Matsuoka et al. Progress in Earth and Planetary Science
https://doi.org/10.1186/s40645-018-0245-y(2018) 5:80

Progress in Earth andPlanetary Science

RESEARCH ARTICLE

Open Access

Deep learning approach for detecting
tropical cyclones and their precursors inthe simulation by a cloud-resolving global
nonhydrostatic atmospheric modelDaisuke Matsuoka1,2* , Masuo Nakano3, Daisuke Sugiyama1 and Seiichi Uchida4

Abstract
We propose a deep learning approach for identifying tropical cyclones (TCs) and their precursors. Twenty year
simulated outgoing longwave radiation (OLR) calculated using a cloud-resolving global atmospheric simulation isused for training two-dimensional deep convolutional neural networks (CNNs). The CNNs are trained with 50,000
TCs and their precursors and 500,000 non-TC data for binary classification. Ensemble CNN classifiers are applied to
10 year independent global OLR data for detecting precursors and TCs. The performance of the CNNs isinvestigated for various basins, seasons, and lead times. The CNN model successfully detects TCs and theirprecursors in the western North Pacific in the period from July to November with a probability of detection(POD) of 79.9–89.1% and a false alarm ratio (FAR) of 32.8–53.4%. Detection results include 91.2%, 77.8%, and74.8% of precursors 2, 5, and 7 days before their formation, respectively, in the western North Pacific. Furthermore,
although the detection performance is correlated with the amount of training data and TC lifetimes, it is possible toachieve high detectability with a POD exceeding 70% and a FAR below 50% during TC season for several ocean basins,
such as the North Atlantic, with a limited sample size and short lifetime.
Keywords: Tropical cyclogenesis, Cloud resolving atmospheric model, Deep learning, Convolutional neural network

Introduction
Tropical cyclones (TCs), also referred to as typhoons,cyclones, and hurricanes, cause significant damage tohuman life, agriculture, forestry, fisheries, and infrastructure. For example, Typhoon Lionrock in 2016
caused record-breaking heavy rainfall, which resulted insevere floods and the loss of 23 lives in Japan. Moreover, TCs occasionally form very close to and approachcountries at low latitudes (e.g., the Philippines) with
rapid intensification. Therefore, accurate prediction of
TC track and intensity is necessary. Early prediction of
TC formation is important not only from an academic
but also from a disaster mitigation perspective.
* Correspondence: daisuke@jamstec.go.jp
1Center for Earth Information Science and Technology (CEIST), Japan Agency
for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi,
Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan
2
PRESTO, Japan Science and Technology Agency (JST), 4-1-8 Honcho,
Kawaguchi, Saitama 332-0012, Japan
Full list of author information is available at the end of the article

TCs form from convective disturbances in the tropics(Riehl 1954). Dynamic environmental conditions (e.g.,
small vertical wind shear, low-level cyclonic vorticity,and non-zero planetary vorticity) and thermodynamically favorable environmental conditions (e.g., sea surface temperature > 26 °C, existence of convective
instability, and mid-tropospheric moisture) necessary
for TC formation were proposed in the pioneering
work of Gray (1968, 1975). However, because only asmall percentage of convective disturbances in the tropics develop into TCs under favorable environmental
conditions (Emanuel 1989), accurate and early prediction of TC formation is still a developing area of research. The Japan Meteorological Agency (JMA)
extended the Dvorak method (Dvorak 1975; Dvorak
1984), which estimates TC intensity based on satellite
infrared imagery (IR), to tropical depressions (maximum sustained surface wind speed < 17.5 m s−1). Thisextension, known as early-stage Dvorak analysis (EDA),© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, andreproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to
the Creative Commons license, and indicate if changes were made.

