Improving Land Surface Hydrological Simulations in China Using CLDAS Meteorological Forcing Data

The accuracy of land surface hydrological simulations using an offline land surface model(LSM)depends largely on the quality of the atmospheric forcing data.In this study,Global Land Data Assimilation System(GLDAS)forcing data and the newly developed China Meteorological Administration Land Data Assimilation System(CLDAS)forcing data are used to drive the Noah LSM with multiple parameterizations(Noah-MP)and to explore how the newly developed CLDAS forcing data improve land surface hydrological simulations over China's Mainland.The monthly soil moisture(SM)and evapotranspiration(ET)simulations are then compared and evaluated against observations.The results show that the Noah-MP driven by the CLDAS forcing data(referred to as CLDASNoah-MP)significantly improves the simulations in most cases over China's Mainland and its eight river basins.CLDASNoahMP increases the correlation coefficient(R)values from 0.451 to 0.534 for the SM simulations at a depth range of 0–10 cm in China's Mainland,especially in the eastern monsoon area such as the Huang–Huai–Hai Plain,the southern Yangtze River basin,and the Zhujiang River basin.Moreover,the root-mean-square error is reduced from 0.078 to0.068 m3 m-3 for the SM simulations,and from 12.9 to 11.4 mm month-1 for the ET simulations over China's Mainland,especially in the southern Yangtze River basin and Zhujiang River basin.This study demonstrates that,by merging more in situ and remote sensing observations in regional atmospheric forcing data,offline LSM simulations can better simulate regional-scale land surface hydrological processes.

HRCLDAS与ERA5-Land气温日统计数据在山东地区的对比评估

基于山东省2021年3月—2022年2月1519个气象观测站2 m气温观测数据,对中国气象局高分辨率陆面数据同化系统(High Resolution China Meteorological Administration Land Data Assimilation System,HRCLDAS)和欧洲中期天气预报中心第五代陆面再分析数据集(ERA5-Land)逐小时2 m气温分析的日统计数据(平均气温、最高气温、最低气温)进行对比评估。结果显示:(1)HRCLDAS/ERA5-Land日统计平均气温、最高气温、最低气温的均方根误差分别为0.1/1.2℃、0.6/1.9℃、0.4/1.7℃,表明HRCLDAS具有更高的精度,且在不同地理区域、不同海拔高度的表现均优于ERA5-Land,大部地区的偏差(-0.5~0.5℃)远低于ERA5-Land(-2.0~2.0℃)。(2)两套数据对高温及寒潮过程的监测能力对比评估表明,HRCLDAS能够捕捉到大部分的高温以及寒潮过程,其与观测的高温日数及寒潮日数空间分布较为相似,但对影响范围存在一定的低估;ERA5-Land则只能监测到部分高温及寒潮过程,并对高温日数与寒潮日数存在严重的低估。

Land Response to Atmosphere at Different Resolutions in the Common Land Model over East Asia

Towards a better understanding of hydrological interactions between the land surface and atmosphere, land surface mod- els are routinely used to simulate hydro-meteorological fluxes. However, there is a lack of observations available for model forcing, to estimate the hydro-meteorological fluxes in East Asia. In this study, Common Land Model （CLM） was used in offline-mode during the summer monsoon period of 2006 in East Asia, with different forcings from Asiaflux, Korea Land Data Assimilation System （KLDAS）, and Global Land Data Assimilation System （GLDAS）, at point and regional scales, separately. The CLM results were compared with observations from Asiaflux sites. The estimated net radiation showed good agreement, with r = 0.99 for the point scale and 0.85 for the regional scale. The estimated sensible and latent heat fluxes using Asiaflux and KLDAS data indicated reasonable agreement, with r = 0.70. The estimated soil moisture and soil temperature showed similar patterns to observations, although the estimated water fluxes using KLDAS showed larger discrepancies than those of Asiaflux because of scale mismatch. The spatial distribution of hydro-meteorological fluxes according to KLDAS for East Asia were compared to the CLM results with GLDAS, and the GLDAS provided online. The spatial distributions of CLM with KLDAS were analogous to CLM with GLDAS, and the standalone GLDAS data. The results indicate that KLDAS is a good potential source of high spatial resolution forcing data. Therefore, the KLDAS is a promising alternative product, capable of compensating for the lack of observations and low resolution grid data for East Asia.

