Published as a conference paper at ICLR 2022

H URRICANE F ORECASTING : A N OVEL M ULTIMODALM ACHINE L EARNING A PPROACH
Léonard Boussioux∗ , Cynthia Zeng∗
Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, USA
leobix@mit.edu
Théo Guénais
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
Dimitris Bertsimas
Sloan School of Management and Operations Research Center, Massachusetts Institute of Technology,
Cambridge, MA, USA

A BSTRACTThis paper describes a novel machine learning (ML) framework for tropical cyclone intensity and track forecasting, combining multiple ML techniques and utilizing diverse data sources. Our multimodal framework, called Hurricast, efficiently combines spatial-temporal data with statistical data by extracting featureswith deep-learning encoder-decoder architectures and predicting with gradientboosted trees. We evaluate our models in the North Atlantic and Eastern Pacificbasins on 2016-2019 for 24-hour lead time track and intensity forecasts and show
they achieve comparable mean absolute error to current operational forecast models while computing in seconds. Furthermore, the inclusion of Hurricast into an
operational forecast consensus model could improve over the National Hurricane
Center’s official forecast, thus highlighting the complementary properties withexisting approaches. In summary, our work demonstrates that utilizing machinelearning techniques to combine different data sources can lead to new opportunities in tropical cyclone forecasting.

1

I NTRODUCTION

A tropical cyclone (TC) is a low-pressure system originating from tropical or subtropical waters
and develops by drawing energy from the sea. It is characterized by a warm core, organized deep
convection, and a closed surface wind circulation about a well-defined center. Every year, tropical
cyclones cause hundreds of deaths and billions of dollars of damage to households and businesses(Grinsted et al., 2019). Therefore, producing an accurate prediction for TC track and intensity with
sufficient lead time is critical to undertake life-saving measures.
The forecasting task encompasses the track, intensity, size, structure of TCs, and associated storm
surges, rainfall, and tornadoes. Most forecasting models focus on producing track (trajectory) forecasts and intensity forecasts, i.e., intensity measures such as the maximum sustained wind speed ina particular time interval. Current operational TC forecasts can be classified into dynamical models, statistical models, and statistical-dynamical models (Cangialosi, 2020). Dynamical models,also known as numerical models, utilize powerful supercomputers to simulate atmospheric fields’
evolution using dynamical and thermodynamical equations (Biswas et al., 2018; ECWMF, 2019).
Statistical models approximate historical relationships between storm behavior and storm-specificfeatures and, in general, do not explicitly consider the physical process (Aberson, 1998; Knaff et al.,2003). Statistical-dynamical models use statistical techniques but further include atmospheric variables provided by dynamical models (DeMaria et al., 2005). Lastly, consensus models typically∗

Equal contribution

1

Published as a conference paper at ICLR 2022

combine individual operational forecasts with a simple or weighted average (Cangialosi, 2020; Cangialosi et al., 2020).
In addition, recent developments in Deep Learning (DL) enable Machine Learning (ML) models to
employ multiple data processing techniques to process and combine information from a wide rangeof sources and create sophisticated architectures to model spatial-temporal relationships. Several
studies have demonstrated the use of Recurrent Neural Networks (RNNs) to predict TC trajectory
based on historical data (Moradi Kordmahalleh et al., 2016; Gao et al., 2018; Alemany et al., 2019).
Convolutional Neural Networks (CNNs) have also been applied to process reanalysis data and satellite data for track forecasting (Mudigonda et al., 2017; Lian et al., 2020; Giffard-Roisin et al., 2020)and storm intensification forecasting (Chen et al., 2019; Su et al., 2020).
This paper introduces a machine learning framework, called Hurricast (HUML), for both intensity and track forecasting by combining several data sources using deep learning architectures andgradient-boosted trees.
Our contributions are three-fold:
1. We present novel multimodal1 machine learning techniques for TC intensity and track predictions by combining distinct forecasting methodologies to utilize multiple individual data
sources. Our Hurricast framework employs XGBoost models to predict using statisticalfeatures based on historical data and spatial-temporal features extracted with deep learning
encoder-decoder architectures from atmospheric reanalysis maps.
2. Evaluating in the North Atlantic (NA) and Eastern Pacific (EP) basins, we demonstrate that
our machine learning models produce comparable results to currently operational models
for 24-hour lead time for both intensity and track forecasting tasks.
3. Based on our testing, adding one machine learning model as an input to a consensus model
can improve the performance, suggesting the potential for incorporating machine learning
approaches for hurricane forecasting.

