Digital Typhoon: Long-term Satellite Image Dataset for the Spatio-Temporal Modeling of Tropical Cyclones
Authors: Asanobu Kitamoto
Jared Hwang
Bastien Vuillod
Lucas Gautier
Yingtao Tian
Tarin Clanuwat
Venue: NeurIPS 2023 Datasets and Benchmarks Spotlight (2023)
Keywords: meteorology
typhoon
tropical cyclone
climate
satellite image
dataset
TL;DR: The Digital Typhoon dataset, a 40+ year-long, high-quality typhoon satellite image dataset, is released for machine learning research on tropical cyclones.

Abstract: This paper presents the official release of the Digital Typhoon dataset, the longest typhoon satellite image dataset for 40+ years aimed at benchmarking machine learning models for long-term spatio-temporal data. To build the dataset, we developed a workflow to create an infrared typhoon-centered image for cropping using Lambert azimuthal equal-area projection referring to the best track data. We also address data quality issues such as inter-satellite calibration to create a homogeneous dataset. To take advantage of the dataset, we organized machine learning tasks by the types and targets of inference, with other tasks for meteorological analysis, societal impact, and climate change. The benchmarking results on the analysis, forecasting, and reanalysis for the intensity suggest that the dataset is challenging for recent deep learning models, due to many choices that affect the performance of various models. This dataset reduces the barrier for machine learning researchers to meet large-scale real-world events called tropical cyclones and develop machine learning models that may contribute to advancing scientific knowledge on tropical cyclones as well as solving societal and sustainability issues such as disaster reduction and climate change. The dataset is publicly available at http://agora.ex.nii.ac.jp/digital-typhoon/dataset/ and https://github.com/kitamoto-lab/digital-typhoon/.

Digital Typhoon: Long-term Satellite Image Dataset for the Spatio- Temtooal Modeling of Tropical Cyclones
Asanobu Kitamoto 1.2
Jared Hwang
Bastien Vuillod
Lucas Gautier":
Yingtao Tian
Tarin Clanuwat
National Institute of Informatics, Japan
Typhoon Science and Technology Research Center, Yokohama National University, Japan University of Southern California, USA Grenoble-INP, Ensimag, France
Université Clermont Auvergne, ISIMA, France Google DeepMind
Abstract
This paper presents the official release the Digital Typhoon dataset, the longest typhoon satellite image dataset for 40+ years aimed at benchmarking machine learning models for long-term spatio-temporal data. To build the dataset, de- veloped a workflow to create an infrared typhoon-centeree image for cropping using Lambert azimuthal equal-area projection referring the best track data. We also address data quality issues such as inter-satellite calibration to create a homogeneous dataset. To take advantage of the dataset, we organized machine learning tasks by the types and targets of inference, with other tasks for me- teorological analysis, societal impact, and climate change. The benchmarking results on the analysis, forecasting, and reanalysis for the intensity suggest that the dataset is challenging for recent deep learning models, due to many choices that affect the performance of various models. This dataset reduces the barrier for machine learning researchers to meet large-scale real-world events called tropical cyclones and develop machine learning models that may contribute to advancing scientific knowledge on tropical cyclones as well as solving societal and sustain- ability issues such as disaster reduction and climate change. The dataset is publicly available at htthtp ex. nii ac pig..aa-oooitttttatttttoooooo dataset/ and htttss///githie http://ooo...tttttoooooaaaaaaaaa
Introduction
Tropical cyclones, also known as typhoons and hurricanes in certain regions, have been the critical target of research due to their substantial societal impact [I]]. To reduce the impact of tropical cyclones, the meteorological community, along with other earth science communities, has been developing both a theoretical and empirical understanding of tropical cyclones through efforts such as advancing satellite remote sensing and atmospheric simulation models of higher spatial, temporal, and spectral resolutions for better analysis and forecasting.
Metcorologists have also developed an empirical method, known as the Dvorak technique 10 to estimate the intensity of tropical cyclone based on time-series observation data collected from worldwide ground sensor networks, meteorological satellites, and reconnaissance flights. This technique consists of manual procedure to estimate tropical cyclone intensity based on the cloud
37th Conference on Neural Information Processing Systems (NeurIPS 2023) Track on Datasets and Benchmarks.

