Cloud Base Height Retrieval Methods for FY-4A Based on Ensemble Learning

Fig. 3. Variation of RMSE of eight types of clouds with decision trees number in RF model and GBT model. (a) Variation of RMSE with decision trees number in RF model; (b) variation of RMSE with decision trees number in GBT model

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Fig. 4. Variation of RMSE of eight types of clouds with maximum depth of decision trees in RF model and GBT model. (a) Variation of RMSE with maximum depth of decision trees in RF model; (b) variation of RMSE with maximum depth of decision trees in GBT model

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Fig. 5. Variation of RMSE of samples on training and validation datasets with decision trees number in RF model and GBT model. (a) Variation of RMSE with decision trees number in RF model; (b) variation of RMSE with decision trees number in GBT model

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Fig. 6. Variation of RMSE of samples on training and validation datasets with maximum depth of decision trees in RF model and GBT model. (a) Variation of RMSE with maximum depth of decision trees in RF model; (b) variation of RMSE with maximum depth of decision trees in GBT model

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Fig. 7. Retrieval results of models of two schemes on test dataset. (a) Scheme one; (b) scheme two

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Fig. 8. Retrieval flow of CBH for FY-4A in practical application

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Fig. 9. CBH retrieved from models of two schemes and the comparison with CBH from CloudSat. (a) Cloud types obtained according by the model proposed in Ref. [32]; (b) CBH retrieved from the cloud types of Fig. 9(a) and the model of scheme one; (c) CBH retrieved from the model of scheme two; (d) comparison between the cloud types of CloudSat and the model proposed in Ref. [32], and comparison among CBH retrieved from models of two schemes and CBH from CloudSat on CloudSat track

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Table 1. Parameters of FY-4A/AGRI channels
View table
Table 1. Parameters of FY-4A/AGRI channels
Channel number Center wavelength /μm Spatial resolution /km
1 0.47 1.0
2 0.65 0.5
3 0.825 1.0
4 1.375 2.0
5 1.61 2.0
6 2.25 2.0
7 3.75（H） 2.0
8 3.75（L） 4.0
9 6.25 4.0
10 7.1 4.0
11 8.5 4.0
12 10.7 4.0
13 12.0 4.0
14 13.5 4.0

Table 2. Related cloud products of FY-4A/AGRI and corresponding channels
View table
Table 2. Related cloud products of FY-4A/AGRI and corresponding channels
Cloud product of FY-4A/AGRI Center wavelength of optical channel /μm
Cloud top height（CTH） 11.0，12.09，13.55
Cloud optical thickness（COT）and cloud effective radius（CER） 0.65，2.25

Table 3. Numbers of all types of clouds after data matching
View table
Table 3. Numbers of all types of clouds after data matching
Cloud type Number
Ci 73255
As 71000
Ac 41412
St/Sc 104792
Cu 24535
Ns 40397
Dc 10719
Multi 65603

Table 4. Decision trees number of two models for all types of clouds
View table
Table 4. Decision trees number of two models for all types of clouds
Cloud type Decision trees number of RF model Decision trees number of GBT model
Ci 13 93
As 9 71
Ac 13 83
St/Sc 11 7
Cu 13 7
Ns 13 7
Dc 5 3
Multi 11 27

Table 5. Maximum depth of decision trees of two models for all types of clouds
View table
Table 5. Maximum depth of decision trees of two models for all types of clouds
Cloud type Maximum depth in RF Maximum depth in GBT
Ci 8 5
As 8 5
Ac 7 4
St/Sc 9 6
Cu 4 5
Ns 8 8
Dc 5 6
Multi 8 5

Table 6. Retrieval results of two models for all types of clouds on the test dataset

View table

Table 6. Retrieval results of two models for all types of clouds on the test dataset

Cloud	Mean value /m	RF model				GBT model
Cloud	Mean value /m	RMSE /m	MAE /m	R	MRE /%	RMSE /m	MAE /m	R	MRE /%
Ci	9238.5	1060.6	841.4	0.7249	9.47	1054.8	836.5	0.7285	9.41
As	4918.6	1524.5	1180.9	0.6481	41.82	1515.8	1173.7	0.6532	41.65
Ac	2954.9	1357.2	1063.7	0.6396	66.68	1352.2	1060.0	0.6431	66.51
St/Sc	973.2	620.0	372.5	0.6062	43.45	669.5	394.4	0.5921	46.06
Cu	1075.6	1000.3	634.4	0.5018	78.63	1035.0	663.5	0.5132	84.22
Ns	1266.4	1338.8	896.2	0.4246	106.35	1371.7	933.2	0.4210	113.18
Dc	712.9	534.5	369.5	0.1847	60.12	538.4	375.6	0.1831	61.73
Multi	2285.0	2044.7	1593.7	0.4046	148.92	2044.9	1605.5	0.4069	150.72

