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Related Concept Videos

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 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 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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Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Downsampling01:20

Downsampling

220
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Types of Coprecipitation01:10

Types of Coprecipitation

763
Coprecipitation is the contamination of a precipitate by otherwise soluble species and occurs via different processes. In colloidal precipitates, coprecipitation occurs via surface adsorption. For instance, barium sulfate has a primary layer of adsorbed barium ions and a secondary layer of nitrate counterions. This results in contamination of the precipitate by barium nitrate.
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A Generative Deep Learning Approach to Stochastic Downscaling of Precipitation Forecasts.

Lucy Harris1, Andrew T T McRae1, Matthew Chantry2

  • 1Department of Physics University of Oxford Oxford UK.

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|January 2, 2023
PubMed
Summary

Generative adversarial networks (GANs) improve precipitation forecasts by enhancing low-resolution weather model data. This deep learning approach creates more accurate, high-resolution precipitation maps, outperforming existing downscaling methods.

Keywords:
deep learningdownscalingmachine learningneural networkspostprocessingprecipitation forecasting

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Area of Science:

  • Meteorology
  • Artificial Intelligence
  • Computer Vision

Background:

  • Precipitation forecasts lack accuracy due to sub-grid scale processes not resolved by global weather models.
  • Generative adversarial networks (GANs) excel at super-resolution tasks in computer vision, adding fine-scale detail to coarse images.

Purpose of the Study:

  • To enhance the accuracy and resolution of low-resolution weather forecasting model precipitation data.
  • To apply GANs and Variational Autoencoder-GANs (VAE-GANs) for precipitation downscaling using radar data as ground truth.

Main Methods:

  • Utilized GANs and VAE-GANs to learn super-resolution for precipitation fields.
  • Trained models using low-resolution weather forecast data and high-resolution radar measurements as ground truth.
  • Accounted for forecast errors during the downscaling process.

Main Results:

  • Developed models that match statistical properties of state-of-the-art post-processing methods.
  • Generated high-resolution, spatially coherent precipitation maps.
  • Achieved favorable comparisons against existing downscaling methods in CRPS scores, power spectrum analysis, and calibration assessment.

Conclusions:

  • GANs and VAE-GANs offer a powerful approach for precipitation downscaling and accuracy improvement.
  • The developed models demonstrate robust performance across various scenarios, including heavy rainfall.
  • This deep learning technique advances the reliability of precipitation forecasting.