Special Issue on Radar Rainfall and Operational Hydrology

Weather radars operate by emitting short pulses of coherent microwave energy. When a target is encountered, such as a building, airplane, bird, or precipitation droplet, the emitted energy is scattered in all directions. Small amounts of energy, known as backscatter, are returned to the radar where they are detected and recorded. The intensity of the returned signal is then related to the size of the object and analyzed according to the time required for the pulse to reach the target and return. This provides information regarding the range and Doppler velocity of the target relative to the radar. The S-band radar is a 10-cm wavelength. It is designed for long-distance surveillance since its wavelength penetrates rainfall with little attenuation. However, European radar systems predominantly use lower (less than 5-cm) wavelength (C-band) radar. X-band radars with low wavelengh (2.5–4 cm) are also in use for short-range weather observations. Shorter-wavelength radars suffer attenuation caused by absorption and scattering of the electromagnetic radiation that degrades performance as the distance from the radar increases, thus requiring increased density of radar systems. Under most conditions, the useful range of a 10-cm wavelength (S-band) radar is considered to be close to 180 km even though the system produces precipitation estimates up to 230 km. As distance increases, the beam becomes increasingly higher above average ground level because the radar’s lowest elevation angle is 0.5°, the Earth is curved, and there is atmospheric refraction.

A primary advantage of radar-derived rainfall data is the density of measurements that is not obtainable by rain gauges alone. Combining these two sensor systems produces better rainfall estimates that more-accurately characterize rainfall over a watershed. Using radar rainfall data, hydrologists benefit from having more information about rainfall rates at high resolution in space and time over large areas. How radar measures rainfall rates depends on assumptions about the number and sizes of raindrops in a representative volume of the atmosphere. Various reflectivity (Z) and rainfall rate (R) relationships have been derived from a theoretical or empirical basis. Depending on storm type and the power of the radar, a range of Z-R relationships is possible. Once an appropriate Z-R relationship is selected, comparison with rain-gauge accumulations is carried out to remove any systematic error, known as bias. After bias removal, differences between radar rainfall and gauge rainfall estimates remain as random error. Dramatic progress has been made in the recent decade toward achieving representative rainfall measurement through a variety of methods. More promising methods include dual-polarization and postprocessing algorithms to account for attenuation and other artifacts in short-wavelength radars such as X-band and C-band radars.

Radar data provide rainfall amount products with two primary spatial grid configurations, i.e., Cartesian and degree-azimuth range. The radar rainfall data are limited by relying on the measurement of raindrop reflectivity, which can be affected by factors such as raindrop size and signal reflection by other objects. Because the reflected signal measured by the radar is proportional to the sum of the sixth power of the diameter of the raindrops in a given radar volume, small changes in the size of raindrops can have a dramatic effect on the radar’s estimate of rainfall. For this reason, and due to overshooting beams and radar miscalibration, radar-derived rainfall data generally are scaled to match the volume measured at coincident rain gauges using bias-adjustment techniques. Radar rainfall outputs are generally in the native polar coordinate system of the radar or resampled to a grid in Cartesian coordinates. Aggregation or disaggregation of gridded radar rainfall data is often necessary with grid-based distributed hydrologic models because the model grid is not at the same resolution as the radar rainfall data input or has a different geographic projection. Besides file-format manipulation, the link between radar rainfall data and hydrologic models requires spatial aggregation from one grid to another or to subbasin areas. Aggregation from polar coordinates to basins or to rectangular grids is usually accomplished with spatial analysis tools that can handle spatial data in different geographic projections.

While radar rainfall data are increasingly available over the web, data processing, quality assurance/quality control, and radar rainfall data calibration using rain-gauge data all require specialized knowledge and experience. Use of unadjusted radar data without the proper understanding, or the application of tools for quality control and correcting for bias, could lead to serious errors in the analysis. The methods of estimating precipitation from radar have changed appreciably since the original deployment of weather radar networks, and these methods have continued to evolve over last several years. Distributed hydrologic models are particularly well-suited to utilizing radar rainfall. Understanding the importance of radar rainfall estimation errors within a modeling context has particular advantages. Some errors tend to average out over a watershed area, while others may dramatically increase systematic errors. Use of real-time and postanalysis quality control and postprocessing will yield improved radar rainfall data for hydrologic applications. Distributed hydrologic models designed from the outset to utilize high-resolution rainfall rates from multiple sensors (radar, satellite, and rain gauge) allow detailed estimations at almost any location in a watershed characterized with geospatial data relating to topography, soils, and land cover. Besides hydrologic design, radar rainfall can also provide timely inputs to operational decisions for water control structures and flood warning systems, and can also guide emergency-management personnel in taking proactive steps to protect life and property from flooding. Considering the need for precipitation measurements at different spatial and temporal scales for hydrologic analysis and forecasting, radar-derived rainfall estimation is clearly one of the most significant advances in hydrologic engineering and practice.

This special issue includes selected peer-reviewed technical papers that were presented at the 2014 International Symposium on Weather Radar and Hydrology (2014 WRaH) that was organized by the Environment and Water Resources Institute (EWRI) of ASCE, and was held in Washington, DC. The papers in this special issue can be grouped into three categories: (1) six papers are on radar rainfall data estimation, improvement, and validation, (2) five papers are on application of radar rainfall data, and (3) four papers are on use of radar rainfall for flood forecasting.

Six papers in this issue deal with quantitative precipitation estimation (QPE), multisensor QPE, assessment of biases in radar-based precipitation data estimates, use of pre-NEXRAD (NEXt Generation RADar) reflectivity data to obtain precipitation estimates, creation of multisensor precipitation products, and evaluation of artifacts in multisensor precipitation estimates. Five papers discuss the use of radar rainfall for hydrologic web-mapping-based products, urban hydrology applications using dual-polarization X-band radar, evaluation of tropical rainfall measuring mission (TRRM)-precipitation combined with rain-gauge observations using hydrologic models, radar adjustment using runoff from urban-area, and riverine design flood applications. Four papers mainly focus on case studies related to urban flood forecasting applications with use of an X-band multiparameter radar network, a radar-based flood alert system, operational quality control, and enhancement of radar data to support regional flash-flood warning systems, and an operational hydrometeorological forecast system.

As guest editors of this special issue, we sincerely hope that the topic of radar rainfall estimation and operational hydrology is useful to researchers and practicing engineers alike as this discipline of hydrologic engineering progresses in the future.