Atmospheric Motion Vectors (AMVs) from satellite measurements serve as critical indicators of atmospheric dynamics, playing an essential role in enhancing the prediction precision of Numerical Weather Prediction (NWP) models through data assimilation. The implementation of finer satellite-derived vector products has the potential to significantly augment the accuracy of atmospheric flow field data in high resolution regional NWP model simulations, thereby fulfilling the burgeoning requirements of operational weather nowcasting and forecasting. This study is focused on the development of Mesoscale Atmospheric Motion Vector (MAMV) products, which are distinguished by their exceptional quality and spatiotemporal resolution, leveraging data from the Geostationary high-speed imager aboard the Fengyun-4B geostationary meteorological satellite (FY-4B/GHI). MAMVs of FY-4B/GHI feature an enhanced horizontal resolution of 3 km, enabling more accurate identification and monitoring of non-geostrophic flow patterns of mesoscale weather systems, as well as their fast-evolving dynamical structures and characteristics. Furthermore, a comparative analysis with radiosonde measurements highlights the precision of MAMV products, as evidenced by a Speed Bias (SB) of 0.37 m/s, a speed Root Mean Square Error (sRMSE) of 4.68 m/s, and a direction Root Mean Square Error (dRMSE) of 26.35°. The prospects of high-resolution satellite wind field data holds great potential for propelling scientific advancement and enriching our comprehension of atmospheric dynamics. This is particularly valuable in the context of typhoon monitoring and forecasting, where such data can lead to significant improvements in predictive capabilities.

Ground-based weather radar data plays an essential role in monitoring severe convective weather. The detection of such weather systems in time is critical for saving people’s lives and property. However, the limited spatial coverage of radars over the ocean and mountainous regions greatly limits their effective application. In this study, we propose a novel framework of a deep learning-based model to retrieve the radar composite reflectivity factor (RCRF) maps from the Fengyun-4A new-generation geostationary satellite data. The suggested framework consists of three main processes, i.e., satellite and radar data preprocessing, the deep learning-based regression model for retrieving the RCRF maps, as well as the testing and validation of the model. In addition, three typical cases are also analyzed and studied, including a cluster of rapidly developing convective cells, a Northeast China cold vortex, and the Super Typhoon Haishen. Compared with the high-quality precipitation rate products from the integrated Multi-satellite Retrievals for Global Precipitation Measurement, it is found that the retrieved RCRF maps are in good agreement with the precipitation pattern. The statistical results show that retrieved RCRF maps have an R-square of 0.88-0.96, a mean absolute error of 0.3-0.6 dBZ, and a root-mean-square error of 1.2-2.4 dBZ.

Nowcasting of convective weather is a challenging and significant task in operational weather forecasting system. In this study, a new convolution recurrent neural network (ConvRNN)-based regression network for convective weather prediction is proposed, which is named as the Convective Weather Nowcasting Net (CWNNet). The CWNNet adopts the joint observations of Fengyun-4A geostationary satellite and the ground-based Doppler weather radar data of the last 0–1-hour as the inputs of the model to predict the radar reflectivity factor maps of next 0-1 hour. The statistical validating results clearly demonstrate that the mean values of the probability of detection, false alarm ratio, threat score, root mean square error and mean absolute error evaluating the performance of CWNNet for 1-h nowcasting reach 0.87, 0.137, 0.71, 3.365 dBZ and 1.038 dBZ, respectively. Due to that the geostationary meteorological satellite is capable of capturing the features of convective initiation, the CWNNet shows a good performance in convective initiation nowcasting. Besides, several case studies also further indicate that the CWNNet can predict convective initiation more than 30 minutes in advance by monitoring the convective clouds. The CWNNet based on the joint satellite and radar data shows a better nowcasting performance than that only employing single data source. Thus, it can effectively produce more reliable nowcasting for convective weather events.

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.

A strong convective storm is a disastrous weather system with a small spatio-temporal scale. It often occurs suddenly and can cause huge disasters. Thus, it is necessary to improve the forecast accuracy of strong convective storms. Overshooting cloud top (OT) is the product of strong updrafts in convective storms, which can penetrate the tropopause and enter the lower stratosphere. OT is closely related to severe weather and can influence water vapor transport and the material exchange between the troposphere and stratosphere. Therefore, the timely detection of OT can help improve the accuracy of forecasting. In this study, we develop a new objective OT detection algorithm based on geostationary satellite observations from 2006 to 2017. The accuracy of the new algorithm in identifying OT is verified by manually comparing it with the radar echo images and the cloud images of MODIS 250 m. Then, the OT is statistically analyzed in a long time series. It is found that OT events are mainly concentrated in equatorial and low latitude regions, with higher frequency in summer. There are obvious differences between OT events on land and sea. Additionally, this dataset also reveals the close connection between the seasonal shift of OT and the seasonal average precipitation distribution around the globe. This study provides a scientific basis for determining the geographical characteristics of OT frequency and explores the application of this OT objective detection algorithm in the operational forecast of strong convective weather. We hope this study can benefit OT monitoring in operational weather forecasting.