天气预报真的是怎么做的?- 江南体育网页版- - - - -地球科学堆江南电子竞技平台栈交换 最近30从www.hoelymoley.com 2023 - 07 - 08 - t21:31:32z //www.hoelymoley.com/feeds/question/22490 https://creativecommons.org/licenses/by-sa/4.0/rdf //www.hoelymoley.com/q/22490 7 天气预报真的是怎么做的? 客人 //www.hoelymoley.com/users/22910 2021 - 07 - 03 - t19:38:05z 2021 - 07 - 05 - t23:26:29z < p >我想了解具体过程的气象学家天气预报办公室产生不同类型的天气预报。我理解数值天气模型是如何工作的,但我想学如何变成了一个预测模型输出和扩展它是改善由技术熟练的气象学家。< / p > < p >我发现一位年长的参考从1993年开始,有一些信息工作流,< a href = " https://esrl.noaa.gov/gsd/eds/gfesuite/pubs/AWIPS-Forecast-Preparation-System.pdf " rel = " noreferrer " > https://esrl.noaa.gov/gsd/eds/gfesuite/pubs/AWIPS-Forecast-Preparation-System.pdf < / >,但这可能是过时的和不谈气象。< / p > < p >有很多不同的预报产品从文本图形产品,我的问题可能过于宽泛,但到目前为止我还没有找到太多的信息,所以我不想被限制太多。< / p > < p >什么具体的模型输出预测看,他们使用当地的观察和经验扩展做什么? < / p > //www.hoelymoley.com/questions/22490/-/22500 # 22500 6 答案由f。索普是一个天气预报如何真的做了吗? f.thorpe //www.hoelymoley.com/users/543 2021 - 07 - 05 - t06:07:19z 2021 - 07 - 05 - t06:07:19z < p >美国国家气象局实际上有一个很好的< a href = " https://www.weather.gov/about/forecast-process " rel = " noreferrer " >预测总结页面< / >这回答你的问题:< / p > < blockquote > < p >我们的科学家彻底审查当前观测使用技术,如雷达、卫星和数据从各种各样的地面和机载仪器现状的全貌。江南登录网址app下载预测往往依赖于计算机程序来创建一个所谓的“分析”,这是一个简单的图形表示当前的条件。一旦创建完成评估和分析,预测者使用各种各样的数值模型,统计和概念模型和年的本地经验来确定当前的条件会随着时间改变。数值模拟是完全根深蒂固的在预测过程中,和我们的预测者每天复习这些模型的输出。通常,模型会产生不同的结果,在这种情况下,预测将决定哪个模型执行给定的情况或寻求一个混合的解决方案。< / p > < /引用> < p >他们也有一个很好的“< a href = " https://www.weather.gov/about/nws " rel = " noreferrer " > < / >“页;论述了区域办事处和< a href = " https://www.weather.gov/rah/virtualtourfcstcreation " rel = " noreferrer " >预测过程的虚拟之旅< / >。< / p > //www.hoelymoley.com/questions/22490/-/22503 # 22503 7 答案由BarocliniCplusplus天气预报真的是怎么做的? BarocliniCplusplus //www.hoelymoley.com/users/704 2021 - 07 - 05 - t23:26:29z 2021 - 07 - 05 - t23:26:29z < p >虚拟旅游是好的,我不知道为什么没有一个公认的答案。所以我把我的答案扔到戒指。我推荐你试试< a href = " https://www.meted.ucar.edu/bom/intro_nwp/index.htm " rel = " noreferrer " >数值天气预报和预测彗星模块< / >(它是免费的,如果让一个帐户)。比我的答案,可能会帮助更多,这是基于预测类,我和我的经验作为一个天气预报员在大学的学生服务。< / p > < p >从外面,预测过程是一个非常神秘的事情。但在现实中不是这样的。不过,它是高度主观的任务和人均受到某种程度的变异(因此一些人比其他人更好的预测)。它也需要大量的时间去做。这是我的如果我想成为严格的模式。<李> < / p > < ol >知道我预测。< /李> < / ol > < p >位置是很重要的。我知道气候学。 Climatology can give a "first guess" on the range of values that can be reasonably considered. The previous day's weather can also be a good first guess. If I am well acquainted with the area, then this becomes a shorter and shorter step.

  1. Start with observations.

What is happening now? What is reality saying? The amount of space that is looked at needs to be proportionate to the forecast time.

  1. Make inferences on the current state and causes of weather features.

What patterns are seen? Was map analysis done? Are there certain areas that are colder than others? Are there places that have clouds and others that don't have clouds? What does the radar say? Why, why why why why? If you know the mechanisms that are generating weather now, then you can understand how they might evolve.

  1. Examine how the weather models have started.

Before you use a weather model, you should understand it. Garbage in=garbage out, sometimes. How well did the model do today? If it is overpredicting temperature right now, will it continue overpredicting the temperature? Will the errors that occurred upstream yesterday occur today?

  1. Look at how the weather models progress. Question if it aligns with my knowledge and experience.

Taking a model at face value might work for research purposes (unless you are researching the model itself), but shouldn't be done on a practical level or in a rush. What does Model Output Statistics (MOS) say?

  1. Choose the right numbers or features.

This is probably the step that requires the least amount of explanation. Though the more intricate the type of forecast, the harder and harder this becomes. Does it actually require numbers, or is there some sort of GIS software (like for hurricane trajectory or lightning forecast)?

  1. Verify

This can't be stated enough. You must verify how well you did. Decisions need to be made on how the forecast will be verified. What data sources do you know of for verification? If I could move this up to number 1 and still have this make sense, I would. Because this is what you should start off with. This actually goes part and parcel with starting with observations, since observations are what you start a forecast with. Understand the processes of why your forecast was off. This will serve you in the future and in the next forecast.

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