量子粒子群模糊神经网络碳酸盐岩流体识别方法研究

根据不同流体性质在角度道集上所反映特征的差异,构建了多属性角度叠加数据体组合流体识别因子.并将量子粒子群与模糊神经网络相结合,利用量子粒子群方法来优化模糊神经网络中的连接权值和隶属函数参数,并进行一系列的改进措施,显著提高了算法的全局寻优能力.将近远角度叠加数据体组合流体识别因子作为改进模糊神经网络的输入,流体性质作为输出,同时引入“相控流体识别”的思想,利用碳酸盐岩储集相进行控制,建立了碳酸盐岩流体识别模型.通过塔中实际井区进行验证,证明该方法能够提高流体的识别精度,具有很好的实际应用价值.     

Accumulation over the Greenland Ice Sheet as Represented in Reanalysis Data

Annual precipitation,evaporation,and calculated accumulation from reanalysis model outputs have been investigated for the Greenland Ice Sheet (GrIS),based on the common period of 1989-2001.The ERA-40 and ERA-interim reanalysis data showed better agreement with observations than do NCEP-1 and NCEP-2 reanalyses.Further,ERA-interim showed the closest spatial distribution of accumulation to the observation.Concerning temporal variations,ERA-interim showed the best correlation with precipitation observations at five synoptic stations,and the best correlation with in situ measurements of accumulation at nine ice core sites.The mean annual precipitation averaged over the whole GrIS from ERA-interim (363 mm yr 1) and mean annual accumulation (319 mm yr 1) are very close to the observations.The validation of accumulation calculated from reanalysis data against ice-core measurements suggests that further improvements to reanalysis models are needed.     

An ocean data assimilation system in the Indian Ocean and west Pacific Ocean

The development and application of a regional ocean data assimilation system are among the aims of the Global Ocean Data Assimilation Experiment. The ocean data assimilation system in the regions including the Indian and West Pacific oceans is an endeavor motivated by this goal. In this study, we describe the system in detail. Moreover, the reanalysis in the joint area of Asia, the Indian Ocean, and the western Pacific Ocean(hereafter AIPOcean) constructed using multi-year model integration with data assimilation is used to test the performance of this system. The ocean model is an eddy-resolving,hybrid coordinate ocean model. Various types of observations including in-situ temperature and salinity profiles(mechanical bathythermograph, expendable bathythermograph, Array for Real-time Geostrophic Oceanography, Tropical Atmosphere Ocean Array, conductivity–temperature–depth, station data), remotely-sensed sea surface temperature, and altimetry sea level anomalies, are assimilated into the reanalysis via the ensemble optimal interpolation method. An ensemble of model states sampled from a long-term integration is allowed to change with season, rather than remaining stationary. The estimated background error covariance matrix may reasonably reflect the seasonality and anisotropy. We evaluate the performance of AIPOcean during the period 1993–2006 by comparisons with independent observations, and some reanalysis products. We show that AIPOcean reduces the errors of subsurface temperature and salinity, and reproduces mesoscale eddies. In contrast to ECCO and SODA products, AIPOcean captures the interannual variability and linear trend of sea level anomalies very well. AIPOcean also shows a good consistency with tide gauges.     

夏季南亚高压对其邻近UTLS区域臭氧分布的影响

利用欧洲中期数值预报中心（ECMWF）提供的ERA5高分辨率月平均再分析资料,分析了1991—2020年共30 a夏季6—8月南亚高压对其邻近上对流层—下平流层区域臭氧（O₃）分布的影响。结果表明：ERA5资料能清晰地刻画出南亚高压夏季6—8月的月变化及振荡特征,同时能很好地反映出对应的臭氧低值这一现象。此外,ERA5高分辨率资料还能揭示出臭氧低值的范围和强度变化与南亚高压的范围和强度变化之间存在紧密联系。当南亚高压异常偏强（弱）时,邻近区域的臭氧低值增强（减弱）,当南亚高压中心位置发生振荡时,臭氧浓度纬向偏差的中心也随之发生变化,这种变化在南亚高压偏强年时更明显。但这种联系有一定复杂性,不同月份的相关性存在差异。南亚高压对臭氧浓度的这种影响主要与对流层的空气输送有关,臭氧低值中心位置和范围的变化与位涡低值、位温纬向偏差负值区域分布相对应。     

