HYDROMETEOROLOGY RESEARCH

Fang, M., J. Zhang, J. K. Williams, J. A. Craig, 2008: Three-Dimensional Mosaic of the Eddy Dissipation Rate Fields from WSR-88Ds. Extended Abstracts, The 88th AMS annual conference, New Orleans, LA, USA, AMS, P4.5. [Available from Ming Fang, 408C, Wadsack Dr., Norman, OK, USA, 73072.]

A national 3-D mosaic of Eddy Dissipation Rate (EDR) is being developed, prototyped, and evaluated through collaboration between the National Center for Atmospheric Research (NCAR) and NOAA’s National Severe Storms Lab (NSSL) under the auspices of the FAA Aviation Weather Research Program’s Turbulence and Advanced Weather Radar Techniques (AWRT) Research Teams. The EDR field is an indicator of in-cloud turbulence intensity derived from individual WSR-88Ds’ spectrum width data by the NEXRAD Turbulence Detection Algorithm (NTDA), which was developed at NCAR by the Turbulence Research Team. The NTDA software has been delivered to the National Weather Service and will be implemented operationally on all WSR-88Ds beginning in the spring of 2008, providing EDR and associated confidence data as a polar-grid Level III field. A national 3-D mosaic of the EDR field will provide a high-resolution, rapid update, in-cloud turbulence product for use in aviation safety decision support products. In particular, the Turbulence Research Team plans to incorporate it into a new rapid-update version of the Graphical Turbulence Guidance product, which will directly address convective turbulence for the first time.

The EDR mosaic has been developed using NTDA data from 20 radars covering the Chicago to Washington DC region that are being generated at NCAR and transferred to NSSL in real-time. A mosaic scheme previously developed by the AWRT Research Team for creating 3-D reflectivity mosaics was used as a starting point, but differences between EDR and reflectivity has required a number of adjustments; in addition, the 3-D mosaic scheme was modified to utilize the confidence values produced by the NTDA. The prototype regional 3-D in-cloud turbulence mosaic was evaluated based on comparisons with EDR values obtained from an automated measurement and reporting system on United Airlines aircraft. Continuing evaluation and tuning efforts are expected to lead to enhancements in the current mosaic scheme and establishment of a methodology that will eventually be used in the operational national 3-D in-cloud turbulence mosaic.

Fang, M., R. J. Doviak, P. Zhang, 2008: An Analytical Expression For Doppler Spectra Related to TerminalVelocity With Non-uniform Drop Size Distribution. Extended Abstracts, The 88th AMS annual meeting, New Orleans, LA, USA, AMS, P2.27. [Available from Ming Fang, 408C, Wadsack Dr., Norman, OK, USA, 73072.]

Starting from the correlation function and neglecting other spectrum broadening mechanisms, an analytical expression for the Doppler spectrum is related to the drop’s terminal velocity and size distribution if there is a unique relationship between drop’s diameter and its terminal velocity. The derivation does not require drop size distribution to be homogeneous. This generalized expression reduces to previously derived expression if drop size distribution is uniform.

Fang, M., R. J. Doviak, 2008: WSR-88D Observed Spatial Spectra of Turbulence in Precipitation. Extended Abstracts, The 88th AMS annual meeting, New Orleans, LA, USA, AMS, 12.6. [Available from Ming Fang, 408C, Wadsack Dr., Norman, OK, USA, 73072.]

Different algorithms are designed to isolate the turbulent component from radar measured Doppler velocity. Spatial spectra along the quasi-horizontal direction are then obtained in stratiform rain, storms and squall lines. The slope of horizontal spectra in stratiform rain and storms is close to -5/3 on a log-log graph up to at least scales of 10 km and 7 km respectively. The spectrum in a squall line has a steeper slope than -5/3 up to scales at least 17 km. The scales at low wave number end on the spectra are so large that the spectra could not be due to three-dimensional isotropic turbulence but to two-dimensional turbulence.

Gourley, J. J., P. Tabary, J. Parent-du-Chatelet, 2007: Empirical estimation of attenuation from differential propagation phase measurements at C-band. Journal of Applied Meteorology and Climatology, 46, 306-317.

Gourley, J. J., P. Tabary, J. Parent-du-Chatelet, 2007: A fuzzy logic algorithm for the separation of precipitating from non-precipitating echoes using polarimetric radar observations. Journal of Atmospheric and Oceanic Technology, 24, 1439-1451.

Langston, C., J. Zhang, K. Howard, 2007: Four-Dimensional Dynamic Radar Mosaic. Journal of Atmospheric and Oceanic Technology, 24, 776-790.

Vasiloff, S. V., D. J. Seo, K. H. Howard, J. Zhang, D. H. Kitzmiller, C. Coauthors, 2007: Improving QPE and Very Short Term QPF. Bulletin of the American Meteorological Society, 88, 1899-1911.