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has been utilized in operational forecasts since 2001
(Tsuchiya et al. 2001), and the JMA issues early warnings on typhoon occurrence 1 day before its formation
based on EDA. The National Hurricane Center (NHC)
and Central Pacific Hurricane Center (CPHC) also use
the advanced Dvorak method for predicting TC genesis
with lead time and accuracies of 48 h and 15–57%, respectively (Cossuth et al. 2013). Yamaguchi and Koide(2017) demonstrated that that the predictability of TC
genesis could be improved to 35–79% by combining
the Dvorak method and multi-model ensemble forecasts. On the other hand, with recent advancements inhigh-performance computing and numerical weather
prediction, TC formation could be simulated 2 weeks inadvance in case studies of eight typhoons in August
2004 (Nakano et al. 2015), Hurricane Sandy in 2012,
and Super Typhoon Haiyan in 2013 (Xiang et al. 2015).
In recent years, deep learning, a machine learning
method based on neural networks, has been receiving
increasing attention and is being applied to various pattern recognition tasks (Krizhevsky et al. 2012; Simonyan
and Zisserman 2015). In meteorology, several studies
have proposed applying deep neural networks to existing
TC detection (Liu et al. 2016; Kim et al. 2017), tornado
prediction (Trafalis et al. 2014), hurricane pathway prediction (Kordmahalleh et al. 2015), and extreme rain fall
prediction (Gope et al. 2016). Although several studies
have used deep learning approaches for TCs after theirformation, no research has considered this approachfor detecting TCs before their formation.
In general, there are two approaches to detecting extreme events such as TCs: the model-driven approach(deductive approach), including numerical simulation,and the data-driven approach (inductive approach), including machine learning. The model-driven approachhas the limitation that the prediction error increases
with lead time because numerical models are inherently dependent on initial values. On the other hand,machine learning, as a data-driven approach, requiresa large amount of high-quality training data. Most related works use reanalysis data and/or satellite observational data and labeled data as TCs or precursors
based on the best track data provided by meteorological agencies. However, best track data for a TC’soccurrence well ahead of its formation is limited because the best track data is basically generated using
the EDA technique and are limited in accuracy andelapsed time. For example, the best track data from theRegional Specialized Meteorological Center (RSMC),
Tokyo, captures precursors 60 h before TC formation,
on average, whereas simulation data from cloud-resolving global atmospheric models (Kodama et al.
2015) and TC tracking algorithms capture TC formation 107 h ahead, on average.

In this work, we adopted the deep learning approach to detect precursors of TCs before their formation using only two-dimensional (2D) Outgoing
Longwave Radiation (OLR) data, which is equivalent
to IR and is a good proxy of atmospheric deep convection and cloud cover. In our 2D deep convolutional neural networks (CNNs), we use 30 year
cloud-resolving global atmospheric simulation data
(20 year data for training and 10 year data for verification) and a TC tracking algorithm for automatic labeling. Although the basic concept, simulation data, andTC tracking algorithm of this work are the same as
those in our previous conference paper (Matsuoka et
al. 2017), the present study improves the deep learning architecture and investigates predictive ability forvarious basins, seasons, and elapsed times.
The manuscript is organized as follows. The “Data”
section presents the climate simulation data and TC
tracking algorithm. The “Method” section explains thetraining data preparation, deep convolutional neuralnetworks, and evaluation metrics of prediction results.
The “Results and discussion” section examines thedetection results, including detectability for each ocean
basin, spatial detectability, seasonal detectability, andlong-term detectability. The “Conclusions” section
provides a summary of the main conclusions of thepresent work.

Matsuoka et al. Progress in Earth and Planetary Science

Data
Climate simulation data

Thirty year atmospheric simulation data were produced by the Nonhydrostatic Icosahedral Atmospheric Model (NICAM) with a 14 km horizontal
resolution (Kodama et al. 2015). This model employs
fully compressible nonhydrostatic equations andguarantees the conservation of mass and energy.
Equations were discretized by the finite volume
method. One characteristic feature of this model isthat it explicitly calculates deep convective circulations without using any cumulus parameterizations.
Moist processes are calculated using a single-momentbulk cloud microphysics scheme (NSW6) (Tomita2008). HadISST (Rayner et al. 2003) is used for lower
boundary condition. The seasonal march of TC genesis, TC track, and TC intensity in each basin is wellsimulated, as described in Kodama et al. (2015). The
dataset includes simulated OLR, precipitation, windvelocity, pressure, temperature, water vapor, andcloud (liquid, ice, rain, snow, and graupel) for 30 years
since January 1979. OLR and precipitation are output
every hour, and other physical quantities are output
every 6 h. An example of simulation results of OLR is
depicted in Fig. 1a. Three TCs and three precursors
are reproduced at 0:00:00 08/17/2008 UTC.

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Fig. 1 a An example of the simulated OLR. Blue and red squares show TCs and their precursors, respectively. Numeric values next to the squares
represent elapsed time (positive numbers: TCs, negative numbers: precursors). b Detection results of TCs (blue dots) and their precursors (reddots) during 1979–2008 from NICAM. The definition of basins is based on Fudeyasu et al. (2014)

This model is suitable for reproduction of tropical phenomena such as TCs (Nakano et al. 2015; Nakano et al.
2017a; Nakano et al. 2017b) and the Madden–Julian

oscillation (MJO) (Miura et al. 2007). For additional details
on this model, please see the original and review papers(Tomita and Satoh 2004; Satoh et al. 2014).

