ORCID Profile
0000-0003-0500-8514
Current Organisations
University of Reading
,
National Centre for Atmospheric Science
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Publisher: Elsevier BV
Date: 08-2022
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-7411
Abstract: & & & & This work considers the sub-seasonal predictability of two sets of weather regimes for South East Asia: a two-tiered assignment, that first considers large-scale patterns and then assigns synoptic-scale regimes, and a flat classification, which only considers the synoptic scale. In the two-tiered approach, the tier 1 large-scale regimes, which capture ENSO and seasonal variations, are each partitioned into South East Asia regional clusters that capture synoptic variability.& & & & & & / & & & & & The sub-seasonal predictability of both the standard and tiered regimes is assessed using UKMO GloSea5 hindcasts and forecasts for lead times of up to 5 weeks. We find that the GloSea5 system presents an accurate representation of the regimes& #8217 climatology and a good level of skill for their assignment. Nonetheless, the predictability depends on the specific regimes and some significant forecast drifts are also identified. Additionally, the predictive skill of high impact precipitation events obtained statistically from the prediction of the regimes is assessed and compared with the probabilistic precipitation forecasts of the GloSea5 ensemble.& & & & & & & / & & & & & A description of the regime classification methodology and their connections to seasonal and synoptic phenomena will be discussed in a separate presentation, titled & #8220 Weather regimes in South East Asia: connections with synoptic phenomena and high impact weather& #8221 and presented by Emma Howard.& & & & / &
Publisher: American Meteorological Society
Date: 06-2020
Abstract: Weather and climate variations on subseasonal to decadal time scales can have enormous social, economic, and environmental impacts, making skillful predictions on these time scales a valuable tool for decision-makers. As such, there is a growing interest in the scientific, operational, and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) time scales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) time scales, while the focus broadly remains similar (e.g., on precipitation, surface and upper-ocean temperatures, and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal variability and externally forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation initialization shock and drift understanding the onset of model systematic errors bias correction, calibration, and forecast quality assessment model resolution atmosphere–ocean coupling sources and expectations for predictability and linking research, operational forecasting, and end-user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Programme (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis.
Publisher: Elsevier BV
Date: 12-2022
Publisher: Wiley
Date: 24-12-2021
DOI: 10.1002/QJ.4227
Abstract: Two sets of weather patterns describing variability in 850 hPa winds in Southeast Asia are presented and compared. Patterns are calculated using EOF/ k ‐means clustering with and without imposing a separation between planetary‐scale and regional‐scale circulation features. The former are labelled as tiered patterns while the latter are referred to as flat. The ability of the patterns to distinguish between known modes of tropical circulation variability is examined. This includes climate modes such as the seasonal monsoons, the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD) as well as sub‐seasonal modes including cold surges, phases of the MJO and Boreal summer intraseasonal oscillation (BSISO), tropical cyclones, Borneo vortices and equatorial waves. All these modes are well captured by the weather patterns except for the equatorial waves and the IOD. The tiered patterns are shown to better describe large‐scale modes of variability, while the flat patterns better describe the synoptic variability. Both sets of weather patterns are then used to study the likelihood of heavy precipitation depending on synoptic circulation by considering the regime‐conditioned probability of high‐percentile precipitation using the satellite‐derived Global Precipitation Measurement (GPM) dataset. It is shown that the pattern centroids explain up to 10% of the seasonally anomalous precipitation over land, and that a perfect weather pattern forecast would outperform a perfect MJO forecast. These weather patterns show promising potential in extending the useful forecast range for the risk of heavy precipitation, dependent on their forecastability.
