scholarly journals A Doppler Radar Emulator with an Application to the Detectability of Tornadic Signatures

2007 ◽  
Vol 24 (12) ◽  
pp. 1973-1996 ◽  
Author(s):  
Ryan M. May ◽  
Michael I. Biggerstaff ◽  
Ming Xue
Keyword(s):  
Pulse Propagation ◽  
Doppler Radar ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Advanced Regional Prediction System ◽  
Adaptive Sensing ◽  
Regional Prediction ◽  
Sampling Intervals ◽  
Half Power ◽  
The Impact

Abstract A Doppler radar emulator was developed to simulate the expected mean returns from scanning radar, including pulse-to-pulse variability associated with changes in viewing angle and atmospheric structure. Based on the user’s configuration, the emulator samples the numerical simulation output to produce simulated returned power, equivalent radar reflectivity, Doppler velocity, and Doppler spectrum width. The emulator is used to evaluate the impact of azimuthal over- and undersampling, gate spacing, velocity and range aliasing, antenna beamwidth and sidelobes, nonstandard (anomalous) pulse propagation, and wavelength-dependent Rayleigh attenuation on features of interest. As an example, the emulator is used to evaluate the detection of the circulation associated with a tornado simulated within a supercell thunderstorm by the Advanced Regional Prediction System (ARPS). Several metrics for tornado intensity are examined, including peak Doppler velocity and axisymmetric vorticity, to determine the degradation of the tornadic signature as a function of range and azimuthal sampling intervals. For the case of a 2° half-power beamwidth radar, like those deployed in the first integrated project of the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA), the detection of the cyclonic shear associated with this simulated tornado will be difficult beyond the 10-km range, if standard metrics such as azimuthal gate-to-gate shear from a single radar are used for detection.

scholarly journals Analysis and Forecast of a Tornadic Thunderstorm Using Multiple Doppler Radar Data, 3DVAR, and ARPS Model

2013 ◽  
Vol 2013 ◽  
pp. 1-18
Author(s):  
Edward Natenberg ◽  
Jidong Gao ◽  
Ming Xue ◽  
Frederick H. Carr
Keyword(s):  
Doppler Radar ◽  
Initial Conditions ◽  
Three Dimensional ◽  
Radar Data ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Upper Level ◽  
Using Data ◽  
Nwp Model ◽  
The Impact

A three-dimensional variational (3DVAR) assimilation technique developed for a convective-scale NWP model—advanced regional prediction system (ARPS)—is used to analyze the 8 May 2003, Moore/Midwest City, Oklahoma tornadic supercell thunderstorm. Previous studies on this case used only one or two radars that are very close to this storm. However, three other radars observed the upper-level part of the storm. Because these three radars are located far away from the targeted storm, they were overlooked by previous studies. High-frequency intermittent 3DVAR analyses are performed using the data from five radars that together provide a more complete picture of this storm. The analyses capture a well-defined mesocyclone in the midlevels and the wind circulation associated with a hook-shaped echo. The analyses produced through this technique are used as initial conditions for a 40-minute storm-scale forecast. The impact of multiple radars on a short-term NWP forecast is most evident when compared to forecasts using data from only one and two radars. The use of all radars provides the best forecast in which a strong low-level mesocyclone develops and tracks in close proximity to the actual tornado damage path.

scholarly journals The Impact of a Stochastically Perturbing Microphysics Scheme on an Idealized Supercell Storm

2017 ◽  
Vol 146 (1) ◽  
pp. 95-118 ◽  
Author(s):  
Xiaoshi Qiao ◽  
Shizhang Wang ◽  
Jinzhong Min
Keyword(s):  
Stochastic Methods ◽  
Microphysics Scheme ◽  
Advanced Regional Prediction System ◽  
Recursive Filters ◽  
Supercell Storm ◽  
Regional Prediction ◽  
Double Moment ◽  
The Impact ◽  
Temperature Tendency ◽  
Stochastically Perturbed