Effect of 2-m Temperature Data Assimilation in the CMA-MESO 3DVAR System

Assimilation of surface observations including 2-m temperature(T_(2m))in numerical weather prediction(NWP)models remains a challenging problem owing to differences between the elevation of model terrain and that of actual observation stations.NWP results can be improved only if surface observations are assimilated appropriately.In this study,a T_(2m)data assimilation scheme that carefully considers misrepresentation of model and station terrain was established by using the three-dimensional variational data assimilation(3DVAR)system of the China Meteorological Administration mesoscale model(CMA-MESO).The corresponding forward observation operator,tangent linear operator,and adjoint operator for the T_(2m)observations under three terrain mismatch treatments were developed.The T_(2m)data were assimilated in the same method as that adopted for temperature sounding data with additional representative errors,when station terrain was 100 m higher than model terrain;otherwise,the T_(2m)data were assimilated by using the surface similarity theory assimilation operator.Furthermore,if station terrain was lower than model terrain,additional representative errors were stipulated and corrected.Test of a rainfall case showed that the observation innovation and analysis residuals both exhibited Gaussian distribution and that the analysis increment was reasonable.Moreover,it was found that on completion of the data assimilation cycle,T_(2m)data assimilation obviously influenced the temperature,wind,and relative humidity fields throughout the troposphere,with the greatest impact evident in the lower layers,and that both the area and the intensity of rainfall were better forecasted,especially for the first 12hours.Long-term continuous experiments for 2–28 February and 5–20 July 2020,further verified that T_(2m)data assimilation reduced deviations not only in T_(2m)but also in 10-m wind forecasts.More importantly,the precipitation equitable threat scores were improved over the two experimental periods.In summary,this study confirmed that the T_(2m)data assimilation scheme that we implemented in the kilometer-scale CMA-MESO 3DVAR system is effective.

不同陆面模式对我国地表温度模拟的适用性评估

基于CLDAS大气驱动数据驱动CLM3.5陆面模式和3种不同参数化方案下的Noah-MP陆面模式模拟得到的地表温度,利用中国气象局2009-2013年2000多个国家级地面观测站地表温度进行质量评估。结果表明:从时间分布看,模拟地表温度与观测的偏差及均方根误差均呈季节性波动;从空间分布看,模拟地表温度与观测的偏差及均方根误差在中国东部地区相对于中国西部地区更小。选择Noah-MP陆面模式3种不同参数化方案模拟结果进行对比,结果表明:Noah-MP模式的非动态植被方案不变时,考虑植被覆盖度的二流近似辐射传输方案的Noah-MP陆面模式模拟的地表温度优于考虑太阳高度角和植被三维结构的二流近似辐射传输方案Noah-MP陆面模式模拟的地表温度;选择动态植被方案的Noah-MP陆面模式模拟的地表温度优于选择非动态植被方案的NoahMP陆面模式;总体而言,考虑动态植被方案的Noah-MP陆面模式模拟的地表温度优于其他两种参数化方案的Noah-MP陆面模式以及CLM3.5陆面模式模拟的地表温度。

