The capability to measure rainfall advanced dramatically in the last quarter of the twentieth century. The advances have been paced by remote-sensing technologies including both ground-based weather radar and satellite-borne instruments. The most dramatic developments have centered around the capability to monitor precipitation globally from satellite sensors. This measurement capability provides a variety of avenues for hydroclimatological analysis and forecasting. Advances in ground-based radar technologies and deployment of dense networks of rain gages has enhanced the ability to measure rainfall at short time scales (less than 1 h) and small spatial scales (less than 1 km). These time and space scales are often most relevant for water management applications. A brief summary of rainfall measurement and analysis capabilities is presented in the following three sections and organized by the three principal measurement technologies: rain gage, radar, and satellite.


Networks of rain gages play a key role in hydrologic applications ranging from flood forecasting to design of high-hazard structures and water supply management. A wide variety of recording and nonrecording rain gages are used for hydrologic applications. Review and discussion of rain gage technologies are presented in the work by Sumner (1988).

There exist several inherent sources of error that affect all types of rain gages. All rain gages suffer from errors due to modification of the wind field by the gage [see Robinson and Rodda (1969) for detailed discussions]. The magnitude of errors depends on wind speed, siting characteristics, and type of precipitation (Groisman

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and Legates, 1994; Sevruk, 1982, 1989; Nystuen et al., 1996; Steiner et al., 1999; McCollum and Krajewski, 1998; Larson and Peck, 1974). Rain gage measurement of rainfall is especially difficult in a variety of settings, including mountain ridges, forests, and water bodies. Measurement errors for snow are typically much larger than for rain and are generally in the form of catch deficiencies (Groisman and Legates, 1994).

Rain gage networks serve as the basis for climatological assessments of precipitation that are used for a wide range of applications (see, e.g., Frei and Schaer, 1998). Three of the principal types of climatological analyses that are used for water management applications are illustrated in Figures 1 to 3. Assessments of average rainfall conditions, in a variety of forms, are central to activities involving industrial, municipal, and agricultural water use. Mean annual precipitation is shown in Figure

1 [see also Groisman and Legates (1994) for a discussion of biases in rain gage analyses of mean precipitation]. Global assessments of continental precipitation have been developed from rain gage observations by Legates (1987) [see also Legates and Wilmott (1990)]. Precipitation frequency analysis plays a central role in engineering design problems, especially in urban areas (Urbonas and Roesner, 1993). The 15-min, 100-year rainfall magnitude for the United States (Frederick et al., 1977) is illustrated in Figure 2. The network of gages that have the temporal resolution to provide short-term precipitation frequency analyses, such as those in Figure 2, is far less dense than the rain gage network used to produce mean annual precipitation maps. Consequently, it is difficult to assess the true geographic variability of extreme rainfall rates. It is likely that geographic features, such as mountains and land-water boundaries, exert a pronounced influence on the frequency of extreme rainfall rates. The density of the network, however, is not adequate to resolve these geographic variations. Design of high-hazard structures, such as spillways on major dams, is determined through probable maximum precipitation (PMP) analyses (Hansen, 1987; WMO, 1986). Rain gage data sets, in the form of storm catalogs, play a central role in PMP analyses. Storm catalogs for PMP analyses consist of gage observations from specific events. Consequently, the density of gage observations in regions experiencing catastrophic rainfall is critical for PMP analyses. The 6-h, 200 mi2 PMP for the eastern United States is shown in Figure 3. The greatest uncertainties in PMP analyses are for small areas (less than 200 mi2), short time periods (6h and less), and for regions of complex terrain (National Research Council, 1994).


Implementation of the NEXRAD (next-generation weather radar) system of WSR-88D (weather surveillance radar—1988 Doppler) radars has resulted in dramatic advances in rainfall measurement capabilities for the United States (Klazura and Imy, 1993). Operational National Weather Service (NWS) rainfall products derived from WSR-88D observations provide rainfall analyses for the United States at 1-h time resolution and spatial resolution of approximately 4 km (Hudlow et al., 1991).

Figure 1 Mean annual precipitation (inches) for the United States from rain gage observations.

Figure 1 Mean annual precipitation (inches) for the United States from rain gage observations.

Figure 2 The 100-year, 15-min rainfall magnitudes (inches) for the United States east of the Rocky Mountains.

The hourly digital product (HDP) rainfall estimates are created by the WSR-88D radar product generator on a 131 x 131, 4-km grid centered at each radar site. The range over which rainfall products are constructed for each site is approximately 230km. The algorithm used to construct this product (Fulton et al., 1998) consists of the following steps: (1) quality control, including identification and elimination of anomalous propagation returns, (2) conversion of radar reflectivity factor to rainfall rate through a Z-R relationship, (3) correction for range effects, (4) aggregation of rainfall estimates to hourly, 4-km grid scale, and (5) bias correction using rain gage observations. The HDP product is the base rainfall product from the NEXRAD system. Detailed assessments of HDP algorithm performance are presented in Smith et al. (1996b) and Baeck and Smith (1998) [see also Joss and Waldvogel (1989), Wilson and Brandes (1979), and Anagnostou and Krajewski (1998)].

