Stratospheric Network for the Assessment of Predictability (SNAP)

Lead Research Organisation: University of Reading
Department Name: Meteorology

Abstract

Forecasting the weather from days to two weeks in advance has typically focused on the troposphere, the layer of the atmosphere closest to the ground. A typical weather forecast first attempts to estimate what the atmosphere is like now, and then extrapolates forward in time, using a complex model of the atmosphere based on the basic physical laws of motion. Over the last 15 years, evidence has been growing that different parts of the atmosphere and Earth system can also be exploited to improve weather forecasts. One of these regions is the stratosphere, the layer directly above the troposphere. Because, temperatures increase with height in the stratosphere, winds and weather systems are quite different, and a distinct community of scientific researchers who study the stratosphere exists around the world. Through the work of this community, many weather forecasting centres have been encouraged to look to the stratosphere to improve their weather forecasts and have been modifying their weather forecasting models accordingly. What has been missing, however, is a concerted effort to understand how best to make use of the stratosphere to improve weather forecasts and to determine how much weather forecasts might benefit.

This proposal will fund a new international scientific network which will bring scientists from around the world together to study the stratosphere and how it might be used to improve weather forecasts. The network is made up of scientists from universities and weather forecasting centres around the world and is supported by two other international scientific research bodies. The network will allow scientists to come together to discuss current research in this area and to plan and carry out a new experiment which will compare the stratosphere and its impact on weather forecasts in their weather forecasting models. At the end of the research project, the network members will work together to produce a report which will provide guidance to all weather forecasting centres on the use of the stratosphere for weather forecasting.

Planned Impact

The key beneficiaries of the research will be the weather forecasting centres who participate in the network and will gain valuable insight into how to maximise potential gains in tropospheric predictability from the stratosphere. Since they will be part of the network, it will be very easy to communicate the key results of the project to them through the two project workshops and the report that the network will produce. In the UK, the Met Office is a core member of the network and will benefit strongly from the research which meets their own strategic goals as expressed in their letter of support.

Since all of the weather forecasting centres provide their forecasts to a broad range of governmental and non-governmental customers, improving the skill of weather forecasts provides a direct way that research in this area can benefit the UK and other economies and general public well-being. It is worth noting that previous research has suggested that some of the most recent periods of disruptive weather in the UK, particularly the extreme cold conditions in December 2009, have been linked in part to stratospheric weather.

Publications

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Description In this project we assessed the predictability of the stratosphere in a number of different weather forecasting models. We needed to do this because in recent years the stratosphere has been implicated in tropospheric predicability and so it is important to understand how skilful forecasts of the stratosphere can be made and what limits their predictability.

Over a number of studies we were able to examine the predicability of the stratosphere in both the northern and southern hemisphere for a number of different weather forecast models. We found that:
1. It was possible to predict stratospheric variability up to 10 days in advance of significant events, although in some cases there were still significant discrepancies in the way in which models reproduced specific stratospheric properties
2. The prediction was limited by the skill of the models in predicting tropospheric processes

Following this work, we shared the data widely amongst collaborators who are beginning to use the data collected for their own work. We were also able to start to think about processes which determined how strongly stratospheric variability was linked to the surface. A publication at the end of the project showed how these kind of events can contribute strongly to enhanced tropospheric predictability and in our future work we plan to follow up on this aspect.
Exploitation Route The dataset of stratospheric forecasts we produced is publicly available through the BADC and can be exploited by others.
Sectors Energy

Environment

 
Description Partnership with WCRP/SPARC 
Organisation World Climate Research Programme
Country Switzerland 
Sector Academic/University 
PI Contribution SNAP team members lead the ongoing international part of the network as a core project of WCRP/SPARC. Recently we agreed that they would continue to support this initiative after the funded NERC portion of the network has ceased.
Collaborator Contribution Dr Charlton-Perez leads the WCRP/SPARC portion of the SNAP project, providing international leadership on stratospheric predicability and related initiatives with SPARC (which is one of the core projects of WCRP). This leadership has led to enhanced collaboration between SNAP and the sub-seasonal to seasonal prediction project of WWRP and a renewal of the SNAP project within SPARC for the next three years. A new co-chair from outside of the initial SNAP network (Amy Butler) has recently been appointed and we are involved in initiatives to promote the use of S2S data within the SPARC community and are producing a chapter for a forthcoming book on S2S predictability.
Impact We have produced two articles for the SPARC Newsletter on the SNAP project and are working on a chapter for a forthcoming book on S2S predictability.
Start Year 2016