Sources of Power: How People Make Decisions

The National Mosaic and Multi-sensor QPE (Quantitative Precipitation Estimation), or “NMQ”, system was initially developed from a joint initiative between the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory, the Federal Aviation Administration's Aviation Weather Research Program, and the Salt River Project. Further development has continued with additional support from the National Weather Service (NWS) Office of Hydrologic Development, the NWS Office of Climate, Water, and Weather Services, and the Central Weather Bureau of Taiwan. The objectives of NMQ research and development (R&D) are 1) to develop a hydrometeorological platform for assimilating different observational networks toward creating high spatial and temporal resolution multisensor QPEs for f lood warnings and water resource management and 2) to develop a seamless high-resolution national 3D grid of radar reflectivity for severe weather detection, data assimilation, numerical weather prediction model verif...

National Mosaic and Multi-Sensor QPE (NMQ) System: Description, Results, and Future Plans

Rapid advancements of computer technologies in recent years made the real-time transferring and integration of high-volume, multisource data at a centralized location a possibility. The Multi-Radar Multi-Sensor (MRMS) system recently implemented at the National Centers for Environmental Prediction demonstrates such capabilities by integrating about 180 operational weather radars from the conterminous United States and Canada into a seamless national 3D radar mosaic with very high spatial (1 km) and temporal (2 min) resolution. The radar data can be integrated with high-resolution numerical weather prediction model data, satellite data, and lightning and rain gauge observations to generate a suite of severe weather and quantitative precipitation estimation (QPE) products. This paper provides an overview of the initial operating capabilities of MRMS QPE products.

Multi-Radar Multi-Sensor (MRMS) Quantitative Precipitation Estimation: Initial Operating Capabilities

This paper contains a description of the most recent version of the hydrometeor classification algorithm for polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D). This version contains several modifications and refinements of the previous echo classification algorithm based on the principles of fuzzy logic. These modifications include the estimation of confidence factors that characterize the possible impacts of all error sources on radar measurements, the assignment of the matrix of weights that characterizes the classification power of each variable with respect to every class of radar echo, and the implementation of a class designation system based on the distance from the radar and the parameters of the melting layer that are determined as functions of azimuth with polarimetric radar measurements. These additions provide considerable flexibility and improve the discrimination between liquid and frozen hydrometeors. The new classification scheme utilizes all available polarimetric va...

The Hydrometeor Classification Algorithm for the Polarimetric WSR-88D: Description and Application to an MCS

https://ntrs.nasa.gov/api/citations/20140010540/downloads/20140010540.pdf

Global precipitation measurement: Methods, datasets and applications

As part of the evolution and future enhancement of the Next Generation Weather Radars (NEXRAD), the National Severe Storms Laboratory recently upgraded the KOUN Weather Surveillance Radar-1988 Doppler (WSR-88D) to include a polarimetric capability. The proof of concept was tested in central Oklahoma during a 1-yr demonstration project referred to as the Joint Polarization Experiment (JPOLE). This paper presents an overview of polarimetric algorithms for rainfall estimation and hydrometeor classification and their performance during JPOLE. The quality of rainfall measurements is validated on a large dataset from the Oklahoma Mesonet and Agricultural Research Service Micronet rain gauge networks. The comparison demonstrates that polarimetric rainfall estimates are often dramatically superior to those provided by conventional rainfall algorithms. Using a synthetic R(Z, KDP, ZDR) polarimetric rainfall relation, rms errors are reduced by a factor of 1.7 for point measurements and 3.7 for areal estimates [when ...

The joint polarization experiment: Polarimetric rainfall measurements and hydrometeor classification

A key advantage of the National Weather Radar Testbed Phased Array Radar (PAR) is the capability to adaptively scan storms at higher temporal resolution than is possible with the Weather Surveillance Radar-1988 Doppler (WSR-88D): 1 min or less versus 4.1 min, respectively. High temporal resolution volumetric radar data are a necessity for rapid identification and confirmation of weather phenomena that can develop within minutes. The purpose of this paper is to demonstrate the PAR’s ability to collect rapid-scan volumetric data that provide more detailed depictions of quickly evolving storm structures than the WSR-88D. Scientific advantages of higher temporal resolution PAR data are examined for three convective storms that occurred during the spring and summer of 2006, including a reintensifying supercell, a microburst, and a hailstorm. The analysis of the reintensifying supercell (58-s updates) illustrates the capability to diagnose the detailed evolution of developing and/or intensifying areas ...

