A Machine-Learning-Based Study on All-Day Cloud Classification Using Himawari-8 Infrared Data

2024-12-07 Figure 1. Illustration of the observation area of the Himawari-8 satellite . Figure 1. Illustration of the observation a

Figure 1.
Illustration of the observation area of the Himawari-8 satellite .

figure 2 .
Cloud datum for 10 channel of AHI .

Figure 3.
General flow diagram .

Figure 3.
General flow diagram.

figure 4 .
Confusion matrix of the AInfraredCCM.

figure 5 .
Classification performance graphs of different models. (a–e) graph of the precision , recall , and f1 – score of classification result of 5 model in different cloud type .

Figure 6.
Classification result of different model at 05:00 UTC on June 29 , 2019 . (a) visible datum , (b) thermal infrared data, (c) the brightness temperature map ; (d–h) sequentially present the classification results of GradientBoost, LightGBM, rf, AdaBoost, and AInfraredCCM, respectively.

figure 7 .
Evaluation of classification metrics of the AInfraredCCM for daytime/nighttime. (a,b) bar graph of precision , recall , and f1 – score of different type of cloud classification result ; (c,d) confusion matrix of different type of cloud classification in daytime and nighttime .

figure 7 .
Evaluation of classification metrics of the AInfraredCCM for daytime/nighttime. (a,b) bar graph of precision , recall , and f1 – score of different type of cloud classification result ; (c,d) confusion matrix of different types of cloud classification in daytime and nighttime.

Figure 8.
plot of classification result at 04:20 UTC on May 15 , 2019 . (a) rgb ; (b) bright temperature ; (c) ainfraredccm result ; (d,e) CLTYPE, AInfraredCCM results, and label; (f) the combine cloud product of the CPR / CALIOP and Himawari-8 datum ; (g) vertical profile of the cloud type along the orbit of the CPR / CALIOP .

Figure 8.
plot of classification result at 04:20 UTC on May 15 , 2019 . (a) rgb ; (b) bright temperature ; (c) ainfraredccm result ; (d,e) CLTYPE, AInfraredCCM results, and label; (f) the combine cloud product of the CPR / CALIOP and Himawari-8 datum ; (g) vertical profiles of the cloud types along the orbit of the CPR/CALIOP.

figure 9 .
evaluation metric of the model across 4 season ; (a–h) are the line graphs and confusion matrix of precision, recall, and f1 – score in 4 seasons.

Figure 10.
Bright temperatures (K), AInfraredCCM classification results, and Himawari-8 CLTYPE for different seasons. (a–d) Cloud map moments in the following order: 15 May 2019 UTC 02:50; 5 August 2017 UTC 05:00; 10 October 2018 UTC 03:30; and 1 February 2019 UTC 06:40, respectively.

Table 1 .
Himawari-8 band parameter and application .

Bands	Channel Type	Center wavelength ( μm )	Spatial Resolution (km)	Main Applications
7	Midwave IR	3.9	2	Natural disasters, low cloud (fog) observation
8	Water vapor	6.2	2	Observation of water vapor volume in the upper and middle layers
9	6.9	2	observation of water vaporization in the mesosphere
10	7.3	2
11	Longwave IR	8.6	2	Cloud phase identification and SO2 detection
12	9.6	2	Measurement of total ozone
13	10.4	2	Observation of cloud images and cloud top conditions
14	11.2	2	Observation of cloud images and sea surface water temperature
15	12.4	2	Observation of cloud images and sea surface water temperature
16	13.3	2	Measurement of cloud height

Table 2.
Cloud type of this study.

Cloud Label	Label of CPR/CALIOP	Label of CLTYPE	Name of Cloud
0	0 ( clear )	0 ( clear )	clear
1	1 ( ci )	1, 2 (ci, Cs)	ci (ci, Cs)
2	8 ( Dc )	3 (Dc)	Dc
3	3 ( Ac )	4 (Ac)	Ac
4	2 ( As )	5 (As)	As
5	7 (n)	6 (n)	n
6	6 ( Cu )	7 ( Cu )	Cu
7	5 (Sc)	8 (Sc)	Sc
8	4 (St)	9 ( St )	St

Table 3.
Information of dataset.