2

DATA

In this study, we employed three kinds of data dated since 1980: historical storm data, reanalysis
maps, and operational forecast data. We use all storms from the seven TC basins since 1980 thatreach 34 kt maximum intensity at some time, i.e., are classified at least as a tropical storm, andwhere more than 60 h of data are available after they reached the speed of 34 kt for the first time.
First, we collected historical storm data through the post-season storm analysis dataset IBTrACS(Knapp et al., 2010) maintained by the National Oceanic and Atmospheric Administration. Second,
we obtained reanalysis data from the ERA-5 data set, which contains hourly high spatial resolution
reanalysis maps (ERA5, 2017). We extracted nine reanalysis maps for each TC time step, corresponding to three different features, geopotential z, u and v components of the winds, at threeatmospheric altitudes (see Figure 1).
Finally, to use as benchmark, we obtained operational forecast data from the Automated Tropical Cyclone Forecasting (ATCF) data set, maintained by the US National Hurricane Center (NHC)(Sampson & Schrader, 2000; National Hurricane Center, 2021). We selected the strongest operational forecasts with a sufficient number of cases concurrently available. More details on all data
sources are in Appendix A.

3

M ETHODOLOGYOur Hurricast framework makes predictions based on time-series data with different formats: threedimensional vision-based reanalysis maps and one-dimensional historical storm data consisting of
numerical and categorical features.
1
Multimodality in machine learning refers to the simultaneous use of different data formats, including, forexample, tabular data, images, time series, free text, audio.

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Published as a conference paper at ICLR 2022

Figure 1: Representation of the nine reanalysis maps extracted for each time step, corresponding togeopotential z, u and v components of the winds, repeatedly extracted at three atmospheric altitudes,
225, 500, and 700hPa. Each map is of size 25◦ × 25◦ , centered on the TC center location, and eachpixel corresponds to the average field value at the given latitude and longitude degree.
Overall, we adopt a three-step approach to combine the multiple data sources. We first extracta one-dimensional feature representation (embedding) from each reanalysis maps sequence, using
encoder-decoder architectures. Second, we concatenate this one-dimensional embedding with thestatistical data to form a one-dimensional vector. Third, we make our predictions using gradientboosted tree XGBoost models (Chen & Guestrin, 2016) trained on the selected features.
At a given time step (forecasting case), we perform two 24-hour lead time forecasting tasks: intensity prediction, i.e., predicting the maximum sustained wind speed at a 24-hour lead time; anddisplacement prediction, i.e., the latitude and longitude storm displacement in degrees between given
time and forward 24-hour time. Figure 2 illustrates the three-step pipeline.
1. Feature Extraction

3. Forecasting

2. Concatenation

Vision Data: Reanalysis Maps…
t-21 ht-18 hFeatureextraction

VisionEmbeddingst

XGBoostModel

Statistical Data
t-21 ht-18 hIntensity or
Track
Forecast…

…

StatisticalDatatFigure 2: Representation of our multimodal machine learning framework using the two data sources.
3.1

F EATURE E XTRACTION

We experimented with encoder-decoder architectures trained with a supervised learning fashion to
perform feature extraction. The encoder component consists of a Convolutional Neural Network.
We compare two decoder variations. The first one relies on Gated Recurrent Units (GRUs) (Chunget al., 2014), a well-suited recurrent neural network to model temporal dynamic behavior in sequen3

