TRMM降水数据在东北地区的精度验证与应用

引用本文

刘小婵, 赵建军, 张洪岩, 郭笑怡, 张正祥, 浮媛媛. .TRMM降水数据在东北地区的精度验证与应用.自然资源学报, 2015,30(6): 1047-1056
LIU Xiao-chan, ZHAO Jian-jun, ZHANG Hong-yan, GUO Xiao-yi, ZHANG Zheng-xiang, FU Yuan-yuan. .Accuracy Validation and Application of TRMM Precipitation Data in Northeast China.Journal of Natural Resources,2015,30(6): 1047-1056 复制到剪切板

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TRMM降水数据在东北地区的精度验证与应用

刘小婵, 赵建军, 张洪岩^*, 郭笑怡, 张正祥, 浮媛媛

东北师范大学地理科学学院,长春 130024

第一作者简介：刘小婵（1990- ）,女,福建厦门人,硕士研究生,主要从事遥感与GIS应用研究。E-mail: liuxc225@nenu.edu.cn

*通信作者简介：张洪岩,教授,博士,主要从事GIS应用与环境遥感研究工作。E-mail: zhy@nenu.edu.cn

收稿日期:2014-07-10 网络出版日期:2015-06-20

基金:中国科学院地理科学与资源研究所资源与环境信息系统国家重点实验室开放基金项目“中国东北地表物候遥感反演及交叉验证”.中国博士后科学基金资助项目（2014M561272）.中央高校基本科研业务费专项资金（14QIVJJ025）.吉林省博士后科研项目启动经费（RB201353）.吉林省科技发展计划资助项目（20150520069JH）

摘要

利用东北地区2000—2007年的APHRODITE降水数据,基于GWR方法对TRMM降水数据进行修正,分析新的TRMM降水数据精度,并基于修正的TRMM降水数据对东北地区降水进行时空分布特征分析。结果表明：①APHRODITE降水数据与观测数据之间的线性相关性更高、均方根误差 RMSE更小,数据具有较高的精度;②修正后的TRMM降水数据相关系数 R有所提高,且 RMSE值均有降低。整体来看,TRMM降水数据的降水量数值偏大于观测值;③修正TRMM降水数据在5—10月的误差相对较小,整体来看,在大部分区域的误差在0~30%之间;④东北地区降水分布极不均匀,整体呈从东南向西北减少趋势。11月到翌年3月的降水稀少,降水主要集中在夏季,其中7月降水量最大。

关键词: 东北地区; TRMM; GWR; 精度验证

中图分类号:P426.6 文献标志码:A 文章编号:1000-3037(2015)06-1047-10

Accuracy Validation and Application of TRMM Precipitation Data in Northeast China

LIU Xiao-chan, ZHAO Jian-jun, ZHANG Hong-yan, GUO Xiao-yi, ZHANG Zheng-xiang, FU Yuan-yuan

School of Geographical Sciences, Northeast Normal University, Changchun 130024, China

Abstract

As the important component of the global water cycle, precipitation is the key parameter in hydrology, meteorology and climate. Conventional interpolated observed precipitation data cannot reflect the spatial variation due to the limitation of the number of stations. In recent decades, with the development of remote sensing and meteorological satellite technology, satellite remote sensing images has become an important source of spatial precipitation data to detect rainfall information. In this study, APHRODITE precipitation data in Northeast China from 2000 to 2007 are used. We adjust the TRMM precipitation data based on GWR method, validate the accuracy of the adjusted data, and analyze the spatial and temporal distribution characteristics of precipitation in Northeast China based on the adjusted TRMM precipitation data. The conclusions are: 1) Correlation coefficient between APHRODITE and observed data is higher, and the root mean square error is smaller, so APHRODITE data have a higher accuracy. 2) The adjusted TRMM precipitation data have a higher correlation coefficient and a smaller RMSE value. Overall, the TRMM precipitation is higher than that of observed data. 3) High value of R mainly exists in the northern, eastern and southeastern regions, low-values mainly exist in the western and central regions. 4) The BIAS of adjusted TRMM precipitation data is relatively small from May to October. Overall, the BIAS of most areas ranges in 0-30%. 5) The distribution of precipitation is uneven in Northeast China, which reduces from the southeast to the northwest. The rainfall mostly happens in summer while less happens from November to the following March. The largest rainfall happens in July.

Keyword: Northeast China; TRMM precipitation; GWR; accuracy validation

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降水是全球地表物质交换、水文循环等过程的重要组成部分, 是水文学、生态学等研究的最基础资料。近几十年来, 遥感技术的迅速发展以及相应遥感产品数据的产生, 为区域降水研究提供了新的手段。1997年, 由美国国家宇航局和日本国家空间发展局共同研制的热带测雨卫星（Tropical Rainfall Measuring Mission, TRMM）的成功发射为与降水有关的研究提供了数据支持^[1], 国内外学者借助其开展了一系列的研究^{[1, 2, 3, 4, 5, 6]}。2006年, 由日本综合地球环境研究所（RIHN）和日本气象厅研究所（MRI/JMA）联合的APHRODITE（Asian Precipitation-Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources）计划, 通过整编亚洲各国和地区的雨量站观测数据, 建立了一套逐日的网格化降水数据集^[7]。这也是目前唯一覆盖整个亚洲地区的长时间（从1951年开始）陆地降水网格化数据^{[8, 9, 10]}, 可以作为评估不同降水数据的基准数据。

国内外许多学者针对TRMM降水数据的精度已做了不少工作, 研究表明TRMM降水数据与台站、雷达观测数据相比, 具有较好的精度。部分学者也对APHRODITE在中国大陆的适用性进行了评估, 发现该数据与观测数据的相关系数超过0.9, 能够解释降水空间分异^[8]。同时, 基于雨量站观测资料的APHRODITE降水数据在插值过程中考虑了地形影响^[11]。

东北地区是我国重要的粮食产区, 气候年际变率大, 属气候脆弱带, 且降水的局域性和突发性很强。作为对农业生产起决定性作用的气象要素, 研究东北地区的降水规律对该地区生产具有重要意义^[12]。为此, 本文将APHRODITE数据作为“ 真实值” , 基于GWR方法对TRMM数据进行修正, 并对修正的TRMM降水数据进行精度验证, 最后利用2000— 2007年修正的TRMM降水数据分析东北地区降水分布的时空分布特征。

1 研究区概况

本文研究区域为东北地区, 由于TRMM数据本身区域范围的限制, 包括辽宁省、吉林省、黑龙江省（除黑龙江北部区域）和内蒙古自治区东部的部分区域, 位于东经115° 05′ ~135° 02′ , 北纬38° 40′ ~50° 。东北地区位于北半球的中高纬度, 地处欧亚大陆的东岸, 属温带大陆性季风气候, 是我国最大的商品粮基地和农业生产最具发展潜力的地区之一。该区气候年际变率大, 是典型的气候脆弱区之一^{[13, 14, 15]}。研究表明该地区受干旱和暴雨洪涝等气候灾害影响严重。而降水是造成干旱和洪涝的影响因素之一, 因此对东北地区降水规律进行研究十分必要^{[12, 16]}。

2 数据与方法

2.1 数据来源与预处理

本文使用的TRMM数据为TRMM卫星第7版本3级产品（3B43 Version7）的月降水资料, 数据来自于NASA（http://mirador.gsfc.nasa.gov/）, 空间分辨率为0.25° × 0.25° , 时间分辨率为1个月, 数据覆盖全球50° S~50° N范围的地区, 时段为2000— 2007年。采用的APHRODITE数据为APHRO_MA_V1101数据集, 数据下载于http://www.chikyu.ac.jp/precip/cgi-bin/aphrodite/script/aphrodite_cgi.cgi/register, 空间范围为60° E~150° E, 15° S~55° E, 空间分辨率为0.25° × 0.25° , 时间分辨率为 d, 时间序列同TRMM数据。地面气象资料来源于中国气象科学数据共享服务网（http://cdc.cma.gov.cn/home.do）, 考虑资料的完整性, 最终选取东北地区93个站点2000— 2007年的中国地面气候资料日值数据集（图1）。

