National Weather Center Convection/NWP Seminar Series
Organizers: Dr. Louis Wicker, Ryan Sobash
This seminar is held Fridays at 3:00 p.m. in NWC 5600 during the regular OU spring and fall semesters.
|08/30/13||Kristen Cassady||SoM||A Comparison of Microphysical Parameterization Sensitivities in Comma-Head Snow Events and Evaluation with PIREPS
Numerical model experiments are conducted to assess how the presence of supercooled liquid water (SLW) is dependent on the choice of microphysical parameterization for select winter storms that were observed to have elevated regions of SLW. The schemes considered include the Thompson, Milbrandt-Yau, Morrison, and NSSL. The Thompson scheme routinely produces less SLW than the other schemes. Sensitivity experiments reveal this is in large part a consequence of aggressive scavenging of cloud water from snow. In some environments, the Morrison scheme produces considerably more SLW than the other experiments. It is unclear why this happens. The output from each experiment is compared to pilot reports of icing to determine which scheme yields the most accurate results. According to all statistical measures that we tried, the Morrison scheme performs the best. However, the results are not significantly different between the schemes.
|09/13/13||Ariel Cohen||SoM/SPC||The 5 June 2012 Central Montana Tornado Event
During the afternoon and evening of 5 June 2012, central Montana experienced a rare tornado event for that region, with three confirmed tornadoes from two supercell thunderstorms. While the background environment supporting this event was generally consistent with that corresponding to a severe thunderstorm and tornado event in the mid latitudes, particular factors supporting this event in central Montana were seasonally anomalous. We present climatological anomalies, with mid-level lapse rates and deep moisture content being particularly high for this case. In support of future pattern recognition and forecasting of such an event, for which large potential impact could exist, the synoptic and mesoscale environment evolution, and the radar evolution associated with this event are investigated. Impact-based decision support services and detailed tornado assessments from the National Weather Service in Great Falls will also be discussed.
|10/11/13||No seminar||OU/TX Holiday|
|10/18/13||Jeffrey Snyder||SoM||Observations and Simulations of Polarimetric Weather Radar Signatures in Supercells
Polarimetric weather radars, with the additional information collected, allow one to infer scatterer and precipitation properties considerably more easily than single-polarization radars. Given the relationship between the thermodynamic, mass, and kinematic fields and the resulting structure of the microphysical quanities within convective storms, examining the polarimetric characteristics of hydrometeors and the polarimetric structure of supercells provides potentially valuable information about processes and storm-relevant variables that are currently unobservable by radar. In this study, two previously-undocumented polarimetric signatures – the low-reflectivity ribbon and the area of anomalously low co-polar cross-correlation coefficient to the left of the bounded weak echo region -- observed by two mobile, X-band, polarimetric radars are examined. Since there are appreciable differences in scattering properties of some hydrometeors at different radar frequencies and most of the past work on polarimetric signatures in supercells has used S band radar data, examples of other signatures observed by X-band radars are also presented.
Given the recent development of advanced multimoment microphysics schemes and polarimetric radar emulators, examining the polarimetric structure of simulated supercells can help elucidate the microphysical and kinematic structure of simulated polarimetric signatures. To do so, a series of idealized high-resolution simulations are performed using eight different vertical shear profiles in an attempt to investigate the relationships between observed signatures, the structure of microphysical and kinematic fields within the simulated supercells, and potential sensitivies to vertical wind shear variations. Many of the previously-observed signatures are reproduced in the simulations; results from these simulations are reported.
|10/25/13||Ryan Sobash||SoM||Forecasts of convection initiation and early evolution on 29 May 2012 using EnKF-based assimilation of surface and radar data
The 29 May 2012 convective episode produced very large hail (> 4” diameter), 80 mph wind gusts, and a brief tornado, near and within the OKC metropolitan area, with estimated losses totaling 500 million dollars. Analyses and forecasts of this event were produced using the ensemble Kalman filter (EnKF) by assimilating surface and radar datasets. Surface data, including data from surface mesoscale networks (mesonets), were assimilated at 5-minute intervals between 18 UTC and 21 UTC. Both surface and WSR-88D data were assimilated at 5-minute intervals between 21 UTC and 23 UTC, following convection initiation (CI). 50-member, 6-hour, ensemble forecasts were initialized each hour between 18 UTC and 23 UTC.
The first part of the presentation will focus on the ensemble predictions of CI. The frequent assimilation of surface data, especially the use of mesonet data, improved the forecast of CI timing and placement within the domain, especially for convection developing along a surface dry line. Surface data assimilation reduced a surface moisture bias that was present due to model error. Experiments where mesonet data were withheld, or where surface data were assimilated less frequently, produced less accurate forecasts of CI and possessed larger surface moisture errors. The improved surface state at 21 UTC also led to changes in the forecast convective mode after 00 UTC. The ability of sub-hourly assimilation of mesonet data to improve forecasts of CI has not been previously documented.