Published as a conference paper at ICLR 2022

tial data. The second one uses Transformers (Vaswani et al., 2017), a state-of-the-art architecture forsequential data. While the GRUs model the temporal aspect through a recurrence mechanism, theTransformers utilize attention mechanisms and positional encoding (Bahdanau et al., 2015; Vaswani
et al., 2017) to model long-range dependencies.
First, we trained the encoder-decoder architectures end-to-end using the Adam optimizer (Kingma& Ba, 2014). We used a mean squared error loss with either an intensity or track objective and added
an L2 regularization on the network’s weights. We then froze the encoder-decoder’s weights after
training was completed.
To perform feature extraction from a given input sequence of reanalysis maps and statistical data, we
passed them through the whole frozen encoder-decoder, except the last fully-connected layer (seeFigure 3). The second fully connected layer after the GRU or the pooling layer after the Transformer
output a vector of relatively small size, e.g., 1024 or 128 features, to compress information andprovide predictive features. This vector constitutes our one-dimensional reanalysis maps embeddingthat we extract from the initial 45,000 (8 × 9 × 25 × 25) features forming the spatial-temporal input.
Figure 3 illustrates the encoder-decoder architectures (see Appendix B for more details).
CNN-encoder GRU-decoder

Statistical Data

...

...

ht-18hCNN
Statistical Data

Embedtt-18h

Intensity or
Track
Forecast

ht
GRU

Statistical Data

t
ht

CNN

CNN
Statistical DataStatt

ht-21hFeatureextraction

Statt-21h
Embedt-18h
ht-18hStatt-18h

Fully Connected Layer

Statt-18h

ht-18hStatistical Data

Embedt-21h

Pooling layer

GRU

CNN

Transformertht-21hEmbedt-18h

Featureextraction
Fully Connected Layer 3

Statistical Data

Statt-21h

ht-21hFully Connected Layer 2

CNN

GRU

Fully Connected Layer 1

t-18h

Embedt-21h

...

CNN

...

t-21h

CNN-encoder Transformer-decoder
t-21h

Intensity or
Intensity or
Track
Track Forecast
Forecast

Embedtht
Statt

Figure 3: Schematic of the encoder-decoder networks for an 8-time step TC sequence. The CNN
produces a one-dimensional representation of the reanalysis maps at each time step. Then, we
concatenate these embeddings with the corresponding statistical features to create a sequence of
inputs fed sequentially to the GRU or as a whole to the Transformer. In the GRU-decoder, the
GRU sequentially outputs hidden states passed to the next time step. Finally, we concatenate all
the successive hidden states and pass them through three fully connected layers to predict intensity
or track with a 24-hour lead time. We extract our spatial-temporal embeddings as the output of thesecond fully connected layer. In the Transformer-decoder, the Transformer outputs a new 8-timestep
sequence that we average feature-wise and feed into one fully connected layer to predict intensity or
track. We extract our spatial-temporal embeddings as the output of the pooling layer.

4

E XPERIMENTS AND R ESULTS

We separated the data set chronologically into training (80% of the data, ranging from 1980 to 2011),
validation (10% of the data, ranging from 2012 to 2015), and testing (10% of the data, ranging
from 2016 to 2019). The training and validation sets consist of TCs from all basins, whereas we
restrict the testing set to only NA and EP basins where operational forecasts are available to compare
performances. We computed the mean absolute error (MAE) on the predicted 1-minute maximum
sustained wind speed in 24 hours to evaluate our intensity forecasts’ performance. To evaluate track
forecasts’ performance, we computed the mean geographical distance error in kilometers between
the actual position and the predicted position in 24 hours, using the Haversine formula2 .
Standalone ML models produce a comparable performance to standalone operational models. For both intensity and track forecasting tasks, the best Hurricast model HUML-(stat/viz,
xgb/cnn/transfo) achieve better performance than operational statistical models, and comparable
2
The Haversine metric calculates the great-circle distance between two points — in our case, the shortest
distance between these two points over the Earth’s surface.

4

Published as a conference paper at ICLR 2022

results to operational dynamical models. Results for 24-hour lead time intensity and track forecasting are displayed in Table 1 and Table 2 respectively. Details on the operational models and HUML
models can be found in Appendix in Tables 4 and 5. These results highlight that machine learning
approaches can emerge as a new methodology to currently existing forecasting methodologies in thefield. In addition, we believe there is potential for improvement if given more available data sources.
Machine learning models bring additional insights to consensus models. Consensus models
often perform better than individual models by averaging errors and biases. In particular, the official NHC forecast (OFCL) is a consensus model. Therefore, to experiment if ML models cancomplement operational models to improve the official forecast, we built two types of consensus
models: (i) OP-consensus, a simple average of the standalone operational models included in ourbenchmark; (ii) HUML/OP-consensus, a simple average of HUML-(stat/viz, xgb/cnn/transfo) andthe other standalone operational models included in our benchmark. As shown in Tables 1 and 2,
consensus models have a lower MAE than standalone models. In addition, the HUML/OP-averageconsensus demonstrates the potential to improve the NHC Official Forecast (OFCL) by including an
ML model.
Table 1: Mean absolute error (MAE) and standard deviation of the error (Error sd) of standalone
Hurricast models, standalone operational forecasts, operational consensus, and consensus experiments on the same test set between 2016 and 2019, for 24-hour lead time intensity forecasting task.
Bold values highlight the best performance in each category.
Model Type