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TC tracking

Methods/Experimental

To detect TCs and precursors, we employed a TC
tracking algorithm for six-hourly outputs of the horizontal components of wind, temperature, and sea levelpressure (SLP). This algorithm was originally proposedby Sugi et al. (2002) and optimized for NICAM data by
Nakano et al. (2015) and Yamada et al. (2017). In thefirst step, grid points at which the SLP was 0.5 hPa lessthan the mean of its surrounding area (eight-neighbor
grids) were selected as candidate TC centers. In thisstep, grid points that met the following criteria wereconsidered as “TCs”: (i) the maximum wind speed at
10 m is greater than 17.5 m/s, (ii) the maximum relative vorticity at 850 hPa is greater than 1.0 × 10−3 s−1,(iii) the sum of temperature deviations at 300, 500, and
700 hPa is greater than 2 K, (iv) the wind speed at 850 hPa
is greater than that at 300 hPa, (v) the duration of each detected storm is greater than 36 h, and (vi) the TC isformed within a limited range of latitudes (30° S–30° N).
In the second step, these grid points were connectedwith nearest neighbors in time, and tracks of “precursors” (before becoming TCs), “TCs,” and “extratropical
cyclones” were subsequently obtained.
Figure 1b shows the result of applying the above
algorithm to the 30 year NICAM data. TCs and precursors are represented by blue and red dots. In 30
years, 2532 TCs were detected (72–103 TCs peryear). The numbers of TC tracks, detected samples,
positive samples in training data, and average lifetime in each ocean basin (North Indian Ocean, western North Pacific, eastern North Pacific, North
Atlantic, South Indian Ocean, South Pacific, andSouth Atlantic) are listed in Table 1. The definitionof basins is taken from Fudeyasu et al. (2014) and illustrated in Fig. 1b. Detected TCs and precursors
were used not only for labeling data, but also forground truth. Ground truth was provided beforehand as the center point of TCs and precursors toevaluate identification results.

Training data preparation

In this work, we performed a CNN-based binary classification that categorizes 2D cloud data (OLR) into “developing TCs and their precursors” or “non-developing
depressions.” Binary classification is the task of categorizing (or classifying) objects into two groups (positive
examples and negative examples) on the basis of classification rules. Before classification, it is necessary to
prepare a labeled training data set, which is a series ofsample data with the labels (positive or negative).
In the present work, at first glance, it appears natural
to categorize the data into the following three classes:
TCs, precursors, and non-developing depressions. However, since they are defined by the threshold value ofmaximum wind speed, it is difficult to identify themfrom cloud images. Figure 2a indicates a 2D histogramof cloud cover and elapsed time of all detected TCs including precursors by using the TC tracking algorithm.
Here, we define cloud cover as (OLRmax − OLRmean)/(OLRmax − OLRmin), where OLRmean is the mean value of
OLR in 64 × 64 grids, and OLRmax = 300.0 W/m2 andOLRmin = 100.0 W/m2. Figure 2a shows the developing
phase and dissipation phase in which the cloud cover
increases and decreases over time, respectively. All
precursors (elapsed time < 0) were in the developing
phase; therefore, we could identify both precursors andTCs in the developing phase (inside yellow dotted line)
under the same category “developing TCs and theirprecursors” (referred to here as “TCs”) for labeling supervised data. The range was 30.0–95.0% for cloud
cover and was from 10 days before to 7 days after formation, which could cover 97.0% of all precursors of
TCs (in Matsuoka et al. 2017, the cloud cover range
was 30.0–90.0% and covered 92.0% of all precursors ofTCs). The other category, “non-developing depressions” (hereinafter referred as “non-TCs”), are lowpressure clouds that were candidates for TCs but did
not satisfy criteria (i)–(vi). For the binary classification,

Table 1 The numbers of TC tracks, detected samples, positive samples in training data, and average lifetime in each basin
Numberof TC
tracksAverage lifetime [day]Number of detected samples (number of positive samples in training data)

TCs

TCs

Pre-TCs

Pre-TCs

North Indian Ocean

169

4.6

2.9

3011 (1422)

2162 (1184)

Western North Pacific

754

6.8

3.1

21,546 (9549)

13,514 (8023)

Eastern North Pacific

589

4.8

7.8

11,880 (4478)

14,392 (7976)

North Atlantic

125

4.4

4.1

2582 (788)1767 (758)South Indian Ocean

525

5.5

3.7

7989 (4757)

7989 (4148)

South Pacific

367

4.2

4.0

6649 (3503)

5346 (3193)

South Atlantic

3

1.6

2.3

22 (15)27 (26)

Total

2532

5.4

4.5

53,679 (24,512)

25,488 (25,308)

