ERA再分析陆面温度资料在浙江省的适用性

利用浙江省2016年71个气象台站观测的日平均气温和地表(0cm)温度资料对ERA再分析陆面温度资料的适用性进行了初步评估,通过计算2套再分析资料(ERA5和ERA-Interim)与观测资料之间的相关系数、平均偏差、平均绝对偏差、均方根误差和纳什效率系数等统计参数,综合评估了ERA再分析资料在浙江省的适用性。结果表明:①ERA2套再分析资料与观测值均较为接近,均能够较好地再现浙江省2m气温的时空分布特征且变化相关系数均高于0.98,日绝对偏差较小。②对于地表温度,2套再分析资料的适用性要差于气温,主要表现在2套再分析地表温度均低于观测值,且夏季的偏差显著大于其他季节,但绝大多数站点相关系数高于0.9,均方根误差高于2℃。总体来说,2套ERA再分析陆面温度资料对浙江省具有较好的适用性,ERA5整体上优于ERA-Interim,地表温度的改善更明显。     

三种风场再分析资料在辽宁省海岸带的比较与评估

利用1981-2015年辽宁省海岸带20个气象站常规观测资料与同期ERA-Interim、JRA-55和CFSR三种再分析资料进行对比分析,讨论了风速场再分析资料在辽宁省海岸带的适应性问题。结果表明,三种再分析资料与观测资料的大风相关性均通过显著性检验,ERA资料相关性最好,但ERA的最大小时风速明显较观测资料偏小。三种再分析资料在空间上的偏差有明显的不均匀性,绥中、兴城一带偏差较小,旅顺口站偏差较大;CFSR资料最大小时风速的偏差绝对值小于1的站点最多,适用性较好;ERA资料的大风次数与观测资料最为接近,且14:00(北京时,下同)最大小时风速和大风次数的偏差均较08:00偏小。三个海区最大小时风速平均绝对误差的差别不显著,基本在1.5~3 m·s-1之间,而大风次数的误差较显著,渤海海峡误差相对较大,黄海北部次之,渤海北部误差最小,其中CFSR资料的风速平均绝对误差最小。观测的最大小时风速和大风次数呈显著减少趋势,JRA和CFSR资料呈缓慢减少趋势,而ERA资料呈增加趋势;ERA和JRA资料的年代际曲线波动平稳,不能表现出观测资料的年代际变化趋势,CFSR资料与观测值最为接近,但是变化趋势较观测资料缓慢。在长期变化趋势空间分布上,CFSR资料的可信度相对较好,JRA资料次之,ERA资料差别较大。     

三套再分析降水资料在华南地区的适用性评估

利用1980-2017年观测站降水数据(OBS)为参照标准,对目前广泛应用的三套再分析资料,即ERAI资料、JRA-55资料和CFSR资料在华南地区降水空间分布和年际变化的再现能力进行分析与评估。结果表明,不同资料对不同季节降水的再现能力存在显著差异,三者对降水年际变化的刻画都较为优秀,但对降水空间分布描述偏差较大,且在夏季最为明显;三者之间对比,ERAI对春季和冬季降水时空变化表现能力较为优秀,但在秋季表现一般;CFSR降水与实测时空相关性较好,但都存在降水系统性高估,在夏季最为严重,偏差达到5 mm·d~(-1);JRA-55对夏季和秋季降水刻画相对最佳,但对冬季降水存在高估。     

基于陆面水文耦合模式CLHMS的淮河流域水文过程的模拟评估及其不确定性分析

利用最新的CFSR(Climate Forecast System Reanalysis)再分析及观测的降水和地表气温资料驱动陆面水文耦合模式CLHMS(Coupled Land surface and Hydrologic Model System),对淮河流域1980~2003年共24年的水文水循环过程进行了模拟,系统评估了CLHMS对淮河流域水文过程的模拟能力及其不确定性。分析结果表明,CLHMS模式对淮河流域水文过程具有良好的模拟能力,模式尤其对湿润年份流域的水量平衡以及河道流量的季节、年际变化具有很强的模拟能力,而对降水偏少的干旱年份,模式模拟的河道流量通常会高于观测实况,与实况间存在着一定的偏差,而这也是导致CLHMS对流域水文过程模拟能力存在显著年代际差异的主要原因。基于三组不同降水强迫的流域水文过程模拟结果比较表明,降水驱动资料准确与否是陆面水文模拟最主要的不确定性来源之一,正是由于CFSR再分析降水与观测降水之间存在较大的差异,从而导致CFSR降水驱动下模式模拟的淮河流域河道流量与观测存在较大的偏差,其模拟性能相对较差。进一步分析还表明,可以保持较强降水日变化的时间解集方法,也是保证合理模拟流域水文过程的重要因素。     

