（圖：Getty上的環(huán)球歷史檔案館/環(huán)球圖片集團）

時間：2022-04-09 16:21:01 | 來源：行業(yè)動態(tài)

時間：2022-04-09 16:21:01 來源：行業(yè)動態(tài)

業(yè)界已經(jīng)不乏關(guān)于人工智能及其應(yīng)用的好處的討論，包括約會、營銷和社交媒體到太空探索和醫(yī)學(xué)進步。每個行業(yè)都受到人工智能工具的影響，包括氣象行業(yè)。

Meteorology has always grappled with the problem of big data. I would even propose that the science was the epitome of big data before the word became mainstream. Due to the multivariate and chaotic nature of weather, for more than half of a century meteorologists have dealt with terabytes of data and modeling variables to produce an accurate forecast. Today, we are still processing data now on the scale of petabytes thanks to the Internet of Things, more sensors, and ensemble modeling. Writer Ted Alcorn estimates that, Todays (weather) models incorporate about 100 million pieces of data each day, a level of complexity comparable to simulations of the human brain or the birth of the universe.

氣象學(xué)一直都在努力解決大數(shù)據(jù)的問題。筆者甚至認(rèn)為，在大數(shù)據(jù)這個詞成為主流之前，氣象學(xué)就是大數(shù)據(jù)的縮影。天氣具有多變性和混沌性，半個多世紀(jì)以來，氣象學(xué)家一直在試著處理兆兆字節(jié)（TB）級別的數(shù)據(jù)和建模變量做準(zhǔn)確的天氣預(yù)測。到了今天，我們還是在處理數(shù)據(jù)，數(shù)據(jù)規(guī)模到了千兆兆字節(jié)（PB）級別，拜物聯(lián)網(wǎng)、更多的傳感器和集合建模所賜。寫手Ted Alcorn估計，今天的（天氣）模型每天用到約1億條數(shù)據(jù)，其復(fù)雜程度堪比對人腦或宇宙誕生的模擬。

But computing power and the advancement of technology such as AI have allowed us to not only analyze the data quicker and easier, but also learn from historical data for better situational awareness and decision-making. Within the weather community, AI is being applied to several different challenges. One focus is to make a better weather forecast.

但計算能力和人工智能等技術(shù)在不斷進步，我們現(xiàn)在不僅能夠更快、更容易地分析數(shù)據(jù)，而且還能從歷史數(shù)據(jù)中 學(xué)習(xí)，從而獲得更好的態(tài)勢感知和做出更好的決策。人工智能在氣象界能夠用于應(yīng)對幾個不同的挑戰(zhàn)。其中的一個重點是做出更好的天氣預(yù)報。

Forecasting is increasingly becoming more accurate. Today a five-day forecast has a 90% accuracy, the same as a three-day forecast 25 years ago. Short-term predictions, or now casting in hourly time spans, is more challenging particularly due to micro changes at the surface. Scientists at DeepMind and the University of Exeter have partnered with the U.K. Met Office to build a nowcasting system using AI that would overcome these challenges to make more accurate short-term predictions, including for critical storms and floods. Another research study is looking at the efficiency of modeling and how AI can analyze past weather patterns to predict future events, more efficiently and more accurately.