沙颖河上游区降水特性分析

介绍了淮河干流沙颖河水系上游区的区域概况、气候情况及水汽来源,分析了该区副热带高压活动与降水的关系、多年平均降水量的时空分布规律以及地形对降水量的影响。     

拜泉县生态农业建设与三效益同步增长

拜泉县自1986年实施生态农业战略以来,紧紧围绕生态农业建设规划,分阶段实施．整体推进,为拜泉县向持续、稳定、高效生态农业迈进创造了良好的生态环境基础。着重分析了拜泉县粮食生产由高到低,再由低到高,各种灾害由大到小,由多到少的变化过程。证明了生态农业县具有较大的生命力,是实现农业现代化的必由之路。     

GNSS天顶对流层延迟的短时天气预报分析

针对天顶对流层延迟(ZTD)与大气可降水量(PWV)相关性特征及变化规律,该文提出基于无气象元素ZTD短时天气监测及预报的方法.该文以北京市2013-2015年BJFS站 日均数据为例,采用GZTD和Saastamoinen+GPT2模型拟合,得到传统的无气象元素的PWV模型.并利用2016年BJFS站ZTD和气象数据,计算出PWV1(含气象元素)和PWV2(无气象元素)数据,通过定量数据分析得出:PWV1与PWV2、ZTD相关系数分别达到0.633 2,0.967 6;PWV2与ZTD相关性为0.649 8.从短期数据,BJFS站6-9月的ZTD与PWV1相关性系数为0.969 6.这证明了 ZTD能够真实反映PWV1的真实变化趋势,同时也说明ZTD应用于短时天气预报的可行性.最后针对北京地区2016年的降雨、降雪和雾霾等极端天气事件,采用ZTD方法对其进行了分析研究,并取得了较好的效果.     

利用CORS站研究多因子分季节可降水量转换模型

针对GNSS气象学中可降水量转换过程较为复杂的问题,利用2017年江苏CORS站及其并址探空数据,分析大气可降水量(PWV)与天顶对流层延迟(ZTD)、温度和气压之间的相关性,采用线性回归拟合法建立多因子分季节PWV模型,并用2017-2018年数据验证模型精度.结果表明PWV与ZTD有很强的线性相关性,相关系数为0.980 3,PWV与温度和气压有较强的相关性,相关系数分别为0.611 2和-0.613 6;全年PWV模型中,单、双、多因子模型的RMS分别为2.88、1.70和0.49 mm,精度依次提高;多因子PWV模型中,分季节PWV模型较全年PWV模型的RMS分别提高0.23、0.20、0.18和0.37 mm,精度明显优于全年模型.因此,多因子分季节PWV模型预测精度优于1 mm,满足GNSS监测水汽的气象应用精度要求.     

西藏高原测站降水与TRMM估测降水一致性评估

利用124个测站2011—2012年6—8月逐小时降水资料,分辨率为0.25°×0.25°的TRMM估测降水和DEM 高程数据,采用相关系数、相对误差和准确性指标,分析了西藏高原TRMM估测降水整体表现能力及海拔高度对降水估测影响。结果表明：TRMM估测降水在西藏高原整体趋势较一致,降水量级偏大,次数偏多;平均无降水准确率远高于平均有降水准确率,漏测率低而空测率高,降水量大的测站TRMM估测能力相对强。西藏高原上大部分测站处于相对低洼（河谷）地带,海拔高度差较小的区域TRMM估测降水与测站降水误差小,较大的区域误差则大。     

科尔沁沙地1961—2021年主要气象要素的变化特征——以奈曼旗为例北大核心CSCD

为了分析全球气候变化背景下科尔沁沙地主要气象要素的变化特征,基于逐月站点气象数据,采用趋势分析、Mann-Kendall突变分析和小波分析等研究方法,分析了1961—2021年奈曼旗主要气象要素(气温、降水量和蒸发量)的多尺度时间变化特征。结果表明:在全球气候变化下奈曼旗各主要气象要素变化显著,其中气温以0.21℃/10a速率极显著升高,降水以-9.2 mm/10a速率极显著减小,蒸发量以32.50 mm/10a速率不显著增加;从季节变化来看,春季和秋季气温、降水和蒸发量均表现为增加趋势,夏季和冬季温度和蒸发量增加,降水量减少。各气象要素出现突变点的时间不同,其中气温为1971年左右,降水为1978年和1987年,蒸发量为2002、2009、2013年。各气象要素在研究时段内均表现出明显的周期变化,其中气温为3~7、14~23、34~43 a,降水量为3~6、8~11、13~23、43 a,蒸发量为5~7、11~16、27、35 a。     

