ANN-Based Estimation of Low-Latitude Monthly Ocean Latent Heat Flux by Ensemble Satellite and Reanalysis Products
暂无分享,去创建一个
Kun Jia | Yunjun Yao | Ke Shang | Xiaozheng Guo | Yuhu Zhang | Junming Yang | Xiangyi Bei | Xiaowei Chen | Xiaotong Zhang | Yufu Li | Xiaotong Zhang | K. Jia | Yunjun Yao | Yuhu Zhang | Yufu Li | Ke Shang | Junming Yang | Xiaozheng Guo | Xiaowei Chen | Xiangyi Bei
[1] Zhang Chun,et al. A Survey of Selective Ensemble Learning Algorithms , 2011 .
[2] Ka-Ming Lau,et al. On the Annual Cycle of Latent Heat Fluxes over the Equatorial Pacific Using TAO Buoy Observations , 1998 .
[3] W. Large,et al. Open Ocean Momentum Flux Measurements in Moderate to Strong Winds , 1981 .
[4] Bomin Sun,et al. Mean and Variability of the WHOI Daily Latent and Sensible Heat Fluxes at In Situ Flux Measurement Sites in the Atlantic Ocean , 2004 .
[5] P. Tavella,et al. Estimating the Instabilities of N Clocks by Measuring Differences of their Readings , 1994 .
[6] D. Huntley,et al. A Modified Inertial Dissipation Method for Estimating Seabed Stresses at Low Reynolds Numbers, with Application to Wave/Current Boundary Layer Measurements , 1988 .
[7] Matthew A. Lazzara,et al. Evaluation of four global reanalysis products using in situ observations in the Amundsen Sea Embayment, Antarctica , 2016 .
[8] Faliang Huang,et al. Research on Ensemble Learning , 2009, 2009 International Conference on Artificial Intelligence and Computational Intelligence.
[9] Chun-Xia Zhang,et al. A Survey of Selective Ensemble Learning Algorithms: A Survey of Selective Ensemble Learning Algorithms , 2011 .
[10] LuAnne Thompson,et al. Evaluation of a hybrid satellite‐ and NWP‐based turbulent heat flux product using Tropical Atmosphere‐Ocean (TAO) buoys , 2005 .
[11] Andrej Pázman,et al. Nonlinear Regression , 2019, Handbook of Regression Analysis With Applications in R.
[12] Hans C. Graber,et al. Momentum and heat fluxes via the eddy correlation method on the R/V L'Atalante and an ASIS buoy , 2003 .
[13] Guolin Feng,et al. Spatial-temporal variation characteristics of global evaporation revealed by eight reanalyses , 2014, Science China Earth Sciences.
[14] Siddhartha Bhattacharyya,et al. A review of machine learning in scheduling , 1994 .
[15] M. Mccabe,et al. Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .
[16] Mehdi Khashei,et al. An artificial neural network (p, d, q) model for timeseries forecasting , 2010, Expert Syst. Appl..
[17] C. Frantzidis,et al. Response to Reviewers Reviewer #1 , 2010 .
[18] D. Bourras. Comparison of Five Satellite-Derived Latent Heat Flux Products to Moored Buoy Data , 2006 .
[19] Antonio Fiaschi,et al. Contents lists available at ScienceDirect , 2011 .
[20] Bertrand Chapron,et al. Review and assessment of latent and sensible heat flux accuracy over the global oceans , 2017 .
[21] E. F. Bradley,et al. Bulk parameterization of air‐sea fluxes for Tropical Ocean‐Global Atmosphere Coupled‐Ocean Atmosphere Response Experiment , 1996 .
[22] Hui,et al. Seasonal variability of turbulent heat fluxes in the tropical Atlantic Ocean based on WHOI flux product , 2007 .
[23] Qu Shaohou,et al. Observation research of the turbulent fluxes of momentum, sensible heat and latent heat over the West Pacific Tropical Ocean Area , 1989 .
[24] M. T. Chahine,et al. Global Energy and Water Cycle Experiment (GEWEX) , 1994 .
[25] W. Large,et al. Sensible and Latent Heat Flux Measurements over the Ocean , 1982 .
[26] Gong Yan. Long-Term Trend Analysis of Latent Heat in Tropical Oceans , 2013 .
[27] S. Liang,et al. Improving global terrestrial evapotranspiration estimation using support vector machine by integrating three process-based algorithms. , 2017 .
[28] Jonas Ardö,et al. Estimation of high-resolution terrestrial evapotranspiration from Landsat data using a simple Taylor skill fusion method , 2017 .
