全球共享降水数据在我国的适用性研究
摘要
水文学科未来发展很大程度上取决于能否获得模型率定和校正所需要的充足数据,遥感技术能够在这个过程中起到关键作用。遥感数据能够通过共享平台获得,以实现数据的共享和流动。全球目前能够共享的遥感数据除了直接的卫星图片外,还有众多的基于卫星遥感数据源制作成的产品。本文针对卫星降水观测数据在我国的适用性进行了详细的分析比较,并对差异性进行深入探讨,以期为无资料地区的水文研究提供数据支撑。
论文的主要研究内容及取得的成果包括：
(1)首先对卫星观测降水数据GPCP (Global Precipitation Climatology Project)和CMAP (the CPC Merged Analysis of Precipitation)在我国陆地区域进行了时空分析。①1980—2006年12个月空间相关分析发现,GPCP与CMAP数据在东经100°E以东适用性好于东经100°E以西的区域,同时研究还发现其他季节的相关性要好于冬季。这主要与卫星降水产品对于降雪的反演原理还不是很清楚有关。②空间差异性研究表明东北区、黄淮海区、长江及以南区、西北及西南区的一致性呈递减趋势。分析认为对于西北及西南等台站分布较疏的地区而言,GPCP与CMAP不同的融合方法是造成两者差异大的原因；而在北纬40°N以南的我国大部分地区,两者均使用基于GPI算法融合的卫星数据,只是GPCP在使用前经过了微波数据的校正,而CMAP数据则没有,这可能是造成黄淮海区域与长江及以南区域差异的内在原因。北纬40°N以北两者差异小的主要原因是数据源均来源于SSM/I。结果得出GPCP数据是更适合研究我国降水特征的卫星降水产品。
(2)其次,基于GPCP数据与气象台站多年平均、季平均、月平均降水量等不同时间尺度分析得出,降水较少月份GPCP数据与气象台站的相关性要高于降水量较多的月份,这主要是与台站实测降水对于局地性降水代表性差有关。GPCP与台站数据空间一致性研究结果表明：对于东北区、黄淮海区、长江及以南区域研究的56个流域中各评级时间段内,当面积达到7万km2以上时,GPCP各评价时间段相对误差达到12%以下。而由于西北及西南的广大地区GPCP相对误差太大,GPCP数据基本不适用。分析原因认为GPCP数据网格内的站点疏密对结果造成直接影响；另外一个原因与我国各地区降水类型有关。
(3)比较分析了我国四个不同地区GPCP数据的精度,结果表明：GPCP数据精度最高的区域位于长江及以南区,站点个数在4个以上,面积4.9万km2,精度能达到11%以上；黄淮海区的精度次之,站点个数为5,面积阈值为6.7万km2,精度达到12%；东北区也能达到较高精度,相对误差不超过15%,流域面积大于7万km2,站点个数达到5个以上。西南及西北区GPCP数据误差较大,只能在个别地区适用。
(4)基于GPCP数据应用线性回归、差积曲线、M-K检验方法、小波等方法,研究了东北某河流1980—2006年降水演变规律,并应用PREC/L数据进行了检验。证明年、春、夏、秋季GPCP数据均能很好地表征流域的降水变化,但是冬季的结果较差。这主要与微波对于降雪的反演机理还不明确有关。
The future development of hydro- disciplinary depends largely on the data which is required to improve the model availability and adequacy for calibration. Remote sensing technology can play a key role in this process. Remote sensing data can be obtained through a shared platform, which allows data shared and flowed. The current global remote sensing data can be shared except satellite pictures. There are a number of satellite-based remote sensing data products. In this paper, we compare the applicability of the satellite observations of precipitation in China, and discuss the differences of them. We look forward to providing data to support the no information region hydrological study.
