Delbert Willie

Evaluation of Multisensor Quantitative Precipitation Estimation in Russian River Basin

AbstractAn important goal of combining weather radar with rain gauge data is to provide reliable estimates of rainfall rate and accumulation and to further identify intense precipitation and issue flood warnings. Scanning radars provide the ability to observe precipitation over wider areas within shorter timeframes compared to rain gauges, leading to improved situational awareness and more accurate and reliable warnings of future precipitation and flooding events. The focus of this study is on evaluating the performance of the multi-radar multi-sensor (MRMS) system with and without the impact of a local gap filling radar. The challenge of using radar and rain gauges to provide accurate rainfall estimates in complex terrain is investigated. The area of interest is the Russian River basin north of San Francisco, CA, which lies within the National Oceanic and Atmospheric Administration (NOAA) Hydrometeorology Testbed (HMT). In this complex mountainous terrain, the challenge of obtaining reliable quantitative...

Attenuation margin requirements in a networked radar system for observation of precipitation

In recent years, it becomes increasingly possible to move the operating frequency of weather radar systems from non-attenuating lower frequencies, such as at S-band, to attenuating higher frequencies, such as at X-band. However, wave is more easily extinct in rain at higher frequencies in which case there will be missing observations. Therefore, rain attenuation is one of the important metrics in radar system design and an extra attenuation margin needs to be applied to the allocation of power budget to meet the required sensitivity. The NSF Engineering Research Center (ERC) for Collaborative and Adaptive Sensing of the Atmosphere (CASA) is advancing a new sensing paradigm using networked short-range radar systems to avoid problematic earth curvature blockage. The CASA ERC has developed a networked radar test bed — Integrated Project 1 (IP1) — in southwestern Oklahoma, using 4 X-band radar of 40 km range to cover an area of 7, 000 km2. In this paper the attenuation margin performance are analyzed in the network context and the metric to design a networked radar system is formed.

Attenuation statistics for X band radar design

It is well known that the reliability of radar measurements to characterize targets is dependent upon the signal-to-noise ratio of echoes. The power of received signal becomes weak as range increases as described by radar equation in free space. For weather radar, the echoes can suffer extra power loss due to propagation in rain media. A set of system parameters need to be determined in radar design to assure the received signal in sufficient quality at given range. However, rain attenuation is dependent on rain intensity and path length. Therefore attenuation statistics can predict the power loss due to precipitation. This is especially important for radar designers to leverage the transmit power and maximum range. The higher the frequency the greater the amount of power attenuation the signal will suffer as it travels along its path mainly through precipitation as well as other attenuating medium. Conventionally long range weather radar operates at S- band where the attenuation is almost negligible and the application of X-band radars to weather sensing has seen limited application. X-band radars typically operate at frequencies in the neighborhood of 8-12 GHz with respective wavelengths between 3.75 cm to 2.5 cm, and S-band radars operate in the range of 2-4 GHz with wavelengths between 15 cm and 7.5 cm respectively. However the use of X-band radars in comparison to S-band radars has some advantages particularly in the physical size of the radar where the width of the dish decreases as the frequency of the radar increases. Therefore, the costs of building and maintaining a smaller radar can outweigh the costs of larger radars. This can then lead to the usage of multiple radars in place of a single large radar. Further more, the size of an X-band radar can lends itself to portability. So with the size advantage the statistics can give insight into X-band radar design.

Deployment and performance of the NASA D3R during the GPM OLYMPEx field campaign

The NASA D3R was successfully deployed and operated throughout the NASA OLYMPEx field campaign. A differential phase based attenuation correction technique has been implemented for D3R observations. Hydrometeor classification has been demonstrated for five distinct classes using Ku-band observations of both convection and stratiform rain. The stratiform rain hydrometeor classification is compared against LDR observations and shows good agreement in identification of mixed-phase hydrometeors in the melting layer.

Deployment and performance of the NASA D3R during GPM IFloods field campaign

The Iowa Flood Studies (IFloodS) field experiment was conducted to better understand the strengths and limitations of Global Precipitation Measurement (GPM) mission satellite products in the context of hydrologic applications. The NASA dual-frequency dual-polarization Doppler radar (D3R), designed as part of the GPM ground validation program, participated in the IFloodS field campaign to characterize precipitation properties at Ku- and Ka-band frequencies. This paper presents the deployment of the D3R and summarizes the D3R observations during the IFloodS field campaign. The quality of the D3R measurements is evaluated by comparing with the NASA NPOL S-band radar observations. In addition, the capability for rainfall estimation using the D3R is also described and validated using ground gauge measurements.

Regional polarimetric quantitative precipitation estimation over South Carolina

Quantitative precipitation estimation (QPE) continues to be one of the principal objectives for weather researchers and forecasters. The purpose of this research is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies used to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that covers South Carolina for a severe rainfall event occurring October 2, 2015 through October 4, 2015. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP.

Evaluation of multisensor quantitative precipitation estimation methodologies

The use of weather sensing radar measurements along with corresponding gauge data in multisensor applications seek to provide reliable estimates of rainfall rate and accumulation versus single radar. Radar rainfall estimators have a number of advantages over gauges including the ability to observe precipitation over wider areas within shorter timeframes and providing advanced warning of impending precipitation events. The radar reflectivity-rainfall (Z-R) relations are traditionally used for quantitative precipitation estimation (QPE).

Two-year assessment of nowcasting performance in the CASA system

Nowcasting refers to short-term automated forecasting (0–6 hours or less) of high-impact weather events such as heavy rainfall that can produce severe flooding. Accurate and efficient nowcasting can be used to assist emergency managers in the decision-making process. This paper presents an evaluation of nowcasting performance within the Collaborative Adaptive Sensing of the Atmosphere (CASA) system from the 2009–2010 Integrative Project 1 (IP1) experiment. The nowcasting methodology consists of the Dynamic and Adaptive Radar Tracking of Storms (DARTS) method for motion estimation and a sinc kernel-based advection method. Radar reflectivity fields were predicted up to 10 min into the future. Previous analysis is extended to include data over a two year period using Critical Success Index (CSI), False Alarm Ratio (FAR), Probability of Detection (POD), and Mean Absolute Error (MAE) scores for evaluation. Analysis of the categorical scores (i.e., CSI, POD, FAR) relative to scoring threshold and neighborhood is also presented.

Regional polarimetric quantitative precipitation estimation over the Pigeon River Basin

Quantitative precipitation estimation (QPE) from radar measurements remains a principal objective for weather researchers and forecasters. The objective of this paper is to present the development of a regional dual polarization QPE process known as the RAdar Multi-Sensor QPE (RAMS QPE). This scheme applies the dual polarization radar rain rate estimation algorithms developed at Colorado State University into an adaptable QPE system. The methodologies to combine individual radar scans, and then merge them into a mosaic are described. The implementation and evaluation is performed over a domain that occurs near the Pigeon River Basin near Asheville, NC. In this mountainous locale, beam blockage, beam overshooting, orographic enhancement, and the unique climactic conditions complicate the development of reliable QPEs from radar. The QPE precipitation fields evaluated in this analysis will stem from the dual polarization radar data obtained from the local NWS WSR-88DP radars as well as the NASA NPOL research radar.