5B.1 OBSERVATIONS OF TORNADOGENESIS USING A MOBILE, PHASED-ARRAY, DOPPLER RADAR

Similar documents
Mobile, phased-array, X-band Doppler radar observations of tornadogenesis in the central U. S.

Recent observations of tornadoes using a mobile, rapid-scan, polarimetric, X-band, Doppler radar

7 AN ADAPTIVE PEDESTAL CONTROL ALGORITHM FOR THE NATIONAL WEATHER RADAR TESTBED PHASED ARRAY RADAR

RODGER A. BROWN NOAA/National Severe Storms Laboratory, Norman, OK

10R.1 DUAL-POLARIZATION OBSERVATIONS OF TORNADOES AT CLOSE RANGE MADE WITH A MOBILE X-BAND DOPPLER RADAR

THE DETECTABILITY OF TORNADIC SIGNATURES WITH DOPPLER RADAR: A RADAR EMULATOR STUDY

P11.7 DUAL-POLARIZATION, MOBILE, X-BAND, DOPPLER-RADAR OBSERVATIONS OF HOOK ECHOES IN SUPERCELLS

5.4 Comparison of Storm Evolution Characteristics: The NWRT and WSR-88D

Mobile, Phased-Array, Doppler Radar Observations of Tornadoes at X Band

P PRELIMINARY ANALYSIS OF THE 10 JUNE 2010 SUPERCELLS INTERCEPTED BY VORTEX2 NEAR LAST CHANCE, COLORADO

Meteorological Data Results from the Atmospheric Imaging Radar

P165 Mobile, X-band, polarimetric Doppler radar observations of the 23 May 2016 Woodward, Oklahoma tornadoes

The Vertical Structure of a Tornado near Happy, Texas, on 5 May 2002: High-Resolution, Mobile, W-band, Doppler Radar Observations

results, azimuthal over-sampling and rapid update Y. Umemoto 1, 2, T. Lei 3, T. -Y. Yu 1, 2 and M. Xue 3, 4

P13.6 Rapid-Scan DOW 3D GBVTD and Traditional Analysis of Tornadogenesis

Lecture 15: Doppler Dilemma, Range and Velocity Folding, and Interpreting Doppler velocity patterns

P1.10 Synchronization of Multiple Radar Observations in 3-D Radar Mosaic

NOTES AND CORRESPONDENCE. An Example of the Use of Mobile, Doppler Radar Data for Tornado Verification

Meteorological Studies with the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter

(Preliminary) Observations of Tropical Storm Fay Dustin W Phillips Kevin Knupp, & Tim Coleman. 34th Conference on Radar Meteorology October 8, 2009

3.23 IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL

Tornado Structure and Risk. Center for Severe Weather Research. The DOW Perspective

LIGHTNING ACTIVITY AND CHARGE STRUCTURE OF MICROBURST PRODUCING STORMS

OBSERVATIONS OF A SUPERCELL AND WEAK TORNADO MADE WITH A RAPID-SCAN, POLARIMETRIC, MOBILE RADAR

P15.13 DETECTION OF HAZARDOUS WEATHER PHENOMENA USING DATA ASSIMILATION TECHNIQUES

POLARIMETRIC RADAR OBSERVATION OF A TORNADO AT C-BAND

24 TH CONFERENCE ON SEVERE LOCAL STORMS, OCTOBER 2008, SAVANNAH, GEORGIA

PROFESSIONAL EXPERIENCE:

CIRCULATION AND AREAL CONTRACTION RATE AS DETECTED AND MEASURED BY DOPPLER RADAR

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

P6.18 THE IMPACTS OF THUNDERSTORM GEOMETRY AND WSR-88D BEAM CHARACTERISTICS ON DIAGNOSING SUPERCELL TORNADOES

Convective-scale Warn-on-Forecast The Future of Severe Weather Warnings in the USA?

P4.10. Kenichi Kusunoki 1 * and Wataru Mashiko 1 1. Meteorological Research Institute, Japan

Supercells. Base lecture and Graphics created by The COMET Program May University Corporation for Atmospheric Research

Numerical Prediction of 8 May 2003 Oklahoma City Supercell Tornado with ARPS and Radar Data Assimilation

TORNADO AND TORNADOGENESIS EVENTS SEEN BY THE NOXP, X-BAND, DUAL- POLARIZATION RADAR DURING VORTEX2 2010

165 HIGH-RESOLUTION PHASED ARRAY RADAR OBSERVATIONS OF AN OKLAHOMA HAILSTORM PRODUCING EXTREMELY-LARGE HAIL

826 High-Resolution TDWR Evolution of the Newcastle-Moore, Oklahoma Tornado of 20 May 2013

The multiple uses of the Navy's Twin Otter Doppler Wind Lidar (TODWL) for atmospheric research