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	图1 研究区位置及气象站点分布Fig. 1 Study area and rain gauge stations

对TRMM数据进行拼接、投影变换、裁剪, 得到研究区2000— 2007年TRMM月数据; 将TRMM的月数据进行求和, 获得TRMM的年数据。对APHRODITE数据进行格式转换、波段批量提取、像元统计, 得到APHRODITE年数据。

2.2 方法

2.2.1 数据验证指标

本研究中, 采用相关系数R（Correlation Coefficient）、均方根误差RMSE（Root Mean Square Error）、相对误差BIAS等指标对降水数据进行评判。其中, 相关系数R表示两数据之间的线性相关程度, RMSE用来评估误差的整体水平, BIAS反映TRMM数据与观测数据在数值上的偏离程度。其计算公式如下：

$\begin{matrix} R = \frac{\overset{n}{\sum_{i = 1}} (x_{i} - \overset{̅}{x}) (y_{i} - \overset{̅}{y})}{\sqrt[]{\overset{n}{\sum_{i = 1}} {(x_{i} - \overset{̅}{x})}^{2} \overset{n}{\sum_{i = 1}} {(y_{i} - \overset{̅}{y})}^{2}}} \end{matrix}$ （1）

$\begin{matrix} RMSE = \sqrt[]{\frac{\overset{n}{\sum_{i = 1}} {(x_{i} - y_{i})}^{2}}{n}} \end{matrix}$ （2）

$\begin{matrix} BIAS = \frac{\overset{n}{\sum_{i = 1}} x_{i}}{\overset{n}{\sum_{i = 1}} y_{i}} - 1 \end{matrix}$ （3）

式中： $\begin{matrix} x_{i} \end{matrix}$ 、 $\begin{matrix} y_{i} \end{matrix}$ 分别对应遥感降水数据和气象站观测数据, $\begin{matrix} \overset{̅}{x} \end{matrix}$ 为遥感降水平均值, $\begin{matrix} \overset{̅}{y} \end{matrix}$ 为气象站观测平均值, $\begin{matrix} n \end{matrix}$ 为气象站点个数。

2.2.2 地理加权回归模型（GWR）

GWR模型是1996年由Fotheringham等率先提出^[17], 最近被广泛用于空间异质性研究的方法^{[18, 19]}, 其基本思想是变量间的关系随着空间位置的变化而变化, 通过估算研究区内每一给定位置的相关变量与解释变量的参数来建立回归模型^[20]。GWR模型是普通线性回归模型的扩展。本研究利用2000— 2007年的TRMM降水数据与APHRODITE数据建立GWR回归模型（公式（4））, 生成研究区内APHRODITE对应TRMM数据的系数项数据、常数项数据, 并进一步利用GWR方法估算新的TRMM降水数据, 从而修正原始TRMM降水数据。

$\begin{matrix} y_{i} = β_{0 i} (u) + β_{1 i} (u) x_{1 i} \end{matrix}$ （4）

式中： $\begin{matrix} y_{i} \end{matrix}$ 为第i个点的TRMM降水量, $\begin{matrix} x_{1 i} \end{matrix}$ 为第i个点的APHRODITE降水量, $\begin{matrix} β \end{matrix}$ 为相应的系数项, $\begin{matrix} u \end{matrix}$ 为某一个空间坐标; $\begin{matrix} i = 1, \dots, n \end{matrix}$ , 表示样本点的个数。

3 结果与分析

3.1 TRMM数据/APHRODITE数据精度比较

将研究区2000— 2007年93个气象站点对应的观测数据、TRMM降水数据、APHRODITE数据按4个季节（春季：3— 5月; 夏季：6— 8月; 秋季：9— 11月; 冬季：12月到翌年2月）叠加, 得到季度降水数据; 4个季度降水数据再叠加, 得到年降水数据。利用ArcGIS提取各个气象站点对应的观测值、TRMM降水值、APHRODITE降水值, 分别比较两种数据与观测数据在不同时间尺度下的相关系数R、均方根误差RMSE（图2、3）。

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	图2 两种数据与观测数据在不同时间尺度下的相关系数Fig. 2 The correlation coefficient of TRMM/APHRODITE on different time scales

结果显示（图2）, TRMM降水数据与观测数据在1月份和冬季的相关系数较低, 特别是1月份, 相关系数R只有0.37; 而APHRODITE数据的相关系数R整体较高, 均大于0.9。这说明相比于TRMM降水数据, 基于台站资料插值建立的APHRODITE数据与站点观测数据的一致性更好, 精度更高。

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	图3 两种数据与观测数据在不同时间尺度下的均方根误差Fig. 3 The root mean square error of TRMM/APHRODITE on different time scales

从两种数据与观测数据的均方根误差结果（图3）来看, TRMM数据的均方根误差RMSE值更大, 这也进一步说明APHRODITE数据的精度与准确性。比较1— 12月的结果发现, 两种数据在夏季月份的均方根误差较大, 可能是因为降水量大容易导致雷达信号衰退, 从而使得数据探测出现偏差。

3.2 TRMM数据修正与精度验证

以研究区内93个气象站点2000— 2007年的APHRODITE降水数据为“ 真实值” , 利用GWR回归方程估算新的TRMM数据, 完成TRMM数据修正。分别以研究区内93个气象站点2000— 2007的月、季度、年观测数据为自变量, 其对应的月、季度、年的原始TRMM降水数据、修正TRMM降水数据为因变量, 进行一元线性回归分析, 并计算不同时间尺度的相关系数R、均方根误差RMSE（表1）。

表1 不同时间尺度原始TRMM数据、修正TRMM数据与站点观测数据的相关系数与均方根误差 Table 1 The correlation coefficient and root mean square error between original TRMM and observed precipitation and between adjusted TRMM and observed precipitation on different time scales

东北地区冬季的降水形式表现为降雪, 该天气状况将对观测的降水数据产生影响。由表1可以看出：① 除冬季和1月, 原始数据不同时间尺度的相关系数R均大于0.8, RMSE介于5.32~108.68之间; ② 修正后的数据相关系数R均有所提高, 除1月、12月及冬季外, 相关系数均大于0.9, 且RMSE值均有降低。可见, 原始TRMM降水数据、修正TRMM降水数据与气象站观测数据之间具有较好的线性相关和一致性, 且修正后的TRMM降水数据精度更高。

从相对误差BIAS的结果来看（表2）, TRMM数据在暖季（4— 10月）的相对误差较小, 而在冷季（11月到翌年3月）存在较大的误差。总体来看, TRMM降水数据的降水量比观测值偏大, 存在一定程度上的高估。这可能是由于卫星本身的特点所致。卫星主要是通过微波传感器等设备探测云内降水粒子和云粒子与微波的相互作用达到测量降水的目的^[21], 不能很好地估计历时较短、降水较小的降水过程^{[1, 4]}。

表2 不同时间尺度下原始TRMM数据、修正TRMM数据与站点观测数据的相对误差 Table 2 The BIAS between original TRMM and observed precipitation and between adjusted TRMM and observed precipitation on different time scales

