Deep Learning for Hurricane Track Forecasting from Aligned Spatio-temporal Climate Datasets
AfﬁliationAddress
Abstract
The forecast of hurricane trajectories is crucial for the protection of people andproperty, but machine learning techniques have been scarce for this so far.
Wepropose a neural network fusing past trajectory data and reanalysis atmosphericimages (wind and pressure 3D ﬁelds).
We used a moving frame of reference thatfollows the storm center for the 24h tracking forecast.
The network is trained toestimate the longitude and latitude displacement of hurricanes and depressionsfrom a large database from both hemispheres (more than 3000 storms since 1979,sampled at a 6 hour frequency).
The advantage of the fusion network is demonstrated and a comparison with current forecast models shows that deep methodscould provide a valuable and complementary prediction
Introduction
Cyclones, hurricanes or typhoons are words designating the same phenomena: rare and complexevents characterized by strong winds surrounding a low pressure area.
Their trajectory and intensityforecasts are crucial for the protection of people and property.
However, their evolution dependson many factors at different scales, altitudes and time, which leads to difﬁculties in their modelling.
Today, current national forecasts are typically driven by consensus methods able to combine differentdynamical models 1 .
Statistical forecasting models, on the other hand, still perform poorly withrespect to dynamical models, even though the database of past hurricanes is constantly growing.
Moreover, a large number of physical variables (pressure, wind ﬁelds..) are now available on griddedearth maps from the reanalysis 2 , and could be integrated in a statistical or learning method.
However, only few machine learning methods are tackling the tracking forecast problem.
One of themuses a sparse recurrent neural network from only trajectory data (Moradi Kordmahalleh et al.
(2016))and was tested on 6h- and 12h-forecast on only 4 hurricanes.
Another study uses storm tracks andreanalysis maps as input for a hybrid ConvNet - LSTM network in order to learn the (x,y) trackingcoordinates (Mudigonda et al. and showed their 6h-forecast results.
The regional map (forimage-like physical inputs) was ﬁxed and of size 160 x 80 deg (longitude/latitude).
However, a ﬁxedregion has three major limitations.
Firstly, the tracked storm must stay in the region (while tracksoften cross oceans, see Fig.
1a), forcing the selection of a large region, even if it is constrained bymemory issues (Mudigonda et al.
Moreover, learning local phenomena on a large and notcentered image can be difﬁcult.
Finally, it prevents information transfer between storms coming fromdifferent basins or regions, while ground truth data is scarce.
In a recent work (same authors, 2018),we showed the advantage of using a moving reference CNN model for forecasting hurricanes tracks 6hours into the future with respect to the other learning methods (30km error with respect to more
[Figure]
[Figure]
a
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than 60km). However, a 6h-forecast is of no use for catastrophe planning and it is not possible tocompare to current forecasts as the smallest standard is 24 hours. In this work, we propose to use a moving frame of reference that follows the storm center for a24h-forecast tracking task. We pose the tracking problem as the estimation of the displacement vector(cid:126)d between current and future locations.
Moreover, we propose to use the reanalysis data as croppedimages (25 x 25 degrees) centered on the storm location.
That way, the computation is reduced andwe can learn from storms coming from a large number of hurricane basins from both hemispheres.
We include past temporal information by adding the reanalysis maps from previous time steps.
Wepropose a fusion convolutional neural network taking into account past trajectories and reanalysisimages (wind ﬁelds and pressure), and we treat each time step of a storm as a training data point.
Tracking Data and Reanalysis Data Processing
Tracking Data from Both Hemispheres.
The raw storm track data is composed of more than3000 extra-tropical and tropical storm tracks since 1979 extracted from the NOAA database IBTrACS Knapp et al.
(2010), see Fig.
1a.
The tracks are deﬁned by the 6-hourly center locations (latitudeand longitude).
They come from both hemispheres and the number of records per storm varies from2 to 120 (total: more than 90,000 time steps).
A storm’s future displacement (here in 24h) can bepredicted from its historical displacement.
We deﬁne a displacement as the vector (cid:126)d = ( δlon t , δlat t )between two successive locations of one storm, t being a multiple of 6 hours.
We used as features thetwo past displacements of the storm.
We added also some “ 0 D-features” from the IBTrACS database:the current latitude, longitude, and max. sustained windspeed, the Jday predictor (DeMaria et al.(2005)), and the current distance to land.
In total, 9 features per time step are extracted.
Reanalysis Data.
The trajectory of a storm depends on large scale atmospheric physical phenomena.
We applied a sparse feature selection technique (automatic relevance determination, based on linearregression) over 10 available reanalysis ﬁelds on pressure levels from the ERA-interim database (Deeet al.
It highlighted the usefulness of wind ﬁelds and geopotential heights (that can be seenas pressure maps).
Thus, we extracted them on the neighborhood of the storm at every time step t .
Speciﬁcally, we extracted the u-wind, v-wind and z ﬁelds on a 25x25 degree grid centered on thecurrent storm location, at 3 atmospheric pressure levels ( 700 / 500 / 225 hPa ).
The choice of the 3pressure levels was inspired by related work in the literature on statistics forecast models (DeMariaet al.
In order to capture the dynamics, we extracted the wind ﬁelds measured at t − 6 h atthe same locations: the data can be seen as 9 small videos of 2 frames each.
The Model
General Framework: Fusing Convolutional Networks.
