Long-Term Typhoon Trajectory Prediction: A Physics-Conditioned Approach Without Reanalysis Data
Authors: Young-Jae Park
Minseok Seo
Doyi Kim
Hyeri Kim
Sanghoon Choi
Beomkyu Choi
Jeongwon Ryu
Sohee Son
Hae-Gon Jeon
Yeji Choi
Venue: ICLR 2024 spotlight (2024)
Keywords: Weather Forecasting
Typhoon Trajectory Forecasting
Tropical Cyclone
Climate Change
Primary area: applications to physical sciences (physics, chemistry, biology, etc.)
TL;DR: Real-time 72-hour typhoon trajectory prediction using the NWP model.

Abstract: In the face of escalating climate changes, typhoon intensities and their ensuing damage have surged. Accurate trajectory prediction is crucial for effective damage control. Traditional physics-based models, while comprehensive, are computationally intensive and rely heavily on the expertise of forecasters. Contemporary data-driven methods often rely on reanalysis data, which can be considered to be the closest to the true representation of weather conditions. However, reanalysis data is not produced in real-time and requires time for adjustment since prediction models are calibrated with observational data. This reanalysis data, such as ERA5, falls short in challenging real-world situations. Optimal preparedness necessitates predictions at least 72 hours in advance, beyond the capabilities of standard physics models. In response to these constraints, we present an approach that harnesses real-time Unified Model (UM) data, sidestepping the limitations of reanalysis data. Our model provides predictions at 6-hour intervals for up to 72 hours in advance and outperforms both state-of-the-art data-driven methods and numerical weather prediction models. In line with our efforts to mitigate adversities inflicted by \rthree{typhoons}, we release our preprocessed \textit{PHYSICS TRACK} dataset, which includes ERA5 reanalysis data, typhoon best-track, and UM forecast data.

Published as a conference paper at ICLR 2024
LONG-TERM TYPHOON TRAJECTORY PREDICTION: A PRYSICS-CONDITIONE) APPROACH WITHOUT RE- ANALYSIS DATA
Young-Jae Park "1 Minseok Doyi Kim², Hyeri Kim", Sanghoon Choi², Beomkyu Choi², Jeongwon Ryu², Sohee Son", Hae-Gon Jeon', Yeji Choi² Gwangju Institute of Science and Technology, SI Analytics, 3 The University of Manchester
ABSTRACT
In the face of escalating climate changes, typhoon intensities and their ensuing damage have surged. Accurate trajectory prediction crucial for effective damage control. Traditional physics-based models, while comprehensive, are computa- tionally intensive and rely heavily on the expertise of forecasters. Contemporary data-driver methods often rely on reanalysis data, which can be considered to be the closest to the true representation of weather conditions. However, reanalysis data is not produced in real-time and requires time for adjustment because pre- diction models are calibrated with observational data. This reanalysis data, such ERAS, falls short in challenging real-world situations. Optimal preparedness necessitates predictions at least 72 hours in advance, beyond the capabilities standard physics models. In response to these constraints, present approach that harnesses real-time Unified Model (UM) data, sidestepping the limitations of reanalysis data. Our model provides predictions 6-hour intervals for up 72 hours in advance and outperforms both state-of-the-art data-driven methods and numerical weather prediction models. In line with efforts mitigate adversities inflicted by typhoons, we release our preprocessed PHYSICS TRACK dataset, which includes ERA5 reanalysis data, typhoon best-track, and UM forecast data.
INTRODUCTION
As global warming accelerates, the intensity of typhoons on the rise (Walsh al., 2016; IPCC, 2023). Accurate typhoon trajectory prediction is of paramount importance to allow enough time for emergency management and organize evacuation efforts. This warming period more critical in countries that lack sufficient infrastructure to forecast typhoons. Many nations utilize numerical weather prediction (NWP) models for typhoon trajectory forecasting and base their typhoon pre- paredness measures this forecasting information. NWP models make predictions for atmospheric variables like geopotential height, wind vectors, and temperature using atmospheric governing equa- tions. Forecasters employ the specific characteristics of typhoons in their NWP output to infer the typhoon trajectory.
However, interpreting the outputs of NWP models depends the expertise of the forecasters, as well as the use of other specialized tracking algorithms, such as the European Centre for Medium- Range Weather Forecasts (ECMWF) Tracker (Bi et al., 2023; Lam al., 2022). To address these issues, data- driven typhoon trajectory prediction methods have been proposed (Huang et al., 2023; Rüttgers et al., 2019). Additionally, methods to generate reanalysis data for applying ECMWF Tracker (ECMWF, 2021) have also emerged (Pathak et al., 2022; Espeholt et al., 2022; Lam et al., 2022; Nguyen et al., 2023). Yet, they possess the following limitations: Weather forecasting typically uses the ECMWF ReAAnalssis-55 (ERA5) (Dee et al., 2011) data for training and inference. However, ERAS data goes through post-correction processes, including data assimilation based on NWP forecast data. As result, although ERA5 data shows high accuracy, it is not accessible until 3-5 days after the typhoon.
Y.JJ. Park and M.-S Seo provided equal contributions to this work.

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Prowarc 583
300
u-wind
Gespoccatial,
Figure 1: Visualization and comparison of the UM and ERA5 data pressure levels of 250, 500, and 700 hPa, along with difference map of two datasets. The u- u-wind and v-wind represent zonal wind and meridional wind, respectively. The UM forecast data at lead time of +72 hours, and the ERA5 data corresponds to that forecasted time. This analysis covers the Western North Pacific basin, with latitudes ranging from 0 to 59. N and longitudes from E 179.75%F.
Consequently, they are unsuitable for real-time tasks like typhoon trajectory prediction, In addition, existing data-driven real-time trajectory prediction models (Huang et al., 2022) restrict their forecasts to less than 24 hours. This considerably shorter prediction window, compared to the NWP models, makes them incapable of planning effective damage control for typhoons.
For data-driven typhoon trajectory prediction, the dataset known as Best Track (Knapp et al., 2010) has been a cornerstone. Documenting the intensity and central points of typhoons, its records extend back to 1950. Correspondingly, the ERA5 reanalysis data, which provides invaluable insights as an additional physics- conditioned dataset, is accessible for the same time range. In contrast, the Unified Model (UM) (Brown et al., 2012) dataset is produced in near-real time, including geopotential height, wind vectors, and other atmospheric variables using primitive equations such as the conservation of momentum, mass, energy, and water mass. However, the UM dataset inherently has several limitations.
The UM data records only include data from 2010 to the present. Furthermore, while the UM dataset adheres to physical formulas, it does not guarantee an exact representation of real-world values and can have potential errors (Brown al., 2012). Figure I and Table 1 compare the UM dataset with ERA5. As illustrated, the UM data significantly differs from ERA5 in several areas. Mcanwhile, as Table 1 reveals, while the UM data has only 3-hour data acquisition delay, it exhibits larger errors.
