{"id":83872,"date":"2019-02-06T13:37:43","date_gmt":"2019-02-06T21:37:43","guid":{"rendered":"https:\/\/www.esri.com\/about\/newsroom\/?post_type=arcuser&#038;p=83872"},"modified":"2024-05-10T16:56:32","modified_gmt":"2024-05-10T23:56:32","slug":"forecasting-weather-with-big-data-in-the-cloud","status":"publish","type":"arcuser","link":"https:\/\/www.esri.com\/about\/newsroom\/arcuser\/forecasting-weather-with-big-data-in-the-cloud","title":{"rendered":"Forecasting Weather with Big Data in the Cloud"},"author":1432,"featured_media":0,"menu_order":0,"template":"","format":"standard","meta":{"_acf_changed":false,"sync_status":"","episode_type":"","audio_file":"","podmotor_file_id":"","podmotor_episode_id":"","castos_file_data":"","cover_image":"","cover_image_id":"","duration":"","filesize":"","filesize_raw":"","date_recorded":"","explicit":"","block":"","itunes_episode_number":"","itunes_title":"","itunes_season_number":"","itunes_episode_type":"","_links_to":"","_links_to_target":""},"categories":[10432,23392,10992],"tags":[871,151,281,138062,137952],"arcuser_issues":[63142],"class_list":["post-83872","arcuser","type-arcuser","status-publish","format-standard","hentry","category-arcgis-enterprise","category-cloud-computing","category-managing-gis","tag-big-data","tag-iot","tag-location-intelligence","tag-science","tag-weather","arcuser_issues-fall-2017"],"acf":{"short_description":"With an abundance of satellites and remote-sensing devices monitoring weather systems all over the world, meteorologists now have more data avai\u2026","pdf":{"host_remotely":false,"file":"","file_url":""},"flexible_content":[{"acf_fc_layout":"image","image":83882,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"<em>With an abundance of satellites and remote-sensing devices monitoring weather systems all over the world, meteorologists now have more data available to them than ever before. But more data doesn\u2019t necessarily translate into improved predictions.<\/em>\r\n\r\nBecause of the size and complexity of weather patterns, they can be very difficult to predict more than a few days in advance. Researchers continually refine their instruments and processes to better understand the climate and make more accurate weather forecasts.\r\n\r\nEsri partner Weather Decision Technologies, Inc. (WDT), uses advanced GIS from Esri to better organize and analyze this big data. WDT provides weather forecasting and mapping services to many industries: energy corporations to help them predict electrical outages and keep offshore oil rigs safe; agriculture agencies for crop insurance; freight transportation companies to aid with route design; and concert and sporting event organizers for planning and safety.\r\n\r\n\u201cThe amount of data that we collect is enormous, about one terabyte per day,\u201d said Matt Gaffner, GIS solutions expert at WDT. \u201cOver the years, we have assembled an archive of almost half a petabyte of weather data.\u201d\r\n<h2>Analyzing Clouds in the Cloud<\/h2>\r\nThe company currently uses ArcGIS Server to develop all its map services for customers, though it plans to upgrade to\u00a0ArcGIS Enterprise\u00a0later this year.\r\n\r\n\u201cOne of the great things about the ArcGIS Server platform is that it\u2019s extremely easy to publish live, dynamic, rapidly updated data and then host it as a service for use with other applications,\u201d said Gaffner. \u201cThis allows our users to quickly add past, present, and future weather data to their maps and apps.\u201d It also enables WDT\u2019s partners that build apps for specific vertical markets, such as utilities, to add weather data to their apps using WDT\u2019s map services.\r\n\r\nCloud computing is key to WDT\u2019s operations. The company uses Amazon Web Services (AWS) to deploy the analytical and mapping services that ArcGIS Server provides. \u201cThere are so many advantages available to us by using Amazon Web Services,\u201d Gaffner pointed out.