Potential of radar-estimated rainfall for plant disease risk forecast.

Lack of site-specific weather information has been a major limitation in application of decision support systems in plant disease management because existing weather stations are too sparse to account for local variability. Magarey et al. (17) stressed the need for site-specific weather information and extensively discussed problems associated with deployment of on-site weather stations for high-resolution weather information. To circumvent the shortcomings, they described a system known as model output enhancement technique, originally introduced by Kelly et al. (11), where interpolated upper air forecasts from a mesoscale numerical model are extrapolated to ground surface at 1 km resolution using geophysical data. Rainfall is one of the most spatially variable weather factors (1,8,14). Sufficient information on relationships between rain gauge density and rainfall spatial variability is lacking, but available data suggest that a rain gauge spacing of less than 5 km would be required to explain greater than 90% of the variability (1,8). For a rain gauge network, such a high degree of spatial resolution would be very costly and impractical. Currently, distances between existing weather stations often exceed 50 km, even for so-called densely populated regional networks, and are greater than 100 km for first-order National Weather Service (NWS) Stations (15). Rain gauges provide point source measurements. Hence, without a dense rain gauge network, spatial interpolations for locations without weather stations often produce inaccurate precipitation measurements. A wide variety of fungal and bacterial plant pathogens are dispersed by rain splash and/or wind-driven aerosols. In addition, rain provides high humidity (or free water) for propagule germination and infection and, consequently, many plant disease epidemics are associated with periods of rainfall (3). In semi-arid climates, like the Texas Panhandle, rain is the principal source of high humidity required for activity of many plant pathogens. It has been shown to be a major driving force behind sorghum ergot epidemics, caused by Claviceps africana, in the Texas Panhandle (25). During the last few years, we have been experimenting with the application of radar rainfall estimates for site-specific ergot risk assessment as an alternative to gauge-based measurement (26). Radar-based precipitation estimates have greater spatial coverage and resolution than gauge-based point precipitation estimates (19). Therefore, radar-estimated rainfall is more applicable than gauge-based rainfall estimates for interpolation and regional risk assessment. There have been reports of the successful use of radar precipitation estimates for site-specific plant disease management (7,12). However, questions often arise about the accuracy of radar-estimated rainfall and its relationship to traditional gaugebased rainfall measurements. In this letter, we discuss some of the factors associated with both radar and gauge rainfall measurements, the relationship between the two methods, and the potential of radar rainfall for site-specific disease risk assessment. NEXRAD (Next Generation Weather Radar), also known as WSR-88D (Weather Surveillance Radar 1988–Doppler) or simply Doppler radar, so named after C. J. Doppler, the discoverer of the Doppler shift, has been operational in the United States since the early 1990s. One of the basic concepts of Doppler radar technology is the ability to detect a phase shift in pulse energy of a reflected signal (Doppler shift) after coming in contact with an object in motion (such as rain drops). The weather surveillance radar is supported and operated by three governmental agencies including the NWS (5). Deployment of NEXRAD is considered to be a major step in revolutionizing the weather forecast system in the United States (5,13). This radar system has shown marked improvements in sensitivity and delivery of meteorological and hydrological products over the earlier non-Doppler weather radars (4,21). NEXRAD generates a large number of diverse meteorological and hydrological products, including rainfall and wind velocity (13). Perhaps the most appealing product of NEXRAD to plant pathologists is the radar’s spatial resolution of real-time hourly rainfall estimates. The precipitation rates are computed on a 1 km × 1° grid and averaged on a polar grid of 1° azimuthal and 2 km radial increments out to a range of 230 km (5,18). Figure 1 is a NEXRAD image of a rainstorm that passed through the northern Texas Panhandle on 11 August 1997. The smallest pixel has a resolution of 2 km radial length and the shades of gray depict levels of rainfall accumulation during the 24-h period. The processing of radar data is a multistage procedure. The raw radar reflectivity is converted into rainfall rate (stage I) using the Z-R relationship (described below). The next stage (stage II) is computing of hourly mean-field rain gauge-radar corrections followed by regionally mosaicking of the hourly rainfall product (stage III) from multiple overlapping radars (5). Recently, a new procedure (known as multisensor precipitation estimator) has been introduced to improve rainfall quantification. In this latest addition, rain gauge, radar, and satellite data are merged (on a pixel-by-pixel basis) to generate optimal multisensor rainfall grids. The final hourly regional precipitation product is remapped onto a polar stereographic projection called hydrologic rainfall analysis project (HRAP) grid with a spatial resolution of 4 km × 4 km (5,24). The HRAP grid is a coordinate system used for defining individual grid cells. One of the formats in which the NEXRAD rainfall data are archived is a binary format called XMRG (NetCDF is another), which can be converted into ASCII for use in Arc/Info. A program that converts XMRG files to ASCII is available from the NWS (available online from the NWS National Oceanic and Atmospheric Administration [NOAA]). Alternatively, arrangements can be made with the NWS to obtain data in ASCII format. Measurement of precipitation by radar is influenced by several factors that can be possible sources of error and a few of these factors are briefly described here. Radar reflectivity is converted to rainfall rate using Z-R (reflectivity-rainfall rate) relations of the Corresponding author: C. M. Rush; E-mail address: cm-rush@tamu.edu