Application of Direct Assimilation of ATOVS Microwave Radiances to Typhoon Track Prediction

In order to solve the difficult problem of typhoon track prediction due to the sparsity of conventional data over the tropical ocean, in this paper, the No. 0205 typhoon Rammasun of 4-6 July 2002 is studied and an experiment of the typhoon track prediction is made with the direct use of the Advanced TIROS-N Operational Vertical Sounder (ATOVS) microwave radiance data in three-dimensional variational data assimilation. The prediction result shows that the experiment with the ATOVS microwave radiance data can not only successfully predict the observed fact that typhoon Rammasun moves northward and turns right, but can also simulate the action of the fast movement of the typhoon, which cannot be simulated with only conventional radiosonde data. The skill of the typhoon track prediction with the ATOVS microwave radiance data is much better than that without the ATOVS data. The typhoon track prediction of the former scheme is consistent in time and in location with the observation. The direct assimilation of ATOVS microwave radiance data is an available way to solve the problem of the sparse observation data over the tropical ocean, and has great potential in being applied to typhoon track prediction.

THE IMPACT OF CUMULUS PARAMETERIZATIONS AND MICROPHYSICS SCHEMES UNDER DIFFERENT COMBINATIONS ON TYPHOON TRACK PREDICTION

This study examines the effects of cumulus parameterizations and microphysics schemes on the track forecast of typhoon Nabi using the Weather Research Forecast model. The study found that the effects of cumulus parameterizations on typhoon track forecast were comparatively strong and the typhoon track forecast of Kain-Fritsch (KF) was superior to that of Betts-Miller (BM). When KF was selected, the simulated results would be improved if microphysics schemes were selected than otherwise. The results from Ferrier, WSM6, and Lin were very close to those in the best track. KF performed well with the simulations of the western extension and eastern contraction changes of a North Pacific high as well as the distribution and strength of the typhoon wind field.

TYPHOON TRACK PREDICTION EXPERIMENTS USING T_(106)L_(19)

A series of 96-h typhoon track prediction experiments were carried out using medium range forecasting system of NMC by adding BOGUS typhoon (simplified as B-TC) into the first guess field or the analysis field in order to provide longer time typhoon track forecast. The results show that T106L19 could provide a better forecast to typhoon tracks when the B-TC was added, especially when the typhoon vortex is even weaker. The sensitive experiments on where to add the B-TC show that the results from adding the B-TC into the first guess field are better. The results also show that the initialization smoothes the B-TC a lot and this will affect the typhoon track prediction.

Preliminary Study of Sensitive Areas for Several Tropical Cyclone Track Prediction Cases in 2007

Conditional （CNOP） obtained by nonlinear optimal perturbation the ensemble-based calculation method is employed to find possible sensitive areas for improving 48-h or more than 48-h tropical cyclone （TC） track predictions in several cases affecting China in 2007. These sensitive areas are examined by observing system simulation experiments （OSSEs）. Results show that these sensitive areas improve TC track predictions for 48 h or more to different extents. Further analysis is performed to determine the distribution characteristics of sensitive areas in these cases. Results show that areas south of Luzon and over surrounding oceans are significant for 48-h or more than 48-h TC track predictions, especially 60-h to 72-h track predictions. Areas over oceans north or east to Taiwan Island seem to be secondary sensitive for 48-h or more than 48-h TC track predictions.

Adaptive Track Predicting Control for Target Tracking Control Systems

According to the characteristic of maneuvering targets tracking system, adaptive track predicting control is proposed from the point of predicting the motion track of the maneuvering target. For this method, least mean square（LMS） adaptive filter is applied to estimate the future track of the target. The structure of this filter is simple and the calculation amount is small. It is therefore suitable to being used in real-time control system. Testing results have proved that the control method can improve the tracking precision for maneuvering targets obviously.

