Accurate estimation of dew point temperature(Tdew)plays a very important role in the fields of water resource management,agricultural engineering,climatology and energy utilization.However,there are few studies on the...Accurate estimation of dew point temperature(Tdew)plays a very important role in the fields of water resource management,agricultural engineering,climatology and energy utilization.However,there are few studies on the applicability of local Tdew algorithms at regional scales.This study evaluated the performance of a new machine learning algorithm,i.e.,gradient boosting on decision trees with categorical features support(Cat Boost)to estimate daily Tdew using limited local and cross-station meteorological data.The random forests(RF)algorithm was also assessed for comparison.Daily meteorological data from 2016 to 2019,including maximum,minimum and average temperature(Tmax,Tmin and Tmean),maximum,minimum and average relative humidity(RHmax,RHmin and RHmean),maximum,minimum and average global solar radiation(Rsmax,Rsmin and Rsmean)from three weather stations in Hunan of China were used to evaluate the CatBoost and RF algorithms.The results showed that both algorithms achieved satisfactory estimation accuracy at the target stations(on average RMSE=1.020℃,R^(2)=0.969,MAE=0.718℃and NRMSE=0.087)in the absence of complete meteorological parameters(with only temperature data as input).The Cat Boost algorithm(on average RMSE=1.900℃and R^(2)=0.835)was better than the RF algorithm(on average RMSE=2.214℃andR^(2)=0.828).The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters,and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter algorithms.The developed methodology is helpful to predict Tdew at regional scale.展开更多
MODIS atmospheric profile products(MOD07_L2 and MYD07_L2)have been widely used for near-surface dew point temperature(T_(d))estimation.However,their accuracy over large scale has seldom been evaluated.In this study,we...MODIS atmospheric profile products(MOD07_L2 and MYD07_L2)have been widely used for near-surface dew point temperature(T_(d))estimation.However,their accuracy over large scale has seldom been evaluated.In this study,we validated these two products comprehensively against 2153 stations over China's Mainland.MOD07_L2 was suggested by our study because it achieved higher accuracy in either of two frequently-used methods.To be specific,the root-meansquare error(RMSE)achieved by MOD07_L2 and MYD07_L2 was 5.82 and 7.42℃,respectively.On this basis,a recent ground-based correction method was modified to further improve their accuracy.Our focus is to investigate whether this ground-based approach is applicable to large-scale remote sensing applications.The results show that this new method showed great potential for T_(d) estimation independently from ground observations.Through the introduction of MODIS land surface products,the RMSE it achieved for MOD07_L2 and MYD07_L2 was 5.23 and 5.59℃,respectively.Further analysis shows that it was particularly useful in capturing the annual average T_(d) patterns.The R2,RMSE,and bias of annual average daily mean T_(d) estimates were 0.95,1.84℃,and 0.53℃,and those achieved for annual average instantaneous T_(d) estimates were 0.94,2.09℃,and 0.75℃,respectively.展开更多
Remote sensing techniques are a useful tool for continuous observation of the Earth at global scale.However,products derived from remote sensing data require a rigorous validation using in situ data.Moderate Resolutio...Remote sensing techniques are a useful tool for continuous observation of the Earth at global scale.However,products derived from remote sensing data require a rigorous validation using in situ data.Moderate Resolution Imaging Spectroradiometer(MODIS)is not really a sounding instrument,but it does have 16 infrared bands(bands 20-36 covering the spectral range from 3μm to 14μm)that allow the retrieval of temperature and moisture profiles as well as total column integrated magnitudes.In this paper we show the results obtained in the evaluation of MOD07 daytime and nighttime products over the Iberian Peninsula during the decade from 2000 to 2010 using nine radiosonde stations.Although MODIS limitations in comparison with other sounding instruments,the validation provided satisfactory results,with bias(MOD07 minus radiosonde)<0.3 cm and a standard deviation of 0.5 cm for the total column water vapor,and bias around 1 K on average with standard deviations between 2 K and 3 K for air temperature at different pressure levels.On average,bias was positive and below 2 K with standard deviations around 5 K for the dew point temperature case.Large errors were found in this case for pressure levels higher than 50 hPa.展开更多
基金the Shandong Provincial Natural Science Fund(ZR2020ME254 and ZR2020QD061).
