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Over half a billion smartphones worldwide are now capable of measuring atmospheric pressure, providing a pressure network of unprecedented density and coverage. This paper describes novel approaches for the collection, quality control, and bias correction of such smartphone pressures. An Android app was developed and distributed to several thousand users, serving as a test bed for onboard pressure collection and quality-control strategies. New methods of pressure collection were evaluated, with a focus on reducing and quantifying sources of observation error and uncertainty. Using a machine learning approach, complex relationships between pressure bias and ancillary sensor data were used to predict and correct future pressure biases over a 4-week period from 10 November to 5 December 2016. This approach, in combination with simple quality-control checks, produced an 82% reduction in the average smartphone pressure bias, substantially improving the quality of smartphone pressures and facilitating their use in numerical weather prediction.

Over a half-billion smartphones are now capable of measuring atmospheric pressure, potentially providing a global surface observing network of unprecedented density and coverage. An earlier study by the authors described an Android app, uWx, that served as a test bed for advanced quality control and bias correction strategies. To evaluate the utility and quality of the resulting smartphone pressure observations, ensemble data assimilation experiments were performed for two case studies over the Pacific Northwest. In both case studies, smartphone pressures improved the analyses and forecasts of assimilated and nonassimilated variables. In case I, which considered the passage of a front across the region, cycled smartphone pressure assimilation consistently improved 1-h forecasts of the altimeter setting, 2-m temperature, and 2-m dewpoint. During a postfrontal period, cycled smartphone pressure assimilation improved mesoscale forecasts of hourly precipitation accumulation. In case II, which considered a major coastal windstorm, cycling experiments assimilating smartphone pressures improved 10-m wind forecasts as well as the predicted track and intensity. For both cases, free-forecast experiments initialized with smartphone data produced forecast improvements extending several hours, suggesting the utility of crowdsourced smartphone pressures for short-term numerical weather prediction.

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. 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. Using citizen observations to improve operational weather forecasts. Noncorrected forecast of temperature (left) and analysed (corrected) field (right). Coloured circles are the observations (in °C)

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. 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. It was concluded that crowdsourced observations are likely to be useful for NWP. Finally, it is recommended that a platform for sharing thoughts, data and results is worked upon moving forward.

My first impression is that we have the usual crazily eclectic hodge-podge of bands playing at Glastonbury, stretching right across the music spectrum.

From our recent reader research we know that, uniquely among young people buying magazines, NME readers value a strong editorial voice and seek out named writers across the magazine. I'll admit that up to now we haven't always drawn enough attention to this but it's something we're rectifying in the new-look NME when we add byline photographs to our major pieces along with a host of other improvements and innovations.

The compilation, Future's Burning, will mark the return next month of Nude Records. Nude founder Saul Galpern put the label - which formed in 1992 and was home to Suede - on ice in 2002 after the end of its link with Sony's Sine. Galpern has joined forces with V2 for the album, which also features The Dead 60s, The Duke Spirit and Kaiser Chiefs.