DeepTC: ConvLSTM Network for Trajectory Prediction of Tropical Cyclone using Spatiotemporal Atmospheric Simulation Data
Abstract
Accurate forecasting of tropical cyclone trajectory is important because it canhave a great impact on the safety of people and infrastructure.
This paper introduces a novel data-driven tropical cyclone path prediction model called DeepTC .
The proposed model makes use of data generated using the Weather Researchand Forecasting model, which simulates spatiotemporal atmospheric conditions.
Additionally, the proposed model utilizes convolutional long short-term memory,which is effective when operating on spatial data over time.
Experimental resultsdemonstrate that our methodology is promising, conﬁrming that DeepTC learnsthe spatiotemporal dynamics of the atmosphere by the WRF effectively.
Introduction
Tropical cyclones (TCs)—known as hurricanes in the Atlantic and Northeast Paciﬁc, cyclones in the South Paciﬁc and Indian Ocean, and typhoons in the Northwest Paciﬁc—are one of the most severenatural disasters.
TCs with wind speeds exceeding 31 m/s at their center lead to tens of thousandsof people being evacuated and rescued each year.
As a result, the precise prediction of the path ofan impending storm would help local authorities in their decision making regarding the time andlocation of evacuation.
Acknowledging the importance of TC prediction as a critical spatiotemporal domain problem,meteorologists rely on numerical weather prediction (NWP) models such as the Weather Researchand Forecasting (WRF) model, Model for Prediction Across Scales (MPAS), and Community
Atmosphere Model ver 5.0 (CAM5) to predict wind speeds, temperature, air pressure, and otheratmospheric indicators, which help to predict the approach path of a TC over its lifetime.
Extant studies, through use of modern deep-learning techniques, have recently attempted to predictthe trajectory as well as detect the center of TC.
Studies using RNN [1], GA (genetic algorithm)[2], ANN (Artiﬁcial Neural Network) [3] trained with latitude, longitude, wind speed, and pressure,and LSTM with track data [4] with a theoretical toy model-Lorenz 96 [5] have been introduced.
However, these studies have a limitation that the results still need to be veriﬁed with a real atmosphericmodel.
There also are trials for center detection[6] and intensity estimation [7, 8] with CNN usingsatellite images.
[9] has tried detect center using re-analysis data after TC, but the approach has adrawback as a forecaster because re-analysis data is not available when forecasting.
To the best ofour knowledge, none of the above-mentioned studies employed WRF simulation dataset, which isone of the high-level, rich, and realistic spatiotemporal data of atmosphere as well as possible data atthe time of forecast.
In this study, we propose DeepTC , a model capable of predicting the trajectory of TCs using simulationresults generated by the WRF numerical model through use of a recently proposed deep learning technique—ConvLSTM network.
The key idea is that, by training the proposed model with ﬁvedifferent conditioned simulations generated by WRF and the best track 1 for each TC, we exploit DeepTC as an ensemble track forecasting model.
Through the above exercise, DeepTC can learnunique characteristics of differently conditioned simulations, thereby facilitating the generation ofmore accurate cyclone-trajectory predictions.
In practice, weather forecasters predict the path of a TCby taking into consideration prediction results of multiple WRF models based on present atmosphericconditions, their own experience, and synoptic situations.
In this study, we attempt to use DeepTC inthe role of the predictor.
Data
WRF is a mesoscale numerical weather prediction model designed for both atmospheric research andoperational forecasting applications [10].
It predicts the future state of atmospheric conditions, startingfrom initial data—usually operational global atmospheric data.
WRF is mainly used for regionalmodels, which require a boundary condition on the global atmospheric data.
Although the interval forupdating boundary conditions can be changed, in this study, we update the boundary conditions ofglobal data every six hours.
WRF as a regional weather prediction model is computationally efﬁcientand represents mesoscale weather phenomena better than global atmospheric models.
Therefore, ithas been one of the most popular numerical models among atmospheric scientists since the 2000s.
The WRF simulation performed in this study covered the area of the 7 × 239 × 279 grid we set 2 ,conﬁgured at a horizontal resolution of 30 km and adaptive vertical level up to 50 hPa.
Originally,
WRF generated data for the area 29 × 239 × 279 grid, but we selected 7 levels (5, 7, 9, 12, 14,16, 19) in the vertical plane, which is meaningful for TCs with our domain knowledge in order toreduce computational cost.
At this resolution, each snapshot of the atmospheric states in the WRFoutput corresponds to a grid.
One simulation produces over 100 variables; however, of these, only25 variables, deemed to be the most signiﬁcant for cyclone tracking, were used for the training ofour model.
These selected variables are listed in Table 1, and some are visualized in Figure 1 with [Table] VAPOR [11].