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Best Track
Satellite Image
Map Projection
Dataset
Seasons: 44 (1978-2022)
Typhoons: 1,099
Images: 189,364
Figure 1: Overview of the Digital Typhoon dataset.
patterns of satellite images and temporal model for intensity change. The method was originally developed in the United States in the 1970s and later adopted by meteorological agencies worldwide become the standard procedure. However, experts are aware of its heuristic and subjective nature, as it relies on empirical, rather than theoretical, human interpretation of observation data. Solutions to this problem include more objective and automated versions the Dvorak technique 39] 38 and
citizen science project to take advantage of collective intelligence [5)
clear that the Dvorak technique naturally fits into the machine learning framework by using images input and intensity values as output. Hence there is growing interest in both the machine
learning community 42 g 36 33] and the meteorology community [8] to take advantage of the big data of tropical cyclones for developing data-driven approaches. One of the authors, Asanobu Kitamoto, started the Digital Typhoon project in 1999 with the aim of applying machine learning to typhoon analysis and forecasting [21 26).
[6]. The first step was to develop homogeneous satellite image
dataset for machine learning as in Figure T The second step was to apply machine learning algorithms available at the time, such as SVM (24). Generative Topographic Mapping (22), and content-basee baaee
image retrieval (23), which is later evolved into deep learning-basse models for classification and
regression tasks [46 41, combined with fisheye preprocessing [16]. The third step was to release the
website "Digital Typhoon" in 2003 for browsing and searching datasets [25). The remaining problem
was the lack of public datasets for machine learning. There have been attempts to download the dataset via scraping of the website (c.g. (531), but the dataset created in this way is of lower quality.
Here we introduce the Digital Typhoon dataset, the longest typhoon satellite image dataset. This dataset reduces the burden of researchers start machine learning on tropical cyclones without solid domain knowledge of meteorology and satellite remote sensing. We also illustrate the variety of tasks so that researchers can concentrate on building and evaluating machine-learning models.
2 Related Work
2.1 Track Datasets
The track data includes the 'annotation' of tropical cyclones, such as location, intensity, and wind circles, based on the interpretation of metcorological experts following the established procedure (c.g. Dvorak Technique). The best estimate, obtained from retrospective analysis after collecting all the information from the start to the end of life, called the best track dataset.
The Digital Typhoon dataset targets the Western North Pacific basin, and the Japan Meteorological Agency (JMA) is designated as the regional center maintain the best track dataset. Globally, the International Best Track Archive for Climate Stewardship (IBTrACS) (28 collects the best track from meteorological agencies worldwide and creates a comprehensive track dataset since 1842.
IBTrACS shows an interesting variation of the best track; namely the location and intensity of the same tropical cyclone show discrepancies across metcorological agencies [48). This fact suggests

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6000
0xc
3017
0030
MGO
2005
Cride
(a) Number of images for each grade
(b)
Transition matrix for the (c) Number of typhoons by the numher of images
grade
Figure 2: Visualization statistics of the Digital Typhoon dataset.
that the interpretation the observation data not unique, or not the ground truth a strict sense. Nonetheless, regard the best track as the ground truth for most machine learning tasks, because is the best estimate available. a reanalysis task, however, we could critically evaluate the quality of the best track [17].
2.2 Image Datasets
The image dataset has information about the spatial distribution of physical properties such as cloud patterns as grid data. The observation dataset [27 30 derived from sensor observation that measures the physical properties of the atmosphere, while the simulation dataset, both typhoon-relatee
337 and the global atmosphere 44 I 3 [ is generated as the representation of the atmosphere in
simulation model. Observation datasets and simulation datasets are linked through data assimilation, which is a statistical method to integrate observation datasets into a simulation model.
The Digital Typhoon dataset is an observation dataset, and it offers a richer detail of tropical cyclones with higher temporal and spatial resolutions than the simulation dataset. In addition, data quality issues in the observation dataset, such as sensor noise, missing data, and long-ten sensor calibration, handled properly so that machine learning models are not significantly affected by those issues.
Digital Typhoon Dataset
3.1 Dataset Overview
The Digital Typhoon dataset is created from the comprehensive satellite image archive of the Japanese geostationary satellite series, Himawari, from Himawari-1 to Himawari-9, Although those images not copyrighted, some data are not accessible for free, and old satellite images have old formats for which open-source parsers are difficult to find. Hence we developed our own parsers for all generations of satellites, and the workflow to create typhoon-centered images by referring the best track, as shown in Figure []]
Using this workflow, created the Digital Typhoon dataset by integrating metadata and images. The metadata contains hourly best-track data with additional information about the file name and each image's ' quality. The formatting of the best track data aligns with the original best track data sourced from the JMA. On the other hand, the images feature a 2D array of brightness temperatures around the typhoon's 's center, formatted in HDF5.
