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Seasonal ensemble generator for radar rainfall using copula and autoregressive model

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Abstract

Uncertainty analysis of radar rainfall enables stakeholders and users have a clear knowledge of the possible uncertainty associated with the rainfall products. Long-term empirical modeling of the relationship between radar and gauge measurements is an efficient and practical method to describe the radar rainfall uncertainty. However, complicated variation of synoptic conditions makes the radar-rainfall uncertainty model based on historical data hard to extend in the future state. A promising solution is to integrate synoptic regimes with the empirical model and explore the impact of individual synoptic regimes on radar rainfall uncertainty. This study is an attempt to introduce season, one of the most important synoptic factor, into the radar rainfall uncertainty model and proposes a seasonal ensemble generator for radar rainfall using copula and autoregressive model. We firstly analyze the histograms of rainfall-weighted temperature, the radar-gauge relationships, and Box and Whisker plots in different seasons and conclude that the radar rainfall uncertainty has strong seasonal dependence. Then a seasonal ensemble generator is designed and implemented in a UK catchment under a temperate maritime climate, which can fully model marginal distribution, spatial dependence, temporal dependence and seasonal dependence of radar rainfall uncertainty. To test its performance, 12 typical rainfall events (4 for each season) are chosen to generate ensemble rainfall values. In each time step, 500 ensemble members are produced and the values of 5th to 95th percentiles are used to derive the uncertainty bands. Except several outliers, the uncertainty bands encompass the observed gauge rainfall quite well. The parameters of the ensemble generator vary considerably for each season, indicating the seasonal ensemble generator reflects the impact of seasons on radar rainfall uncertainty. This study is an attempt to simultaneously consider four key features of radar rainfall uncertainty and future study will investigate their impacts on the outputs of hydrological models with radar rainfall as input or initial conditions.

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Acknowledgments

The first author would like to thank the University of Bristol and China Scholarship Council for providing the necessary support and funding for his PhD research. The authors acknowledge the British Atmospheric Data Centre for providing the data.

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Authors and Affiliations

  1. Key Laboratory of Virtual Geographic Environment of Ministry of Education, School of Geography Science, Nanjing Normal University, Nanjing, China

    Qiang Dai

  2. WEMRC, Department of Civil Engineering, University of Bristol, Bristol, UK

    Qiang Dai, Dawei Han, Lu Zhuo & Jun Zhang

  3. NOAA/NESDIS Center for Satellite Applications and Research, College Park, MD, USA

    Tanvir Islam

  4. Cooperative Institute for Research in the Atmosphere, Colorado State University, Fort Collins, CO, USA

    Tanvir Islam

  5. Hydrological Sciences, NASA Goddard Space Flight Center, Greenbelt, MD, USA

    Prashant K. Srivastava

  6. Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD, USA

    Prashant K. Srivastava

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  1. Qiang Dai
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  2. Dawei Han
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  3. Lu Zhuo
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  4. Jun Zhang
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  5. Tanvir Islam
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  6. Prashant K. Srivastava
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Correspondence to Qiang Dai.

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Dai, Q., Han, D., Zhuo, L. et al. Seasonal ensemble generator for radar rainfall using copula and autoregressive model. Stoch Environ Res Risk Assess 30, 27–38 (2016). https://doi.org/10.1007/s00477-014-1017-x

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  • DOI: https://doi.org/10.1007/s00477-014-1017-x

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