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Fig. 2 a Histogram of cloud cover and elapsed time for developing TCs and their precursors. Positive and negative values of elapsed time
indicate before formation (precursors) and after formation (TCs), respectively. b, c Examples of training data for binary classification of b
developing TCs and precursors (positive examples) and c non-TCs (negative examples)

we labeled “TCs” and “non-TCs” data as “positive” and“negative” examples, respectively.
Examples of “TCs” and “non-TCs” are shown in Fig. 2b,c. Although these figures are visualized images of OLR,
the actual training and test data are single-precision
floating point numbers. Their horizontal sizes wereapproximately 1000 km × 1000 km (64 × 64 grids). For
training, 20 year data (1979–1998) were used, and theremaining 10 year data (1999–2008) were used for prediction tests. The numbers of positive data (TCs andprecursors) and negative data (non-TCs) for training
were approximately 50,000 and 1000,000, respectively(the numbers of positive data in training data in eachbasin are listed in Table 1). Generally, the numbers of
positive and negative data are often set to same number inbinary classification. In this work, in order to train the
CNN with a vast number of negative data, ten training data
sets including the same 50,000 positive data and 50,000
randomly chosen negative data were generated for ten
deep CNNs. By combining multiple CNNs, the influenceof initial value dependence becomes smaller than when
only single CNN is used (Freund and Schapire, 1997;
Kearns and Valiant 1989; Breiman 1996; Breiman 2001).
Training and prediction using deep convolutional neuralnetworks

We used a 2D deep CNN for binary classification(Table 2). CNNs are algorithms of neural networks used
for image recognition and classification and for directly
learning visual patterns from images. CNNs usuallyconsist of convolutional layers, pooling layers, and fully
connected layers (LeCun et al. 1999; Krizhevsky et al.

2012). Convolutional layers extract local features (feature maps) of input images, pooling layers allow spatial
invariance by reducing the resolution of the image, andfully connected layers determine which features most correlate to a particular class. Dropout is a regularization
technique where randomly selected neurons are ignored during training for preventing overfitting in aneural network.
Our CNN architecture comprises four convolutional
layers, three pooling layers, and three fully connected
layers. Input data were 64 × 64 grids of OLR data
Table 2 The architecture of our deep CNN. The parameters of
the input layer, convolutional layers, pooling layers, and fully
connected layers are denoted as [input data size] (e.g., 64 × 64),
[filter size]@[number of feature maps] (e.g., 3 × 3@32),[pooling window size] (e.g., 2 × 2), and [number of units](e.g., 2048), respectively
Layer

Shape

Input

64 × 64

Convolution 1

3 × 3@32

Convolution 2

3 × 3@64

Pooling

2×2

Convolution 3

3 × 3@64

Pooling

2×2

Convolution 4

7 × 7@128

Pooling

2×2

Fully-connected

2048

Fully-connected

2048

Fully-connected2

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consisting of single-precision real numbers, and output
was generated in two classes (1 or positive: TCs; 0 or
negative: non-TCs). Hyper parameters were optimizedon the basis of the cross-validation test, which was conducted to evaluate the performance of the CNN using arandom part of the training data. We examined the validation accuracy of 216 combinations of architecture
settings: the number of convolutional (1–5) and poolinglayers (1–5), the number of parameters in the fully connected layer (128, 256, 512, 1024, 2048), drop out ratio(0.2, 0.3, 0.4, 0.5), size of the convolutional filter (3 × 3,
5 × 5, 7 × 7), and number of feature maps (16, 32, 64,
128). Accordingly, the architecture with the highest
level of performance was adopted, as shown in Table 2.
The Adam optimizer (Kingma and Ba 2015) was applied to the CNN to update the network parameter tominimize the loss function called binary cross entropy.
Batch normalization (Ioffe and Szegedy, 2015) was also
applied to the CNN to minimize the initial-value dependence of the parameters.
The source code for deep learning was implemented
in Python 3.6.3 with Keras (TensorFlow backend)(Chollet 2015), running on an NVIDIA Tesla P100 (1node). Training 100,000 data over one epoch consumed
approximately 3 min.
The accuracy of ten CNN classifiers using 100,000 data
(50,000 for each of the two classes) for training and 5000
data for cross-validation is shown in Table 3. The maximum, minimum, and average values were 90.99%, 89.58%,
and 90.30%, respectively (the number of epochs ranged
over 19–46). The metric “Accuracy” is defined as follows:

