Joe Zagrodnik (AgWeatherNet Postdoctoral Reseacher)<\/p>\n
Dave Brown (AgWeatherNet Director)<\/p>\n
Growers rely on weather data to manage their crops and make decisions about irrigation, pest and disease prevention, frost mitigation, labor allocation, and more. Traditionally, meteorological observations have been obtained through automated weather stations such as those maintained by AgWeatherNet, but it is cost prohibitive to place a full professional weather station in every field and orchard so growers must account for differences in weather conditions between their location and the nearest weather station.<\/p>\n
One proposed solution that has recently received attention is the concept of \u201cvirtual\u201d weather stations. A virtual weather station uses gridded weather data from commercial providers interpolated to any latitude and longitude. Virtual\u00a0weather\u00a0data is inexpensive and ubiquitous with many different commercial providers delivering\u00a0weather\u00a0data to online tools and mobile apps. Data providers start with a \u201cgrid\u201d output from global\u00a0weather\u00a0models, then apply various proprietary statistical corrections and interpolations to deliver estimated\u00a0weather\u00a0data down to the nearest 3 miles or less.<\/p>\n
AgWeatherNet meteorologists have completed an intensive analysis of\u00a0virtual\u00a0weather\u00a0data from a leading commercial provider (DarkSky) and found the accuracy for air temperature to be inadequate for many agricultural decision support needs. For 8.5 years of AgWeatherNet\u00a0station\u00a0data with replicate air temperature sensors at 156 locations (445,102 daily comparisons):<\/p>\n
\u2022 24% of DarkSky daily high temperatures were off by more than<\/strong> 2\u00b0F; <\/p>\n <\/p>\n <\/p>\n It would be great if inexpensive virtual weather data could replace weather stations. And virtual weather data might well be better than using weather station data from 20 miles away or at a very different elevation. But growers should be aware that virtual weather data is not nearly as accurate as a weather station. The mean error for AWN weather station air temperature is < 0.3 \u00b0F, and generally < 1 \u00b0F for quality commercial sensors from companies like Davis Instruments, METER or Pessl. \u00a0Washington State topography, nocturnal inversions, and cold air flows combine to generate complex microclimates that make weather data interpolation challenging (particularly at night).<\/p>\n AgWeatherNet meteorologists are actively researching\u00a0weather\u00a0data interpolation methods. But our understanding and experience suggest that for any location in Washington where a\u00a0virtual\u00a0weather\u00a0product is to be used, at least some ground truth data should be collected for site-specific calibration and validation.<\/p>\n For many locations, interpolation and virtual weather stations will not work, and site-specific instrumentation provides the only accurate\u00a0weather\u00a0data solution. With the support of the Whatcom and Skagit County Conservation Districts, AgWeatherNet has added additional stations for these two counties. \u00a0And AgWeatherNet now offers a free \u201cTier 3\u201d service, ingesting data from approved private all-in-one weather stations and affordable temperature\/relative humidity sensors (https:\/\/weather.wsu.edu\/?p=115550<\/a>) and making data from these private installations available on AgWeatherNet and AWNfarm platforms.<\/p>\n <\/p>\n Questions: Email AgWeatherNet staff at weather@wsu.edu.<\/a><\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n <\/p>\n [\/textblock][\/column][\/row]<\/p>\n\n
\n\u2022 21% of DarkSky daily low temperatures were off by more than<\/strong> 4\u00b0F, with a systematic 1\u00b0F warm bias;<\/p>\n![\"\"](\"https:\/\/wpcdn.web.wsu.edu\/extension\/uploads\/sites\/37\/2021\/03\/Picture1.png\")
<\/a>\n
<\/a><\/p>\n\n
<\/a><\/p>\n