Radar improvement helps forecasters to “see” storms better

Radars are a vital tool for weather forecasters because they provide a detailed picture of storms as they’re happening. A new radar technique is improving the picture for forecasters, helping them provide more accurate information about rain and snow storms.

An example of the RBRN technique.

Developed by researchers at University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies and NOAA’s National Severe Storms Laboratory, the improvement is now being shared with collaborators an ocean away. The United States-based engineers collaborated with meteorologists at the United Kingdom Met Office on the technique called Radial-by-Radial Noise estimator, or RBRN, to improve radar signal returns in storms.

“Through this unique collaboration paradigm, we’ve proven that scientific partnerships can transcend geographical, political and proprietary boundaries,” said Sebastian Torres, CIMMS researcher leading NSSL’s Advanced Radar Techniques Team. “The atmosphere knows no geographical boundaries. Better forecasts in the UK can provide improved information to the United States, and vice versa, as we continue to build partnerships to help save lives, property and minimize the economic impact of severe weather in the U.S.”

Weather radars often pick up noise from various sources like the sun or man-made devices similarly to how a radio or television sometimes retrieve a static signal. The RBRN analyzes radar beam data in real-time and performs several tests to ensure the noise can be detected and measured.

“Accurate measurement of noise on weather radars is critical as it impacts the accuracy of radar data and plays a key role in data quality control,” Torres said.

In addition, a portion of energy from the radar beam may also be absorbed by particles in its path before the radar beam energy is returned.

“This weakens the echoes from locations far from the radar and gives the wrong impression that storms in these locations are weaker than they truly are,” Torres said. “Because the particles in the radar beam path emit noise, the noise measured by RBRN can be used to correct for the weakening or attenuation of echoes as the radar beam intersects storms. The operational RBRN estimator significantly improves the quality of radar data, especially for weak returns associated with snow storms and gust fronts.”

Reducing and accurately measuring contamination from the noise in the radar data equates to better information and more accurate forecasts for the public.

The RBRN was originally developed by CIMMS Researcher Igor Ivic and became operational in the U.S. NEXRAD radar network in 2014.

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Significant Publication: Collaborative Efforts between the United States and United Kingdom to Advance Prediction of High-Impact Weather

The following significant paper publication was reported to headquarters the week of May 19. NOAA authors are bolded.

  1. Collaborative Efforts between the United States and United Kingdom to Advance Prediction of High-Impact Weather”

By John S. Kain (NSSL) , Steve Willington, Adam J. Clark (NSSL), Steven J. Weiss (NWS SPC), Mark Weeks, Israel L. Jirak (NWS SPC), Michael C. Coniglio (NSSL), Nigel M. Roberts, Christopher D. Karstens (OU CIMMS/NSSL), Jonathan M. Wilkinson, Kent H. Knopfmeier (OU CIMMS/NSSL), Humphrey W. Lean, Laura Ellam, Kirsty Hanley, Rachel North, Dan Suri.

Published in May 2017 American Meteorological Society’s Bulletin of American Meteorological Society, pages 937-948.

Significance: The Met Office brought expertise gained from its efforts using convection-allowing models (CAMs) to better represent the convective storms that bring flash flooding in the United Kingdom. The infusion of Met Office models and perspectives dovetailed exceptionally well with the rapidly growing National Severe Storms Laboratory and Storm Prediction Center proficiency in using CAMs to help predict tornadoes, large hail, and damaging winds. The successful collaborative efforts of the Hazardous Weather Testbed, NSSL, SPC, and Met Office are demonstrating that international collaboration can provide synergy, efficiency, and important scientific advances when it is strongly supported at both grassroots and institutional levels.

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Collaboration improves UK and US radar techniques to improve forecasts

The national weather radar system used throughout the United States by NOAA National Weather Service  forecasters to “see” weather across the country is unique because it can be upgraded and modified with the newest capabilities, unlike other systems worldwide.

Because of this, and the need to work with experts in radar signal processing for improving the quality of radar data, international partners from the United Kingdom Met Office are collaborating with researchers from The University of Oklahoma Cooperative Institute for Mesoscale Meteorological Studies at the NOAA National Severe Storms Laboratory to develop new techniques for U.K.-based radars.

The U.K. Met Office operated a radar system that did not allow changes and was considered a “commercial off-the-shelf solution.”

“Most weather services in the world purchase radar systems from companies and in those systems, the signal processor is typically a black box,” said Sebastian Torres, senior research scientist with OU CIMMS and NSSL. “The signal processor is a key component in all weather radar systems. Its job is to convert echoes received by the radar into weather images. It’s something most weather services don’t really have access to. They know how it works but they can’t change or improve anything.”

The U.K. Met Office decided to build its own signal processor for their radar systems. This allows a similar degree of flexibility to that of the NEXRAD radars, also known as the WSR-88D (weather surveillance radar-88 Doppler), operated in the United States. NOAA offered some of its tested techniques to the U.K. Met Office and in return received access to valuable data it could use for future research and operations.

Inside every NEXRAD radar is a rotating parabolic antenna. As the antenna rotates, it travels up and around while sending out pulses of electromagnetic energy. When radars send and receive these pulses, buildings and other structures may obstruct the radar’s view, contaminating the storm data.

To help keep unwanted objects from impacting storm data, Torres and fellow CIMMS Researcher David Warde developed two complementary signal-processing techniques for the WSR-88D. One technique, called CLEAN-AP, or Clutter Environment Analysis using Adaptive Processing filter, removes unwanted radar echoes from objects on the ground. The other one, called WET or Weather Environment Thresholding, intelligently decides when the CLEAN-AP filter should be applied. This prevents slow-moving storms from being confused with stationary objects.

NSSL and CIMMS researchers Sebastian Torres and David Warde (second and third person from the left) visited the UK Met Office in Exeter from February 22-26, 2016 to support implementation of CLEAN-AP on the UK weather radar network.

 

“The goal of CLEAN-AP and WET is to clean the data as much as possible so the forecasters have the best data available to make warnings and forecasts,” Torres said.

Through collaboration with the U.K. Met Office, who implemented CLEAN-AP and WET, the techniques were fine-tuned and improved. Both techniques are being transferred to the NOAA National Weather Service, and CLEAN-AP is licensed by OU to U.S. weather radar manufacturer Baron.

CLEAN-AP before and after

 

Another CIMMS Researcher, Igor Ivic, developed a third product transferred to the U.K. called the Radial-by-Radial Noise Estimator. RBRN  improves the quality of radar data by removing “noise,” the radar equivalent of radio static or television static. It was implemented on the U.S. NEXRAD network as part of ongoing research-to-operations efforts at NSSL and CIMMS.

“If you have noise and you can remove it from the radar returns, then you get just the signal, and that can be used to get better quality data,” Torres said.

Torres called the collaboration a “win-win” situation because the information exchange, as well as the new technologies and techniques that have been developed are good for both the U.S. and U.K.

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