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天气预报真的是怎么做的?

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。

我发现一位年长的参考工作流从1993年开始,有一些信息,https://esrl.noaa.gov/gsd/eds/gfesuite/pubs/AWIPS-Forecast-Preparation-System.pdf但是,这可能是过时的,不谈气象方面。

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。

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?

回答

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。我建议你试试(数值天气预报和预报彗星模块)[1](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。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。1。 Know where I am forecasting for. Location is important. What do I know about the climatology. 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. 2. 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. 3. 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. 4. 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? 5. 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? 6. 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)? 7. 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. [1]: https://www.meted.ucar.edu/bom/intro_nwp/index.htm

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\ begingroup美元精彩的资源,谢谢!关键的火险天气模块也是伟大的,因为它显示了如何准备一个火前景基于数据和观察:meted.ucar.edu/fire/crit_fire \ endgroup美元
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2021年7月7日在0:16

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