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University of Oklahoma
1.
Dahl, Nathan.
Coupling the Advanced Regional Prediction System and the Discrete Event Specification Fire Spread Model to Predict Wildfire Behavior.
Degree: PhD, 2014, University of Oklahoma
URL: http://hdl.handle.net/11244/10403 
► The cost of wildfire suppression in the United States has risen dramatically over the last 20 years. As the interface between wildland and urban areas…
(more)
▼ The cost of wildfire suppression in the United States has risen dramatically over the last 20 years. As the interface between wildland and urban areas expands, increased emphasis is being placed on rapid, efficient deployment of firefighting resources. Various numerical models of wildfire spread have been developed to assist wildfire management efforts over the last several decades; however, the use of coupled fire-weather models to capture important feedbacks between the wildfire and the atmosphere is a relatively new development.
This research evaluates a coupled system consisting of the Advanced Regional Prediction System (ARPS) atmospheric model and the raster-based Discrete Event Specification Fire Spread model (DEVS-FIRE). After the theoretical basis of coupled fire-atmosphere modeling and the basic design of previous vector-based models are outlined, idealized tests, verification using data from the FIREFLUX experiment, and case studies of the September 2000 Moore Branch Fire and the April 2011 Rock House Fire are presented. The current version of ARPS/DEVS-FIRE produces mixed results; broader-scale feedbacks appear to be represented somewhat skillfully, but the model also exhibits systematic flaws, which are exacerbated by efforts to depict fine-scale feedbacks or fire spread in high-wind cases. These results demonstrate the importance of coupled modeling and suggest improvements that must be made to ARPS/DEVS-FIRE before reliable results may be obtained.
Advisors/Committee Members: Xue, Ming (advisor), Brewster, Keith (committee member), Fiedler, Brian (committee member), Hong, Yang (committee member), Shapiro, Alan (committee member).
Subjects/Keywords: Physics; Atmospheric Science.
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APA (6th Edition):
Dahl, N. (2014). Coupling the Advanced Regional Prediction System and the Discrete Event Specification Fire Spread Model to Predict Wildfire Behavior. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/10403
Chicago Manual of Style (16th Edition):
Dahl, Nathan. “Coupling the Advanced Regional Prediction System and the Discrete Event Specification Fire Spread Model to Predict Wildfire Behavior.” 2014. Doctoral Dissertation, University of Oklahoma. Accessed April 22, 2021.
http://hdl.handle.net/11244/10403.
MLA Handbook (7th Edition):
Dahl, Nathan. “Coupling the Advanced Regional Prediction System and the Discrete Event Specification Fire Spread Model to Predict Wildfire Behavior.” 2014. Web. 22 Apr 2021.
Vancouver:
Dahl N. Coupling the Advanced Regional Prediction System and the Discrete Event Specification Fire Spread Model to Predict Wildfire Behavior. [Internet] [Doctoral dissertation]. University of Oklahoma; 2014. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11244/10403.
Council of Science Editors:
Dahl N. Coupling the Advanced Regional Prediction System and the Discrete Event Specification Fire Spread Model to Predict Wildfire Behavior. [Doctoral Dissertation]. University of Oklahoma; 2014. Available from: http://hdl.handle.net/11244/10403
University of Oklahoma
2.
Gasperoni, Nicholas.
Forecast Sensitivity to Observations using Data Denial and Ensemble-based Methods over the Dallas-Fort Worth Testbed.
Degree: PhD, 2018, University of Oklahoma
URL: http://hdl.handle.net/11244/54641
► The ‘Nationwide Network of Networks’ (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system.…
(more)
▼ The ‘Nationwide Network of Networks’ (NNoN) concept was introduced by the National Research Council to address the growing need for a national mesoscale observing system. Part of this growing need is the continued advancement toward accurate high-resolution numerical weather prediction. The research testbed known as the Dallas – Fort Worth (DFW) Urban Demonstration Network was created to experiment with many kinds of mesoscale observations that could be used in a data assimilation system, in order to identify observational systems that are most impactful on high-resolution forecasts. Many observation systems have been implemented for the DFW testbed, including Earth Networks (ERNET) Weather Bug surface stations, Citizen Weather Observer Program (CWOP) amateur surface stations, Global Science and Technology (GST) mobile truck observations, CASA X- band radars, SODARs, and radiometers. These ‘nonconventional’ observations are combined with conventional operational data from METARs, mesonet, aircraft, rawinsondes, profilers, and operational radars to form the testbed network. A principal component of the NNoN effort is the quantification of observation impact from several different sources of information. This dissertation covers two main themes related to quantifying the impact that observations have on forecasts.