We used 12 3-Dimensional (in the vertical and horizontal directions) variables, such asx-wind component (U) and y-wind component (V), and 13 2-Dimensional (in the horizontal direction)variables, such as surface pressure and sea surface temperature.
The trajectory data was from the Japan Meteorological Agency ´ s (JMA ´ s) ofﬁcial best track information, which has a precision of onedecimal place.
[Table]
In this study
[Table]
WRF simulation data were generated for 50 TCs that drifted in close proximity to the Korean peninsula, including Rusa, Maemi, Megi, and Ewiniar.
Simulations were performed onlyfor the signiﬁcant period of each TC (i.e., the period for which the TC was most destructive).
Forinstance, for Rusa, from the total lifetime of the typhoon, simulation data was generated for only 6days and 6 hours (from 2002.08.26 00:00 h, lat 22.1N long 146.7E to 2002.09.01 00:00 h, lat 38.0Nlong 128.7E).
We started the simulations every six hours, as shown in Figure 2.
Speciﬁcally, onesimulation has ﬁve temporal sequences with a six-hour interval between the successive data 24 hours
[Table]
[Table]
schemes) to build the[-movement of typhoons from themultiple models. A total of 75 simulations were generated for RUSA, as a result, 5400 simulationswere performed for all 50 typhoons for a complete dataset. The dataset was then randomly dividedinto training (60%, about 450 GB), validation (20%, about 150 GB), and test data (20%, about 
 GB).-]
Approach & Experiment
The proposed study aims to tackle the problem of reliably predicting the trajectory of a TC usingtest and training data.
To solve this problem, we have used the ConvLSTM network [12], which isknown to be effective in operations involving spatial data.
It replaces matrix multiplications withconvolution operations performed at each gate in the LSTM cell.
As a result, it is able to captureunderlying spatial features from multi-dimensional data.
The architecture of DeepTC utilizing ConvLSTM is presented in Figure 3.
The proposed modelconﬁguration (many-to-many), thus provides a means of predicting the next sequence of TC positionsfrom the next sequence of simulated atmospheric conditions.
The model receives ﬁve items of data(X), including 3-D (12 channels) and 2-D (13 channels) variables at regulated time intervals of 6 h. For 3-D data, the Conv3DLSTM cell is invoked to facilitate 3-D convolution, and for 2-D data, the
Conv2DLSTM cell is invoked.
We used four kernels with 3 × 3 shape and four kernels with 3 × 3 × 3shape.
Tensors from each cell were ﬂattened, concatenated and fed to a fully-connected (FC) layer.
FC layers ( 24 × 2 ) were added to the output of each sequence cell to generate two output values,which represent the expected latitude and longitude.
The cost function for the model was set as theroot mean squared error (RMSE) of latitude and longitude for each output node.
We trained the model with the Adam optimizer at a learning rate of 0.001.
We used random shufﬂingmini-batches for learning; the mini-batch size was set to 10.
The training epoch was 200, and it tookapproximately 2 h to complete one epoch.
The RMSE was used to measure the prediction accuracy.
The testbed environment conﬁguration had dual GPUs (NVIDIA Titan Xp 12 GB) and 128 GB of RAM, and we used TensorFlow 1.8 and Python 3.6.2 for implementation.
In our experiments, we observed that using too many kernels (e.g., 16 and 32) caused overﬁtting, andusing eight kernels produced stable learning.
Then, we checked the performance of the test dataseton the model built with the eight kernels.
The result were 3.81 and 2.6 (RMSE, degree) at 150 and200 epochs, respectively (Table 2).
There was a about 49% of error drop compared to models builtwith two ﬁlters on average.
For reference, one degree in latitude is approximately 8 km and onedegree in longitude is approximately 11 km, and the average diameter of a TC center is known to beapproximately 30km.
For further analysis, we measured RMSE at each time (Figure 5) by calculatingthe error from each output node.
We found a tendency that the error increases with time.
It seemsthat WRF simulation errors increased over time and DeepTC learned this simulation data. [Figure] [Figure]
[Figure]
Conclusion
In the preliminary study, we applied the WRF simulation datasets to ConvLSTM, as we aim toreliably predict the trajectory of TCs.
The key contribution of this study is that we have proposedan ensemble model, which utilizes multiple WRF models that are widely used in actual TC pathpredictions, with a deep learning technique.
By utilizing meteorological variables (such as U, V, W,and SST), which are inﬂuential on TC path, we have predicted the movement of the TCs.
The initialexperimental results have demonstrated that our methodology is promising.
The main reason for thisis the ConvLSTM can easily learn on the spatial and temporal dynamics of the atmosphere simulatedby the WRF.
In our future endeavors, we will experiment with a dataset by split by TCs.
Finally, theauthors are working towards synthesizing trajectory prediction results through use of a model trainedby satellite images to enhance overall system performance.