THE STRUCTURE OF TROPICAL CYCLONE BY TOVS AND ITS APPLICATION IN NUMERICAL WEATHER PREDICTION

The TOVS data are used to study the structure of a number of tropical cyclones for the year 2000. Differences are found to some extent between what is found and classic conceptual models in that (1) the horizontal structure is asymmetric and variable so that the low-value centers at low levels of the geopotential height field (or the high-value centers at high levels) do not necessarily coincide with the high-value centers of the temperature field; (2) the vertical structure is also variable in the allocation of the anomalies of the geopotential height field between low values at low levels and high values at high levels. It is especially noted that the centers of the anomalies are tilting at both high and low levels or the high level is only at the edge of a high-pressure zone. There is not any significant high-value anomalous center in a corresponding location with the tropical cyclone. The structure of tropical cyclone in the TOVS is also used as reference to modify the structure of typhoon BOGUS in the numerical prediction model system of tropical cyclones. It is found that the modified BOGUS performs better in coordinating with the environment and predicting the track of the tropical cyclone. The demonstration is two-fold the structure of the typhoon BOGUS is such that it means much in the track prediction and the use of the TOVS-based tropical cyclone structure really helps in improving it. It provides the foundation for modification and evolution of typhoon BOGUS.

SPATIAL TRAJECTORY PREDICTION OF VISUAL SERVOING

Target tracking is one typical application of visual servoing technology. It is still a difficult task to track high speed target with current visual servo system. The improvement of visual servoing scheme is strongly required. A position-based visual servo parallel system is presented for tracking target with high speed. A local Frenet frame is assigned to the sampling point of spatial trajectory. Position estimation is formed by the differential features of intrinsic geometry, and orientation estimation is formed by homogenous transformation. The time spent for searching and processing can be greatly reduced by shifting the window according to features location prediction. The simulation results have demonstrated the ability of the system to track spatial moving object.

Unusual tracks:Statistical,controlling factors and model prediction

The progress of research and forecast techniques for tropical cyclone(TC)unusual tracks(UTs)in recent years is reviewed.A major research focus has been understanding which processes contribute to the evolution of the TC and steering flow over time,especially the reasons for the sharp changes in TC motion over a short period of time.When TCs are located in the vicinity of monsoon gyres,TC track forecast become more difficult to forecast due to the complex interaction between the TCs and the gyres.Moreover,the convection and latent heat can also feed back into the synoptic-scale features and in turn modify the steering flow.In this report,two cases with UTs are examined,along with an assessment of numerical model forecasts.Advances in numerical modelling and in particular the development of ensemble forecasting systems have proved beneficial in the prediction of such TCs.There are still great challenges in operational track forecasts and warnings,such as the initial TC track forecast,which is based on a poor pre-genesis analysis,TC track forecasts during interaction between two or more TCs and track predictions after landfall.Recently,artificial intelligence(AI)methods such as machine learning or deep learning have been widely applied in the field of TC forecasting.For TC track forecasting,a more effective method of center location is obtained by combining data from various sources and fully exploring the potential of AI,which provides more possibilities for improving TC prediction.

Large tropical cyclone track forecast errors of global numerical weather prediction models in western North Pacific basin

Although tropical cyclone(TC)track forecast errors(TFEs)of operational warning centres have substantially decreased in recent decades,there are still many cases with large TFEs.The International Grand Global Ensemble(TIGGE)data are used to study the possible reasons for the large TFE cases and to compare the performance of different numerical weather prediction(NWP)models.Forty-four TCs in the western North Pacific during the period 2007-2014 with TFEs(+24 to+120 h)larger than the 75 th percentile of the annual error distribution(with a total of 93 cases)are identified.Four categories of situations are found to be associated with large TFEs.These include the interaction of the outer structure of the TC with tropical weather systems,the intensity of the TC,the extension of the subtropical high(SH)and the interaction with the westerly trough.The crucial factor of each category attributed to the large TFE is discussed.Among the TIGGE model predictions,the models of the European Centre for Medium-Range Weather Forecasts and the UK Met Office generally have a smaller TFE.The performance of different models in different situations is discussed.