The main conclusions of this paper include the following aspects:
Firstly, we analyze GPCP and CMAP precipitation data in the China's land area. Found that:GPCP and CMAP have a better correlation east of 100°E than the west of 100°E. Another result is that the correlation is worse in the winter than the other seasons. This is mainly because of satellite precipitation products for the principle of inversion of snow is not very clear. The spatial difference indicats that the Northeast area, the Huanghuai sea area, Yangtze River and south of the area, northwest and the southwest area's conformity showing a decreasing progressively tendency. Analysis for the areas, which has little station distribution of it, for example northwest and southwest etc, has a big difference between GPCP and CMAP. We think it is relate to different fusion method in the process of production between them. From the 40°N to the south of most regions of China, both are using of satellite data which based on the GPI algorithm. GPCP before using it after microwave data calibration, but CMAP data is not. This may be the internal causes of difference of Huang-huai-hai region and the Yangtze and south of the Yangtze River area. In the north of 40°N, the difference is small because of the data source is derived from the SSM/I. Results indicate that GPCP is more suitable for China's rainfall characteristics.
Secondly, based on GPCP data and the rain gause, we analysis different time scales including the annual average、quarter average and average month, finding that the rainfall in less GPCP data and rain gause has a better correlation than the rainfall in more. This is mainly because the precipitation stations for local precipitation representative poor. GPCP and rain gause space station consistency show that:the 56 river basin which belong to the northeast area, south of the Yangtze river and yellow sea area. When the area is above 70000 km, GPCP and rain gause's relative error is below 12%. In the large areas of the northwest and southwest, relative error is too big. We analyze the reason that density of meteorological stations plays a very important role, and another reason is relevant to type of precipitation in each region.
Thirdly, to compare of our four different areas GPCP data accuracy, the results indicate that:The highest accuracy of GPCP data is the Yangtze River and south of the Yangtze River area. In this area when the number of sites in above 4, and the area above 49000 km2, precision can reach more than 11%. The accuracy of the yellow sea area is less than the Yangtze River and south of the Yangtze River area, when the number of site above 5, area threshold for 67000 km2, precision can reach 12%. The northeast area also can achieve a high precision, a relative error less than 15%, a basin area more than 70000 km2, the number of sites more than 5. Because of the Northwest and southwest area GPCP data error poor, can be only applied in individual region.
Finally, based on GPCP data using linear regression, residual mass curve, M-K inspection, and wavelet methods, this paper researches a typical river basin evolution of precipitation from 1980 year to 2006. And using PREC/L data verifies the conclusion. That year, spring, summer, autumn GPCP data can be a very good characterization of precipitation, but the result of the winter was poor.
论文的主要研究内容及取得的成果包括：
(1)首先对卫星观测降水数据GPCP (Global Precipitation Climatology Project)和CMAP (the CPC Merged Analysis of Precipitation)在我国陆地区域进行了时空分析。①1980—2006年12个月空间相关分析发现,GPCP与CMAP数据在东经100°E以东适用性好于东经100°E以西的区域,同时研究还发现其他季节的相关性要好于冬季。这主要与卫星降水产品对于降雪的反演原理还不是很清楚有关。②空间差异性研究表明东北区、黄淮海区、长江及以南区、西北及西南区的一致性呈递减趋势。分析认为对于西北及西南等台站分布较疏的地区而言,GPCP与CMAP不同的融合方法是造成两者差异大的原因；而在北纬40°N以南的我国大部分地区,两者均使用基于GPI算法融合的卫星数据,只是GPCP在使用前经过了微波数据的校正,而CMAP数据则没有,这可能是造成黄淮海区域与长江及以南区域差异的内在原因。北纬40°N以北两者差异小的主要原因是数据源均来源于SSM/I。结果得出GPCP数据是更适合研究我国降水特征的卫星降水产品。
(2)其次,基于GPCP数据与气象台站多年平均、季平均、月平均降水量等不同时间尺度分析得出,降水较少月份GPCP数据与气象台站的相关性要高于降水量较多的月份,这主要是与台站实测降水对于局地性降水代表性差有关。GPCP与台站数据空间一致性研究结果表明：对于东北区、黄淮海区、长江及以南区域研究的56个流域中各评级时间段内,当面积达到7万km2以上时,GPCP各评价时间段相对误差达到12%以下。而由于西北及西南的广大地区GPCP相对误差太大,GPCP数据基本不适用。分析原因认为GPCP数据网格内的站点疏密对结果造成直接影响；另外一个原因与我国各地区降水类型有关。
(3)比较分析了我国四个不同地区GPCP数据的精度,结果表明：GPCP数据精度最高的区域位于长江及以南区,站点个数在4个以上,面积4.9万km2,精度能达到11%以上；黄淮海区的精度次之,站点个数为5,面积阈值为6.7万km2,精度达到12%；东北区也能达到较高精度,相对误差不超过15%,流域面积大于7万km2,站点个数达到5个以上。西南及西北区GPCP数据误差较大,只能在个别地区适用。
