Advanced Radar Research Center

Norman, OK, United States

Advanced Radar Research Center

Norman, OK, United States
SEARCH FILTERS
Time filter
Source Type

A University of Oklahoma research team with the Advanced Radar Research Center has developed the first numerical polarimetric radar simulator to study and characterize scattering mechanisms of debris particles in tornadoes. Characterizing the debris field of a tornado is vital given flying debris cause most tornado fatalities. Tornado debris characteristics are poorly understood even though the upgrade of the nation's radar network to dual polarimetric radar offers potentially valuable capabilities for improving tornado warnings and nowcasting. "These results are important for operational weather forecasters and emergency managers," says Nick Anderson, program director in the National Science Foundation Division of Atmospheric and Geospace Sciences, which funded the research. "An improved understanding of what weather radars tell us about tornado debris can help provide more accurate tornado warnings, and quickly direct emergency personnel to affected areas." "With this simulator, we can explain in great detail to the operational weather community the tornadic echo from the polarimetric radar," said Robert Palmer, ARRC executive director. "The signal received by the dual polarimetric radar is not easily understood because rain is mixed with the debris. The knowledge we gain from this study will improve tornado detection and near real-time damage estimation." Numerous controlled anechoic chamber measurements of tornadic debris were conducted at the Radar Innovations Laboratory on the OU Research Campus to determine the scattering characteristics of several debris types--leaves, shingles and boards. Palmer, D.J. Bodine, B.L.Cheong, C.J. Fulton and S.M. Torres, the center, and the OU Schools of Electrical and Computer Engineering and Meteorology, developed the simulator to provide comparisons for actual polarimetric radar measurements. Before this study, there were many unanswered questions related to tornado debris scattering, such as knowing how the size, concentration and shape of different debris types affect polarimetric variables. How the radar identifies the debris is equally as important. Orientation of debris makes a difference as well as how the debris falls through the atmosphere. Overall, understanding debris scattering characteristics aid in the discovery of the relationship between debris characteristics, such as lofting and centrifuging, and tornado dynamics. OU team members were responsible for various aspects of this study. Coordination of damage surveys and collection of debris samples were led by Bodine. Field experiments were designed by team members in collaboration with Howard Bluestein, OU School of Meteorology. Electromagnetic simulations and anechoic chamber experiments were led by Fulton. The signal processing algorithms were developed by Torres and his team. Cheong led the simulation development team.


VIDEO:  Researchers use special simulations to study tornado debris and how it interacts with deadly tornadoes. view more Researchers have developed the first numerical polarimetric radar simulator to study and characterize the scattering of debris particles in tornadoes. (See video) The results of their study are published in the Institute of Electrical and Electronics Engineers (IEEE) journal Transactions on Geoscience and Remote Sensing. "These results are important for operational weather forecasters and emergency managers," says Nick Anderson, program director in the National Science Foundation's (NSF) Division of Atmospheric and Geospace Sciences, which funded the research. "An improved understanding of what weather radars tell us about tornado debris can help provide more accurate tornado warnings and quickly direct emergency personnel to affected areas." Current polarimetric radars, also called dual-polarization radars, transmit radio wave pulses horizontally and vertically. The pulses measure the horizontal and vertical dimensions of precipitation particles. The radars provide estimates of rain and snow rates, accurate identification of the regions where rain transitions to snow during winter storms, and detection of large hail in summer thunderstorms. But polarimetric radars have limitations the new research aims to address. "With this simulator, we can explain in great detail to the operational weather community [weather forecasters] the tornadic echo from polarimetric radar," says Robert Palmer, an atmospheric scientist at the University of Oklahoma (OU) and co-author of the paper. Palmer is also director of the university's Advanced Radar Research Center. "The knowledge gained from this study will improve tornado detection and near real-time damage estimates." Characterizing debris fields in tornadoes is vital, scientists say, because flying debris causes most tornado fatalities. The researchers conducted controlled measurements of tornado debris to determine the scattering characteristics of several debris types, such as leaves, shingles and boards. The orientation of the debris, the scientists found, makes a difference in how it scatters and falls through the atmosphere -- and where it lands. Additional co-authors of the paper include OU's David Bodine, Boon Leng Cheong (lead author), Caleb Fulton, Sebastian Torres, and Takashi Maruyama of the Disaster Prevention Research Institute at Japan's Kyoto University. The paper's co-authors designed the field experiments in collaboration with atmospheric scientist Howard Bluestein of OU.