Variations of Surface Heat Fluxes over the Tibetan Plateau before and after the Onset of the South Asian Summer Monsoon during 1979–2016

As the "Third Pole of the World," the Tibetan Plateau (TP) is an important thermal forcing to the South Asian summer monsoon (ASM) and even the global atmospheric circulation. In this paper, surface heat fluxes from the ERA-Interim reanalysis data during March-October of 1979-2016 in the TP and its surrounding areas are examined and analyzed. The results are as follows.(1) From March to May (before the ASM onset), the main body of the TP is dominated by sensible heat flux, which increases rapidly with high (low) values in the west (east), while the change of latent heat flux is small but it increases with time.(2) From June to August (after the ASM onset), sensible heat flux over the TP decreases, while latent heat flux increases rapidly with high (low) values in the east (west).(3) From September to October (after the ASM withdrawal), sensible and latent heat fluxes are comparable to each other in strength, again with high (low) sensible heat flux in the west (east).(4) During 1979-2016, surface sensible heat flux in the whole TP shows a slightly downward trend, while latent heat flux shows an increasing trend. Specifically, in the western TP, sensible (latent) heat flux shows a weak decreasing (an increasing) trend;while in the eastern TP, sensible (latent) heat flux decreases (increases obviously). These variations are consistent with the observed warming and moistening in the TP region. The above results are useful for further analysis of the change of atmospheric heat sources and surface heat fluxes over the TP based on the data from the Third Tibetan Plateau Atmospheric Science Experiment (TIPEX-Ⅲ).     

长时间序列多源土壤湿度产品在中国地区的比较分析

研究利用实测数据对遥感产品(ECV CCI)、全球陆面同化系统产品(GLDAS/CLM、NOAH2.7、NOAH3.3、VIC、MOSAIC)、再分析资料(ERA-Interim、NCEP/DOE)等不同来源的8套长时间序列土壤湿度产品进行时空比较。结果表明:上述产品均可以模拟出中国不同区域土壤湿度的空间分布;从各产品平均态与实测数据的偏差、均方根误差、相关系数统计结果来看,NOAH模式和ERA-Interim表现最好,MOSAIC和NCEP/DOE与实测相差较大;从区域尺度上来看,所有产品在东北区域表现最好,在华南、西南南区和西北西区较差;ERA-Interim有效的模拟了土壤湿度的年际变化。     

Evaluation of Reanalysis Products with in situ GPS Sounding Observations in the Eastern Himalayas

Previous studies have shown that reanalysis products contain large uncertainties in the Tibetan Plateau(TP),and the biases are location dependent.Therefore,these products must be evaluated with in situ observations prior to their applications.In this study,the authors compare the results of several reanalysis projects with independent sounding observations recorded in the Yarlung Tsangpo River valley in the Eastern Himalayas in June 2010.These reanalysis projects include Climate Forecast System Reanalysis(CFSR),Interim European Centre for Medium Range Weather Forecasts Reanalysis(ERA- Interim),Japanese 25-year Reanalysis(JRA-25),Modern Era Retrospective Analysis for Research and Applications(MERRA),National Center for Environmental Prediction and the Department of Energy Reanalysis 2(NCEP-R2),and NCEP Final Analysis(FNL).Statistical quantities such as average,mean bias(MB),root-mean-square difference(RMSD),and correlation coefficient(R) of temperature,specific humidity,u-wind,and v-wind between 100 hPa and 650 hPa were calculated.The authors determined that the performance of each product differed with variables at different levels.The average profiles of the variables were captured by the reanalysis products,with large biases appearing at lower levels.ERA-Interim and NCEP-R2 showed the best and worst performances,respectively,for all variables.This study suggests that users should select appropriate reanalysis products according to their specific purposes for TP research.     