As result, the dataset comprises total of 1,099 typhoons and 189,364 images. Figure visualizes some of the statistics of the dataset. It is complete record of typhoons occurring in the Western North Pacific region (ranging from 100 to 180 degrees east of the northern hemisphere), from the 1978 season through the 2022 season, with missing typhoons in 1979 and 1980 due to the unavailability of satellite data. The length the dataset, spanning 44 typhoon seasons (years), the longest typhoon image dataset. We call it the longest dataset because Japanese geostationary satellite images for typhoons before 1978 were lost forever, and our dataset went back to the oldest satellite image preserved. Hence it provides a unique opportunity to challenge long-term datasets.

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Table 1: Comparison between the Digital Typhoon dataset and the HURSAT dataset.
Digital Typhoon dataset
HURSAT dataset
Temporal coverage
1978-2022 (present)
1978-2015
Temporal resolution
one hour Himawari
three hours
Target satellites
SMS, GOES, Meteosat, Himawari, FY2
Spatial coverage
Western North Pacific basin 5km
All basins (Global)
Spatial resolution
8km
Image coverage
512x512 pixels (1250km from the center) infrared (others on the Website)
301 x301 pixels (1100km from the center)
Spectral coverage
visible, infrared, water vapor, near IR. split window
Map projection
Azimuthal equal-area projection
Equirectangular projection
Calibration
Recalibration
ISCCP
Data format
HDF5
NetCDF
Best track
Japan Metcorological Ageney
IBTrACS
Dataset browsing
Digital Typhoon website
Download only
The Digital Typhoon dataset can reduce the burden of machine learning researchers to study tropical cyclones. First, it opens up access tropical cyclone data processed from long-term satellite data. Second, it offers a homogeneous dataset created by the image processing workflow based on expertise in metcorology and satellite remote sensing. Third, massive computations process hundreds of terabytes of original satellite data to create a machine-learning dataset are not necessary, comprehensive explanation of the workflow for the creation of the dataset is provided in the Appendix.
The Digital Typhoon dataset is available the official page http: http://agora, ex exx.nii. jpp with an open data license, namely the Creative Commons Attri- bution 4.0 International (CC BY 4.0) License.
3.2
Comparison with the HURSAT Dataset
Among satellite image datasets of tropical cyclones, Hurricane Satellite Data (HURSAT) dataset (27 (30 from The National Oceanic and Atmospheric Administration (NOAA) is the most notable dataset size and coverage. Table provides a comparative summary the Digital Typhoon and HURSAT datasets. There are distinct variations between the two enumerated below.
Temporal coverage The Digital Typhoon dataset continually updated, and the longest tropical cyclone image dataset worldwide. On the other hand, the HURSAT dataset stopped updating in 2015.
Temporal resolution The Digital Typhoon dataset has temporal resolution of one hour which is higher than the HURSAT dataset's three-hour resolution. A freeufryy change such as rapid intensification more sensitive to temporal resolution.
Spatial coverage The Digital Typhoon dataset specifically targets the Western North Pacific basin, whereas the HURSAT dataset encompasses all basins.
Spatial resolution The Digital Typhoon dataset possesses a spatial resolution of approximately 5km, superior the HURSAT dataset's roughly 8km (0.07 degree). A small-scale structure such as the eye of a tropical cyclone more sensitive to spatial resolution.
Spectral coverage The Digital Typhoon dataset incorporates the infrared (IR) channel, while the HURSAT dataset has more channels. It should be noted, however, that the Digital Typhoon website has the same spectral coverage, and the dataset can be easily extended to cover these channels.
Map projection The Digital Typhoon dataset utilizes the Lambert azimuthal equal-area projection, maintaining the spherical shape of the tropical cyclone, while the HURSAT dataset employs the

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equirectangular (lat/long) grid, causing shape distortion in higher latitude or peripheral areas. Figure ! shows an example of distortion when a typhoon is observed in the north.
Dataset browsing The Digital Typhoon dataset can be browsed via the Digital Typhoon website, which offers additional data. In contrast, the HURSAT dataset is solely available for download.
