In December 2018, the Danish Meteorological Institute organised an international meeting on the subject of crowdsourced data in numerical weather prediction (NWP) and weather forecasting. The meeting, spanning 2 days, gathered experts on crowdsourced data from both meteorological institutes and universities from Europe and the United States. Scientific presentations highlighted a vast array of possibilities and progress being made globally. Subjects include data from vehicles, smartphones, and private weather stations. Two groups were created to discuss open questions regarding the collection and use of crowdsourced data from different observing platforms. Common challenges were identified and potential solutions were discussed. While most of the work presented was preliminary, the results shared suggested that crowdsourced observations have the potential to enhance NWP. A common platform for sharing expertise, data, and results would help crowdsourced data realise this potential.
ABSTRACT:The investigation of weather forecast errors has focussed mainly on assessing the physical causes in numerical weather forecasting, such as errors in the initial state and model deficiencies. The framework and context of this paper are different. A 30 year time series (1979-2011) of 1 and 2 day human forecasts of temperature in Finland were used to investigate the statistical and climatological characteristics of maximum and minimum temperature forecast errors. Error dependence on different flow types and weather regimes was also analysed. The error statistics indicate that the forecast root-mean-square error has halved, and the forecast hit rate within a 2.5 ∘ C error threshold has increased from 70% to 85-90% during the 30 year period. Large forecast errors of >5 ∘ C are presently very rare, and they are mostly seen as positive biases in extreme cold temperatures and inversions. Otherwise, the forecasts nowadays are mostly unbiased with only small negative biases associated with Foehn events. Forecast error extremes were found during winter, with a secondary peak in spring and under high pressure periods in non-westerly and especially northeasterly airflows, dry air mass and relatively weak winds. The summertime maximum temperature errors are typically associated with opposite weather situations that occur most typically with cyclonic circulations, moist air masses and strong winds.KEY WORDS temperature forecast; forecast verification; forecast error; weather pattern; human forecast
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