Abstract The concept of stochastic parameterization provides an opportunity to represent spatiotemporal errors caused by microphysics schemes that play important roles in supercell simulations. In this study, two stochastic methods, the stochastically perturbed temperature tendency from microphysics (SPTTM) method and the stochastically perturbed intercept parameters of microphysics (SPIPM) method, are implemented within the Lin scheme, which is based on the Advanced Regional Prediction System (ARPS) model, and are tested using an idealized supercell case. The SPTTM and SPIPM methods perturb the temperature tendency and the intercept parameters (IPs), respectively. Both methods use recursive filters to generate horizontally smooth perturbations and adopt the barotropic structure for the perturbation r, which is multiplied by tendencies or parameters from this parameterization. A double-moment microphysics scheme is used for the truth run. Compared to the multiparameter method, which uses randomly perturbed prescribed parameters, stochastic methods often produce larger ensemble spreads and better forecast the intensity of updraft helicity (UH). The SPTTM method better predicts the intensity by intensifying the midlevel heating with its positive perturbation r, whereas it performs worse in the presence of negative perturbation. In contrast, the SPIPM method can increase the intensity of UH by either positive or negative perturbation, which increases the likelihood for members to predict strong UH.

Clarifying remotely-retrieved precipitation of shallow marine clouds from the NSF/NCAR Gulfstream V

2021 ◽  
Author(s):  
Mampi Sarkar ◽  
Paquita Zuidema ◽  
Virendra Ghate
Keyword(s):  
Doppler Radar ◽  
Rain Attenuation ◽  
Doppler Velocity ◽  
Distribution Width ◽  
Raindrop Size Distribution ◽  
Remote Measurements ◽  
Raindrop Size ◽  
Rain Rates ◽  
The Impact

AbstractPrecipitation is a key process within the shallow cloud lifecycle. The Cloud System Evolution in the Trades (CSET) campaign included the first deployment of a 94 GHz Doppler radar and 532 nm lidar. Despite a larger sampling volume, initial mean radar/lidar retrieved rain rates (Schwartz et al. 2019) based on the upward-pointing remote sensor datasets are systematically less than those measured by in-situ precipitation probes in the cumulus regime. Subsequent retrieval improvements produce rainrates that compare better to in-situ values, but still underestimate. Retrieved shallow cumulus drop sizes can remain too small and too few, with an overestimated shape parameter narrowing the raindrop size distribution too much. Three potential causes for the discrepancy are explored: the gamma functional fit to the dropsize distribution, attenuation by rain and cloud water, and an underaccounting of Mie dampening of the reflectivity. A truncated exponential fit may represent the dropsizes below a showering cumulus cloud more realistically, although further work would be needed to fully evaluate the impact of a different dropsize representation upon the retrieval. The rain attenuation is within the measurement uncertainty of the radar. Mie dampening of the reflectivity is shown to be significant, in contrast to previous stratocumulus campaigns with lighter rain rates, and may be difficult to constrain well with the remote measurements. An alternative approach combines an a priori determination of the dropsize distribution width based on the in-situ data with the mean radar Doppler velocity and reflectivity. This can produce realistic retrievals, although a more comprehensive assessment is needed to better characterize the retrieval errors.

scholarly journals The Tornadic Vortex Signature: An Update

2012 ◽  
Vol 27 (2) ◽  
pp. 525-530 ◽  
Author(s):  
Rodger A. Brown ◽  
Vincent T. Wood
Keyword(s):  
Data Collection ◽  
Doppler Radar ◽  
Core Region ◽  
Doppler Velocity ◽  
Vortex Center ◽  
Vortex Model ◽  
The Core ◽  
Lower Elevation ◽  
Simulation Results ◽  
Half Power

Abstract A tornadic vortex signature (TVS) is a degraded Doppler velocity signature of a tornado that occurs when the core region of a tornado is smaller than the half-power beamwidth of the sampling Doppler radar. Soon after the TVS was discovered in the mid-1970s, simulations were conducted to verify that the signature did indeed represent a tornado. The simulations, which used a uniform reflectivity distribution across a Rankine vortex model, indicated that the extreme positive and negative Doppler velocity values of the signature should be separated by about one half-power beamwidth regardless of tornado size or strength. For a Weather Surveillance Radar-1988 Doppler (WSR-88D) with an effective half-power beamwidth of approximately 1.4° and data collected at 1.0° azimuthal intervals, the two extreme Doppler velocity values should be separated by 1.0°. However, with the recent advent of 0.5° azimuthal sampling (“superresolution”) by WSR-88Ds at lower elevation angles, some of the extreme Doppler velocity values unexpectedly were found to be separated by 0.5° instead of 1.0° azimuthal intervals. To understand this dilemma, the choice of vortex model and reflectivity profile is investigated. It is found that the choice of vortex model does not have a significant effect on the simulation results. However, using a reflectivity profile with a minimum at the vortex center does make a difference. The revised simulations indicate that it is possible for the distance between the peak Doppler velocity values of a TVS to be separated by 0.5° with superresolution data collection.