News Article | May 1, 2017
Site: www.eurekalert.org

IMAGE:  A University of Oklahoma research team with the Advanced Radar Research Center has developed the first numerical polarimetric radar simulator to study and characterize scattering mechanisms of debris particles in... view more A University of Oklahoma research team with the Advanced Radar Research Center has developed the first numerical polarimetric radar simulator to study and characterize scattering mechanisms of debris particles in tornadoes. Characterizing the debris field of a tornado is vital given flying debris cause most tornado fatalities. Tornado debris characteristics are poorly understood even though the upgrade of the nation's radar network to dual polarimetric radar offers potentially valuable capabilities for improving tornado warnings and nowcasting. "These results are important for operational weather forecasters and emergency managers," says Nick Anderson, program director in the National Science Foundation Division of Atmospheric and Geospace Sciences, which funded the research. "An improved understanding of what weather radars tell us about tornado debris can help provide more accurate tornado warnings, and quickly direct emergency personnel to affected areas." "With this simulator, we can explain in great detail to the operational weather community the tornadic echo from the polarimetric radar," said Robert Palmer, ARRC executive director. "The signal received by the dual polarimetric radar is not easily understood because rain is mixed with the debris. The knowledge we gain from this study will improve tornado detection and near real-time damage estimation." Numerous controlled anechoic chamber measurements of tornadic debris were conducted at the Radar Innovations Laboratory on the OU Research Campus to determine the scattering characteristics of several debris types--leaves, shingles and boards. Palmer, D.J. Bodine, B.L.Cheong, C.J. Fulton and S.M. Torres, the center, and the OU Schools of Electrical and Computer Engineering and Meteorology, developed the simulator to provide comparisons for actual polarimetric radar measurements. Before this study, there were many unanswered questions related to tornado debris scattering, such as knowing how the size, concentration and shape of different debris types affect polarimetric variables. How the radar identifies the debris is equally as important. Orientation of debris makes a difference as well as how the debris falls through the atmosphere. Overall, understanding debris scattering characteristics aid in the discovery of the relationship between debris characteristics, such as lofting and centrifuging, and tornado dynamics. OU team members were responsible for various aspects of this study. Coordination of damage surveys and collection of debris samples were led by Bodine. Field experiments were designed by team members in collaboration with Howard Bluestein, OU School of Meteorology. Electromagnetic simulations and anechoic chamber experiments were led by Fulton. The signal processing algorithms were developed by Torres and his team. Cheong led the simulation development team. The study, "SimRadar: A Polarimetric Radar Time-Series Simulator for Tornadic Debris Studies," will be published in the May issue of the Institute of Electrical and Electronics Engineers Transactions on Geoscience and Remote Sensing. This work is supported by the National Science Foundation with grant number AGS-1303685. There were significant results from the collaboration between the center and the Disaster Prevention Research Institute in Kyoto University. Note to editors: An animation has been developed for the simulation of the three types of tornadic debris used in this study, which included leaves (green), shingles (pink) and boards (orange). The OU team has the ability, however, to simulate other types of debris. Download the animation at https:/ .