巧用数学知识 智解地理问题

正在给学生复习气候类型判读时,我举2013四川高考题(5~6题)为例。图1是北半球亚热带某地降水量逐月累计曲线图。读图回答1~2题。(2013年四川卷)     

近20a雅鲁藏布江流域冻融侵蚀演变趋势

西藏冻融侵蚀面积约占中国的冻融侵蚀面积的73%。采用1990年、2000年和2010年3期TM影像、土地利用图和植被类型图相叠加,对雅鲁藏布江(简称雅江)流域近20 a的冻融侵蚀进行目视解译,并且统计冻融侵蚀强度和侵蚀面积。结合气温、降水和植被覆盖度的变化,探讨引起冻融侵蚀面积和强度变化的原因。研究结果表明:1.近20 a雅江流域冻融侵蚀总面积略有下降,下降比例约为3.5%;轻度侵蚀区持续增加,面积由1990年的21213.6 km2增加到2010年的31 526.2 km2,增加幅度为56%;中度侵蚀区持续减少,面积由1990年的55 964.26km2减少到2010年的42 718.12 km2,减少幅度为23.6%;重度侵蚀区面积变化不显著。至2010年雅江流域的侵蚀格局发生改变,由原先的中度侵蚀主体型逐步向轻度侵蚀、中度侵蚀混合型过渡。2.雅江流域冻融侵蚀主要分布在北部高海拔地区,南部湖盆谷地分布较少,流域内冻融侵蚀主要集中在海拔4 500~6 000 m范围内。近20 a间流域内重度侵蚀区逐渐向东移动,由日喀则北部地区渐移至拉萨、那曲、昌都一线。侵蚀强度整体呈现减弱趋势,在日喀则地区,林芝地区和山南地区均出现轻度侵蚀逐渐替代中度侵蚀的现象。     

Climate transformation to warm-humid and its effect on river runoff in the source region of the Yellow River

The change characteristics and trends of the regional climate in the source region of the Yellow River, and the response of runoff to climate change, are analyzed based on observational data of air temperature, precipitation, and runoff at 10 main hydrological and weather stations in the region. Our results show that a strong signal of climate shift from warm-dry to warm-humid in the western parts of northwestern China （Xinjiang） and the western Hexi Corridor of Gansu Province occurred in the late 1980s, and a same signal of climate change occurred in the mid-2000s in the source region of the Yellow River located in the eastern part of northwestern China. This climate changeover has led to a rapid increase in rainfall and stream runoff in the latter region. In most of the years since 2004 the average annual precipitation in the source region of the Yellow River has been greater than the long-term average annual value, and after 2007 the runoff measured at all of the hydrologic sections on the main channel of the Yellow River in the source region has also consistently exceeded the long-term average annual because of rainfall increase. It is difficult to determine the prospects of future climate change until additional observations and research are conducted on the rate and temporal and spatial extents of climate change in the region. Nevertheless, we predict that the climate shift from warm-dry to warm-humid in the source region of the Yellow River is very likely to be in the decadal time scale, which means a warming and rainy climate in the source region of the Yellow River will continue in the coming decades.     

The boreal summer intraseasonal oscillation simulated by four Chinese AGCMs participating in the CMIP5 project

The performances of four Chinese AGCMs participating in the Coupled Model Intercomparison Project Phase 5 （CMIP5） in the simulation of the boreal summer intraseasonal oscillation （BSISO） are assessed. The authors focus on the major characteristics of BSISO： the intensity, significant period, and propagation. The results show that the four AGCMs can reproduce boreal summer intraseasonal signals of precipitation; however their limitations are also evident. Compared with the Climate Prediction Center Merged Analysis of Precipitation （CMAP） data, the models underestimate the strength of the intraseasonal oscillation （ISO） over the eastern equatorial Indian Ocean （IO） during the boreal summer （May to October）, but overestimate the intraseasonal variability over the western Pacific （WP）. In the model results, the westward propagation dominates, whereas the eastward propagation dominates in the CMAP data. The northward propagation in these models is tilted southwest-northeast, which is also different from the CMAP result. Thus, there is not a northeast-southwest tilted rain belt revolution off the equator during the BSISO＇s eastward journey in the models. The biases of the BSISO are consistent with the summer mean state, especially the vertical shear. Analysis also shows that there is a positive feedback between the intraseasonal precipitation and the summer mean precipitation. The positive feedback processes may amplify the models＇ biases in the BSISO simulation.