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相关概念视频

Precipitation and Co-precipitation01:17

Precipitation and Co-precipitation

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Precipitation and coprecipitation methods can be used to separate a mixture of ions in a solution. In qualitative inorganic analysis, ions that form sparingly soluble precipitates with the same reagent are separated based on the differences in solubility products. For example, consider the separation of Cu(II) and Fe(II) ions by precipitation as insoluble sulfides. First, copper(II) sulfide is precipitated by the addition of acidic H2S, where the dissociation of H2S is suppressed. Adding H2S...
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Precipitation Processes01:12

Precipitation Processes

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The experimental conditions in a gravimetric analysis should be optimized to maximize the particle size and purity of the obtained precipitate. Ideally, the concentration of the precipitating reagent should be low with effective stirring to maintain low relative supersaturation for the growth of large crystals. In homogeneous precipitation, the precipitant is slowly generated by a chemical reaction in the solution to avoid local reagent excesses. For example, urea decomposes gradually to...
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Precipitation Gravimetry01:03

Precipitation Gravimetry

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Precipitation gravimetry is based on converting an analyte into a sparingly soluble precipitate, which is separated by filtration and weighed. An ideal precipitate should be pure, insoluble, of known composition, and easily filtered from the reaction mixture.
In determining nickel by gravimetric analysis, a precipitant of ethanolic dimethylglyoxime is added to a hot nickel salt solution. This is quickly followed by the dropwise addition of dilute ammonia solution until precipitation occurs. A...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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Global Climate Change01:50

Global Climate Change

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Throughout its ~4.5 billion year history, the Earth has experienced periods of warming and cooling. However, the current drastic increase in global temperatures is well outside of the Earth’s cyclic norms, and evidence for human-caused global climate change is compelling. Paleoclimatology, the study of ancient climate conditions, provides ample evidence for human-caused global climate change by comparing recent conditions with those in the past.
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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相关实验视频

Updated: May 24, 2025

Early Detection of Cyanobacterial Blooms and Associated Cyanotoxins using Fast Detection Strategy
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基于卫星遥感数据的全天云属性和发生概率数据集.

Longfeng Nie1,2,3, Yuntian Chen4,5, Dongxiao Zhang6,7,8,9

  • 1Pengcheng Laboratory, Shenzhen, 518000, P. R. China.

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|March 5, 2025
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概括

CldNet版本2.0 (CldNetV2) 增强了云的分类和属性预测,为气象研究提供了关键的全天数据集. 这一进步提高了对气候的理解,并填补了夜间数据缺口.

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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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Measurement of Aerosols Optical Thickness of the Atmosphere using the GLOBE Handheld Sun Photometer
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Last Updated: May 24, 2025

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Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
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科学领域:

  • 气象学和气候科学 气象学和气候科学
  • 遥感 遥感 遥感 遥感
  • 人工智能在地球观测中的作用

背景情况:

  • 准确的云属性数据集对于气象研究,气候研究和应用至关重要.
  • 现有的卫星云产品往往缺乏全面的夜间数据和多样化的云属性信息.
  • CldNet版本2.0建立在之前的工作基础上,将云识别功能扩展到全日夜周期.

研究的目的:

  • 引入CldNet版本2.0 (CldNetV2) 进行增强的云类型分类和属性预测.
  • 为了生成全面的,全天云特性和发生概率的数据集.
  • 为了解决当前希马瓦里云产品的局限性,特别是在夜间条件下.

主要方法:

  • 从基础的CldNet中利用转移学习和模型参数共享技术.
  • 开发一个深度学习模型来分类云类型和预测多个云属性.
  • 在年度,季节性和月度时间尺度上统计分析云类型发生概率,区分全天,白天和夜间.

主要成果:

  • CldNetV2成功地对云类型进行了分类,并预测了额外的云属性,从而创建了有价值的夜间数据集.
  • 生成的数据集包括各种时间尺度和条件 (全天,白天,夜晚) 的云类型发生概率.
  • 使用CALIPSO,ERA5和可视化的独立验证证实了CldNetV2云产品的可靠性.

结论:

  • CldNetV2显著提升了全天云属性和事件概率评估的能力.
  • 公开发布的数据集为气象环境评估和气候研究提供了宝贵的资源.
  • 这项工作有助于通过全面的云数据更好地理解和建模大气过程.