Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–5 December 2018

Hintz, Kasper S.; O'Boyle, Katharine; Dance, Sarah L.; Al-Ali, Saja; Ansper, Ivar; Blaauboer, Dick; Clark, Matthew R.; Cress, Alexander; Dahoui, Mohamed; D'Arcy, R J; Hyrkkänen, Juhana; Isaksen, Lars; Kaas, Eigil; Korsholm, Ulrik Smith; Lavanant, M.; Bloa, Gwenaelle Le; Mallet, Emilie; McNicholas, Callie; Onvlee-Hooimeijer, Jeanette; Sass, Bent Hansen; Siirand, Valeria; Vedel, Henrik; Waller, John C.; Yang, Xiaohua

doi:10.1002/asl.921

Cited by 26 publications

(24 citation statements)

References 22 publications

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“…The different sources of data typically include recent measurements of precipitation and a previous forecast valid at the same time the measurements are made (Dance et al, 2019). The development of convection‐permitting NWP models has necessitated international research efforts in data assimilation over the past few years (Dance et al, 2019; Flack, Skinner, et al, 2019; Hintz et al, 2019). Unlike large‐scale models that rely on some form of parametrisation process to add in convective features, convection‐permitting NWP models can represent convective structures directly (Clark et al, 2016).…”

Section: Advances In Meteorological Monitoring and Forecastingmentioning

confidence: 99%

Operational and emerging capabilities for surface water flood forecasting

et al. 2021

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Surface water (or pluvial) flooding is caused by intense rainfall before it enters rivers or drainage systems. As the climate changes and urban populations grow, the number of people around the world at risk of surface water flooding is increasing. Although it may not be possible to prevent such flooding, reliable and timely flood forecasts can help improve preparedness and recovery. Unlike riverine and coastal flooding where forecasting methods are well established, surface water flood forecasting presents a unique challenge due to the high uncertainties around predicting the location, timing, and impact of what are typically localized events. Over the past 5 years, there has been rapid development of convection‐permitting numerical weather prediction models, ensemble forecasting, and computational ability. It is now theoretically feasible to develop operational surface water forecasting systems. This paper identifies three approaches to surface water forecasting utilizing state‐of‐the‐art meteorological forecasts: empirical‐based scenarios, hydrological forecasts linked to presimulated impact scenarios, and real‐time hydrodynamic simulation. Reviewing operational examples of each approach provides an opportunity to learn from international best practice to develop targeted, impact‐based, surface water forecasts to support informed decision‐making. Although the emergence of new meteorological and hydrological forecasting capabilities is promising, there remains a scientific limit to the predictability of convective rainfall. To overcome this challenge, we suggest that a rethink of the established role of flood forecasting is needed, alongside the development of interdisciplinary solutions for communicating uncertainty and making the best use of all available data to increase preparedness. This article is categorized under: Engineering Water > Engineering Water

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Section: Advances In Meteorological Monitoring and Forecastingmentioning

confidence: 99%

Operational and emerging capabilities for surface water flood forecasting

et al. 2021

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“…While uWx demonstrated that smartphone pressures could be retrieved frequently and efficiently, the quality of the observations remained poor. Since smartphone pressure sensors have biases which are systematic (Price et al ., 2018; Hintz et al ., 2019a), the primary source of uncertainty was attributed to location/elevation errors. This realisation facilitated a machine learning approach to predict and bias‐correct smartphone pressure errors, using only data from smartphone sensors and GPS.…”

Section: Crowd‐sourced Observations and Data Assimilationmentioning

confidence: 99%

Crowd‐sourced observations for short‐range numerical weather prediction: Report from EWGLAM/SRNWP Meeting 2019

Hintz

McNicholas

Randriamampianina

et al. 2021

Atmospheric Science Letters

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Crowd-sourced observations (CSO) offer great potential for numerical weather prediction (NWP). This paper offers a synthesis of progress, challenges and opportunities in this area based on a special session of the EWGLAM Meeting in 2019, concentrating on high-resolution limited-area models (LAMs). Two main application areas of CSO are described: data assimilation and verification. One part of data assimilation developments concentrates on smartphone pressure observations, which represent a large volume of data. However, special care has to be taken about data protection and the quality of observations. In this paper, two examples are presented: the SMAPS experiment from Denmark and the uWx experiment from the United States. Another data assimilation topic is citizen observations with low-cost weather sensors; here an example from Norway is presented using Netatmo stations. The other application area is the use of CSO for model verification. One novel method developed in the United Kingdom is applying social media data to detect severe weather events. This approach is especially important because one future application area of LAM NWP models is impact-oriented warnings.

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“…Barometric pressure obtained via smartphones has been suggested by Mass and Madaus (2014) as a data source to improve forecasts of mesoscale phenomena. McNicholas and Mass (2018) and Hintz et al (2019a) assimilated smartphone pressure observations and found them to increase forecast skill. The Weather Observations Website (WOW) project is examining how to collect data from personal weather stations (PWS) as additional data (Met Office 2017).…”

Section: Introductionmentioning

confidence: 99%

“…A comprehensive overview of current crowdsourcing activities in NWP is given by Hintz et al (2019b), and Krennert et al (2018) give a more general overview of recent crowdsourcing activities within Europe.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Wind Speed and Roughness Length Using Smartphones: Method and Quality Assessment

Hintz

Vedel

Kaas

et al. 2020

Journal of Atmospheric and Oceanic Technology

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Crowdsourced data is now seen as a potential source of high-resolution observations in the atmospheric sciences. In this paper we investigate a potential data source, wind observations obtained using anemometers connected to handheld smartphones. The aim of this paper is twofold: to assess the quality of raw and height extrapolated wind measurements from the handheld anemometer against professional-grade SYNOP stations, and to use this data of opportunity to infer a more accurate estimation of terrain roughness lengths. Roughness lengths are essential in numerical weather prediction; however, they are often poorly determined. Roughness lengths are also necessary when correcting near-surface wind observations for height offsets. For the analysis we performed a series of field experiments measuring wind profiles using handheld anemometers at roughly 2 m above ground. These raw measurements were then extrapolated to 10 m height using roughness lengths from three different sources. The extrapolation enabled us to compare the quality of roughness lengths estimated from smartphone measurements to those from traditional sources, as well as assess the quality of these wind measurements against the professional-grade stations. We find the handheld wind measurements are comparable in quality to wind measurements from SYNOP stations at 10 m height, and that for some cases the handheld measurements can be more representative than SYNOP stations only about a kilometre away. To determine the roughness lengths, we examine a method based on the turbulent intensity derived from the high-frequency signal of the smartphone wind measurements. Under certain circumstances, the roughness lengths obtained with the approach presented here are superior to traditional sources.

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Collecting and utilising crowdsourced data for numerical weather prediction: Propositions from the meeting held in Copenhagen, 4–5 December 2018

Cited by 26 publications

References 22 publications

Operational and emerging capabilities for surface water flood forecasting

Operational and emerging capabilities for surface water flood forecasting

Crowd‐sourced observations for short‐range numerical weather prediction: Report from EWGLAM/SRNWP Meeting 2019

Estimation of Wind Speed and Roughness Length Using Smartphones: Method and Quality Assessment

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