predicted positive example as positive,” “correctly predicted negative examples as negative,” “incorrectly predicted negative example as positive,” and “incorrectlypredicted positive examples as negative,” respectively.
Compared with the average accuracy of ten classifiersin Matsuoka et al. (2017), which was 86.60%, the average accuracy increased by 3.7 percentage point. Note
that, although simulation data and the TC tracking algorithm were the same as in Matsuoka et al. 2017, in
this study, the target range of cloud cover was expanded from 30.0–80.0% to 30.0–95.0%.
In the present study, in order to effectively train imbalanced data (positive 50,000, negative 1000,000), tenclassifiers (Classifier 1, 2, …, 10) were generated bytraining ten sets of 100,000 data on the same neuralnetwork, as shown in Fig. 3a. Each classifier was trainedwith the same 50,000 positive data and randomly selected 50,000 negative data. In this manner, our CNNs
could train 50,000 positive examples and 500,000 negative examples simultaneously.
To verify the model’s performance, classifiers trainedusing the 20 year data were applied to the test data (untrained 10 year simulation data). Candidate regions in thetest data to be predicted by applying trained classifiers
were clipped with a sliding window, which is widely used
for object detection (Kumar 2013). We slid a rectangulararea (approximately 1000 × 1000 km) with a 125 km(eight-grid) stride and continued sliding the window over
the whole data within latitudes of 30° N to 30° S because
three pooling layers of our CNN assumed eight grids ofhorizontal shift. Furthermore, in order to reduce the number of candidate regions, we set a limit to the cloud coverin the range of 30.0 to 95.0% and 50% or more over sea
areas. In this manner, 97.0% of precursors of TCs in thesimulation data could be covered.
Our ensemble CNNs output the ensemble averageusing the weight value of each trained classifier, as
shown in Fig. 3b. The weight value given by the accuracy of the ten classifiers is listed in Table 3. The
final probability p for detecting the presence of developing TCs and precursors in an arbitrary region isdefined as follows:

Matsuoka et al. Progress in Earth and Planetary Science

Accuracy ¼

TP þ TNTP þ TN þ FP þ FN

ð1ÞHere, TP (true positive), TN (true negative), FP (false
positive), and FN (false negative) correspond to “correctly

Table 3 Accuracy values (also used as weights) of the ten classifiers
Model number: i

Accuracy (weight: wi)Classifier 1

0.9099Classifier 2

0.9050Classifier 3

0.9013

Classifier 40.9014

Classifier 50.8958Classifier 6

0.9085Classifier 7

0.8987

Classifier 8

0.9065Classifier 90.9025Classifier 100.9007p¼

10
1 X
w i xi10 i¼1 wið2Þ

where wi is the weight value of classifier i, and xi is theoutput value obtained by Classifier i (0: non-TCs, 1:
TC). When the threshold value pth is given, arbitrarycandidate areas that satisfy p ≥ pth are regarded as positive. Although we adopt binary classification to facilitate
the evaluation of prediction results, we can also output
detection results as probabilistic information using p.

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Fig. 3 The flow charts of the proposed ensemble CNNs: a training and cross-validation phase and b prediction phase. Hyper parameters of the
CNN architecture are listed in Table 2

Evaluation metrics of prediction results

The false alarm ratio (FAR) and the probability of detection (POD) are often used as evaluation metrics ofprediction results in weather forecasting (Jolliffe andStephenson, 2003; Wilks 2006; Barnes et al. 2007). FAR,
incorrectness of positive prediction, is defined using“correctly predicted positive examples as positive (TP,true positive)” and “incorrectly predicted negative
examples as positive (FP false positive)” as follows:
FAR ¼

FP
TP þ FPð3Þ

As shown in Fig. 4a, when a positive predicted area
captures the ground truth, the area is TP. Similarly,
when a positive predicted area does not capture theground truth, the area is FP. In the example of Fig. 4a,

TP is 3, FP is 1, and FAR is 25.0%. Although FAR isclosely related to Precision = TP/(TP + FP), which is
widely used in computer vision and pattern recognition(Forsyth 2011), Precision is one minus FAR and means
correctness of positive prediction.
POD is another important metric in prediction; it indicates the amount of ground truth that can be correctly predicted. POD is conceptually the same as
Recall = TP/(TP + FN) used in computer vision except
for cases in which multiple positive predicted areas
overlap, as shown in Fig. 4b. This is because the denominator of POD is the value of the ground truth,
whereas the denominator of Recall is the number ofpredicted areas corresponding to the ground truth.
Therefore, TP is given to TCs instead of prediction
area, and we define the POD at multiple areas with thesame detected ground truth as follows:Matsuoka et al. Progress in Earth and Planetary Science(2018) 5:80

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Fig. 4 Conceptual diagram explaining our evaluation metrics: a accuracy and FAR and b POD

POD ¼

number of correctly predicted TCs
number of TCs

ð4Þ

Here, the numbers of both TCs and correctly predicted TCs include precursors as mentioned above.

