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  • American Meteorological Society  (3)
  • Hannachi, Abdelwaheb  (3)
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  • American Meteorological Society  (3)
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  • 1
    Online Resource
    Online Resource
    Simple Nonparametric Techniques for Exploring Changing Probability Distributions of Weather
    American Meteorological Society ; 2005
    In:  Journal of Climate Vol. 18, No. 21 ( 2005-11-01), p. 4344-4354
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 18, No. 21 ( 2005-11-01), p. 4344-4354
    Abstract: Anthropogenic influences are expected to cause the probability distribution of weather variables to change in nontrivial ways. This study presents simple nonparametric methods for exploring and comparing differences in pairs of probability distribution functions. The methods are based on quantiles and allow changes in all parts of the probability distribution to be investigated, including the extreme tails. Adjusted quantiles are used to investigate whether changes are simply due to shifts in location (e.g., mean) and/or scale (e.g., variance). Sampling uncertainty in the quantile differences is assessed using simultaneous confidence intervals calculated using a bootstrap resampling method that takes account of serial (intraseasonal) dependency. The methods are simple enough to be used on large gridded datasets. They are demonstrated here by exploring the changes between European regional climate model simulations of daily minimum temperature and precipitation totals for winters in 1961–90 and 2071–2100. Projected changes in daily precipitation are generally found to be well described by simple increases in scale, whereas minimum temperature exhibits changes in both location and scale.
    Type of Medium: Online Resource
    ISSN: 1520-0442 , 0894-8755
    URL: Article
    DOI: 10.1175/JCLI3518.1
    RVK:
    UA 4548
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2005
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Observed Trends and Teleconnections of the Siberian High: A Recently Declining Center of Action
    American Meteorological Society ; 2005
    In:  Journal of Climate Vol. 18, No. 9 ( 2005-05-01), p. 1411-1422
    Details
    In: Journal of Climate, American Meteorological Society, Vol. 18, No. 9 ( 2005-05-01), p. 1411-1422
    Abstract: This study investigates variability in the intensity of the wintertime Siberian high (SH) by defining a robust SH index (SHI) and correlating it with selected meteorological fields and teleconnection indices. A dramatic trend of –2.5 hPa decade−1 has been found in the SHI between 1978 and 2001 with unprecedented (since 1871) low values of the SHI. The weakening of the SH has been confirmed by analyzing different historical gridded analyses and individual station observations of sea level pressure (SLP) and excluding possible effects from the conversion of surface pressure to SLP. SHI correlation maps with various meteorological fields show that SH impacts on circulation and temperature patterns extend far outside the SH source area extending from the Arctic to the tropical Pacific. Advection of warm air from eastern Europe has been identified as the main mechanism causing milder than normal conditions over the Kara and Laptev Seas in association with a strong SH. Despite the strong impacts of the variability in the SH on climatic variability across the Northern Hemisphere, correlations between the SHI and the main teleconnection indices of the Northern Hemisphere are weak. Regression analysis has shown that teleconnection indices are not able to reproduce the interannual variability and trends in the SH. The inclusion of regional surface temperature in the regression model provides closer agreement between the original and reconstructed SHI.
    Type of Medium: Online Resource
    ISSN: 1520-0442 , 0894-8755
    URL: Article
    DOI: 10.1175/JCLI3352.1
    RVK:
    UA 4548
    Language: English
    Publisher: American Meteorological Society
    Publication Date: 2005
    detail.hit.zdb_id: 246750-1
    detail.hit.zdb_id: 2021723-7
    Location Call Number Limitation Availability
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    Online Resource
  • 3
    Online Resource
    Online Resource
    Archetypal Analysis of Geophysical Data Illustrated by Sea Surface Temperature
    American Meteorological Society ; 2022
    In:  Artificial Intelligence for the Earth Systems Vol. 1, No. 3 ( 2022-07)
    Details
    In: Artificial Intelligence for the Earth Systems, American Meteorological Society, Vol. 1, No. 3 ( 2022-07)
    Abstract: The ability to find and recognize patterns in high-dimensional geophysical data is fundamental to climate science and critical for meaningful interpretation of weather and climate processes. Archetypal analysis (AA) is one technique that has recently gained traction in the geophysical science community for its ability to find patterns based on extreme conditions. While traditional empirical orthogonal function (EOF) analysis can reveal patterns based on data covariance, AA seeks patterns from the points located at the edges of the data distribution. The utility of any objective pattern method depends on the properties of the data to which it is applied and the choices made in implementing the method. Given the relative novelty of the application of AA in geophysics it is important to develop experience in applying the method. We provide an assessment of the method, implementation, sensitivity, and interpretation of AA with respect to geophysical data. As an example for demonstration, we apply AA to a 39-yr sea surface temperature (SST) reanalysis dataset. We show that the decisions made to implement AA can significantly affect the interpretation of results, but also, in the case of SST, that the analysis is exceptionally robust under both spatial and temporal coarse graining. Significance Statement Archetypal analysis (AA), when applied to geophysical fields, is a technique designed to find typical configurations or modes in underlying data. This technique is relatively new to the geophysical science community and has been shown to be beneficial to the interpretation of extreme modes of the climate system. The identification of extreme modes of variability and their expression in day-to-day weather or state of the climate at longer time scales may help in elucidating the interplay between major teleconnection drivers and their evolution in a changing climate. The purpose of this work is to bring together a comprehensive report of the AA methodology using an SST reanalysis for demonstration. It is shown that the AA results are significantly affected by each implementation decision, but also can be resilient to spatiotemporal averaging. Any application of AA should provide a clear documentation of the choices made in applying the method.
    Type of Medium: Online Resource
    ISSN: 2769-7525
    URL: Article
    DOI: 10.1175/AIES-D-21-0007.1
    Language: Unknown
    Publisher: American Meteorological Society
    Publication Date: 2022
    Location Call Number Limitation Availability
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    Online Resource
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