P5.5 WSR-88D MANIFESTATIONS OF THE OWL HORN SIGNATURE. Matthew R. Kramar National Weather Service WFO Amarillo, TX

P10.1 TORNADOGENESIS IN A SIMULATED HP SUPERCELL

P7.1 STUDY ON THE OPTIMAL SCANNING STRATEGIES OF PHASE-ARRAY RADAR THROUGH ENSEMBLE KALMAN FILTER ASSIMILATION OF SIMULATED DATA

The observation of severe local storms and tornadoes

Dual-Doppler and Single-Doppler Analysis of a Tornadic Storm Undergoing Mergers and Repeated Tornadogenesis

HIGH-RESOLUTION RADAR ANALYSIS DURING TORNADOGENESIS FROM OU-PRIME ON 10 MAY 2010

11A.2 Forecasting Short Term Convective Mode And Evolution For Severe Storms Initiated Along Synoptic Boundaries

P3.17 THE DEVELOPMENT OF MULTIPLE LOW-LEVEL MESOCYCLONES WITHIN A SUPERCELL. Joshua M. Boustead *1 NOAA/NWS Weather Forecast Office, Topeka, KS

Multi-day severe event of May 2013

Dual-Wavelength Polarimetric Radar Analysis of the May 20th 2013 Moore, OK, Tornado

14.1 GBVTD-RETRIEVED NEAR-SURFACE AXISYMMETRIC VORTEX STRUCTURE IN A TORNADO AND IN TORNADO-LIKE VORTICES OBSERVED BY A W-BAND RADAR DURING VORTEX2

Chapter 3 Convective Dynamics Part V ñ Bright Bands, Bow Echoes and MCCs. Bright band associated with stratiform precipitation in a squall line system

Adaptive Radar Data Quality Control and Ensemble-Based Assimilation for Analyzing and Forecasting High-Impact Weather

Robin L. Tanamachi* Howard B. Bluestein School of Meteorology, University of Oklahoma, Norman, Oklahoma

P5.7 MERGING AMSR-E HYDROMETEOR DATA WITH COASTAL RADAR DATA FOR SHORT TERM HIGH-RESOLUTION FORECASTS OF HURRICANE IVAN

Meteorological Studies with the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter

A Climatology of supercells in Romania

GBVTD-retrieved near-surface vortex structure in a tornado and tornado-like vortices observed by a W- band radar during VORTEX2

P1.13 GROUND BASED REMOTELY SENSED HIGH TEMPORAL RESOLUTION STABILITY INDICES ASSOCIATED WITH SOUTHERN GREAT PLAINS TORNADO OUTBREAKS

P3.36 An Evaluation of the Potential Impact of SAILS on the Warning Decision Making of the 31 May 2013 El Reno, OK Storm

Refereed Publications (A. Shapiro)

OBSERVATIONS OF CLOUD-TO-GROUND LIGHTNING IN THE GREAT PLAINS

Meteorology 311. RADAR Fall 2016

P1.6 Simulation of the impact of new aircraft and satellite-based ocean surface wind measurements on H*Wind analyses

Doppler Radar Observations of Dust Devils in Texas

ASSESMENT OF THE SEVERE WEATHER ENVIROMENT IN NORTH AMERICA SIMULATED BY A GLOBAL CLIMATE MODEL

16.4 SENSITIVITY OF TORNADOGENESIS IN VERY-HIGH RESOLUTION NUMERICAL SIMULATIONS TO VARIATIONS IN MODEL MICROPHYSICAL PARAMETERS

The Integration of WRF Model Forecasts for Mesoscale Convective Systems Interacting with the Mountains of Western North Carolina

P5.16 OBSERVED FAILURE MODES OF THE WSR-88D VELOCITY DEALIASING ALGORITHM DURING SEVERE WEATHER OUTBREAKS

The Next Generation Polarimetric Airborne Doppler Radar

THE IMPACT OF DIFFERENT DATA FIELDS ON STORM-SSCALE DATA ASSIMILATION

Chapter 3 Convective Dynamics 3.4. Bright Bands, Bow Echoes and Mesoscale Convective Complexes

P13A.7 Multi-pass objective analyses of radar data: Preliminary results

Using Wavelet Analysis to Detect Tornadoes from Doppler Radar Radial-Velocity Observations

Is Spectral Processing Important for Future WSR-88D Radar?