News Article | December 6, 2016
Site: www.eurekalert.org

Robert D. Palmer, Ph.D., University of Oklahoma meteorology professor, associate vice president for research and executive director of the Advanced Radar Research Center, has been named an Institute of Electrical and Electronics Engineering Fellow. Among a select group of recipients recommended for the prestigious honor, Palmer is being recognized for contributions to atmospheric and meteorological radar science. "Professor Robert Palmer has brought distinction to the University of Oklahoma in numerous ways: scientifically, academically and through service that reaches a wide array of private and public sector activities. His most recent and great honor of being made a fellow in the institute adds to this record of distinction to OU. We are particularly thrilled since this also brings much deserved distinction to Bob Palmer," said Berrien Moore, vice president for Weather and Climate Programs, director of the National Weather Center and dean of the OU College of Atmospheric and Geographic Sciences. While at OU, Palmer has been deeply committed to providing students a rigorous education in weather radar. In close collaboration with colleagues in the Norman weather radar community, Palmer led the development of a unique interdisciplinary curriculum in radar meteorology. Soon after joining OU, Palmer established the Advanced Radar Research Center, which is rapidly gaining recognition as one of the world's strongest academic centers in radar meteorology. In recent years, Palmer has focused on the application of advanced radar signal processing techniques to observations of severe weather, particularly related to phased-array radars and other innovative system designs. He has been published widely in the area of radar remote sensing of the atmosphere, with an emphasis on generalized imaging problems, spatial filter design, and clutter mitigation using advanced array and signal processing techniques. Palmer, an OU graduate with a doctoral degree in electrical engineering, is actively engaged with his profession through involvement with the American Meteorological Society, the American Geophysical Union and the Institute of Electrical and Electronics Engineering. Internationally, he has been committed to the development of a vibrant exchange program with Kyoto University in Japan, focused on studies of the atmosphere using modeling and advanced remote sensing methods. He has received several awards for his research and teaching activities and is an American Meteorological Society Fellow as well. The Institute of Electrical and Electronics Engineering Grade of Fellow is conferred by the Board of Directors upon a person with an outstanding record of accomplishments in any of the fields of interest. The total number selected in any one year cannot exceed one-tenth of one- percent of the total voting membership. Fellow is the highest grade of the institute's membership and is recognized by the technical community as a prestigious honor and an important career achievement. The Institute of Electrical and Electronics Engineering is the leading professional association for advancing technology for humanity. Through its 400,000 plus members in 160 countries, the association is a leading authority on a wide variety of areas ranging from aerospace systems, computers and telecommunications to biomedical engineering, electric power and consumer electronics.


Li Z.,Tsinghua University | Yang D.,Tsinghua University | Hong Y.,University of Oklahoma | Hong Y.,Advanced Radar Research Center
Journal of Hydrology | Year: 2013

In the present study, four high-resolution multi-sensor blended precipitation products, TRMM Multisatellite Precipitation Analysis (TMPA) research product (3B42 V7) and near real-time product (3B42 RT), Climate Prediction Center MORPHing technique (CMORPH) and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN), are evaluated over the Yangtze River basin from April 2008 to March 2012 using the gauge data. This regional evaluation is performed at temporal scales ranging from annual to daily, based on a number of diagnostic statistics. Gauge adjustment greatly reduces the bias in 3B42 V7, a post real-time research product. Additionally, it helps the product maintain a stable skill level in winter. When additional indicators such as spatial correlation, Root Mean Square Error (RMSE), and Probability of Detection (POD) are considered, 3B42 V7 is not always superior to other products (especially CMORPH) at the daily scale. Among the near real-time datasets, 3B42 RT overestimates annual rainfall over the basin; CMORPH and PERSIANN underestimate it. In particular, the upper Yangtze always suffers from positive bias (>1mmday-1) in the 3B42 RT dataset and negative bias (-0.2 to -1mmday-1) in the CMORPH dataset. When seasonal scales are considered, CMORPH exhibits negative bias, mainly introduced during cold periods. The correlation between CMORPH and gauge data is the highest. On the contrary, the correlation between 3B42 RT and gauge data is more scattered; statistically, this results in lower bias. Finally, investigation of the probability distribution functions (PDFs) suggests that 3B42 V7 and 3B42 RT are consistently better at retrieving the PDFs in high-intensity events. Overall, this study provides useful information about the error characteristics associated with the four mainstream satellite precipitation products and their implications regarding hydrological applications over the Yangtze River basin. © 2013 Elsevier B.V.