Model Name

Hurricast(HUML)
Methods
Standalone
Operational
Forecasts
Operational
Consensus
Consensus
Experiments

HUML-(stat, xgb)
HUML-(stat/viz, xgb/cnn/gru)
HUML-(stat/viz, xgb/cnn/transfo)
GFSO
Decay-SHIPS
HWRF
FSSE
OFCL
OP-average consensus
HUML/OP-average consensus

Eastern Pacific Basin
Comparison on 877 cases
MAE (kt) Error sd (kt)
10.6
10.5
10.3
10.0
10.3
9.8
15.7
14.7
11.7
10.4
10.6
11.0
9.7
9.5
10.0
10.1
9.6
9.7
9.2
9.0

North Atlantic Basin
Comparison on 899 cases
MAE (kt) Error sd (kt)
10.7
9.3
10.8
9.2
10.4
8.8
14.2
14.1
10.2
9.3
9.7
9.0
8.5
7.8
8.5
8.1
8.5
7.9
8.3
7.6

Table 2: Mean absolute error (MAE) and standard deviation of the error (Error sd) of standalone
Hurricast models, standalone operational forecasts, operational consensus, and consensus experiments on the same test set between 2016 and 2019, for 24-hour lead time track forecasting task.
Bold values highlight the best performance in each category.
Model Type

Model Name

Hurricast(HUML)
Methods
Standalone
Operational
Forecasts

HUML-(stat, xgb)
HUML-(stat/viz, xgb/cnn/gru)
HUML-(stat/viz, xgb/cnn/transfo)
CLP5
HWRF
GFSO
AEMN
FSSE
OFCL
OP-average consensus
HUML/OP-average consensus

Operational
Consensus
Consensus
Experiments

5

Eastern Pacific Basin
Comparison on 837 cases
MAE (km) Error sd (km)
81
47
72
43
72
43
121
67
67
42
65
45
60
37
56
47
54
33
55
37
50
32

North Atlantic Basin
Comparison on 899 cases
MAE (km) Error sd (km)
144
108
111
79
109
71
201
149
75
49
71
54
73
55
69
53
71
56
64
48
61
42

C ONCLUSION

This study develops a novel multimodal machine learning framework for tropical cyclone intensity
and track forecasting utilizing historical storm data and meteorological reanalysis data. We present
5Published as a conference paper at ICLR 2022

a three-step pipeline to combine multiple machine learning approaches, consisting of (1) deep feature extraction, (2) concatenation of all processed features, (3) prediction. We demonstrate that a
successful combination of deep learning techniques and gradient-boosted trees can achieve strong
predictions for both track and intensity forecasts, producing comparable results to current operational forecast models, especially in the intensity task.
We show that multimodal encoder-decoder architectures can successfully serve as a spatial-temporalfeature extractor for downstream prediction tasks. In particular, this is also the first successful application of a Transformer-decoder architecture in tropical cyclone forecasting. Furthermore, consensus models that include machine learning models could benefit the NHC’s official forecast for
both intensity and track, thus demonstrating the potential value of developing machine learning approaches as a new branch methodology for tropical cyclone forecasting. Moreover, once trained, ourmodels run in seconds, showing practical interest for real-time forecast, the bottleneck lying only inthe data acquisition. In conclusion, our work demonstrates that machine learning can be a valuableapproach to providing alternatives in tropical cyclone forecasting.

