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Chun-Chieh Wu
,
Guo-Yuan Lien
,
Jan-Huey Chen
, and
Fuqing Zhang

Abstract

A new tropical cyclone vortex initialization method based on the ensemble Kalman filter (EnKF) is proposed in this study. Three observed parameters that are related to the tropical cyclone (TC) track and structure—center position, velocity of storm motion, and surface axisymmetric wind structure—are assimilated into the high-resolution Weather Research and Forecasting (WRF) model during a 24-h initialization period to develop a dynamically balanced TC vortex without employing any extra bogus schemes. The first two parameters are available from the TC track data of operational centers, which are mainly based on satellite analysis. The radial wind profile is constructed by fitting the combined information from both the best-track and the dropwindsonde data available from aircraft surveillance observations, such as the Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR).

The initialized vortex structure is consistent with the observations of a typical vertical TC structure, even though only the surface wind profile is assimilated. In addition, the subsequent numerical integration shows minor adjustments during early periods, indicating that the analysis fields obtained from this method are dynamically balanced. Such a feature is important for TC numerical integrations. The results here suggest that this new method promises an improved TC initialization and could possibly contribute to some high-resolution numerical experiments to better understand the dynamics of TC structure and to improve operational TC model forecasts. Further applications of this method with sophisticated data from The Observing System Research and Predictability Experiment (THORPEX) Pacific Asian Regional Campaign (T-PARC) will be shown in a follow-up paper.

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Chun-Chieh Wu
,
Jan-Huey Chen
,
Po-Hsiung Lin
, and
Kun-Hsuan Chou

Abstract

Since 2003, a field program has been conducted under the name of Dropwindsonde Observations for Typhoon Surveillance near the Taiwan Region (DOTSTAR). As the name DOTSTAR suggests, targeted observation is one of its key objectives. The prerequisite for designing the observing strategy is to identify the sensitive areas, which would exert great influence on the results of numerical forecast or the extent of the forecast error.

In addition to various sensitivity products already adopted in DOTSTAR, a new way to identify the sensitive area for the targeted observation of tropical cyclones based on the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (NCAR) Mesoscale Model (MM5) is proposed in this paper. By appropriately defining the response functions to represent the steering flow at the verifying time, a simple vector, adjoint-derived sensitivity steering vector (ADSSV), has been designed to demonstrate the sensitivity locations and the critical direction of typhoon steering flow at the observing time. Typhoons Meari and Mindulle of 2004 have been selected to show the use of ADSSV. In general, unique sensitive areas 36 h after the observing time are obtained.

The proposed ADSSV method is also used to demonstrate the signal of the binary interaction between Typhoons Fungwong and Fengshen (2002). The ADSSV is implemented and examined in the field project, DOTSTAR, in 2005 as well as in the surveillance mission for Atlantic hurricanes conducted by the Hurricane Research Division. Further analysis of the results will be vital to validate the use of ADSSV.

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Jan-Huey Chen
,
Melinda S. Peng
,
Carolyn A. Reynolds
, and
Chun-Chieh Wu

Abstract

In this study, the leading singular vectors (SVs), which are the fastest-growing perturbations (in a linear sense) to a given forecast, are used to examine and classify the dynamic relationship between tropical cyclones (TCs) and synoptic-scale environmental features that influence their evolution. Based on the 72 two-day forecasts of the 18 western North Pacific TCs in 2006, the SVs are constructed to optimize perturbation energy within a 20° × 20° latitude–longitude box centered on the 48-h forecast position of the TCs using the Navy Operational Global Atmospheric Prediction System (NOGAPS) forecast and adjoint systems. Composite techniques are employed to explore these relationships and highlight how the dominant synoptic-scale features that impact TC forecasts evolve on seasonal time scales.

The NOGAPS initial SVs show several different patterns that highlight the relationship between the TC forecast sensitivity and the environment during the western North Pacific typhoon season in 2006. In addition to the relation of the SV maximum to the inward flow region of the TC, there are three patterns identified where the local SV maxima collocate with low-radial-wind-speed regions. These regions are likely caused by the confluence of the flow associated with the TC itself and the flow from other synoptic systems, such as the subtropical high and the midlatitude jet. This is the new finding beyond the previous NOGAPS SV results on TCs. The subseasonal variations of these patterns corresponding to the dynamic characteristics are discussed. The SV total energy vertical structures for the different composites are used to demonstrate the contributions from kinetic and potential energy components of different vertical levels at initial and final times.

Full access
Y. Qiang Sun
,
Fuqing Zhang
,
Linus Magnusson
,
Roberto Buizza
,
Jan-Huey Chen
, and
Kerry Emanuel

Abstract

In their comment, Žagar and Szunyogh raised concerns about a recent study by Zhang et al. that examined the predictability limit of midlatitude weather using two up-to-date global models. Zhang et al. showed that deterministic weather forecast may, at best, be extended by 5 days, assuming we could achieve minimal initial-condition uncertainty (e.g., 10% of current operational value) with a nearly perfect model. Žagar and Szunyogh questioned the methodology and the experiments of Zhang et al. Specifically, Žagar and Szunyogh raised issues regarding the effects of model error on the growth of the forecast uncertainty. They also suggested that estimates of the predictability limit could be obtained using a simple parametric model. This reply clarifies the misunderstandings in Žagar and Szunyogh and demonstrates that experiments conducted by Zhang et al. are reasonable. In our view, the model error concern in Žagar and Szunyogh does not apply to the intrinsic predictability limit, which is the key focus of Zhang et al. and the simple parametric model described in Žagar and Szunyogh does not serve the purpose of Zhang et al.

Open access
Fuqing Zhang
,
Y. Qiang Sun
,
Linus Magnusson
,
Roberto Buizza
,
Shian-Jiann Lin
,
Jan-Huey Chen
, and
Kerry Emanuel

Abstract

Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

Open access