风云气象卫星观测在短时临近天气预报中的定量应用进展(特邀)

Progress in Quantitative Applications of Fengyun Meteorological Satellite Observations in Weather Nowcasting(Invited)

经过半个世纪的发展,我国风云气象卫星已经成为全球观测系统的重要组成部分,为灾害天气监测和预警、数值天气预报、气候研究和预测、环境监测和预报等提供了关键观测资料。特别是最近10多年来,随着新一代风云气象卫星的发展,定量应用已成为业务应用的主要方式。本文综述了风云气象卫星在短时临近天气预报应用中的最新进展,并提出未来需要研究和解决的短时临近天气预报应用中的关键科学和技术问题。

Significance Severe local storms,hail,squall lines,and tornadoes significantly affect daily life,social activities,and economic development.Despite their importance,understanding the mechanisms of severe storms and improving their forecasts remains a challenging task.Nowcasting focuses on high-impact weather(HIW)events that develop rapidly and have short durations.After half a century of development,Fengyun meteorological satellites have become a crucial component of the global observation network.They provide essential data for monitoring severe weather,generating early warnings,and contributing to numerical weather forecasting,climate projections,environmental assessments,and predictive analyses.Notably,in the past decade,the advent of the new generation of Fengyun satellites has brought quantitative products to the forefront of operational use.We review the latest advances in the applications of Fengyun meteorological satellites in short-term weather nowcasting and highlight the principal scientific and technical challenges that future research endeavors need to address.Progress China has actively utilized the new generation Fengyun meteorological satellite data to improve near real-time(NRT)forecasting and nowcasting capabilities.The China Meteorological Administration(CMA)assimilates these observation data into numerical weather prediction(NWP)models to enhance short-range and middle-range weather forecasts.In addition,the National Satellite Meteorological Center(NSMC)of the CMA processes these data to produce and distribute quantitative information on the atmosphere,clouds,and precipitation.These quantitative products,delivered to users on time through advanced communication and data distribution technologies,are crucial for NRT nowcasting applications and have played a significant role in monitoring and early warning of HIW events.Besides operational Fengyun satellite products,progress has been made in developing new products and prediction models for 0-6 h forecasts,particularly using data from the Fengyun-4 series.1 New Products and Applications 1)Radar composite reflectivity estimation(RCRE).Ground-based weather radar observations are commonly used to track convective storms;however,the radar network’s coverage is limited,especially in mountainous and marine areas.Fengyun-4 satellites provide extensive coverage and NRT observations,compensating for radar’s limitations.Since the physical properties of clouds can be reflected in both ground-based radar and satellite observations,a connection exists between the two.Using deep learning methods,Yang et al.developed the radar composite reflectivity estimation(RCRE)using Fengyun-4A AGRI observations.Independent validation indicates that RCRE accurately reproduces radar echoes’position,shape,and intensity.This RCRE product is operationally used by the National Meteorological Center(NMC)and provides synthetic radar data for nowcasting applications where ground-based radar is unavailable.2)Automatic recognition of convection clouds.Monitoring convective clouds from satellites is vital for nowcasting.Traditional techniques rely on thresholds,such as using the 240-258 K range to identify convective clouds from 11µm brightness temperature images.For rapidly changing convective systems,these methods are often regional,seasonal,and weather-dependent.To address this,the K-means clustering method is used to analyze cloud types over China from AGRI infrared band brightness temperature measurements.This method enables users to select regions of interest and automatically identify convective systems and other cloud types in NRT,improving quantitative precipitation estimation(QPE)from satellite IR data.For instance,this product can enhance convective cloud precipitation estimation and provide valuable information on convection coverage and intensity,especially in areas without radar observations.Figure 1 shows the automatic identification of convective clouds based on Fengyun-4A on July 30,2023.Due to the northward influence of the typhoon’s peripheral cloud system,the northern and central parts of Shanxi,Hebei,Beijing,and Tianjin are completely covered by large areas of convective clouds,with maximum hourly precipitation exceeding 40 mm/h.The convective clouds correspond