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71.
The effects of past climate change on the northern limits of maize planting in Northeast China 总被引:8,自引:0,他引:8
Northeast China (NEC) is one of the major agricultural production areas in China and also an obvious region of climate warming. We were motivated to investigate the impacts of climate warming on the northern limits of maize planting. Additionally, we wanted to assess how spatial shifts in the cropping system impact the maize yields in NEC. To understand these impacts, we used the daily average air temperature data in 72 weather stations and regional experiment yield data from Jilin Province. Averaged across NEC, the annual air temperature increased by 0.38 °C per decade. The annual accumulated temperature above 10 °C (AAT10) followed a similar trend, increased 66 °C d per decade from 1961 to 2007, which caused a northward expansion of the northern limits of maize. The warming enabled early-maturing maize hybrids to be sown in the northern areas of Heilongjiang Province where it was not suitable for growing maize before the warming. In the southern areas of Heilongjiang Province and the eastern areas of Jilin Province, the early-maturing maize hybrids could be replaced by the middle-maturing hybrids with a longer growing season. The maize in the northern areas of Liaoning Province was expected to change from middle-maturing to late-maturing hybrids. Changing the hybrids led to increase the maize yield. When the early-maturing hybrids were replaced by middle-maturing hybrids in Jilin Province, the maize yields would increase by 9.8 %. Similarly, maize yields would increase by 7.1 % when the middle-maturing hybrids were replaced by late-maturing hybrids. 相似文献
72.
Xinhui Bi Bernd R.T. Simoneit Guoying Sheng Shexia Ma Jiamo Fu 《Atmospheric Research》2008,88(3-4):256-265
Total suspended particles (TSP), collected during June 2002 to July 2003 in Guangzhou, a typical economically developed city in South China, were analyzed for the organic compound compositions using gas chromatography–mass spectrometry (GC/MS). Over 140 organic compounds were detected in the aerosols and grouped into different classes including n-alkanes, hopanoids, polycyclic aromatic hydrocarbons, alkanols, fatty acids, dicarboxylic acids excluding oxalic acid, polyols/polyacids, lignin products, phytosterols, phthalates and water-soluble sugars. The total amounts of the identified organic compounds including unresolved complex mixture (UCM) ranged from 3112 ng/m3 in spring to 5116 ng/m3 in winter, comprising on seasonal average 2.8% of TSP. Primary organic compounds peaked in winter although there are no heating systems burning fuels in Guangzhou. The highest saccharide levels occurred in fall due to agricultural activities. This study demonstrated that utilization of fossil fuels, biomass burning, soil resuspension and plastic/refuse burning are the major contributors to the identified organic compounds in the urban atmosphere of South China. 相似文献
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在低纬地区,风速的垂直切变也是很明显的,作者解释了这些纬向气流的垂直切变对低纬长波性质以及对不同模态的相互作用的影响,并且发现切变对低纬波动的影响具有明显的选择性. 相似文献
76.
基于卫星遥感图象纹理特征的云类识别方法及软件设计 总被引:4,自引:0,他引:4
介绍了基于卫星遥感资料,运用纹理学识别云类的原理、方法及软件设计。理论与实际分析表明,不同的云类对纹理敏感,纹理特征量是识别云类的良好因子,软件设计充分考虑了模块化,可视化与交互性。 相似文献
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Bin Wang June-Yi Lee In-Sik Kang J. Shukla C.-K. Park A. Kumar J. Schemm S. Cocke J.-S. Kug J.-J. Luo T. Zhou B. Wang X. Fu W.-T. Yun O. Alves E. K. Jin J. Kinter B. Kirtman T. Krishnamurti N. C. Lau W. Lau P. Liu P. Pegion T. Rosati S. Schubert W. Stern M. Suarez T. Yamagata 《Climate Dynamics》2009,33(1):93-117
We assessed current status of multi-model ensemble (MME) deterministic and probabilistic seasonal prediction based on 25-year (1980–2004) retrospective forecasts performed by 14 climate model systems (7 one-tier and 7 two-tier systems) that participate in the Climate Prediction and its Application to Society (CliPAS) project sponsored by the Asian-Pacific Economic Cooperation Climate Center (APCC). We also evaluated seven DEMETER models’ MME for the period of 1981–2001 for comparison. Based on the assessment, future direction for improvement of seasonal prediction is discussed. We found that two measures of probabilistic forecast skill, the Brier Skill Score (BSS) and Area under the Relative Operating Characteristic curve (AROC), display similar spatial patterns as those represented by temporal correlation coefficient (TCC) score of deterministic MME forecast. A TCC score of 0.6 corresponds approximately to a BSS of 0.1 and an AROC of 0.7 and beyond these critical threshold values, they are almost linearly correlated. The MME method is demonstrated to be a valuable approach for reducing errors and quantifying forecast uncertainty due to model formulation. The MME prediction skill is substantially better than the averaged skill of all individual models. For instance, the TCC score of CliPAS one-tier MME forecast of Niño 3.4 index at a 6-month lead initiated from 1 May is 0.77, which is significantly higher than the corresponding averaged skill of seven individual coupled models (0.63). The MME made by using 14 coupled models from both DEMETER and CliPAS shows an even higher TCC score of 0.87. Effectiveness of MME depends on the averaged skill of individual models and their mutual independency. For probabilistic forecast the CliPAS MME gains considerable skill from increased forecast reliability as the number of model being used increases; the forecast resolution also increases for 2 m temperature but slightly decreases for precipitation. Equatorial Sea Surface Temperature (SST) anomalies are primary sources of atmospheric climate variability worldwide. The MME 1-month lead hindcast can predict, with high fidelity, the spatial–temporal structures of the first two leading empirical orthogonal modes of the equatorial SST anomalies for both boreal summer (JJA) and winter (DJF), which account for about 80–90% of the total variance. The major bias is a westward shift of SST anomaly between the dateline and 120°E, which may potentially degrade global teleconnection associated with it. The TCC score for SST predictions over the equatorial eastern Indian Ocean reaches about 0.68 with a 6-month lead forecast. However, the TCC score for Indian Ocean Dipole (IOD) index drops below 0.40 at a 3-month lead for both the May and November initial conditions due to the prediction barriers across July, and January, respectively. The MME prediction skills are well correlated with the amplitude of Niño 3.4 SST variation. The forecasts for 2 m air temperature are better in El Niño years than in La Niña years. The precipitation and circulation are predicted better in ENSO-decaying JJA than in ENSO-developing JJA. There is virtually no skill in ENSO-neutral years. Continuing improvement of the one-tier climate model’s slow coupled dynamics in reproducing realistic amplitude, spatial patterns, and temporal evolution of ENSO cycle is a key for long-lead seasonal forecast. Forecast of monsoon precipitation remains a major challenge. The seasonal rainfall predictions over land and during local summer have little skill, especially over tropical Africa. The differences in forecast skills over land areas between the CliPAS and DEMETER MMEs indicate potentials for further improvement of prediction over land. There is an urgent need to assess impacts of land surface initialization on the skill of seasonal and monthly forecast using a multi-model framework. 相似文献