The first part is the quantification of impact using data denial experiments, or observational simulation experiments. The GSI-based EnKF data assimilation system was used together with the WRF-ARW model to examine impacts of observations assimilated for forecasting convection initiation (CI) in the 3 April 2014 hailstorm case.
Data denial experiments were conducted testing the impact of high-frequency (5-min) assimilation of nonconventional data on the timing and location of CI, as well as on the development of storms as they progress through the testbed domain. Results using ensemble probability of reflectivity and neighborhood ensemble probability of hail show nonconventional observations were necessary to capture local details in the dryline structure causing localized enhanced convergence and leading to CI. Diagnosis of denial-minus-control fields showed the cumulative influence each observing network had on the resulting CI forecast. It was found that most of this impact came from the assimilation of thermodynamic observations. Accurate metadata is found to be crucial to the application of nonconventional observations in high-resolution assimilation and forecasts systems.
The second part of this dissertation explored the application of the ensemble- based forecast sensitivity to observations (EFSO). First, tests using a global two-layer model were performed to identify areas of improvement in the localization methods needed to make EFSO estimates accurate. Due to the time-forecast component, localization of the EFSO metric is more complicated than during traditional assimilation because as forecast time increases the error correlation structures evolve with the flow. Experiments made use of the…
Advisors/Committee Members: Wang, Xuguang (advisor), Carr, Frederick (committee member), Xue, Ming (committee member), Brewster, Keith (committee member), Devegowda, Deepak (committee member).
Subjects/Keywords: forecast sensitivity to observations; convective-scale data assimilation; ensemble forecasting; Atmospheric Sciences
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APA (6th Edition):
Gasperoni, N. (2018). Forecast Sensitivity to Observations using Data Denial and Ensemble-based Methods over the Dallas-Fort Worth Testbed. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/54641
Chicago Manual of Style (16th Edition):
Gasperoni, Nicholas. “Forecast Sensitivity to Observations using Data Denial and Ensemble-based Methods over the Dallas-Fort Worth Testbed.” 2018. Doctoral Dissertation, University of Oklahoma. Accessed April 22, 2021.
http://hdl.handle.net/11244/54641.
MLA Handbook (7th Edition):
Gasperoni, Nicholas. “Forecast Sensitivity to Observations using Data Denial and Ensemble-based Methods over the Dallas-Fort Worth Testbed.” 2018. Web. 22 Apr 2021.
Vancouver:
Gasperoni N. Forecast Sensitivity to Observations using Data Denial and Ensemble-based Methods over the Dallas-Fort Worth Testbed. [Internet] [Doctoral dissertation]. University of Oklahoma; 2018. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11244/54641.
Council of Science Editors:
Gasperoni N. Forecast Sensitivity to Observations using Data Denial and Ensemble-based Methods over the Dallas-Fort Worth Testbed. [Doctoral Dissertation]. University of Oklahoma; 2018. Available from: http://hdl.handle.net/11244/54641
University of Oklahoma
3.
Stratman, Derek.
Sensitivities of 1-km Forecasts of Tornadic Supercells to Microphysics Parameterizations, Assimilated Radar Data, and Assimilation Techniques.
Degree: PhD, 2016, University of Oklahoma
URL: http://hdl.handle.net/11244/34808
► The aim of this study is to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an…
(more)
▼ The aim of this study is to examine the impact of using five different microphysics parameterization schemes, including single-, double-, and triple-moment microphysics, in an efficient high-resolution data assimilation system suitable for nowcasting and short-term forecasting with low latencies. In addition to testing the sensitivity to microphysics, the impact of gap-filling radars and variations in analysis cycling and incremental analysis updating (IAU) techniques are explored using a variety of verification methods.