Publisher: Copernicus GmbH
Date: 04-03-2021
DOI: 10.5194/EGUSPHERE-EGU21-7472
Abstract: & & & & & & & & / & & & & & A tiered set of weather regimes describing variability in 850& hPa& winds in South East Asia (SEA) is presented and compared to a corresponding non-tiered set of weather regimes. The tiered regimes are calculated in two parts: the first tier computed by applying EOF/K-means clustering on a planetary scale domain which partitioning seasonal variation and ENSO, and the second tier obtained by EOF/K-means clustering on a smaller SE Asia regional domain, partitioning the synoptic variability within each of the& first tier& regimes. This identifies synoptic weather phenomena with multi-day persistence. In contrast, the un-tiered (& #8220 flat& #8221 ) clustering approach uses a standard EOF/K-means classification in the regional domain without conditional dependence on large-scale, with the number of regimes set to match the tiered regimes.& & & & & / & & & & & These regimes are used to study the likelihood of extreme precipitation depending on synoptic circulation. We consider the conditional probability depending on regime type of synoptic weather events including cold surges, phases of the MJO and BSISO, tropical cyclones, Borneo Vortices and equatorial waves. We then study the regime-conditioned probability of high percentile TRMM precipitation. We find that a perfect regime forecast would have greater skill than the GloSEA5 precipitation forecast for lead times longer than approximately one week. The tiered regimes distinguish a greater fraction of considered modes of variability, while the flat regimes better distinguish the precipitation variability.& & & & & / & & & & & The predictability of these regimes will be discussed in a separate presentation, titled & #8220 Weather regimes in South East Asia: Sub-seasonal predictability of the regimes and the associated high impact weather& #8221 and presented by Paula Gonzalez.& & & & / &
Publisher: American Meteorological Society
Date: 06-2009
Abstract: The Madden–Julian oscillation (MJO) interacts with and influences a wide range of weather and climate phenomena (e.g., monsoons, ENSO, tropical storms, midlatitude weather), and represents an important, and as yet unexploited, source of predictability at the subseasonal time scale. Despite the important role of the MJO in climate and weather systems, current global circulation models (GCMs) exhibit considerable shortcomings in representing this phenomenon. These shortcomings have been documented in a number of multimodel comparison studies over the last decade. However, diagnosis of model performance has been challenging, and model progress has been difficult to track, because of the lack of a coherent and standardized set of MJO diagnostics. One of the chief objectives of the U.S. Climate Variability and Predictability (CLIVAR) MJO Working Group is the development of observation-based diagnostics for objectively evaluating global model simulations of the MJO in a consistent framework. Motivation for this activity is reviewed, and the intent and justification for a set of diagnostics is provided, along with specification for their calculation, and illustrations of their application. The diagnostics range from relatively simple analyses of variance and correlation to more sophisticated space–time spectral and empirical orthogonal function analyses. These diagnostic techniques are used to detect MJO signals, to construct composite life cycles, to identify associations of MJO activity with the mean state, and to describe interannual variability of the MJO.
Publisher: Copernicus GmbH
Date: 19-09-2023
DOI: 10.5194/GMD-2023-165
Publisher: Wiley
Date: 11-12-2022
DOI: 10.1002/QJ.4378
Abstract: While skilful forecasts of heavy rainfall are highly desirable for weather warnings and mitigating impacts, forecasting such events is notoriously difficult, even with the most advanced numerical weather prediction models, due to the strong dependence on convective‐scale processes. The large‐scale circulation, on the other hand, is typically more predictable. Weather patterns (WPs) are a set of circulation types obtained statistically that can be used to characterize regional weather and harness the predictability of the large‐scale circulation. In this work we produce pattern‐conditioned probabilistic rainfall forecasts by projecting the horizontal winds from the Met Office GloSea5 prediction system on to WPs and then using the observed relationship between each WP and rainfall estimated by satellite. The WPs are derived following a novel two‐tier clustering technique: the WPs in the first tier represent planetary‐scale variability, such as El Niño–Southern Oscillation (ENSO), while the WPs in the second tier capture synoptic‐scale variability. We investigate WP predictability as well as the improvement in skill of subseasonal rainfall forecasts gained by this technique. GloSea5 predicts the WP occurrence with skill extending beyond lead day 10. The pattern‐conditioned rainfall forecasts were evaluated against climatological forecasts and model‐simulated rainfall hindcasts. We show that the pattern‐conditioned forecasts are skilful and outperform the model‐simulated rainfall hindcasts for lead times extending to days 10–20, depending on the specific exceedance criteria and region. Spatial aggregation leads to increased levels of skill, but not to a significant extension of the skilful prediction horizon. These results constitute a fundamental step for the development of subseasonal prediction systems for Southeast Asia.
Publisher: Elsevier BV
Date: 08-2021
Location: United Kingdom of Great Britain and Northern Ireland
Location: United Kingdom of Great Britain and Northern Ireland
No related grants have been discovered for Steven Woolnough.