In this study, we address these issues by introducing a Long- Term Typhoon Trajectory Prediction method (LT3P). LT3P primarily consists of two components: a physics-comee conditioned encoder and trajectory predictor. The first component, the conditi-coddd encoder, encodes influential variables representing the typhoon's 'ss trajectory and intensity. this initial phase, we harness three atmospheric sitiables-geopotenee (700, 500, and 250 hPaa) sourced from the ERA5 dataset. After this, network parameters are frozen. The next step exclusively focuses the bias corrector from UM data ERA5. This correction process is fine-tuned on UM's gcopotential height and wind vector data from pressure levels 250, 500, and 700 available since 2010 combined with the Best Track dataset. The second component, the trajectory predictor, accepts the typhoon's 's central coordinates as input and forecasts its trajectories for the future 72 hours. Features from the physics-conditioned encoder are cross-attended within this trajectory predictor, facilitating precise physics-conditione typhoon trajectory predictions.
height, zonal, and meridional wind-acros three pressure levels
Three primary contributions be summarized:
We propose, for the first time, a real-time +72 hours typhoon trajectory prediction model without reanalysis data.
We provide the preprocessed dataset PHYSICS TRACK, and training, evaluation, and pretrained weights of LT3P.
In the +72 hours forecast, we achieved state-of-the-art results, outperforming the NWP-based typhoon trajectory forecasting models by significant margins.
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Table I: Comparison of ERA5 and NWP datasets: Highlighting the trade-off between real-time data availability and forecast accuracy. While ERAS boasts high accuracy, it lacks immediacy. In contrast,
NWP provides real-time data but may contain forecasting errors.
Type
Data Availabiity
Real-time
Limitations
Forecast Accuracy F: 93%
ERA5
Reanalysis Dataset
1950 - Present
No
Time delay for datn availability
UM
NWP Model
Apprex. 2010- Present
Yes
Potential for model
in forecasting
IFS
NWP Model
1974 - Present
Yes
Potential for model errors in forecasting
High Middle
GFS
NWP
2015 Present
Yes
Potential model erroes in forecasting
2 RELATED WORK
Typhoon trajectory prediction and human trajectory prediction are closely related in that they focus on modeling future trajectories. Unlike the trajectories of general robots and vehicles (Jiang et al., 2023), typhoon trajectories have high uncertainty because the direction and speed of movement are not deterministic (Golchoubian al., 2023). These characteristics are similar pedestrian trajectory prediction (Bae et al., 2022b) in that the direction and speed of pedestrian movement are not determined and its uncertainty is high.
However, while human predictions focus on capturing social interactions between agents, typhoon predictions consider overall atmospheric and geoscientific variables. In this section, we explore typhoon trajectory prediction that takes into account the benefits of two distinct fields.
2.1 TRAJECTORY PREDICTION
Trajectory prediction has primarily evolved from a focus on human trajectory prediction (Alahi et al., 2016; Gupta et al., 2018; Huang et al., 2019; Mangalam et al., 2020; Salzmann et al., 2020; Gu et al., 2022), as it has been extensively applied human-robot interaction systems (Mavrogiannis et al., 2023).
SocialGAN (Gupta al., 2018) employs a Generative Adversarial Network (GAN) framework for predicting realistic future paths. By leveraging latent space vectors, it is able to generate various outputs that capture the intricate nuances of socially acceptable and multimodal human behaviors. PECNet (Mangalam et al., 2020) offers distinct conditional VAE-based approach by transforming the latent spaces. It aims to strike a balance between the fidelity and diversity of predicted samples through its truncation trick in the latent space. Trajectron++ (Salzmann et al., 2020) provides holistic methodology, predicting the probability of a latent distribution during inference. This robustness arises from its ability adapt distribution parameters based external input, such prior trajectory data. Graph Neural Network (GNN)-based approaches (Huang et al., 2019; Mohamed et al., 2020; Shi et al., 2021; Bac et al., 2022a) have attracted interest in modeling and predicting trajectories in scenarios with dense populations, such as pedestrian movements. Recently, MID (Gu et al., 2022) proposes beghhpeeforming trajectory prediction method. This method enhances the conventional trajectory prediction structure which previously consisted only of long short-tern memory (LSTM), multi-layer perceptron (MLP), and GNN-by integrating Transformer and diffusion processes.
However, when human trajectory prediction methods are applied to typhoon trajectory prediction, the results typically show poor performance (Huang et al., 2022; 2023). Unlike pedestrian scenes with multiple interacting entities, typhoons usually appear singly in pairs at most. This difference implies that the detailed social modeling crucial for pedestrian predictions might be less important in the case of typhoons. While GNN techniques excel in complex interactive settings, their efficacy is reduced by the inherent nature of typhoons.
2.2 TYPHOON TRAJECTORY PREDICTION
Forecasting the trajectory of typhoons is still a complex task, affected by many variables including weather conditions, sea surface temperatures, and topography (Emanuel, 2007). As studies have progressed, forecasting methods have improved, moving from basic statistical models to more advanced contemporary numerical approaches (Wang et al., 2015; Chen & Zhang, 2019), Particularly, NWP systems, which leverage abundance of meteorological data, have emerged as fundamental
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tools for typhoon trajectory prediction. However, the interpretation of NWP model outputs often requires the expertise of meteorologists or relies on complex typhoon trajectory prediction algorithms.
On the other hand, data-driven approaches (Pathak et al., 2022; Espeholt et al., 2022; Lam et al., 2022; Nguyen et al., 2023) demonstrate notable performance in the fields of climate science, suggesting their potential applicability in forecasting typhoon trajectories. Initial attempts integrate Recurrent Neural Networks (RNNs) with specific data types but often lead to unsatisfactory results (Alemany al., 2019). Leveraging a broader range of meteorological data has enhanced prediction accuracy, even though this introduces challenges in data processing and encoding (Giffard-Role et al., 2020). Notably, the recent leading approaches in typhoon trajectory prediction emphasize multi-trajectory forecasts, enabled through GANs (Huang et al., 2022: Dendorfer et al., 2021)
Despite these efforts, data-driven models still exhibit limitations, particularly with respect their long-term forecast performance (Chen et al., 2020; Huang et al., 2023) and real-time. In this context, our work aims to pioneer long-term predictions by integrating physical values from UM data, which are outputs from real-time-capable NWP model, with the power of data-driven methods.
3 METHOD
The LT3P model takes the typhoon center coordinates C. :- [c] (Toon ]ott;= where to represent the input time sequences, while Tio and ylat denote the longitude and latitude. Here, i represents the index in the time sequence. In LT3P, the inputs are the geopotential height and to wind vector from the UM, represented 4p { ( and Wp = pppppePisee respectively. Here, P refers to the pressure levels and use it as P {250, 500, 700 ty represent the output time sequences. LT3P then outputs the future typhoon center coordinates C = (u] loty = {aton ytat) batty imtt)
12.
Hence, LT3P comprises a physics-conditioned model which extracts the representation from & and W. and a trajectory predictor that forecasts the typhoon trajectory. LT3P optimized through dual-branch training strategy. During pre-trained phase, the physics-conditoneed model is trained using ERA5 as in (Nguyen et al., 2023). Subsequently, in a bias correction phase, the trajectory predictor is trained to produce outputs akin to ERA5, even when UM data is used as the input.