3.3 Design Choices
The dataset has several design choices, such as spectral coverage, spatial resolution, temporal resolution, and spatial coverage. In the following, we explain the reasons behind our choices.
Spectral coverage The current dataset includes only the Infrared channel (IR1) (wavelength of around I1um) but does not include any other channels available on the Digital Typhoon website, The following is the summary of the availability of each channel on the website.
IRI (infrared); the data has been available since the beginning (1978).
VIS (visible): the data has been available since the beginning (1978), but images from early satellites were too noisy and not appropriate for a machine learning dataset. In addition, the visible channel is meaningful only during the daytime.
IR2 (infrared) and WV (water vapor): the data has been available since 1995 (Himawari-55,
NIR (near infrared) and other channels: the data has been available since 2005 (2nd genera- tion) or 2015 5 (3rd generation).
As summarized, the IRI is the only channel that is the longest and with fewer data quality issues, and this is the reason we included only the IR1 channel in our first version of the dataset. Future inclusion of multispectral data may offer additional tasks such as multispectral classification and regression.
Spatial resolution The spatial resolution of about 5km per pixel reflects the spatial resolution of the IR1 channel for the first- restteenrration satellites from Himawari-1 to Himawari-5. This resolution has improved to 4km for the second generation and 2km for the third generation. In spite of these progresses in technology, homogeneous dataset. An interesting task in the future is to transfer a machine- learning model from long-term lower-resolution datasets short-term higher-resolution datasets that can take advantage of recent technology for better forecasting.
chose a 5km resolution because it is the best choice create long-term
Temporal resolution The temporal resolution of one hour reflects the temporal resolution of one hour for some of the first-generation satellites after Himawari-3. From Himawari-1 to Himawari-2, the temporal resolution was more than one hour, typically every three hours. For this reason, the data before 1987 has many missing data points as an hourly dataset. This resolution has improved to 30 minutes for the second generation and 10 minutes for the third generation. In spite of these progresses in technology, we chose one hour because it is a representative interval for many types of meteorological observations.
Spatial coverage The current dataset only covers the Western North Pacific basin the northern hemisphere (NH), but the Digital Typhoon website offers the same types of images for the southern hemisphere (SH) the Australian basin using the best track from the Bureau of Meteorology, Australia. Here an interesting question is how model trained in NH can be transferred to SH. From a meteorological point view, tropical cyclones in various basins are considered the same meteorological phenomena, theoretically, the dataset can be created similarly, and machine learning results are transferable. However, we also need to consider many details that may have an impact on the actual results, such different quality of the best track data, and different sensor characteristics and calibration methods for different satellites. A future version of our datasets and benchmarks may address these issues.
4 Machine Learning Tasks
The Digital Typhoon dataset serves two important roles. First, offers a practical real-world dataset and tasks for the machine learning community to explore new models and solutions. Second, it
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provides a tool for meteorologists to apply data-driven approaches in studying tropical cyclones. The following is a summary of tasks in multiple dimensions. Other lists of tasks can be found in the review 55 45
4.1 Types of Inference
Analysis The task is to estimate current values using the current and past data. For instance, estimating the intensity of typhoon falls into an analysis task, as it produces information about the typhoon's intensity using both current and past data. Supervised learning within this task can be further categorized into either classification task or regression task, contingent on whether the target variable categorical numerical. Additionally, unsupervised tasks can be designed for clustering or identifying typhoons with similar characteristics.
Forecasting The task is to produce future predictions based on current and past data. The forecasts can be evaluated with the actual outcomes from the real event which become available over time. The forecasting task has a sub-task called nowcasting, aimed at making short-term forecasting spanning several hours using data-driven extrapolation. Note, however, that weather forecasting theoretically constrained by the atmosphere's chaotic nature, which states that a minor difference in initial conditions can escalate over time.
We call this task 'forecasting' instead of 'prediction' because prediction is ambiguous in machine learning. In meteorology. prediction strictly used to mean future values, but in machine learning prediction could mean the output of machine learning model without temporal dimension. To avoid confusion across disciplines, we use forecasting throughout the paper.
Reanalysis The task produce the best estimate given all obtainable data. This task is especially relevant to producing a uniform dataset spanning a long period of time, such as detecting trends in tropical cyclone activity to study the effects of climate change. As addressed Section 2.1 the best track dataset may contain errors due to technological limitations or inconsistencies from different human experts. Machine learning can potentially aid in evaluating the quality of annotated data.
