福建省复杂地形下旬平均气温分布式模拟
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摘要
为建立高时空分辨率的福建省复杂地形下气温栅格数据集,利用福建省及其周边33个常规气象站观测资料,基于数字高程模型(DEM)数据,综合考虑海拔、太阳总辐射、地表长波有效辐射对旬平均气温的影响,模拟了福建省复杂地形下旬均温的空间分布。结果表明:1)常规站验证结果显示:各旬气温绝对误差平均值(MAE)最小为0.46℃,最大为2.3℃,全年平均为0.87℃;加密站验证结果显示,MAE最大为2.3℃,最小0.5℃,全年平均为0.96℃。2)模拟结果能反映旬均温的宏观分布规律与局地细节特征。宏观范围内,旬均温受纬度影响较大,由北至南气温逐渐升高,沿海地区旬均温整体高于内陆,山区旬均温明显较低;局地范围内,各坡向上气温差异显著,海拔越高、坡度越大,差异越明显;地形因子对旬平均温的影响具有季节差异,具体表现为冬季时地形因子对旬均温的影响最大,秋季次之,春夏季节中地形因子对旬均温的影响最弱。
Observation data of 33 conventional meteorological stations in Fujian Province and its surrounding areas, from 1960 to 2010, and digital elevation model(DEM) data, were used to examine the effects of altitude,total solar radiation, and effective long-wave radiation on mean temperature of a 10-day period over complex terrain in Fujian Province, China. The spatial distribution of 100 m resolution temperatures was simulated using a distributed model of 10-day temperature. The results showed that: 1)the results of the conventional station calibration showed that the maximum of mean absolute bias error(MAE) was 2.3℃, the minimum was 0.46℃,and the average for the whole year was 0.87℃. The calibration results of the meteorological stations showed that the maximum of MAE was 2.3℃, the minimum temperature was 0.5℃, and the annual average was 0.96℃.2)The simulation results can reflect the macroscopic distribution and local details of the 10-day's average temperature. At a macroscopic scale, average temperature was affected by latitude, and temperature gradually increased from north to south. Average temperature in coastal areas was higher than that on land, and average temperature in the mountainous area was significantly lower. At a local scale, due to the influence of topographic factors such as altitude, slope, and aspect, the temperature difference between different aspects was significant.The higher the altitude, the larger the slope, the larger the difference became. The influence of topographic factors on average temperature of 10-day periods exhibited seasonal differences. Topographic factors had the largest influence on 10-day average temperature in winter, and this was reduced. Topographical factors had the weakest influence on average temperature in the spring and summer.
Observation data of 33 conventional meteorological stations in Fujian Province and its surrounding areas, from 1960 to 2010, and digital elevation model(DEM) data, were used to examine the effects of altitude,total solar radiation, and effective long-wave radiation on mean temperature of a 10-day period over complex terrain in Fujian Province, China. The spatial distribution of 100 m resolution temperatures was simulated using a distributed model of 10-day temperature. The results showed that: 1)the results of the conventional station calibration showed that the maximum of mean absolute bias error(MAE) was 2.3℃, the minimum was 0.46℃,and the average for the whole year was 0.87℃. The calibration results of the meteorological stations showed that the maximum of MAE was 2.3℃, the minimum temperature was 0.5℃, and the annual average was 0.96℃.2)The simulation results can reflect the macroscopic distribution and local details of the 10-day's average temperature. At a macroscopic scale, average temperature was affected by latitude, and temperature gradually increased from north to south. Average temperature in coastal areas was higher than that on land, and average temperature in the mountainous area was significantly lower. At a local scale, due to the influence of topographic factors such as altitude, slope, and aspect, the temperature difference between different aspects was significant.The higher the altitude, the larger the slope, the larger the difference became. The influence of topographic factors on average temperature of 10-day periods exhibited seasonal differences. Topographic factors had the largest influence on 10-day average temperature in winter, and this was reduced. Topographical factors had the weakest influence on average temperature in the spring and summer.
引文
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Pape R and Loffler J.2004.Modelling Spatio-Temporal Near-Surface Temperature Variation in High Mountain Landscapes.Ecological Modelling,178(3/4):483-501.
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