In a second stage of WSR-88D rainfall processing, multisensor precipitation analyses employ rain gage observations and the 4-km HDP rainfall fields in an optimal estimation framework developed by Krajewski (1987) and Seo (1998a, 1998b). These rainfall fields are subsequently composited into a regional mosaic.

Radar polarimetric measurements (Zrnic, 1996; Zrnic and Ryzhkov, 1996; Ryzh-kov and Zrnic, 1996; Aydin et al., 1995), which utilize the capability of radar to transmit and receive electromagnetic radiation at alternating polarization, hold promise for providing significant improvements in rainfall estimates. Polarization measurements have been shown to be quite useful for quality control algorithms, including detection of bright band, hail, and AP [anomalous propagation (of radar waves, due to sharp gradients of water and air density)], as well as for algorithms for estimating rainfall rate (Peterson et al., 1999; Zrnic, 1996). The NEXRAD network was designed for eventual implementation of polarization measurements by the WSR-88D.

Radar has provided a significant component of the observational basis for studying storms that produce extreme rainfall. Chappell (1989) and Doswell et al. (1996) summarize key elements of heavy rainfall producing storms with particular emphasis on storms that produce large point rainfall accumulations through small net storm motion [see also Maddox et al. (1979)]. These storms have been termed quasi-stationary convective systems (Chappell, 1989). Houze et al. (1990) provide a detailed summary of radar-derived storm structure for severe thunderstorms in the central United States [see also Perica and Foufoula-Georgiou (1996) and Steiner et al. (1995)].

WSR-88D observations, and the rainfall products derived from these observations, have provided a new playing field for hydrologic application and science. Many hydrologic problems that were previously not possible to address due to an absence of information concerning rainfall, have been attacked from an observational perspective. Numerous examples can be drawn from flood hydrology. Figure 5 illustrates a storm total rainfall analysis constructed for the rapidan storm of June 27, 1995 (Smith et al., 1996a). More than 600 mm of rain fell on the east slope of the Virginia Blue Ridge during a 12-h period resulting in record unit discharge for the United States east of the Mississippi River and catastrophic landslides and debris flows. Fluvial and geomorphic impacts of the rapidan storm rival those described in the classic study by Hack and Goodlett (1960) for the June 1949 storm in the Shenandoah Mountains. The chief difference between studies of the 1949 and 1995 storms is rainfall measurement at the 1-km horizontal scale and 6-min time scale for the 1995 storm that allows direct assessment of hydrologic processes.


Satellite-borne instruments have proven useful for monitoring precipitating cloud system since the 1960s. Steady progress has been made in developing algorithms for retrieving rainfall accumulations from passive satellite observations in the microwave (Negri et al., 1994; Adler et al., 1994) and infrared (Vicente and Scofield, 1997; Huffman et al., 1995; Adler and Negri, 1988) portions of the electromagnetic spectrum. This progress is reflected in rapidly advancing capabilities for hydro-climatological analysis (Kummerow et al., 2000; Adler et al., 2000; Huffman et

subject to underestimation of rain rates in warm cloud top environments and over-estimation of cold top storms in strong wind shear environments.

The Tropical Rainfall Measuring Mission (TRMM) satellite (Simpson et al., 1988) is designed to measure tropical precipitation and its variation. With the inclusion of a precipitation radar, TRMM provides the first opportunity to estimate the vertical profile of the latent heat that is released through condensation. The TRMM rainfall data will be particularly important for studies of the global hydrological cycle and for testing the ability of climate models to simulate climate accurately on the seasonal time scale.

The TRMM instruments for rainfall observation consist of a precipitation radar, a multifrequency microwave radiometer, and a visible and infrared (VIS/IR) radiometer. The precipitation radar provides measurements of the three-dimensional rainfall distribution over both land and ocean. The precipitation radar will permit the measurement of rain over land where passive microwave channels have difficulty. The horizontal resolution is approximately 4 km, the range resolution is 250 m, and the scanning swath width is 220 km. The multichannel microwave radiometer provides information on vertically integrated precipitation, its areal distribution, and its intensity. Rainfall analyses using the microwave radiometer are best suited for open ocean conditions. The visible infrared (IR) scanner provides high-resolution information on cloud coverage, type, and cloud top temperatures and serves as the link between these data and the long and virtually continuous coverage by the geosynchronous meteorological satellites. The instrument, with a swath width of 720 km, will provide cloud distributions by type and height and rain estimates from brightness temperatures at a horizontal resolution of approximately 2 km.

Satellite IR observations from geostationary satellites have been used extensively for assessing the climatology of extreme rainfall producing storms. An extensive climatology has been developed for mesoscale convective complexes (Maddox, 1980) based on IR-based assessments of cloud properties. Numerous studies have examined the links between mesoscale convective complexes (MCCs), and the more general category of mesoscale convective systems, and heavy rainfall [see Houze (1993)].


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