Comparison of polarimetric radar signatures in hailstorms simultaneously observed by C-band and S-band radars.

11.3 Z DR COLUMNS AS A PREDICTIVE TOOL FOR HAIL GROWTH AND STORM EVOLUTION 1. INTRODUCTION

Automated Storm-based Scheduling on the National Weather Radar Testbed Phased Array Radar

Fundamentals of Radar Display. Atmospheric Instrumentation

Preprint, 18 th Conference on Numerical Weather Prediction American Meteorological Society, Park City, UT

Research and Development of Advanced Radar Data Quality Control and Assimilation for Nowcasting and Forecasting Severe Storms

FORENSIC WEATHER CONSULTANTS, LLC

2B.6 LATEST DEVELOPMENT OF 3DVAR SYSTEM FOR ARPS AND ITS APPLICATION TO A TORNADIC SUPERCELL STORM. Guoqing Ge * and Jidong Gao

The LaGrange Tornado during VORTEX2. Part I: Photogrammetric Analysis of the Tornado Combined with Single-Doppler Radar Data

A NEW WAY TO INVESTIGATE DYNAMICS IN A STORM USING DOPPLER SPECTRA FROM WEATHER RADAR

P59 High-frequency Dual-Doppler Analysis of a Retrograding Dryline/Baroclinic Boundary Intersection

Meteorological Studies With the Phased Array Weather Radar and Data Assimilation Using the Ensemble Kalman Filter

30 th Conference on Environmental Information Processing Technologies (2014)

13A. 4 Analysis and Impact of Super-obbed Doppler Radial Velocity in the NCEP Grid-point Statistical Interpolation (GSI) Analysis System

Radar Data Quality Control and Assimilation at the National Weather Radar Testbed (NWRT)

P12.7 MESOCYCLONE AND RFD INDUCED DAMAGING WINDS OBSERVED IN THE 27 MAY 2004 SOUTHWEST OHIO SUPERCELL

EnKF Assimilation of High-Resolution, Mobile Doppler Radar Data of the 4 May 2007 Greensburg, Kansas, Supercell into a Numerical Cloud Model

P5.4 EFFECTS OF RADAR RANGE AND AZIMUTHAL RESOLUTION ON TORNADIC SHEAR SIGNATURES: APPLICATIONS TO A TORNADO DETECTION ALGORITHM

13.2 IMPACT OF CONFIGURATIONS OF RAPID INTERMITTENT ASSIMILATION OF WSR-88D RADAR DATA FOR THE 8 MAY 2003 OKLAHOMA CITY TORNADIC THUNDERSTORM CASE

4.5 A WRF-DART study of the nontornadic and tornadic supercells intercepted by VORTEX2 on 10 June 2010

Tornado and Severe Thunderstorm Warning Forecast Skill and its Relationship to Storm Type

SUPER-RAPID SCAN SATELLITE IMAGERY ANALYSIS OF TWO HAILSTORMS SAMPLED BY HAILSTONE

DOPPLER RADAR AND STORM ENVIRONMENT OBSERVATIONS OF A MARITIME TORNADIC SUPERCELL IN SYDNEY, AUSTRALIA

TWO CASES OF HEAVY RAIN ON THE MEDITERRANEAN SIDE OF THE ALPS IN MAP. Robert Houze 1 and Socorro Medina University of Washington

Warning procedures for extreme events in the Emilia-Romagna Region

Observations of hail cores of tornadic thunderstorms with four polarimetric radars

Transcription:

5B.1 OBSERVATIONS OF TORNADOGENESIS USING A MOBILE, PHASED-ARRAY, DOPPLER RADAR Howard B. Bluestein * and Michael French University of Oklahoma, Norman Ivan Popstefanija ProSensing, Inc., Amherst, Massachusetts Robert Bluth and Jeffrey Knorr Naval Postgraduate School, Monterey, California 1.INTRODUCTION Tornadoes and many severe convective storms evolve on very short time scales (~ 10 s), owing to very high horizontal wind speeds and vertical velocities. This paper documents the formation of a tornado in the U. S. using a mobile, rapidly scanning, Doppler radar. This radar, the MWR-05XP (Meteorological Weather Radar, 2005, X-band, Phased Array), electronically scans in elevation and azimuth over limited sectors, and mechanically in azimuth at high speed. While the radar scans electronically in elevation, the attitude of its beam is held nearly fixed in space because it electronically back scans in azimuth at the same rate as it mechanically scans. Back scanning in azimuth is accomplished by frequency hopping, while scanning in elevation is accomplished by changing the phase delay. Transmitted frequency 9.3 10 GHz (X band) Maximum power (peak) 15 kw Beamwidth (half power) 1.8 0 (azimuth), 2 0 (elevation) Maximum PRF 10 khz Pulse duration/range resolution 1 ms / 150 m Sensitivity ~ -15 dbz@10 km (minimum) Range sampling interval 75 m Mechanical azimuth scanning rate 180 0 s -1 (maximum) Electronic azimuth back-scanning 6 8 0 swath (varies with elevation angle) Electronic elevation scan capability -18 0 to +55 0 with respect to the horizon Table 1. Characteristics of the MWR-05XP. * Corresponding author address: H. Bluestein, School of Meteorology, University of Oklahoma, Norman, OK 73072: e-mail: hblue@ou.edu The most important characteristics of the MWR-05XP are given in Table 1. Other rapidly scanning radars in operation now include the NWRT PAR S-band, fixed-site radar in Norman, OK (Zrnic et al. 2007) and the Rapid-DOW X- band mobile radar at CSWR in Boulder, CO (http://www.cswr.org/docs/radar-conf-rapid- 2001-0327.pdf). The MWR-05XP was first used in spring 2007. In 2008 frequency hopping had not yet been implemented, so the mechanical scanning rate was slowed down to minimize beam smearing. In 2009, frequency hopping was implemented so that beam smearing was eliminated and more independent samples were available. Two modes of data collection are used. The stepped frequency spiral (STF-SP) volume scan sequence is similar to that of the WSR-88D and is used for surveillance. The stepped frequency elevation (STF-E) volumetric sectorscan sequence is used for more focused probing. In 2008 only, when frequency hopping was not available, sector elevation (SE) scans were taken. In 2008, SE volume scans were available with a maximum range of 60 km and updated every 10.8 s up to 20 o elevation angle. In 2009, STF-E scans with a maximum range of 30 km were updated every 10 s up to 55 0 elevation angle. Data were collected for other values of maximum elevation angle and maximum range at similar update times. In general, typical update times of ~ 15 s were available for sector scans in 2008 and ~ 6 7 s in 2009. In 2008 data were collected in several tornadic storms. Tornadogenesis on 1 May and 10 May was captured, but for the former the tornado was at relatively long range (> 20 km)

and for the latter, attenuation was severe because the radar was located just upstream from the parent supercell. The focus of this paper is on a cyclonic tornado and companion anticyclonic vortex that formed in an HP supercell in Kansas on 23 May. Data slightly oversampled in the vertical were collected when the tornado formed at range as close as ~ 16 km, from near the surface to 20 0 elevation angle (~6.5 km AGL). The update time for the volume scans was ~ 13 s over a ~ 50 min period, from ~ 15 min prior to tornadogenesis to well after the tornado had dissipated. 2. PRELIMINARY DISCUSSION OF DATA

conference, 5B.6). [More details on the radar are found in PopStefanija et al. (2009, this conference, P5.9) and on both the radar and the data in Bluestein et al. (2010).] Owing to the strong attenuation by precipitation in the highprecipitation (HP) supercell parent storms, analyses of the cyclonic vortices are not possible at the highest elevation angles. For the anticyclonic vortices, there is less attenuation, so analyses of these vortices are possible up to higher altitudes. Fig. 1. The life cycle of a supercell tornado in Kansas, as documented by the MWR-05XP. Doppler velocity (m s -1 ) at 1 0 elevation angle on 23 May 2008 (24 May UTC). The maximum Doppler wind speeds associated with the vortex signature (sometimes highlighted by a circle) that marks the tornado weaken steadily after 0206:55 and data are not shown until 0217:06, when the maximum wind speeds are much weaker (ellipsis denotes panels that are not shown for the sake of brevity). The color scale for Doppler velocity (m s -1 ) is changed while the isodop maximum exceeds the maximum allowed by the color scheme; when this is done, the new range is indicated at the lower-right hand side of the panel. Range markers shown every 5 km. Fig. 1 depicts the entire life cycle of a cyclonically rotating tornado at low elevation angle, ~ 300 m AGL. Also seen in this storm and in another tornadic storm on the same day are nearby, intense, anticyclonic vortices, both of whose life cycles were captured (one is shown in Fig. 2). Analyses of these features are ongoing; some preliminary results will be shown at the conference by French et al. (2009; this Fig. 2. The behavior of a cyclonic tornado and a neighboring, intense, anticyclonic vortex in Kansas, as documented by the MWR-05XP on 23 May 2008 (24 May in UTC time). Doppler velocity (m s -1 ) at 1 0 elevation angle at (a) 0148:57, (b) 0152:16, and (c) 0155:06. Cyclonic vortex signatures indicated by large circles; anticyclonic vortex signatures indicated by smaller circles. For brevity, only three representative images are shown; data are available as in Fig. 1, every 13 s. Range markers shown every 5 km. Maximum Doppler velocities are ± 40 m s -1. It is seen from the data shown in Figs. 1 and 2 show that there is excellent temporal continuity in the locations of the cyclonic and