4.2 Targets of Inference
Intensity The task makes inferences on the strength and size of typhoon. The categorical grade is used to classify both the strength and type of tropical cyclone. A classification task uses these grades as target variables. On the other hand, the intensity of tropical cyclones is measured numerically by central pressure and maximum sustained wind. An intensity regression task uses either pressure or wind as the target variable. In addition, the metadata includes the radius of the strong wind circle that represents the size of tropical cyclone, so we can also design a regression task for size using the radius the target variable.
Track The task makes inferences on the geographical location of a typhoon. The cyclone's 's center, as estimated by human experts, is represented by latitude and longitude coordinates with a precision of 0.1 degrees. A regression task for predicting the typhoon's 's location uses these latitude and longitude coordinates as target variables.
Formation The task makes inferences on the birth of tropical cyclone, which typically occurs in tropical regions. Among the numerous cloud clusters actively evolving in tropical regions, determining which one will evolve into a tropical cyclone presents a challenging forecasting task, making it target for machine learning applications [6].
The task makes inferences on the transition from tropical cyclone to an extra-tropical cyclone, which typically occurs in mid-latitude regions. Two types of cyclones are conceptually distinct from meteorological perspective, but as a natural phenomenon, they are continuous. The data-driven modeling of a continuous transition process connecting two discrete concepts machine- learning task.
Transition

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Table The statistics of target values.
Target value
Range
Mean
Standard deviation
Central pressure
870-1018 (hPa)
983.8
22.5
Maximum sustained wind
35-140 (knots)
59.2
19.8
4.3 Meteorological Analysis
Machine learning can also be applied to analyze meteorological events tropical cyclones, such as rapid intensification (2] eyewall replacement [14], and overshooting cloud tops [20]. These events may be linked with the forecasting of tropical cyclones, yet their underlying mechanisms not entirely understood. Data-driven methodologies could potentially provide insights that contribute to the development of novel theoretical framework for understanding these phenomena.
4.4 Analysis for Societal Impact
The Digital Typhoon dataset represents the atmospheric observation of tropical cyclone, but its societal impacts are measured by different sources and modalities. For example, hazards measured by heavy rainfall or strong winds, disasters are measured by landslides and flooding, and damages are measured by human casualties and financial loss. To construct a machine learning model analyze and forecast the societal impact, real-world datasets from many sources should be integrated with meteorological datasets. This would enable more comprehensive understanding of the full range of impacts arising from tropical cyclones.
4.5 Analysis for Climate Change
Understanding how long-ter anngterrm tropical cyclone activity is impacted by climate change is a crucial topic in society [32 22 47). Technological and methodological evolution that occurred during the 40+ years lifespan introduces many types of biases in the dataset. While certain biases may be removed by sensor calibration, others are harder to detect such as annotation errors by human experts. The reanalysis of historical data and the creation of a homogeneous dataset can contribute to advancing our knowledge of the relationship between tropical cyclones and climate change.
Benchmarks
5.1 Overview of Benchmarks
Machine learning tasks can be combined create benchmarks for machine learning. We propose three benchmarks. 1) Analysis, 2) Forecasting, and 3) Reanalysis of the intensity of typhoons. The following summarizes some of the technical choices for benchmarking.
Task
Data splitting In metcorological time series, data are auto-correlated and one has to be careful how to split the data before starting train a model 49) At least, random split for the image level must not be used to avoid overestimating the performance due to data leakage in the same typhoon sequence. Our assumption is that every sequence independent, and we do have to consider any leakage across sequences. So, as long as each sequence is treated atomic when splitting the dataset, there is no limitation to using the entire dataset. Hence we apply random splits to the sequence level (split-by-seqeence or the season level (split-by-season). More complex splits can be designed, such as split by satellite generations (1978-2004, 2005-2014, 2015-2022). These designed splits are especially useful for the reanalysis task in Section 5.4
The following benchmarks evaluate the performance by the absolute error of target values because this is easier for domain experts to understand the result. However, for machine learning experts, the relative error of target values is more intuitive. Instead of showing absolute and relative errors for each benchmark, we summarize the statistics of target values so that relative ervors can be roughly estimated. For example, the best result of 10.06 0.09 hPa RMSE in Table 3 less than one standard deviation of error.
Performance metric
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Table 3: The result of the pressure regression task for two architectures and three types of input.
