Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location chang...Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps (i.e., missing data) are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging (UK) and Kriging with an external drift (KED), as appropriate, as if all the ob-servations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients (R2) between the observed and estimated daily series were 〉0.98, and root mean square errors (RMSEs) of the estimated daily mean (Tmean), maximum (Tmax), and minimum (Tmin) of air temperature were 〈3 ℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1 ℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.展开更多
This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, usi...This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, using a regression-based approach. Variables used for modelling include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal temperature data, for the observation period 1971 to 2000, were obtained from 59 meteorological stations. Temperature data from 52 meteorological stations were used to construct the regression model. Data from the other 7 stations were retained for model validation. Seasonal temperature surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most important predictors of local seasonal temperature. Validation determined that regression plus kriging predicts seasonal temperature with a coefficient of determination (R2), between the estimated and observed values, of 0.757 (autumn) and 0.935 (winter). A simple regression model without kriging yields less accurate results in all seasons except for the autumn temperature.展开更多
[ Objectivel The research aimed to study prediction model for spatial distribution of the average temperature based on GIS. [ Method l Average temperature over the years as research object, based on Ordinary Kriging ...[ Objectivel The research aimed to study prediction model for spatial distribution of the average temperature based on GIS. [ Method l Average temperature over the years as research object, based on Ordinary Kriging (OK), Inverse Distance Weight ( IDW), SPLINE and Mixed In- terpolation (MLR), monthly temperature data from 1979 to 2008 at 18 long-term meteorological observation stations in Hainan Island were conduc- ted spatial grid treatment. Via contrasts and analyses on different interpolation methods, the optimum interpolation method for average temperature over the years in Hainan Island was selected. [ Resuitl By error analyses of the four interpolation methods for average temperature in recent 30 years in Hainan Island, it was found that accuracy was MLR 〉 IDW 〉 OK 〉 SPLINE. Spatial interpolation effect of MLR was the best for average temperature in Hainan Island. Spatial distribution of the average temperature in Halnan Island had obvious south-high-north-low latitudinal zonality and vertical zonality of gradually declining as altitude rise. In addition, temperature along coast was slightly higher than that in inland. Lapse rate of the temperature in each month in Hainan Island was 0.38 -0.85℃/100 m, and lapse rate of the annual average temperature was about 0.74 ℃/ 100 m. In different areas, lapse rate of the temperature as altitude was different at different time. [ Condusion] The research provided basis for ob- taining continuous distribution situation of the agricultural meteorological factor and establishing accurate prediction model of the spatial distribution in Hainan Island.展开更多
This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bil...This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.展开更多
The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation sta...The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature (LST) data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression (OLS) and geographical weighted regression (GWR) of monthly mean air temperature (Ta) with monthly mean land surface temperature (Ts) were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July (the warmest month) at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.展开更多
基金funded by the Chinese National Fund Projects (Nos. 41401028, 41201066)by the State Key Laboratory of Frozen Soils Engineering (Project No. SKLFSE201201)
文摘Quality-controlled and serially complete daily air temperature data are essential to evaluating and modelling the influences of climate change on the permafrost in cold regions. Due to malfunctions and location changes of observing stations, temporal gaps (i.e., missing data) are common in collected datasets. The objective of this study was to assess the efficacy of Kriging spatial interpolation for estimating missing data to fill the temporal gaps in daily air temperature data in northeast China. A cross-validation experiment was conducted. Daily air temperature series from 1960 to 2012 at each station were estimated by using the universal Kriging (UK) and Kriging with an external drift (KED), as appropriate, as if all the ob-servations at a given station were completely missing. The temporal and spatial variation patterns of estimation uncertainties were also checked. Results showed that Kriging spatial interpolation was generally desirable for estimating missing data in daily air temperature, and in this study KED performed slightly better than UK. At most stations the correlation coefficients (R2) between the observed and estimated daily series were 〉0.98, and root mean square errors (RMSEs) of the estimated daily mean (Tmean), maximum (Tmax), and minimum (Tmin) of air temperature were 〈3 ℃. However, the estimation quality was strongly affected by seasonality and had spatial variation. In general, estimation uncertainties were small in summer and large in winter. On average, the RMSE in winter was approximately 1 ℃ higher than that in summer. In addition, estimation uncertainties in mountainous areas with complex terrain were significantly larger than those in plain areas.
