The Advanced Radiative Transfer Modeling System(ARMS),a computationally efficient satellite observation op-erator,has been successfully integrated into the YinHe four-dimensional variational data assimilation(YH4DVAR)system.This study investigates the impacts of assimilating Advanced Microwave Sounding Unit-A(AMSU-A)ob-servations from the Meteorological Operational Satellite-C(MetOp-C)on the performance of YH4DVAR.Through a month-long global statistical analysis and a case study of Typhoon Hinnamnor,we evaluate the benefits of AMSU-A data assimilation under clear sky conditions.Key findings are as follows.(1)ARMS achieves simulation accuracy comparable to RTTOV(Radiative Transfer for the Television and InfraRed Observation Satellite Operational Verti-cal sounder)version 11.2,demonstrating only a 0.5％discrepancy in data retention after quality control.(2)Imple-mentation of ARMS as an operator in YH4DVAR enhances forecast accuracy for the 850-hPa temperature and 500-hPa geopotential height in the tropical region.(3)Compared to RTTOV,ARMS has improved the intensity forecast of Typhoon Hinnamnor and reduced mean wind speed errors by approximately 2％and central pressure errors by ap-proximately 1％.ARMS has now been operationally adopted as an alternative observational operator wi-thin YH4DVAR,demonstrating exceptional numerical stability,computational efficiency,and promising potential for future satellite data assimilation applications.

Satellite microwave-sounding radiometer data assimilation under clear-sky conditions typically requires the exclusion of precipitation-affected field-of-view (FOV) regions. However, the traditional scatter index (SI) and cloud liquid water path (CLWP)-based precipitation sounding algorithms from earlier NOAA microwave sounders are built on window channels which are not available from FY-3C/D MWTS-II. To address this limitation, this study establishes a nonlinear relationship between multispectral visible/infrared data from the FY-2F geostationary satellite and microwave sounding channels using an artificial intelligence (AI)-driven approach. The methodology involves three key steps: (1) The spatiotemporal integration of FY-2F VISSR-derived products with NOAA-19 AMSU-A microwave brightness temperatures was achieved through the GEO-LEO pixel fusion algorithm. (2) The fused observations were used as a training set and input into a random forest model. (3) The performance of the RF_SI method was evaluated by using individual cases and time series observations. Results demonstrate that the RF_SI method effectively captures the horizontal distribution of microwave scattering signals in deep convective systems. Compared with those of the NOAA-19 AMSU-A traditional SI and CLWP-based precipitation sounding algorithms, the accuracy and sounding rate of the RF_SI method exceed 94% and 92%, respectively, and the error rate is less than 3%. Also, the RF_SI method exhibits consistent performance across diverse temporal and spatial domains, highlighting its robustness for cross-platform precipitation screening in microwave data assimilation.

The accurate forecasting of tropical cyclones (TCs) is a challenging task. The purpose of this study was to investigate the effects of a dry-mass conserving (DMC) hydrostatic global spectral dynamical core on TC simulation. Experiments were conducted with DMC and total (moist) mass conserving (TMC) dynamical cores. The TC forecast performance was first evaluated considering 20 TCs in the West Pacific region observed during the 2020 typhoon season. The impacts of the DMC dynamical core on forecasts of individual TCs were then estimated. The DMC dynamical core improved both the track and intensity forecasts, and the TC intensity forecast improvement was much greater than the TC track forecast improvement. Sensitivity simulations indicated that the DMC dynamical core-simulated TC intensity was stronger regardless of the forecast lead time. In the DMC dynamical core experiments, three-dimensional winds and warm and moist cores were consistently enhanced with the TC intensity. Drier air in the boundary inflow layer was found in the DMC dynamical core experiments at the early simulation times. Water vapor mixing ratio budget analysis indicated that this mainly depended on the simulated vertical velocity. Higher updraft above the boundary layer yielded a drier boundary layer, resulting in surface latent heat flux (SLHF) enhancement, the major energy source of TC intensification. The higher DMC dynamical core-simulated updraft in the inner core caused a higher net surface rain rate, producing higher net internal atmospheric diabatic heating and increasing the TC intensity. These results indicate that the stronger DMC dynamical core-simulated TCs are mainly related to the higher DMC vertical velocity.