RMSE (hPa)
Full (512 x 512)
Resized (224 224)
Cropped (224 x 224)
ResNet18
10.51 (=0.11)
10.47(=0.20)
10.06 (+:0.0)
ResNet50
11.12 (+0.41)
11.63 (+0.35)
10.09 (+00004
Table 4: The result of the wind regression task for two architectures and three types of input.
RMSE (kt)
Full (512 x 512)
Resized (224 224)
Cropped (224 x 224)
ResNet18
10.21 (#0.19)
10.09 (+0.08)
9.25 (+0.25)
ResNet50
10.05 (100266
10.21 (#0.14)
9.13 (+0.11)
Software and hardware To perform the benchmarks, we developed syython-basede software library pyphoon2, downloadable from https:///itthu: pyphoon2 comes with data loader and components to help build machine learning pipelines. All the experiments were performed on the internal cluster with 6 GPUs consisting of NVIDIA Quadro RTX 6000, NVIDIA Quadro RTX 8000, and NVIDIA Quadro RTX A6000.
com/kiitmotte lab/daiiiallltyhhhoe
5.2 Analysis for the Intensity
We propose classification tasks, which take an image as input and estimate grade as output, and regression tasks, which take image as input and estimate a pressure wind value output. In the JMA best track, grades 3, 4, and 5 denote a tropical cyclone, among which grade is the most intense according to the maximum sustained wind. Grade 2 signifies tropical depression, a type of cyclone weaker than a tropical cyclone. Moreover, grade 6 corresponds to an extra-tropical cyclone, type of cyclone having a different structure from tropical cyclone. Central pressure in hectopascal (hPa) is recorded for all grades, while the maximum sustained wind in knot (kt) is recorded only for grades 3, 4, and 5. In the following, describe the result of the regression task, and the result of the classification task is described in the appendix.
We explored three types of comparisons. First, we compared three architectures, namely VGG [11] ResNet [13 and Vision Transformer (9. Second, we compared models trained on 1) full-resolution images (512 x 512), 2) resized images (224 x 224), and 3) cropped images (224 x 224). In 2), the full region of the image is resized, while in 3), the central region of the image is cropped without resizing. The latter is inspired by the Dvorak technique, which focuses many relevant image features found around the typhoon center. Third, we compared two target values, namely pressure, and wind.
We used the Torch Vision 335 ResNet18 8 and ResNet50 models with learning rate (LR) of 10-4, batch size of 16, and for 50 epochs. An 80/20 train/test split by sequence was used. The ResNet18 and ResNet50 models were trained five and two times respectively. To evaluate, we measured the root mean square error (RMSE) of the prediction from ground truth and their standard deviations (+ std).
Table 3 and Table summarize the results. Firstly, ResNet50 yielded similar results to ResNet18. Secondly, cropping the images around the typhoon center yielded lower RMSE than other choices, indicating that cropping is better than resizing in preserving features around the typhoon center, or removing non-relevant features far from the center. Training a model on the full images did not perform well due to their larger number of pixels. Furthermore, Figure 3 Dillusstraes that regression performs better for weaker typhoons, but worse for stronger typhoons.
5.3 Forecasting for the Intensity
Our previous work used Recurrent Neural Network (RNN) to forecast the pressure directly from images and showed comparable performance with SHIPS 4I.. In this paper, we chose another approach using a convolutional LSTM (50] to predict the next n image frames of a typhoon given the previous 12 image frames and analyze the pressure from the predicted image. We adapted an implementation 40). and used 3-layer ConVLSTTM with 128 hidden dimensions. Due to resource limitations, used 128 128 downsampled images from only the first 24 hours of given typhoon.
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200
200
140
2020
250
200
pod
960
POlE COURS
130
113
80
62
880
metadara
(a) Plots for wind regression by ResNet18 for cropped images.
(b) Plots for pressure regression by ResNet18 for cropped images.
Figure 3: Prediction plots for pressure and wind regressions.
Forecasted
Expected
Figure Results image forecasting by ConvLSTM.
To forecast hours into the future (starting at 1), the 12 preceding frames (t [-11, 0)) passed into the model, which outputs single image serving as its forecast at = 1. Then, images from / [-10, 1]. including the predicted image, are passed back into the model get the prediction for = 2. This process is repeated n times to forecast n hours into the future. As a result, Figure 4 shows that the first predicted frame is perceptively blurred and rapidly deteriorates
advances.
