By far, the most common use of gridded precipitation is for data collected by weather radar. It is possible to create gridded precipitation from gages, for example, using an inverse distance squared scheme between all gages and each grid cell. It is also possible to extract gridded precipitation data from some atmospheric models. The remainder of this section will focus on the common application with weather radar.

Basic Concepts and Equations

Given the radar reflectivity, the rainfall rate for each grid cell can be inferred because the power of the reflected signal is related to the size of and density of the reflecting obstacles. The simplest model to estimate rainfall from reflectivity is a Z-R relationship, and the most commonly-used of these is:

Z=aR^b

in which Z is the reflectivity factor; R is the rainfall intensity; and a and b are empirical coefficients. Thus, as a product of the weather radar, rainfall for cells of a grid that is centered about a radar unit can be estimated. This estimate is the MAP for that cell and does not necessarily suggest the rain depth at any particular point in the cell.

The National Weather Service, Department of Defense, and Department of Transportation (Federal Aviation Administration) cooperatively operate the WSR-88D network. They collect and disseminate the weather radar data to federal government users. The NEXRAD Information Dissemination Service (NIDS) was established to provide access to the weather radar data for users outside of the federal government. Each WSR-88D unit that is designated to support the NIDS program has four ports to which selected vendors may connect. The NIDS vendors, in turn, disseminate the data to their clients using their own facilities, charging the clients for the products provided and for any value added. For example, one NIDS vendor in 1998 was distributing a 1-km x 1-km mosaic of data. This mosaic is a combined image of reflectivity data from several radar units with overlapping or contiguous scans. Combining images in this manner increases the chance of identifying and eliminating anomalies. It also provides a better view of storms over large basins.

The following figure illustrates the advantages of acquiring weather radar data. Figure (a) shows the watershed with a grid system superimposed. Data from a radar unit will provide an estimate of rainfall in each cell of the grid. Commonly these radar-rainfall estimates are presented in graphical format, as illustrated in Figure (b), with color codes for various intensity ranges. (This is similar to the images seen on television weather reports.)

With estimates of rainfall in grid cells, a "big picture" of the rainfall field over a watershed is presented. With this, better estimates of the MAP at any time are possible due to knowledge of the extent of the storm cells, the areas of more intense rainfall, and the areas of no rainfall. By using successive sweeps of the radar, a time series of average rainfall depths for cells that represent each watershed can be developed.

Parameter Estimation

The radar-estimated precipitation should be compared or corrected to correlate with field observations. Radar measures only the movement of water in the atmosphere, not the volume of water falling on the watershed. Precipitation gages must be used to measure the amount of water that actually reaches the ground. Furthermore, radar cannot differentiate liquid and frozen water in the atmosphere. Rain drops and snowflakes have very different reflectance properties and different Z-R relationships must be used. Temperature information must be used to determine the likely state of the precipitation so that the correct Z-R relationship can be used. As noted earlier, the process is complicated by the fact that the gridded precipitation represents an integrated average over the grid cell and not the value at a particular point in the cell, such as a precipitation gage. Because of these and other complications, the work of correcting or calibrating weather radar into a usable precipitation estimate should be done by experienced meteorologists. When such processing is done by the National Weather Service, the final estimate is referred to as a Stage III product. All processing must be done external to the program. The final integrated gridded product is loaded into the program via a HEC-DSS file.