Details of Award
NERC Reference : NE/N008227/1
Seasonal forecasts of East African rainfall
Training Grant Award
- Lead Supervisor:
- Professor C Birch, University of Leeds, School of Earth and Environment
- Grant held at:
- University of Leeds, School of Earth and Environment
- Science Area:
- Atmospheric
- Overall Classification:
- Atmospheric
- ENRIs:
- Environmental Risks and Hazards
- Global Change
- Science Topics:
- Convective cloud & precip
- Boundary Layer Meteorology
- Monsoonal processes
- Atmospheric fluxes
- Land - Atmosphere Interactions
- Atmospheric circulation
- Convection
- Teleconnections
- Large Scale Dynamics/Transport
- Tropospheric Processes
- Atmospheric modelling
- Boundary layer
- Convective precipitation
- Deep convection
- Monsoon systems
- Rainfall
- Weather forecasting
- Water In The Atmosphere
- Abstract:
- This studentship will work closely with the Met Office (MO) CASE partner to improve understanding of the key drivers of East African (EA) droughts and heavy rainfall events, evaluate state-of-the-art EA seasonal weather forecasts and diagnose the sources of model biases, enabling improved predictions for this vulnerable region. EA has one of the world's highest population growths with extremely rapid urbanisation. Its population and economy are heavily reliant on rain-fed agriculture. It is therefore extremely vulnerable to drought, heavy rainfall and flooding. A recent example is the year-long severe drought of 2011 which affected the entire region through reduced rainfall over two consecutive rainy seasons; this caused disease, crop failure, widespread loss of livestock, and the deaths of tens to hundreds of thousands. Limited infrastructure in the rapidly growing cities makes them vulnerable to heavy rainfall and flooding. For example, a severe storm caused significant disruption and flooding in the Dar es Salaam region of Tanzania in April 2014. Given the humanitarian impact of these events, it is crucial to predict them accurately, with the seasonal prediction regularly stated as the key timescale by local governmental and aid agencies. Despite the importance of prediction capability, numerical models are still not able to adequately represent the long and short rainy seasons in EA and skill scores for seasonal forecasts from models involved in the recent ENSEMBLES project were poor for EA compared to those for West and South Africa. Consequently, improvements in seasonal forecasting for EA rainfall would have major benefits. Effective seasonal prediction remains a grand challenge of meteorology. The MO Global Seasonal forecast system (GloSea) is continually being developed to improve predictions on monthly to seasonal timescales. The system was updated in January 2013 to run with a higher resolution ocean model and more ensemble members, which is at the forefront of global seasonal forecasting prediction capability. This configuration has produced significant improvements to the accuracy of seasonal forecasts in the North Atlantic region. Thus far there is limited understanding of how these upgrades have impacted the EA region and recently there has been only very limited work on seasonal forecasting in EA rains within the MO, although forecasts are routinely issued ahead of the rainy seasons: http://www.metoffice.gov.uk/research/climate/seasonal-to-decadal/long-range/forecasts. Improved scientific understanding of seasonal predictability and predictions for EA would have significance to research worldwide. The student will begin by using satellite and in-situ observations of rainfall and other quantities to understand the main drivers of severe events. He/she will then evaluate GloSea and diagnose the processes responsible for any major model biases as a function of forecast lead time. The ability of GloSea to predict extremes in seasonal mean rainfall will be assessed, with a particular focus on extreme years, such as the drought years of 2010-2011 and the floods in 2014. In the latter stages of the project the student will run his/her own simulations or use higher-resolution model simulations made available through other projects for focused studies of the key processes as diagnosed through GloSea. The proposed research has a clear route to impact through the MO and EA partners. The student will spend periods of time working at the MO and interact with operational weather forecasters that focus on the EA region and model developers to translate results research into model and forecast improvements. Both the MO and Leeds have a strong track-record in working with EA Meteorological Agencies and these links will be used to arrange for the student to visit EA for the mutual exchange of knowledge and skills.
- NERC Reference:
- NE/N008227/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Industrial CASE
This training grant award has a total value of £88,304
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - Student Stipend | Total - RTSG |
|---|---|---|
| £17,309 | £59,997 | £11,000 |
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