{"id":2458,"date":"2022-08-05T17:54:30","date_gmt":"2022-08-05T17:54:30","guid":{"rendered":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/?p=2458"},"modified":"2022-08-05T18:23:25","modified_gmt":"2022-08-05T18:23:25","slug":"shall-i-take-an-umbrella-or-not","status":"publish","type":"post","link":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/?p=2458","title":{"rendered":"Shall I take an umbrella or not?"},"content":{"rendered":"
\"\"<\/a>
The man says: \u201cThe weather goes wrong. My phone shows \u2018SUN\u2019.\u201d (Source: roth-cartoons<\/a>)<\/figcaption><\/figure>\n

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Have you ever been in this situation: You are looking into your weather forecast app: There is a 40% chance that it will rain today. What does it mean? Shall I carry my umbrella with me or not? You decide not to take your umbrella with you \u2013 no risk no fun. And then it happens: It rains. <\/em><\/p>\n

Weather affect us all in our daily life. It\u2019s condition decides, whether to have a BBQ, if a farmer has to water it\u2019s field or if an open-air concert will take place. So, having a proper weather forecast would make the life easier. When why aren\u2019t they so accurate as they should be?<\/p>\n

In general one could say: the better the availability of data from satellites or measurement stations and the higher the resolution of the model, the better the forecast. A numerical weather prediction model is based on grid cells. If a phenomenon like a general weather condition (\u201cGro\u00dfwetterlage\u201d) covers several grid cells, it\u2019s easier to simulate, since the model have enough grid points to calculate with. But the smaller the phenomenon like precipitation, the harder it is to reproduce it, especially if a phenomenon happens to be smaller than a grid cell, it normally gets parametrized. So why not increasing the resolution of the model for that, one could ask. You could do that, if you have enough money and the technology for it: The finer the grid of a model, the more computing time and computing power it takes. In addition, weather forecasts are most accurate for the next couple of days. A forecasts beyond a week is less reliable. Since models can\u2019t collect data from the future, they estimate and assume upcoming weather based on data in the past with similar weather conditions. Still, the atmosphere is a complex system and the further away the event, the harder it is to predict due to too much uncertainty in what happens next.<\/p>\n

Precipitation is a special worrier in weather forecast, since its spatial and temporal resolution varies highly. First of all, you have to define, when to count rain as rain: does some droplets count as rain or is rain rain, when it fills up a bucket? If a cloud is supersaturated, precipitation occurs. But on the way down to earth a lot can happen: wind can blow droplets away or droplets collide with obstacles like dust and other particulate matter.<\/p>\n

Individual people often misinterpret the probability of precipitation (PoP) in forecasts or are unsure what the percentage stands for. To take up on the above example (40% PoP) and a reference class could enlighten that: If you look at past events with the exact or similar weather condition, in four out of ten days it was raining in that particular place. [1]<\/a> It tells not, how much time of the day it could rain. This kind of misinterpretation leads to a lack in understanding weather forecasts. So how about using verbal quantifiers instead of probabilistic numbers? A study from Murphy et al. (1980, [2])<\/a>, where they undertook a survey about the perception of precipitation probability forecasts, states, that verbal qualifiers are more confusing than numerical probabilities. The majority of the participants even preferred forecasts \u201cin which uncertainty is expressed in probabilistic terms\u201d. \u00a0Another investigation in the subject of people\u2019s interpretation of PoP by Juanchich and Sirota (2016, [3])<\/a> underlines the use of probabilities instead as verbal qualifiers as more plausible and recommend, to use reference classes, defined as a class of event that is sampled to produce the probability, to improve the interpretation of PoP.<\/p>\n

What I learned out of this confusing with PoP is, when I am planning an outdoor event within the next hours, I will look on special rain tracking apps, which can predict precipitation within the next couple of hours more accurate.<\/p>\n

 <\/p>\n

References:<\/p>\n

[1] Definition by: DWD Glossar – Nierderschlagswahrscheinlichkeit https:\/\/www.dwd.de\/DE\/service\/lexikon\/Functions\/glossar.html?lv3=101912&lv2=101812<\/a>, visited on the 3rd of August, 2022<\/p>\n

[2] Murphy, A.H., Lichtenstein, S., Fischhoff, B. and Winkler, R.L. (1980). Misinterpretations of Precipitation Probability Forecasts. Bulletin of the American Meteorological Society<\/em>, 69<\/em>(7), 695\u2013701. https:\/\/doi.org\/10.1175\/1520-0477(1980)061<0695:MOPPF>2.0.CO;2<\/a><\/p>\n

[3] Juanchich, M. and Sirota, M. (2016) How to improve people’s interpretation of probabilities of precipitation, Journal of Risk Research, 19:3, 388-404, DOI: 10.1080\/13669877.2014.983945<\/a><\/p>\n

 <\/p>\n","protected":false},"excerpt":{"rendered":"

  Have you ever been in this situation: You are looking into your weather forecast app: There is a 40% chance that it will rain today. What does it mean? Shall I carry my umbrella with me or not? You decide not to take your umbrella with you \u2013 no risk no fun. And thenContinue reading “Shall I take an umbrella or not?”<\/span><\/a><\/p>\n","protected":false},"author":107,"featured_media":2463,"comment_status":"open","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_post_was_ever_published":false},"categories":[172],"tags":[183],"class_list":["post-2458","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-posts-2022","tag-weatherforecasts"],"jetpack_featured_media_url":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/wp-content\/uploads\/2022\/08\/rain.jpg","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=\/wp\/v2\/posts\/2458","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=\/wp\/v2\/users\/107"}],"replies":[{"embeddable":true,"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=2458"}],"version-history":[{"count":3,"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=\/wp\/v2\/posts\/2458\/revisions"}],"predecessor-version":[{"id":2462,"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=\/wp\/v2\/posts\/2458\/revisions\/2462"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=\/wp\/v2\/media\/2463"}],"wp:attachment":[{"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=2458"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=2458"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/uncertain2degrees.blogs.uni-hamburg.de\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=2458"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}