R EFERENCES
Sim D. Aberson. Five-day tropical cyclone track forecasts in the north atlantic basin. Weather andForecasting, 13(4):1005 – 1015, 1998.
Sheila Alemany, Jonathan Beltran, Adrian Perez, and Sam Ganzfried. Predicting hurricane trajectories using a recurrent neural network. In Proceedings of the AAAI Conference on Artificial
Intelligence, volume 33, pp. 468–475, 2019.
Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Bengio. Neural machine translation by jointly
learning to align and translate. CoRR, abs/1409.0473, 2015.
Gu-Feng Bian, Gao-Zhen Nie, and Xin Qiu. How well is outer tropical cyclone size represented in
the era5 reanalysis dataset? Atmospheric Research, 249:105339, 2021. ISSN 0169-8095.
Mrinal K Biswas, Sergio Abarca, Bernardet Ligia, Ginis Isaac, Grell Evelyn, Iacono Michael, Kalina
Evan, Liu Bin, Avichal Mehra Kathryn Newman Jason Sippe Vijay Tallapragada Biju Thomas
Weiguo Wang Henry Winterbottom Qingfu, Liu Timothy Marchok, and Zhan Zhang. Hurricane
weather research and forecasting (hwrf) model: 2018 scientific documentation. Developmental
Testbed Center, 2018.
John P. Cangialosi. National hurricane center forecast verification report. National Hurricane Center, 2020. URL https://www.nhc.noaa.gov/verification/pdfs/
Verification_2020.pdf.
John P. Cangialosi, Eric Blake, Mark DeMaria, Andrew Penny, Andrew Latto, Edward Rappaport,
and Vijay Tallapragada. Recent Progress in Tropical Cyclone Intensity Forecasting at the National
Hurricane Center. Weather and Forecasting, pp. 1–30, 07 2020. ISSN 0882-8156.
Rui Chen, Xiang Wang, Weimin Zhang, Xiaoyu Zhu, Aiping Li, and Chao Yang. A hybrid cnn-lstm
model for typhoon formation forecasting. GeoInformatica, 2019.
Tianqi Chen and Carlos Guestrin. Xgboost: A scalable tree boosting system. In Proceedings of the
22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD’16, pp. 785–794. ACM, 2016. ISBN 978-1-4503-4232-2.
Junyoung Chung, Çaglar Gülçehre, KyungHyun Cho, and Yoshua Bengio. Empirical evaluation of
gated recurrent neural networks on sequence modeling. CoRR, abs/1412.3555, 2014.
Mark DeMaria and John Kaplan. A statistical hurricane intensity prediction scheme (ships) for theatlantic basin. Weather and Forecasting, 9(2):209–220, 1994.
Mark DeMaria, Michelle Mainelli, Lynn K. Shay, John A. Knaff, and John Kaplan. Further improvements to the statistical hurricane intensity prediction scheme (ships). Weather and Forecasting,20(4):531 – 543, 2005.
6

