Chandra S. Pathak

Geo-Spatial Comparison of Rain Gauge and NEXRAD Data for Central and South Florida

The South Florida Water Management District (District) is responsible for managing water resources in 16-counties over a 46,439-square kilometer (17,930 square-mile) area. The area extends from Orlando to Key West and from the Gulf Coast to the Atlantic Ocean and contains the country’s second largest lake – Lake Okeechobee and the world famous Everglades wetlands. The District operates approximately 3,000 kilometers (~1,800 miles) of canals, 22 major pump stations and 200 water control structures. Nearreal-time rainfall data are used in operation of these pumps and water control structures. The District uses a network of approximately 287 active rain gauge stations that cover the more populated and environmentally sensitive areas under its management and provide data for this purpose. Four NEXRAD (Next Generation Weather Radar) sites operated by the National Weather Service cover the region. Since 2002 the District began to acquire NEXRAD data coverage. Corporate access of 15-minute, rain gauge-adjusted NEXRAD data, for each of the 2 x 2 km cells in the grid covering the District, was a major objective of the acquisition. The District has been receiving the NEXRAD data two types of datasets - near-real time (NRT) and end-of-month (EOM). District is using ArcIMS based application for NEXRAD data retrieval. The application provides varied spatially and temporally integrated datasets in tabular and image formats. This paper compares results of geo-spatial analysis of clusters of rain gauge and NEXRAD data. The data used in the analysis includes rain gauge data and the NEXRAD rainfall data that was collected during 1995-2005 at 2 x 2 km resolution. A set of clusters of rain gauges and a regular array of analysis blocks that were 20 x 20 km in size for the NEXRAD data were used to account for variability of the rainfall processes and local rainfall patterns. The spatial autocorrelations of the rain gauge and NEXRAD rainfall were identified using a semivariogram approach at daily timescale. The model fitting to the semivariograms were performed on data from 1998-2005. The range parameter of the semivariograms from rain gauge and NEXRAD rainfall data sets were compared.

Evaluation of Rain Gauge Network Density and NEXRAD Rainfall Accuracy

The distribution of rainfall in space and time measured by a rain gauge network is a critical element in the management of precious water resources, whether the network is used alone or in combination with weather radar. In 2004, the District initiated a program to optimize the rain gauge network for purposes of near-real time and end of month adjustment of radar data. The rainfall measurement accuracy achieved by a given network density depends on the range correlation defined by radar and the point variance measured at each rain gauge in the network. The point variance and range parameter of the semivariogram vary across the analysis blocks intersecting the District. A variable number of gauges designed to achieve a constant accuracy requirement can be determined. Gauge-adjusted radar rainfall estimate variations based on rain gauge density were analyzed.

Special Issue on Radar Rainfall Data Analyses and Applications

The use of technology to estimate radar-based precipitation (rainfall) began in the early 1960s. During the early 1990s, the use of this technology proliferated as the National Oceanic and Atmospheric Administration (NOAA) installed many radar stations across the United States as part of the Weather Surveillance Radar– 88-Doppler (WSR-88D) and the Next-Generation Weather Radar (NEXRAD) programs initiated by the National Weather Service (NWS). Currently, more than 160 WSR-88D radar stations are in operation, providing nearly contiguous coverage across most of the United States and a way of estimating the intensity of rain or snowfall. WSR-88D radars operate by emitting short (250 m) 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 WSR-88D radar is a 10-cm wavelength (S-band) radar. It is designed for long-distance surveillance because its wavelength penetrates rainfall with little attenuation. However, European radar systems predominantly use lower (less than 5 cm) wavelength (C-band) radar. Shorter wavelength radars suffer attenuation caused by the absorption and scattering of the electromagnetic radiation that degrades performance as the distance from the radar increases, thus requiring an increased density of radar systems. Under most conditions, the useful range of an S-band radar is considered to be approximately 180 km, although the WSR-88D 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 degree, the Earth is curved, and there is atmospheric refraction. Radar data provide rainfall amount products with two primary spatial resolutions: 4 × 4 km Cartesian, and 2 km × 1° azimuthrange. 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 because of overshooting beams and radar miscalibration, radar-derived rainfall data generally are scaled to match the volume measured at coincident rain gauges by using bias adjustment techniques. There is a statistical tradeoff between rainfall data collected by rain gauges and by radars. Rain gauges can provide point values of rainfall depth and intensity but may not cost effectively provide the spatial distribution of rainfall on a regional scale. Whereas rain gauges might suffice for widespread rain events, a gauge network can miss smaller-scale localized convective rainfall events altogether. For this reason, radar rainfall estimates incorporate coincident rain gauge data in several ways. 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 models because the model grid is not at the same resolution as the radar rainfall data input or has a different geographic projection. In addition to 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 the help of GIS or special-purpose spatial analysis tools that can handle spatial data in geographic projections. High-quality radar rainfall records in the United States have only been kept since the mid-1990s. Moreover, some significant changes in computation algorithms have been implemented during that period. Fifteen years of records are still insufficient to use radar rainfall data for long-term statistical studies. However, short-term storm analyses can be performed using radar rainfall data for a specific, recent historical event coupled with continuous hydrologic modeling. Although radar rainfall data are increasingly available over the Internet, data processing, quality assurance/quality control, and radar rainfall data calibration using rain gauge data, all require specialized skills not widely available in the engineering community. At present, those skill sets are limited to a small number of firms or organizations specializing in radar rainfall estimation for hydrologic applications. The use of governmental unadjusted radar data without proper understanding, or the application of tools for quality control and correcting for bias, could lead to serious errors in hydrologic analyses. The methods of estimating precipitation from radar have changed appreciably since the original deployment of the weather radar network, and these methods will continue to evolve. For example, statistical biases can change over time as algorithms are refined. Thus, a major challenge is the proper interpretation of radar-derived rainfall estimates in long time series of 10 years or more. 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. By 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