三种再分析气温资料在中国西部地区的可信度评价

再分析资料质量检验和可信度分析是其合理使用的必要前提,是提高气候变化研究成果可靠性的根本保证,为了评价NCEP-II、ERA-40和JRA-25三种再分析资料在中国西部地区的可信度,研究对比分析了2m气温产品的质量。结果表明,这三种再分析气温产品都能较好地反映中国西部地区气温的空间分布特征,在地形复杂区域与站点气温的差异比较大,而在地势相对平缓的地区这三种再分析资料都能提供高质量的气温产品。由于同化了地面观测资料,ERA-40、JRA-25气温产品的可信度相对较高,且能够提供更为详尽的大尺度气温变异性。总体上,ERA-40的质量优于其他两种再分析资料。     

Different data,different general circulations? A comparison of selected fields in NCEP/DOE AMIP-II and ECMWF ERA-40 reanalyses

Reanalysis datasets have been very popular for understanding the general circulation as well as verifying general circulation models. The most recent versions of global reanalysis datasets prepared by ECMWF (“ERA-40”) and NCEP (“NDRa2”) are examined in this article. The NDRa2 data are regridded to the resolution (2.5° × 2.5° longitude and latitude) of the ERA-40 public data. Primary variables that both relate to the atmosphere's general circulation and are readily available are compared and contrasted. Significant differences are found in the primary circulation variables and energetics. The zonal mean Hadley cells are stronger in ERA-40, but differences in temperature and moisture make the poleward heat transport by the cells more similar in the two datasets. The subtropical and polar night jet streams are stronger in ERA-40 data as is kinetic energy. The surface energy budgets differ in that ERA-40 data have greater sensible heat flux into the air, while NDRa2 data have greater latent heat flux. The result is NDRa2 has more moisture in the subtropics; ERA-40 data have more moisture in the tropics. Geographically, the two datasets have notable differences in their treatment of the intertropical convergence zone (ICZ). The ICZ over the Atlantic and eastern Pacific is narrower and stronger in ERA-40 data. The ICZ over the western Pacific and Indian oceans is generally stronger in NDRa2 data, one consequence is a stronger tropical easterly jet in NDRa2 data over the Indian Ocean in JJA. Both datasets have a double ICZ in the western half of the Pacific in DJF; in JJA ERA-40 retains that double ICZ but NDRa2 largely does not. Beyond the handling of the ICZ, the datasets differ in tropical zonal mean zonal wind, ERA-40 data in DJF has zonal mean upper troposphere tropical westerlies where NDRa2 data have easterlies; this difference may imply a different amount of interhemispheric communication. The datasets also have strong disagreements in regions of large-scale higher topography.     

Stochastic Post-Processing of CFSR Daily Precipitation across Canada

Reanalyses, based on numerical weather prediction methods assimilating past observations, provide continuous precipitation datasets and represent interesting options for assessing the climatology of regions with sparse station networks (e.g., northern Canada). However, reanalysis series cannot be used directly because of possible biases and mismatch between their spatial and temporal resolutions with that needed for local applications. To address these issues, a Stochastic Model Output Statistics (SMOS) approach was selected to post-process precipitation series simulated by the Climate Forecast System Reanalysis (CFSR) across Canada. This approach uses CFSR precipitation as a covariate and is based on two regression models: the first one is a logistic regression that deals with precipitation occurrence, and the second is a vector generalized linear model for precipitation intensity. At-site post-processed daily precipitation series are randomly generated using the SMOS approach, and selected climate indicators from the Expert Team on Climate Change Detection and Indices, which is jointly sponsored by the Commission for Climatology of the World Meteorological Organization's (WMO) World Climate Data and Monitoring Programme, the Climate Variability and Predictability Programme of the World Climate Research Programme, and the Joint WMO-IOC Technical Commission for Oceanography and Marine Meteorology (CCI/CLIVAR/JCOMM) are estimated and compared with corresponding observed and CFSR values. The two models in the SMOS approach, in addition to adequately correcting systematic biases, produced better predictions than the climatology of the wet and dry and intensity sequences. Additionally, the SMOS generally yields consistent climate indices when compared with those from CFSR without post-processing, though there is still room for improvement for specific indices (e.g., annual maximum of cumulative wet days).