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Journal of Geophysical Research: Atmospheres

A stochastic fractional dynamics model of space-time variability of rain

Authors

  • Prasun K. Kundu,

    Corresponding author
    1. Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, Baltimore, Maryland, USA
    2. Joint Center for Earth Systems Technology, NASA Goddard Space Flight Center, Greenbelt, Maryland, USA
    • Corresponding author: P. K. Kundu, Joint Center for Earth Systems Technology, University of Maryland, Baltimore County, 5523 Research Park Drive, Baltimore, MD 21228, USA. (prasun.k.kundu@nasa.gov)

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  • James E. Travis

    1. Department of Mathematics and Statistics, University of Maryland, Baltimore County, Baltimore, Maryland, USA
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Abstract

[1] Rainfall varies in space and time in a highly irregular manner and is described naturally in terms of a stochastic process. A characteristic feature of rainfall statistics is that they depend strongly on the space-time scales over which rain data are averaged. A spectral model of precipitation has been developed based on a stochastic differential equation of fractional order for the point rain rate, which allows a concise description of the second moment statistics of rain at any prescribed space-time averaging scale. The model is thus capable of providing a unified description of the statistics of both radar and rain gauge data. The underlying dynamical equation can be expressed in terms of space-time derivatives of fractional orders that are adjusted together with other model parameters to fit the data. The form of the resulting spectrum gives the model adequate flexibility to capture the subtle interplay between the spatial and temporal scales of variability of rain but strongly constrains the predicted statistical behavior as a function of the averaging length and time scales. We test the model with radar and gauge data collected contemporaneously at the NASA TRMM ground validation sites located near Melbourne, Florida and on the Kwajalein Atoll, Marshall Islands in the tropical Pacific. We estimate the parameters by tuning them to fit the second moment statistics of radar data at the smaller spatiotemporal scales. The model predictions are then found to fit the second moment statistics of the gauge data reasonably well at these scales without any further adjustment.

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