On 24 May 2011,
Oklahoma experienced an outbreak of tornadoes, including one rated EF-5 and two rated EF-4. The extensive observation network in this area, including the WSR-88D radars, Collaborative Adaptive Sensing of the Atmosphere (CASA) IP-1 X-band radars,
Oklahoma Mesonet, and standard surface data, makes this an ideal case for these tests. Additionally, the real-time configuration of the 1-km ARPS, which used 3DVAR with cloud analysis via IAU, had success providing a good baseline forecast. ARPS forecasts of 0-2h are verified using point-to-point, neighborhood, and object-based verification techniques. The object-based verification technique uses updraft helicity fields to represent mesocyclone centers, which are verified against tornado locations from three supercells of interest. Varying levels of success in the forecasts are found and appear to be dependent on the complexity of storm interaction, with early forecasts of isolated storms exhibiting the most success. Verification scores indicate the multi-moment schemes tend to produce better forecasts, assimilating CASA radar data can improve forecasts for storms within the CASA radar network, and analysis cycling and modified IAU techniques generally contribute to better forecasts.
Advisors/Committee Members: Xue, Ming (advisor), Brewster, Keith (advisor), Carr, Frederick (committee member), Richman, Michael (committee member), Christopher, Weaver (committee member).
Subjects/Keywords: NWP; storm-scale forecasting; data assimilation; microphysics parameterizations
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APA (6th Edition):
Stratman, D. (2016). Sensitivities of 1-km Forecasts of Tornadic Supercells to Microphysics Parameterizations, Assimilated Radar Data, and Assimilation Techniques. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/34808
Chicago Manual of Style (16th Edition):
Stratman, Derek. “Sensitivities of 1-km Forecasts of Tornadic Supercells to Microphysics Parameterizations, Assimilated Radar Data, and Assimilation Techniques.” 2016. Doctoral Dissertation, University of Oklahoma. Accessed April 22, 2021.
http://hdl.handle.net/11244/34808.
MLA Handbook (7th Edition):
Stratman, Derek. “Sensitivities of 1-km Forecasts of Tornadic Supercells to Microphysics Parameterizations, Assimilated Radar Data, and Assimilation Techniques.” 2016. Web. 22 Apr 2021.
Vancouver:
Stratman D. Sensitivities of 1-km Forecasts of Tornadic Supercells to Microphysics Parameterizations, Assimilated Radar Data, and Assimilation Techniques. [Internet] [Doctoral dissertation]. University of Oklahoma; 2016. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11244/34808.
Council of Science Editors:
Stratman D. Sensitivities of 1-km Forecasts of Tornadic Supercells to Microphysics Parameterizations, Assimilated Radar Data, and Assimilation Techniques. [Doctoral Dissertation]. University of Oklahoma; 2016. Available from: http://hdl.handle.net/11244/34808
University of Oklahoma
4.
Kong, Rong.
HYBRID EN3DVAR RADAR DATA ASSIMILATION AND COMPARISONS WITH 3DVAR AND ENKF WITH OSSES AND A REAL CASE.
Degree: PhD, 2017, University of Oklahoma
URL: http://hdl.handle.net/11244/52910
► Studies have shown advantages of the hybrid ensemble-variational data assimilation (DA) algorithms over pure ensemble or variational algorithms, although such advantages at the convective scale,…
(more)
▼ Studies have shown advantages of the hybrid ensemble-variational data assimilation (DA) algorithms over pure ensemble or variational algorithms, although such advantages at the convective scale, in the presence of complex ice microphysics and for radar data assimilation, have not yet been clearly demonstrated, if the advantages do exist. A hybrid ensemble-3DVar (En3DVar) system is developed recently based on the ARPS 3DVar and EnKF systems at the Center for Analysis and Prediction of Storms (CAPS). In this dissertation, hybrid En3DVar is compared with 3DVar, EnKF, and pure En3DVar for radar DA through observing system simulation experiments (OSSEs) under both perfect and imperfect model assumptions. It is also applied to a real case including multiple tornadic supercells. For the real case, radar radial velocity and reflectivity data are assimilated every 5 minutes for 1 hour that is followed by short-term forecasts. DfEnKF that updates a single deterministic background forecast using the EnKF updating algorithm is introduced to have an algorithm-wise parallel comparison between EnKF and pure En3DVar.