Li Z.,Advanced Radar Research Center | Zhang Y.,Advanced Radar Research Center | Wang S.,Advanced Radar Research Center | Li L.,NASA | McLinden M.,NASA
IEEE Transactions on Aerospace and Electronic Systems | Year: 2015

An adaptive pulse compression (APC) algorithm is developed, based on the concept of minimum mean square error (MMSE), that utilizes matched filter (MF) output as its input and is termed Matched-Filter-Reiterative-MMSE (MF-RMMSE). MF-RMMSE allows the use of a smaller processing window than traditional reiterative-MMSE (RMMSE) but achieves identical performance at side-lobe suppression, and thus the computational load is reduced. MF-RMMSE also facilitates the implementation of APC to current radar sensors, since the existing MF implementations can be untouched. The derivation of MF-RMMSE is provided in detail, and its performance is validated through both simulations and actual airborne radar measurements for "mixed-target" observations, which includes both hard targets and distributed weather targets. © 2015 IEEE.


Xue X.,University of Oklahoma | Xue X.,Advanced Radar Research Center | Hong Y.,University of Oklahoma | Hong Y.,Advanced Radar Research Center | And 6 more authors.
Journal of Hydrology | Year: 2013

The objective of this study is to quantitatively evaluate the successive Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products and further to explore the improvements and error propagation of the latest 3B42V7 algorithm relative to its predecessor 3B42V6 using the Coupled Routing and Excess Storage (CREST) hydrologic model in the mountainous Wangchu Basin of Bhutan. First, the comparison to a decade-long (2001-2010) daily rain gauge dataset reveals that: (1) 3B42V7 generally improves upon 3B42V6's underestimation both for the whole basin (bias from -41.15% to -8.38%) and for a 0.25°. ×. 0.25° grid cell with high-density gauges (bias from -40.25% to 0.04%), though with modest enhancement of correlation coefficients (CC) (from 0.36 to 0.40 for basin-wide and from 0.37 to 0.41 for grid); and (2) 3B42V7 also improves its occurrence frequency across the rain intensity spectrum. Using the CREST model that has been calibrated with rain gauge inputs, the 3B42V6-based simulation shows limited hydrologic prediction NSCE skill (0.23 in daily scale and 0.25 in monthly scale) while 3B42V7 performs fairly well (0.66 in daily scale and 0.77 in monthly scale), a comparable skill score with the gauge rainfall simulations. After recalibrating the model with the respective TMPA data, significant improvements are observed for 3B42V6 across all categories, but not as much enhancement for the already-well-performing 3B42V7 except for a reduction in bias (from -26.98% to -4.81%). In summary, the latest 3B42V7 algorithm reveals a significant upgrade from 3B42V6 both in precipitation accuracy (i.e., correcting the underestimation) thus improving its potential hydrological utility. Forcing the model with 3B42V7 rainfall yields comparable skill scores with in situ gauges even without recalibration of the hydrological model by the satellite precipitation, a compensating approach often used but not favored by the hydrology community, particularly in ungauged basins. © 2013 Elsevier B.V.


James B.,Advanced Radar Research Center | Fulton C.,Advanced Radar Research Center
2015 IEEE MTT-S International Microwave Symposium, IMS 2015 | Year: 2015

The decorrelation of spurious products among the elements in a phased array of direct conversion digital receivers is discussed. Intentional phase randomization of each local oscillator and post-distortion prior to digital beamforming are presented as methods of suppression. Results are shown using a representative system, further validating and extending the theoretical results beyond previous lab-bench superheterodyne results. Further limits to dynamic range in such a system are discussed. © 2015 IEEE.