Dimension	number	Variables
predictor	BTs (10)	BT (3.9 μm), BT (6.2 μm), BT (6.9 μm), BT (7.3 μm), BT (8.6 μm), BT (9.6 μm), BT (10.4 μm), BT (11.2 μm), BT (12.4 μm), and BT (13.3 μm)
BTDs (5)	BTD (11.2–7.3 μm), BTD (3.9–11.2 μm), BTD (11.2–12.4 μm), BTD (12.4–10.4 μm), and BTD (7.3–10.4 μm)
Auxiliary data (2)	Latitude and Longitude
Prediction	1	Cloud label from 2B-CLDCLASS-LIDAR and CLTYPE

Table 4.
Classification results of the model on dataset C.

		number of Ever category	Total number
	number of category A clouds	number of category B clouds
Model classification result	number of category A clouds	TA	FB	T1
number of category B clouds	fa	TB	T2
Total number		AS	BS	T

Table 5.
parameter of the AInfraredCCM .

Parameter	Meaning	value
n_estimators	number of trees	204
learning_rate	Magnitude of the iterative model update	0.2122
max_depth	Maximum tree depth	26
min_child_weight	Minimum number of samples required in a leaf node	3

Table 6 .
precision, recall, and f1 – score of the AInfraredCCM.

Cloud Type	precision	Recall	F1-Score
clear	0.85	0.89	0.87
ci	0.90	0.88	0.89
Dc	0.93	0.87	0.90
Ac	0.82	0.74	0.78
As	0.89	0.88	0.89
n	0.95	0.93	0.94
Cu	0.68	0.57	0.60
Sc	0.88	0.93	0.91
St	0.98	0.90	0.94

Table 7.
Optimal parameter combinations for the model.

Algorithm	Parameted Range
Random Forest	1 . max_depth = 73 2. n_estimators = 280
LightGBM	1. learning_rate = 0.095 2. max_depth = 22 3. n_estimators = 252 4. num_leaves = 35
AdaBoost	1. learning_rate = 0.4224 2. max_depth = 74 3. n_estimators = 458 4. min_samples_leaf = 1
GradientBoost	1 . learning_rate = 0.4749 2. max_depth = 37 3. n_estimators = 10

Table 8.
Evaluation for different model .

Algorithm	Accuracy	precision	Recall	F1-Score
Random Forest	82.53 %	0.83	0.76	0.79
LightGBM	74.60 %	0.70	0.64	0.66
GradientBoost	80.96%	0.78	0.77	0.78
AdaBoost	85.83%	0.87	0.83	0.85
AInfraredCCM	86.22%	0.88	0.84	0.86

Table 9 .
Cloud classification model statistical table.