Published as a conference paper at ICLR 2022

ECWMF. PART III: Dynamics and Numerical Procedures. Number 3 in IFS Documentation.
ECMWF, 2019. URL https://www.ecmwf.int/node/19307.
ERA5. Era5 reanalysis, 2017. URL https://doi.org/10.5065/D6X34W69. Accessed2020-09-20.
Song Gao, Peng Zhao, Bin Pan, Yaru Li, Min Zhou, Jiangling Xu, Shan Zhong, and Zhenwei Shi. A
nowcasting model for the prediction of typhoon tracks based on a long short term memory neuralnetwork. Acta Oceanologica Sinica, 37:8–12, 2018.
Sophie Giffard-Roisin, Mo Yang, Guillaume Charpiat, Christina Kumler Bonfanti, Balázs Kégl, andClaire Monteleoni. Tropical cyclone track forecasting using fused deep learning from aligned
reanalysis data. Frontiers in Big Data, 3:1, 2020. ISSN 2624-909X.
Aslak Grinsted, Peter Ditlevsen, and Jens Hesselbjerg Christensen. Normalized us hurricane damage
estimates using area of total destruction, 1900-2018. Proceedings of the National Academy of
Sciences, 116(48):23942–23946, 2019. ISSN 0027-8424.
Kevin Hodges, Alison Cobb, and Pier Luigi Vidale. How well are tropical cyclones represented inreanalysis datasets? Journal of Climate, 30(14):5243 – 5264, 2017.
Diederik Kingma and Jimmy Ba. Adam: A method for stochastic optimization. International
Conference on Learning Representations, 12 2014.
J.A. Knaff, M. DeMaria, C.R. Sampson, and J.M. Gross. Statistical 5-day tropical cyclone intensity
forecasts derived from climatology and persistence. Weather and Forecasting, 18:80 –92, 2003.
K. R. Knapp, M. C. Kruk, D. H. Levinson, H. J. Diamond, and C. J. Neumann. The international
best track archive for climate stewardship (ibtracs): Unifying tropical cyclone best track data,
2010.
Jie Lian, Pingping Dong, Yuping Zhang, Jianguo Pan, and Kehao Liu. A novel data-driven tropical
cyclone track prediction model based on cnn and gru with multi-dimensional feature selection.
IEEE Access, 2020.
Mina Moradi Kordmahalleh, Mohammad Gorji Sefidmazgi, and Abdollah Homaifar. A sparse recurrent neural network for trajectory prediction of atlantic hurricanes. In Proceedings of the Genetic
and Evolutionary Computation Conference 2016, pp. 957–964, 2016.
M. Mudigonda, Sookyung Kim, A. Mahesh, S. Kahou, K. Kashinath, D. Williams, Vincent Michalski, T. O’Brien, and M. Prabhat. Segmenting and tracking extreme climate events using neuralnetworks. 2017.
National Hurricane Center. Automated tropical cyclone forecasting system (atcf), 2021. URL
https://ftp.nhc.noaa.gov/atcf/. Accessed: 2021-04-06.
C. Sampson and Ann J. Schrader. The automated tropical cyclone forecasting system (version 3.2).
Bulletin of the American Meteorological Society, 81:1231–1240, 2000.
Benjamin A. Schenkel and Robert E. Hart. An examination of tropical cyclone position, intensity,and intensity life cycle within atmospheric reanalysis datasets. Journal of Climate, 25(10):3453– 3475, 2012.
Udai Shimada, Hiromi Owada, Munehiko Yamaguchi, Takeshi Iriguchi, Masahiro Sawada, Kazumasa Aonashi, Mark DeMaria, and Kate D. Musgrave. Further Improvements to the Statistical
Hurricane Intensity Prediction Scheme Using Tropical Cyclone Rainfall and Structural Features.
Weather and Forecasting, 33(6):1587–1603, 11 2018. ISSN 0882-8156.
Hui Su, Longtao Wu, Jonathan H. Jiang, Raksha Pai, Alex Liu, Albert J. Zhai, Peyman Tavallali,and Mark DeMaria. Applying satellite observations of tropical cyclone internal structures to rapid
intensification forecast with machine learning. Geophysical Research Letters, 47(17), 2020.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In Advances in Neural Information Processing Systems 30, pp. 5998–6008. 2017.
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A

DATA

A.1

H ISTORICAL S TORM DATA S ET

We obtained historical storm data from the National Oceanic and Atmospheric Administration
through the post-season storm analysis dataset IBTrACS (Knapp et al., 2010). Among the available features, we have selected time, latitude, longitude, and minimum pressure at the center of theTC, distance-to-land, translation speed of the TC, direction of the TC, TC type (disturbance, tropical, extra-tropical, etc.), basin (North-Atlantic, Eastern Pacific, Western Pacific, etc), and maximum
sustained wind speed from the WMO agency (or from the regional agency when not available). Our
overall feature choice is consistent with previous statistical forecasting approaches (DeMaria & Kaplan, 1994; DeMaria et al., 2005; Giffard-Roisin et al., 2020). In this paper, we refer to this data as
statistical data (see Table 3).
We processed statistical data through several steps before inputting it into machine learning models. First, we treated the categorical features using the one-hot encoding technique: for a specificcategorical feature, we converted each possible category as an additional binary feature, with 1 indicating the sample belongs to this category and 0 otherwise. We encoded the basin and the nature of
the TC as one-hot features. Second, we encoded cyclical features using cosine and sine transformations to avoid singularities at endpoints. Features processed using this smoothing technique include
date, latitude, longitude, and storm direction.