Putnam B.J.,University of Oklahoma | Xue M.,Center for Analysis and Predication of Storms | Xue M.,Advanced Radar Research Center | Jung Y.,Center for Analysis and Predication of Storms | And 2 more authors.
Monthly Weather Review | Year: 2014

Doppler radar data are assimilated with an ensemble Kalman Filter (EnKF) in combination with a doublemoment (DM) microphysics scheme in order to improve the analysis and forecast of microphysical states and precipitation structures within a mesoscale convective system (MCS) that passed over western Oklahoma on 8-9 May 2007. Reflectivity and radial velocity data from five operational Weather Surveillance Radar-1988 Doppler (WSR-88D) S-band radars as well as four experimental Collaborative and Adaptive Sensing of the Atmosphere (CASA) X-band radars are assimilated over a 1-h period using either single-moment (SM) or DM microphysics schemes within the forecast ensemble. Three-hour deterministic forecasts are initialized from the final ensemble mean analyses using a SMor DM scheme, respectively. Polarimetric radar variables are simulated from the analyses and compared with polarimetric WSR-88D observations for verification. EnKF assimilation of radar data using a multimoment microphysics scheme for an MCS case has not previously been documented in the literature. The use of DM microphysics during data assimilation improves simulated polarimetric variables through differentiation of particle size distributions (PSDs) within the stratiform and convective regions. The DM forecast initiated from the DM analysis shows significant qualitative improvement over the assimilation and forecast using SM microphysics in terms of the location and structure of the MCS precipitation. Quantitative precipitation forecasting skills are also improved in the DM forecast. Better handling of the PSDs by the DM scheme is believed to be responsible for the improved prediction of the surface cold pool, a stronger leading convective line, and improved areal extent of stratiform precipitation. © 2014 American Meteorological Society.


News Article | October 26, 2015
Site: www.sciencenews.org

Wireless technology dangerously clutters the airwaves that meteorologists rely on to monitor thunderstorms, hurricanes and tornadoes, blacking out large swaths of weather radar maps. Wi-Fi, remote surveillance cameras and other wireless tech emit radio waves that can disrupt those from weather radars. This interference, which creates blind spots on radar images, is a growing problem, meteorologists report October 14 in the Bulletin of the American Meteorological Society. “Interference could hide an approaching tornado or a strong convective system and we wouldn’t have any warning,” says coauthor Elena Saltikoff, a meteorologist at the Finnish Meteorological Institute in Helsinki. Weather radar dishes blast radio waves that ricochet off water droplets in the air. Measuring these echoes allows meteorologists to monitor weather conditions up to hundreds of kilometers away. The returning radio waves can be less than a quintillionth the strength of the original signal, though, making the system vulnerable to devices that emit radio waves on similar frequencies. This disruption looks like blotches and streaks on radar images. While software can remove interference, it often can’t salvage the underlying weather data. Interference has been a meteorological nuisance for decades, but the problem has grown stratospherically, says study coauthor John Cho, an atmospheric radar scientist at the MIT Lincoln Laboratory in Lexington, Mass. In Europe, reports of wireless devices interfering with weather radars went from zero before 2006 to more than 200 in 2012. These incidents largely involved equipment such as Wi-Fi routers that had been hacked to circumvent built-in safeguards meant to reduce interference. In South Africa, interference became so bad that meteorologists switched radar frequencies, a move that cost millions of dollars in new equipment. Even after the switch, operators say they still battle rising interference. “We have to protect these frequencies; otherwise, forecasts and observations of storms will suffer,” says Robert Palmer, a radar meteorologist at the University of Oklahoma’s Advanced Radar Research Center in Norman.

Loading Advanced Radar Research Center collaborators
Loading Advanced Radar Research Center collaborators