EXPECT

EXtending the PrEdiCTability of the Atmosphere over Europe

Started
February 1, 2020
Status
In Progress
Share this post

Abstract

Currently available weather forecasts over Europe tend to have skill up to about one week, although theoretical estimates indicate a theoretical limit of about 3 weeks (Buizza et al., 2015; Domeisen et al., 2018a). After that, predictability often decreases sharply. However, longer term forecasts are possible if information is available that provides an increased probability of a long-lasting event, or an influence from a region that has a slower variability – such as the ocean (e.g. Duchez et al., 2016) – or longer predictability – such as the tropics (e.g. Greatbatch et al., 2015; Scaife et al., 2017; Wulff et al., 2017). The upper atmosphere, i.e. the stratosphere at about 12 – 50km above the Earth’s surface, is such a region that provides increased predictability to Europe after extreme stratospheric events, so-called Sudden Stratospheric Warming (SSW) events (Scaife et al., 2016). These events can provide skill over Europe for up to several weeks to months (Sigmond et al., 2013), with persistently colder than usual weather over Northern and central Europe. One of these events occurred in February 2018 and led to persistent cold weather in Europe in late February and early March after an otherwise mild winter.

SSW events themselves are however only possible to predict several days in advance. An extended prediction of SSW events would therefore significantly benefit forecasts at the surface. While the stratosphere is not the only region with predictive potential for Europe, most other predictors exhibit a pathway through the stratosphere, such as e.g. predictability arising from the tropics (e.g. Domeisen et al., 2015; Butler et al., 2016). It is therefore crucial to understand the predictability of the stratosphere itself. However, SSW events are difficult to classify, and indeed there exists a range of methods and classifications. This project aims to find an automated classification of these events in addition to an automated analysis of predictability over Europe. The main objectives of this project are described here:

  • Only recently have predictions on sub-seasonal to seasonal timescales, i.e. weeks to months, become publicly available. This project aims at extracting novel insights from this data using data science tools.
  • A first step will be an improved classification of stratospheric events, allowing for a flexible definition that includes the predictability aspects of these events.
  • In a second step, this project aims to classify remote predictors of long-term weather variability. In particular, known predictors for stratospheric and tropospheric variability will be evaluated using data science methods and possible new predictors will be identified. This knowledge is expected to lead to an improved predictability of the weather over Europe on weekly to monthly timescales.

People

Scientists

SDSC Team:
PI | Partners

DS3Lab: Data Sciences, Data Systems, & Data Services:

  • Prof. Ce Zhang

More info

Geoscience & Remote Sensing:

  • Dr. William T. Ball

More info

description

Problem:

From a climatological point of view, winter is characterized by the apparition in the stratosphere of a vortex of strong winds over the polar regions; this vortex is generally centered at the pole and have with a circular shape.

In average once every two years, sudden and rapid warmings of the stratosphere disturb the vortex causing displacements and deformations. The impact of such events, known as Sudden Stratospheric Warmings (SSW), are not limited to the stratosphere but also strongly influence the troposphere, i.e., weather at the sea level, during two to three months after an occurrence.

Thus a good prediction of SSW events would enable the production of more accurate weather forecasts at a monthly time scale. However, the stratosphere is a complex environment: the different states of the polar vortex and their relations with SSW events are still not well understood.

Solution

  • Use Data science methodologies to better characterize the relationship between SSWs and the different states of the polar vortex.
  • Improve the prediction of disturbed state of the stratosphere by combining data driven prediction algorithms with the knowledge extracted from the first point.

Impact:

  • Contribute to a better understanding of the coupling between stratosphere and troposphere.
  • Improve the predictability of weather at a seasonal time scale. Accurate seasonal forecasts are particularly important for agriculture planning, public health and natural resources management.

Gallery

Figure 1: Potential Vorticity at 10hPa.

Annexe

Additionnal resources

Bibliography

  1. Baldwin, M. P. and Dunkerton, T. J. (2001). Stratospheric Harbingers of Anomalous Weather Regimes. Science, 294(5542):581-584.
  2. Butler, A. H., Seidel, D. J., Hardiman, S. C., Butchart, N., Birner, T., & Match, A. (2015). Defining sudden stratospheric warming. Bull. Amer. Meteor. Soc., 1–16.
  3. Blume, C., Matthes, K., and Horenko, I. (2012). Supervised Learning Approaches to Classify Sudden Stratospheric Warming Wvents. Journal of the Atmospheric Sciences, 69(6):1824-1840.
  4. Coughlin, K. and Gray, L. J. (2009). A Continuum of Sudden Stratospheric Warmings. Journal of the Atmospheric Sciences, 66(2):531-540.

Publications

Related Pages

More projects

ML4FCC

In Progress
Machine Learning for the Future Circular Collider Design
Big Science Data

CLIMIS4AVAL

In Progress
Real-time cleansing of snow and weather data for operational avalanche forecasting
Energy, Climate & Environment

SEMIRAMIS

Completed
AI-augmented architectural design
Energy, Climate & Environment

4D-Brains

In Progress
Extracting activity from large 4D whole-brain image datasets
Biomedical Data Science

News

Latest news

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science
November 6, 2023

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science

Climate-smart agriculture in sub-Saharan Africa: optimizing nitrogen fertilization with data science

Food insecurity in sub-Saharan Africa is widespread, with crop yields much lower than in many developed regions. The project aims to use laser spectroscopy to measure fluxes and isotopic composition of N2O from maize and potato crops subjected to a range of fertilization levels.
Street2Vec | Self-supervised learning unveils change in urban housing from street-level images
October 31, 2023

Street2Vec | Self-supervised learning unveils change in urban housing from street-level images

Street2Vec | Self-supervised learning unveils change in urban housing from street-level images

It is difficult to effectively monitor and track progress in urban housing. We attempt to overcome these limitations by utilizing self-supervised learning with over 15 million street-level images taken between 2008 and 2021 to measure change in London.
DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging
February 28, 2023

DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging

DLBIRHOUI | Deep Learning Based Image Reconstruction for Hybrid Optoacoustic and Ultrasound Imaging

Optoacoustic imaging is a new, real-time feedback and non-invasive imaging tool with increasing application in clinical and pre-clinical settings. The DLBIRHOUI project tackles some of the major challenges in optoacoustic imaging to facilitate faster adoption of this technology for clinical use.

Contact us

Let’s talk Data Science

Do you need our services or expertise?
Contact us for your next Data Science project!