anticyclonic vortex signatures associated with the tornado and anticyclonic vortex at low levels. In 2009, during VORTEX-2 (Verification of the Origins of Rotation in Tornadoes Experiment-2) in the Plains of the U. S., an excellent dataset was collected depicting the entire life cycle of a tornadic storm at close range (Figs. 3 and 4). Owing to the addition of frequency hopping in 2009, the update time was cut down to ~ 7 s for each volume scan (up to 20 0 ). Some of these data will be shown at the conference. Fig. 3. Photograph of a tornado being probed by the MWR-05XP in southeastern Wyoming during VORTEX-2 at 2218:46 UTC on 5 June 2009. Courtesy of Chad Baldi (ProSensing, Inc.). 2010 during year 2 of VORTEX-2. Several improved capabilities will have been added. It will be possible to change scanning parameters without temporarily stopping data collection. Different PRFs will be implemented on alternate scans to make unfolding aliased Doppler velocity data easier. A pulsed, 2 µm Doppler lidar having 150 m range resolution was added during the summer of 2009 to provide clear-air data collection. It is anticipated that a nonscanning W-band radar will also be added to the facility. One issue that must be addressed is what scan parameters should be selected. There is a tradeoff between the highest elevation angle scanned and the volumetric update time. Currently, we typically scan up to 20 o elevation angle. When a convective storm is relatively far away we can scan to its top. When it gets within close range, we must scan to higher elevation angle to scan to the same altitude, but the update time increases. Our philosophy has been to scan to the top of the storm when there is no tornado, but to scan only to midlevels when a tornado is imminent or is occurring to provide for as rapid updates as possible at low altitudes. Our choice of scanning parameters in the future will be based on our experiences in 2008 and 2009 and what is necessary to track tornadogenesis and/or provide data adequate for assimilation into nonhydrostatic cloud models. 4. ACKNOWLEDGMENTS Fig. 4. Unedited radar reflectivity (dbz) from the MWR-05XP at 2.5 0 elevation angle, of the parent supercell for the tornado seen in Fig. 3. Range markings shown every 2 km. Tornado was located at the weak-echo hole seen at 5.5 km range. 3. FUTURE WORK The MWR-05XP will be used in spring Chad Baldi (ProSensing) led the data collection and field operations. Bethany Seeger (ProSensing) did much of the data processing. Jana Houser (OU) contributed to the field efforts. Jeff Snyder (OU) and Mark Laufensweiler (OU) provided computer-related assistance. Paul Buczynski (NPS) also contributed to the project. This work was supported in part by NSF grant ATM-0637148 to OU and contracts to ProSensing from the Navy SBIR program at the Office of Naval Research. 5. REFERENCES Bluestein, H. B., M. M. French, I. Popstefanija, R. T. Bluth, and J. B. Knorr, 2010: A mobile, phased-array Doppler radar for the study of severe convective storms: The MWR-05XP. Bull. Amer. Meteor. Soc., 91 (accepted subject to revisions).

French, M. M., H. B. Bluestein, I. PopStefanija, R. Bluth, and C. Baldi, 2009: A preliminary analysis of a tornadic supercell using mobile, phased-array Doppler radar data. 34 th Conf. on Radar Meteorology, Williamsburg, Virginia, 5B.6 (not available online) PopStefanija, I., B. Seeger, C. Baldi, R. Bluth, P. Buczynski, and H. B. Bluestein, 2009: Techniques for rapid scanning weather surveillance using the MWR-05XP, a mobile, phased-array, X-band, Doppler radar. 34 th Conf. on Radar Meteorology, Williamsburg, Virginia, P5.9 (not available online) Zrnic, D. S., J. F. Kimpel, D. E. Forsyth, A. Shapiro, G. Crain, R. Ferek, J. Heimmer, W. Benner, T. J. McNellis, and R. J. Voigt, 2007: Agile-beam phased array radar for weather observations. Bull. Amer. Meteor. Soc., 88, 1753 1766.