Vasiloff, S. V., K. H. Howard, 2007: Investigation of a severe microburst near Phoenix, Arizona as seen by a mobile Doppler radar and the KIWA WSR-88D. Extended Abstracts, 13th Conference on Aviation, Range and Aerospace Meteorology, New Orleans, LA, USA, AMS, p4.7.

Vasiloff, S. V., B. Kaney, C. Langston, W. Xia, 2007: The National Severe Storms Laboratory QPE verification system. Extended Abstracts, 24th Conference on IIPS, New Orleans, LA, USA, AMS, 6B.12.

Vasiloff, S. V., D. J. Seo, K. W. Howard, J. Zhang, D. H. Kitzmiller, M. G. Mullusky, W. F. Krajewski, E. A. Brandes, R. M. Rabin, D. S. Berkowitz, H. E. Brooks, J. A. McGinley, R. J. Kuligowski, B. G. Brown, 2007: Improving QPE and Very Short Term QPF: An Initiative for a Community-Wide Integrated Approach. Bulletin of the American Meteorological Society, 88, 1899-1911.

Accurate quantitative precipitation estimates (QPE) and very short term quantitative precipitation forecasts (VSTQPF) are critical to accurate monitoring and prediction of water-related hazards and water resources. While tremendous progress has been made in the last quarter-century in many areas of QPE and VSTQPF, significant gaps continue to exist in both knowledge and capabilities that are necessary to produce accurate high-resolution precipitation estimates at the national scale for a wide spectrum of users. Toward this goal, a national next-generation QPE and VSTQPF (Q2) workshop was held in Norman, Oklahoma, on 28–30 June 2005. Scientists, operational forecasters, water managers, and stakeholders from public and private sectors, including academia, presented and discussed a broad range of precipitation and forecasting topics and issues, and developed a list of science focus areas. To meet the nation's needs for the precipitation information effectively, the authors herein propose a community-wide integrated approach for precipitation information that fully capitalizes on recent advances in science and technology, and leverages the wide range of expertise and experience that exists in the research and operational communities. The concepts and recommendations from the workshop form the Q2 science plan and a suggested path to operations. Implementation of these concepts is expected to improve river forecasts and flood and flash flood watches and warnings, and to enhance various hydrologic and hydrometeorological services for a wide range of users and customers. In support of this initiative, the National Mosaic and Q2 (NMQ) system is being developed at the National Severe Storms Laboratory to serve as a community test bed for QPE and VSTQPF research and to facilitate the transition to operations of research applications. The NMQ system provides a real-time, around-the-clock data infusion and applications development and evaluation environment, and thus offers a community-wide platform for development and testing of advances in the focus areas.

Gourley, J. J., P. Tabary, J. Parent-du-Chatelet, 2006: Data quality of the Meteo-France C-band polarimetric radar. Journal of Atmospheric and Oceanic Technology, 23, 1340-1356.

Gourley, J. J., B. E. Vieux, 2006: A method for identifying sources of model uncertainty in rainfall-runoff simulations.. Journal of Hydrology, 327, 68-80.

Vasiloff, S. V., 2006: Comparison of 2 hour forecasts based on persistence and a cross-correlation technique.. Preprints, 12th Conference on Aviation, Range, and Aerospace Meteorology, Atlanta, GA, USA, AMS, CD-ROM, 3.10. [Available from steven.vasiloff@noaa.gov, National Weather Center, 120 David L. Boren Blvd., Norman, OK, USA, 73072.]

The NCAR Weather Support to Deicing Decision Making System (WSDDM) uses a cross-correlation technique to produce radar echo motion vectors. These vectors are then used to forecast snow water equivalent precipitation based on future echo positions with the focus on airports. It has been shown that WSDDM 30 min forecasts have large skill compared to persistence forecasts (a persistence forecast assumes that the current state will continue). This paper carries this type of comparative analysis out to two hours. Data from winter storms in the upper Midwest are evaluated and point forecasts near Chicago and Minneapolis are determined for both methods. Various echo configurations are used for the tests and include rain/snow bands, echoes from different sectors of synoptic cyclones and echoes with various reflectivity intensities.

Ware, E. C., D. M. Schultz, H. E. Brooks, P. J. Roebber, S. L. Bruening, 2006: Improving snowfall forecasting by accounting for the climatological variability of snow density.. Weather and Forecasting, 21, 94-103.

