Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo (1997) Using the Ensemble Kalman Filter

Godinez, H. C.; Reisner, Jon; Fierro, Alexandre O.; Guimond, Stephen R.; Kao, Jim

doi:10.1175/jas-d-12-022.1

Cited by 14 publications

(16 citation statements)

References 31 publications

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“…6 in Fierro et al 2012). The ENTLN location accuracy varies from tens of meters in dense areas of the network to about 500 m elsewhere.…”

Section: B Data Assimilation Proceduresmentioning

confidence: 99%

“…The ENTLN location accuracy varies from tens of meters in dense areas of the network to about 500 m elsewhere. The cloudscale lightning assimilation technique follows Fierro et al (2012), whereby incremental increases in water vapor mixing ratio in the graupel-rich layer between 08 and 2208C are applied at observed lightning locations. The nudging-induced increase in water vapor mass at a grid point is proportional to the observed gridded flashrate density (per 3-km grid cell herein) and inversely proportional to the simulated graupel mixing ratio at that grid point (threshold for zero nudging also set to 3 g kg…”

Section: B Data Assimilation Proceduresmentioning

confidence: 99%

“…The first technique is a recently developed, computationally inexpensive lightning data nudging method (Fierro et al 2012), while the second technique is the more detailed Advanced Regional Prediction System (ARPS; Xue et al 2001Xue et al , 2003 three-dimensional variational data assimilation (3DVAR) system (Gao et al 1999(Gao et al , 2004Hu et al 2006a,b;Stensrud and Gao 2010;Ge et al 2010Ge et al , 2012Gao et al 2013). The lightning data assimilation technique makes use of two-dimensional gridded total lightning flash rate densities.…”

Section: Introductionmentioning

confidence: 99%

“…Relative to the evaluation of the assimilation of observations into NWP models using variational techniques such as 3DVAR or ensemble techniques such as the ensemble Kalman filter (EnKF, e.g., Evensen 1994Evensen , 2003Houtekamer and Mitchell 1998;Zhang 2005Zhang , 2009Aksoy et al 2009;Godinez et al 2012), comparatively fewer studies have focused their attention on the assimilation of lightning data. Alexander et al (1999) and Chang et al (2001) were the first to demonstrate the utility of assimilating lightning data into a mesoscale model.…”

Section: Introductionmentioning

confidence: 99%

“…Unlike ground flashes, IC flashes have been shown to correlate better with convective strength and updraft development (e.g., MacGorman et al 1989;Wiens et al 2005;Fierro et al 2006;Deierling and Petersen 2008), thus serving as a better proxy for thunderstorm evolution. Several recent studies have demonstrated the utility of assimilating total lightning data toward improving the analysis and short-term forecast of highimpact weather ranging from the mesoscale (e.g., Mansell et al 2007) to the cell scale (Fierro et al 2012). Further motivating a more systematic use of the assimilation of total lightning data is the upcoming first launch of the Geostationary Operational Environmental Satellite-R series (GOES-R; Gurka et al 2006;Goodman et al 2013) in 2015, which will be equipped with the Geostationary Lightning Mapper (GLM; Goodman et al 2013), which is capable of mapping total lightning (CG 1 IC) day and night, year round, with a nearly uniform resolution over the Americas ranging between 8 and 12 km (Gurka et al 2006).…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of a Cloud-Scale Lightning Data Assimilation Technique and a 3DVAR Method for the Analysis and Short-Term Forecast of the 29 June 2012 Derecho Event

Fierro

Gao

Ziegler

et al. 2014

Monthly Weather Review

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This work evaluates the short-term forecast (#6 h) of the 29-30 June 2012 derecho event from the Advanced Research core of the Weather Research and Forecasting Model (WRF-ARW) when using two distinct data assimilation techniques at cloud-resolving scales (3-km horizontal grid). The first technique assimilates total lightning data using a smooth nudging function. The second method is a three-dimensional variational technique (3DVAR) that assimilates radar reflectivity and radial velocity data. A suite of sensitivity experiments revealed that the lightning assimilation was better able to capture the placement and intensity of the derecho up to 6 h of the forecast. All the simulations employing 3DVAR, however, best represented the storm's radar reflectivity structure at the analysis time. Detailed analysis revealed that a small feature in the velocity field from one of the six selected radars in the original 3DVAR experiment led to the development of spurious convection ahead of the parent mesoscale convective system, which significantly degraded the forecast. Thus, the relatively simple nudging scheme using lightning data complements the more complex variational technique. The much lower computational cost of the lightning scheme may permit its use alongside variational techniques in improving severe weather forecasts on days favorable for the development of outflow-dominated mesoscale convective systems.