Table 3: List of features included in our statistical data.
FeatureLatitudeRange[-90.0, 90.0]

Longitude[-180.0, 180.0]

WMO
Wind

[10, 165]

WMO
Pressure
Distance
to Land[880, 1022][0, 4821]

Unit Type
Processing
deg numerical spline interpolanorthtion by IBTrACS,
standardize
deg numerical spline interpolaeast
tion by IBTrACS,
standardize
knots numerical linear interpolation, conversion
to 1-min, standardize
mb
numerical linear interpolation, standardizekm
numerical standardize

Storm
Speed

[0, 69]knots numerical standardize

Storm
Direction[0, 360]

deg

numerical cosine & sine encoding

Storm Displacement
Latitude
Storm Displacement
LongitudeBasin[-2.68, 3.13]

deg

numerical standardize[-3.83, 4.28]

deg

numerical standardize[NA, EP, WP,
NI, SI, SP, SA]

N/A

categorical one-hot encoding[DS, TS, ET,
SS, MX]

N/A

categorical one-hot encoding

Storm
Type

8

Description
Latitude of the center of the hurricane.
Longitude of the center of the hurricane.
Maximum sustained wind speed fromthe WMO agency for the current location.
Wind pressure from the WMO agency
for the current location.
Distance to land from the current position. The IBTrACS land mask includes
islands larger than 1400 km2 .
Translation speed of the system as calculated from the positions in latitude
and longitude.
Translation direction of the system, as
calculated from the positions, pointing
in degrees east of north.
Engineered feature, indicating latitudechange since the last time step (3 hours
ago).
Engineered feature, indicating longitude change since the last time step (3hours ago).
Basins include: NA - North Atlantic, EP- Eastern North Pacific, WP - Western
North Pacific, NI - North Indian, SI South Indian, SP - Southern Pacific, SA- South Atlantic
Storm types include: DS - Disturbance,
TS - Tropical, ET - Extratropical, SS- Subtropical, NR - Not reported, MX- Mixture (contradicting nature reports
from different agencies)

Published as a conference paper at ICLR 2022

A.2

R EANALYSIS M APS

Reanalysis maps are used extensively for atmospheric monitoring, climate research, and climate
predictions. They are assimilated using observational data and provide a comprehensive record of
how weather and climate evolve, based on dynamical aspects of the Earth systems, such as the airpressure, humidity, and wind speed.
In our work, we used the extensive ERA5 reanalysis data set (ERA5, 2017) developed by the European Centre for Medium-Range Weather Forecasts (ECWMF). ERA5 provides hourly estimates ofa large number of atmospheric, land, and oceanic climate variables. The data cover the Earth on a
30 km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80 km.
We extracted (25° × 25°) maps centered at the storm locations across time, given by the IBTrACS
dataset described previously, of resolution 1° × 1°, i.e., each cell corresponds to one degree of
latitude and longitude, offering a sufficient frame size to capture the entire storm. We obtained ninereanalysis maps for each TC time step, corresponding to three different features, geopotential z, uand v components of the winds, at three atmospheric altitudes, 225, 500, and 700 hPa (see Figure1). We chose the three features to incorporate physical information which would influence the TC
evolution, and this choice is motivated by previous literature in applying ML techniques to process
reanalysis maps (Shimada et al., 2018; Chen et al., 2019; Giffard-Roisin et al., 2020).
As a remark, we acknowledge two main limitations from using reanalysis maps for TC forecasting.
First, since they are reanalysis products, they are not available in real-time and thus significantly
hinder operational use. Second, they have deficiencies in representing tropical cyclones (Schenkel& Hart, 2012; Hodges et al., 2017; Bian et al., 2021); for instance, with large TC sizes particularly
being underestimated (Bian et al., 2021).
A.3

O PERATIONAL F ORECAST M ODELSWe obtained operational forecast data from the ATCF data set, maintained by the National Hurricane
Center (NHC) (Sampson & Schrader, 2000; National Hurricane Center, 2021). The ATCF datacontains historical forecasts by operational models used by the NHC for its official forecasting fortropical cyclones and subtropical cyclones in the North Atlantic and Eastern Pacific basins. To
compare the performance of our models with a benchmark, we selected the strongest operational
forecasts with a sufficient number of cases concurrently available: including Decay-SHIPS, GFSO,
HWRF, FSSE, and OFCL for the intensity forecast; CLP5, HWRF, GFSO, AEMN, FSSE, and OFCL
for the track forecast (see detailed list in Table 4).
Table 4: Summary of all operational forecast models included in our benchmark.
Model ID
CLP5
Decay-SHIPS
GFSO
HWRF
AEMN
FSSE
OFCL