Results and discussion
Detection results

This section first introduces one of the best cases ofdetection results under the condition that the numberof TCs and precursors is larger than eight and POD islarger than 80.0%. Similarly, one of the worst cases
under the condition that the number of TCs and precursors is larger than five is also introduced.
Figure 5 shows the best case of detection results during the 10 year period (October 21, 2003, 18:00:00
UTC) for (a) pth = 1.0 and (b) pth = 0.6. Red and whiteboxes represent positive predicted areas (TCs) andnegative predicted areas (non-TCs), respectively. Furthermore, blue and red dots represent the central
points of actual TCs and precursors (as ground truth)
calculated by the TC tracking algorithm, respectively.
In Fig. 5a, five developing TCs and three precursors ofnine ground truths can be correctly predicted; hence
POD is 88.9% (= 8/9). Furthermore, 74 of 82 positive
prediction areas could be correctly predicted; hence, FAR
is 9.8% (= 1–74/82). Figure 5b shows the prediction results

after decreasing the threshold pth to 0.6. In this case,the correctly predicted area (true positive) increases(POD is 100.0%) because the positive predicted area is
expanded. However, the false alarm rate also increases(FAR is 34.1%).
Representing the worst case, prediction results of August 17, 2006 18:00:00 UTC are shown in Fig. 6, in
which many TCs and precursors with less cloud coverwere missed and there are numerous false alarms.
While the PODs range from 20 to 60%, the FARs werehigh (72.7 to 78.3%).
Detectability for each basin

The performance for various pth was evaluated on eachbasin. Figure 8 shows the relationship between POD and
FAR when pth was varied from 10 to 100% in 10% increments. It represents the average value over 10 years(1999–2008) for each basin. The South Atlantic was notconsidered in this study because the number of TCs that
occur in that area is extremely small. Although there aredifferences in values of detection performance depending on the basin, POD and FAR exhibited a trade-off relationship for all cases. When pth is increased, thepositive prediction area is narrowed down, and both
FAR and POD decrease. In contrast, if pth is decreased,
the positive predicted area becomes wider, and both FAR
and POD increase because dropout is reduced.

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Fig. 5 Detection results at 18:00:00 UTC 10/21/2003 with a pth = 1.0 (POD = 88.9% and FAR = 9.8%), and b pth = 0.6 (POD = 100% and FAR = 34.1%).
Numbers next to the blue or red letters indicate the elapsed time of the ground truth of TC or pre-TC, respectively

There are several reasons for the variation in detection performance for different basins. First, it is known
that the pattern of TC genesis is different for each basin(Holland 2008), and the detectability for each pattern
may differ (as will be described in the “Conclusion” section, investigation of the detectability for each generation pattern will be undertaken in future studies).

Second, in general, the performance of supervised machine learning depends on the number of training data
for each pattern. In our results, the correlation coefficient between POD and the number of training data is
0.749, and that between the FAR and the number oftraining data is − 0.756. Lastly, since the cloud patternof TCs is broken over time in the dissipation phase,Matsuoka et al. Progress in Earth and Planetary Science(2018) 5:80

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Fig. 6 Detection results at 18:00:00 UTC 08/17/2006 with a pth = 1.0 (POD = 20.0% and FAR = 72.7%) and b pth = 0.6 (POD = 60% and FAR = 78.3%).
Numbers next to the blue or red letters indicate the elapsed time of the ground truth of the TC or pre-TC, respectively

their detectability should decrease. In other words, thedetectability should be high for TCs with long lifetimes.
The correlation coefficient between POD and average
TC lifetime in each basin is 0.821 and that between
FAR and average lifetime is − 0.802.
For example, as seen in Fig. 7, it was found that thebasin with the best detection performance was thewestern North Pacific and that with the worst detection performance was the North Indian Ocean. In thewestern North Pacific, the number of training data
was the largest (TCs 9549, pre-TCs 8023) and theaverage lifetime was also the longest (6.8 days). On
the other hand, in the North Indian Ocean, the number of training data was relatively small (TCs 1422,

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Fig. 7 Relationship of the POD and FAR with various pth for different basins. The POD and FAR are 10 year average values from 1999 to 2008 foreach basin

pre-TCs 1184) and the average lifetime was also
relatively short (4.6 days).
Spatial detectability

This section first shows the spatial distribution of detection performance for a threshold value of pth = 1.0.
Figure 8a, b shows the spatial distributions of POD and
FAR, respectively. Here, the spatial distributions of
POD and FAR are calculated as their 10 year means at
each grid point and are defined in a 64 × 64 rectangulararea centered on the ground truth. Figure 8c shows thecount of ground truths for both TCs and pre-TCs in
the training data at each grid point. Figure 8d shows
the count of positive prediction areas at each grid
point. Areas with many TCs and pre-TCs are represented by a gray and white dotted line in Fig. 8a, b, d.
In most basins, both POD and FAR appeared to beroughly associated with the ground truth count in the
training data. Especially in the Indian Ocean and thePacific, POD was higher and FAR was lower in regionsthat had higher ground truth counts. As an exception,
POD also exceeds 80% and FAR falls to 60% in a part of
the North Atlantic, despite limited training data. Previous studies reported that there is a pattern of TC