Improvement of NEXRAD Rainfall Data for Central and South Florida

The South Florida Water Management District (District) is responsible for managing water resources in 16-counties over a 46,439-square kilometer (17,930 square-mile) area. The area extends from Orlando to Key West and from the Gulf Coast to the Atlantic Ocean and contains the countrys second largest lake — Lake Okeechobee and the world famous Everglades wetlands. The District operates approximately 3,000 kilometers (∼1,800 miles) of canals and over 500 water control structures. Near-real-time NEXRAD rainfall data and rain gage network is used to manage water resources in South Florida. The District uses a network of approximately 287 active rain gage stations that cover the more populated and environmentally sensitive areas. Five NEXRAD (Next Generation Weather Radar) sites operated by the National Weather Service cover the region. In conjunction with three of the other five water management districts in Florida, the District has acquired processed radar data from OneRain (formerly NEXRAIN Corporation) since July 2002. The 15-minute radar rainfall data was derived from the 2-km x 2-km high-resolution precipitation product, which was produced from NWS Level 3 — NEXRAD reflectivity. To achieve improved accuracy, gage-adjusted radar rainfall data were derived. This paper provides details on improvements that were made to existing radar rainfall data processed and provided for the District. A bias correction methodology was identified to improve data quality and accuracy in the existing radar rainfall data. The method was applied to existing rainfall data and its performance evaluated and assessed. The improvement obtained through reprocessing the existing radar rainfall data was summarized through data comparison of annual totals and by comparison to gage accumulations over the three watershed areas. In addition, Level 2 NEXRAD reflectivity data (also 2-km x 2-km)from surrounding radars were processed to estimate radar rainfall data using a standard Z-R relationship. During a validation event, the radar rainfall data derived from Level 2 produced better agreement and more accurate rainfall than either the existing radar rainfall product or the reprocessed data. The NEXRAD data quality improvement process performed in this study increased the amount of rainfall through application of the spatially variable bias correction, produced more consistent results through verification at control gages using statistical performance measures.

Identifying and Resolving the Barriers and Issues in Using Radar-Derived Rainfall Estimating Technology

This paper was developed to document the barriers and issues in using radar rainfall estimating technology by the water resources engineering community. It also outlines shortand long-term resolutions of those barriers and other issues that were identified. The authors’ intent is that the ASCE Environmental and Water Resources Institute’s (EWRI) Surface Water Hydrology Technical Committee, Radar Rainfall Data and Application Task Committee, will seek to adopt, improve upon, and implement these suggested shortand long-term resolutions.

Future Research and Application Needs of Radar Rainfall Data in Hydrology

The hallmark of recent advances in radar precipitation estimates is the high resolution detection of spatially variable rainfall over large areas. Radar data far exceeds spatial densities of most rain gage networks and is useful for filling in the gaps between gage measurements. While radar provides the spatial and temporal patterns of rainfall, it requires correction or adjustment to remove systematic over- or underestimation of rainfall rates. Research in radar measurement of rainfall is needed to address the accurate conversion of reflectivity to rain rate, how radar system characteristics affect resolution and precision of the rainfall data, and improved methods for bias adjustment to remove systematic errors. First, we examine the origin and characteristics of radar estimation of rainfall, implementation of radar technology and algorithms, and then provide an overview of radar research needs in hydrology. Research and resulting advances in radar hardware and computer algorithms are expected to improve the use and reliability of radar information for civil infrastructure design and flood forecasting operations.

Developing a Relationship between Radar and Rain Gauge Datasets in Central and South Florida

The South Florida Water Management District (SFWMD) is an agency that relies on a network of nearly 300 rain gauges in order to provide rainf all data for use in operations, modeling, water supply planning, and environmental projects. However, the prevalence of convective and tropical rainfall events in South Florida during the wet season presents a challenge in that the current rain gauge netwo rk may not fully capture rain events which demonstrate high spatial variability. NEXRAD (Next Generation Radar) technology offers the advantage of providing a spatial account of rainfall, although the relative quality of radar rainfall measurements remains largely unknown. The intent of this paper is to examine the relationship between gauge adjusted NEXRAD data and corresponding rain gauge measurements in order to assess the relative performance of radar and rain gauge technologies for different conditions .