Table 7. Optimal CBH retrieval model of all types of clouds
View table
Table 7. Optimal CBH retrieval model of all types of clouds
Cloud type Retrieval model
Ci GBT
As GBT
Ac GBT
St/Sc RF
Cu RF
Ns RF
Dc RF
Multi RF

Table 8. Retrieval results of RF model and GBT model on test dataset
View table
Table 8. Retrieval results of RF model and GBT model on test dataset
Model RMSE /m MAE /m R MRE /%
RF 2113.4 1497.6 0.7769 124.60
GBT 2109.1 1498.6 0.7779 124.81

Table 9. Retrieval results of RF model and GBT model for all types of clouds on test dataset

View table

Table 9. Retrieval results of RF model and GBT model for all types of clouds on test dataset

Cloud type	Mean value /m	RF model				GBT model
Cloud type	Mean value /m	RMSE /m	MAE /m	R	MRE /%	RMSE /m	MAE /m	R	MRE /%
Ci	9238.5	2528.7	2178.0	0.6489	23.80	2529.7	2181.5	0.6518	23.84
As	4918.6	1896.3	1532.0	0.6111	41.19	1891.8	1529.5	0.6140	41.04
Ac	2954.9	1637.8	1246.1	0.5566	63.07	1636.4	1247.3	0.5566	63.17
St/Sc	973.2	1098.2	536.6	0.2782	66.40	1095.6	537.7	0.2763	66.69
Cu	1075.6	1734.9	1080.0	0.2450	171.41	1725.9	1076.7	0.2458	170.77
Ns	1266.4	1980.8	1518.9	0.2353	246.69	1977.5	1527.6	0.2320	248.25
Dc	712.9	3090.6	2524.6	-0.0628	545.12	3071.9	2532.3	-0.0578	546.44
Multi	2285.0	3104.5	2369.9	0.3775	297.83	3095.7	2367.4	0.3783	297.88

Table 10. Retrieval results of models of two schemes for all types of clouds on test dataset

View table

Table 10. Retrieval results of models of two schemes for all types of clouds on test dataset

Cloud	Mean value /m	Scheme one				Scheme two
Cloud	Mean value /m	RMSE /m	MAE /m	R	MRE /%	RMSE /m	MAE /m	R	MRE /%
Ci	9238.5	1054.8	836.5	0.7285	9.41	2529.7	2181.5	0.6518	23.84
As	4918.6	1515.8	1173.7	0.6532	41.65	1891.8	1529.5	0.6140	41.04
Ac	2954.9	1352.2	1060.0	0.6431	66.51	1636.4	1247.3	0.5566	63.17
St/Sc	973.2	620.0	372.5	0.6062	43.45	1095.6	537.7	0.2763	66.69
Cu	1075.6	1000.3	634.4	0.5018	78.63	1725.9	1076.7	0.2458	170.77
Ns	1266.4	1338.8	896.2	0.4246	106.35	1977.5	1527.6	0.2320	248.25
Dc	712.9	534.5	369.5	0.1847	60.12	3071.9	2532.3	-0.0578	546.44
Multi	2285.0	2044.7	1593.7	0.4046	148.92	3095.7	2367.4	0.3783	297.88

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Zhuofu Yu, Ya Wang, Shuo Ma, Weihua Ai, Wei Yan. Cloud Base Height Retrieval Methods for FY-4A Based on Ensemble Learning[J]. Acta Optica Sinica, 2023, 43(6): 0601002

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Paper Information

Category: Atmospheric Optics and Oceanic Optics

Received: Apr. 13, 2022

Accepted: Aug. 1, 2022

Published Online: Mar. 13, 2023

The Author Email: Wang Ya (ywang@cma.gov.cn), Ma Shuo (mashuo0601@163.com)

DOI:10.3788/AOS220957

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Parameters of FY-4A/AGRI channels

Table 1. Parameters of FY-4A/AGRI channels

Table 2. Related cloud products of FY-4A/AGRI and corresponding channels

Table 2. Related cloud products of FY-4A/AGRI and corresponding channels

Table 3. Numbers of all types of clouds after data matching

Table 3. Numbers of all types of clouds after data matching

Table 4. Decision trees number of two models for all types of clouds

Table 4. Decision trees number of two models for all types of clouds

Table 5. Maximum depth of decision trees of two models for all types of clouds

Table 5. Maximum depth of decision trees of two models for all types of clouds

Table 6. Retrieval results of two models for all types of clouds on the test dataset

Table 6. Retrieval results of two models for all types of clouds on the test dataset

Table 7. Optimal CBH retrieval model of all types of clouds

Table 7. Optimal CBH retrieval model of all types of clouds

Table 8. Retrieval results of RF model and GBT model on test dataset

Table 8. Retrieval results of RF model and GBT model on test dataset

Table 9. Retrieval results of RF model and GBT model for all types of clouds on test dataset

Table 9. Retrieval results of RF model and GBT model for all types of clouds on test dataset

Table 10. Retrieval results of models of two schemes for all types of clouds on test dataset

Table 10. Retrieval results of models of two schemes for all types of clouds on test dataset