基于CLDAS强迫CLM3.5模式的新疆区域土壤温度陆面过程模拟及验证

利用中国气象局国家气象信息中心研发的中国气象局陆面数据同化系统(China Meteorological Administration Land Data Assimilation System,CLDAS)大气近地面强迫资料,驱动美国国家大气研究中心公用陆面模式(Community Land Model,CLM3.5),对中国新疆地区土壤温度时空分布进行逐小时Off-line模拟(模拟时段为2009—2012年);利用国家土壤温度自动站(新疆区域105站点)数据验证CLDAS驱动场强迫下的CLM3.5模式在中国新疆地区3个土壤层(5cm、20cm和80cm)的土壤温度模拟能力。研究发现:在月变化方面,第1层(5cm)土壤温度模拟与实测值差异最大,在每年7月最大差异达5k左右;第2层(20cm)在每年7月达最大差异(3k左右),而第3层(80cm)在每年7月均模拟的很好。造成这种现象的原因可能因为新疆地区7月前后浅层土壤温度变化剧烈,温度白天最高可达300K以上,昼夜温差大,导致模式不能很好抓住浅层土壤温度的变化趋势。研究还发现,在80cm土壤深度,模式在1月、12月的模拟结果均较前两层差。在日变化方面,研究发现:较浅的两层(5cm和20cm)土壤温度模拟值在夏季和秋季均较差。与月变化模拟结果类似的是,80cm土壤层日变化在1、12月模拟较差,然而在其他时段却模拟的很好。在小时变化方面,分析发现:第1层土壤(5cm)模拟结果在每年的1—4月及9—11月的全天(即24 h),模式也会有不同的偏差:其中,在03UTC—21UTC之间主要表现为模式结果比观测结果偏高,而在日内21UTC—00UTC主要表现为模拟结果偏小。在每年的5—8月,全天模拟值都偏小,其中在09UTC达当日最大值。而距离第2层(20cm)处的土壤温度模拟值在大部分月份都偏差较小(-1K至1k之间),并在日内12UTC偏差达到当日最大值。研究发现,在土壤20cm处,模式模拟的最大值较观测值提前,而第3层(80cm)的土壤温度基本不受日内变化影响,表现较为平稳。造成这种影响的原因可能是因为新疆地区5—8月、9—11月为昼夜温差大,深层土壤温度较浅层土壤温度温差变化小,这也造成了模式对于浅层土壤模拟较深层差的主要原因。总体研究表明:CLDAS驱动场强迫下的CLM3.5模式可较为精确的模拟中国新疆地区多年平均土壤温度时空分布,并较为准确的反映中国新疆地区土壤温度的小时、日、月及年际的变化规律。模式浅温度模拟不好的原因可能与模式参数化方案及地表参数有关,后期将继续修正该问题。

Applicability Assessment of the 1998–2018 CLDAS Multi-Source Precipitation Fusion Dataset over China

Traditional hourly rain gauges and automatic weather stations rarely measure solid precipitation, except for those stations with weighing-type precipitation sensors. Microwave remote sensing has only a low ability to retrieve solid precipitation. In addition, there are no long-term, high-quality precipitation data in China that can be used to drive land surface models. To address these issues, in the China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS), we blended the Climate Prediction Center(CPC) morphing technique(CMORPH) and Modern-Era Retrospective analysis for Research and Applications version 2(MERRA2) precipitation datasets with observed temperature and precipitation data on various temporal scales using multigrid variational analysis and temporal downscaling to produce a multi-source precipitation fusion dataset for China(CLDAS-Prcp). This dataset covers all of China at a resolution of 6.25 km at hourly intervals from 1998 to 2018. We performed dependent and independent evaluations of the CLDAS-Prcp dataset from the perspectives of seasonal total precipitation and land surface model simulation. Our results show that the CLDAS-Prcp dataset represents reasonably the spatial distribution of precipitation in China. The dependent evaluation indicates that the CLDAS-Prcp performs better than the MERRA2 precipitation, CMORPH precipitation, Global Land Data Assimilation System version 2(GLDAS-V2.1) precipitation,and CLDAS-V2.0 winter precipitation, as compared to the meteorological observational precipitation. The independent evaluation indicates that the CLDAS-Prcp dataset performs better than the Global Precipitation Measurement(GPM) precipitation dataset and is similar to the CLDAS-V2.0 summer precipitation dataset based on the hydrological observational precipitation. The simulated soil moisture content driven by CLDAS-Prcp is slightly better than that driven by the CLDAS-V2.0 precipitation, whereas the snow depth simulation driven by CLDAS-Prcp is much better than that driven by the CLDAS-V2.0 precipitation. This is because the CLDAS-Prcp data have included solid precipitation. Overall, the CLDAS-Prcp dataset can meet the needs of land surface and hydrological modeling studies.