3.1 PRELIMINARIES
The NWP forecasting system primarily hinges on a core prediction formula for the geopotential height & and the wind vector W based on conservation laws:
i
$ W
P. T. d = F F p.
(1)
where F refers to interactions among variables following the conservation laws. The variables p. and i denote air density, temperature, and specific humidity, respectively.
Conservation equations underpin the NWP's forecasting. For instance, momentum conservation with the spatial gradient V is expressed by:
Ow
VP
= W
VW
+
(2)
Øt
P
and mass conservation is below:
0p Øt + V (pW) = 0.
(3)
Other conserved quantities include energy, related to geopotential height & and temperature T, and water vapor content or specific humidity q.
Note on Accuracy and Time f. In the NWP system, time indicates progression. For longer predictions up to +72 hours, their accuracy depends on various factors: initial conditions, physics parameterization schemes, spatial resolution, and
While the conservation law captures large-scall atmospheric dynamics effectively, might not work on small-scale details due to local conditions like microclimates, terrain variations, or transient phenomena.
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ERAS
BRAS
Inevtrmc
Doaae
Prevscising
UM
Cirmuce
Asurrancaa $0.00
Trejectury
Trajectery
Phaue2 Bix Cancttin Trjeckary Predkaton
Figure Overview of LT3P. In phase the physics-conditiones model R(-) is trained for the weather forecasting task to encode information from &, W.. and W.. Subsequently, in phase 2, using UM as the input, the typhoon trajectory prediction model D(-) trained in conjunction with the bias-corrector (-)) to make accurate predictions.
The Earth's s atmosphere a chaotic system (Lorenz, 1963). Despite relying on conservation laws, NWP models inevitably prone to emrors due to the inherent imperfections in our understanding of physical processes and the true state of the atmosphere. These discrepancies underscore the importance of data-driven approaches, in spite of the potential error in the UM data.
3.2 PRE-TNANNING ON ERAS DATASET
The ERA5 dataset, accumulated at 6-hour intervals from 1950 to the present, requires a significant amount of time to train model from scratch. To address this challenge, we have adopted strategy of constructing foundational model for typhoon trajectory prediction.
The consioc-conditioned model of LT3P trained for weather forecasting task, similar to (Nguyen et al., 2023), to effectively learn the representations of the highly-related variables for typhoon trajectory prediction in (Giffard-RRosin et al., 2020): geopotential height zonal wind(W..) w). and meridional wind(W..).
As shown Figure 2, the physies-conditioned model consists of an architecture. The encoder and decoder are built using 3D convolutions for spatio-temporal modeling, and the transformer also constructed as a 3D transformer.
The objective of the physics model is as follows:
H W ΣΣΣ
V
Lpre train
(X) X.s.I x1..k.12
(4)
T X
where is the final lead time, V represents variables, with + W.,. and W. for each of the pressure levels. H and W denote height and width, respectively. X is the forecasting value for ERAS from time to l. + ly in the physics-conditioned model, given V input from the period I to l. X is its ground truth (GT).
3.3 BIAS CORRECTION & TYPHOON TRAJECTORY PREDICTION
Bias correction. Since ERAS is reanalysis data, this limits the applicability on real-time tasks, which makes us use the UM data. Hence, the typhoon trajectory prediction model, LT3P, accepts two primary inputs: the representation encoded by the R(.) derived from the physics formula presented in Section. 3.1 using and and the typhoon's center coordinates C.. The model then produces the output Cy.
However, since our R(-) was trained with the ERA5 data, it may struggle with extracting meaningful representations from the UM data due to their domain gap. To solve this problem, LTTP uses the
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Table 2: Ensemble typhoon trajectory prediction results (FDE): Note that while the original MGTCF study only includes 24-hour predictions, for comparison purposes, we modified it to input 48 hours 6-hour intervals and predict +72 hours at 6-hour intervals. Bold indicates the best performance. and underline indicates the second-best performance. Note that Dataset refers to the dataset used for inference.
Metlhed
Real-time
Ditaaet
BRAS
UM
6.6
12b
26hh
30
00.
72.b
SeciaGAN (Uippa
131.31
2009
PBCNa
bilangalan cll 20309 Ens
41.61
58.34
82.30
112.8
147.06
135.92
252 09
279.29
330.54
388.25
645.8L
538.46
MID (Cs ot 2012 Eas
100.31
173
23045
280.82
399.00
422:11
490.00
564.23
638.04
712.0
798.63
381.34
MMSTN (ilsang Socr)plle
173.44
346
19161
1176.36
1932-99
TTMC (CSTa 301r
8778
125.75
317/0
DEPS (CB22 2013)
52.33
08.10
135.20
192104
223.75
25R07
ECMWF-EPS EPS (Ches 2023
57.15
78,87
106.25
135.97
176.02
210.71
NCE3-G22% (17sm 2011)
185
LT57
15.35
107.33
35537
351.1
30014
351152
LT3P comeae UM) Bis
6.30
13.45
32.53
23.81
40.70
60.75
64 36
T1.01
33.04
102 88
110 36
143.00
LTSP (BRA3 Oaly-Bas
5.39
6.30
19.00
38.89
30.03
35.93
30.39
$8.30
66.17
70.0
track from MMSTN MGTCF could obtained hecause they use from specific organizations.
Therefore, the experimental results of each of the two studies were evaluated only 2019.
bias-corrector (-) to adapt the variables generated through UM to the variables of ERA5. The B-))
has the following objective functions:
VVA H w ΣΣΣΣ (X1
L Bias.com correction
-X, X2..k.y
(5)
TXVXHXW
where X is the ERA5 prediction which is bias-corrected version of the UM dataset.
Typhoon Trajectory Prediction. In the second phase, LTTP takes ERA5 data from 1950 to 2010 and UM data from 2010 to 2018 as inputs in order to perform typhoon trajectory prediction. The main model LT3P, which predicts the center coordinates of the typhoon, generates the final trajectory by applying cross-attention between the 3D features from the fR(-)'s transformer and the ID features of the typhoon coordinates from the LT3P model's 's encoder. The cross attention enables LT3P to take fully advantage of both data-driven and physics-based prediction. It also allows to fuse these two features regardless of the shape or length of the features. The cross attention defined as:
QKT
Attention(( (Q, K, V) softmax
V,
Vd
where Q W (R( W)). K- // (Cp), and V W(() (Cp)). The 0 represents the set of parameters associated with the D(-). Here, d2(2)) E N denotes a (flattened) intermediate representation of the transformer implementing 0. w dddd, w Rdxd, and w-( Raxd, are learnable projection matrices.
Therefore, in LT3P, the final objective function is defined as follows:
Ltotal Biasscorrrection + Ltrajectory-
(6)
Note that various trajectory prediction baselines such GAN, CVAE, and diffusion-based models can be applied, whose effectiveness will validated section 4.3.