文摘Accurate estimation of dew point temperature(Tdew)plays a very important role in the fields of water resource management,agricultural engineering,climatology and energy utilization.However,there are few studies on the applicability of local Tdew algorithms at regional scales.This study evaluated the performance of a new machine learning algorithm,i.e.,gradient boosting on decision trees with categorical features support(Cat Boost)to estimate daily Tdew using limited local and cross-station meteorological data.The random forests(RF)algorithm was also assessed for comparison.Daily meteorological data from 2016 to 2019,including maximum,minimum and average temperature(Tmax,Tmin and Tmean),maximum,minimum and average relative humidity(RHmax,RHmin and RHmean),maximum,minimum and average global solar radiation(Rsmax,Rsmin and Rsmean)from three weather stations in Hunan of China were used to evaluate the CatBoost and RF algorithms.The results showed that both algorithms achieved satisfactory estimation accuracy at the target stations(on average RMSE=1.020℃,R^(2)=0.969,MAE=0.718℃and NRMSE=0.087)in the absence of complete meteorological parameters(with only temperature data as input).The Cat Boost algorithm(on average RMSE=1.900℃and R^(2)=0.835)was better than the RF algorithm(on average RMSE=2.214℃andR^(2)=0.828).The accuracy and stability of the CatBoost and RF algorithms were positively correlated with the number of input parameters,and the three-parameter algorithms achieved higher estimation accuracy than the two-parameter algorithms.The developed methodology is helpful to predict Tdew at regional scale.
基金supported by Ministry of Science and Technology of the People’s Republic of China[grant number:2021YFC3000201]National Natural Science Foundation of China[grant number:42071032]Youth Innovation Promotion Association of the Chinese Academy of Sciences[grant number:2020056].
文摘MODIS atmospheric profile products(MOD07_L2 and MYD07_L2)have been widely used for near-surface dew point temperature(T_(d))estimation.However,their accuracy over large scale has seldom been evaluated.In this study,we validated these two products comprehensively against 2153 stations over China's Mainland.MOD07_L2 was suggested by our study because it achieved higher accuracy in either of two frequently-used methods.To be specific,the root-meansquare error(RMSE)achieved by MOD07_L2 and MYD07_L2 was 5.82 and 7.42℃,respectively.On this basis,a recent ground-based correction method was modified to further improve their accuracy.Our focus is to investigate whether this ground-based approach is applicable to large-scale remote sensing applications.The results show that this new method showed great potential for T_(d) estimation independently from ground observations.Through the introduction of MODIS land surface products,the RMSE it achieved for MOD07_L2 and MYD07_L2 was 5.23 and 5.59℃,respectively.Further analysis shows that it was particularly useful in capturing the annual average T_(d) patterns.The R2,RMSE,and bias of annual average daily mean T_(d) estimates were 0.95,1.84℃,and 0.53℃,and those achieved for annual average instantaneous T_(d) estimates were 0.94,2.09℃,and 0.75℃,respectively.
基金We acknowledge funding from European Union[CEOP-AEGIS,project FP7-ENV-2007-1 Proposal No.212921]the Ministerio de Economía y Competitividad[EODIX,project AYA2008-0595-C04-01+1 种基金CEOS-Spain,project AYA2011-29334-C02-01]the Universitat de València[grant number PRECOM13-115366].
文摘Remote sensing techniques are a useful tool for continuous observation of the Earth at global scale.However,products derived from remote sensing data require a rigorous validation using in situ data.Moderate Resolution Imaging Spectroradiometer(MODIS)is not really a sounding instrument,but it does have 16 infrared bands(bands 20-36 covering the spectral range from 3μm to 14μm)that allow the retrieval of temperature and moisture profiles as well as total column integrated magnitudes.In this paper we show the results obtained in the evaluation of MOD07 daytime and nighttime products over the Iberian Peninsula during the decade from 2000 to 2010 using nine radiosonde stations.Although MODIS limitations in comparison with other sounding instruments,the validation provided satisfactory results,with bias(MOD07 minus radiosonde)<0.3 cm and a standard deviation of 0.5 cm for the total column water vapor,and bias around 1 K on average with standard deviations between 2 K and 3 K for air temperature at different pressure levels.On average,bias was positive and below 2 K with standard deviations around 5 K for the dew point temperature case.Large errors were found in this case for pressure levels higher than 50 hPa.
