Estimation of cloud base height for FY-4A satellite based on random forest algorithm

TAN Zhong-Hui; MA Shuo; HAN Ding; GAO Ding; YAN Wei

doi:10.11972/j.issn.1001-9014.2019.03.020

[1] Wang J H,Rossow. Effects of cloud vertical structure on atmospheric circulation in the GIS GCM ［J］. J. Climate, 1998, 11(11): 3010-3029.

[2] Weisz E, Li J, Menzel W P. Comparision of AIRS, MODIS, CloudSat and CALIPSO cloud top height retrievals ［J］. Geophys Res Lett., 2007, 34(17)：L17811.

[5] Menzel Coauthors. MODIS global cloud-top pressure and amount estimation: Algorithm description and results ［J］. J. Appl. Meteor. Climatol., 2008, 47: 1175-1198.

[6] Weisz E, Li Jun, Zhou D K, et al. Cloudy sounding and cloud-top height retrieval from AIRS alone single field-of-view radiance measurements ［J］. Geophys. Res. Lett., 2007, 34: L12802.

[7] Heidinger, Pavolonis M J. Gazing at cirrus clouds for 25 years through a split window. Part I: Methodology ［J］. J. Appl. Meteor. Climatol., 2009, 48: 1100-1116.

[8] Noh Y J, and Coauthors. Cloud-base height estimation from VIIRS. Part II: A statistical algorithm based on A-Train satellite data ［J］. J. Atmos. Oceanic.,2017, 34:585-598.

[9] Sun, Xue-Jin, Li Hao-Ran, Barker H W, et al. Satellite-based estimation of cloud-base heights using constrained spectral radiance matching ［J］. Quart. J. Roy. Meteor. Soc., 2016, 142: 224-232.

[10] Hutchison, Wong E, Ou S C. Cloud base height retrieval during nighttime conditions with MODIS data ［J］. Int. J. Remote Sens., 2006, 27: 2847-2862.

[11] Breiman L. Random forests ［J］. Machine Learning, 2001, 45(1): 5-32.

[12] Vincenzi S, Zucchetta M, Franzoi P, et al. Application of a Random Forest algorithm to predict spatial distribution of the potential yield of Ruditapes philippinarum in the Venice lagoon, Italy ［J］. Ecological Modelling, 2011, 222: 1471-1478.

[13] Grimm R, Behrens T, Marker M, et al. Soil organic carbon concentrations and stocks on Barro Colorado Island-Digital soil mapping using Random Forests analysis ［J］. Geoderma, 2008, 146:102-113.

[14] Prasad A, Iverson L, Liaw A. Newer classification and regression tree techniques: bagging and random forests for ecological prediction ［J］. Ecosystems, 2006, 9:181-199.

[15] Ismail R, Mutanga, O. A comparison of regression tree ensembles: predicting Sirex noctilio induced water stress in Pinus patula forests of KwaZulu-Natal, South Africa ［J］. International Journal of Applied Earth Observation and Geoinformation, 2010, 12: S45-S51.

[16] Abdel-Rahman, E M, Berg van den, Way M, et al. Hand-held spectrometry for estimating thrips (Fulmekiola serrata) incidence in sugarcane ［J］. IEEE International Geoscience and Remote Sensing Symposium (IGARSS), IEEE, Cape Town, South Africa, 2009, 268-271.

[17] Onisimo Mutanga, Elhadi Adam, Moses Azong Cho. High density biomass estimation for wetland vegetation using WorldView-2 imagery and random forest regression algorithm ［J］. International Journal of Applied Earth Observation and Geoinformation, 2012, 18: 399-406.

[19] Miller S D, Coauthors. Estimating three-dimensional cloud structure via statistically blended satellite observations ［J］. J. Appl. Meteor. Climatol., 2014, 53: 437-455.

[20] Holz R E, Ackerman S A, Nagle F W, et al. Global Moderate Resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIPSO ［J］. J.Geophys.Res., 2008, 113: D00A19.

[21] Platnick, et al, The MODIS Cloud Optical and Microphysical Products: Collection 6 Updates and Examples From Terra and Aqua ［J］. IEEE Transactions on Geoscience and Remote Sensing, 2017, 55: 502-525.

[22] Welliver E A. Remote detection of cloud base height using CloudSat and CALIPSO ［master’s thesis］. California: Naval postgraduate school, 2009: 25-40.