EVALUATIONS
In this section, we first describe the dataset and the prepproesssing methods used as well as an implementation detail of our LT3P. We then show quantitative and qualitative evaluations and provide an extensive ablation study to check the effectiveness of each component in ITTP.
4.1 EXPERIMENTAL SETTINGS
Physics Track dataset. We collect the center coordinates of typhoons at 6-hour intervals (00, 06, 12, 18) from 1950 to 2021. We use data corresponding to the Western North Pacific basin, with latitudes ranging from 0 N and longitudes from 100% 179.75 as typhoons overwhelmingly

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Table 3: Results of Stochastic Typhoon Trajectory Predictions (FDE): This table presents results generated from 20 samples, consistent with the conventional approach in NWP-based GEFS (Zhou al., 2022), which employs 20 ensemble members for the final prediction.
Method
Ditunee (km))
54h
STGAT (Bluagg 20199
147.86
190.03
204.79
253.53
259.13
277.05
522.54
PEC Not Manpiam di al. 202m)
28.40
34.34
45.85
57.36
60.44
$1.17
97.84
112.34
150.22
150
169.22
188.74
MID (Ga 2022)
24.54
43.66
56.52
73.41
56.04
116.86
136.46
100.55
154.09
206.5 76
220.05
256.36
NINES (ibaig It B01
19:3
01:21
07:35
0739
135.99
10.30
19130
35135
199717
307/33
106.10
151.3Y
MOTCF (Baase et tl., 2023)
20.44
37.59
52.45
60.29
63.09
104.15
125.90
156.95
15
218 18
240.22
280.00
L1 1512 conosed US0
1.7
231
3.22
3.50
17389
25.33
30337
36.39
01.12
47.36
51377
65.23
1T (FRA fllly)
0.66
2,36
2.34
31.01
76.96
26,99
41.19
00.34
.9.6
Note that best track data from MMSTN and MGTCF could not be obtained bocause they use data from specific organizations. Therefore, the experimental results of each of the two studies were evalunted only in 2019.
occur within this region. (Stull, 2011) Any typhoon that dissipated within 48 hours is excluded from the dataset. The training data uses years 1950-2018 8 and comprises 1,334 typhoons, while the test dataset covers years 2019-2021 I and includes 90 typhoons. Note that for hyperparameter tuning, we train on data from 1950 to 2016 and set the 2017 to 2018 data as the validation set. Subsequently, we evaluate the final performance of model using the entire dataset from 1950 to 2018.
In sync with the duration of the typhoon, we utilize variables in the ERA5 dataset which are closely associated with typhoons, notably the geopotential height and wind vectors. Data for the pressure levels at 250, 500, and 700 hPa collected. For training purposes, the ERA5 dataset is employed from 1950 to 2018, and the data spanning 2019, 2020, and 2021 is set aside. Regarding the real-time data from the UM, we compile data from the years 2010 2021, ensuring the inclusion of the same variables and pressure levels as found in the ERA5 dataset.
Note: All variables in ERA5 and UM are normalized using the formula (variable mean)/ssanas because no specific minimum-maximun range is predetermined for each variable.
For both the ERA5 and UM datasets, information is extracted to match the conditions of the best track. Therefore, the input dimension for ERA5 and UM is set to (B x T x V x H x W) + (B x 12 x 9 240 320). Note that the resolution has been adjusted using bi-linear interpolation.
Evaluation metries. We compare our LT3P with global metcorological agencies, including the Joint Typhoon Warning Center (JTWC), Japan Meteorological Agency (JMA), European Centre for MediuMedaaaaaa Weather Forecasts (ECMWF), National Centers for Environmental Prediction (NCEP), and the United Kingdom Meteorological Office (UKMO). Additionally, the Global Ensemble Forecast System (GEFS) refers to the results of 20 separate generated forecasts (Zhou et al., 2022) and then combines them into an ensemble average forecast, which is to average the sampled forecasting results. Therefore, the data-driven models are evaluated using the ensemble average method to benchmark their performance compared to the ensemble average NWP models. Performance is measured at 6-hour intervals, consistent with existing meteorological agencies.
We use (1) Average Displacement Error (ADE) average Euclidean distance between a prediction and ground-trute trajectory; (2) Final Displacement Error (FDE) Euclidean distance between prediction and ground-truth final destination.
Implementation details. The physies-conditioned model R(-) is comprised of an encoder, translator and decoder. The encoder and decoder are composed of (Conv, LayerNorm, SiLU) (Elfwing et al., 2018)) and (Conv, LayerNorm, SiLU, PixelShuffle). respectively. In the ex- periments, the encoder and decoder consist of four blocks each. The translator is selected as the transformer block and has a total of three blocks. The trajectory predictor D(.) follows GAN (Gupta et al., 2018), CVAE (Mangalam et al., 2020) and diffusion (Gu et al., 2022), respectively, and the predictor.
experimental results in Table 2 and Table 3 are obtained from the diffusion-
For training, we use machine with 8 Nvidia-Quade and we use Adam optimizer with a learning rate of 0.001. Additionally, an Exponential Moving Average with a momentum of 0.999 is applied, and the training is conducted for 2,000 epochs. Note that we did not employ data augmentation, as the geometric and intensity values in typhoon forecasting are extremely sensitive. Applying data augmentation might compromise the integrity of physical rules. For fair comparison, the data-driven baseline models such as SocialGAN (Gupta et al., 2018). STGAT (Huang et al., 2019), PECNet (Mangalam et al., 2020), MID (Gu et al., 2022), and MMSTN (Huang et al., 2022) are trained and evaluated on the best track dataset, while MGTCF (Huang et al., 2023) utilizes both the best track and ERA5 datasets.
RTX 8000 GPUs The batch size is set to 128.
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MED
KALMAEGI LINGLING
(c)
BAXAI
(e) KAMMURI
(1) NEOGURI
Figure 3: Qualitative analysis between LT3P and other trajectory prediction baselines. Note that the typhoon trajectory provided by JMA exists at 24-hour intervals, the three coordinates have been linearly connected. Additionally, the error (in km) the final coordinate is also indicated. The probability map is visualized using kernel density estimation (KDE).
4.2 EXPERIMENTAL RESULTS
Quantitative results. In Table 2, we compare the performance of LT3P with the state-of-the-art human trajectory prediction methods as well as data-driven typhoon prediction methods. The table also includes forecast results from several renowned metcorological agencies, including JTWC, JMA, ECMWF, NCEP, and UKMO, which are based on the conservation laws in Section. 3.2. Our observations indicate that LT3P (ERA5 Only) exhibits the most promising results, with LT3P (Bias- corrected UM) trailing closely behind. It is important to highlight that MID, which has achieved the best results among data-driven human trajectory predictors, performs poorly in ensemble forecasting. This discrepancy arises from the inherent goal of human trajectory prediction models. They have developed to capture social relations between agents, and its probabilistic distribution attempts to cover wide and diverse distribution represent all feasible socially accially-acceptable paths and destination.
