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

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.

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.

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.