从检验的结果可知, 修正后的TRMM降水数据与观测站实测数据相比, 在整体上有较好的精度, 但是整体检验掩盖了单个站点数据与对应格网的TRMM数据之间的差异。由于地形、地势、经纬度、海拔等因素的影响, 降水在空间上呈现差异性, 仅对数据精度进行整体验证是不够的。为此, 本文计算93个气象站点2000— 2007年的月观测降水量数据与其对应格网修正TRMM降水数据的相关系数。从所有站点相关系数R雷达图中可以看出（图4）, 东北地区大部分站点TRMM数据与观测数据之间线性相关程度在0.9~1之间。

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	图4 93个站点相关系数R雷达图Fig. 4 Radar chart of the correlation coefficient of Rbetween TRMM and observed precipitation

借助Thiessen多边形方法, 以93个气象站点为基准, 将研究区划分成93个多边形; 根据Thiessen方法的思想可知, 位于Thiessen多边形上的点到相应离散点的距离最近, 因此, 多边形表示对于站点最大的影响范围, 进而得到相关系数R空间分布规律（图5）。结果显示, 修正后的TRMM降水数据与观测数据之间的相关系数R介于0.88~0.98之间; 高值主要集中研究区的北部、东部及东南部, 大致与小兴安岭、张广才岭、长白山脉所连接成的山脉走向基本一致, 该区域的降水相对也较为丰富; 而低值主要集中在研究区的西部、中部, 该区域降水相对较少。

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	图5 东北地区修正TRMM数据与观测数据R空间分布Fig. 5 Spatial distribution of R between adjusted TRMM and observed precipitation in Northeast China

3.3 修正TRMM数据误差空间分布

修正TRMM降水数据与气象站点观测数据的相对误差BIAS揭示了各个气象站点所对应格网的修正TRMM降水值偏离站点实测值的程度。BIAS大于0, 说明修正TRMM降水值大于观测值, 即高估; BIAS小于0, 说明修正TRMM降水值小于观测值, 即低估; BIAS越接近于0, 说明TRMM数据越准确。根据公式（3）, 计算每一个气象站点不同时间尺度下的相对误差BIAS, 即可得到修正TRMM降水数据对观测数据的高估与低估程度。

借助ArcGIS的普通克里金插值方法对所有月份、季度和年的BIAS值进行插值, 即可得到不同时间尺度下东北地区的数据误差空间分布图（图6）。

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	图6 东北地区不同尺度下数据误差空间分布Fig. 6 Spatial distribution of BIAS on different time scales in Northeast China

结果表明, 从月尺度来看, 5— 10月研究区大部分区域的误差大致在-10%~30%之间, 而在其他月份的误差较大, 特别是1月。从季节尺度来看, 冬季误差最大, 春季误差为10%~60%, 误差最大主要在西部区域, 其余均在30%以下; 夏季误差为-10%~20%, 修正TRMM降水数据在夏季主要表现为高估, 误差在20%以下; 秋季误差为-10%~40%, 大部分区域的误差在10%~30%之间。从年尺度来看, 研究区大部分区域误差在10%~30%之间。总体来看, 修正的TRMM数据在东北大部分区域的误差为0~30%之间, 表现为高估。

3.4 基于修正TRMM数据的东北地区降水时空分布特征分析

3.4.1 东北地区降水年际变化

将8 a的修正TRMM月份降水数据、气象站点观测降水数据合成年数据, 并获得8 a平均降水图（图7）。从图7(a)可以看出, 东北地区的降水量最大的区域位于辽宁省的东南沿海一带和吉林的东南部, 年平均降水量大于900 mm; 最小值出现在内蒙古的东部, 年平均降水量小于300 mm。降水的次大值区域呈狭长的带状分布, 从辽宁南端向北延伸至黑龙江的中部, 年平均降水量在600~700 mm之间, 与小兴安岭、张广才岭、长白山脉所连接成的山脉走向基本一致。降水的次小值区域则位于次大值的两侧, 即为东北主要山脉两侧的东北平原。由此可见, 东北地区的这种降水分布特征与地形分布及季风气流的作用有关。整体来看, 东北地区多年平均降水的空间分布大致是从东南向西北减少。对比图7(a)、图7(b), 可以发现, TRMM年平均降水量介于194~1 070 mm之间, 站点年平均降水量介于168~1 006 mm之间, 二者数值范围特别接近, TRMM降水数据略高于气象站点观测数据, 这也进一步说明TRMM降水数据在东北地区的适用性。

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	图7 东北地区2000— 2007年平均年降水量空间分布Fig. 7 Spatial distribution of annual mean precipitation in Northeast China from 2000 to 2007

3.4.2 东北地区降水年内变化

利用2000— 2007年修正的TRMM降水数据计算得到东北地区月平均降水分布（图8）。由图8可见, 研究区内1— 12月的月平均降水分布规律与年平均降水分布规律基本一致, 整体上空间分布大致呈由东南向西北减少的趋势。11月到翌年3月, 研究区内降水几乎均在50 mm以下, 降水稀少; 4— 5月, 降水量开始呈现增加趋势, 降水量最少的区域位于西部、西南部; 降水主要集中在夏季, 其中7月降水量最大, 最大降水量为264 mm, 主要在辽宁省的宽甸、丹东一带; 9— 10月, 降水量由东南向西北方向开始逐渐减少。由此可见, 东北地区降水分布在各月份呈现极其不均匀性, 区域间降水量差异明显。

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	图8 东北地区2000— 2007年各月平均降水量空间分布Fig. 8 Spatial distribution of monthly mean precipitation in Northeast China from 2000 to 2007

4 结论

本文利用东北地区2000— 2007年的APHRODITE降水数据, 基于GWR方法对TRMM降水数据进行修正, 并对新的TRMM降水数据进行精度验证, 分析其数据误差, 最后基于修正的TRMM降水数据对东北地区降水进行时空分布特征分析, 得到的结论如下：

1）相对于TRMM数据, APHRODITE降水数据与观测数据之间的线性相关性更高（R均大于0.9）, 均方根误差RMSE更小, 说明APHRODITE数据具有较高的精度与准确性。

2）修正后的TRMM降水数据相关系数R有所提高, 除1月、12月及冬季外, 相关系数均大于0.9, 且RMSE值均有降低。从相对误差BIAS的结果可见, TRMM降水数据在暖季误差较小, 而在冷季误差大。整体来看, TRMM降水数据的降水量比观测值偏大。

3）东北地区大部分站点修正后的TRMM数据与观测数据之间具有很明显的线性相关性（R在0.9~1之间）。从对研究区进行Thiessen剖分获取的相关系数R空间分布规律结果发现, 高值主要集中在研究区的北部、东部及东南部, 低值主要集中在研究区的西部和中部。

4）修正TRMM降水数据在5— 10月的误差相对较小, 研究区内大部分区域的误差在-10%~30%之间。总体来看, 修正的TRMM数据在东北地区大部分区域的误差为0~30%。

5）从年平均降水分布结果发现, 东北地区降水分布整体呈从东南向西北减少的趋势。修正TRMM年平均降水量与站点年平均降水量数值范围特别接近, TRMM降水数据略高些。从各月平均降水分布结果可以看出, 11月到翌年3月, 研究区降水稀少, 几乎均在50 mm以下; 降水主要集中在夏季, 其中7月降水量最大, 最大降水量为264 mm。可见, 东北地区降水分布在各月份呈现极其不均匀性, 区域间降水量差异明显。

The authors have declared that no competing interests exist.

参考文献

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2010

0.0

1.672

刘俊峰, 陈仁升, 韩春坛, 等. 多卫星遥感降雨数据精度评价[J]. 水科学进展, 2010, 21(3): 343-348.