/pdf/rapid-sampling-of-severe-storms-by-the-national-weather-1zwr3i19ti.pdf

Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar

Since 2007 the advancement of the National Weather Radar Testbed Phased-Array Radar (NWRT PAR) hardware and software capabilities has been supporting the implementation of high-temporal-resolution (∼1 min) sampling. To achieve the increase in computational power and data archiving needs required for high-temporal-resolution sampling, the signal processor was upgraded to a scalable, Linux-based cluster with a distributed computing architecture. The development of electronic adaptive scanning, which can reduce update times by focusing data collection on significant weather, became possible through functionality added to the radar control interface and real-time controller. Signal processing techniques were implemented to address data quality issues, such as artifact removal and range-and-velocity ambiguity mitigation, absent from the NWRT PAR at its installation. The hardware and software advancements described above have made possible the development of conventional and electronic scanning capabil...

High-Temporal-Resolution Capabilities of the National Weather Radar Testbed Phased-Array Radar

This study describes, illustrates, and validates hail detection by a simplified version of the National Severe Storms Laboratory’s fuzzy logic polarimetric hydrometeor classification algorithm (HCA). The HCA uses four radar variables: reflectivity, differential reflectivity, cross-correlation coefficient, and “reflectivity texture” to classify echoes as rain mixed with hail, ground clutter–anomalous propagation, biological scatterers (insects, birds, and bats), big drops, light rain, moderate rain, and heavy rain. Diagnostic capabilities of HCA, such as detection of hail, are illustrated for a variety of storm environments using polarimetric radar data collected mostly during the Joint Polarimetric Experiment (JPOLE; 28 April–13 June 2003). Hail classification with the HCA is validated using 47 rain and hail reports collected by storm-intercept teams during JPOLE. For comparison purposes, probability of hail output from the Next-Generation Weather Radar Hail Detection Algorithm (HDA) is validated using the same ground truth. The anticipated polarimetric upgrade of the Weather Surveillance Radar-1988 Doppler network drives this direct comparison of performance. For the four examined cases, contingency table statistics show that the HCA outperforms the HDA. The superior performance of the HCA results primary from the algorithm’s lack of false alarms compared to the HDA. Statistical significance testing via bootstrapping indicates that differences in the probability of detection and critical success index between the algorithms are statistically significant at the 95% confidence level, whereas differences in the false alarm rate and Heidke skill score are statistically significant at the 90% confidence level.

Validation of Polarimetric Hail Detection

This study investigates the use of tornadic debris signature (TDS) parameters to estimate tornado damage severity using Norman, Oklahoma (KOUN), polarimetric radar data (polarimetric version of the Weather Surveillance Radar-1988 Doppler radar). Several TDS parameters are examined, including parameters based on the 10th or 90th percentiles of polarimetric variables (lowest tilt TDS parameters) and TDS parameters based on the TDS volumetric coverage (spatial TDS parameters). Two highly detailed National Weather Service (NWS) damage surveys are compared to TDS parameters. The TDS parameters tend to be correlated with the enhanced Fujita scale (EF) rating. The 90th percentile reflectivity, TDS height, and TDS volume increase during tornado intensification and decrease during tornado dissipation. For 14 tornado cases, the maximum or minimum TDS parameter values are compared to the tornado’s EF rating. For tornadoes with a higher EF rating, higher maximum values of the 90th percentile ZHH, TDS height, ...

Pamela L. Heinselman

Papers

The joint polarization experiment: Polarimetric rainfall measurements and hydrometeor classification

Rapid Sampling of Severe Storms by the National Weather Radar Testbed Phased Array Radar

High-Temporal-Resolution Capabilities of the National Weather Radar Testbed Phased-Array Radar

Validation of Polarimetric Hail Detection

Tornado Damage Estimation Using Polarimetric Radar