Blake Allen University of Oklahoma Edward Mansell NOAA / National Severe Storms Laboratory
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Transcript of Blake Allen University of Oklahoma Edward Mansell NOAA / National Severe Storms Laboratory
Assimilation of Pseudo-GLM Observations Into a Storm Scale
Numerical Model Using the Ensemble Kalman Filter
Blake AllenUniversity of Oklahoma
Edward MansellNOAA / National Severe Storms Laboratory
Funding Provided by NOAA-NESDIS/JCSDA
Background: Pseudo-GLM ObsFlash observations were generated from LMA data, using a flash separation algorithm(MacGorman et al 2008) to specify individualflashes.
Each flash was then mapped to an approximately8km x 8km two dimensional grid.
For each flash that crossed a grid box, a flashevent was added to the box’s total.
Example Set Of pGLM Observations
-> Flash extent density ~ 80 min-1
-> Flash extent density ~ 1 min-1
Lat
Lon
May 8, 2003 22:09Z
EnKF SetupEnsemble Square Root filter used to produce analyses at 1, 3,or 5 minute intervals with a 40 member ensemble.
Pseudo-obs were placed at 6500 m height, with avertical localization radius of 35 km and a 16 kmhorizontal localization radius.
Observation error was set to 10%-15% of the maximumflash rate in each storm.
Thermal bubbles and smooth noise (Caya 2005) were used toinitiate convection and create/maintain ensemble spread.
Observation OperatorsFirst guesses at observation operators were produced by finding a linear fit between flash rate and microphysical variables from simulations using an explicit lightning model while assimilating radial velocity radar data.
Various linear relationships between graupel mass andflash rate, graupel volume and flash rate, andNon-inductive charging and flash rate were tested.
The best results were found with the relationshipFlash extent density = (0.017)*(graupel volume)
Observed During STEPS
Moved through an area well-sampled by the STEPS LMA
Radar data available from CHILL radar
Low-shear, low-CAPE environment
Evolved through multiple formations throughout its lifetime
Model Setup: 1km horizontal resolution, 2-moment microphysics with 4 ice categories.
6 June 2000 Airmass Storm
6 June 2000 Results
Ensemble Mean Reflectivity22:30Z June 6 (1 min. assimilation)
Observed Reflectivity22:30Z June 6
6 June 2000 Airmass Storm
6 June 2000 Results
Ensemble Mean Reflectivity00:00Z June 7 (1 min. assimilation)
Observed Reflectivity00:00Z June 7
6 June 2000 Airmass Storm
6 June 2000 Results
Ensemble Mean Reflectivity01:00Z June 7 (1 min. assimilation)
Observed Reflectivity01:00Z June 7
6 June 2000 Airmass Storm
6 June 2000 Results
5 min pGLM assimilation
1 min pGLM assimilation
Vr assim5 min pGLM assimilation
1 min pGLM assimilation
Vr assimilation
Min = 0 m/s
Max = 35 m/s
Min = 0 km3
Max = 700 km3
Maximum Updraft Speed
6 June 2000 Airmass Storm
Graupel Volume > 0.5 g/kg
Moved through an area well-sampled by the Oklahoma LMA
Radar data available from KTLX WSR-88D, and previous work has been done using the storm as a test case for assimilation of radar velocity and reflectivity data (Dowell et al 2010).
High-shear, high-CAPE environment
Produced multiple tornadoes, including a long-tracked F4 that struck Moore OK and other parts of the Oklahoma City metro area.
Model Setup: 1km horizontal resolution, 2-moment microphysics with 4 ice categories.
8 May 2003 Supercell
22:09Z
8 May 2003 Supercell
Ens. Mean reflectivity
Near-surface radar reflectivity around the time of the first tornado
Observed Reflectivity22:09Z22:09Z
Single member reflectivity
8 May 2003 Supercell
Vr assimilation1min pGLM assim
5 min pGLM assimilation
Vr assimilation
1min pGLM assimilation
5 min pGLM assimilation
Min = 0 km3
Max = 12000 km3
Min = 0 m/s
Max = 70 m/s Maximum Updraft Speed
Graupel Volume > 0.5 g/kg
8 May 2003 Results8 May 2003 SupercellProbabiliy of Vorticity > 0.016 s-1 at 1.75 km model height
Vr assimilation 1 minute pGLM assimilation
8 May 2003 Supercell
Conclusions1.) EnKF assimilation of pseudo-GLM data, using a single observation operator,
can produce analyses that capture the basic reflectivity structure of multiple storm types, and can produce maximum updraft speeds comparable to those obtained when assimilating Doppler radar radial velocity.
2.) In the supercell case, using high temporal resolution data captured the development of the low-level mesocyclone.
3.) At lower temporal resolutions, the strength of both storms dropped later in the runs, although this may be case specific – 8 May had a capping inversion and 6 June had weak instability.
4.) Since the GLM will produce observations over a much larger area than radar
observations are available, these results show promise that EnKF assimilation of GLM data can be useful stand-in for assimilation of radial velocity data in areas where radar data is lacking or of poor quality.
Summary / Conclusions
ReferencesA. Caya et al 2005: A comparison between the 4DVAR and the ensemble Kalman filter
techniques for radar data assimilation. Monthly Weather ReviewD. Dowell et al 2010: Ensemble Kalman Filter Assimilation of Radar Observations of the 8 May
2003 Oklahoma City Supercell: Influences of Reflectivity Observations on Storm-Scale Analyses. Monthly Weather Review
D. MacGorman et al 2008: The Thunderstorm Electrification and Lightning Experiment. BAMS
AcknowledgementsThank you to Kristin Calhoun for converting LMA data to pseudo-GLM observations, and to David Dowell for providing radar data for the 6 June case.Funding Provided by NOAA-NESDIS/JCSDA
Questions?
EnKF Assimilation of Pseudo-GLM Observations (8 May 2003 Moore, OK Supercell)
Run A: Flash Rate = 0.017( Graupel Volume ) Run B: Flash Rate = 0.039( NI charge sep. rate)
Simulated Reflectivity Simulated Reflectivity
Probability of Vorticity > 0.016 s-1 Probability of Vorticity > 0.016 s-1
Observation Operator Comparison