ERA-Interim和NCEP/NCAR再分析数据气温和气压值在中天山山区适用性分析

利用在天山山区海拔超过1 500 m的气象站的逐日气温和气压数据与同期经过水平方向和垂直方向插值后ERA-Interim和NCEP/NCAR两套再分析数据进行回归分析,研究再分析数据在天山山区不同季节的适用性,并验证再分析数据偏差与气候区的一致性。结果表明:从整体上,ERA-Interim数据气压和气温的可信度优于NCEP/NCAR数据,但在局部存在差异。两套再分析数据的偏差与气候分区、高程和季节相关。中天山山区再分析数据气温偏差呈现暖偏差,而天山南坡呈现冷偏差。两套再分析数据的气温偏差在高山带呈现暖偏差,在中山带呈现冷偏差。春秋两季气温的偏差小于夏冬两季。气压的偏差在夏季低于其他三季。而偏差可能会导致以再分析数据为驱动的气候模式结果的偏差。     

NCEP/NCAR再分析风速、表面气温距平在中国区域气候变化研究中的可信度分析

采用计算标准化均方根误差、相关分析和EOF分解等多种客观分析统计方法,对NCEP/NCAR再分析风速、表面气温距平在中国区域的可信度进行了研究,结果表明中国东部不同要素距平的标准化均方根误差均比西部地区的小,说明NCEP/NCAR再分析资料的可信度东部比西部要高,可能是受到模式地形和中国地面气象站点“东密西疏”分布格局的较大影响。随着高度升高,NCEP再分析风速距平的误差减小,进一步表明地形对NCEP再分析资料的可信度具有较大影响。另外,冬季再分析风速误差较大的特点在850,500和200 hPa等压面上均存在,表明冬季再分析风速距平的可信度受到再分析模式系统误差的较大影响。相关分析结果和标准化均方根误差计算结果之间具有很好的反向对应关系,即均方根误差大,NCEP再分析资料与实测资料的相关性就差,均方根误差小,则对应两者之间的相关性就较好。标准化均方根误差较小的要素,其NCEP再分析和站点实测资料距平EOF分解得到的特征向量空间分布较为相似,各特征向量对应时间系数的相关性也比较好;反之,标准化均方根误差大的要素,其NCEP再分析和站点实测资料距平EOF分解得到的特征向量空间分布则相差较大,对应时间系数的相关性也比较差,因此采用EOF分解方法,分析对应特征向量空间分布相似性及其时间系数变化的一致性,可以对NCEP再分析资料的可信度有一个更加客观的认识。综合上述各季节、各要素多种方法的分析结果,可以发现NCEP再分析风速距平在春、夏、秋季具有一定的可信度,但冬季的可信度较差;表面气温距平则是冬季的可信度最好,夏季的可信度较差。     

四套再分析土壤湿度资料在中国区域的适用性分析

利用国家气象信息中心提供的"中国农业气象土壤水分数据集(1981-2010年)(V1.0)"中150个农业气象站的土壤湿度观测资料,对2001-2010年CFSR、ERA-Interim、NCEP R-1和NCEP R-2四套再分析土壤湿度资料在中国区域的适用性进行了比较与评估。结果表明:(1)从空间分布来看,四套再分析资料都可以正确描述出中国区域土壤湿度的分布特征,但是NCEP R-1对于青藏高原土壤湿度的模拟存在一定的问题;(2)从时间变化来看,CFSR再分析资料能够较好地描述了土壤湿度的时间变化,而NCEP R-2再分析资料表现得较差;(3)从土壤湿度的季节循环看,在表层,CFSR和ERA-Interim再分析资料模拟地比较好,而NCEP R-1和NCEP R-2再分析资料出现了高估现象;而在深层,除NCEP R-1外,其他三种再分析资料都可以较好地模拟出土壤湿度的季节循环。     