[LIU

Jun-feng

, CHEN

Ren-sheng

, HAN

Chun-tan

, et al. Evaluating TRMM multisatellite precipitation analysis using gauge precipitation and MODIS snow-cover products. Advances in Water Science, 2010, 21(3): 343-348. ]

The Tropical RainfallMeasuringMission(TRMM)Multi-satellite Precipitation Analysis(TMPA)3B 42 product is evaluated using the gaugeprecipitations from 650 weather stations across China at daily, monthly and annual tine steps.The TMPA-3B42 performance accuracy is also assessed using theModerate Resolution Inaging Spectroradiometer (MODIS) daily snow-cover product for the Tibetan Plateau region.Results show that TMPA-3B42 performs well at coarse temporal resolutions.However, less satisfactory result is obtained at the daily tine step.At the same temporal resolution, the humid region TMPA-3B42 performs better than the arid region ones.An exception is found for regionswith the annual precipitation excess the 2500 mm threshold.During winter, the average difference between the TMPA-3B42 product and the MODIS snow-coverproduct is only around 15% at a regional scale.However, the difference can be as high as 40% if only the region's snow covered areas are considered in the product comparison, which indicates that the TMPA-3B42 product is less skilful in winter.The MODIS snow-cover product can thus be used to improve the performance of TMPA-3B42.

利用中国650个台站降水数据,在日、月、年尺度上,分析了多卫星降水分析数据(TMPA3B42)在中国大陆50°N以南地区的适用性,并利用MODIS逐日积雪数据评估了冬季TMPA数据在以青藏高原为主体区的精度。结果表明:TMPA日降水数据精度存在时空不稳定性,且随着时间尺度的增加,TMPA降水数据的精度提高;在同一时间尺度上,TMPA数据精度在降水量大的地区要明显好于降水稀少地区,但年尺度降水大于2500mm地区存在明显低估;TMPA数据冬季区域降水空间误差平均水平在15%,当只考虑积雪区时的降水空间误差平均水平在40%,这说明TMPA对冬季降水空间量较差,但同时这也意味着可以利用MODIS积雪数据修正TMPA冬季降水数据。

... 1997年,由美国国家宇航局和日本国家空间发展局共同研制的热带测雨卫星（Tropical Rainfall Measuring Mission,TRMM）的成功发射为与降水有关的研究提供了数据支持^[1],国内外学者借助其开展了一系列的研究^{[1,2,3,4,5,6]} ...

... 卫星主要是通过微波传感器等设备探测云内降水粒子和云粒子与微波的相互作用达到测量降水的目的^[21],不能很好地估计历时较短、降水较小的降水过程^[1,4] ...

2007

4.769

0.0

2008

3.573

0.0

2007

0.0

2011

0.0

3.304

曾红伟, 李丽娟. 澜沧江及周边流域TRMM 3B43 数据精度检验[J]. 地理学报, 2011, 66(7): 994-1004.

[ZENG

Hong-wei

, LI

Li-juan

. Accuracy validation of TRMM 3B43 data in Lancang River Basin. Acta Geographica Sinica, 2011, 66(7): 994-1004. ]

The Lancang river basin is a typical area of lacking data with complicated terrain and climate characteristics. It is located in the upper reaches of Mekong river basin. There is great potential to carry out hydrological prediction in ungauged basins by using satellite estimate precipitation data. As a precipitation radar satellite, TRMM has been collecting plentiful fine temporal-spatial precipitation data, so it is significant to use the TRMM precipitation data to study hydrological and climatic characteristics in the Lancang river basin. However, it is necessary to check the accuracy of TRMM data before using it. Based on the correlation coefficients and scatter points slope methods, the accuracy of TRMM 3B43 data at monthly time scale during 1998-2009 was validated by using the 35 rain gauges data, which were distributed in the Lancang river basin and its surrounding areas. Then the influence of slope and elevation on the checking result based on Thiessen polygons and K-Means cluster methods was analyzed. Finally, principal component analysis was used to compare the differences of elevation and slope on the accuracy of TRMM 3B43. The results are obtained as follows. (1) Compared with the 35 rain gauges, TRMM 3B43 data displayed good accuracy in the whole study area at monthly time scale. (2) There were significant differences of accuracy among the 35 sites; the TRMM 3B43 data had good accuracy in the upstream and downstream of the research areas, while the middle area was poor, especially Bomi, Gongshan, Deqin and Shangrila. (3) Compared with slope, the influence of elevation on the accuracy of TRMM 3B43 was more complicated, and the accuracy showed that there was a high-low-high variation when the elevation decreased from upstream to downstream, which may be caused by strong spatial heterogeneity of precipitation in this area. (4) The slope of research area had great influence on the accuracy of TRMM 3B43 data, which decreased as the increase of slope, the middle research area has the maximum slope so the accuracy was the worst, while the upstream and downstream had relatively high accuracy due to flat terrain.

1. Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China; 2. Graduate University of Chinese Academy of Sciences, Beijing 100049, China

在地形复杂的澜沧江及周边流域,利用相关系数法、散点斜率法,以研究区内35 个国家基准与基本站观测数据为“真值”对1998-2009 年之间月尺度的TRMM 3B43 降水数据精度进行检验,采用泰森多边形法、K-Means 聚类法分析了高程与坡度对检验结果的影响,借助主成分法比较了高程与坡度对TRMM 3B43 的影响程度。研究表明：在整体上,月尺度的TRMM 3B43 数据具有较高精度；就个体而言,研究区上游与下游地区具有较高的精度,而在波密,以及中游的贡山、德钦、德钦及香格里拉等地区精度较低；高程对TRMM 3B43 数据精度的影响小于坡度,在空间上呈现出较复杂的变化趋势,自托托河—勐腊随着海拔的降低,数据精度出现高值—低值—高值的变化规律；坡度对TRMM 3B43 数据精度有较大影响,坡度越大,数据精度越低。

2012

0.0

1.791

李相虎, 张奇, 邵敏. 基于TRMM数据的鄱阳湖流域降水时空分布特征及其精度评价[J]. 地理科学进展, 2012, 31(9): 1164-1170.

[LI

Xiang-hu

, ZHANG

, SHAO

Min.

Spatio-temproal distribution of precipitation in Poyang Lake Basin based on TRMM data and precision evaluation. Progress in Geography, 2012, 31(9): 1164-1170. ]

Based on the precipitation data of Poyang lake basin based on TRMM (tropical rainfall measuring mission) satellite 3B42 V6 during 1998-2007, this paper studies the spatio-temporal distribution of precipitation. And the precisions of TRMM data are evaluated for different sub-basins, rainfall intensities and seasons through the observation rainfall data obtained from 40 weather stations, which fills in a gap in the previous studies. The results show that the heavy rain events generally occur in the northern part of Poyang lake basin (Xiushui and Raohe sub-basins) based on TRMM and observation data, which can cause signal attenuation of radar and make a large error for TRMM. The class of 10~50 mm makes the greatest contribution to the total rainfall, accounting for about 60%. The temporal distribution of precipitation in the lake basin shows that there is a dry spell from January to mid-March and a wet spell from late March to September, and another dry spell is observed after September. The spatial distribution presents that more rainfall is observed in the eastern and western parts of the basin than in the central parts. It is also found that the rainfall is 300~400 mm less than observed data in the mountainous areas of southern Jiangxi province, which may be affected by elevation and gradient of landform. So, there is a limitation for TRMM to measure the heavy rain events and the rainfall in mountainous areas.