Model	category	feature	time	OA	sample	Reference
rf	Dc, n, Cu, Sc, St, Ac, As, ci, and multi	REF is cloud and BT of 13 channel , cloud top height , cloud optical thickness is cloud , cloud effective radius	day	0.67	272414	Yu et al. (2021) [12]
BP	clear, low cloud, middle cloud, thick cirrus clouds, thin cirrus cloud, deep convective	IR1 (10.3–11.3), IR2 (11.5–12.5), WV (6.3–7.6), IR1-IR2, IR1-WV, IR2-WV	day	0.86	2449	Zhang et al . ( 2012 ) [ 39 ]
CNN	clear, ci, Ac, As, Sc, Dc, n, Cu	All channel of FY-4A	day	0.95	15780	Wang et al. (2023) [40]
rf	clear, low cloud, middle cloud, thin cloud, thick cloud, multilayer cloud, cumulonimbus	r ( 0.64 ) , r ( 1.6 ) , BT ( 11.2 μm ) , BTD ( 11.2–3.9 μm ) , BTD ( 11.2–7.3 μm ) , BTD ( 11.2–8.6 μm ) , BTD ( 11.2–12.3 μm )	day	0.88	127192	Wang et al . ( 2023 ) [ 41 ]
rf	clear, low cloud, middle cloud, thin cloud, thick cloud, multilayer cloud, cumulonimbus	BT (11.2 μm), BTD (11.2–3.9 μm), BTD (11.2–7.3 μm), BTD (11.2–8.6 μm), BTD (11.2–12.3 μm)	Night	0.79	72934	Wang et al . ( 2023 ) [ 41 ]
rf	clear , single , multi	BT (3.9 um), BT (7.3 m), BT (8.6 μm), BT (11.2 μm), BT (12.4 μm), BTD ( 3.9–11.2 μm ) , BTD ( 8.6–11.2 μm ) , BTD ( 11.2–12.4 μm ) , latitude , longitude	day and night	0.79	12553889	Tan et al. (2022) [16]
DNN	clear, single-ice, single-mixed, single-water, multi	BT ( 3.9–13.3 μm ) , cosine of satellite zenith angle , simulate clear – sky radiance	day and night	0.81	1114591	Li et al . ( 2022 ) [ 17 ]
AInfraredCCM	clear, ci, Dc, Ac, As, n, Cu, Sc, St	BT ( 3.9–13.3 μm ) , BTD ( 11.2–9.6 μm ) , BTD (3.9–11.2 μm), BTD (11.2–12.4 μm), BTD (12.4–10.4 μm), BTD (7.3–10.4 μm), latitude, longitude	day and night	0.86	1314275	This study

Table 10 .
Accuracy rate of Himawari-8 CLTYPE and AInfraredCCM.

	Himawari-8 CLTYPE	AInfraredCCM
Full area	0.48	0.86
cloudy area	0.36	0.87
clear sky	0.77	0.85

Table 11.
Cloud classification result of the AInfraredCCM for daytime and nighttime .

time	Cloud Type	clear	ci	Dc	Ac	As	n	Cu	Sc	St
accuracy = 85.82 %
daytime	precision	0.85	0.90	0.92	0.82	0.89	0.95	0.68	0.89	0.98
Recall	0.89	0.88	0.86	0.72	0.88	0.93	0.54	0.92	0.90
f1 – score	0.87	0.89	0.89	0.77	0.88	0.94	0.60	0.91	0.94
Accuracy = 91.45%
nighttime	precision	0.90	0.92	0.99	0.87	0.92	0.96	0.77	0.93	0.97
Recall	0.90	0.91	0.92	0.85	0.93	0.97	0.56	0.96	0.96
f1 – score	0.90	0.91	0.95	0.86	0.93	0.96	0.65	0.94	0.96

Table 12.
Results of the four seasonal classifications.

Season	Cloud Type	clear	ci	Dc	Ac	As	n	Cu	Sc	St
Accuracy = 86.61%
Spring	precision	0.85	0.90	0.93	0.83	0.90	0.96	0.69	0.90	0.97
Recall	0.90	0.87	0.87	0.75	0.87	0.94	0.56	0.93	0.90
f1 – score	0.88	0.88	0.90	0.79	0.89	0.95	0.62	0.92	0.93
Accuracy = 85.60%
Summer	precision	0.84	0.91	0.95	0.82	0.87	0.95	0.66	0.88	0.97
Recall	0.88	0.89	0.89	0.74	0.88	0.93	0.50	0.92	0.93
f1 – score	0.86	0.90	0.92	0.78	0.88	0.94	0.57	0.90	0.95
Accuracy = 85.87%
Autumn	precision	0.85	0.90	0.93	0.81	0.88	0.95	0.68	0.88	0.99
Recall	0.89	0.87	0.90	0.74	0.88	0.93	0.54	0.93	0.90
f1 – score	0.87	0.89	0.91	0.77	0.88	0.94	0.60	0.90	0.94
Accuracy = 87.27%
winter	precision	0.87	0.91	0.86	0.84	0.91	0.95	0.69	0.89	0.99
Recall	0.91	0.89	0.88	0.75	0.88	0.93	0.55	0.93	0.87
f1 – score	0.89	0.90	0.87	0.79	0.90	0.94	0.60	0.91	0.92