[29] Judith A. Curry,et al. Which Bulk Aerodynamic Algorithms are Least Problematic in Computing Ocean Surface Turbulent Fluxes , 2003 .
[30] Zhiping Wen,et al. Variations of latent heat flux during tropical cyclones over the South China Sea , 2014 .
[31] Li Xiao-we,et al. Prospects on future developments of quantitative remote sensing , 2013 .
[32] R. G. Fleagle,et al. BOMEX: An Appraisal of Results. , 1972, Science.
[33] Masahisa Kubota,et al. An introduction to J-OFURO3, a third-generation Japanese ocean flux data set using remote-sensing observations , 2018, Journal of Oceanography.
[34] S. Chou. A comparison of airborne eddy correlation and bulk aerodynamic methods for ocean-air turbulent fluxes during cold-air outbreaks , 1993 .
[35] S. Schubert,et al. MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications , 2011 .
[36] C. W. Fairall,et al. Inertial-dissipation methods and turbulent fluxes at the air-ocean interface , 1986 .
[37] H. Roquet,et al. Determination of ocean surface heat fluxes by a variational method , 1993 .
[38] Wade T. Crow,et al. Optimal averaging of soil moisture predictions from ensemble land surface model simulations , 2015 .
[39] Jean-Michel Brankart,et al. Improved Turbulent Air–Sea Flux Bulk Parameters for Controlling the Response of the Ocean Mixed Layer: A Sequential Data Assimilation Approach , 2009 .
[40] Wu Xiaoda. Advances in uncertainty analysis for the validation of remote sensing products: Take leaf area index for example , 2014 .
[41] M. Wesely,et al. Comments on “Bulk Parameterization of Air-Sea Exchanges of Heat and Water Vapor Including the Molecular Constraints at the Interface” , 1980 .
[42] Junming Yang,et al. Fusion of Five Satellite-Derived Products Using Extremely Randomized Trees to Estimate Terrestrial Latent Heat Flux over Europe , 2020, Remote. Sens..
[43] Ping Shi,et al. Validation of Satellite-Derived Daily Latent Heat Flux over the South China Sea, Compared with Observations and Five Products , 2013 .
[44] Charles R. Cobb,et al. Stone Tool Traditions in the Contact Era , 2003 .
[45] Antonio J. Busalacchi,et al. A Pilot Research Moored Array in the Tropical Atlantic (PIRATA) , 1998 .
[46] Scott E. Simmons. Stone Tool Traditions in the Contact Era. Charles R. Cobb, editor. 2003. The University of Alabama Press, Tuscaloosa, ix + 214 pp. $34.95 (paper), ISBN 0-8173-1372-9. , 2006, American Antiquity.
[47] Daniel R. Cayan,et al. Latent and sensible heat flux anomalies over the northern oceans : driving the sea surface temperature , 1992 .
[48] Daehyeon Cho,et al. Study on the ensemble methods with kernel ridge regression , 2012 .
[49] Robert N. Miller,et al. Data assimilation into nonlinear stochastic models , 1999 .
[50] R. Hodur. The Naval Research Laboratory’s Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) , 1997 .
[51] Dawei Han,et al. Machine Learning Techniques for Downscaling SMOS Satellite Soil Moisture Using MODIS Land Surface Temperature for Hydrological Application , 2013, Water Resources Management.
[52] Gilles Depuis,et al. INFLUENCE DE LA FERTILISATION AZOTEE ET DE L’ECARTEMENT ENTRE LES RANGS DE CEREALES-ABRI RECOLTEES COMME FOURRAGE SUR L’ETABLISSEMENT DE LA LUZERNE , 1983 .
[53] L. J. Mangum,et al. TOGA-TAO: A Moored Array for Real-time Measurements in the Tropical Pacific Ocean , 1991 .
[54] Nils H. Schade,et al. Regional Evaluation of ERA-40 Reanalysis Data with Marine Atmospheric Observations in the North Sea Area , 2013 .
[55] Xiufeng He,et al. Uncertainties in remotely sensed precipitation data over Africa , 2016 .
[56] Martina Giese,et al. Joint Global Ocean Flux Study (JGOFS) , 1994, Environmental science and pollution research international.
[57] J. Janssen,et al. DOES WIND STRESS DEPEND ON SEA-STATE OR NOT? – A STATISTICAL ERROR ANALYSIS OF HEXMAX DATA , 1997 .