Furthermore, it is noteworthy that our model consistently outperforms the numerical prediction models of the established operational forecast centers. These experimental results demonstrate the potential of real-time NWP data to serve as an alternative to reanalysis data in the field of real-time typhoon trajectory prediction. Note that studies in (Bi al., 2023; Lam et al., 2022), which predict reanalysis data and then use the -rtteker for typhoon trajectory prediction, are excluded in our comparisons because code and weights are unavailable, and there are no access the ECMWF-tracker (ECMWF, 2021).
Table shows the performance results from our stochastic trajectory prediction and the existing approaches to data-driven trajectory prediction. We report the results have the lowest error among 20 generated trajectories. As revealed in Table 3, LT3P stands out the ofate-of-the-art model for stochastic trajectory prediction. We also observe significant performance improvements in other data-driven trajectory prediction models. These encouraging results can attributed to the inherent strength of existing data-driven methods, which are designed to generate diverse and wide range of trajectory predictions.
Qualitative Results Figure 3 presents the qualitative results for ensemble and stochastic predictions. In Figure 3-(c), only the JMA model and our LT3P predict the change trajectory of typhoons like DANAS well. These results highlight the importance of physics values in typhoon trajectory prediction. Figure 3-(f), I.TPP superiority is also confirmed as well. In addition, MID, using only coordinates, outperforms the physics-basee based JMA. Such instances show the promising potential of data-driven models. Additionally, in the case of stochastic results, LT3P does not aim for a wide and diverse range of predictions, but focuses generating forecasts that closely approximate the actual typhoon trajectory. These qualitative results are supporting evidence for LT3P's strong performance in ensemble forecasting, reported in Table 2.
Figure 4 presents the differences in zonal wind field between UM ho-hour forecast and ERAS before and after the bias correction of UM. The figure shows that the UM exhibits significantly smaller bias
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Oenodd ENG
KALMABCI
TTe LINGLING
66 DANAS
6:0 PAXAI
(el KAMMURE
NEOSUM
Figure 4: Zonal wind bias of the UM before and after bias correction with respect to ERA5.
Table 4: Ablation study on the efficiency of each com- ponent of LT3P.
Table 5: Ablation study back- bone models.
Component
Metric ADE/DD (km)
LM
Joini Training
Pre Training
Blas correction
Backbone
Metric
AIMEPFII [km
219.46 1900.92
SocalGAN (Cupla al. 2018)
695597 155.25
80.39 / 190.75
PECNet (Mangalam 2020)
85.62 198.11
MID (Cin al., 2022)
63.25 143.03
after the bias- correctore phase than the original UM field. These show that the atmospheric conditions across the entire region, including the trajectories of the typhoons, have been well corrected LT3P.
4.3 ABLATION STUDY
To assess the efficacy of each component within the LT3P framework, systematic analysis is conducted: a baseline trained solely on the UM, a joint training scheme trained on ERA5 from 1950- 2009 and the UM from 2010-2018. an extended method incorporating phase pre-training and, the full LT3P pipeline with the bias correction. The results are reported in Table 4. The empirical findings in Table 4 suggest that all components, barring the UM Only training, yield good results. In particular, the LT3P configuration, which leverages all the components, achieves the best performance.
Table presents the experimental results when applying well-known trajectory prediction models such as GAN, CVAE, and diffusion- based methods to the LT3P framework. In Table 5, the diffusion-basee method achieves the better performance than the others. Moreover, the methods, including GAN, CVAE, and diffusion, exhibit significant performance improvements when incorporating UM data compared to using only coordinate data (refer to the prediction result on 72 hours Table 2). These findings indicate that the performance gains can be attributed to the LT3P framework itself rather than to the backbone models.
CONCLUSION
We propose the Long-Term Typhoon Trajectory Prediction (LT3P) for real-time typhoon trajectory prediction without reanalysis data. To the best of our knowledge, LT3P is the first data-driven typhoon trajectory prediction that employs real-time NWP dataset. Unlike methods that generate reanalysis data, LT3P predicts the typhoon's central coordinates without any need for an additional forecaster and algorithms. addition, our LT3P does not require weather forecasting experts, making it easily accessible for various institutions with limited meteorological infrastructure. Using extensive evaluations, we confirm that our IT3P achieved state-of-the-art performance in typhoon trajectory prediction. However, regions of tropical cyclones. In future work, we plan to apply it to all kinds of tropical cyclones. Lastly, we hope to contribute the field of climate AI by releasing our PHYSICS TRACK dataset, training, test codes of LT3P, and pretrained weights to the public.
model has only been applied to typhoons and has not been applied to other
ACKNOWLEDGMENTS
This work in part supported by the Institute of Information communications Technology Planning & Evaluation (ITTP) grant funded by the Korea government (MSIT) (No.2019-00 0011-99-8822 Artificial Intelligence Graduate School Program (GIST) and 10.2001-0-0200 Artificial Intelligence Innovation Hub) and GIST-MIT Research Collaboration grant funded by the GIST in 2024.

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A APPENDIX
A.1 TRAJECTORY PREDICTOR IMPLEMENTATION DETAILS.
SocialGAN Gupta et al. (2018) SocialGAN comprises sequential LSTM and MLP-based encoders, decoders, discriminators, and pooling module.
The hidden dimension for both the encoder and decoder, as well as the bottleneck dimension, is set to 32, while the discriminator's hidden dimension is 64.
Furthermore, the encoder and decoder of SocialGAN are configured with a single LSTM layer. The LT3P, utilizing the GAN-based predictor SocialGAN, adheres the Ltrajectory equation as follows:
(R) Lirajectory min [Traje,ty Trajiptt,les
where & 10. To implement SocialGAN, used the official repository".
PECNet Mangalam al. (2020) To construct a typhoon trajectory predictor based on conditional variational autoencoder (CVAE), we have chosen PECNet.
PECNet consists of an encoder Epst for encoding past trajectory coordinates, an encoder Eway used for predicting waypoints, latent encoder Ejstntt, decoder D latent, and predict for predicting future trajectories.
The network architecture of PECNet is as follows: Ewwy has channel dimensions 2-8-16-16, Fepust has dimensions 16-312-256-16 Fimem has dimensions 32-8-50-32, Diaten has dimensions 32-1024-512- 1024-2, and Pprediet has dimensions 32- 22-1004-5122256222
All the sub-networks are multi-layer perceptrons with ReLU non-linearity.
The LT3P's loss function Ltrajectory based PECNet is defined by the equation below:
+ d2 d2llo
$cl +
Strajectory = A
Tr
(8)
where G is the endpoint, and T represents the trajectories.
The first term is the KL divergence term used for training the variational autoencoder, the second term average endpoint loss (AEL) trains Eend. Epust. Etatent and Dintent and the third term average trajectory loss (ATL) trains the entire module together.
We integrated the LT3P with PECNet using the official 2ode?.
MID Gu et al. (2022) To design a typhoon trajectory predictor based on diffusion, we select MID.
Particularly, MID possesses a transformer-base encoder and decoder.