B

Model name or type
CLIPER5 Climatology and Persistence
Decay Statistical Hurricane Intensity
Prediction Scheme
Global Forecast System model
Hurricane Weather Research andForecasting model
GFS Ensemble Mean Forecast
Florida State Super Ensemble
Official NHC Forecast

Model type
Statistical (baseline)
Statistical-dynamical

Forecast
Track
Intensity

Multi-layer global dynamical
Multi-layer regional dynamical

Track, Intensity
Track, Intensity

Ensemble
Corrected consensus
Consensus

Track
Track, Intensity
Track, Intensity

E NCODER -D ECODER A RCHITECTURES D ETAILSThe CNN-encoder At each time step, the corresponding nine reanalysis maps are fed into the
CNN-encoder, which produces one-dimensional embeddings. The CNN-encoder consists of three
convolutional layers, with ReLU activation and MaxPool layers in between, then followed by two
fully connected layers.
Next, we concatenate the reanalysis maps embeddings with processed statistical data corresponding
to the same time step. Note that at this point data is still sequentially structured as 8 time steps to bepassed on to the GRU-decoder or the Transformer-decoder.
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Published as a conference paper at ICLR 2022

The GRU-decoder Our GRU-decoder consists of two unidirectional layers. The data sequenceembedded by the encoder is fed sequentially in chronological order into the GRU-decoder. For eachtime step, the GRU-decoder outputs a hidden state representing a “memory” of the previous time
steps. Finally, a track or intensity prediction is made based upon these hidden states concatenated
all together and given as input to fully-connected layers (see Figure 3).
The Transformer-decoder Conversely to the GRU-decoder, the sequence is fed as a whole into
the Transformer-decoder. The time-sequential aspect is lost since attention mechanisms allow eachhidden representation to attend holistically to the other hidden representations. Therefore, we add
a positional encoding token at each timestep-input, following standard practices (Vaswani et al.,2017). This token represents the relative position of a time-step within the sequence and reintroduces some information about the inherent sequential aspect of the data and experimentally
improves performance.
Then, we use two Transformer layers that transform the 8 time steps (of size 142) into an 8-timestep
sequence with similar dimensions. To obtain a unique representation of the sequence, we average theoutput sequence feature-wise into a one-dimensional vector, following standard practices. Finally, a
track or intensity prediction is made based upon this averaged vector input into one fully-connected
layer.
B.1

S UMMARY OF H URRICAST M ODELSThis section lists all the Hurricast models reported in this paper and Table 5 summarizes the methodologies employed.
Table 5: Summary of the various versions of the Hurricast framework for which we report results.
Models differ in architecture and data used and are named based on these two characteristics.
N◦
1
2
3
4

Name
HUML-(stat, xgb)
HUML-(stat/viz, xgb/cnn/gru)
HUML-(stat/viz, xgb/cnn/transfo)
HUML/OP-average

Data Used
Statistical
Statistical, Vision embeddings
Statistical, Vision embeddings
Operational forecasts,
HUML-(stat/viz, xgb/cnn/transfo)

ML Methods
XGBoost
XGBoost, Feature extraction with CNN, GRU
XGBoost, Feature extraction with CNN, Transformers
Simple average

Models 1-3 are variations of the three-step framework described in Figure 2, with the variation of
input data source or processing technique. Model 1, HUML-(stat, xgb), has the simplest form,
utilizing only statistical data. Models 2-3 utilize statistical and vision data and are referred to as
multimodal models. They utilize vision features extracted with the encoder-decoder, with GRU andTransformer decoders, respectively. Model 4 is a simple average consensus of a few operational
forecasts models used by the NHC and our Model 3, HUML-(stat/viz, xgb/cnn/transfo). We use
Model 4 to explore whether the Hurricast framework can benefit current operational forecasts bycomparing its inclusion as a member model.

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