Finalists for Harry Otten Prize 2017 to give presentations at EMS meeting in Dublin
The Board of the Harry Otten Foundation has selected three finalists for the Harry Otten Prize for 2017. They are Professor Lee Chapman (School of Geography, Earth and Environmental Sciences, University of Birmingham, UK), Tom de Ruijter (Meteogroup, Wageningen, Netherlands), and Gert-Jan Steeneveld and Sytse Koopmans (Wageningen University).
The three finalists will present talks about their ideas at the 2017 meeting of the European Meteorological Society (EMS), to be held 4-8 September 2017 in Dublin, Ireland. (https://www.ems2017.eu/ ). The talks will be in PSE3 (Plenary Sessions and Special Events-3) from 14:00 to 15:30 on Monday September 4 in the Gallery.
After the presentations the Board will meet to select the first-place winner of the prize and possible second and third prize winners. The winner(s) will be announced and awarded their prizes during the EMS Awards session (PSE4) on Tuesday, 5 September from 18:15 to 19:30 in the Theatre.
The titles and abstracts for the three talks are presented below:
High-Resolution Monitoring of Weather Impacts on Infrastructure Networks
Lee Chapman
The impacts of weather and climate on infrastructure are numerous. From a business perspective, the largest opportunities exist in the prediction of smaller impacts where preventative action can be taken by operators/end-users to reduce the severity of the weather event, for example, winter road maintenance, railway buckling, leaves-on-the-line, wind impacts on power cabling etc. Advances in modelling mean that these impacts can now be predicted at a high resolution (e.g. route based forecasting for winter road maintenance) so that mitigation activities can be actioned at vulnerable sections of the infrastructure network.
However, whilst high-resolution models have been in operational use for the last decade, in an environment of increasing litigation, practitioners remain nervous about making decisions solely based on model output. This means that the verification of forecasts is now needed on a scale previously not required, and it is only with this step that end-users will be responsive to using methods which will save money without compromising safety on the network (e.g. selective salting for winter road maintenance where only the coldest sections of road are treated or localised rail speed restrictions in hot weather as opposed to the blanket restrictions currently used).
Hence, there is a clear and pressing need for high-resolution infrastructure monitoring, but existing techniques are simply not capable of producing this solution. Point measurements using traditional sensors are too expensive to install in the numbers required and therefore lack the spatial resolution. Mobile measurements provide an alternative, but these lack the temporal resolution to provide the full picture. Therefore, it is proposed that the emerging Internet of Things could be transformative in this sector, providing the enabling technology to saturate our infrastructure networks with low-cost sensors. In doing so, it will not only provide unprecedented monitoring of weather impacts on infrastructure networks, but would also open the door to a new generation of nowcasting products harnessing the benefits of high resolution observations. These two developments combined will enabling the targeting of costly mitigation efforts more effectively than ever before.
Making use of errors in consumer weather data to derive advanced weather parameters
Tom de Ruijter
Consumer weather stations are widely present in the current age of technology, with currently over 200 thousand online stations online world-wide. These stations and their data do not come without problems as they are not maintained and checked by professional meteorologists. While error filtering is possible, we could also use the information within these structural errors.
This work highlights two out of many possible uses for such structural measurement errors. First, it is possible to derive night-time effective cloud cover maps based on cooling behavior. Second, it is possible to derive the wet-solid precipitation type distinction based on errors in community precipitation measurements.
The night-time cloud cover prediction is mainly based on the effect that most consumer stations are not placed in ventilated Stevenson screens and are more sensitive to changes in long wave radiation, caused by for example clear spells. We derive a machine learning model to nowcast effective cloud cover.
The wet-solid precipitation type is based on the ‘shortcoming’ that consumer precipitation measurement cups freeze over and fill up when exposed to solid precipitation. When combined with radar or satellite images, the precipitation type can accurately be deduced.
In the presentation, we provide a detailed explanation as well as several use cases and initial verification results.
CrowDat@ssimilation: Assimilation of crowdsourced meteorological data in NWP models to improve small-scale weather forecasts.
Gert-Jan Steeneveld and Sytse Koopmans
Crowdsourcing in meteorology has become more and more a valuable approach. Hobby meteorologists put forward their collected data on websites as netatmo.com, weathersignal.com, wunderground.com. Moreover air temperatures can be estimated from smartphone battery temperatures. This development helps to understand the weather in data-scarce regions. So far, crowdsourced data has mainly been used for academic research by evaluating them against routine observations, and for model validation. However, here we propose to develop the CrowDat@ssimilation tool to evaluate the value of crowdsourced observations in numerical weather prediction via data-assimilation into initial fields of the NWP model WRF. The highest relevance is expected in small-scale processes, and therefore the CrowDat@ssimilation tool will explore three weather phenomena with strong horizontal gradients: 1) the urban heat island effect, 2) a squall line, and 3) a sea breeze.