Ben Pickering

Ben Pickering

Profile

Ben Pickering is a postgraduate researcher studying hydrometeor classification (rain, snow, hail e.t.c) from the newly upgraded dual-polarisation UK radar network (C-Band, 5.60 – 5.65 GHz). Ben is focused on developing verification techniques for these categorical, discreet products. This will be achieved by developing a new framework of 'big data' observations: in-situ such as the FAAM aircraft, and ground-based observations like Met Office stations, crowdsourced weather reports and the newly installed Disdrometer Verification Network (DiVeN) for comparison. Ben developed the DiVeN network of 14 laser disdrometers in 2017 as part of his PhD work on radar verification which took several months and thousands of miles of travel around the UK to install.


Funding Awards

NERC: Developing Robust Calibration Methods for Deployable Radars (£42.2k) (supporting researcher)

AMS Travel bursary to present oral talk at 39th Radar Meteorology Conference, Nara, Japan (£1.4k)

Met Office MOAP Collaboration Fund (£1.25k)

NCAS Visiting Scientist Programme (£1.2k)

RMetS Legacy Fund (£1.2k)

DiVeN (£4.5k)

CASE Travel Fund (£8.0k)

NERC funded studentship with CASE sponsorship (£11.0k + £4.0k)


Students

Jonathan Coney (2019-20) Goldmine or Bust? Crowdsourced Meteorological Data for Atmospheric Science.

Dongqi Lin (2017-18) Identifying the Microphysical Difference in UK Precipitation for Periods with and without Radar Bright Band.


Field Campaigns

Lead PI of DiVeN (Feb 2017 - ongoing, 3 years+)

RELAMPAGO (Nov-Dec 2018, 3 weeks)

PECAN (May-July 2015, 6 weeks)


Disdrometer Verification Network (DiVeN)

A network of 14 laser precipitation instruments installed around the UK in 2017.

Live Data – Journal Paper – NetCDF Dataset – News Article – Science Poster – Campaign Video – RMetS Talk


Conferences

12-16 January 2020 – 100th American Meteorological Society Annual Meeting, Boston, U.S. (Oral Talk)

16-20 September 2019 – 39th International Conference on Radar Meteorology, Nara, Japan (Oral Talk)

2-3 July 2019 – UK Atmospheric Science Conference, Birmingham, UK (Poster)

8 October 2018 – ICAS Away Day, Leeds, U.K. (Poster)

1-6 July 2018 – 10th European Conference on Radar, Ede, Netherlands

28 Aug - 1 Sep 2017 – 38th International Conference on Radar Meteorology, Chicago, U.S. (Poster)

11-12 July 2017 – RMetS 'Impact of Science' Conference, Exeter, U.K. (Poster)

3 November 2016 – ICAS Away Day, Leeds, U.K. (Poster)


Invited Talks & Appearances

RMetS Scottish Local Centre, Edinburgh; The Golden Age of Weather Radar: summary of PhD work with wider context (26th March 2021)

Sky News; talking about the unseasonably warm weather at the time in relation to the jet stream and climate change (4th April 2019)

RMetS PhD Student Showcase; presenting research to a public non-scientific audience. Slides: docdro.id/F7MFxVX (31MB) (18th April 2018)

Livestreamed Interview with University of Leeds; talking about how the weather is forecast; why snow is difficult to forecast, my research to improve observations and what it’s like to be a PhD student at Leeds (8th December 2017).

RMetS Special Interest Group Meeting on Low-Cost Instrumentation; talk at University of Birmingham about DiVeN being low-cost. Slides: docdro.id/FsvhaIh (50MB) (19th October 2017)


Past (completed) work


Contact

Prefered Email: ben.pickering@ncas.ac.uk
Twitter: @wx_radar


Memberships / Associations

Holder of a UK CAA Permission for Commercial Operations (PfCO) document (which permits UAS research flights)

STEM Ambassador

RMetS Ambassador

Associate Fellow of the Royal Meteorological Society

Member of the American Meteorological Society

Member of the Aerosol Society

Member of the Priestley International Centre for Climate

Committee member of UK RADAS (Research Association for the use of Drones in the Atmospheric Sciences)

Committee member of RMetS Yorkshire Local Centre

Committee member of RMetS Student Conference 2020

Co-organiser of Chatmosphere, Dynamics Group meetings, Radar Group meetings


 

Research interests

Generally I am interested in observations of the atmosphere, which is an area we can, and must, improve upon in order to continue advances in weather forecasting. These advances are essential as humanity faces great societal changes with the climate emergency such as renewable energy forecasting and agricultural strains, and new industries like drones and autonomous vehicles. My interests can be broadly assigned to three areas:

Unmanned Aerial Systems (UAS / UAV, aka Drones)

I believe there is an emerging new field to be found in the development of UAS for meteorological uses. In particular, a vertically profiling instrumented UAS that can land, recharge and fly again without human interaction would provide sufficient data for next-gen numerical weather prediction, with significant enhancement of forecasts of the atmospheric boundary layer. This will enable greater accuracy of forecasts for solar and wind energy, air quality, fog, and will be essential for future UAS industries such as inner-city delivery and transport.

Opportunistic Meteorological Measurements

We need more observations of the atmosphere to support higher resolution models. One opportunity to retrieve these observations is to use existing sensors that are not intended for meteorological use, and turning them into something useful. Examples include sensors on autonomous vehicles (cameras, lidar, temperature and humidity, windscreen wiper speed), publicly available webcams or traffic cameras, street light sensors (for solar radiation) or even the attenuation between cellular towers (for precipitation). Home weather stations are an obvious example, but there are many challenges in the standardisation of sensors and representativity depending on the owner’s exact placement of the modules.

Machine Learning and Data Assimilation

Machine learning is an exciting new field which, when applied correctly, is very powerful. The previous categories above require high quality machine learning (e.g. using computer vision to recognise meteorological conditions or clouds, in cameras) to succeed. Data assimilation uses similar mathematical techniques to some machine learning methods, and is the key link between meteorological observations and forecasting, which we must get right if new observations are to have any value. Atmospheric radar is a good example of an untapped source of numerical weather prediction initialisation data; it is extremely difficult to give radar data fundamental physical meaning, and other potential observations such as camera images of clouds are the same. This field must be given equal consideration when developing techniques to utilise novel observations.

Other interests include hydrometeor type observations, flood forecasting, martian meteorology to enable human settlements, renewable energy forecasting, air quality forecasting, forecast communication.

Qualifications

  • M.Met. 1st Class (Hons), Meteorology and Climate with a year in Oklahoma, University of Reading

Research groups and institutes

  • Institute for Climate and Atmospheric Science