The second part of the presentation will focus on the forecasts following CI, when WSR-88D and surface data were assimilated. The 23 UTC forecast captured much of the observed convective evolution, including the tracks of several long-lived supercells. Surface data assimilation played a significant role during this period as well. Forecasts from an experiment that assimilated only radar data contained several large errors due to a poor representation of the mesoscale environment. Some parts of the forecast were especially sensitive to the assimilation of reflectivity observations and the vertical localization of those observations. Finally, using innovation statistics, several reflectivity biases were identified in the analyses due to errors in the microphysics parameterization, the reflectivity forward operator, and biases in the environmental wind profile. The applicability of these results for future warn-on-forecast systems will be discussed.
|11/01/13||Jacob Carlin||SoM||Improved Retrieval of Hydrometeor Mixing Ratios Using Polarimetric Radar Data and the Hydrometeor Classification Algorithm for Assimilation into Storm-Scale NWP Models
Hydrometeor mixing ratios for rain (qr), hail (qh) and snow (qs) are typically estimated from the radar reflectivity factor Z in the process of assimilating radar data into storm-scale numerical weather prediction (NWP) models with single-moment microphysics. The existing q – Z relations are basic and assume a Rayleigh scattering regime. In addition, overly simplistic hydrometeor classification schemes that are only functions of Z and the background temperature T are typically used to identify the hydrometeor type before applying the q – Z formula. Double-moment microphysics schemes are generally accepted to be superior to single- moment schemes, but difficulties also exist in their implementation as they require an estimate of the concentration of the various hydrometeor types.
This study, which describes the major principles for q retrievals in convective storms containing both rain and hail, employs the benefits of polarimetric radar observations, which offer additional insight into hydrometeor shape, behavior, composition, and orientation beyond the radar reflectivity factor. A broad range of hail size distributions were considered with the intent of making these retrievals widely applicable. The retrieval procedures were developed based on the results of polarimetric radar observations in hail-bearing storms, disdrometer measurements, and simulations using a one-dimensional model of melting hail combined with a forward radar operator. The methodology for the separate estimation of qr containing a rain-hail mixture is also presented and discussed. In addition, results from a thunderstorm simulation run using the Hebrew University Cloud Model (HUCM) were used to study Z-q relation differences for both rain and hail among different regions within the storm as well as their temporal evolution.
|11/08/13||Mike Vandenburg||SoM||Skill of 1- and 4-km WRF-ARW Forecasts of Storm Motion during The 2010 VORTEX2 Convective Season
In this study, we compare the ability of WRF-ARW model simulations at grid spaces of 1- and 4-km to predict observed storm motion from the 2010 VORTEX2 convective season. In addition, the 1-km model data is smoothed to the 4-km model grid in order to determine if smoothing has any effect on the interpretation of the 1-km results. To facilitate all of this, cells are tracked using an object-based tracking algorithm with 5-minute reflectivity data as input. The resulting observed and modeled storm tracks are compared to four environment-based storm motion predictors: Bunkers’ and Rasmussen-Blanchard right-moving storm motions, 850-300 mb mean wind, and the 0-6 km vertical shear vector. These variables are derived from soundings computed by the RUC and WRF-ARW for the observed and modeled environments, respectively.
Differences in both the raw observed and modeled storm motions as well as differences in their deviations from the environmental shear are computed. In addition, analogous right-moving storms between the observations and the models are identified manually; each analogous storm track is divided into thirds in order to determine how each model’s ability to predict storm motion changes throughout the lifecycle of a storm.
From comparison of the storm motion deviations, it is apparent that the 4-km and smoothed 1-km model data under-predict the relative proportion of left-moving storms and storms that do not deviate much from the mean 0-6-km shear. Raw output from the 1-km simulation better approximates this proportion, though still underestimates it. This indicates that the modeled storms in the 1-km simulation that are too small to be resolved on the 4-km grid tend to be left-movers. Generally, the 1-km simulation is shown to be the best representation of the observed storm motion in terms of both velocity magnitude and direction.
|11/15/13||Chris Kerr||SoM||Assimilation of cloud-top temperature and radar observations for an idealized splitting supercell
The Geostationary Operational Environmental Satellite-R Series (GOES-R) will provide frequent cloud top observations on the convective scale that could be used to help initialize convective storms in numerical weather prediction models. To evaluate the potential value of cloud top temperature observations for data assimilation, an Observing System Simulation Experiment (OSSE) approach is used. Synthetic cloud top temperature observations from an idealized splitting supercell created using the Weather Research and Forecasting (WRF) model are assimilated along with synthetic radar reflectivity and radial velocity using an Ensemble Kalman Filter (EnKF) from the Data Assimilation Research Testbed (DART). The “Truth” run has a 320 km x 240 km domain with 41 vertical levels and 2 km horizontal grid spacing. A 50-member ensemble, initialized only with random perturbations to low-level moisture and the u/v fields, has the same domain specifications. Observations are assimilated every 5 min for 2.5 h, and additive noise is used to maintain ensemble spread.