We then trained a ResNet18 model on predicted images, as well as the first 24 images of every typhoon, to predict the pressure given 128 128 image. As result, Table that the model produces a larger RMSE and eтror as advances due to the blur of predicted images. A future adaptation may be to train both the ConyLSTM and ResNet in black box, such that the loss is minimized by image reproduction and pressure prediction.
Both models were trained on the same 80/20 train/test split by sequence. The ConvLSTM was trained once for 230 epochs with a starting LR of 10-4, and used Conealnn scheduler (34 with 100 steps. The ResNet model used modified first convolutional layer with kernel and stride size of (2, 2) and (1, 1). It was trained five times with an LR of 10 5 for 34 epochs. These hyperparameters were chosen they produced more consistent results given the smaller image sizes.
5.4 Reanalysis for the Intensity
The goal of this paper is to create a homogeneous long-term dataset, and the purpose of the reanalysis task is to identify biases and inconsistencies the dataset to factors such as technological evolution arising from satellite sensors, methodological evolution arising from the improvement of the Dvorak technique to annotate tropical cyclones. One approach this challenge to design special data splits to analyze historical factors.

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Table Results of pressure forecasting for 12-hours by ResNet18 (values in hPa).
1
2
3
6
12
RMSE
10.24 0.73
10.52 + 0.79 11.00 + 0.87 12.10 + 0.85
14.69 0.91
Table 6: The results of regression tasks for each satellite generation (values in hPa).
RMSE
Train the First
Train the Second
Train the Third
Test the First
10.04 (#:0.77
9.92 (+:009)
10.03 (+0.10)
Test the Second
12.80 (4=0.19)
11.05 (#=0.10)
11.17 (+0010)
Test the Third
10.34 (+-0.17) 10.03 (+0008)
9.94 (++0.16)
Our previous work studied this task by training the model using recent data and testing past data to analyze the trend of model performance, indicating that old satellite data may have different characteristics [1]. In this paper, split the dataset into three buckets by satellite generations, namely the first generation (1978-2004), the second generation (2005-2014) 14),, and the third generation (2015-2022), and train and test ResNet18 8 model for the regression task.
Input images were resized 224 x 224 in the same way as the analysis task. 208 sequences (the size of the smallest generation) were then randomly sampled from each generation five times and split into 80/20 train/test sets. A ResNet18 model was trained on each bucket, each for 101 epochs with a batch size of 16 and learning rate of 10-4; these parameters were chosen for their consistent results. Each of the three models was then tested on the test set of each bucket, such that a model trained on the first bucket was tested on the first, second, and third buckets.
Table shows that all three models performed roughly similarly on all three buckets, and no dataset bias was immediately reflected in the quality of the models. An expansion on the reanalysis task experiment we performed described in the Appendix.
5.5 Comparison with Other Approaches
Machine learning is not the only approach for data-driven analysis and forecasting of tropical cyclones. For the analysis of intensity, the Dvorak technique has been the most popular method among meteorologists. In addition, for the forecasting of intensity, computational approaches represent a typhoon in a simulation model and compute the future based on the theory of the atmosphere. This approach, however, has limitations due to spatial and temporal resolutions, and intensity forecasting still considered a difficult challenge. Instead, metcorologists have developed empirical methods, such as SHIPS 12 [56] with linear regression on hand-crafter metcorological features, or a similar approach using XGBoost 4 This paper focused on the comparison of machine learning models, but the real challenge for domain experts is comparing not only machine learning approaches but also computational, empirical, or manual approaches in the context of real-world solutions for tropical cyclones, such as disaster reduction. This paper is starting point for this grand challenge.
6 Conclusion
We have introduced the Digital Typhoon dataset for machine learning and meteorology communities to promote data-driven research on tropical cyclones. Our dataset offers a unique opportunity to benchmark various types of machine learning models, especially spatio-temporal models for long- term time-series images. A solution is not only valuable for machine learning benchmarking but also has the potential to contribute to advancing scientific knowledge on tropical cyclones as well as solving societal and sustainability issues such as disaster reduction and climate change.
Acknowledgments and Disclosure of Funding
Three of the authors, Jared Hwang, Bastien Vuillod, and Lucas Gautier, have been supported by the international internship program of the National Institute of Informatics.