中国自动土壤水分观测资料质量控制方法设计与效果检验

土壤湿度资料对气候变化、农业干旱监测、农业气象预报与服务等研究具有重要意义,为剔除土壤湿度观测资料中的异常数据,本文提出了一套适用于全国自动土壤水分观测资料的质量控制方法。首先,以2014年全国自动土壤水分观测资料为基础,根据资料中异常数据的特征将异常数据分为四类。其次,从界限值检查、内部一致性检查、时间一致性检查等方面提出:异常极值检查、异常增大检查、异常减小检查、异常恒定检查四类方法。最后,利用2014—2015年全国观测资料以及中国气象局陆面数据同化系统(CLDAS)土壤体积含水量数据集产品(V2.0)对各检查方法应用效果进行检验,结果表明:(1)四类检查方法均可判断出自动土壤水分观测资料中的疑误数据;(2)四类检查方法的判定结果在时间连续性及空间分布上具有一定的一致性;(3)该质量控制方法可减小观测数据与CLDAS数据之间的均方根误差(RMSE)。目前,该方法已应用于我国气象资料处理业务系统。

GLDAS和CMIP5产品的中国土壤湿度-降水耦合分析及变化趋势

利用GLDAS同化产品和12个CMIP5模式的输出结果,从土壤湿度对降水影响的两个中间环节出发,通过分析陆面耦合指数ILH、潜热通量—抬升凝结高度耦合指数ILCL以及抬升凝结高度ZLCL间接研究中国区域土壤湿度与降水间耦合特征,并对1958～2013年及RCP4.5辐射强迫情景下50年（2006～2055年）的4个代表性区域夏季耦合强度的年代际变化特征进行分析。研究发现：1958～2013年期间,内蒙古阴山山脉附近、新疆和青海的部分地区为夏季中国土壤湿度与降水耦合的最强区域;陆面耦合指数ILH变化幅度从高到低依次出现在华北、华南、内蒙古中部和西北地区,并在20世纪70年代中到80年代中发生转折。2006～2055年的平均而言,预估内蒙古阴山山脉附近仍为耦合最强区;与历史时期（1958～2005年）比较,新疆中部和内蒙古阴山山脉附近的耦合指数ILH增大,而广西和广东地区的则减小;对于耦合指数ILH的年代际变化（2006～2055年）,2026～2035年间华北最大而华南最小,西北地区变化不大,而内蒙古中部地区的耦合强度逐渐增大。

再分析资料和陆面数据同化资料土壤湿度产品在中国北方地区的适用性评估

土壤湿度是地球系统模拟的重要参数之一,准确获得其时空分布和变化特征是研究陆-气相互作用的基础。再分析资料和陆面数据同化资料均可提供全球或区域高分辨率土壤湿度产品,但在使用前需要对其进行评估分析。利用土壤湿度观测数据,计算ERA5、ERA5-Land、NCEP-DOE R2、CRA40再分析资料和GLDAS-Noah、GLDAS-CLSM、CLDAS陆面数据同化资料土壤湿度产品与观测数据的中位数、模拟偏差、相关系数等统计指标,并分季节和气候区讨论不同土壤湿度产品在中国北方地区的模拟效果。结果表明:整体来看,CRA40与观测值的相关性最好,ERA5和ERA5-Land分别对干中心、湿中心模拟效果更好,GLDAS-Noah对于较干土壤地区模拟略偏湿,CLDAS对较湿土壤地区模拟结果以系统性偏干为主,NCEP-DOE R2和GLDAS-CLSM模拟效果较差;ERA5、ERA5-Land、NCEP-DOE R2、GLDAS-Noah和CLDAS在所有季节均为模拟正偏差,春季模拟效果较好的是CRA40、ERA5-Land,夏季和秋季ERA5-Land、ERA5和CRA40与观测值相关性较好,不同产品模拟的冬季土壤湿度和观测值相关性是全年中最小的;不同土壤湿度产品在干旱区以模拟偏湿为主,GLDAS-Noah模拟效果最佳,但模拟土壤湿度峰值和谷值的出现时间较观测较早,GLDAS-Noah、CRA40、ERA5能较好模拟季风区干、湿土壤的持续时段和土壤湿度变化振幅,大部分产品能模拟出夏季风影响过渡区较干土壤和较湿土壤的出现时间。

3种再分析资料基本统计量比较

为了评估中国气象部门整理的资料的特色和应用价值,使用统计方法检验了中国资料和NCEP/NCAR、ECMWF再分析资料7月100 hPa、500 hPa位势高度场和1月海平面气压场的气候均值和年际方差的差异显著性,并比较了1月和7月北半球主要大气活动中心面积、强度指数的年际变化差异和相关程度。结果表明:1)ECMWF再分析资料7月100 hPa、500 hPa位势高度场的气候均值都显著小于中国资料,且其历年值分别小于1 660、588 dagpm,与中国资料相比不适合用于研究南亚高压、副热带高压;2)中国资料是由单层等压面图上直接读数得到的,更接近实际观测值,更适宜于诊断单个等压面上的气压系统;3)3种资料冬季蒙古高压、阿留申低压的年际变化一致性要好于夏季南亚高压、副热带高压。