南极探空与两套再分析资料的对比分析

基于南极18个站点探空气象观测数据对欧洲中期天气预报中心的再分析数据(ERA-Interim)和美国国家环境预报中心的再分析数据(NECP)在南极地区高层大气的适用性进行验证。结果表明:在南极上空,随着高度抬升,探空气象观测数据与两套再分析数据中四个气象要素的差值均逐渐变大,再分析数据数值愈加偏离实际观测数值。两套再分析数据的位势高度和温度与探空观测数据偏差较小;风向则和探空观测数据相差甚远;两套再分析数据的风速与探空观测数据在300 hPa偏差较大。在季节变化中,南极的春季,再分析数据中的位势高度和温度与探空观测数据相差较大,在其他季节相差相对较小。再分析数据中的风速与探空观测数据在南极的夏季相差较小。再分析数据中的风向与探空观测数据存在较大偏差,且差值没有明显的季节变化。尽管两套再分析数据都存在很大偏差,但ERA-Interim数据整体上优于NCEP数据。对比分析也表明,采用这些再分析资料作为初始条件和边界条件驱动南极区域大气模式将带来较大的误差。未来需要加强南极探空观测,改进再分析资料同化和数值模拟系统。     

ERA-interim气温、地温再分析数据在青海东部农业区的适用性评估

利用1999—2018年逐月ERA-interim再分析数据，选取青海省东部农业区的15个气象站点平均气温和土壤温度，采用误差和相关性分析，对比两种资料时间和站点空间变化规律，并对ERA-interim再分析资料的准确性进行验证评估。结果表明：ERA-interim再分析数据平均气温、5和15 cm土壤温度均低于观测值，分别偏低3.5、4.3和4.5℃；观测值与订正前后的再分析资料月际和季节变化基本一致，空间变化均表现出从东南部向西北部减小的趋势，而且最大值和最小值出现的站点完全一致。近20年来，观测值与再分析资料平均气温均呈现上升趋势，5和15 cm土壤温度观测值表现为升高趋势，而再分析数据呈减小趋势。与订正前相比，订正后的再分析资料与观测值的平均偏差和均方根误差明显减小，同时平均偏差和均方根误差年际变化均呈先增大后减小的趋势。年内平均偏差和均方根误差均呈现“M”型，最大值均出现在4月，最小值出现在12或1月。东部农业区地形复杂、海拔高度差异是再分析资料比观测值偏低的主要原因，利用回归方程实现平均气温和土壤温度再分析数据的订正，有效降低了ERA-interim再分析数据的偏差，提高再...     

中国地表太阳辐射再分析数据与观测的比较

利用我国地表太阳辐射台站资料和海上观测资料与同期的NCEP/NCAR, NCEP/CFSR再分析资料进行比较,检验再分析资料是否能够反映中国地区的太阳辐射特征。结果表明:1979年之前NCEP/NCAR太阳辐射资料的可信度较低,存在虚假的明显上升趋势,1979年之后两套再分析资料的可信度均较高,在我国东部和低纬度地区的可信度好于西部和高纬度地区;由逐6 h再分析数据直接计算得到的逐日太阳辐射比实际观测偏低,剔除太阳辐射为零的情况计算逐日资料更合理。在大陆地区,NCEP/NCAR,NCEP/CFSR再分析资料与台站太阳辐射资料的1979—2009年共31年平均误差分别为10.37 W·m-2和-42.68 W·m-2,误差的标准差分别为12.31 W·m-2和4.19 W·m-2;在海洋区域,NCEP/NCAR,NCEP/CFSR再分析资料与海上观测太阳辐射资料的平均误差分别为-161.19 W·m-2和-179.66 W·m-2,误差的标准差分别为37.07 W·m-2和35.36 W·m-2。与大陆台站资料相比,海上观测与再分析资料的误差偏大,这可能与海上观测资料较少,限制了NCEP模式的评估和改进有关。     

Evaluation of Precipitation in Multi-Generation Reanalyses with Land Observations over Zhejiang Province

