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Jian Li and Rucong Yu

1. Introduction Rainfall is one of the most important factors that determine the climate of a region, and advancing the knowledge of observed rainfall characteristics is always a key concern for meteorologists and climatologists. Amount, frequency, and intensity are three primary and widely used parameters to describe the rainfall features. Taking the late-summer [July and August (JA)] rainfall in contiguous China for example, we can get an overall picture of rainfall characteristics based on

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Mateusda Silva Teixeira and Prakki Satyamurty

1. Introduction Although the monthly distribution of rainfall in southern Brazil is quite uniform ( Rao and Hada 1990 ), day-to-day variability is high. The sources for rainfall in this region are mainly cold fronts and mesoscale convective complexes (MCC; Velasco and Fritsch 1987 ). The extratropical cyclones approaching South America from the west move to east-southeast after crossing the Andes while the associated cold fronts move to north-northeast, producing convective activity in

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J. Vaze, D. A. Post, F. H. S. Chiew, J.-M. Perraud, J. Teng, and N. R. Viney

1. Introduction Conceptual rainfall–runoff models have been widely used for catchment water balance studies across the world. The observed runoff used to calibrate and validate these models is recorded at the catchment outlet and as such is an aggregated response of spatially variable rainfall across the catchment. There are uncertainties associated with the rainfall data, and the measured point rainfall data are usually available only at limited locations within a catchment or close to the

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Ching-Sen Chen, Yi-Leng Chen, Che-Ling Liu, Pay-Liam Lin, and Wan-Chin Chen

1. Introduction Even though monsoons are planetary-scale circulations, monsoon rainfalls are frequently localized in nature because of the terrain and local winds ( Ramage 1971 ; Watanabe and Ogura 1987 ; Ogura and Yoshizaki 1988 ; Akaeda et al. 1995 ; Li et al. 1997 ; Yeh and Chen 1998 ; Chen 2000 , and others). The Central Mountain Range (CMR) runs through Taiwan in a nearly north–south direction at an average height of about 2 km and with peaks near 4 km ( Fig. 1 ). With steep terrain

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Marc Schleiss, Sabine Chamoun, and Alexis Berne

1. Introduction Space–time variability of rainfall is an important source of uncertainty that must be properly taken into account. A distinctive feature of rainfall variability at the mesogamma and mesobeta scales (i.e., from 1 to 200 km) is intermittency ( Kundu and Siddani 2011 ; Schleiss et al. 2011 ). Intermittency limits the available water resources in time and space and directly affects the environment and the ecosystems (e.g., Porporato and Rodriguez-Iturbe 2004 ; Mandapaka et al

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Qi Hu and Song Feng

1. Introduction Rainfall during the 3-month period July–September accounts for nearly 50% of the annual precipitation in the semiarid southwest United States. This relatively wet period, often referred to as the southwest U.S. monsoon season, is of considerable interest and importance, especially its interannual variation. Several causes potentially influencing the monsoonal rainfall variation have been identified in recent years, including 1) a “land memory” effect, which describes anomalous

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Sytske K. Kimball

1. Introduction As a hurricane makes landfall, it moves from shallow coastal waters to solid land, leading to a rapid decrease in surface latent heat fluxes, the hurricane’s primary energy source. In addition, surface roughness increases substantially. These rapidly changing conditions affect the pattern, extent, and intensity of damaging winds and rainfall. The underlying physical processes are complex, occur over a very short time (less than a day), and depend on many coexisting factors

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Manuel Lonfat, Robert Rogers, Timothy Marchok, and Frank D. Marks Jr.

cyclone rainfall has been identified as a high priority for the research and operational communities ( Marks et al. 1998 ). While significant improvements have been made in forecasts of tropical cyclone track (e.g., Aberson 2003 , 2001 ) and, to a lesser extent, intensity ( Knaff et al. 2003 ; DeMaria et al. 2005 ), much less attention has been focused on improving forecasts of rainfall [quantitative precipitation forecasting (QPF)] from tropical cyclones. Only recently have efforts been made to

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Jonathan J. Gourley, Yang Hong, Zachary L. Flamig, Li Li, and Jiahu Wang

1. Introduction Accurate rainfall measurement is needed for a variety of applications vital to the economy, natural resources, and social infrastructure including agriculture, flash flood detection and prediction, water resources management, drinking water supplies, dam operations, transportation, hydroelectric power generation, water quality modeling, debris flow prediction, etc. However, accurately measuring rainfall has been a challenge to the research community predominantly because of its

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Gabriele Villarini and Witold F. Krajewski

1. Introduction Rain gauges represent the most direct and accurate way of measuring rainfall. Unfortunately, this strength is counterbalanced by the fact that these measurements are representative of a small area around the instrument. The capability of capturing the high spatial variability of the precipitation systems is unavoidably related to the availability of dense networks. Currently, dense rain gauge networks are available almost exclusively in a research context and cover only limited

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