genesis unique to the basin (Ritchie and Holland 1999;
Yoshida and Ishikawa 2013; Fudeyasu and Yoshida,2018). For example, Russell et al. (2017) reported that
72% of TCs in the North Atlantic are caused by African
Easterly Waves (AEW). Accordingly, our results may
indicate that TCs and pre-TCs caused by the AEW areeasy to detect using CNNs.
Next, the detection performance of the TC area andpre-TC area are compared. In each basin, pre-TC areas
are located at lower latitudes than the TC area. As
shown in Fig. 8b, the FAR of the pre-TC area washigher than that of the TC area. Although there was no
significant difference in POD between the TC andpre-TC areas, the count of positive predictions was larger in the pre-TC area than in the TC area. That is, thecount of misdetection is larger in the pre-TC area thanin the TC area. Intuitively, the pattern of developed
TCs is simpler than that before formation, and therefore, it is reasonable that the detectability of TCs ishigher than that of pre-TCs.
In the western South Pacific and South Atlantic, although the FAR was close to 100%, there were few positive prediction areas. In other words, the number of
misdetections (false positives) was small. In contrast,

Matsuoka et al. Progress in Earth and Planetary Science(2018) 5:80

Page 12 of 16

Fig. 8 Ten year means of a POD and b FAR with pth = 1.0, respectively. c Spatial distribution of the count of ground truth in the training data of
TCs and precursors. d Spatial distribution of the count of positive prediction areas

near the equator of the Indian Ocean and the eastern
Pacific, although the FAR was also close to 100%, thenumber of positive prediction areas was large. That is,
the number of misdetections was large in these areas.
Seasonal detectability

Seasonal detectability, monthly mean of POD, and FARfrom 1999 to 2008 in each basin are shown in Fig. 9.
Monthly variability of the number of training data
(positive) and ground truth in each month and eachbasin are also shown in the same figure. In each basin,monthly changes in POD and FAR almost correspondto the number of training data. In other words, seasonal
TCs can be detected without generating numerous false
alarms. In particular, seasonal TCs in the western North
Pacific (from July to November) could be detectedwith a POD of 79.0–89.1% and a FAR of 32.8–53.4%(pth = 1.0). Similarly, seasonal TCs in the South Indian
Ocean (from December to March) could be detectedwith a POD of 76.7–78.0% and a FAR of 31.1–40.3%.
Furthermore, seasonal TCs in the North Atlantic(from August to November) could be detected with a
POD of 75.0–78.2% and a FAR of 36.7–51.0%.
In contrast, numerous false alarms were generated
during seasons with a low frequency of TCs. In the western North Pacific, although POD was not very low(65.7–83.0%) during January to May, the FAR was

remarkably high (75.4–95.3%). It is noteworthy that
POD in the North Pacific is unlikely to decrease evenduring seasons with a low frequency of TCs, except formonths with extremely small numbers of positive
ground truth. In the western North Pacific in December,the POD was 78.7% and the FAR was 65.9% (pth = 1.0).
Similarly, in the eastern North Pacific in November, thePOD was 72.6% and the FAR was 68.4% (pth = 1.0).
Long-term detectability

Detectability (POD), the number of training data (positive), and ground truth of each elapsed time frame indifferent basins are shown in Fig. 10. In each basin, theday with the highest POD is the day TCs formed(elapsed time = 0–2 days). One of the reasons for this isthat this period is the transition period from the developing phase to the dissipation phase for most TCs, as
shown in Fig. 2a. Obviously, fully developed TCs areeasy to recognize. Another reason is that the training of
the CNNs tends to focus on large samples near thisperiod to reduce errors.
The POD decreases as lead time is increased because
it is difficult to detect the precursors many days in advance. For example, 91.2%, 77.8%, and 74.8% of precursors 2, 5, and 7 days before their formation can bedetected in the western North Pacific, respectively(Fig. 10b). Similarly, 91.7%, 76.2%, and 70.1% ofMatsuoka et al. Progress in Earth and Planetary Science(2018) 5:80

Page 13 of 16

Fig. 9 POD and FAR (pth = 1.0 and pth = 0.6) and the number of data (training and test data) for each month in each basin. Note that the POD
cannot be defined when the number of TCs is zero according to Eq. (2). In the same manner, the FAR cannot be calculated when the positive
prediction area is zero, according to Eq. (3)Matsuoka et al. Progress in Earth and Planetary Science(2018) 5:80

Page 14 of 16

Fig. 10 Average-max-min charts of POD (pth = 1.0 and pth = 0.6) and bar graphs of the number of data (training and test data) for each elapsed
time frame in each basin

(2018) 5:80

Page 15 of 16

precursors 2, 5, and 7 days ahead can be detected in
the eastern North Pacific, respectively (Fig. 10c). Notethat the FAR could not be calculated in each elapsed
time frame because TP could not be defined in each
elapsed time frame.
In contrast to these basins, long-term detectability
decreases rapidly in basins with less training data, suchas the North Indian Ocean (Fig. 10a) and the North
Atlantic (Fig. 10d). In most basins, the number ofground truth matches of test data is small from 7 to
14 days before the formation of precursors. Therefore,
this time frame involves large errors and decreased reliability of POD. Note that these results are reference
values because the POD and FAR could not be evaluated simultaneously.

