The importance of data assimilation components for initial conditions and subsequent error growth

Despite a specific data assimilation method,data assimilation(DA)in general can be decomposed into components of the prior information,observation forward operator that is given by the observation type,observation error covariances,and background error covariances.In a classic Lorenz model,the influences of the DA components on the initial conditions(ICs)and subsequent forecasts are systematically investigated,which could provide a theoretical basis for the design of DA for different scales of interests.The forecast errors undergo three typical stages:a slow growth stage from 0 h to 5 d,a fast growth stage from 5 d to around 15 d with significantly different error growth rates for ensemble and deterministic forecasts,and a saturation stage after 15 d.Assimilation strategies that provide more accurate ICs can improve the predictability.Cycling assimilation is superior to offline assimilation,and a flow-dependent background error covariance matrix(Pf)provides better analyses than a static background error covariance matrix(B)for instantaneous observations and frequent time-averaged observations;but the opposite is true for infrequent time-averaged observations,since cycling simulation cannot construct informative priors when the model lacks predictive skills and the flow-dependent Pf cannot effectively extract information from low-informative observations as the static B.Instantaneous observations contain more information than time-averaged observations,thus the former is preferred,especially for infrequent observing systems.Moreover,ensemble forecasts have advantages over deterministic forecasts,and the advantages are enlarged with less informative observations and lower predictive-skill model priors.

Evaluation of a Regional Ensemble Data Assimilation System for Typhoon Prediction

An ensemble Kalman filter(EnKF) combined with the Advanced Research Weather Research and Forecasting model(WRF) is cycled and evaluated for western North Pacific(WNP) typhoons of year 2016. Conventional in situ data, radiance observations, and tropical cyclone(TC) minimum sea level pressure(SLP) are assimilated every 6 h using an 80-member ensemble. For all TC categories, the 6-h ensemble priors from the WRF/EnKF system have an appropriate amount of variance for TC tracks but have insufficient variance for TC intensity. The 6-h ensemble priors from the WRF/EnKF system tend to overestimate the intensity for weak storms but underestimate the intensity for strong storms. The 5-d deterministic forecasts launched from the ensemble mean analyses of WRF/EnKF are compared to the NCEP and ECMWF operational control forecasts. Results show that the WRF/EnKF forecasts generally have larger track errors than the NCEP and ECMWF forecasts for all TC categories because the regional simulation cannot represent the large-scale environment better than the global simulation. The WRF/EnKF forecasts produce smaller intensity errors and biases than the NCEP and ECMWF forecasts for typhoons, but the opposite is true for tropical storms and severe tropical storms. The 5-d ensemble forecasts from the WRF/EnKF system for seven typhoon cases show appropriate variance for TC track and intensity with short forecast lead times but have insufficient spread with long forecast lead times. The WRF/EnKF system provides better ensemble forecasts and higher predictability for TC intensity than the NCEP and ECMWF ensemble forecasts.

On the relationship between tropical cyclone size and environmental helicity in the South China Sea

本文利用2001–2019年间的ERA5再分析数据集和热带气旋(TC)最佳路径观测,分析了中国南海TC的外围尺度与环境变量之间的关系.研究发现,低层环境螺旋度与TC外围尺度具有显著的正相关.大的环境螺旋度有利于TC外围对流的增强和组织化,与之对应的径向入流和角动量输送有利于TC外围尺度的维持或扩张.此外,螺旋度的非对称分布与TC外围尺度的非对称性结构也密切相关.环境螺旋度作为一个包含TC外围对流强度和组织化程度等信息的综合动力因子,为理解TC外围尺度变化提供了不同的视角.

Typhoon Track,Intensity,and Structure:From Theory to Prediction

To improve understanding of essential aspects that influence forecasting of tropical cyclones(TCs),the National Key Research and Development Program,Ministry of Science and Technology of the People's Republic of China conducted a five-year project titled“Key Dynamic and Thermodynamic Processes and Prediction for the Evolution of Typhoon Intensity and Structure”(KPPT).Through this project,new understandings of TC intensification,including outer rainbanddriven secondary eyewall formation and the roles of boundary layer dynamics and vertical wind shear,and improvements to TC data assimilation with integrated algorithms and adaptive localizations are achieved.To promote a breakthrough in TC intensity and structure forecasting,a new paradigm for TC evolution dynamics(i.e.,the correlations,interactions,and error propagation among the triangle of TC track,intensity,and structure)is proposed;and an era of dynamic-constrained,big-data driven,and strongly coupled data assimilation at the subkilometer scale and seamless prediction is expected.

Objective satellite methods including AI algorithms reviewed for the tenth International workshop on tropical cyclones(IWTC-10)

Here we explore the latest four years(2019–2022)of using satellite data to objectively analyze tropical cyclones(TC)and issue recommendations for improved analysis.We first discuss new methods of direct retrieval from SAR and geostationary imagers.Next,we survey some of the most prominent new techniques in AI and discuss their major capabilities(especially accuracy in nonlinear TC behavior,characterization of model uncertainty and creation of synthetic satellite imagery)and limitations(especially lack of transparency and limited amount of training data).We also identify concerns with biases and unlabeled uncertainties in the Best Track records as being a first-order limitation for further progress in objective methods.The article concludes with recommendations to improve future objective methods,especially in the area of more accurate and reliable training data sets.

Variability of microphysical characteristics in the “21·7” Henan extremely heavy rainfall event

In this study, significant rainfall microphysical variability is revealed for the extremely heavy rainfall event over Henan Province in July 2021(the “21·7” Henan EHR event) using a dense network of disdrometers and two polarimetric radars.The broad distributions of specific drop size distribution(DSD) parameters are identified in heavy rainfall from the disdrometer observations, indicating obvious microphysical variability on the surface. A K-means clustering algorithm is adopted to objectively classify the disdrometer datasets into separate groups, and distinct DSD characteristics are found among these heavy rainfall groups. Combined with the supporting microphysical structures obtained through radar observations, comprehensive microphysical features of the DSD groups are derived. An extreme rainfall group is dominantly formed in the deep convection over the plain regions, where the high number of concentrations and large mean sizes of surface raindrops are underpinned by both active ice-phase processes and efficient warm-rain collision-coalescence processes in the vertical direction. Convection located near orographic regions is characterized by restricted ice-phase processes and high coalescence efficiency of liquid hydrometeors, causing the dominant DSD group to comprise negligible large raindrops. Multiple DSD groups can coexist within certain precipitation episodes at the disdrometer stations, indicating the potential microphysical variability during the passage of convective system on the plain regions.