\r\n\r\nWith cloud services, the company can stand up multiple machine instances of ArcGIS Server to determine the operational stack that runs the best. \u201cFor one thing, it allows us to implement the \u2018fail faster\u2019 mantra. Using the ArcGIS Amazon Machine Image (AMI) capability, we can easily stand up a version of the server in the cloud and try something new\u2014like using different machine hardware specifications or configuring data services differently\u2014to see if it works or not. If it works, great. If it doesn\u2019t work, then we stand up another instance and try something else.\u201d\r\n\r\n\u201cAWS provides us with reliability because if something goes wrong, we can easily replicate ArcGIS on another machine in the cloud,\u201d Gaffner said. \u201cIt also provides us with load balancing\u2014that is, we can redistribute the many requests we receive for weather data and map services between our servers. This extra demand normally happens when the weather changes and storms begin to develop. Our datasets increase in size then because there is more radar data accumulating, and our customers need access to that data.\u201d\r\n\r\nWDT is currently doing a lot with time-enabled map services. For example, users can loop through the last 60 minutes of radar data to see where a storm has been and where it is headed. WDT is also launching a time-enabled global forecasts map service that will provide both daily and hourly forecasts for all the normal weather variables\u2014temperature, precipitation, wind speed, and direction\u2014out to 10 days. Because the company is serving out bigger datasets to accommodate more requests, using cloud services makes WDT\u2019s servers scalable at critical times.\r\n<h2>Environmental Conditions Affect Businesses\u2014and Decisions<\/h2>"},{"acf_fc_layout":"image","image":83892,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"WDT\u2019s ever-growing collection of big geospatial datasets allows users to conduct unique analyses across time and space. For example, one of its customers\u2014an oil and gas company\u2014wanted to determine how much the weather affected the productivity of its crews in Oklahoma versus Colorado.\r\n\r\n\u201cThey wanted to figure out the productivity of each, but they wanted to statistically normalize that comparison by taking into consideration the local weather conditions,\u201d explained Gaffner.\r\n\r\nDuring the summer, it could be 90 degrees in both Oklahoma and eastern Colorado, but with more humidity in Oklahoma, the heat index would likely be higher than in Colorado. \u201cBy using historical weather data,\u201d said Gaffner, \u201cwe were able to perform this analysis and found that weather can impact crews in two ways: decrease worker efficiency under heat stress and push the heat index above a critical threshold where workers are required to take mandatory breaks.\u201d"},{"acf_fc_layout":"image","image":83902,"image_position":"center","orientation":"horizontal","hyperlink":""},{"acf_fc_layout":"content","content":"WDT is also considering the impact that the Internet of Things (IoT) will have on geospatial analysis.\r\n\r\n\u201cTake, for example, the connected car,\u201d said Gaffner. \u201cWe can provide real-time weather or a weather forecast to your car that might tell you, \u2018Hey, you should probably stop driving to avoid that storm,\u2019 or, \u2018There\u2019s a line of thunderstorms moving through your area; you might as well stay at work for another 20 minutes and wait until it passes through and then drive home.\u2019\u201d\r\n\r\nIf weather and environmental data is combined, it can help mitigate risk and enable people to make smarter decisions. And, as Gaffner hopes, the confluence of big data with smart analysis can save lives and property in the long run.\r\n\r\nFor more information on WDT, visit\u00a0<a href=\"http:\/\/wdtinc.com\/\">wdtinc.com<\/a>."},{"acf_fc_layout":"pdf","file":84002}],"references":null},"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Forecasting Weather with Big Data in the Cloud | Fall 2017 | ArcUser<\/title>\n<meta name=\"description\" content=\"With an abundance of satellites and remote-sensing devices monitoring weather systems all over the world, meteorologists now have more data available to them than ever before.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.esri.com\/about\/newsroom\/arcuser\/forecasting-weather-with-big-data-in-the-cloud\" \/>\n<meta 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