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“…6 in Fierro et al 2012). The ENTLN location accuracy varies from tens of meters in dense areas of the network to about 500 m elsewhere.…”

Section: B Data Assimilation Proceduresmentioning

confidence: 99%

Section: B Data Assimilation Proceduresmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Evaluation of a Cloud-Scale Lightning Data Assimilation Technique and a 3DVAR Method for the Analysis and Short-Term Forecast of the 29 June 2012 Derecho Event

Fierro

Gao

Ziegler

et al. 2014

Monthly Weather Review

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Data assimilation for identification of cardiovascular network characteristics

Lal

Mohammadi

Nicoud

2016

Numer Methods Biomed Eng

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International audienceA method to estimate the hemodynamics parameters of a network of vessels using an Ensemble Kalman filter is presented. The elastic moduli (Young's modulus) of blood vessels and the terminal boundary parameters are estimated as the solution of an inverse problem. Two synthetic test cases and a configuration where experimental data is available are presented. The sensitivity analysis confirms that the proposed method is quite robust even with a few numbers of observations. The simulations with the estimated parameters recovers target pressure or flow rate waveforms at given specific locations, improving the state of the art predictions available in the literature. This shows the effectiveness and the efficiency of both the parameters estimation algorithm and the blood flow model

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The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

Hari Prasad,

Prasad,

Sateesh

et al. 2023

Quart J Royal Meteoro Soc

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The accurate prediction of high‐impact weather systems using cloud‐resolving models is still a challenge among researchers. This study evaluates the consistency of the combination of the three‐dimensional variational technique within the Gridpoint Statistical Interpolation assimilation system (GSI‐3DVAR) and nudging in the same modelling system on short‐range forecasts of three heavy rainfall events from the southwest monsoon season of 2021 over the Indian subcontinent. Three experiments have been conducted (i) control (CNTL): assimilated conventional and satellite observations; (ii) radar and lightning data assimilation (RLDA): assimilated radar reflectivity and lightning proxy reflectivity data along with all observations used in CNTL; and (iii) lightning data assimilation (LDA): same as RLDA but without the assimilation of radar data; particularly done to test the impact of assimilation of only lightning data. The model‐simulated rainfall is evaluated by using the Integrated Multi‐Satellite Retrievals (IMERG) for Global Precipitation Measurement (GPM IMERG) rainfall data. The intercomparison of LDA and RLDA for event 1 highlighted that both represent the convective regions reasonably better than CNTL, but RLDA outperforms LDA and thus further assimilation experiments are done with RLDA. RLDA provided reasonably accurate forecasts compared to CNTL, which is evident in the spatial distribution of rainfall and area‐averaged three‐hourly accumulated rainfall. Verification metrics for the three selected heavy rainfall events reveal that an optimal forecast performance (especially in the first six hours of free forecast) is obtained by the simulation with assimilation of radar and lightning data during the pre‐forecast period, through correcting the position and timing of convective centres. The probability of detection (POD) values are higher for light rainfall categories than for the heavy rain categories. POD values were higher in RLDA than CNTL throughout simulation for all three events. For all these three selected events, fractions skill scores (FSS) of RLDA are always better than CNTL with different neighbourhood sizes for different threshold values throughout the forecast period.

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Determining Key Model Parameters of Rapidly Intensifying Hurricane Guillermo (1997) Using the Ensemble Kalman Filter

Cited by 14 publications

References 31 publications

Evaluation of a Cloud-Scale Lightning Data Assimilation Technique and a 3DVAR Method for the Analysis and Short-Term Forecast of the 29 June 2012 Derecho Event

Evaluation of a Cloud-Scale Lightning Data Assimilation Technique and a 3DVAR Method for the Analysis and Short-Term Forecast of the 29 June 2012 Derecho Event

Data assimilation for identification of cardiovascular network characteristics

The impact of Indian radar and lightning data assimilation on the short‐range forecasts of heavy rainfall events

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