应用风云气象卫星及融合降水资料改进地表参数模拟

气象卫星资料不仅对天气、气候研究非常重要,对于地表参数模拟和预报也具有重要意义。本文首次将全国自动站观测、卫星降水估计和地面观测融合降水资料(CMORPH)以及风云二号D星(FY-2D)积雪覆盖率数据应用到了高分辨率陆面资料同化系统(u-HRLDAS)。融合降水资料用于驱动u-HRLDAS,同时用于计算雪水当量;积雪覆盖率资料作为u-HRLDAS强迫变量。区域模拟结果表明,积雪覆盖率对于地表反照率、地表温度以及地气交换通量模拟有极其重要的影响。密云站土壤湿度模拟结果表明,融合降水资料准确度优于全球陆面资料同化系统(GLDAS)再分析资料。小汤山站单点验证结果表明,应用融合降水资料及卫星积雪覆盖率资料可以改进地表温度及地气交换通量的模拟。

改进的CLDAS降水驱动对中国区域积雪模拟的影响评估

积雪因为其特定的属性在气候变化和水文循环中扮演着重要角色,在大气和陆面之间起到了调节能量和水交换的显著作用,而陆面驱动数据的质量直接决定着模式对积雪的模拟效果。本文采用CLDAS(CMA Land Data Assimilation System)和改进后的降水驱动(CLDAS-Prcp)分别驱动Noah3.6陆面模式对积雪变量进行模拟,并对中国主要的积雪区东北区域、新疆区域、青藏高原区域的积雪覆盖率、雪深、雪水当量的模拟效果进行了评估。结果表明,CLDAS-Prcp改善了原有驱动在冬季由于低估降水所造成的模拟积雪量偏少的情况;东北区域模拟结果与观测的时间变率最为一致,积雪覆盖率、雪深、雪水当量的相关系数分别为0.42,0.78,0.93;而雪水当量的改进效果最明显,均方根误差和偏差分别减小了54.8%和83.1%,相关系数提高了0.47;同时,CLDAS-Prcp不仅能反映积雪变量的年际变率,而且能够较准确地反映出强度较大的突发降雪事件。

气象-气溶胶资料联合同化对秋季PM_(2.5)浓度模拟的影响研究

相比冬季大范围静稳条件下的污染堆积过程,秋季气象条件更加复杂和局地化,气象条件模拟不确定性给秋季气溶胶模拟带来了更大难度,且目前研究较少考虑气象-气溶胶因素在线模拟和联合同化。使用WRF/Chem模式和格点统计差值(GSI)三维变分同化系统,2015年10月进行了为期1个月的气象-气溶胶资料联合同化及模拟试验,并基于此讨论了气象-气溶胶资料联合同化对秋季PM_(2.5)浓度模拟的影响。结果表明,WRF/Chem模式可以模拟出秋季污染天气过程,但对华北平原和中东部地区存在高估、西北部存在低估现象;同化地面PM_(2.5)浓度观测资料可以改进对PM_(2.5)浓度的模拟,上述两个地区的偏差均得到订正,6 h预报偏差均降低至6μg/m3以内;重点针对华北地区的分析表明,秋季PM_(2.5)污染过程与特殊气象条件(湿度升高、风场辐合、区域输送)密切相关,因此在地面PM_(2.5)观测资料同化基础上增加常规气象资料同化,能进一步提高对华北平原气象-污染过程的表达,PM_(2.5)浓度预报相关系数从0.86提高至0.89。气象-气溶胶联合资料同化能更加准确地模拟秋季气溶胶污染过程,为更好地开展污染成因和在气象预报框架下开展气象-气溶胶相互影响研究提供了基础。