Based on various statistical indices, the abilities of multi-generation reanalyses, namely the NCEP/NCAR Reanalysis 1 (R1), the NCEP-DOE Reanalysis 2 (R2) and the NCEP Climate Forecast System Reanalysis (CFSR), to reproduce the spatiotemporal characteristics of precipitation over Zhejiang Province are comprehensively compared. The mean absolute bias percentages for three reanalyses are 20% (R1), 10% (R2) and 37% (CFSR). R2 (R1) gives the best (worst) general depiction of the spatial characteristics of the observed precipitation climatology, whereas a significant wet bias is noticed in CFSR. All reanalyses reasonably reproduce the interannual variability with the correlation coefficients of 0.72 (R1), 0.72 (R2) and 0.84 (CFSR). All reanalyses well represent the first two modes of the observed precipitation through Empirical Orthogonal Function analysis, with CFSR giving the best capture of the principal components. The root-mean-square error (RMSE) is the largest (smallest) in CFSR (R2). The large RMSE of CFSR in summer (especially in June) contributes mostly to its systematic wet bias. After 2001, the wet-bias of CFSR substantially weakens, probably attributed to increasing observations assimilated in the CFSR. On a monthly basis, the percentage of neutral bias cases are similar for all reanalyses, while the ratio of positive (negative) bias cases for CFSR is distinctly larger (smaller) than that of R1 and R2. The proportions of negative bias cases for R1 and R2 begin to increase after 2001 while keeping stable for CFSR. On a daily basis, all reanalyses give good performances of reproducing light rain; however, the reflection of moderate rain and heavier rain by CFSR is better than R1 and R2. Overall, despite being a third-generation reanalysis product, CFSR does not exhibit comprehensive superiorities over R1 and R2 in all aspects on a regional scale.     

An assessment of the surface climate in the NCEP climate forecast system reanalysis

This paper analyzes surface climate variability in the climate forecast system reanalysis (CFSR) recently completed at the National Centers for Environmental Prediction (NCEP). The CFSR represents a new generation of reanalysis effort with first guess from a coupled atmosphere?Cocean?Csea ice?Cland forecast system. This study focuses on the analysis of climate variability for a set of surface variables including precipitation, surface air 2-m temperature (T2m), and surface heat fluxes. None of these quantities are assimilated directly and thus an assessment of their variability provides an independent measure of the accuracy. The CFSR is compared with observational estimates and three previous reanalyses (the NCEP/NCAR reanalysis or R1, the NCEP/DOE reanalysis or R2, and the ERA40 produced by the European Centre for Medium-Range Weather Forecasts). The CFSR has improved time-mean precipitation distribution over various regions compared to the three previous reanalyses, leading to a better representation of freshwater flux (evaporation minus precipitation). For interannual variability, the CFSR shows improved precipitation correlation with observations over the Indian Ocean, Maritime Continent, and western Pacific. The T2m of the CFSR is superior to R1 and R2 with more realistic interannual variability and long-term trend. On the other hand, the CFSR overestimates downward solar radiation flux over the tropical Western Hemisphere warm pool, consistent with a negative cloudiness bias and a positive sea surface temperature bias. Meanwhile, the evaporative latent heat flux in CFSR appears to be larger than other observational estimates over most of the globe. A few deficiencies in the long-term variations are identified in the CFSR. Firstly, dramatic changes are found around 1998?C2001 in the global average of a number of variables, possibly related to the changes in the assimilated satellite observations. Secondly, the use of multiple streams for the CFSR induces spurious jumps in soil moisture between adjacent streams. Thirdly, there is an inconsistency in long-term sea ice extent variations over the Arctic regions between the CFSR and other observations with the CFSR showing smaller sea ice extent before 1997 and larger extent starting in 1997. These deficiencies may have impacts on the application of the CFSR for climate diagnoses and predictions. Relationships between surface heat fluxes and SST tendency and between SST and precipitation are analyzed and compared with observational estimates and other reanalyses. Global mean fields of surface heat and water fluxes together with radiation fluxes at the top of the atmosphere are documented and presented over the entire globe, and for the ocean and land separately.     