Tropical cyclone track and intensity prediction skill of GFS model over NIO during 2019&2020

The Tropical Cyclone(TC)track prediction using different NWP models and its verification is the critical task to provide prior knowledge about the model errors,which is beneficial for giving the model guidance-based real-time cyclone warning advisories.This study has attempted to verify the Global Forecast System(GFS)model forecasted tropical cyclone track and intensity over the North Indian Ocean(NIO)for the years 2019 and 2020.GFS is one of the operational models in the India Meteorological Department(IMD),which provides the medium-range weather forecast up to 10 days.The forecasted tracks from the GFS forecast are obtained using a vortex tracker developed by Geophysical Fluid Dynamics Laboratory(GFDL).A total of 13 tropical cyclones formed over the North Indian Ocean,eight during 2019 and five in 2020 have been considered in this study.The accuracy of the model predicted tracks and intensity is verified for five days forecasts(120 h)at 6-h intervals;the track errors are verified in terms of Direct Position Error(DPE),Along Track Error(ATE)and Cross-Track Error(CTE).The annual mean DPE over NIO during 2019(51–331 km)is lower than 2020(82–359 km),and the DPE is less than 150 km up to 66 h during 2019 and 48 h during 2020.The positive ATE(76–332 km)indicates the predicted track movement is faster than the observed track during both years.The positive CTE values for most forecast lead times suggest that the predicted track is towards the right side of the observed track during both years.The cyclone Intensity forecast for the maximum sustained wind speed(Max WS)and central mean sea level pressure(MSLP)are verified in terms of mean error(ME)and root mean square error(RMSE).The errors are lead time independent.However,most of the time model under-predicted the cyclone intensity during both years.Finally,there is a significant variance in track and intensity errors from the cyclone to cyclone and Bay of Bengal basin to the Arabian Sea basin.

USE OF A NEW STEERING FLOW METHOD TO PREDICT TROPICAL CYCLONE MOTION

A tropical cyclone is a kind of violent weather system that takes place in warmer tropical oceans and spins rapidly around its center and at the same time moves along surrounding flows. It is generally recognized that the large-scale circulation plays a major role in determining the movement of tropical cyclones and the effects of steering flows are the highest priority in the forecasting of tropical cyclone motion and track. This article adopts a new method to derive the steering flow and select a typical swerving track case (typhoon Dan, coded 9914) to illustrate the validity of the method. The general approach is to modify the vorticity, geostropical vorticity and divergence, investigate the change in the non-divergent stream function, geoptential and velocity potential, respectively, and compute a modified velocity field to determine the steering flow. Unlike other methods in regular use such as weighted average of wind fields or geopoential height, this method has the least adverse effects on the environmental field and could derive a proper steering flow which fits well with storm motion. Combined with other internal and external forcings, this method could have wide application in the prediction of tropical cyclone track.

A Composite Model Predictive Control Strategy for Furnaces

Tube furnaces are essential and primary energy intensive facilities in petrochemical plants. Operational optimization of furnaces could not only help to improve product quality but also benefit to reduce energy consumption and exhaust emission. Inspired by this idea, this paper presents a composite model predictive control(CMPC)strategy, which, taking advantage of distributed model predictive control architectures, combines tracking nonlinear model predictive control and economic nonlinear model predictive control metrics to keep process running smoothly and optimize operational conditions. The controllers connected with two kinds of communication networks are easy to organize and maintain, and stable to process interferences. A fast solution algorithm combining interior point solvers and Newton's method is accommodated to the CMPC realization, with reasonable CPU computing time and suitable online applications. Simulation for industrial case demonstrates that the proposed approach can ensure stable operations of furnaces, improve heat efficiency, and reduce the emission effectively.

Approach of Synthesizing Model Predictive Control and Its Applications for Rotary Kiln Calcination Process

An actual control demand of rotary kiln is taken as background. By analyzing and improving approach of MPC （synthesizing model predictive control）, an effective strategy which applies complex S-MPC in actual industrial process is designed. Firstly, after analyzing the main components technology and calcination reaction mechanism in detail, the calcining belt state-space model of rotary kiln is built using PO-Moesp （past-output multivariable output error state space model identification） method. Then, calcining belt temperature predictive control system is de signed. The control system combines time-delay gain scheduled, output-tracking, recursive subspace adaptive and other methods, and forms the off-line/on-line predictive controller of rotary kiln. At last, MATLAB is applied for simulation, experiments run in constant value tracking and servo tracking situation. Simulation results show its ef- fectiveness and feasibility.