In areas with less data such as the North Atlantic,
training data from other basins may have a positive influence on prediction results. However, in areas with
sufficient data, such as the North Western Pacific,
training according to each basin might improve detectability. In order to verify and improve detection performance, it is necessary to analyze the influence of thedata in different basins by training the generation
patterns and environmental factors in each basin.
This paper describes the preliminary results of detecting precursors of tropical cyclones using only simulated
OLR; we plan to use other proxies of convection suchas rain rate and mixing ratio of solid water for improving the detection performance. Furthermore, we alsoplan to apply our ensemble CNNs to reanalysis dataand satellite observation data for practical use. For thispurpose, data of different spatial resolutions and different variables or satellite channels must be considered.
Furthermore, time-sequence data as well as comparative analyses with the results of the Early Dvorak
Method are also required.

Matsuoka et al. Progress in Earth and Planetary Science

Conclusions
In this work, the detectability of TCs and their precursors for each basin, season, and lead time was investigated based on a deep neural network approach using
20 year simulated OLR by the NICAM. From the results of applying the CNNs to untrained 10 year simulated OLR, the following conclusions can be drawn: Particularly in the western North Pacific, we could

successfully detect TCs and their precursors during
July to November with POD values of 79.0–89.1%
and FAR values of 32.8–53.4%. Detection results
include 91.2%, 77.8%, and 74.8% of precursors 2, 5,
and 7 days before their formation, respectively.
 Although the detection performance was approximately
consistent with the number of training data and TC
lifetime, the detection performance in the North
Atlantic was not relatively low despite limited
training data and short lifetimes. In particular, theaverage POD and FAR values in the North Atlantic
during September to October were 74.8% and40.9%, respectively.
These results suggest the high potential of the datadriven approach for studying tropical cyclogenesis.
In contrast, the limitations of our framework are
as follows:Abbreviations
CNN: Convolutional neural network; NICAM: Nonhydrostatic ICosahedral
Atmospheric Model
Acknowledgements
We are grateful to Dr. C. Kodama for the production of simulation data and
Dr. Y. Yamada for the production of best-track data on tropical cyclones. We
also thank Editage for the English language review.
Funding
This work was supported by JST, PRESTO Grant Number JPMJPR1777, JSPS
KAKENHI Grant Number 16 K13885, 26700010, and 17 K13010.
Availability of data and materials
The data used in this study are available from the authors upon request.
Authors’ contributions
DM proposed the topic and conceived and designed the study. MN analyzed
the data and helped in their interpretation. DS conducted the experimental
study. SU collaborated with the corresponding author in the preparation of themanuscript. All authors have read and approved the final manuscript.
Competing interests
The authors declare that they have no competing interest.

Publisher’s Note

 Since the candidate regions are narrowed down by

Springer Nature remains neutral with regard to jurisdictional claims in publishedmaps and institutional affiliations.

the threshold value of cloud cover (30–95%), theycannot be detected when the cloud cover isextremely small (< 30%) or extremely large (> 95%).
 Our method considers developing TCs and precursors
as one category. To evaluate the detection
performance of only pre-TCs, it is necessary toclassify them by improving the CNN model.
 Our CNN classifiers may have model-specific biases
arising from training using only NICAM data.

Author details
1
Center for Earth Information Science and Technology (CEIST), Japan Agency
for Marine-Earth Science and Technology (JAMSTEC), 3173-25 Showa-machi,
Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan. 2PRESTO, Japan Scienceand Technology Agency (JST), 4-1-8 Honcho, Kawaguchi, Saitama 332-0012,
Japan. 3Department of Seamless Environmental Prediction Research, Japan
Agency for Marine-Earth Science and Technology (JAMSTEC), 3173-25
Showa-machi, Kanazawa-ku, Yokohama, Kanagawa 236-0001, Japan.
4
Graduate School and Faculty of Information Science and ElectricalEngineering, Kyushu University, 744 Motooka, Nishi-ku, Fukuoka 819-0395,
Japan.

Matsuoka et al. Progress in Earth and Planetary Science(2018) 5:80

Received: 28 March 2018 Accepted: 19 November 2018

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