Tropical intraseasonal rainfall variability in the CFSR

While large-scale circulation fields from atmospheric reanalyses have been widely used to study the tropical intraseasonal variability, rainfall variations from the reanalyses are less focused. Because of the sparseness of in situ observations available in the tropics and strong coupling between convection and large-scale circulation, the accuracy of tropical rainfall from the reanalyses not only measures the quality of reanalysis rainfall but is also to some extent indicative of the accuracy of the circulations fields. This study analyzes tropical intraseasonal rainfall variability in the recently completed NCEP Climate Forecast System Reanalysis (CFSR) and its comparison with the widely used NCEP/NCAR reanalysis (R1) and NCEP/DOE reanalysis (R2). The R1 produces too weak rainfall variability while the R2 generates too strong westward propagation. Compared with the R1 and R2, the CFSR produces greatly improved tropical intraseasonal rainfall variability with the dominance of eastward propagation and more realistic amplitude. An analysis of the relationship between rainfall and large-scale fields using composites based on Madden-Julian Oscillation (MJO) events shows that, in all three NCEP reanalyses, the moisture convergence leading the rainfall maximum is near the surface in the western Pacific but is above 925?hPa in the eastern Indian Ocean. However, the CFSR produces the strongest large-scale convergence and the rainfall from CFSR lags the column integrated precipitable water by 1 or 2?days while R1 and R2 rainfall tends to lead the respective precipitable water. Diabatic heating related to the MJO variability in the CFSR is analyzed and compared with that derived from large-scale fields. It is found that the amplitude of CFSR-produced total heating anomalies is smaller than that of the derived. Rainfall variability from the other two recently produced reanalyses, the ECMWF Re-Analysis Interim (ERAI), and the Modern Era Retrospective-analysis for Research and Applications (MERRA), is also analyzed. It is shown that both the ERAI and MERRA generate stronger rainfall spectra than the R1 and more realistic dominance of eastward propagating variance than R2. The intraseasonal variability in the MERRA is stronger than that in the ERAI but weaker than that in the CFSR and CMORPH.     

Surface mean temperature from the observational stations and multiple reanalyses over the Tibetan Plateau

The Tibetan Plateau (TP), also called the “Third pole”, is sensitive to climate change due to extensive areas at high elevation presently dominated by snow and ice. In this study, observed surface temperature trends at 150 stations over the TP during 1979–2018 are analyzed and compared with surface temperatures from multiple reanalyses (NCEP1, NCEP2, ERA-Interim, MERRA, JRA55). Observed warming at the stations has a mean annual rate of 0.46 °C/decade during 1979–2018. Although all reanalyses underestimate observed temperatures (cold bias), most reproduce much of the inter-decadal variations of surface temperature shown in the observations. Absolute errors of mean surface temperature (reanalysis minus observation) are closely correlated with elevation errors, suggesting that parts of the cold bias can be interpreted by elevation errors of reanalysis. After elevation-temperature correction, about half of the cold bias is typically eliminated, more for both ERA-Interim and JRA55. Compared with the observations, corrected NCEP2 surface temperatures still have larger cold biases, and fail to capture the overall warming over the TP. Since the elevation-temperature correction fails to improve trend magnitudes even when a significant proportion of the bias has been removed, this suggests that a more sophisticated modeling of the lapse rate in each reanalysis is required to realistically model warming trends across complex topography.

     

