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Yixin Wen, Yang Hong, Guifu Zhang, Terry J. Schuur, Jonathan J. Gourley, Zac Flamig, K. Robert Morris, and Qing Cao

Abstract

Ground-based polarimetric weather radar is arguably the most powerful validation tool that provides physical insight into the development and interpretation of spaceborne weather radar algorithms and observations. This study aims to compare and resolve discrepancies in hydrometeor retrievals and reflectivity observations between the NOAA/National Severe Storm Laboratory “proof of concept” KOUN polarimetric Weather Surveillance Radar-1988 Doppler (WSR-88D) and the spaceborne precipitation radar (PR) on board NASA’s Tropical Rainfall Measuring Mission (TRMM) platform. An intercomparison of PR and KOUN melting-layer heights retrieved from 2 to 5 km MSL shows a high correlation coefficient of 0.88 with relative bias of 5.9%. A resolution volume–matching technique is used to compare simultaneous TRMM PR and KOUN reflectivity observations. The comparisons reveal an overall bias of <0.2% between PR and KOUN. The bias is hypothesized to be from non-Rayleigh scattering effects and/or errors in attenuation correction procedures applied to Ku-band PR measurements. By comparing reflectivity with respect to different hydrometeor types (as determined by KOUN’s hydrometeor classification algorithm), it is found that the bias is from echoes that are classified as rain–hail mixture, wet snow, graupel, and heavy rain. These results agree with expectations from backscattering calculations at Ku and S bands, but with the notable exception of dry snow. Comparison of vertical reflectivity profiles shows that PR suffers significant attenuation at lower altitudes, especially in convective rain and in the melting layer. The attenuation correction performs very well for both stratiform and convective rain, however. In light of the imminent upgrade of the U.S. national weather radar network to include polarimetric capabilities, the findings in this study will potentially serve as the basis for nationwide validation of space-based precipitation products and also invite synergistic development of coordinated space–ground multisensor precipitation products.

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Yixin Wen, Pierre Kirstetter, Yang Hong, Jonathan J. Gourley, Qing Cao, Jian Zhang, Zac Flamig, and Xianwu Xue

Abstract

Over mountainous terrain, ground weather radars face limitations in monitoring surface precipitation as they are affected by radar beam blockages along with the range-dependent biases due to beam broadening and increase in altitude with range. These issues are compounded by precipitation structures that are relatively shallow and experience growth at low levels due to orographic enhancement. To improve surface precipitation estimation, researchers at the University of Oklahoma have demonstrated the benefits of integrating the Tropical Rainfall Measuring Mission (TRMM) Precipitation Radar (PR) products into the ground-based NEXRAD rainfall estimation system using a vertical profile of reflectivity (VPR) identification and enhancement (VPR-IE) approach. However, the temporal resolution of TRMM limits the application of VPR-IE method operationally. To implement the VPR-IE concept into the National Mosaic and Multi-Sensor QPE (NMQ) system in real time, climatological VPRs from 11 years of TRMM PR observations have been characterized for different stratiform/convective rain types, seasons, and surface rain intensities. Then, these representative profiles are used to adjust ground radar–based precipitation estimates in the NMQ system based on different precipitation structures. This study conducts a comprehensive evaluation of the newly developed climatological VPR-IE (CVPR-IE) method on winter events (January, February, and December) in 2011. The statistical analysis reveals that the CVPR-IE method provides a clear improvement over the original radar QPE in the NMQ system for the study region. Compared to physically based VPRs from real-time PR measurements, climatological VPRs have limitations in representing precipitation structure for individual events. A hybrid correction scheme incorporating both climatological and real-time VPR information is desired for better skill in the future.

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Sheng Chen, Jonathan J. Gourley, Yang Hong, Qing Cao, Nicholas Carr, Pierre-Emmanuel Kirstetter, Jian Zhang, and Zac Flamig

Abstract

In meteorological investigations, the reference variable or “ground truth” typically comes from an instrument. This study uses human observations of surface precipitation types to evaluate the same variables that are estimated from an automated algorithm. The NOAA/National Severe Storms Laboratory’s Multi-Radar Multi-Sensor (MRMS) system relies primarily on observations from the Next Generation Radar (NEXRAD) network and model analyses from the Earth System Research Laboratory’s Rapid Refresh (RAP) system. Each hour, MRMS yields quantitative precipitation estimates and surface precipitation types as rain or snow. To date, the surface precipitation type product has received little attention beyond case studies. This study uses precipitation type reports collected by citizen scientists who have contributed observations to the meteorological Phenomena Identification Near the Ground (mPING) project. Citizen scientist reports of rain and snow during the winter season from 19 December 2012 to 30 April 2013 across the United States are compared to the MRMS precipitation type products. Results show that while the mPING reports have a limited spatial distribution (they are concentrated in urban areas), they yield similar critical success indexes of MRMS precipitation types in different cities. The remaining disagreement is attributed to an MRMS algorithmic deficiency of yielding too much rain, as opposed to biases in the mPING reports. The study also shows reduced detectability of snow compared to rain, which is attributed to lack of sensitivity at S band and the shallow nature of winter storms. Some suggestions are provided for improving the MRMS precipitation type algorithm based on these findings.

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Suzanne Van Cooten, Kevin E. Kelleher, Kenneth Howard, Jian Zhang, Jonathan J. Gourley, John S. Kain, Kodi Nemunaitis-Monroe, Zac Flamig, Heather Moser, Ami Arthur, Carrie Langston, Randall Kolar, Yang Hong, Kendra Dresback, Evan Tromble, Humberto Vergara, Richard A Luettich Jr., Brian Blanton, Howard Lander, Ken Galluppi, Jessica Proud Losego, Cheryl Ann Blain, Jack Thigpen, Katie Mosher, Darin Figurskey, Michael Moneypenny, Jonathan Blaes, Jeff Orrock, Rich Bandy, Carin Goodall, John G. W. Kelley, Jason Greenlaw, Micah Wengren, Dave Eslinger, Jeff Payne, Geno Olmi, John Feldt, John Schmidt, Todd Hamill, Robert Bacon, Robert Stickney, and Lundie Spence

The objective of the Coastal and Inland Flooding Observation and Warning (CI-FLOW) project is to prototype new hydrometeorologic techniques to address a critical NOAA service gap: routine total water level predictions for tidally influenced watersheds. Since February 2000, the project has focused on developing a coupled modeling system to accurately account for water at all locations in a coastal watershed by exchanging data between atmospheric, hydrologic, and hydrodynamic models. These simulations account for the quantity of water associated with waves, tides, storm surge, rivers, and rainfall, including interactions at the tidal/surge interface.

Within this project, CI-FLOW addresses the following goals: i) apply advanced weather and oceanographic monitoring and prediction techniques to the coastal environment; ii) prototype an automated hydrometeorologic data collection and prediction system; iii) facilitate interdisciplinary and multiorganizational collaborations; and iv) enhance techniques and technologies that improve actionable hydrologic/hydrodynamic information to reduce the impacts of coastal flooding. Results are presented for Hurricane Isabel (2003), Hurricane Earl (2010), and Tropical Storm Nicole (2010) for the Tar–Pamlico and Neuse River basins of North Carolina. This area was chosen, in part, because of the tremendous damage inflicted by Hurricanes Dennis and Floyd (1999). The vision is to transition CI-FLOW research findings and technologies to other U.S. coastal watersheds.

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