Editorial Type:
Article
Article Type:
Research Article

A Seasonal Rainfall Performance Probability Tool for Famine Early Warning Systems

Nicholas S. Novella Climate Prediction Center, NOAA/National Centers for Environmental Prediction, College Park, and Innovim, LLC, Greenbelt, Maryland

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Wassila M. Thiaw Climate Prediction Center, NOAA/National Centers for Environmental Prediction, College Park, Maryland

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Print Publication:
01 Dec 2016
DOI:
https://doi.org/10.1175/JAMC-D-16-0111.1
Page(s):
2575–2586
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Abstract

This paper reports on the development of a new statistical tool that generates probabilistic outlooks of seasonal precipitation anomaly categories over Africa. Called the seasonal performance probability (SPP), it quantitatively evaluates the probability of precipitation to finish at predefined percent-of-normal anomaly categories corresponding to below-average (<80% of normal), average (80%–120% of normal), and above-average (>120% of normal) conditions. This is accomplished by applying methods for kernel density estimation (KDE), which compute smoothed, continuous density functions on the basis of more than 30 years of historical precipitation data from the Africa Rainfall Climatology, version 2, dataset (ARC2) for the remaining duration of a monsoon season. Discussion of various parameterizations of KDE and testing to determine optimality of density estimates (and thus performance of SPP for operational monitoring) are presented. Verification results from 2006 to 2015 show that SPP reliably provides probabilistic outcomes of seasonal rainfall anomaly categories by after the early to midstages of rain seasons for the major monsoon regions in East Africa, West Africa, and southern Africa. SPP has proven to be a useful tool by enhancing operational climate monitoring at CPC for its prognostic capability for famine early warning scenarios over Africa. These insights are anticipated to translate into better decision-making in food security, planning, and response objectives for the U.S. Agency for International Development/Famine Early Warning Systems Network (USAID/FEWS NET).

Denotes Open Access content.

Corresponding author address: Nicholas S. Novella, Climate Prediction Center, National Centers for Environmental Prediction, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740. E-mail: nicholas.novella@noaa.gov

Abstract

This paper reports on the development of a new statistical tool that generates probabilistic outlooks of seasonal precipitation anomaly categories over Africa. Called the seasonal performance probability (SPP), it quantitatively evaluates the probability of precipitation to finish at predefined percent-of-normal anomaly categories corresponding to below-average (<80% of normal), average (80%–120% of normal), and above-average (>120% of normal) conditions. This is accomplished by applying methods for kernel density estimation (KDE), which compute smoothed, continuous density functions on the basis of more than 30 years of historical precipitation data from the Africa Rainfall Climatology, version 2, dataset (ARC2) for the remaining duration of a monsoon season. Discussion of various parameterizations of KDE and testing to determine optimality of density estimates (and thus performance of SPP for operational monitoring) are presented. Verification results from 2006 to 2015 show that SPP reliably provides probabilistic outcomes of seasonal rainfall anomaly categories by after the early to midstages of rain seasons for the major monsoon regions in East Africa, West Africa, and southern Africa. SPP has proven to be a useful tool by enhancing operational climate monitoring at CPC for its prognostic capability for famine early warning scenarios over Africa. These insights are anticipated to translate into better decision-making in food security, planning, and response objectives for the U.S. Agency for International Development/Famine Early Warning Systems Network (USAID/FEWS NET).

Denotes Open Access content.

Corresponding author address: Nicholas S. Novella, Climate Prediction Center, National Centers for Environmental Prediction, NOAA Center for Weather and Climate Prediction, 5830 University Research Court, College Park, MD 20740. E-mail: nicholas.novella@noaa.gov
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Journal of Applied Meteorology and Climatology

  • Bowden, J. H., and F. H. M. Semazzi, 2007: Empirical analysis of intraseasonal climate variability over the Greater Horn of Africa. J. Climate, 20, 5715–5731, doi:10.1175/2007JCLI1587.1.

    • Search Google Scholar
    • Export Citation

  • Dinku, T., P. Ceccato, E. Grover-Kopec, M. Lemma, S. J. Connor, and C. F. Ropelewski, 2007: Validation of satellite rainfall products over East Africa’s complex topography. Int. J. Remote Sens., 28, 1503–1526, doi:10.1080/01431160600954688.

    • Search Google Scholar
    • Export Citation

  • Hagos, S. M., and K. H. Cook, 2007: Dynamics of the West African monsoon jump. J. Climate, 20, 5264–5284, doi:10.1175/2007JCLI1533.1.