基于1998-2007 年热带测雨卫星(TRMM) 3B42 V6 降雨数据分析鄱阳湖流域降雨时空分布特征, 并利用40个气象站观测日降雨数据对TRMM数据在不同子流域、不同降雨强度及不同季节里的精度进行了对比分析, 弥补了以往只评价整体精度的不足。结果显示:鄱阳湖流域北部地区修水、饶河子流域较易出现暴雨, 导致雷达信号衰减, 使TRMM对大雨强降雨的探测出现较大偏差;流域内降雨以10~50 mm为主, 其雨量占到总雨量的60%;流域降雨在年内1-3 月中旬为干旱少雨期, 3 月下旬-9 月初为湿润多雨期, 9-12 月再次进入干旱少雨期;而空间分布呈东、西部大, 中部小的格局;同时发现, 在赣南山区TRMM降雨较观测雨量低300~400 mm, 这可能受高程和坡度的影响, 使TRMM对山区降雨的探测精度也出现较大偏差。

2009

1.118

0.0

... 2006年,由日本综合地球环境研究所（RIHN）和日本气象厅研究所（MRI/JMA）联合的APHRODITE（Asian Precipitation-Highly-Resolved Observational Data Integration Toward Evaluation of Water Resources）计划,通过整编亚洲各国和地区的雨量站观测数据,建立了一套逐日的网格化降水数据集^[7] ...

2012

0.0

1.802

韩振宇, 周天军. APHRODITE 高分辨率逐日降水资料在中国大陆地区的适用性[J]. 大气科学, 2012, 36(2): 361-373.

[HAN

Zheng-yu

, ZHOU

Tian-jun

. Assessing the quality of APHRODITE high-resolution daily precipitation dataset over contiguous China. Chinese Journal of Atmospheric Sciences, 2012, 36(2): 361-373. ]

A daily gridded precipitation dataset (APHRO) for Asia was created by the Asian Precipitation-Highly Resolved Observational Data Integration Towards the Evaluation of Water Resources (APHRODITE) project in Japan. The resolution of this dataset is 0.25°×0.25°, and the length is from 1951 to 2007. This study aims to assess the quality of the APHRO rainfall in contiguous China from the perspective of climatological mean, rainfall classes, and long-term trend. Daily rainfall records observed by 559 rain gauges are used for the comparison. The results are as follows: (1) For the mean states, the APHRO shows similar distribution of precipitation amount to station data, and can accurately characterize the seasonal migration of rain-belt. However, compared with station data, this dataset underestimates precipitation intensity, but overestimates precipitation frequency.(2) For the distribution of different classes of precipitation, annual mean precipitation amount for heavy rainfall derived from the APHRO data is lower, while the amounts for light and moderate rainfall are higher than the station data.(3) The trends of precipitation amount in China during 1956－2005 derived from two dataset are identical, the trends of precipitation frequency are also highly consistent, and both show an “increase in the west but decrease in the east” pattern. A large difference between the two datasets is found in the spatial pattern of precipitation intensity trends. The precipitation intensity derived from station data shows an increasing trend throughout the Chinese mainland in the past 50 years; the APHRO data exhibit a similar trend in the southeastern coastal region and northwestern China, but a different pattern in northern China, northeastern China, and Jianghuai region. In addition, analysis on the seasonality of interdecadal variability indicates that the characteristics of “southern China food and northern China drought” and “Jiangnan late spring drought” derived from the APHRO data are different from those derived from station data. The APHRO data tend to underestimate the trends.

日本APHRODITE (Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation of Water Resources) 研究计划建立了一套高分辨率的逐日亚洲陆地降水数据集 (简称APHRO), 时间从1951年到2007年, 空间分辨率为0.25°×0.25°。本文从气候态、不同等级降水量分布以及长期变化等方面分析, 通过与中国559个台站观测资料对比, 考察了该降水资料在中国地区的适用性。结果表明: (1) 气候态分布方面, APHRO降水量与台站资料一致, 并且可以准确表征雨带的季节性移动; 但是, 与台站资料相比, 这套资料的降水强度偏小, 而降水频率偏大。 (2) 考察不同等级降水量的分布发现, APHRO资料的暴雨年降水量相对偏少, 而小雨和中雨的年降水量偏多。 (3)APHRO资料揭示的近50年降水量的变化趋势与台站资料大致相同, 并能反映年平均降水频率 “西增东减” 的趋势; 两套资料近50年的年平均降水强度在东南沿海和西北部等区域都呈增加趋势, 但APHRO在华北、东北和江淮区域的降水强度变化趋势与台站资料差异显著。此外, 在年代际变化方面, APHRO资料对中国东部降水夏季 “南涝北旱” 和 “江南春旱” 强度的描述与台站资料有所不同, 降水量的变化趋势偏弱。

... 这也是目前唯一覆盖整个亚洲地区的长时间（从1951年开始）陆地降水网格化数据^[8,9,10],可以作为评估不同降水数据的基准数据 ...

... 9,能够解释降水空间分异^[8] ...

2013

0.0

2.221

阚宝云, 苏凤阁, 童凯, 等. 四套降水资料在喀喇昆仑山叶尔羌河上游流域的适用性分析[J]. 冰川冻土, 2013, 35(3): 710-722.

[KAN

Bao-yun

, SU

Feng-ge

, TONG

Kai

, et al. Analysis of the applicability of four precipitation datasets in the upper reaches of the Yarkant River, the Karakorum. Journal of Glaciology and Geocryology, 2013, 35(3): 710-722. ]

The applicability of four precipitation products to the upper reaches of the Yarkant River is compared. The four datasets are: CMORPH, TMPA 3B42 v6, APHRODITE and ITPCAS. The characteristics of the temporal and spatial distributions of the four precipitation datasets are analyzed for 2003-2009. The datasets are also used as the forcing data to drive the variable infiltration capacity semi-distributed hydrology model. It is found that the spatial distribution of ITPCAS agrees reasonably with glacier distribution, followed by TMPA 3B42 v6 and APHRODITE. Among the four datasets, only CMORPH can roughly follow the seasonal variation of the mean gauge precipitation estimation. Runoff simulations suggest that CMORPH precipitation shows a potential as hydrological model input data for the Yarkant River, while the other precipitation datasets seem to possibly underestimate the precipitation in the catchment.

分析了2003-2009年基于卫星观测的降水数据CMORPH、 TMPA 3B42 v6、中国科学院青藏高原研究所的融合数据ITPCAS和基于地面台站的APHRODITE (2003-2007)四套降水数据集在叶尔羌河上游流域的时空分布特征, 并以这四套降水数据为驱动, 利用VIC分布式水文模型对叶尔羌河上游流域的降水径流进行模拟.结果表明: 在空间分布上, 四套降水资料在叶尔羌河上游流域的差异较大, ITPCAS的空间分布与流域冰川的分布较一致, 基于冰川区即为大降水区的基本认知, 初步认为ITPCAS的空间分布比较合理; 其次是TMPA 3B42 v6和APHRODITE; 在流域的年降水量和季节分配量上, 由于缺乏高海拔地区的实测降水资料, 无法准确回答各套降水资料在量级上是否合理; 在时间序列上, 四套降水资料与流域站点降水(库鲁克栏杆站和塔什库尔干站的平均降水)存在着不同程度的差异.但从整体上看, CMORPH数据在一定程度上能够反映流域的月降水变化过程, 而APHRODITE和ITPCAS只能在个别年份对流域的降水描述较好; 在径流模拟上, 卫星降水数据CMORPH显示了作为水文模型输入数据的较大潜力; 而其他降水资料在叶尔羌河径流模拟中, 与实测径流在量和季节分配上可能存在较大偏差.

... 这也是目前唯一覆盖整个亚洲地区的长时间（从1951年开始）陆地降水网格化数据^[8,9,10],可以作为评估不同降水数据的基准数据 ...

2014

0.0

1.672

李兰海, 尚明, 张敏生, 等. APHRODITE 降水数据驱动的融雪径流模拟[J]. 水科学进展, 2014, 25(1): 53-59.

