Rain is the most common form of precipitation and is the dominant moisture input to the hydrologic cycle except for latitudes north of 50° N in North America and Eurasia (Peixoto and Oort, 1992). Conceptually, rainfall measurement is straightforward, but there are more than 40 rain gauge designs used throughout the world (Linacre, 1992). These gauges display little agreement on the size of the canister opening or on the height of the gauge above the ground. In addition, a high-density rain gauge network is required to observe total areal
Fig. 4.2. Standard rain gauge used in the United States. (Photo by author.)
rainfall from rain gauge observations, and such high-density rain gauge networks are generally not available (Liu, 2003). Measured rainfall is expressed as a depth of water in cm or mm for a specified time.
Rainfall measurement is typically accomplished by catching rain in a flat bottomed, vertically sided canister. It is assumed that the amount or depth collected per unit area of the gauge aperture is the same as the amount that falls per unit area on the surrounding surface (DeFelice, 1998). The precipitation falling into the cylinder is amplified for ease of measurement by a funnel that directs the precipitation catch to an inside can. The cross-sectional area of the inner can is one-tenth the area of the aperture to facilitate measurements of 1 mm. Depth is measured periodically using a graduated dipstick or continuously using a weighing mechanism or other mechanical devices. Since the rain gauge is basically a cylinder sitting in the airstream, turbulence over the mouth of the canister represents the major accuracy problem (Groisman and Easterling, 1995; Peck, 1997). Other systematic errors in rain gauge measurements are due to wetting of the internal walls of the gauge, evaporation from the gauge, splashing into or out of the gauge, and blowing snow (Legates and Willmott, 1990).
The WMO adopted a standard gauge based on the British design. This gauge has a cylinder diameter of 127 mm. The rim is 1 m above the ground to prevent water splashing into the cylinder (Linacre, 1992). Rain gauges used in the United States have slightly different dimensions than those of the WMO gauge. The standard U.S. National Weather Service rain gauge has a 203 mm diameter opening that is 800 mm above the ground (Fig. 4.2). The majority of rain gauges in the United States are not routinely equipped with wind shields to combat the turbulence known to influence instrument accuracy. Consequently, rain gauge measurements tend to underestimate true precipitation by 5% to 40% with an average bias of 9% (Groisman and Legates, 1994; Duchon and Essenberg, 2001). Wind influences are a particular problem when snow occurs. Snow may accumulate in the receiving canister and then be blown away before it melts or the wind may reduce the amount of snow that collects in the canister producing a bias of up to 50% (Groisman and Easterling, 1995). In general, the standard rain gauge underestimates the snow contribution, and snowfall measurement with the standard gauge is not recommended.
When rain gauges cannot be observed daily, storage or recording gauges are employed. A storage gauge has an enlarged receiving vessel to accommodate greater accumulations of rainfall, but attention must be given to reducing evaporation losses between observations. Recording rain gauges are used in automated or remote weather stations and are classified on the basis of the mechanism used in the recording process (Linacre, 1992). The three major types are weighing, float and siphon, and tipping-bucket. The tipping-bucket type is widely used for realtime data transmissions. Detailed characteristics of storage and recording rain gauges are discussed by WMO (1996), DeFelice (1998), and Guyot (1998).
In the absence of a rain gauge at a selected point, multiple regression and other statistical techniques are employed to estimate point rainfall for the site. However, the predictor variables may be unique for a given site and rainfall accumulation period. Elevation, slope, aspect, latitude, distance from a water body, and distance from an existing rainfall gauge are common predictors. Estimating errors are smallest when the distance to the nearest rain gauge is smallest and when the averaging period for rainfall is longest (Linacre, 1992). Still, it is important to remember that intense and highly localized rainfall can produce enormous differences over small distances.
Correlations between large-scale atmospheric variables and observed surface precipitation have demonstrated encouraging results for estimating point precipitation. Hayes et al. (2002) show that 850 hPa wind and humidity radiosonde data satisfactorily estimate station data in mountainous regions of western Washington state, and they suggest interpolation for points between stations should be successful.
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