• Acta Optica Sinica
  • Vol. 43, Issue 6, 0601002 (2023)
Zhuofu Yu1, Ya Wang2、*, Shuo Ma1、**, Weihua Ai1, and Wei Yan1
Author Affiliations
  • 1College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410000, Hunan, China
  • 2National Satellite Meteorological Center, China Meteorological Administration, Beijing 100081, China
  • show less
    DOI: 10.3788/AOS220957 Cite this Article Set citation alerts
    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 Copy Citation Text show less
    Flow chart of data matching between FY-4A and CloudSat
    Fig. 1. Flow chart of data matching between FY-4A and CloudSat
    Schemes of CBH retrieval for FY-4A designed in this paper
    Fig. 2. Schemes of CBH retrieval for FY-4A designed in this paper
    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
    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
    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
    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
    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
    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
    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
    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
    Retrieval results of models of two schemes on test dataset. (a) Scheme one; (b) scheme two
    Fig. 7. Retrieval results of models of two schemes on test dataset. (a) Scheme one; (b) scheme two
    Retrieval flow of CBH for FY-4A in practical application
    Fig. 8. Retrieval flow of CBH for FY-4A in practical application
    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
    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
    Channel numberCenter wavelength /μmSpatial resolution /km
    10.471.0
    20.650.5
    30.8251.0
    41.3752.0
    51.612.0
    62.252.0
    73.75(H)2.0
    83.75(L)4.0
    96.254.0
    107.14.0
    118.54.0
    1210.74.0
    1312.04.0
    1413.54.0
    Table 1. Parameters of FY-4A/AGRI channels
    Cloud product of FY-4A/AGRICenter 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 2. Related cloud products of FY-4A/AGRI and corresponding channels
    Cloud typeNumber
    Ci73255
    As71000
    Ac41412
    St/Sc104792
    Cu24535
    Ns40397
    Dc10719
    Multi65603
    Table 3. Numbers of all types of clouds after data matching
    Cloud typeDecision trees number of RF modelDecision trees number of GBT model
    Ci1393
    As971
    Ac1383
    St/Sc117
    Cu137
    Ns137
    Dc53
    Multi1127
    Table 4. Decision trees number of two models for all types of clouds
    Cloud typeMaximum depth in RFMaximum depth in GBT
    Ci85
    As85
    Ac74
    St/Sc96
    Cu45
    Ns88
    Dc56
    Multi85
    Table 5. Maximum depth of decision trees of two models for all types of clouds
    Cloud

    Mean

    value /m

    RF modelGBT model
    RMSE /mMAE /mRMRE /%RMSE /mMAE /mRMRE /%
    Ci9238.51060.6841.40.72499.471054.8836.50.72859.41
    As4918.61524.51180.90.648141.821515.81173.70.653241.65
    Ac2954.91357.21063.70.639666.681352.21060.00.643166.51
    St/Sc973.2620.0372.50.606243.45669.5394.40.592146.06
    Cu1075.61000.3634.40.501878.631035.0663.50.513284.22
    Ns1266.41338.8896.20.4246106.351371.7933.20.4210113.18
    Dc712.9534.5369.50.184760.12538.4375.60.183161.73
    Multi2285.02044.71593.70.4046148.922044.91605.50.4069150.72
    Table 6. Retrieval results of two models for all types of clouds on the test dataset
    Cloud typeRetrieval model
    CiGBT
    AsGBT
    AcGBT
    St/ScRF
    CuRF
    NsRF
    DcRF
    MultiRF
    Table 7. Optimal CBH retrieval model of all types of clouds
    ModelRMSE /mMAE /mRMRE /%
    RF2113.41497.60.7769124.60
    GBT2109.11498.60.7779124.81
    Table 8. Retrieval results of RF model and GBT model on test dataset

    Cloud

    type

    Mean value /mRF modelGBT model
    RMSE /mMAE /mRMRE /%RMSE /mMAE /mRMRE /%
    Ci9238.52528.72178.00.648923.802529.72181.50.651823.84
    As4918.61896.31532.00.611141.191891.81529.50.614041.04
    Ac2954.91637.81246.10.556663.071636.41247.30.556663.17
    St/Sc973.21098.2536.60.278266.401095.6537.70.276366.69
    Cu1075.61734.91080.00.2450171.411725.91076.70.2458170.77
    Ns1266.41980.81518.90.2353246.691977.51527.60.2320248.25
    Dc712.93090.62524.6-0.0628545.123071.92532.3-0.0578546.44
    Multi2285.03104.52369.90.3775297.833095.72367.40.3783297.88
    Table 9. Retrieval results of RF model and GBT model for all types of clouds on test dataset
    CloudMean value /mScheme oneScheme two
    RMSE /mMAE /mRMRE /%RMSE /mMAE /mRMRE /%
    Ci9238.51054.8836.50.72859.412529.72181.50.651823.84
    As4918.61515.81173.70.653241.651891.81529.50.614041.04
    Ac2954.91352.21060.00.643166.511636.41247.30.556663.17
    St/Sc973.2620.0372.50.606243.451095.6537.70.276366.69
    Cu1075.61000.3634.40.501878.631725.91076.70.2458170.77
    Ns1266.41338.8896.20.4246106.351977.51527.60.2320248.25
    Dc712.9534.5369.50.184760.123071.92532.3-0.0578546.44
    Multi2285.02044.71593.70.4046148.923095.72367.40.3783297.88
    Table 10. Retrieval results of models of two schemes for all types of clouds on test dataset
    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
    Download Citation