[LI

Lan-hai

, SHANG

Ming

, ZHANG

Min-sheng

, et al. Snowmelt runoff simulation driven by APHRODITE precipitation dataset. Advances in Water Science, 2014, 25(1): 53-59. ]

A snowmelt runoff model (SRM) was modified and applied to simulate the snowmelt runoff process in the Kaidu River basin in the Xinjiang Autonomous Region of China. The modified model was driven by the precipitation product of the Asian Precipitation-Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) of the Water Resources project. The daily effective active temperature was used in the model as the degree-days to replace the daily mean temperature for estimating the snowmelt rate. In order to reflect the effect of seasonal frozen soil on runoff, the accumulated active temperature was proposed to represent the state of seasonal frozen soil, and the runoff coefficient was modified using the accumulated active temperature. Using the meteorological and hydrological data observed from the Kaidu River basin, along with snow products from the Moderate Resolution Imaging Spectroradiometer (MODIS), the modified SRM was employed to simulate the runoff in the Kaidu River basin during snowmelt seasons. SRM was calibrated and validated using 2000 and 2006 data, respectively. Based on the coefficient of determination and the difference between simulated and observed runoff, calibrated and validated results simulated by the modified SRM using the daily effective active temperature as the degree-days were much better than that using the daily average temperature as the degree-days. The result shows that the interpolated APHRODITE precipitation data, the modified degree-days and runoff coefficient, expressed with the effective accumulated temperature as the SRM model inputs, can reflect the snowmelt runoff process in the Kaidu River Basin and greatly improve the snowmelt runoff simulation accuracy.

针对流域内气象观测站点稀少和融雪径流过程的特点，利用APHRODITE降水数据进行插值，应用日有效活动温度改进度日数；依据季节性冻土受有效活动积温影响的特点，建立有效活动积温与径流系数的关系，提高模型中融雪速率和径流系数的计算精度。结合气象、水文资料和MODIS遥感积雪产品，应用改进的融雪径流模型（SRM）对开都河流域2000年与2006年融雪期的径流进行了率定和验证模拟。改进模型在率定期和验证期的模拟结果远远优于用日平均温度作为度日数的结果。结果表明，用APHRODITE降水数据及改进的度日数和径流系数作为SRM模型参数输入，能够较好模拟开都河流域融雪径流过程，大大提高模型模拟精度。

... 这也是目前唯一覆盖整个亚洲地区的长时间（从1951年开始）陆地降水网格化数据^[8,9,10],可以作为评估不同降水数据的基准数据 ...

2014

4.904

0.0

... 同时,基于雨量站观测资料的APHRODITE降水数据在插值过程中考虑了地形影响^[11] ...

2008

0.0

2.068

蔡福, 明惠青, 陈鹏狮, 等. 1961 年—2004 年东北地区四季降水的时空演变特征[J]. 资源科学, 2008, 30(10): 1456-1462.

[CAI

, MING

Hui-qing

, CHEN

Peng-shi

, et al. Spatio-temporal trends in seasonal precipitation in Northeastern China from 1961 to 2004. Resources Science, 2008, 30(10): 1456-1462. ]

Seasonal precipitation data interpolated using Anusplin software from 1961 to 2004 with data from 146 weather stations in northeastern China were transformed to annual grid data using a GIS platform. Data from 26 weather stations were reserved in order to test the accuracy of the interpolation. The mean relative error for the four seasons was 14.3%, 14.1%, 18.1% and 25.1%. Then, spatio-temporal characteristics of seasonal precipitation in northeast China over the same period were evaluated with spatial analysis and time series analysis. The results showed that for temporal trends, the regional average seasonal gross precipitation (RASGP) in spring indicated a slight increase over the study period, with a clear increase before the 1980s and decrease after then. In summer, the RASGP decreased, especially after 1985. There was a slight decrease of RASGP in autumn, especially evident in the 1990s but recovering after 2000. The RASGP exhibited traits of inter-decadal change, and a slight increasing trend in winter with large variation over the last decade. For spatial distribution of seasonal gross precipitation from 1961 to 2004, in spring, northern and part of eastern Heilongjiang province as well as eastern Jilin province were the main regions where trends of increased precipitation were significant, ranging from 3mm/10a to 9 mm/10a. Areas where precipitation obviously decreased, with a range from 3mm/10a to 7 mm/10a, were mainly located in parts of central Liaoning province. In summer, precipitation decreased in the great majority of northeastern china. Central Heilongjiang province and the Liaodong peninsula were the main areas where precipitation decreased markedly, ranging from 20mm/10a to 40mm/10a. Most areas located in parts of eastern Heilongjiang as well as central and southern Jilin province were regions where precipitation decreased markedly, from 6mm/10a to 10 mm/10a in autumn. Hiemal precipitation increased from 1mm/10a to 3 mm/10a in most of Heilongjiang province and Jilin province, while decreasing in southern and part of western Liaoning province, from 1mm/10a to 2 mm/10a.

利用Anusplin插值软件对东北地区146站1961年～2004年四季降水量进行空间化处理，各季区域平均MRE分别为春季14.3%、夏季14.1%；秋季18.1%；冬季25.1%。对栅格数据采用空间分析和时间序列分析相结合的方法进行四季降水时空演变特征分析，结果表明：从时间上看，春季，整个时段呈弱增多趋势，但不显著，1980年代前增加，之后减少；夏季呈减少趋势，1985以后呈显著减少趋势；秋季整个时段呈弱的减少趋势，1990年代开始显著减少，2000年以后略有增加；冬季年代际变化特征明显，呈波动增多趋势，近10年增幅较大。从空间分布上看，春季，黑龙江北部和东部部分地区、吉林东部降水显著增多，增幅为(3～9)mm/10年，辽宁中部部分地区降水显著减少，减幅为(3～7)mm/10年。夏季，东北大部地区降水以减少为主，黑龙江中部和辽东半岛地区显著减少，减幅为(20～40)mm/10年。秋季，东北大部地区降水减少，黑龙江东部局部、吉林中部和南部部分地区显著减少，减幅为(6～10)mm/10年。冬季，黑龙江大部、吉林大部降水显著增多，增幅为(1～3)mm/10年，辽宁南部和西部部分地区显著减少，减幅为(1～2)mm/10年。

... 作为对农业生产起决定性作用的气象要素,研究东北地区的降水规律对该地区生产具有重要意义^[12] ...

... 而降水是造成干旱和洪涝的影响因素之一,因此对东北地区降水规律进行研究十分必要^[12,16] ...

2009

0.0

姜晓艳, 刘树华, 马明敏, 等. 东北地区近百年降水时间序列变化规律的小波分析[J]. 地理研究, 2009, 28(2): 354-362.

[JIANG

Xiao-yan

, LIU

Shu-hua

, MA

Ming-min

, et al. A wavelet analysis of the precipitation time series in Northeast China during the last 10years. Geographical Research, 2009, 28(2): 354-362. ]

In recent 100 years, global warming is increasing remarkably, and there are even greater uncertainty, changing precipitation trend and regional differences. In this study, a wavelet analysis method of Morlet is used to research the periodical regulations at different time scales of the precipitation time series during the latest nearly 100 years in Northeast China. Based on the monthly and annual precipitation data of Harbin, Shenyang, Changchun and Dalian weather stations covering the period from 1905 to 2005, the periodic oscillation of precipitation and the points of abrupt change at different time scales along the time series are discovered and the main periods of every serial are confirmed. The result shows that the precipitation of Northeast China has a decreasing trend, with rates of -5.2mm/10a, and -12.7mm/10a, -7.1mm/10a, -2.7mm/10a, 1.3mm/10a for Northeast China, Changchun, Harbin, Dalian and Shenyang respectively. There are multi-time scales of periodical change, which present appears different traits at different phases. There are periodic oscillations of 2a-3a, 5a-6a, 10a and 50a for the seasonal and annual precipitation variations. The local characteristics of time-frequency for wavelet analysis can demonstrate the detailed structures of precipitation. The wavelet analysis can be an alternative approach to the analysis of climate multi-time scales characteristics and the forecast of short-term climate variations.