基金Project supported by the Natural Science Foundation of ZhejiangProvince (No. 30295) and the Key Project of Zhejiang Province (No.011103192), China
文摘This paper presents a Zhejiang Province southeastern China seasonal temperature model based on GIS techniques. Terrain variables derived from the 1 km resolution DEM are used as predictors of seasonal temperature, using a regression-based approach. Variables used for modelling include: longitude, latitude, elevation, distance from the nearest coast, direction to the nearest coast, slope, aspect, and the ratio of land to sea within given radii. Seasonal temperature data, for the observation period 1971 to 2000, were obtained from 59 meteorological stations. Temperature data from 52 meteorological stations were used to construct the regression model. Data from the other 7 stations were retained for model validation. Seasonal temperature surfaces were constructed using the regression equations, and refined by kriging the residuals from the regression model and subtracting the result from the predicted surface. Latitude, elevation and distance from the sea are found to be the most important predictors of local seasonal temperature. Validation determined that regression plus kriging predicts seasonal temperature with a coefficient of determination (R2), between the estimated and observed values, of 0.757 (autumn) and 0.935 (winter). A simple regression model without kriging yields less accurate results in all seasons except for the autumn temperature.
基金Supported by "Project 211" Construction Item,Hainan UniversityBasic Science Research Business Expense,Rubber Research Institute ,CATAS[YWFZX09-03(N)]Special Item of the Modern Agricultural Industrial Technology System Construction(CARS-34)
文摘[ Objectivel The research aimed to study prediction model for spatial distribution of the average temperature based on GIS. [ Method l Average temperature over the years as research object, based on Ordinary Kriging (OK), Inverse Distance Weight ( IDW), SPLINE and Mixed In- terpolation (MLR), monthly temperature data from 1979 to 2008 at 18 long-term meteorological observation stations in Hainan Island were conduc- ted spatial grid treatment. Via contrasts and analyses on different interpolation methods, the optimum interpolation method for average temperature over the years in Hainan Island was selected. [ Resuitl By error analyses of the four interpolation methods for average temperature in recent 30 years in Hainan Island, it was found that accuracy was MLR 〉 IDW 〉 OK 〉 SPLINE. Spatial interpolation effect of MLR was the best for average temperature in Hainan Island. Spatial distribution of the average temperature in Halnan Island had obvious south-high-north-low latitudinal zonality and vertical zonality of gradually declining as altitude rise. In addition, temperature along coast was slightly higher than that in inland. Lapse rate of the temperature in each month in Hainan Island was 0.38 -0.85℃/100 m, and lapse rate of the annual average temperature was about 0.74 ℃/ 100 m. In different areas, lapse rate of the temperature as altitude was different at different time. [ Condusion] The research provided basis for ob- taining continuous distribution situation of the agricultural meteorological factor and establishing accurate prediction model of the spatial distribution in Hainan Island.
文摘This research was an effort to select best imputation method for missing upper air temperature data over 24 standard pressure levels. We have implemented four imputation techniques like inverse distance weighting, Bilinear, Natural and Nearest interpolation for missing data imputations. Performance indicators for these techniques were the root mean square error (RMSE), absolute mean error (AME), correlation coefficient and coefficient of determination ( R<sup>2</sup> ) adopted in this research. We randomly make 30% of total samples (total samples was 324) predictable from 70% remaining data. Although four interpolation methods seem good (producing <1 RMSE, AME) for imputations of air temperature data, but bilinear method was the most accurate with least errors for missing data imputations. RMSE for bilinear method remains <0.01 on all pressure levels except 1000 hPa where this value was 0.6. The low value of AME (<0.1) came at all pressure levels through bilinear imputations. Very strong correlation (>0.99) found between actual and predicted air temperature data through this method. The high value of the coefficient of determination (0.99) through bilinear interpolation method, tells us best fit to the surface. We have also found similar results for imputation with natural interpolation method in this research, but after investigating scatter plots over each month, imputations with this method seem to little obtuse in certain months than bilinear method.
基金National Natural Science Foundation of China,No.41030528No.41001278
文摘The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature (LST) data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression (OLS) and geographical weighted regression (GWR) of monthly mean air temperature (Ta) with monthly mean land surface temperature (Ts) were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July (the warmest month) at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.