[58] Siegfried Schubert,et al. NASA's Modern Era Retrospective-Analysis for Research and Applications (MERRA): Early Results and Future Directions , 2006 .
[59] Jérôme Vialard,et al. Supplement to RAMA: The Research Moored Array for African—Asian—Australian Monsoon Analysis and Prediction , 2009 .
[60] Christopher W. Fairall,et al. An assessment of buoy-derived and numerical weather prediction surface heat fluxes in the tropical Pacific , 2006 .
[61] Huanhuan Yuan,et al. Retrieving Soybean Leaf Area Index from Unmanned Aerial Vehicle Hyperspectral Remote Sensing: Analysis of RF, ANN, and SVM Regression Models , 2017, Remote. Sens..
[62] Zhixia Guo,et al. Evaluation of twelve evapotranspiration products from machine learning, remote sensing and land surface models over conterminous United States , 2019, Journal of Hydrology.
[63] Jia Xu,et al. MODIS-Based Estimation of Terrestrial Latent Heat Flux over North America Using Three Machine Learning Algorithms , 2017, Remote. Sens..
[64] Minha Choi,et al. Stand-alone uncertainty characterization of GLEAM, GLDAS and MOD16 evapotranspiration products using an extended triple collocation approach , 2018 .
[65] T. M. Chin,et al. Multi-reference evaluation of uncertainty in earth orientation parameter measurements , 2003 .
[66] T. C. Hsiang,et al. A Bayesian View on Ridge Regression , 1975 .
[67] R. Anderson,et al. A study of wind stress and heat flux over the open ocean by the inertial-dissipation method , 1993 .
[68] Ravinesh C. Deo,et al. Global solar radiation prediction by ANN integrated with European Centre for medium range weather forecast fields in solar rich cities of Queensland Australia , 2019, Journal of Cleaner Production.
[69] Ponnuthurai N. Suganthan,et al. Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article] , 2016, IEEE Computational Intelligence Magazine.
[70] M. Mcphaden. The Tropical Atmosphere Ocean Array Is Completed , 1995 .
[71] Jian Wang,et al. Evaluation of SMAP, SMOS-IC, FY3B, JAXA, and LPRM Soil Moisture Products over the Qinghai-Tibet Plateau and Its Surrounding Areas , 2019, Remote. Sens..
[72] Robert C. Bolles,et al. Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography , 1981, CACM.
[73] Hannah M. Cooper,et al. Quantification of sawgrass marsh aboveground biomass in the coastal Everglades using object-based ensemble analysis and Landsat data , 2018 .
[74] S. Liang,et al. A satellite-based hybrid algorithm to determine the Priestley–Taylor parameter for global terrestrial latent heat flux estimation across multiple biomes , 2015 .
[75] H. Cai,et al. Evaluation of SVM, ELM and four tree-based ensemble models for predicting daily reference evapotranspiration using limited meteorological data in different climates of China , 2018, Agricultural and Forest Meteorology.
[76] Vagner G. Ferreira,et al. Uncertainties of the Gravity Recovery and Climate Experiment time-variable gravity-field solutions based on three-cornered hat method , 2016 .
[77] P. Tavella,et al. A revisited three-cornered hat method for estimating frequency standard instability , 1993 .
[78] Robert A. Weller,et al. Objectively Analyzed Air–Sea Heat Fluxes for the Global Ice-Free Oceans (1981–2005) , 2007 .
[79] Michael A. Brunke,et al. An Assessment of the Uncertainties in Ocean Surface Turbulent Fluxes in 11 Reanalysis, Satellite-Derived, and Combined Global Datasets , 2011 .
[80] Stephan Bakan,et al. HOAPS: A new satellite-derived freshwater flux climatology , 2002 .
[81] Margaret J. Yelland,et al. The Use of the Inertial Dissipation Technique for Shipboard Wind Stress Determination , 1994 .
[82] Mark A. Bourassa,et al. A Flux Parameterization Including the Effects of Capillary Waves and Sea State , 1999 .
[83] Lior Rokach,et al. Ensemble learning: A survey , 2018, WIREs Data Mining Knowl. Discov..
[84] Peter Schlüssel,et al. Evaluation of Satellite-Derived Latent Heat Fluxes , 1997 .
[85] Raymond T. Pollard,et al. The Joint Air-Sea Interaction Experiment—JASIN 1978 , 1978 .
[86] Axel Schweiger,et al. Evaluation of Seven Different Atmospheric Reanalysis Products in the Arctic , 2014 .
[87] Arun Kumar,et al. NCEP dynamical seasonal forecast system 2000 , 2002 .