We devise three-layer transformer as the core network for our MID, where the transformer dimension is set to 512, and 4 attention heads are applied.
We employ fully-connected layer upsample the input of the model from dimension 2 the transformer dimension and another fully-connected layer to upsample the observed trajectory feature f the same dimension.
We utilize three fully-connected layers to progressively downsample the Transformer output sequence the predicted trajectory, achieving dimensions of 512-256-2.
The loss function of the forward process of MID follows the equation below:
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Figure 5: Result of applying LT3P to Typhoon CHANTHU, which occurred in 2021. Green dots represent the inputs, and blue dots indicate the Ground Truth (GT), and red dots the prediction results.
L(O, v0) - Ee.so E(,, (yn, k, N, x)2
(9)
where ~ (O, 1). ys = V /n + v1 -1 and the training is performed at each step A: E
(1.2,.... KJ. We have set k to 20.
We used the official code² for implementation, and we also provide the code for the MID--basdd LTTP 4
subsectionPhysies Track Dataset Details.
Table ?? presents the statistics of our physics track dataset. Although the total number of entries in the ERA5 dataset is 171,348, our dataset begins from various time points / and extends to +72 hours as input, resulting even more combinations.
Table 6: Statistics of the Physics track dataset. Note that UM data has +72 forecasting values each point in time, so the total amount of data is large.
Number of Samples
Time period
Data shape (V, H, W)
Data size
UM ERAS Best Track
525,588
2010~2021
(9 x 254 x 226)
676G
171.348
1950~2021
x 240 x 320) (T x X x Y).
150G
1,424
1950~~2021
6.6M
Figure displays samples from the Best Track for the years 2019, 2020, and 2021.
As evident from the illustration, there's significant uncertainty regarding the direction of a typhoon's movement.
Typhoon durations can also vary widely, with some lasting over two weeks while others persist for as briefly as three days. This variability underscores the complexity and challenges involved in predicting typhoon trajectories.
Figure 7 is the project page for the Physics track dataset.
We will provide individual data for typhoons occurring each year, along with their corresponding ERA5 and UM data.
Not only that, but we will also make the dataset available for download in its entirety.
Furthermore, explanations for each modality will be included on the project page.
A.2 INFERENCE IN ÖTHER TYPES OF TROPICAL CYCLONES
This paper focuses on the trajectory of typhoons, which is of tropical cyclones occurring in the North Western Pacific. However, tropical cyclones also occur various other regions.
"httss//gihub..oo//utaaanapp hhan:://gtthaaaccmmm//r/2//////m
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(a) 2019
(b) 2020
(c) 2021
Figure 6: The image visualizes the typhoon trajectories from the Joint Typhoon Warning Center (ITWC)'s Best Track for the years 2019, 2020, and 2021 that we utilized.
Physics Track Dataset
Select Year: 2011 91.TEP.
North Western Pacific: 2021
Typa
Number
Name
Perlod
MSMANN
GP GPMNUPR,GM Obs.
GCON -WNAIIAASSSS Ob
Track
D1W/
DUJUAN
2001 Fab 2001
Viu Track
12, 2001 May
Track
T5
D4W)
CHO-AAN
May 20, 2201-11nm 08, 2027
Vie Track
Figure 7: Physics track dataset project page overview.
To assess the potential of our LT3P model in areas outside the North Western Pacific, conduct zero-shot validations on TAUKTAE and CHALANE, which occur the North Indian Ocean and the South Indian Ocean, respectively, using model trained only the North Western Pacific.
As shown in Figure 9, despite not being trained on the North Indian Ocean and South Indian Ocean, the model retains some level of performance.
This experimental result suggests that the performance is consistent because LT3P uses UM prediction values, which follow physical rules and thus do not exhibit large errors.
However, due to regional differences the characteristics of tropical cyclones, the model exhibits larger errors than the average error for typhoons in the North Western Pacific.
These results indicate that additional training is required for LT3P to operate effectively in other regions.
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hPa
500 kPa
LPa
u-wind, Wa
W.
Geopotential height,
Figure 8: Visualization of all variables in the UM dataset.
GT
Imput
246km
PECNE
SGAN
MID
456km
600
1557 km
LT3P
202xm
1580km
319km
(a)
TAUKTAE
(b) CHALANE
Figure 9: Inferences on different types of tropical cyclones using LT3P and other trajectory prediction baselines.
Nevertheless, the fact that the performance is somewhat maintained suggests that LT3P has the potential to function properly in different regions.
100
810
300
08
36
00
140
(b)
(c)
Figure 10: Result of applying LT3P to Typhoon CHANTHU, which occurred in 2021. Green dots represent the inputs, and blue dots indicate the Ground Truth (GT), and red dots are the prediction results.
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A.3 FAILURE CASE ANALYSIS.
Figure 10 presents the qualitative analysis results for Typhoon CHANTHU, which occurred in 2021. As shown the figure, even though is the same the Typhoon CHANTHU, LT3P predicted its trajectory well in the early stage (Figure 10-(a)) and in the late stage (Figure 10-(c)), but not as accurately during the middle phase, as shown in Figure 10-(b). Particularly, as seen in Figure 10-(b), we discovered that LTTP's performance degrades when the typhoon remains stationary for a long time. We speculate that this experimental result due to the scarcity of data on stationary typhoons within the dataset.
A.4 TEMPORAL CORRELATION CORFFICIENT
(a)
UM (Imput)
UM UM (Pree)
(b)
Figure 11: Qualitative analysis results for bias- correcete UM. (a) provides an overview of TCC calculation, while (b) demonstrates how the bias-corrected UM corrects the position of the center of the typhoon. The white diamond represents the center of the typhoon, and the red bounding box indicates the cropped area around the coordinate center.
To calculate the temporal correlation coefficient (TCC) scores between bias-corrected UM and ERA5 image sequences, we first compute the hame-by-frame differences:
AP = Pi+1* - P:
(10)
AG G G;
(11)
where and G are the sequences of predicted and ground truth images, respectively, and indexes the frames. We then flatten these differences obtain feature vectors for each time step:
p flatten AP)
(12)
g flatten( AG)
(13)
The covariance matrix C for the flattened vectors is computed as:
N
C N
Ep. (p. - p) - g)r
(14)
where N is the number of time steps, and and g are the means of the vectors p and g respectively. The standard deviation for each vector is calculated as follows:
Pp V diag( diag(Cpp) (Cpp)
(15)
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UM (aput)
MSE.
VCC9.17
TOCHUP
1OC
TOC00.0
TOC89.1
UM (Prdd)
MSE:1.3 25
MSS:L40
JCC051
TOCK0.0
TOCHS4
TCU967
TOCS9.57
TOCA000
PRAS (GET)
KALMAEGI
(b) LINGLING
DANAS
(d) FAXAI
(e) KAMMURI
(i NEOGURI
ZU5
Figure 12: Comparison with zonal wind variables with 700 hPa: From top to bottom, the columns UM (NWP), bias-corrected UM (output), and ERAS (ground truth). All MSE and temporal correlation coefficient (TCC) calculations are based on the original physical values.
og V diag( Cgg)
(16)
where Cpp and Cgg represent the diagonal elements of the covariance matrix corresponding to and g. The TCC then computed as:
C pg
TCC
(17)
JpØg
where Cpg is the off--iiaonal element of the covariance matrix representing the covariance between P and g. The final TCC score the average of these values across all features.