scholarly journals A study of the influence of forest gaps on fire–atmosphere interactions

2016 ◽  
Vol 16 (13) ◽  
pp. 8499-8509 ◽  
Author(s):  
Michael T. Kiefer ◽  
Warren E. Heilman ◽  
Shiyuan Zhong ◽  
Joseph J. Charney ◽  
Xindi Bian
Keyword(s):  
Sensible Heat Flux ◽  
Forest Canopies ◽  
Advanced Regional Prediction System ◽  
Forest Gaps ◽  
Low Intensity ◽  
Regional Prediction ◽  
Surface Sensible Heat Flux ◽  
Atmospheric Perturbations ◽  
The Impact ◽  
Enhanced Surface

Abstract. Much uncertainty exists regarding the possible role that gaps in forest canopies play in modulating fire–atmosphere interactions in otherwise horizontally homogeneous forests. This study examines the influence of gaps in forest canopies on atmospheric perturbations induced by a low-intensity fire using the ARPS-CANOPY model, a version of the Advanced Regional Prediction System (ARPS) model with a canopy parameterization. A series of numerical experiments are conducted with a stationary low-intensity fire, represented in the model as a line of enhanced surface sensible heat flux. Experiments are conducted with and without forest gaps, and with gaps in different positions relative to the fire line. For each of the four cases considered, an additional simulation is performed without the fire to facilitate comparison of the fire-perturbed atmosphere and the background state. Analyses of both mean and instantaneous wind velocity, turbulent kinetic energy, air temperature, and turbulent mixing of heat are presented in order to examine the fire-perturbed atmosphere on multiple timescales. Results of the analyses indicate that the impact of the fire on the atmosphere is greatest in the case with the gap centered on the fire and weakest in the case with the gap upstream of the fire. It is shown that gaps in forest canopies have the potential to play a role in the vertical as well as horizontal transport of heat away from the fire. Results also suggest that, in order to understand how the fire will alter wind and turbulence in a heterogeneous forest, one needs to first understand how the forest heterogeneity itself influences the wind and turbulence fields without the fire.

Impact of Configurations of Rapid Intermittent Assimilation of WSR-88D Radar Data for the 8 May 2003 Oklahoma City Tornadic Thunderstorm Case

2007 ◽  
Vol 135 (2) ◽  
pp. 507-525 ◽  
Author(s):  
Ming Hu ◽  
Ming Xue
Keyword(s):  
Data Assimilation ◽  
Doppler Radar ◽  
Initial Conditions ◽  
Radar Data ◽  
Oklahoma City ◽  
Advanced Regional Prediction System ◽  
Initial Stage ◽  
Regional Prediction ◽  
Cloud Temperature ◽  
Adjustment Scheme

Abstract Various configurations of the intermittent data assimilation procedure for Level-II Weather Surveillance Radar-1988 Doppler radar data are examined for the analysis and prediction of a tornadic thunderstorm that occurred on 8 May 2003 near Oklahoma City, Oklahoma. Several tornadoes were produced by this thunderstorm, causing extensive damages in the south Oklahoma City area. Within the rapidly cycled assimilation system, the Advanced Regional Prediction System three-dimensional variational data assimilation (ARPS 3DVAR) is employed to analyze conventional and radar radial velocity data, while the ARPS complex cloud analysis procedure is used to analyze cloud and hydrometeor fields and adjust in-cloud temperature and moisture fields based on reflectivity observations and the preliminary analysis of the atmosphere. Forecasts for up to 2.5 h are made from the assimilated initial conditions. Two one-way nested grids at 9- and 3-km grid spacings are employed although the assimilation configuration experiments are conducted for the 3-km grid only while keeping the 9-km grid configuration the same. Data from the Oklahoma City radar are used. Different combinations of the assimilation frequency, in-cloud temperature adjustment schemes, and the length and coverage of the assimilation window are tested, and the results are discussed with respect to the length and evolution stage of the thunderstorm life cycle. It is found that even though the general assimilation method remains the same, the assimilation settings can significantly impact the results of assimilation and the subsequent forecast. For this case, a 1-h-long assimilation window covering the entire initial stage of the storm together with a 10-min spinup period before storm initiation works best. Assimilation frequency and in-cloud temperature adjustment scheme should be set carefully to add suitable amounts of potential energy during assimilation. High assimilation frequency does not necessarily lead to a better result because of the significant adjustment during the initial forecast period. When a short assimilation window is used, covering the later part of the initial stage of storm and using a high assimilation frequency and a temperature adjustment scheme based on latent heat release can quickly build up the storm and produce a reasonable analysis and forecast. The results also show that when the data from a single Doppler radar are assimilated with properly chosen assimilation configurations, the model is able to predict the evolution of the 8 May 2003 Oklahoma City tornadic thunderstorm well for up to 2.5 h. The implications of the choices of assimilation settings for real-time applications are discussed.

