely matching FRESCO-S and ROCINN, especially when fitting the O-2-A band. COT and cloud-top pressure (CTP) show better correlation with VIIRS data when fitting the O-2-A band than the O-2-B band. For thick clouds, fitting stray light is not advisable, and a specific a priori value of COT is unnecessary. Using the HITRAN2020 dataset provides more accurate spectral simulations. Fitting the O-2-A band yields results closer to both FRESCO-S and VIIRS, while the O-2-B band yields results closer to ROCINN and serves as a useful supplement. Excluding high-residual wavelengths in the fitting window further improves retrievals.

DISAMAR (determining instrument specifications and analysing methods for atmospheric retrieval) is a computer model developed to simulate the retrieval of properties of atmospheric trace gases, aerosols, clouds, and the ground surface from passive remote sensing observations in a wavelength range from 270 to 2400 nm. It is being used for the TROPOMI/Sentinel-5P and Sentinel-4/5 missions to derive Level-1b product specifications. It is also used in some research to obtain aerosol and trace gas properties, but its application to cloud properties retrieval is limited. This study presents the retrieval of cloud pressure and cloud optical thickness as well as surface pressure for cloud free based on TROPOMI Oxygen-A (Band 6) and Oxygen-B (Band 5) band measurements, and compares the results with FRESCO and NPP-Suomi Level 2 cloud property data. Different cross section datasets including JPL2008, HITRAN 2008 and HITRAN2020 are also tested in this study. In conclusion, for surface pressure retrieval, using O2-A band gives more reliable results than O2-B band and is easier to converge in the calculation, especially over land surface. But while over sea surface, using O2-B band in retrieval performs better than O2-A band. Secondly, the retrieval based on the cross section file JPL2008 shows better results when using O2-A band, but HITRAN2020 gives better results when using O2-B band. Thirdly, setting appropriate a-priory value in DISAMAR and removing some of the wavelengths with high residual simulated reflectivity can significantly improve the results , both in terms of convergence and reduction of validation error. The cloud pressure correlation coefficient between the retrieval and NPP or FRESCO data is 0.85 and 0.99 respectively, while the cloud optical thickness has a correlation coefficient of 0.77 between retrieval and NPP COT datasets.

As climate change drives increased frequency and intensity of extreme precipitation and flooding worldwide, posing escalating threats to public safety and economic assets, accurate and real-time satellite-based precipitation estimation is essential for operational large-scale hydrometeorological analysis and disaster monitoring. NASA's Integrated Multi-satellitE Retrievals for GPM (IMERG Final Run) combines information from "all" satellite microwave observations with gauge correction and climatological adjustment to produce precipitation estimates at 0.1° spatial and 30-min temporal resolution. However, its latency of approximately 3.5 months restricts its utility for real-time applications, despite outperforming mainstream satellite precipitation datasets in representing rainfall patterns and variability. We present Huayu, a novel machine learning-based real-time satellite precipitation retrieval system that relies solely on infrared observations from the FengYun-4B geostationary satellite to provide a more accurate precipitation estimate at a finer spatiotemporal resolution (15 min, 0.05°) over a 120° by 120° domain. Performance evaluations demonstrate that Huayu achieves strong consistency with rain gauge observations, yielding a critical success index (CSI) of 0.693 - representing a 3.43

The Fengyun 4B (FY-4B) as an operational satellite was launched on 3 June 2021. The advanced geostationary radiation imager (AGRI) aboard on the FY-4B has ability to monitor aerosols information. The objective of this study is to introduce the operational algorithm for retrieving land aerosol based on FY-4B/AGRI data, which is called as an adaptive dark-target algorithm. First, on the basis of FY-4B/AGRI cloud mask and snow cover products, a novelty identification scheme was proposed to classify the pixels into cloud, haze and clear categories. Then, aiming at the characteristics of FY-4B/AGRI instrument, the adaptive relationships of surface reflectance varying with the normalized vegetation index or view zenith angle were established by atmospheric correction. By validating the AGRI retrievals every 15 min with AERONET AOD, AGRI performance (R: 0.95, bias: -0.014, RMSE: 0.091, within expected error (EE:): 81.99%) was comparable to that of Aqua/MODIS deep blue AOD (R: 0.95, bias: 0.024, RMSE: 0.095, EE: 78.57%), while have the largest difference with those of MYDDT. At daily mean scale, the statistics of AGRI AOD against AERONET performed well (R: 0.93, bias: 0.062, RMSE: 0.18, EE: 64%). Additionally, the agreement of AGRI retrievals with AERONET AOD varied with observation moments and stations. The FY-4B/AGRI operational aerosol product based on adaptive dark target algorithm were proved to be robust.

The heterogeneous land surface spanning the Yellow River irrigated oasis and the adjacent Kubuqi and Ulan Buh Desert (Hetao area) in Inner Mongolia, China, has been noted to frequently generate planetary boundary layer convergence line (BLCL), providing an important source of low-level lifting for convection initiation (CI). As the first field experiment to collect comprehensive observations of vegetation-contrast-resulting thermal circulations that consistently generate BLCLs and lead to CI, the Desert-Oasis Convergence Line and Deep Convection Experiment (DECODE) was conducted from 5 July to 9 August 2022 in the Hetao area. Two oasis and four desert observation sites were set up in the region that exhibits the highest frequency of BLCL and CI occurrences, equipped with a suite of advanced instruments probing land-atmosphere interactions, planetary boundary layer processes, and evolution of BLCLs and their associated CI, including Doppler lidars, microwave radiometers, soil temperature and moisture sensors, eddy covariance systems, portable radiosondes, C-band polarimetric Doppler radar, aircraft, and Geostationary High-speed Imager onboard FY-4B satellite. DECODE captured 29 BLCLs (16 with CI), 66 gust fronts, 12 horizontal convective rolls, and one tornado. The observations unveiled full thermal circulations spanning the desert-oasis boundary characterized by a horizontal width of-25 km, a convergence height of-1 km above ground level (AGL), and divergence from 2 to-3.5 km AGL, with vertical wind speeds of up to 2 m s-1. Future publications stemming from DECODE will delve into a spectrum of scientific inquiries, including but not limited to land surface and boundary layer processes, BLCL dynamics, CI mechanisms, convective organization, predictability, and model evaluation.