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14


Reviews:
[{"review_id": "l9m2BboqOv", "reviewer": "Reviewer_Tmq2", "summary": "", "questions": "", "limitations": "The dataset's focus on the Western North Pacific basin limits its global applicability. However, this limitation is understandable given the scope of the project.", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "S1. The dataset's extensive temporal coverage is remarkable, making it a significant contribution to the field.\n\nS2. The integration with the Digital Typhoon website and the provision of a Python package enhance the dataset's accessibility and usability.\n\nS3. The paper is well-structured and provides a clear overview of the dataset's creation, its potential applications, and benchmark results.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "iYtKv71cNf", "reviewer": "Reviewer_fsVs", "summary": "", "questions": "", "limitations": "Nothin beyond what authors have reported in the paper. This is a very challenging ML task, and the development of this dataset can advance ML models that can be applied for such a task that would hopefully result in identifying opportunities for further improvement of the dataset. ", "rating": 8, "confidence": 5, "soundness": null, "presentation": null, "contribution": null, "strengths": "- This is a very comprehensive dataset in terms of temporal coverage, and tasks that are enabled. \n- The samples are well organized, and authors have provided detailed explanation how the dataset is curated. \n- Users are informed how they should sample the dataset given it's inherent properties like auto-correlation. \n", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "Nx053wmB0J", "reviewer": "Reviewer_zc1f", "summary": "", "questions": "", "limitations": "It is unclear how transferable would this dataset be across other regions. It would help the reader to know if there are no known practical differences between tropical cyclones in the Western North Pacific basin and in other basins. Similarly, it would be helpful to read about whether or not and how this dataset could be applied to tasks utilizing data from other satellites. Other limitations are mostly related with clarity, as described in the other comments. I see no potential negative societal impact.", "rating": 9, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "This paper seeks to bridge practitioners and researchers on the fields of deep learning, atmospheric/meteorology researchers, and remote sensing, enabling multidisciplinary studies on the field of tropical cyclones, bringing together authors with different expertise. This submission tackles this gap by providing a dataset that is ready to use (common data format, abstracting the different formats that had been used in the Himawari mission throughout the years, and with easy to ingest labels) and large enough for deep learning applications, while also incorporating some of the field-specific techniques used in tropical cyclones assessments (remote sensing TIR data, cyclones spatial features and relation to their severity classification). In terms of presentation, this paper presents the well thought reasoning used to put together this dataset, and includes a compact but complete overview of the dataset along with a comparison with one another siilar dataset in the field. It also includes the results (RMSE) of experimenting the dataset on three different deep learning models for different tasks. These experiments follow a very good description of tasks and applications for the dataset. The authors also mention different, well thought split strategies for the training and test data. In the preparation of the data, the authors discuss a very interesting point about the effects of different projections utilized when acquiring and utilizing satellite data of cyclones.\n\n\n\n ", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "NDPAIwGNZt", "reviewer": "Reviewer_SUZt", "summary": "", "questions": "", "limitations": "N/A", "rating": 6, "confidence": 4, "soundness": null, "presentation": null, "contribution": null, "strengths": "- This is currently the longest temporal span typhoon satellite image dataset available.\n- The authors have provided a website for convenient access to the dataset and have made the corresponding code available on GitHub.\n- The paper is written in an easy-to-understand manner and is very friendly to people who have no background in meteorological knowledge.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}, {"review_id": "G57fhlHO0C", "reviewer": "Reviewer_JoXz", "summary": "", "questions": "", "limitations": "Limitations and tradeoffs in the dataset are discussed. One concern I have is that it was not clear from the abstract or introduction that the dataset is not global; this is not an issue, but it would be better to be clear about it early.", "rating": 7, "confidence": 3, "soundness": null, "presentation": null, "contribution": null, "strengths": "1. This is a quite useful dataset, as there are not many high-quality datasets for extreme meteorological events. Such datasets are valuable for many societal applications, and for ML research.\n2. The dataset is a companion to the Digital Typhoon website, which I found useful for examining the data.\n3. A companion Python package for working with the data is provided.\n4. A number of benchmark results on different tasks are provided as a basis for research.", "weaknesses": "", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": ""}]

Author rebuttal:

Meta-review: The reviewers are positive about this submission and are largely satisfied with how the authors have addressed their concerns. The authors are encouraged to address the outstanding suggestion to "include a brief text about the first comment (use of IR band)"
Decision: Accept (Spotlight)