利用1905~2005年东北地区哈尔滨、长春、沈阳和大连的降水时间序列资料,采用距平和Morlet小波分析方法,研究了东北地区降水变化的多时间尺度的周期性性变化规律,并对东北地区近期降水状况进行了预测。结果表明:近百年来东北地区年降水量呈现较显著下降趋势,整个东北地区降幅为-5.2mm/10a;长春为-12.7mm/10a;哈尔滨为-7.1mm/10a;大连为-2.7mm/10a;沈阳略为上升趋势为1.3mm/10a。东北地区的年降水量存在着区域性的多重时间尺度下的周期变化特征,2a~3a、5a~6a,10a和50a左右的长期振荡周期具有全域性;长春、哈尔滨年降水的主要控制周期是20a左右;5a~6a的短周期和50年的长周期变化也对年降水有较大影响。

... 该区气候年际变率大,是典型的气候脆弱区之一^[13,14,15] ...

2003

0.0

2.498

吴正方, 靳英华, 刘吉平, 等. 东北地区植被分布全球气候变化区域响应[J]. 地理科学, 2003, 23(5): 564-570.

[WU

Zheng-fang

, JIN

Ying-hua

, LIU

Ji-ping

, et al. Response of vegetation distribution to global climate changes in Northeast China. Scientia Geographica Sinica, 2003, 23(5): 564-570. ]

In recent years, IPCC emphasizes the assessment of regional responses to global climate change, and tries to understand the climate change impacts on regional/global scales and to make mitigation adaptation.Northeast China in temperate monsoon climate is the vulnerable and sensitive area of climate change.The impact of global climate change on the vegetation in Northeast China is an important researching field.By using GCMs scenarios, the regional assessment of vegetation distribution response to climate change has been done.According to the characteristics of eco-climate and bio-geography in Northeast China, some index limits of Holdridge life zone classification system are modified.Based on bio-temperature (BT) and potential evapotranspiration ratio (PER, determined by Thornthwaite model), Northeast China climate are classified as 3 thermal zones, i.e.cold temperate zone(BT 1.25).And then taking annual precipitation( P , mm) into account we divide Northeast China vegetation into 9 life zones, i.e.Cold temperate moist forests, Cold temperate humid forests, Temperate humid forests, Warm temperate humid forests, Temperate semi-humid forests/meadow, Temperate semi-humid meadow prairie, Temperate semi-arid prairie, Warm temperate semi-humid meadow prairie and Warm temperate semi-arid prairie.General Circulation Models(GCMs) simulates climate warming scenarios induced by the anthropogenic Green-House Gases(GHGs) concentration increasing in atmosphere.For Northeast China, the analysis indicate that under CO 2 concentration doubling annual mean temperature increase by 3℃-4℃ and precipitation by 9%-12%, but the potential evapotranspiration increase much more than precipitation increment because of temperature increasing.Thus regional scenario of climate change in Northeast China would be with a high possibility of warming and aridity.Response to the climate change, the eco-climatic environment and distribution of vegetation life zone may change greatly in Northeast China.The assessment of regional impacts on vegetation shows:1) Under climate warming scenarios, the warm-temperate and temperate zones enlarge, but cool-temperate zone becomes small and even disappears in the northeast, which would result in the boundaries of vegetation life zones transferring northward obviously and 2 cold temperate life zones would move to a higher altitude or move out from this region; 2) At the same time, the humid area shrink, and semi-humid area and semi-arid area expend, which would make forest range shrink and grassland expand in Northeast China.

根据东北地区生态气候环境和生物地理规律对Holdridge生命地带分类系统进行修正,将东北地区植被分为寒温带湿润森林、寒温带潮湿森林、温带湿润森林、暖温带湿润森林、温带半湿润森林草甸草原、温带半湿润草甸草原、温带半干旱典型草原、暖温带半湿润草甸草原和暖温带半干旱典型草原等9 个生命地带并分析了其空间分布特征。运用大气环流模式分析东北地区由于温室气体增加导致的气候变化趋势。以此为基础评价东北地区植被分布的区域响应。全球气候变暖情景下,东北地区暖温带和温带范围明显扩大,而寒温带范围缩小甚至退出东北地区,植被分布界限显著北移;同时湿润区面积减少半湿润区和半干旱区扩大,导致森林面积缩小草原面积扩大。

... 该区气候年际变率大,是典型的气候脆弱区之一^[13,14,15] ...

2013

0.0

贺伟, 布仁仓, 熊在平, 等. 1961—2005 年东北地区气温和降水变化趋势[J]. 生态学报, 2013, 33(2): 519-531.

[HE

Wei

, BU

Ren-cang

, XIONG

ZAI-ping

, et al. Characteristics of temperature and precipitation in Northeastern China from 1961 to 2005. Acta Ecologica Sinica, 2013, 33(2): 519-531. ]

Northeastern China is one of the regions that would be mostly affected by the changing climates, and created the particular climate pattern and characteristics under climate changes. The pattern and characteristics of climate changes in Northeastern China was analyzed with the methods of the linear regression method, cumulative anomaly method, Mann-Kendall test method and Morlet wavelets analysis method based on daily average air temperature and daily precipitation data observed at 96 meteorological stations covered the Northeastern China from 1961 to 2005. The linear regression method was used to study the variation trends of mean temperature and precipitation on annual-scale and seasonal-scale in recent 45 years. The cumulative anomaly method and Mann-Kendall test method were used to test trends and abrupt changes of annual mean temperature and precipitation. The Morlet wavelet analysis method was used to detect the change interval of annual mean temperature and precipitation. The linear trend rates for annual and seasonal mean temperature and annual and seasonal precipitation were calculated at each meteorological station. Those rates were then interpolated using inverse distance weighted (IDW) interpolation to reveal their spatial distributions. The results showed that the climate in this region showed a significant warming trend, the annual mean temperature varied between 2.45 ℃ and 5.72 ℃,and the annual mean temperature has increased by 0.38 ℃/10 a ( P P >0.05), the precipitation in 1980s was more than in the other decades, and the abrupt change in precipitation occurred during in 1982-1983. The seasonal precipitation showed various trends, the spring precipitation and winter precipitation showed increasing trend, but the summer precipitation and autumn precipitation showed decreasing trend. The Liaodong Peninsula and southern Changbai Mountain showed significantly decreasing trend in precipitation, whereas, the northern Da Hinggan Mountains and Songnen Plain showed obviously increasing trend. The climate change interval analysis revealed that the annual mean temperature fluctuated significantly with the interval of 11 years, 24 years and 6 years, and the annual mean precipitation fluctuated with interval of 16 years and 6 years, respectively. For this region, the climate was trended to be warmer and dryer in recent 45 years and the temperature and the precipitation showed different interval.

利用东北地区96个气象站1961-2005年的日平均气温和日降水量资料,采用线性倾向率法,累积距平法,Mann-Kendall法和Morlet小波分析等方法,对东北地区近45年来的气候变化和突变现象进行了研究。结果表明:近45年来,东北地区年平均气温变化在2.45-5.72 ℃之间,年均温呈现显著上升趋势,气候倾向率为0.38 ℃/10 a( P P >0.05),1981-1990年为降水最多的年代,1982-1983年间发生了降水量由少到多的突变。四季降水量变化呈现不同的趋势,其中春季和冬季降水量呈现增多的趋势,夏季和秋季降水量呈现减少的趋势。降水量减少较明显的地区为辽东半岛和长白山南段,降水量增多较明显的地区为大兴安岭北部和松嫩平原。Morlet小波分析结果表明,东北地区年平均气温存在11 a的强显著周期,此外还有24 a和6 a尺度的变化周期;东北地区年降水量存在16 a的强显著周期和6 a的小尺度变化周期。通过以上分析,近45 a东北地区总体气候呈现明显暖干化趋势。

... 该区气候年际变率大,是典型的气候脆弱区之一^[13,14,15] ...