scholarly journals Sensitivity of Surface Analyses over the Western United States to RAWS Observations

2008 ◽  
Vol 23 (1) ◽  
pp. 145-158 ◽  
Author(s):  
David T. Myrick ◽  
John D. Horel
Keyword(s):  
United States ◽  
Wind Speed ◽  
Winter Season ◽  
Western United States ◽  
Cold Pool ◽  
Federal State ◽  
Advanced Regional Prediction System ◽  
Average Analysis ◽  
Regional Prediction ◽  
The Impact

Abstract Federal, state, and other wildland resource management agencies contribute to the collection of weather observations from over 1000 Remote Automated Weather Stations (RAWS) in the western United States. The impact of RAWS observations on surface objective analyses during the 2003/04 winter season was assessed using the Advanced Regional Prediction System (ARPS) Data Assimilation System (ADAS). A set of control analyses was created each day at 0000 and 1200 UTC using the Rapid Update Cycle (RUC) analyses as the background fields and assimilating approximately 3000 surface observations from MesoWest. Another set of analyses was generated by withholding all of the RAWS observations available at each time while 10 additional sets of analyses were created by randomly withholding comparable numbers of observations obtained from all sources. Random withholding of observations from the analyses provides a baseline estimate of the analysis quality. Relative to this baseline, removing the RAWS observations degrades temperature (wind speed) analyses by an additional 0.5°C (0.9 m s−1) when evaluated in terms of rmse over the entire season. RAWS temperature observations adjust the RUC background the most during the early morning hours and during winter season cold pool events in the western United States while wind speed observations have a greater impact during active weather periods. The average analysis area influenced by at least 1.0°C (2.5°C) by withholding each RAWS observation is on the order of 600 km2 (100 km2). The spatial influence of randomly withheld observations is much less.

Using a Low-Order Model to Detect and Characterize Tornadoes in Multiple-Doppler Radar Data

2009 ◽  
Vol 137 (4) ◽  
pp. 1230-1249 ◽  
Author(s):  
Corey K. Potvin ◽  
Alan Shapiro ◽  
Tian-You Yu ◽  
Jidong Gao ◽  
Ming Xue
Keyword(s):  
Cost Function ◽  
Wind Field ◽  
Doppler Radar ◽  
Radar Data ◽  
Horizontal Wind ◽  
Order Model ◽  
Advanced Regional Prediction System ◽  
Regional Prediction ◽  
Objective Detection ◽  
Low Order

Abstract A new multiple-Doppler radar analysis technique is presented for the objective detection and characterization of tornado-like vortices. The technique consists of fitting radial wind data from two or more radars to a simple analytical model of a vortex and its near-environment. The model combines a uniform flow, linear shear flow, linear divergence flow (all of which compose a broadscale flow), and a modified combined Rankine vortex (representing the tornado). The vortex and its environment are allowed to translate. The parameters in the low-order model are determined by minimizing a cost function that accounts for the discrepancy between the model and observed radial winds. Since vortex translation is taken into account, the cost function can be evaluated over time as well as space, and thus the observations can be used at the actual times and locations where they were acquired. The technique is first tested using analytically simulated observations whose wind field and error characteristics are systematically varied. An Advanced Regional Prediction System (ARPS) high-resolution numerical simulation of a supercell and associated tornado is then used to emulate an observation dataset. The method is tested with two virtual radars for several radar-sampling strategies. Finally, the technique is applied to a dataset of real dual-Doppler observations of a tornado that struck central Oklahoma on 8 May 2003. The method shows skill in retrieving the tornado path and radar-grid-scale features of the horizontal wind field in the vicinity of the tornado. The best results are obtained using a two-step procedure in which the broadscale flow is retrieved first.