Even though the long-short-term memory (LSTM) networks are designed for predicting time-series events, they are difﬁcult to train and [Figure] [Figure]
Conv- [Figure]
Conv-
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ConvFC-
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FC
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FC-
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FC-2 FC [Figure] 3FC- [Figure]
FC-
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FC-
[Figure] FC-1 FC- [Figure] Pressure
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Wind CNN
Past tracks + meta NN
Fusion Network 12 64 64 4 576 1288 86 64 64 10
88+8+9 8+8+8 2+2+
[Figure]
(fusion)
[Figure]
simpler CNNs can often outperform LSTMs (Bai et al. (2018)), and encode time frames as differentchannels already proved its efﬁciency (de Bezenac et al. Because of the different natureof the data sources, it is not straightforward to mix all the data into a neural network (NN).
Wepropose a fusion of three different NN architectures (see Fig. 3).
The Wind CNN and Pressure
CNN are convolutional NN that take atmospheric ﬁelds as input, while the Past tracks + meta NNis a small network which takes 0D features as input.
Each stream network ﬁrst learn its parametersindependently for the same task, i.e. predicting the 24h-forecast displacement (cid:126)d = ( δlon 24 h , δlat 24 h ) .
We then integrate the three networks into a fusion network and retrain it (see Fig. 3).
Wind CNN and Pressure CNN.
The two CNN networks are very similar, however the type of datais different thus different learning rates were need, that is why we separate them into two networks.
The Wind CNN data consists in 12 channels, and 6 channels for the Pressure CNN (concatenation ofevery dimension).
We used a typical CNN architecture alternating convolutional layers (Conv layer)and max-pooling layers with fully connected layers at the end (Simonyan and Zisserman (2014)).
All hidden layers are equipped with the rectiﬁcation (ReLU) non-linearity and batch normalization.
We have evaluated different conﬁgurations (from one to four Conv layers) of Wind CNN on ourvalidation set before selecting 3 Conv layers.
Past tracks + meta NN.
We designed a small neural network (two fully connected layers) represented as the green stream in Fig.
It is able to learn the future displacement from its pastdisplacements and other handcrafted data (see section 2).
We use two past displacements (fromt − 12 h to t − 6 h and from t − 6 h to t ) because more past tracks did not improve the performance.
Combining Neural Networks.
Once the three individual stream networks are trained, we concatenate their 3 last layers and add a layer at the end of the network as the fused output layer.
We initializeto zero the weights of the new connections in these 3 layers (across streams).
We then re-train thewhole fused network by allowing every weight to be optimized.
The number of fused layers (3) wasdetermined by comparing different conﬁgurations.
Algorithmic Details.
The storms were randomly separated in 3 sets as follows: train (60%) / valid(20%) / test (20%).
All time instants were treated independently within each set and the input datawas standardized.
The loss function was set as the mean square error (MSE) in kilometers betweenthe forecast and the true storm location at t + 24 h .
We added an L2 penalty on the weights of themodel ( coef.
= 0 . 01 ).
The training was performed by the Adam optimizer, and each model convergedwithin 200 epochs.
Every evaluation was repeated three times and an average score was computed.
Our implementation uses PyTorch 4.0 on 4 TitanX GPUs with [Figure] [Figure] Atlanticerrors(km
[Figure]
East Paciﬁc errors (km)
[Figure] std mean std [Figure] Fusion
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Experimental Evaluation
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Fig.
3 shows the 24h-forecast results on the test set (14,256 time steps) in absolute distance error [Figure]
We can see the improvement of fusing networks with respect to the Wind CNN, Pressure CNN and
Past tracks + meta NN.
We also compared our fusion model CNN with existing forecasting models:
BCD5 is a statistical model which is often used to benchmark other storm track forecasting methods,and OFCL is the National Hurricane Center ofﬁcial forecast (consensus of dynamical models).
3 Weextracted the BCD5 prediction results of years 1989-2016 in the Atlantic and Eastern Paciﬁc basins.
We compare in Table 1 our fusion network with the statistical BCD5 on the test hurricane instantswhere both methods provided a forecast (4349 time instants from 258 storms).
On both basins, ourfusion network behaves better than the BCD5 model on average.
Such comparison is not possiblewith the OFCL as this model is modiﬁed every year and they only provide forecasts of the version N of the model for the year N. We don’t know the performance of the recent models on previousyears, and it would be unfair for them to compare with old results (obtained with earlier, less efﬁcientmodels). Analyzing the mean errors per year, our deep learning model performs better than the OFCLforecast until year 2010 for the Paciﬁc basin (2005 for the Atlantic).
During the years 2010, the
OFCL method improved and its mean errors per year are smaller than ours.
We also compared qualitatively the predictions with both OFCL and BCD5 models for recent stormsof the test set, as the Hermine hurricane in 2016 (Fig. 4).
The small bars connect each pair ofpredicted and ground truth location (after 24 hours).
The larger the length, the larger the error.
Eventhough the ofﬁcial OFCL model has globally smaller forecast errors, on some time points our model outperforms the OFCL.
Moreover, the 3 forecasts have often different directions.
If we don’t expectto perform better than a current ofﬁcial ensemble of dynamical models, a neural network model canhelp the current forecast modellers by providing a complementary prediction that could be integratedin a consensus method.
Conclusion
We designed a neural network for the storm track 24h-forecasting using a moving frame of referenceable to use a common dataset and a common training for every hurricane of both hemispheres.
We demonstrated the beneﬁt of coupling past displacements and registered reanalysis images.
Bycomparing results with current forecast models, we think that such a different approach can bebeneﬁcial if integrated in a consensus method.