Four assimilations are completed to explore the relative value of cloud top temperature and radar observations. One experiment only assimilates radar data, while another experiment only assimilates cloud top temperature data. Two experiments assimilate combined radar/satellite observations with the observation types assimilated in different order. A rather weak correlation is found between cloud top temperature and dynamical variables, while larger correlations are found between cloud top temperature and microphysical variables. Results show that the assimilation of cloud top temperature data produces a convective storm in the ensemble, although the storm is weaker and larger than in the truth run. The addition of radar observations greatly improves the storm structure and reduces the over-prediction of storm extent. A 1-hour post-assimilation forecast is made for each experiment to gain some insight into the overall efficiency of assimilating radar data along with cloud top temperature observations.
|11/22/13||Ben Herzog||SoM||Total Lightning Information in a 5-Year Thunderstorm Climatology|
Rescheduled for 3pm Monday December 2nd, NWC 5930
Total lightning information has been shown to be proportional to storm intensity. The Geostationary Lightning Mapper (GLM), which will be on board the Geostationary Operational Environmental Satellite-R series (GOES-R) satellite, will provide total lightning information across the contiguous United States. This information will be available at a temporal resolution of 1 minute, thus providing a proxy of storm strength that will be available more frequently than traditional measures of storm intensity.
In an effort to increase the value of the information that will be provided by the GLM, the goal of this study is to enhance the understanding of total lightning information by exploring it in the framework of a 5-year climatology. In this study, total lightning information is examined across 3 different geographic domains (Central Oklahoma, Northern Alabama, and Washington, D.C.), with each domain corresponding to the location of a Lightning Mapping Array. Specifically, this research; a) determines how total lightning attributes of thunderstorms vary between different geographic regions, b), examines relationships between total lightning attributes and radar-derived attributes in thunderstorms, and c) determines how total lightning attributes vary between hazardous and non-hazardous storms.
|11/29/13||No seminar||Thanksgiving Break|
|12/06/13||Jill Hardy||SoM||Probabilistic Flash Flood Forecasting using Stormscale Ensembles
Flash flooding is one of the most costly and deadly natural hazards in the US and across the globe. The loss of life and property from flash floods could be mitigated with better guidance from hydrological models, but these models have limitations. For example, they are commonly initialized using rainfall estimates derived from weather radars. This introduces a problem for forecasting flash floods because the time interval between observations of heavy rainfall and a flash flood can be on the order of minutes, particularly for small basins in urban settings. Increasing the lead time for these events is critical for protecting life and property. Therefore, this study advances the use of quantitative precipitation forecasts (QPFs) from the stormscale CAPS ensemble system into a distributed hydrological model setting to yield basin-specific, probabilistic flash flood forecasts (PFFFs).
Rainfall error characteristics of the individual CAPS members are first diagnosed and quantified in terms of structure, amplitude, and location (SAL; Wernli et al., 2008). To compute the PFFFs, we considered the June 14, 2010 Oklahoma City flash flood event. This new approach is shown to: 1) identify the specific basin scales within the broader regions that are forecast to be impacted by flash flooding based on cell movement, rainfall intensity, duration, and the basin’s susceptibility factors such as initial soil moisture conditions; 2) yield probabilistic information about the forecast hydrologic response; and 3) improve lead time by using stormscale NWP ensemble forecasts.
Subscribe to the listserv for this seminar series here:
To schedule a seminar, send your talk title (or a working title) to "louis.wicker at noaa dot gov" and "rsobash at ou dot edu". Seminar dates towards the end of the semester tend to fill up quickly, so reserve early! Unless you are a graduate student doing your required departmental seminar, your seminar need not fill the entire hour, nor be carefully polished. We encourage presentations (short or long) on works in progress!
To those presenting, e-mail your abstract to the organizers, as well as Marcia Pallutto (SoM), no later than one week before your seminar. The abstract will be placed in the weekly seminar e-mail and on this webpage.
For accommodations based on disability, or more details, please contact the School of Meteorology at 325-6561. Visitor parking is available for all University visitors. However, faculty/staff/students must have a current multi-purpose parking permit. Additional parking is available at the Lloyd Noble Center for those individuals who do not have a parking permit. OU ID must be presented at security desk.
Spring 2013 | Spring 2012 | Spring 2011 | Fall 2011