(4)基于GPCP数据应用线性回归、差积曲线、M-K检验方法、小波等方法,研究了东北某河流1980—2006年降水演变规律,并应用PREC/L数据进行了检验。证明年、春、夏、秋季GPCP数据均能很好地表征流域的降水变化,但是冬季的结果较差。这主要与微波对于降雪的反演机理还不明确有关。
The future development of hydro- disciplinary depends largely on the data which is required to improve the model availability and adequacy for calibration. Remote sensing technology can play a key role in this process. Remote sensing data can be obtained through a shared platform, which allows data shared and flowed. The current global remote sensing data can be shared except satellite pictures. There are a number of satellite-based remote sensing data products. In this paper, we compare the applicability of the satellite observations of precipitation in China, and discuss the differences of them. We look forward to providing data to support the no information region hydrological study.
The main conclusions of this paper include the following aspects:
Firstly, we analyze GPCP and CMAP precipitation data in the China's land area. Found that:GPCP and CMAP have a better correlation east of 100°E than the west of 100°E. Another result is that the correlation is worse in the winter than the other seasons. This is mainly because of satellite precipitation products for the principle of inversion of snow is not very clear. The spatial difference indicats that the Northeast area, the Huanghuai sea area, Yangtze River and south of the area, northwest and the southwest area's conformity showing a decreasing progressively tendency. Analysis for the areas, which has little station distribution of it, for example northwest and southwest etc, has a big difference between GPCP and CMAP. We think it is relate to different fusion method in the process of production between them. From the 40°N to the south of most regions of China, both are using of satellite data which based on the GPI algorithm. GPCP before using it after microwave data calibration, but CMAP data is not. This may be the internal causes of difference of Huang-huai-hai region and the Yangtze and south of the Yangtze River area. In the north of 40°N, the difference is small because of the data source is derived from the SSM/I. Results indicate that GPCP is more suitable for China's rainfall characteristics.
Secondly, based on GPCP data and the rain gause, we analysis different time scales including the annual average、quarter average and average month, finding that the rainfall in less GPCP data and rain gause has a better correlation than the rainfall in more. This is mainly because the precipitation stations for local precipitation representative poor. GPCP and rain gause space station consistency show that:the 56 river basin which belong to the northeast area, south of the Yangtze river and yellow sea area. When the area is above 70000 km, GPCP and rain gause's relative error is below 12%. In the large areas of the northwest and southwest, relative error is too big. We analyze the reason that density of meteorological stations plays a very important role, and another reason is relevant to type of precipitation in each region.
Thirdly, to compare of our four different areas GPCP data accuracy, the results indicate that:The highest accuracy of GPCP data is the Yangtze River and south of the Yangtze River area. In this area when the number of sites in above 4, and the area above 49000 km2, precision can reach more than 11%. The accuracy of the yellow sea area is less than the Yangtze River and south of the Yangtze River area, when the number of site above 5, area threshold for 67000 km2, precision can reach 12%. The northeast area also can achieve a high precision, a relative error less than 15%, a basin area more than 70000 km2, the number of sites more than 5. Because of the Northwest and southwest area GPCP data error poor, can be only applied in individual region.
Finally, based on GPCP data using linear regression, residual mass curve, M-K inspection, and wavelet methods, this paper researches a typical river basin evolution of precipitation from 1980 year to 2006. And using PREC/L data verifies the conclusion. That year, spring, summer, autumn GPCP data can be a very good characterization of precipitation, but the result of the winter was poor.
引文
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