The rendition of the Atlantic Warm Pool in the reanalyses

The Atlantic Warm Pool (AWP) region, which is comprised of the Gulf of Mexico, Caribbean Sea and parts of the northwestern tropical Atlantic Ocean, is one of the most poorly observed parts of the global oceans. This study compares three ocean reanalyses, namely the Global Ocean Data Assimilation System of National Centers for Environmental Prediction (NCEP), the Climate Forecast System Reanalysis (CFSR) of NCEP, and the Simple Ocean Data Assimilation (SODA) for its AWP variation. The surface temperature in these ocean reanalyses is also compared with that from the Extended Range SST version 3 and Optimally Interpolated SST version 2 SST analyses. In addition we also compare three atmospheric reanalyses: NCEP-NCAR (R1), NCEP-DOE (R2), and CFSR for the associated atmospheric variability with the AWP. The comparison shows that there are important differences in the climatology of the AWP and its interannual variations. There are considerable differences in the subsurface ocean manifestation of the AWP with SODA (CFSR) showing the least (largest) modulation of the subsurface ocean temperatures. The remote teleconnections with the tropical Indian Ocean are also different across the reanalyses. However, all three oceanic reanalyses consistently show the absence of any teleconnection with the eastern equatorial Pacific Ocean. The influence of the AWP on the tropospheric temperature anomalies last for up to a one season lead and it is found to be relatively weak in R1 reanalyses. A simplified SST anomaly equation initially derived for diagnosing El Niño Southern Oscillation variability is adapted for the AWP variations in this study. The analysis of this equation reveals that the main contribution of the SST variation in the AWP region is from the variability of the net heat flux. All three reanalyses consistently show that the role of the ocean advective terms, including that associated with upwelling in the AWP region, is comparatively much smaller. The covariance of the SST tendency in the AWP with the net heat flux is large, with significant contributions from the variations of the surface shortwave and longwave fluxes.     

Comparison of spatial interpolation methods for gridded bias removal in surface temperature forecasts

All numerical weather prediction (NWP) models inherently have substantial biases, especially in the forecast of near-surface weather variables. Statistical methods can be used to remove the systematic error based on historical bias data at observation stations. However, many end users of weather forecasts need bias corrected forecasts at locations that scarcely have any historical bias data. To circumvent this limitation, the bias of surface temperature forecasts on a regular grid covering Iran is removed, by using the information available at observation stations in the vicinity of any given grid point. To this end, the running mean error method is first used to correct the forecasts at observation stations, then four interpolation methods including inverse distance squared weighting with constant lapse rate (IDSW-CLR), Kriging with constant lapse rate (Kriging-CLR), gradient inverse distance squared with linear lapse rate (GIDS-LR), and gradient inverse distance squared with lapse rate determined by classification and regression tree (GIDS-CART), are employed to interpolate the bias corrected forecasts at neighboring observation stations to any given location. The results show that all four interpolation methods used do reduce the model error significantly, but Kriging-CLR has better performance than the other methods. For Kriging-CLR, root mean square error (RMSE) and mean absolute error (MAE) were decreased by 26% and 29%, respectively, as compared to the raw forecasts. It is found also, that after applying any of the proposed methods, unlike the raw forecasts, the bias corrected forecasts do not show spatial or temporal dependency.     

An Evaluation of Temperature and Precipitation Surface-Based and Reanalysis Datasets for the Canadian Arctic, 1950–2010

The spatial and temporal consistency of seasonal air temperature and precipitation in eight widely used gridded observation-based climate datasets (CANGRD, CRU-TS3.1, CRUTEM4.1, GISTEMP, GPCC, GPCP, HadCRUT3, and UDEL) and eight reanalyses (20CR, CFSR, ERA-40, ERA-Interim, JRA25, MERRA, NARR, and NCEP2) was evaluated over the Canadian Arctic for the 1950–2010 period. The evaluation used the CANGRD dataset, which is based on homogenized temperature and adjusted precipitation from climate stations, as a reference. Dataset agreement and bias were observed to exhibit important spatial, seasonal, and temporal variability over the Canadian Arctic with the largest spread occurring between datasets over mountain and coastal regions and over the Canadian Arctic Archipelago. Reanalysis datasets were typically warmer and wetter than surface observation-based datasets, with CFSR and 20CR exhibiting biases in total annual precipitation on the order of 300?mm. Warm bias in 20CR exceeded 12°C in winter over the western Arctic. Analysis of the temporal consistency of datasets over the 1950–2010 period showed evidence of discontinuities in several datasets as well as a noticeable increase in dataset spread in the period after approximately 2000. Declining station networks, increased automation, and the inclusion of new satellite data streams in reanalyses are potential contributing factors to this phenomenon. Evaluation of trends over the 1950–2010 period showed a relatively consistent picture of warming and increased precipitation over the Canadian Arctic from all datasets, with CANGRD giving moistening trends two times larger than the multi-dataset average related to the adjustment of the station precipitation data. The study results indicate that considerable care is needed when using gridded climate datasets in local or regional scale applications in the Canadian Arctic.     