CLDAS融合土壤相对湿度产品适用性评估及在气象干旱监测中的应用

基于2008—2017年全国自动气象观测站逐旬土壤相对湿度观测数据,综合评估中国气象局陆面数据同化系统(CMA Land Data Assimilation System,CLDAS)0~20 cm层融合土壤相对湿度产品在中国地区的适用性,评估表明CLDAS土壤相对湿度产品在中国东北、西北、江南大部及华南等地区存在较大系统性误差,总体上适用性较差。为消除CLDAS土壤相对湿度产品的系统性误差,采用回归订正法、7旬滑动平均订正法和临近加权前旬订正法对CLDAS土壤相对湿度产品进行误差订正处理,对订正结果评估发现:订正处理后CLDAS土壤相对湿度产品与站点观测的相关性显著增加,系统偏差基本消除,适用性明显提高,3种订正方法中临近加权前旬订正法的订正效果最优。最后,采用经不同方法订正后的CLDAS土壤相对湿度产品对2017年5月东北—华北地区一次气象干旱个例进行重现,对比验证表明:相对其他两种订正方法,经临近加权前旬订正法处理后的CLDAS土壤相对湿度产品能更为精准地重现2017年5月东北—华北地区气象干旱的落区和强度。

Evaluation and Hydrological Application of CMADS Reanalysis Precipitation Data against Four Satellite Precipitation Products in the Upper Huaihe River Basin, China

Satellite-and reanalysis-based precipitation products are important data source for precipitation, particularly in areas with a sparse gauge network. Here, five open-access precipitation products, including the newly released China Meteorological Assimilation Driving Datasets for the Soil and Water Assessment Tool(SWAT) model(CMADS)reanalysis dataset and four widely used bias-adjusted satellite precipitation products [SPPs;i.e., Tropical Rainfall Measuring Mission(TRMM) Multisatellite Precipitation Analysis 3B42 Version 7(TMPA 3B42V7), Climate Prediction Center(CPC) morphing technique satellite–gauge blended product(CMORPH-BLD), Climate Hazards Group Infrared Precipitation with Station Data(CHIRPS), and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record(PERSIANN-CDR)], were assessed. These products were first compared with the gauge observed data collected for the upper Huaihe River basin, and then were used as forcing data for streamflow simulation by the Xin’anjiang(XAJ) hydrological model under two scenarios with different calibration procedures. The performance of CMADS precipitation product for the Chinese mainland was also assessed. The results show that:(1) for the statistical assessment, CMADS and CMORPH-BLD perform the best, followed by TMPA 3B42V7, CHIRPS, and PERSIANN-CDR, among which the correlation coefficient(CC) and rootmean-square error(RMSE) values of CMADS are optimal, although it exhibits certain significant negative relative bias(BIAS;-22.72%);(2) CMORPH-BLD performs the best in capturing and detecting rainfall events, while CMADS tends to underestimate heavy and torrential precipitation;(3) for streamflow simulation, the performance of using CMADS as input is very good, with the highest Nash–Sutcliffe efficiency(NSE) values(0.85 and 0.75 for calibration period and validation period, respectively);and(4) CMADS exhibits high accuracy in eastern China while with significant negative BIAS, and the performance declines from southeast to northwest. The statistical and hydrological evaluations show that CMADS and CMORPH-BLD have high potential for observing precipitation. As high negative BIAS values showed up in CMADS evaluation, further study on the error sources from original data and calibration algorithms is necessary. This study can serve as a reference for selecting precipitation products in datascarce regions with similar climates and topography in the Global Precipitation Measurement(GPM) era.

三维变分数值气象预报在弹道解算中应用研究

利用数值天气预报快速获取高空气象参数是当前战时远程火炮射击诸元解算重点研究内容之一,但是数值天气预报初始场误差大,直接影响了气象参数预报精度。采用三维变分同化法,同化NCEP ADP全球高空观测天气数据,结合背景场气象资料使用WRF进行气象预报,并对其应用于有风状态下的6自由度弹道的解算效果进行了分析。结果表明:同化试验对风速预报改善作用显著,应用循环同化后的预报结果较未同化数值预报结果,可使弹道计算射程X方向射程的预测相对误差减小了约0.14%;Z方向侧偏的预测相对误差减小了约3.89%。