Cloud detection is an essential step in the application of hyperspectral infrared (HIR) data. In this paper, a new cloud detection method using a LightGBM algorithm based on principal component (PC) space is proposed for HIR data from the High Spectral Infrared Atmospheric Sounder (HIRAS). Considering the difference of infrared radiation between ocean and land, day and night, this paper respectively builds the LightGBM day-land, day-sea, night-land and night-sea models. The truth cloud fraction of the HIRAS field of view (FOV) is determined by the collocated cloud masks of the Medium Resolution Spectral Imager-II (MERSI) onboard the FY-3D satellite. The HIRAS infrared channels were transformed into the leading K PCs as predictors through principal component analysis (PCA), with the advantages of reducing the correlation of infrared channels, accelerating model convergence and prediction. The cloudy FOVs in global datasets are randomly down-sampling to alleviate the impact of dataset imbalance. The validation experiments have shown that the LightGBM model has high accuracy for the completely cloudy and completely clear-sky FOVs. However, the partially cloudy FOVs are sometimes detected as the clear-sky. It may be because these partially cloudy FOVs have prominent clear-sky radiation properties. The cloud detection performance of the LightGBM model (accuracy=0.93, HSS=0.85) for land HIR data is better than that of sea HIR data (accuracy=0.89, HSS=0.65). It may be because there are more partially cloudy FOVs in the ocean. In addition, the imbalance of the ocean dataset significantly reduced its Heidke skill score (HSS=0.65). Compared with the HIRAS Lie cloud cover product, the accuracy of the LightGBM model for land (sea) HIR data is increased by about 0.22 (0.07). The time complexities of the algorithms have shown that the cloud detection speed of the LightGBM model is approximately 670 times that of the HIRAS/MERSI collocation cloud detection method. The higher cloud detection accuracy and faster efficiency are helpful to the operational application of the LightGBM cloud detection method.

To use infrared observation data from the High Spectral Infrared Atmospheric Sounder (HIRAS) which is onboard the FengYun 3D (FY-3D) satellite, we have proposed a new cloud detection method based on random forest (RF). The true cloud distribution of field of views (FOVs) is generated by the collocated cloud masks of the Medium Resolution Spectral Imager-II (MERSI). The long-wave infrared radiations of 781 channels in the HIRAS FOVs are used as the input features of the model. The matched observation data of HIRAS and MERSI in East Asia (May 2019 to April 2020) are used as training and testing datasets. Given the significant differences in the radiation characteristics between land and sea, we respectively build the sea and land cloud detection models based on random forest. Both of them have achieved good cloud detection performance. The sea model produced slightly higher performance (ACC of 0.96, a FAR of 0.03, an F1-score of 0.96, and AUC of 0.99) than the land model (ACC of 0.95, FAR of 0.04, F1-score of 0.96, and AUC of 0.99). The RF cloud detection models have adequate generalization performances for the observations of HIRAS at different times and regions. Besides, the RF cloud detection models have faster computing efficiency and lower data dependency than HIRAS-MERSI matching method. The validation experiments have shown that the RF models can detect the dense cloud scenes and the large clear-sky areas with higher accuracy. However, the RF model has relatively low detection accuracy for broken clouds and thin clouds. This may be because the infrared radiation properties of these cloud FOVs and clear-sky FOVs are relatively similar.

Mesoscale eddies play an important role in modulating the ocean circulation. Many previous studies on the three-dimensional structure of mesoscale eddies were mainly based on composite analysis, and there are few targeted observations for individual eddies. A cyclonic eddy surveyed during an oceanographic cruise in the Northwest Pacific Ocean is investigated in this study. The three-dimensional structure of this cyclonic eddy is revealed by observations and simulated by the four-dimensional variational data assimilation (4DVAR) system combined with the Regional Ocean Modeling System. The observation and assimilation results together present the characteristics of the cyclonic eddy. The cold eddy has an obvious dual-core structure of temperature anomaly. One core is at 50–150 m and another is at 300–550 m, which both have the average temperature anomaly of approximately −3.5°C. The salinity anomaly core is between 250 m and 500 m, which is approximately −0.3. The horizontal velocity structure is axis-asymmetric and it is enhanced on the eastern side of the cold eddy. In the assimilation experiment, sea level anomaly, sea surface temperature, and in situ measurements are assimilated into the system, and the results of assimilation are close to the observations. Based on the high-resolution assimilation output results, the study also diagnoses the vertical velocity in the mesoscale eddy, which reaches the maximum of approximately 10 m/d. The larger vertical velocity is found to be distributed in the range of 0.5 to 1 time of the normalized radius of the eddy. The validation of the simulation result shows that the 4DVAR method is effective to reconstruct the three-dimensional structure of mesoscale eddy and the research is an application to study the mesoscale eddy in the Northwest Pacific by combining observation and assimilation methods.

Evaporation duct is an abnormal refraction atmospheric structure frequently appearing on the sea surface. Accurately obtaining the evaporation duct height (EDH) has significant effects on the effective application of electromagnetic system equipment. Aiming at the problems of large error and strong data dependence of current evaporation duct prediction models, we take the measured meteorological observation data as the starting point and propose the principal component regression evaporation duct prediction Model (PCR model) which applies the principal component regression (PCR) in this field for the first time. We select the famous theoretical Paulus-Jeske (PJ) model and machine learning Gradient Boosting Regression (GBR) model as the baseline methods, and conduct the prediction accuracy comparison experiment, cross-prediction experiment, and PCR model optimization experiment successively. The results show that compared with the GBR model, the EDH prediction accuracy of the PCR model is significantly improved in five sub-areas and the generalization ability is also obviously enhanced. The accuracy of the PCR model optimized by combining reanalysis data is greatly improved compared with its unoptimized version.