We calculate the TCC for typhoons by cropping 50 x 50 pixel area around the Ground Truth (GT) typhoon locations. Figure 11-(a) presents an example of TCC. As shown in the figure, the correlation coefficient is calculated across all predicted frames. Additionally, seen in Figure 11-(b), although the bias-corrected UM appears blurred, accurately represents the center of the typhoon. These results support the high TCC values of the bias-corrected UM, serving as evidence of its effectiveness, as shown in Figure 12
A.5 MORE DETAILS OF ABLATION STUDY
The ERA5 dataset has been accumulating data from 1950 to the present, whereas the UM dataset has been available since 2010. Therefore, if only the UM dataset is used, the best track typhoon dataset is also only available from 2010. The number of typhoons since 2010 is approximately 200, which a small fraction compared to the total of 1.424 typhoons that have occurred since 1950. Consequently, the performance of UM-only training is significantly limited. In contrast, joint training setting, which uses ERA5 data from 1950 to 2009 and the UM dataset from 2010 to 2021, can utilize the entire typhoon dataset. This dataset is about six times larger than UM-only dataset, which seems to significantly improve the performance of data-driven models due to the increased amount of data. Training a physics-comee consiic-coniiionedd model using only ERA5 and then inferring with UM data seems to result in decreased performance compared to joint training, likely due to the domain gap between ERA5 and UM. However, Bias-correction appears to have improved performance by reducing this domain gap. However, it is evident that joint training has the most significant impact on the performance enhancement. These experimental results indicate that joint training has great potential, especially when ERA5 cannot be used for real-time inference. Furthermore, while we used a physics-conditioned model as foundation model considering computational resources, future work could also explore methods like domain adaptation.
A.6 LIMITATION AND FUTURE WORK
Our work has been trained and validated exclusively on typhoons, which are tropical cyclones occurring in the North Western Pacific. Therefore, due to the varying progression directions of
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tropical cyclones in different regions, it is impractical to directly apply model trained in the North Western Pacific to other types or regions of tropical cyclones, even if their physical characteristics are similar. Additionally, future work, we plan to apply LTTP to tropical cyclones occurring globally and provide codes and training recipes for Climate AI. Furthermore, since ITTP is fundamentally a probabilistic prediction model, it requires the selection of a path. In our future work, we plan to conduct research on a selector that chooses single path, rather than simply relying on ensemble average methods.
model still relies NWP models, making it unusable without them. In
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A.7
QUALITATIVE EXPERIMENT RESULTS OF LT3P
bep58000 p010
parc
WOrN WWN Datrors
EectkAus JAWA (3
Figure 13: Results showcase trajectory predictions using LT3P for the DANAS Typhoon. The green dots are input, the red dots are prediction, and the blue dots are GT.
bep052001 MARR port
10
WOOVII MAN SUAKE
be012003 MAR
WIOSI NAN SOAVE
10
240
16
340
15
Figure 14: Results showcase trajectory predictions using LT3P for the NAKRI Typhoon. The green dot input, the red dot is prediction, and the blue dot is GT.
20


Reviews:
[{"review_id": "fEOGUyoqZK", "reviewer": "Reviewer_k3qU", "summary": "This paper introduces a framework for data-driven typhoon prediction based on cross-attention between encoded representations of the past trajectory points as well as global weather data (geopotential and wind directions). The paper shows good results in forecasting the typhoon trajectories, especially compared to physics-based baselines.", "questions": "- First sentence of methods section: 1) There is no index *i* for none of the variables (e.g. it should be $c_i$, I think); 2) $t_p$ is the input sequence **length** (not the input sequence). Similarly for $t_f$; 3) $p$ is given different meanings for $C_p$ and the pressure levels $p \\in P$. This is confusing, can you use different letters, please?\n- Why are geopotential height and wind vectors inputs taken from the output timesteps $i=t_p+1, \\dots, t_p+t_f$? Should it not be $i=1, \\dots, t_p$?\n- 3.1 Preliminaries (not preliminarily)\n- Tone down \"is too large scale to train a model from scratch\". There exist models trained from scratch on ERA5...\n- \"$\\tilde X$ is the forecasting values for ERA5 from time $t$ to $t+t_f$\". Shouldn't it be $t+1$ to $t+t_f$?\n- I don't completely understand how the bias correction phase works, is the UM data matched with ERA5 based on the timestamp?", "limitations": "", "rating": 3, "confidence": 4, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "Overall I believe that the paper has great potential for presenting an interesting case study with strong empirical results on this important problem, but in its current form the lack of clarity and details for better understanding the study are severe issues.\n\n- The results seem strong and quite significant gains over physics-based baselines (coming from various meteorological institutions) are reported. I am not familiar with the typhoon track forecasting literature though.\n- Open-sourcing the data will be valuable. When doing so, the authors should put care into making it easily accessible and well-documented\n- Training on NWP data such as UM instead of ERA5 is a good study to make but has limited originality.", "weaknesses": "- Clarity can be improved significantly. Firstly, there are a lot of grammar mistakes worth fixing with a grammar checker. Secondly, many implementation details are conveyed very unclearly or details are missing. This inhibits understanding the significance of the paper and would hurt reproducibility. For example:\n    - The method seems to have been tested using a GAN, a CVAE, and a diffusion model, but implementation details are largely lacking\n    - Unclear meaning and implementation details of *\"so it is matched through the latitude and longitude mapping and the interpolation method\"*\n    - Unclear what exactly the *\"ensemble method\"* refers to and how exactly it is performed in practice for all the models.\n    - Please include more ablations, e.g. with UM and joint training and bias-correction but without pre-training. The whole ablation study section is a bit hard to understand and should be expanded (in the appendix if needed).\n- Are the baselines in the first set of rows of Table 2 (e.g SocialGAN) all off-the-shelf pre-trained human trajectory prediction models? If so, it is not surprising at all that they perform so badly, and it would be good to retrain them on your data. Additionally, why is Ruttgers et al. not included in the benchmarking?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "UcukxLg7vo", "reviewer": "Reviewer_j264", "summary": "The paper introduces the Long-Term Typhoon Trajectory Prediction (LT3P), a novel approach for real-time typhoon trajectory prediction without the need for reanalysis of data. LT3P is a data-driven model that utilizes a real-time Numerical Weather Prediction (NWP) dataset, making it unique in its field. The model is designed to predict the central coordinates of a typhoon, eliminating the need for additional forecasters and algorithms. LT3P is accessible to various institutions, even those with limited meteorological infrastructure. The paper includes extensive evaluations, demonstrating that LT3P achieves state-of-the-art performance in typhoon trajectory prediction. However, the model is currently applied only to typhoons and has not been tested on other types of tropical cyclones. The authors plan to extend its application in future work and contribute to the field of climate AI by releasing their dataset, training, test codes of LT3P, and pre-trained weights to the public.", "questions": "- How well does the LT3P model generalize to different regions and conditions of typhoon occurrences? Have there been any specific challenges in adapting the model to various geographical locations?