Will coupling the Met Office forecast model to an ocean model improve weather predictions in the tropics?

CASE award with The Met Office (MATTHEWS_UENV20ARIES)

Will coupling the Met Office forecast model to an ocean model improve weather predictions in the tropics?

CASE award with The Met Office (MATTHEWS_UENV20ARIES)

Project Description

Supervisors

Prof Adrian Matthews, School of Environmental Sciences, University of East Anglia

Dr Ben Webber, School of Environmental Sciences, University of East Anglia

Dr Tim Graham, Met Office

Dr Prince Xavier, Met Office

 

Scientific background:

Numerical weather prediction (NWP) is the main tool behind weather forecasting of rainfall, temperature, winds and other meteorological variables. These forecasts are relied on for business planning, to protect life and property and by the general public. NWP is based on computer models of the physical laws that govern the atmosphere. The Met Office (CASE partner) runs one of the most advanced NWP systems in the world. As part of their ongoing drive to improve forecasts, they are moving beyond their “atmosphere-only” model and trialling a new “coupled ocean-atmosphere” NWP system including ocean processes. This is likely to improve forecast skill, particularly in the Tropics, where there are long-lived weather systems that interact strongly with the ocean, such as the Madden-Julian Oscillation (MJO), which is hard to forecast and linked to hazardous weather.

Research methodology:

You will join a team of weather, climate and NWP experts at UEA and the Met Office. You will assess the forecast skill of atmosphere-only and coupled configurations of the Met Office NWP model to quantify the benefit of coupling, focusing on the Tropics and the MJO. Additionally, you will perform a process-based analysis to determine the atmospheric and oceanic mechanisms behind any increase in skill. You will provide recommendations to the Met Office for future development of their coupled NWP system.

Training:

You will be trained in the analysis of NWP model output using the python programming language, including use of specific python modules developed by the Met Office for analysing and visualising model output. Depending on your background, you will be trained in atmospheric physics, meteorology and oceanography to equip you for the process-based analysis. In addition to frequent teleconferences with the Met Office co-supervisors, you will undertake visits to the Met Office for training in running and analysing the Met Office model. You will present your work at an international scientific conference.

Person specification:

You will be a physical sciences graduate (physics, mathematics, meteorology, oceanography, or similar), with a keen interest in applying theoretical understanding to solve real world environmental problems. Programming skills (e.g., Python, Matlab, Java) will be beneficial.

 

References

  • 1. Birch CE, Webster S, Peatman SC, Parker DJ, Matthews AJ, Li Y, Hassim ME, 2016: Scale interactions between the MJO and the western Maritime Continent. J. Climate, 29, 2471-2492. doi: 10.1175/JCLI-D-15-0557.1.
  • 2. Brunet G, Shapiro M, Hoskins B, Moncrieff M, Dole R, Kiladis GN, Kirtman B, Lorenc A, Mills B, Morss R, Polavarapu S, Rogers D, Schaake J, Shukla J, 2010: Collaboration of the weather and climate communities to advance subseasonal-to-seasonal prediction. Bull. Amer. Meteorol. Soc., 91, 1397-1406, doi: 10.1175/2010BAMS3013.1.
  • 3. Peatman SC, Matthews AJ, Stevens DP, 2014: Propagation of the Madden-Julian Oscillation through the Maritime Continent and scale interaction with the diurnal cycle of precipitation.Quart. J. Roy. Meteorol. Soc., 140, 814-825, doi: 10.1002/qj.2161.
  • 4. Webber BGM, Matthews AJ, Heywood KJ, 2010: A dynamical ocean feedback mechanism for the Madden-Julian Oscillation. Quart. J. Roy. Meteorol. Soc., 136, 740-754, doi: 10.1002/qj.604.
  • 5. Williams KD, Copsey D, Blockley EW, Bodas-Salcedo A, Calvert D, Comer R, Davis P, Graham T, Hewitt HT, Hill R, Hyder P, Ineson S, Johns TC, Keen AB, Lee RW, Megann A, Milton SF, Rae JGL, Roberts MJ, Scaife AA, Schiemann R, Storkey D, Thorpe L, Watterson IG, Walters DN, West A, Wood RA, Woolings T, Xavier PK, 2018: The Met Office global coupled model 3.0 and 3.1 (GC3.0 and GC3.1) configurations. J. Adv. Model. Earth Sys., 11, 357-380, doi: 10.1002/2017MS001115.

Key Information

  • This project has been shortlisted for funding by the ARIES NERC Doctoral Training Partnership, and will involve attendance at mandatory training events throughout the course of the PhD.
  • Successful candidates who meet UKRI’s eligibility criteria will be awarded a NERC studentship - UK and EU nationals who have been resident in the UK for 3 years are eligible for a full award.
  • Excellent applicants from quantitative disciplines with limited experience in environmental sciences may be considered for an additional 3-month stipend to take advanced-level courses in the subject area (see https://www.aries-dtp.ac.uk/supervisors/additional-funding/).
  • This studentship will start on 1st October 2020, and the closing date for applications is 12:00 on 7th January 2020.
  • Shortlisted applicants will be interviewed on 18/19 February 2020.
  • For further information, please contact the supervisor.
  • Please note that the joint NERC-ESRC ARIES-SeNSS studentship projects have different deadlines and funding arrangements. For full details please visit https://senss-dtp.ac.uk/aries-senss-joint-studentship, or contact SeNSS.dtp@uea.ac.uk.

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