A comparative study of the Indian summer monsoon hydroclimate and its variations in three reanalyses

This study examines the Indian summer monsoon hydroclimate in the National Centers for Environmental Prediction (NCEP)-Department of Energy (DOE) Reanalysis (R2), the Climate Forecast System Reanalysis (CFSR), and the Modern Era Retrospective-Analysis for Research and Applications (MERRA). The three reanalyses show significant differences in the climatology of evaporation, low-level winds, and precipitable water fields over India. For example, the continental evaporation is significantly less in CFSR compared to R2 and MERRA. Likewise the mean boreal summer 925?hPa westerly winds in the northern Indian Ocean are stronger in R2. Similarly the continental precipitable water in R2 is much less while it is higher and comparable in MERRA and CFSR. Despite these climatological differences between the reanalyses, the climatological evaporative sources for rain events over central India show some qualitative similarities. Major differences however appear when interannual variations of the Indian summer monsoon are analyzed. The anomalous oceanic sources of moisture from the adjacent Bay of Bengal and Arabian Sea play a significant role in determining the wet or dry year of the Indian monsoon in CFSR. However in R2 the local evaporative sources from the continental region play a more significant role. We also find that the interannual variability of the evaporative sources in the break spells of the intraseasonal variations of the Indian monsoon is stronger than in the wet spells. We therefore claim that instead of rainfall, evaporative sources may be a more appropriate metric to observe the relationship between the seasonal monsoon strength and intraseasonal activity. These findings are consistent across the reanalyses and provide a basis to improve the predictability of intraseasonal variability of the Indian monsoon. This study also has a bearing on improving weather prediction for tropical cyclones in that we suggest targeting enhanced observations in the Bay of Bengal (where it is drawing the most moisture from) for improved analysis during active spells of the intraseasonal variability of the Indian monsoon. The analysis suggests that the land–atmosphere interactions contribute significant uncertainty to the Indian monsoon in the reanalyses, which is consistent with the fact that most of the global reanalyses do not assimilate any land-surface data because the data are not available. Therefore, the land–atmosphere interaction in the reanalyses is highly dependent on the land-surface model and it’s coupling with the atmospheric model.     

Evaluation of Surface Relative Humidity in China from the CRA-40 and Current Reanalyses

Recently, the China Meteorological Administration (CMA) released a new Global Atmospheric Reanalysis (CRA-40) dataset for the period 1979-2018. In this study, surface relative humidity (RH) from CRA-40 and other current reanalyses (e.g., CFSR, ERA5, ERA-Interim, JRA-55, and MERRA-2) is comprehensively evaluated against homogenized observations over China. The results suggest that most reanalyses overestimate the observations by 15％-30％ (absolute difference) over the Tibetan Plateau but underestimate the observations by 5％-10％ over most of northern China. The CRA-40 performs relatively well in describing the long-term change and variance seen in the observed surface RH over China. Most of the reanalyses reproduce the observed surface RH climatology and interannual variations well, while few reanalyses can capture the observed long-term RH trends over China. Among these reanalyses, the CFSR does poorly in describing the interannual changes in the observed RH, especially in Southwest China. An empirical orthogonal function (EOF) analysis also suggests that the CRA-40 performs better than other reanalyses to capture the first two leading EOF modes revealed by the observations. The results of this study are expected to improve understanding of the strengths and weaknesses of the current reanalysis products and thus facilitate their application.     

浙江省温度和相对湿度释用技术及其效果检验分析

基于多模式的要素预报和浙江省乡镇站点观测资料,结合全省天气统计特点,利用最优集成方案,改进了温度和相对湿度的1~7 d预报。统计检验发现,模式的温度预报在浙江省中南部主要表现为系统性偏差,在浙江省北部平原地区主要为随机误差。使用滑动平均误差订正后,浙中南等地形复杂地区温度预报的系统性偏差明显减小。在模式订正基础上,使用动态集成进一步减小了浙北平原地区温度预报的随机误差。基于温湿关系,使用改进后的温度预报对相对湿度预报进行订正。与传统的加权平均方法相比,改进后的温度预报均方根误差减小16.7%,相对湿度预报均方根误差减小13.8%,对改善浙江省精细化预报有一定参考意义。