\n\n- Could you provide more insight into why LT3P outperforms other state-of-the-art models and established meteorological agencies’ predictions? What specific features or methodologies contribute to its superior performance?\n\n- How does the dependency on real-time NWP datasets affect the model’s performance, especially in scenarios where real-time data might be sparse or inaccurate?\n\n- How does the LT3P model handle uncertainties in typhoon trajectory prediction, and what measures are in place to ensure the reliability of its predictions?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- Innovative Approach: LT3P is one of the first data-driven models for real-time typhoon trajectory prediction that utilizes a real-time NWP dataset, distinguishing it from other methods in the field.\n\n- Extensive Evaluations: The paper includes comprehensive evaluations, showcasing the model's state-of-the-art performance in typhoon trajectory prediction.\n\n- Contribution to Climate AI: The authors plan to release their dataset, training, test codes of LT3P, and pre-trained weights to the public, contributing valuable resources to the field of climate AI.", "weaknesses": "- Limited Application: The model has only been applied to typhoons and has not been tested on other types of tropical cyclones, limiting its current applicability.\n\n- Dependence on Real-Time NWP Dataset: The model's performance is dependent on the availability and accuracy of the real-time NWP dataset, which could be a potential limitation.\n\n- Need for Future Work: While the paper outlines plans for future work, including extending the application to all kinds of tropical cyclones, these aspects have not yet been addressed or tested.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "iK34VCnsla", "reviewer": "Reviewer_y7ao", "summary": "- This paper proposes a encoder-decoder type weather prediction model, focusing on the typhoon trajectory prediction. \n- A notable point is the use of two different reanalysis data. A precise but slow-to-retrieve ERA5 data is used to pretrain the encoder-decoder module. A rough but fast-to-retrieve UM data is then used to fine-tune the encoder-decoder, as well the trajectory predictor. \n- Reanalysis data is usually used only for off-line training, However, the UM data is known fir its quick release (3 hours after observations),  one can use the UM for computing the ERA5-like embedding for precise trajectory prediction. \n- Enables 72 hours ahead prediction based on a UM reanalysis data.", "questions": "I'm a little bit confused about how to handle the \"Lead time\" in evaluations in a fair way. \n\nThe manuscript says that one needs to wait for three hours to obtain the UM reanalysis data. This means the proposed method loses the lead time of three hours. So, the score of \"6-hour leadtime\" should be understood as \"3-hour leadtime\"?", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 2, "contribution": 2, "strengths": "- It is novel to use re-analysis data for a prediction (inference) phase. This can change the possible applications of the reanalysis data, usually used for offline computations only. \n- An error correction scheme to utilize the quick but erroneous UM reanalysis effectively sounds interesting.", "weaknesses": "- If I read correctly, several explanations about network architecture, losses, ... are missing. This would prevent the re-production by fellow researchers. \n  * Number of layers, embedding dimensions, total amounts of learnable parameters, ..\n  * L_{trajectory} not defined? \n\n- The reported quantitative results In the Tables are surprisingly good. However, I have the following concerns. \n  * Inconsistent trends of the scores of existing (compared) models. As I read the MGTCF paper, the authors reported that the MGTCF performs clearly better than SGAN, which is different from Tables 2, and 3. \n  * The scores (Distance) of the proposed LT3P update the current SotA by order of magnitude. I checked the most recent works such as (Bi+ 2023, Lam+ 2022) but the distance scores are roughly in the same order as the existing methods. I feel the current manuscript does not sufficiently explain why this huge jump happens, although the ablation study tells that it seems the joint training with reanalysis is a key factor.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "obJY7NWIFS", "reviewer": "Reviewer_cnGt", "summary": "This paper proposes a new data-based model for typhoon trajectory prediction, using current and passed typhoon locations as well as unified model (UN) pressure and wind maps.", "questions": "cf. section 'weaknesses'.\n\nQ1) in your state-of-the art comparison, which one uses UN, which uses only trajectory information?\nQ2) how come the Bias-corrected is sometimes better than ERA5 only? Any insights?\nQ3) Could you comment a bit more on the Figure 4? it looks like the bias-correction is actually losing the details, just fitting better to the distribution of the ERA5. Maybe there is more to see? The values (SSIM, mean) are not very convincing.\nQ4) How many different typhoons are used in the training set?\nQ5) Figure 1: what is the scale of the image? how many km (or give the lat/lon) in both directions?\n\nMinor comments/grammar/typos:\n\n- p. 2 'the UM' --> not yet defined (except Abstract)\n- 'But, it is not without the drawbacks.' sentence issue\n- 'while the UM data has only a 3-hour delay compared to ERA5' --> not clear: is it 3 hour less than ERA5? \n- LSTM, MLP, ... not defined acronyms\n- 'on a conservation laws'\n- 'hyperparameters & a architecture'\n- 'The ERA5 dataset has been accumulated at 6-hour intervals from 1950 to the present, and is too large scale to train a model from scratch. It is computationally inefficient to search optimal hyperparameters & a architecture for trajectory predictions.' --> not clear.\n- what do you mean by 'this architecture is computationally efficient when training with large-scale ERA5 dataset?\n- (6) L_trajectory is not defined.\n- 'and use it as our backbone architecture' --> we missing?\n- in Evaluation Metrics, MID not defined.\n- Fig 3 : we are viewing probability cones? It is not clear\n- 'bais-corrected'\n- ssim: not defined", "limitations": "", "rating": 8, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "The paper is interesting for both its application results compared to state-of-the-art, and also presents an interesting methodological framework. Indeed, getting rid of reanalysis data (which are not available in real time) is an important asset. The way to do so, first by learning the prediction of the physic variables maps using reanalysis data (which has more history than UN maps, and is also more precise), then to combine using a cross-attention both 'corrected' UN prediction with trajectory prediction, seems to give very good results.\nI think this paper would fit at ICLR, yet I have some questions which I would like to know the answer, and also a proofreading should be performed.", "weaknesses": "1) The study is limited to one region, as it looks that the UN map has a fixed latitude and longitude values. How was it chosen, what if typhoons are in the borders, or even going outside? It also means that your model can't easily be applied on other regions? \n\n2) Since real-time computation is the goal, it would be important to give computation times values.\n\n3) It is not clear how the 'probability cones' are obtained: stochasticity is mentioned only in the result part, not in the method.\n\n4) I understand that the number of data is limited, but I would like to know if a validation set was used to fine-tune the hyper-parameters or if it was done using the 2019-2021 years. Please explain better. \n\n5) Finally, it would be interesting to see one of the 'worst' cases also in Figure 3, with a comment on it.\n\n6) Many typos are present, see below.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (spotlight)