Assimilation of T-TREC-Retrieved Winds from Single-Doppler Radar with an Ensemble Kalman Filter for the Forecast of Typhoon Jangmi (2008)

2014 ◽  
Vol 142 (5) ◽  
pp. 1892-1907 ◽  
Author(s):  
Mingjun Wang ◽  
Ming Xue ◽  
Kun Zhao ◽  
Jili Dong
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Doppler Radar ◽  
Correlation Technique ◽  
Coverage Area ◽  
Advanced Regional Prediction System ◽  
Typhoon Structure ◽  
Spatial Coverage ◽  
Regional Prediction ◽  
Maximum Range

Abstract A tropical cyclone (TC) circulation Tracking Radar Echo by Correlation technique (T-TREC) developed recently is applied to derive horizontal winds from single Doppler radar reflectivity Z data (combined with radial velocity Vr data when available). The typically much longer maximum range of Z observations compared to Vr data allows for much larger spatial coverage of the T-TREC-retrieved winds (VTREC) when a TC first enters the maximum range of a coastal radar. Retrieved using data from more than one scan volume, the T-TREC winds also contain valuable cross-beam wind information. The VTREC or Vr data at 30-min intervals are assimilated into the Advanced Regional Prediction System (ARPS) model at 3-km grid spacing using an ensemble Kalman filter, over a 2-h window shortly after Typhoon Jangmi (2008) entered the Vr coverage area of an operational weather radar of Taiwan. The assimilation of VTREC data produces analyses of the typhoon structure and intensity that more closely match observations than analyses produced using Vr data or the reference Global Forecast System (GFS) analysis. Subsequent 28-h forecasts of intensity, track, structure, and precipitation are also improved by assimilating VTREC data. Further sensitivity experiments show that assimilation of VTREC data can build up a reasonably strong vortex in 1 h, while a longer assimilation period is required to spin up the vortex when assimilating Vr. Although the difference between assimilating VTREC and Vr is smaller when the assimilation window is longer, the improvement from assimilating VTREC is still evident. Assimilating Z data in addition to Vr or VTREC results in little further improvement.

Ku-Band High-Speed Scanning Doppler Radar for Volcanic Eruption Monitoring

2019 ◽  
Vol 14 (4) ◽  
pp. 630-640
Author(s):  
Masayuki Maki ◽  
Shinobu Takahashi ◽  
Sumiya Okada ◽  
Katsuyuki Imai ◽  
Hiroshi Yamaguchi ◽  
...  
Keyword(s):  
Volcanic Eruption ◽  
High Speed ◽  
Doppler Radar ◽  
Volcanic Eruptions ◽  
Radio Station ◽  
Japan Meteorological Agency ◽  
Doppler Spectrum ◽  
Doppler Velocity ◽  
Eruption Column ◽  
Ku Band

This paper presents the major specifications and characteristics of the Ku-band high-speed scanning Doppler radar for volcano observation (KuRAD) introduced to Kagoshima University in March 2017 as well as the results of a test observation at Sakurajima. KuRAD is a Doppler radar for research with a wavelength of approximately 2 cm and uses a 45 cm diameter Luneberg lens antenna as a transmitting and receiving antenna to observe the development of a volcanic eruption column immediately following eruption at a maximum rotation speed of 40 rpm. The maximum transmitter power is 9.6 W and the maximum observational range is 20 km. Observed data includes radar reflectivity factor, Doppler velocity, and Doppler spectrum width. Another feature of KuRAD is an obtained radio station license for observation of a total of seven active volcanos in Kyushu. To assess the basic performance of KuRAD, we carried out test observations of volcanic eruptions at Sakurajima, Kagoshima Prefecture, Japan and collected a total of 87 eruptions (20 of which are explosive eruptions and 7 of which had 3,000 m or higher eruptive smoke from vents). From the eruption data of Showa vent on May 2, 2017, it was confirmed that KuRAD could monitor the three-dimensional internal structure of a volcanic eruption column immediately following eruption. Eruption data from Minamidake of Sakurajima on March 5, 2018, showed that KuRAD successfully observed the eruptive smoke reaching a height of 4,000 m, although the eruptive smoke was covered with clouds and could not be detected by optical instruments of the Japan Meteorological Agency.

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