  • Herman, A., V. B. Kumar, P. A. Arkin, and J. V. Kousky, 1997: Objectively determined 10-day African rainfall estimates created for famine early warning systems. Int. J. Remote Sens., 18, 2147–2159, doi:10.1080/014311697217800.

    • Search Google Scholar
    • Export Citation

  • Herrmann, S., and K. Mohr, 2011: A continental-scale classification of rainfall seasonality regimes in Africa based on gridded precipitation and land surface temperature products. J. Appl. Meteor. Climatol., 50, 2504–2513, doi:10.1175/JAMC-D-11-024.1.

    • Search Google Scholar
    • Export Citation

  • Hutchinson, P., 1992: The Southern Oscillation and prediction of “Der” season rainfall in Somalia. J. Climate, 5, 525–531, doi:10.1175/1520-0442(1992)005<0525:TSOAPO>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • International Research Institute for Climate and Society, 2013: Descriptions of the IRI climate forecast verification scores. Columbia University International Research Institute for Climate and Society Tech. Doc., 27 pp. [Available online at http://iri.columbia.edu/wp-content/uploads/2013/07/scoredescriptions.pdf.]

  • Lall, U., Y. Moon, and K. Bosworth, 1993: Kernel flood frequency estimators: Bandwidth selection and kernel choice. Water Resour. Res., 29, 1003–1015, doi:10.1029/92WR02466.

    • Search Google Scholar
    • Export Citation

  • Liebmann, B., I. Blade, G. N. Kiladis, L. M. V. Carvalho, G. B. Senay, D. Allured, S. Leroux, and C. Funk, 2012: Seasonality of African precipitation from 1996 to 2009. J. Climate, 25, 4304–4322, doi:10.1175/JCLI-D-11-00157.1.

    • Search Google Scholar
    • Export Citation

  • Love, T. B., V. Kumar, P. Xie, and W. Thiaw, 2004: A 20-year daily Africa precipitation climatology using satellite and gauge data. 14th Conf. on Applied Climatology, Seattle, WA, Amer. Meteor. Soc., P5.4. [Available online at https://ams.confex.com/ams/pdfpapers/67484.pdf.]

  • Mason, I., 1982: A model for assessment of weather forecasts. Aust. Meteor. Mag., 30, 291–303.

  • Novella, N. S., and W. M. Thiaw, 2013: African Rainfall Climatology version 2 for famine early warning systems. J. Appl. Meteor. Climatol., 52, 588–606, doi:10.1175/JAMC-D-11-0238.1.

    • Search Google Scholar
    • Export Citation

  • Rajagopalan, B., U. Lall, and D. G. Tarboton, 1993: Simulation of daily precipitation from a nonparametric renewal model. Utah State University Rep. WP-93-HWR-UL/003, 24 pp. [Available online at http://digitalcommons.usu.edu/cgi/viewcontent.cgi?article=1145&context=water_rep.]

  • Rajagopalan, B., U. Lall, and D. G. Tarboton, 1997: Evaluation of kernel density estimation methods for daily precipitation resampling. Stoch. Hydrol. Hydraul., 11, 523–547, doi:10.1007/BF02428432.

    • Search Google Scholar
    • Export Citation

  • Sheather, S. J., 2004: Density estimation. Stat. Sci., 19, 588–597, doi:10.1214/088342304000000297.

  • Sheather, S. J., and M. C. Jones, 1991: A reliable data-based bandwidth selection method for kernel density estimation. J. Roy. Stat. Soc., 53B, 683–690.

    • Search Google Scholar
    • Export Citation

  • Silverman, B. W., 1986: Density Estimation for Statistics and Data Analysis. Monogr. Stat. Appl. Probab., No. 26, Chapman and Hall, 176 pp.

  • Verdin, J., C. Funk, G. Senay, and R. Choularton, 2005: Climate science and famine early warning. Philos. Trans. Roy. Soc., 360B, 2155–2168, doi:10.1098/rstb.2005.1754.

    • Search Google Scholar
    • Export Citation

  • Wand, M. P., and M. C. Jones, 1995: Kernel Smoothing. Monogr. Stat. Appl. Probab., No. 60, Chapman and Hall, 224 pp.

  • Wilks, D. S., 2011: Statistical Methods in the Atmospheric Sciences. 3rd ed. Elsevier, 676 pp.

  • Williams, A. P., and C. Funk, 2011: A westward extension of the warm pool leads to a westward extension of the Walker circulation, drying eastern Africa. Climate Dyn., 37, 2417–2435, doi:10.1007/s00382-010-0984-y.

    • Search Google Scholar
    • Export Citation
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