well with the radar observations[Fig.1(b)].3)Cloud-base height.The cloud top height(CTH)product is well-established and widely used,while cloud base height(CBH)is challenging to obtain due to weak signals in passive remote sensing observations.However,CBH is crucial for understanding vertical atmospheric motion,aviation safety,and weather analysis.The physical method for retrieving CBH involves converting cloud optical thickness into physical thickness and subtracting it from CTH.The uncertainty of optical thickness is the main error source for CBH retrieval using the physical method.To overcome this limitation,a machine learning model trained on satellite-based lidar(CALIOP from CALIPSO satellite)observations,which has good accuracy but limited coverage,has been used to derive CBH by combining NWP products and Fengyun-4 AGRI observations as input.This algorithm provides a CBH product with the same coverage as CTH(AGRI full disk).Independent validation shows an overall root mean square error(RMSE)of 1.87 km.This CBH product,along with the traditional CTH product,offers valuable information on cloud structure and physical thickness,enhancing nowcasting applications.2 Prediction Models Using Fengyun-4 Data for Nowcasting 1)Storm-warning in pre-convection environment.Severe local storms typically have three stages:pre-convection,initiation,and development.Identifying the pre-convection environment is crucial for nowcasting and providing warnings before radar observations.By integrating high spatiotemporal resolution AGRI observations from the Fengyun-4 series with CMA NWP products,key factors in the pre-convection environment can be analyzed.Li et al.developed the storm warning in the pre-convection environment version 2.0(SWIPE2.0)model for China and surrounding areas using machine learning techniques.This model identifies potential convective systems and classifies cloud clusters into strong,medium,or weak convection.SWIPE2.0 predicts storm occurrence and intensity 0-2 h ahead of radar observations and is used in NRT applications by the NMC/CMA.For example,the SWIPE2.0 model issued a severe convective warning for a cloud mass located in the western part of Gansu province at 14:30 on July 10,2023(Beijing time).At that time,the ground-based radar reflectivity of about 20 dBz or lower is mainly near the provincial boundary,while the satellite warning signals did not correspond to ground-based radar signals,indicating that precipitation had not yet occurred.At 14:34,the red severe convective warning signal still existed,and its range expanded slightly to the southeast.As the cloud developed and moved towards the southeast,it produced precipitation greater than 1 mm/h between 15:00 and 16:00,with some local areas experiencing rainfall exceeding 5 mm/h.SWIPE2.0 provides early warnings for local convection before ground-based radar observations.2)Satellite image extrapolation.Similar to radar extrapolation,satellite image extrapolation is essential for short-term forecasting and applications such as solar photovoltaic power generation.The rapid advancement of artificial intelligence has led to the adoption of data-driven machine learning methods in satellite image extrapolation.Xia et al.developed an hourly cloud cover prediction algorithm using high spatiotemporal resolution geostationary satellite images.This model predicts cloud images for the next 0-4 h and estimates cloud cover over photovoltaic stations.Independent validation shows reliable and stable performance in the first two hours,with an average correlation coefficient of nearly 0.9 between predicted and observed cloud cover.Compared to previous methods of only being able to perform 10-30 min of extrapolation,the new approach greatly improves accuracy and forecasting time,making it valuable for regional short-term warnings.Conclusions and Prospects As a key member of the global observing system,the Fengyun meteorological satellite system has significantly enhanced observation capabilities,short-term monitoring,and early warning.However,challenges remain in applying Fengyun satellite data for nowcasting,particularly in achieving low latency and high-quality products with high spatiotemporal resolution.With ongoing advancements in Fengyun satellite technology,quantitative nowcasting applications are entering a new era.The future direction involves combining Fengyun satellite quantitative products,NWP products,ground-based measurements including radar,and other multi-source data with artificial intelligence to improve the identification,monitoring,and early warning of severe weather events.