CLDAS土壤相对湿度数据不同时空尺度适用性评估

基于2020—2021年中国气象局陆面数据同化系统(CLDAS)模拟的逐日土壤相对湿度和土壤水分自动站观测的逐小时土壤相对湿度资料,采用多个统计对比指标,在日和月时间尺度、点和区域空间尺度系统性地评估了CLDAS模拟的土壤相对湿度适用性。结果表明:CLDAS模拟的土壤相对湿度与观测值具有一致的逐日变化规律;0~10 cm、0~20 cm层次CLDAS模拟值与观测值较为接近,0~50 cm层次模拟值普遍低于观测值;各层次上,CLDAS模拟值与观测值的相关系数普遍大于0.6,均方根误差普遍小于30%。区域尺度上,0~10 cm层次CLDAS模拟值和观测值相关系数为0.78~0.95,以华南最高;均方根误差为5.70%~17.26%,以华东最小;偏差为−6.63%~15.80%,以华中偏差绝对值最小。0~20 cm层次CLDAS模拟值和观测值相关系数为0.78~0.95,以东北和内蒙古最高;均方根误差为4.45%~14.03%,以华东最小;偏差为−5.36%~12.56%,以华东偏差绝对值最小。0~50 cm层次CLDAS模拟值和观测值相关系数为0.68~0.97,以东北最高;均方根误差为4.00%~15.83%,以华东最小;偏差为−9.83%~9.62%,以东北偏差绝对值最小。区域月尺度CLDAS模拟值和观测值整体的相关性较好,相关系数在6—10月较高;均方根误差普遍小于15%,华东各个层次逐月均方根误差均较小。总体来看,CLDAS模拟的土壤相对湿度空间上在华东、华中和东北精度最高,时间上在6—10月精度最高。

Evaluation of CLDAS and GPM Precipitation Products over the Tibetan Plateau in Summer 2005–2021 Based on Hourly Rain Gauge Observations

Accurate,reliable,and high spatiotemporal resolution precipitation products are essential for precipitation research,hydrological simulation,disaster warning,and many other applications over the Tibetan Plateau(TP).The Global Precipitation Measurement(GPM) data are widely recognized as the most reliable satellite precipitation product for the TP.The China Meteorological Administration(CMA) Land Data Assimilation System(CLDAS) precipitation fusion dataset(CLDAS-Prcp),hereafter referred to as CLDAS,is a high-resolution,self-developed precipitation product in China with regional characteristics.Focusing on the TP,this study provides a long-term evaluation of CLDAS and GPM from various aspects,including characteristics on different timescales,diurnal variation,and elevation impacts,based on hourly rain gauge data in summer from 2005 to 2021.The results show that CLDAS and GPM are highly effective alternatives to the rain gauge records over the TP.They both perform well for precipitation amount and frequency on multiple timescales.CLDAS tends to overestimate precipitation amount and underestimate precipitation frequency over the TP.However,GPM tends to overestimate both precipitation amount and frequency.The difference between them mainly lies in the trace precipitation.CLDAS and GPM effectively capture rainfall events,but their performance decreases significantly as intensity increases.They both show better accuracy in diurnal variation of precipitation amount than frequency,and their performance tends to be superior during nighttime compared to the daytime.Nevertheless,there are some differences of the two against rain gauge observations in diurnal variation,especially in the phase of the diurnal variation.The performance of CLDAS and GPM varies at different elevations.They both have the best performance over 3000–3500 m.The elevation dependence of CLDAS is relatively minor,while GPM shows a stronger elevation dependence in terms of precipitation amount.GPM tends to overestimate the precipitation amount at lower elevations and underestimate it at higher elevations.CLDAS and GPM exhibit unique strengths and weaknesses;hence,the choice should be made according to the specific situation of application.

区域尺度近地表气候要素驱动数据研制的研究综述

区域和流域尺度的陆面、水文和生态模拟与同化,都需要高分辨率的大气近地表层风、温、压、湿、辐射、降水等资料作为驱动。介绍了流域尺度驱动数据的重要性,对现阶段大气驱动数据的制备方法进行评论,总结了降水、辐射、气温和其他近地表要素的若干产品,探讨了北美陆面数据同化系统、全球陆面数据同化系统、欧洲陆面数据同化系统和中国西部陆面数据同化系统中大气驱动数据的研究思路和研究成果,最后提出了现阶段制备流域尺度驱动数据存在的问题,并针对中国西部复杂地形的高时空分辨率驱动数据制备提出一些看法。