2009

0.0

1.081

张运福, 胡春丽, 赵春雨, 等. 东北地区降水年内分配的不均匀性[J]. 自然灾害学报, 2009, 18(2): 89-94.

[ZHANG

Yun-fu

, HU

Chun-li

, ZHAO

Chun-yu

, et al. Intra-annual in homogeneity characteristics of precipitation in North-east China. Journal of Natural Disasters, 2009, 18(2): 89-94. ]

利用东北地区93个台站1961-2005年的逐日降水资料,通过降水集中度和降水集中期,讨论了该地区降水的年内分配不均匀分布特征.结果表明,东北地区的降水集中度自东南向西北逐渐递减;集中期具有南北高中问低的变化特点.从长期趋势上看,降水集中度的减小趋势显著,在该地区的东北部和西北部各有一个趋势明显的区域(通过0.05的显著性检验),但集中期的减小趋势不显著,只有2个台站通过显著性检验.年降水量与降水集中度和降水集中期的相关系数表明:东北地区年降水量与降水集中度呈显著的正相关,即年降水量越多(少)的地区,年内降水越集中(均匀);年降水量与降水集中期的关系并不显著.

... 而降水是造成干旱和洪涝的影响因素之一,因此对东北地区降水规律进行研究十分必要^[12,16] ...

2002

0.0

... GWR模型是1996年由Fotheringham等率先提出^[17],最近被广泛用于空间异质性研究的方法^[18,19],其基本思想是变量间的关系随着空间位置的变化而变化,通过估算研究区内每一给定位置的相关变量与解释变量的参数来建立回归模型^[20] ...

1996

0.0

2008

0.0

2013

0.0

3.304

姚永慧, 张百平. 基于MODIS数据的青藏高原气温与增温效应估算[J]. 地理学报, 2013, 68(1): 95-107.

[YAO

Yong-hui

, ZHANG

Bai-ping

. MODIS-based estimation of air temperature and heating-up effect of the Tibetan Plateau. Acta Geographica Sinica, 2013, 68(1): 95-107. ]

Time series of MODIS land surface temperature (LST) data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, were used to estimate and map the spatial distribution of monthly mean air temperatures of the Tibetan Plateau and neighboring areas. Time series and regression analyses of monthly mean land surface temperature (Ts) and monthly mean air temperature (Ta) were conducted using both ordinary linear regression (OLS) and geographical weighted regression (GWR) methods. Analysis shows that recorded Ta is rather closely related to Ts, and that the GWR method has a much better result (adjusted R 2 > 0.91, root mean square error (RMSE)=1.16-1.58℃) for estimating Ta than OLS. The GWR model, with MODIS Ts and altitude as independent variables, was thus used to estimate Ta for the Tibetan Plateau. For more than 80% of the stations, the Ta retrieved from Ts had residuals lower than 2℃. Analysis of the spatial pattern of retrieved Ta data showed that the mean Ta of the summer half year was higher than 0℃ even at high altitudes of 5000±600 m of the plateau, especially in the warmest month (July) the Ta in high mountain areas with altitudes of 4000-5500 m could reach as high as 10 ℃. This may help explain why the highest timber line in the northern hemisphere is located on the Tibetan Plateau. According to our results, Ta in July was probably 6-10℃ warmer in the inner plateau than in the outer plateau at any given elevation which resulted from the heating up effect of the Plateau.

利用2001-2007 年MODIS地表温度数据、137 个气象观测台站数据和ASTERGDEM数据, 采用普通线性回归分析方法(OLS)及地理加权回归分析方法(GWR), 研究了高原月均地表温度与气温的相关关系, 最终选择精度较高的GWR分析方法, 建立了高原气温与地表温度、海拔高度的回归模型。各月气温GWR回归模型的决定系数(Adjusted R 2 ) 都达到了0.91 以上(0.91~0.95), 标准误差(RMSE) 介于1.16~1.58℃;约70%以上的台站各月残差介于-1.5~1.5℃之间, 80%以上的台站的残差介于-2~2℃之间。根据该模型, 估算了青藏高原气温, 并在此基础上, 将高原及周边地区7 月份月均气温转换到4500 m和5000 m海拔高度上, 对比分析高原内部相对于外围地区的增温效应。研究结果表明:(1) 利用GWR方法, 结合地面台站的观测数据和MODIS Ts、DEM等, 对高原气温估算的精度高于以往普通回归分析模型估算的精度(RMSE=2~3℃), 精度可以提高到1.58℃;(2) 高原夏半年海拔5000 m左右的高山区气温能达到0℃以上, 尤其是7 月份, 海拔4000~5500 m的高山区的气温仍能达到10℃左右, 为山地森林的发育提供了温度条件, 使高原成为北半球林线分布最高的地方;(3) 高原的增温效应非常突出, 初步估算, 在相同的海拔高度上高原内部气温要比外围地区高6~10℃。

2013

0.0

1.841

吕洋, 杨胜天, 蔡明勇, 等. TRMM 卫星降水数据在雅鲁藏布江流域的适用性分析[J]. 自然资源学报, 2013, 28(8): 1414-1425.

[Lü

Yang

, YANG

Sheng-tian

, CAI

Ming-yong

, et al. The applicability analysis of TRMM precipitation data in the Yarlung Zangbo River Basin. Journal of Natural Resources, 2013, 28(8): 1414-1425. ]

The Yarlung Zangbo River is a trans-boundary river of crucial importance to China with great potentiality in hydropower resources. However, the precipitation gauge stations are dramatically sparse in this basin due to high elevation, complex topography and severe climate, especially in the western part of the basin. Precipitation estimation from satellites provides potential alternatives in precipitation measurements in regions where conventional precipitation gauges are not readily available. In this study, the performance of TRMM (Tropical Rainfall Measurement Mission) monthly and daily precipitation data over 1998－2010 were evaluated in the Yarlung Zangbo River Basin with rain gauges from 16 meteorological stations. The evaluation revealed strong correlation and little numerical biases between TRMM monthly precipitation data and rain gauges in general, while the correlation coefficient in Bomi station was comparatively lower and the biases in Jiangzi station and Nanmulin station were comparatively higher. While on the daily basis, the correlation between TRMM precipitation and rain gauges is relatively weak. On the basis of the evaluation, the spatial and temporal patterns of the average annual and monthly precipitation over the basin were analyzed with TRMM monthly precipitation data. There is an increasing trend from west to east and strong spatial variability in the average annual precipitation. Precipitation peaks in July and most of the total annual precipitation occur during June to September. Precipitation hardly occurs during December to February next year.

以雅鲁藏布江流域为研究区,利用16个气象站点的实测降水量在月尺度和日尺度上验证了TRMM(Tropical Rainfall Measurement Mission)卫星降水数据的精度,并在此基础上基于TRMM月降水数据分析了雅鲁藏布江流域的降水时空分布特征。结果表明:在整体上,TRMM月降水数据与站点实测降水量相关系数 R =0.902,斜率 K =0.849,数据精度较高,数值上比站点实测降水量略微偏低;就单个站点而言,大部分站点相关系数较高,偏差较小,但波密站相关系数相对较低,江孜站和南木林站数据偏差相对较大。TRMM日降水数据与站点实测降水量相关系数 R =0.466,斜率 K =0.451,数据精度较低,与站点实测降水量一致性较差。在降水空间分布上,雅鲁藏布江流域整体呈现由西向东逐渐递增的趋势,不同区域间差异极其明显;在降水时间分布上,大部分降水集中在6至9月,12月至第二年2月很少有降水发生。