FengYun-4A (FY-4A)'s Geostationary Interferometric Infrared Sounder (GIIRS) is the first hyperspectral infrared sounder on board a geostationary satellite, enabling the collection of infrared detection data with high temporal and spectral resolution. As clouds have complex spectral characteristics, and the retrieval of atmospheric profiles incorporating clouds is a significant problem, it is often necessary to undertake cloud detection before further processing procedures for cloud pixels when infrared hyperspectral data is entered into assimilation system. In this study, we proposed machine-learning-based cloud detection models using two kinds of GIIRS channel observation sets (689 channels and 38 channels) as features. Due to differences in surface cover and meteorological elements between land and sea, we chose logistic regression (lr) model for the land and extremely randomized tree (et) model for the sea respectively. Six hundred and eighty-nine channels models produced slightly higher performance (Heidke skill score (HSS) of 0.780 and false alarm rate (FAR) of 16.6% on land, HSS of 0.945 and FAR of 4.7% at sea) than 38 channels models (HSSof 0.741 and FAR of 17.7% on land, HSS of 0.912 and FAR of 7.1% at sea). By comparing visualized cloud detection results with the Himawari-8 Advanced Himawari Imager (AHI) cloud images, the proposed method has a good ability to identify clouds under circumstances such as typhoons, snow covered land, and bright broken clouds. In addition, compared with the collocated Advanced Geosynchronous Radiation Imager (AGRI)-GIIRS cloud detection method, the machine learning cloud detection method has a significant advantage in time cost. This method is not effective for the detection of partially cloudy GIIRS's field of views, and there are limitations in the scope of spatial application.

Hyper-spectral infrared radiance data play an important role in cloud detection. To improve the cloud detection accuracy, this study proposes a novel cloud detection method based on the logistic regression model that uses the Infrared Atmospheric Sounding Interferometer (IASI) radiance data of four characteristic channels as the training features. Due to significant differences in the terrain between the land and the sea, the data from the oceans and continents are trained separately. Thereafter, the proposed scheme is verified and compared with existing methods. The results show that the accuracy of the proposed method (97% at sea and 88% on land) outperforms that of the existing Advanced Very High Resolution Radiometer (AVHRR)/IASI scheme (75% at sea and 55% on land). In addition, the proposed method uses only IASI observations as input and thus does not require the use of other auxiliary data.

Firstly, the annual variation of sandstorm and strong sandstorm weather process in China from 2000 to 2012 is analyzed according to the “Sand-Dust Weather Yearbook” (2012). Secondly, based on the ERA-Interim Reanalysis from ECMWF and MISR data from the Terra satellite, we investigate the correlation between different dust weather process and land meteorological elements. Finally, the temporal and spatial distribution features of the aerosol optical depth (AOD) in the Taklamakan Desert is studied. And we compare the Taklamakan Desert AOD with nationwide AOD. The results show that: (1) the frequency of sandstorm and strong sandstorm has shown a downward trend and the occurrence of sandstorm decreases more in recent years. (2) In the Taklamakan Desert, the number of sandstorm is positively correlated with the surface temperature, meanwhile, negatively related to the surface relative humidity. (3) In all seasons, the average of AOD in Taklamakan Desert is higher than that of the whole country, and there are obvious differences among the four seasons.

In addition to the traditionally complex land surface emissivity models, it is possible to direct real-time retrieve emissivity over land surface in a large-scaled temporal and spatial region from satellite microwave observations. We investigated the performance of the real-time dynamic retrieval emissivity against the monthly averaged emissivity atlas. As indicated by the results, the dynamic retrieval algorithm can obviously reduce the first guess departure of low-level channels which are sensitive to the surface and the simulated background brightness temperatures with real-time dynamic emissivity against the observations have a better agreement over the land surface and higher correlation. Therefore, the dynamic retrieval emissivity algorithm has the potential to improve the effect of satellite data assimilation.

Data assimilation of satellite observations in cloud and rain regions is a worldwide problem. In the paper, we designed the background field and their error covariance of three cloud water variables (cloud cover, cloud liquid water and cloud ice water) at first. The experiment results demonstrate that 1D-Var (One-dimensional variational data assimilation) can retrieve the three variables efficiently. The cloud liquid water and cloud ice water can be obviously improved by 1D-Var. However, the retrieved effect of low cloud cover is still to be improved. It has the positive potential to assimilate the satellite observations in cloud and rain regions.