Abstract

Knowledge of the spatial–temporal variation of rain fields is required for the planning and optimization of wide-area high-frequency terrestrial and satellite communication networks. This article presents data and a method for characterizing multi-resolution statistical/dynamic parameters describing the spatial–temporal variation of rain fields across ocean climate in North-Western Europe. The data are derived from the NIMROD network of rain radars. The characterizing parameters include 1) statistical distribution of point 1 min rainfall rates, 2) spatial and temporal correlation functions of rainfall rate, and 3) the probability of rain/no rain. The main contributions of this article are the assessment of the impact of varying spatial and temporal integration lengths on these parameters, their dependencies on the integration volumes and area sizes, and the model for both temporal and spatial correlation parameters.

Highlights

  • R AIN-INDUCED attenuation of microwave signals at frequencies above 10 GHz is the dominant dynamic impairment on high-capacity satellite and terrestrial links [1], [2]

  • This article has presented the outcome of an extensive study of 5 years (2005–2009) of rain radar data spanning Oceanic climate in North-Western Europe

  • Four key characteristics of rainfall rate have been studied for a range of spatial and temporal integration lengths: 1) the annual statistical distribution; 2) the annual spatial correlation function; 3) the temporal correlation function; and 4) the point probability of rain/no rain

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Summary

Introduction

R AIN-INDUCED attenuation of microwave signals at frequencies above 10 GHz is the dominant dynamic impairment on high-capacity satellite and terrestrial links [1], [2]. Network planners and designers of physical layer fade mitigation techniques [7], [8] require knowledge of rain characteristics at smaller space (L) and time (T ) scales than are typically available from radar or rain gauge measurements. This provides impetus for the development of rain models which can be used to predict rain rates at fine scales. Manuscript received July 13, 2018; revised May 4, 2019; accepted June 9, 2019. Date of publication July 29, 2019; date of current version November 27, 2019.

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