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- Author or Editor: Nicolas Fauchereau x
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Abstract
Weather regimes (WRs), also known as synoptic types, are defined as recurrent patterns that have been used to categorize variability in atmospheric circulation. However, defining the optimal number of patterns can often be arbitrary, and there are common shortcomings when oversimplifying a wide range of synoptic conditions and weather outcomes. We build on previous work that has defined regional WRs and objectively ascribe an optimal number of once-daily weather patterns for Aotearoa New Zealand (ANZ) using affinity propagation combined with K-means clustering. Nine primary WRs for ANZ were classified based on once-daily geopotential height spatial patterns, but these patterns still retained a wide degree of spatial variability. Subsidiary clusters were subsequently defined within each primary WR by applying affinity propagation and K-means clustering to reveal the largest within-cluster differences based on joint daily temperature and precipitation anomalies. Up to three subsidiary patterns in each of the primary regimes were revealed, with a total of 21 unique daily patterns emerging from the two-tier classification. Subsidiary WRs reveal subtle differences in the location and intensity of regional-scale pressure anomalies, pressure gradients, and wind flow over both main islands that lead to large differences in surface weather anomalies. Impacts of atmospheric variability related to each subsidiary WR are exemplified by different spatial outcomes for rainfall and temperature (including intensity of anomalies) at regional and subregional levels. The approach presented in this study has utility for enhancing prediction of weather outcomes, including extreme weather, and can also be applied more widely over a range of time scales to improve understanding of weather and climate linkages.
Abstract
Weather regimes (WRs), also known as synoptic types, are defined as recurrent patterns that have been used to categorize variability in atmospheric circulation. However, defining the optimal number of patterns can often be arbitrary, and there are common shortcomings when oversimplifying a wide range of synoptic conditions and weather outcomes. We build on previous work that has defined regional WRs and objectively ascribe an optimal number of once-daily weather patterns for Aotearoa New Zealand (ANZ) using affinity propagation combined with K-means clustering. Nine primary WRs for ANZ were classified based on once-daily geopotential height spatial patterns, but these patterns still retained a wide degree of spatial variability. Subsidiary clusters were subsequently defined within each primary WR by applying affinity propagation and K-means clustering to reveal the largest within-cluster differences based on joint daily temperature and precipitation anomalies. Up to three subsidiary patterns in each of the primary regimes were revealed, with a total of 21 unique daily patterns emerging from the two-tier classification. Subsidiary WRs reveal subtle differences in the location and intensity of regional-scale pressure anomalies, pressure gradients, and wind flow over both main islands that lead to large differences in surface weather anomalies. Impacts of atmospheric variability related to each subsidiary WR are exemplified by different spatial outcomes for rainfall and temperature (including intensity of anomalies) at regional and subregional levels. The approach presented in this study has utility for enhancing prediction of weather outcomes, including extreme weather, and can also be applied more widely over a range of time scales to improve understanding of weather and climate linkages.
Abstract
An automated cloud band identification procedure is developed that captures the meteorology of such events over southern Africa. This “metbot” is built upon a connected component labeling method that enables blob detection in various atmospheric fields. Outgoing longwave radiation is used to flag candidate cloud band days by thresholding the data and requiring detected blobs to have sufficient latitudinal extent and exhibit positive tilt. The Laplacian operator is used on gridded reanalysis variables to highlight other features of meteorological interest. The ability of this methodology to capture the significant meteorology and rainfall of these synoptic systems is tested in a case study. Usefulness of the metbot in understanding event-to-event similarities of meteorological features is demonstrated, highlighting features previous studies have noted as key ingredients to cloud band development in the region. Moreover, this allows the presentation of a composite cloud band life cycle for southern Africa events. The potential of metbot to study multiscale interactions is discussed, emphasizing its key strength: the ability to retain details of extreme and infrequent events. It automatically builds a database that is ideal for research questions focused on the influence of intraseasonal to interannual variability processes on synoptic events. Application of the method to convergence zone studies and atmospheric river descriptions is suggested. In conclusion, a relation-building metbot can retain details that are often lost with object-based methods but are crucial in case studies. Capturing and summarizing these details may be necessary to develop a deeper process-level understanding of multiscale interactions.
Abstract
An automated cloud band identification procedure is developed that captures the meteorology of such events over southern Africa. This “metbot” is built upon a connected component labeling method that enables blob detection in various atmospheric fields. Outgoing longwave radiation is used to flag candidate cloud band days by thresholding the data and requiring detected blobs to have sufficient latitudinal extent and exhibit positive tilt. The Laplacian operator is used on gridded reanalysis variables to highlight other features of meteorological interest. The ability of this methodology to capture the significant meteorology and rainfall of these synoptic systems is tested in a case study. Usefulness of the metbot in understanding event-to-event similarities of meteorological features is demonstrated, highlighting features previous studies have noted as key ingredients to cloud band development in the region. Moreover, this allows the presentation of a composite cloud band life cycle for southern Africa events. The potential of metbot to study multiscale interactions is discussed, emphasizing its key strength: the ability to retain details of extreme and infrequent events. It automatically builds a database that is ideal for research questions focused on the influence of intraseasonal to interannual variability processes on synoptic events. Application of the method to convergence zone studies and atmospheric river descriptions is suggested. In conclusion, a relation-building metbot can retain details that are often lost with object-based methods but are crucial in case studies. Capturing and summarizing these details may be necessary to develop a deeper process-level understanding of multiscale interactions.