In the perfect-model OSSEs, DfEnKF and pure En3DVar are compared and are found to perform differently when using the same localization radii. The serial (EnKF) versus global (pure En3DVar) nature of the algorithms, and direct filter update (EnKF) versus variational minimization (En3DVar) are the major reasons for the differences. Hybrid En3DVar for radar DA is also compared with 3DVar, EnKF, DfEnKF, and pure En3DVar. Experiments are conducted first to obtain the optimal configurations for different algorithms before they are compared; the optimal configurations include the optimal background decorrelation scales for 3DVar, optimal localization radii for EnKF, DfEnKF, and pure En3DVar, as well as the optimal hybrid weights for hybrid En3DVar. When the algorithms are tuned optimally, hybrid En3DVar does not outperform EnKF or pure En3DVar, although their analyses are all much better than 3DVar. When ensemble background error covariance is a good estimation of the true error distribution, pure ensemble-based DA methods can do a good job, and the advantage of including static background error covariance B in hybrid DA is not obvious.
In the imperfect-model OSSEs, model errors are introduced by using different microphysical schemes in the truth run (Lin scheme) and in the ensemble forecasts (WSM6 scheme). Experiments are conducted to obtain the optimal configurations for different algorithms, similar to those in perfect-model OSSEs. Hybrid En3DVar is then found to outperform EnKF and pure En3DVar (3DVar) for better capturing the hail analyses below the freezing level (intensity of the storm). The advantage of hybrid En3DVar over pure ensemble-based methods is most obvious when ensemble background errors are systematically underestimated. In addition, the impact of adding a mass continuity constraint in 3DVar, pure and hybrid En3DVar is also examined. Overall, adding the mass continuity constraint improving the analyses by…
Advisors/Committee Members: Xue, Ming (advisor), Shapiro, Alan (committee member), Parsons, David (committee member), Kong, Fanyou (committee member), Brewster, Keith (committee member), Xiao, Xiangming (committee member).
Subjects/Keywords: meteorology; radar data assimilation
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APA ·
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MLA ·
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CSE |
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Manager
APA (6th Edition):
Kong, R. (2017). HYBRID EN3DVAR RADAR DATA ASSIMILATION AND COMPARISONS WITH 3DVAR AND ENKF WITH OSSES AND A REAL CASE. (Doctoral Dissertation). University of Oklahoma. Retrieved from http://hdl.handle.net/11244/52910
Chicago Manual of Style (16th Edition):
Kong, Rong. “HYBRID EN3DVAR RADAR DATA ASSIMILATION AND COMPARISONS WITH 3DVAR AND ENKF WITH OSSES AND A REAL CASE.” 2017. Doctoral Dissertation, University of Oklahoma. Accessed April 22, 2021.
http://hdl.handle.net/11244/52910.
MLA Handbook (7th Edition):
Kong, Rong. “HYBRID EN3DVAR RADAR DATA ASSIMILATION AND COMPARISONS WITH 3DVAR AND ENKF WITH OSSES AND A REAL CASE.” 2017. Web. 22 Apr 2021.
Vancouver:
Kong R. HYBRID EN3DVAR RADAR DATA ASSIMILATION AND COMPARISONS WITH 3DVAR AND ENKF WITH OSSES AND A REAL CASE. [Internet] [Doctoral dissertation]. University of Oklahoma; 2017. [cited 2021 Apr 22].
Available from: http://hdl.handle.net/11244/52910.
Council of Science Editors:
Kong R. HYBRID EN3DVAR RADAR DATA ASSIMILATION AND COMPARISONS WITH 3DVAR AND ENKF WITH OSSES AND A REAL CASE. [Doctoral Dissertation]. University of Oklahoma; 2017. Available from: http://hdl.handle.net/11244/52910
. 
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