Accurately forecasting snowfall is a challenge. In particular, one poorly understood component of snowfall forecasting is determining the snow ratio. The snow ratio is the ratio of snowfall to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 < ratio < 9:1), average (9:1 <= ratio <= 15:1), and light (ratio > 15:1). A Web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural network is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snowfall, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. For example, mean low-level temperatures greater than −2.7°C rarely (less than 5% of the time) produce snow ratios greater than 25:1, whereas mean low-level temperatures less than −10.1°C rarely produce snow ratios less than 10:1. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snowfall, given a forecasted value of liquid equivalent. For example, liquid equivalent amounts 2.8–4.1 mm (0.11–0.16 in.) rarely produce snow ratios less than 14:1, and liquid equivalent amounts greater than 11.2 mm (0.44 in.) rarely produce snow ratios greater than 26:1. The surface wind speed plays a minor role by decreasing snow ratio with increasing wind speed. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

Available online at ://http://www.cimms.ou.edu/~schultz/pubs/wareetal06.pdf.

Zhang, J., S. Wang, 2006: An Automated 2D Multipass Doppler Radar Volocity Dealiasing Scheme. Journal of Atmospheric and Oceanic Technology, 23, 1239-1248.

Arthur, A. T., G. M. Cox, N. R. Kuhnert, D. L. Slayter, K. W. Howard, 2005: The National Basin Delineation Project. Bulletin of the American Meteorological Society, 86, 1443-1452.

Fang, M., R. J. Doviak, 2005: Corrections to and considerations of the spectrum width equation. Preprints, 32nd Conference on Radar Meteorology, Albuquerque, NM, USA, AMS, CD-ROM, P4R.2.

Gourley, J. J., B. E. Vieux, 2005: A Method for Evaluating the Accuracy of Quantitative Precipitation Estimates from a Hydrologic Modeling Perspective. Journal of Hydrometeorology, 6, 115-133.

Zhang, J., K. Howard, J. J. Gourley, 2005: Constructing three-dimensional multiple radar reflectivity mosaics: examples of convective storms and stratiform rain echoes. Journal of Atmospheric and Oceanic Technology, 22, 30-42.

Fang, M., R. J. Doviak, V. Melnikov, 2004: Spectrum width measured by WSR-88D: Error sources and statistics of various weather phenomena. Journal of Atmospheric and Oceanic Technology, 21, 888-904.

Langston, C., J. Zhang, K. Howard, 2004: Four-dimensional dynamic radar mosaic. Preprints, 11th Conference on Aviation, Range, and Aerospace Meteorology, Hyannis, MA, USA, American Meteorological Society, CD-ROM, P5.11.

Langston, C., J. Zhang, 2004: An automated algorithm for radar beam occultation. Preprints, 11th Conference on Aviation, Range, and Aerospace Meteorology, Hyannis, MA, USA, American Meteorological Society, CD-ROM, P5.16.

Zhang, J., K. Howard, W. Xia, C. Langston, S. Wang, Y. Qin, 2004: Three-dimensional high-resolution national radar mosaic. Preprints, 11th Conference on Aviation, Range, and Aerospace Meteorology, Hyannis, MA, USA, American Meteorological Society, CD-ROM, 3.5.

Zhang, J., S. Wang, 2004: An automated 2-D multi-pass velocity dealiasing scheme. Preprints, 11th Conference on Aviation, Range, and Aerospace Meteorology, Hyannis, MA, USA, American Meteorological Society, CD-ROM, 5.5.

Zhang, J., S. Wang, B. Clarke, 2004: WSR-88D reflectivity quality control using horizontal and vertical reflectivity structure. Preprints, 11th Conference on Aviation, Range, and Aerospace Meteorology, Hyannis, MA, USA, American Meteorological Society, CD-ROM, P5.4.

Gourley, J. J., B. E. Vieux, 2003: A hydrologic approach to evaluating quantitative precipitation estimates. Preprints, 31st International Conference on Radar Meteorology, Seattle, WA, USA, American Meteorological Society, 714-717.

Gourley, J. J., B. Kaney, R. A. Maddox, 2003: Evaluating the calibrations of radars: A software approach. Preprints, 31st International Conference on Radar Meteorology, Seattle, WA, USA, American Meteorological Society, 459-462.

Gourley, J. J., C. M. Calvert, 2003: Automated detection of the bright band using WSR-88D radar data. Weather and Forecasting, 18, 585-599.

Gourley, J. J., B. E. Vieux, 2003: The effects of radar-derived rainfall uncertainties on forecasts from a distributed hydrologic model. Weather Radar Information and Distributed Hydrological Modelling. International Association of Hydrological Sciences Publication, 282, 130-137.

Zhang, J., K. Howard, W. Xia, J. J. Gourley, 2003: Comparison of Objective Analysis Schemes for the WSR-88D Radar Data. Preprints, 31st International Conference on Radar Meteorology, Seattle, WA, USA, American Meteorological Society, 907-910.