很多取决于两个地方之间的地理范围。但总的来说往往会有一些相互依赖,甚至地方大型之间的距离,随着大规模的上层大气是由[波型][1]:[![在这里输入图像描述][2]][2]这个流模式[全球持续][3]。此外,事实上,太平洋的温度(对天气的影响几乎全世界)[4]显示重要的互连可以:[![在这里输入图像描述][5]][5]最独立的现象会中尺度现象(当地的)像海风雷暴和湖泊效应雪……尽管他们有一些公平依赖风向和政权的压力,参与更大的模式。之间的依赖两个相当遥远的位置虽然经常会很小。另一方面,很常见有几天下雨相隔数千英里不仅仅是有点相关,但是直接的结果相同的特性,例如这方面从2013年1月,维基百科[救了][6]:[![在这里输入图像描述][7]][7]所以从根本上有一些,至少小,全球降水事件在任何两个地点之间的互连。但是如果你可以选择和考虑地理位置(距离/环境)和气象因素(季节),你可能可以实证的结果基本上是独立的,随着大规模的模式之间的联系是一个次要的因素与其他因素的影响。* *换句话说,有一个沉淀在任意两个地点之间的依赖关系部分和一个独立的部分。统计依赖往往非常(非常)小的许多位置的选择,但更近的位置,在某些季节/天气事件。** If you are, for example, designing a school science fair experiment, you would likely struggle if you wanted to see [which animal specie can predict the weather][8] (spoiler: none!) by looking at snowfall totals, because there's quite a lot of dependence between the weather at the sites since they are mostly in the same region and their winter/spring climate is heavily tied to the large-scale upper atmospheric pattern and storm system tracks, so that would likely swamp out any "contribution" from the animal (in addition, geographical differences, such as terrain and relationship to bodies of water, would be another huge uncontrolled independent factor). On the other hand, if you were wanting to compare the influence of industrial pollution on the number of rainfall days, and carefully chose, for example, distant tropical coastal sites (perhaps a site in SE Asia versus one in the Caribbean) with similar weather influences/patterns, and looked at large datasets... you might be able to fairly inspect the magnitude of such influence for that regime. I often found meteorological phenomena to be challenging to design simple science fair projects around, as there are so many different interconnected variables, and it is hard to control most and leave one independent. That's why many meteorological studies are often focused more on [multivariate analysis][9] of large datasets and effects like [urban heat island][10] took quite a bit of careful analysis to recognize. It's not impossible, but to find two locations that are for all intents and purposes seeing independent weather events, while still not being controlled by other factors like their climate regimes/topography, can be quite challenging. **But while fundamentally no precipitation events are ever absolutely perfectly independent, with the right distance and location considerations their interdependence can be so negligible so as to consider the precipitation independent phenomena. I would consult a meteorologist to analyze how independent two such sites may be.** [1]: https://www.weather.gov/jetstream/basic [2]: https://i.stack.imgur.com/gtLkT.png [3]: https://www.weather.gov/jetstream/longshort [4]: https://www.climate.gov/news-features/featured-images/global-impacts-el-ni%C3%B1o-and-la-ni%C3%B1a [5]: https://i.stack.imgur.com/NBI1n.png [6]: https://en.wikipedia.org/wiki/Squall_line#/media/File:Eastern_US_squall_line_30_January_2013_radar_mosaic.png [7]: https://i.stack.imgur.com/dL6k5.png [8]: https://www.cleveland.com/weather/blog/2018/02/meet_20_other_weather-predicti.html [9]: https://en.wikipedia.org/wiki/Multivariate_statistics [10]: https://en.wikipedia.org/wiki/Urban_heat_island