Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments

Wheatley, Dustan M.; Knopfmeier, Kent H.; Jones, Thomas A.; Creager, Gerald J.

doi:10.1175/waf-d-15-0043.1

Cited by 126 publications

(68 citation statements)

References 45 publications

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“…To accurately forecast rapidly developing precipitating systems of isolated convective cells, observations at high spatial and temporal resolution need to be assimilated to mitigate the rapid nonlinear and non‐Gaussian error growth in observation intervals and obtain appropriate initial conditions for an atmospheric forecast model. Many previous studies successfully improved convective predictability by assimilating ground‐based radar observations into limited area models with high resolution domains (e.g., Kawabata et al, ; Miyoshi, Kunii, et al, ; Miyoshi, Lien, et al, ; Snook et al, ; Stensrud et al, ; Wheatley et al, ; Zhang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

et al. 2019

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Improving the predictability of sudden local severe weather is a grand challenge for numerical weather prediction. Recently, the capability of geostationary satellites to observe infrared radiances has been significantly improved, and it is expected that the “Big Data” from the new generation geostationary satellites could contribute to improving convective predictability. We examined the potential impacts of assimilating frequent infrared observations from a new generation geostationary satellite, Himawari‐8, on convective predictability. We implemented the real‐data experiment in which Himawari‐8 all‐sky moisture‐sensitive infrared radiances of band 8 (6.2 μm) and band 10 (7.3 μm) were assimilated into the high‐resolution (2 km) limited area model, Japan Meteorological Agency's Non‐Hydrostatic Model, every 10 min by the Local Ensemble Transform Kalman Filter. The frequent infrared observations from Himawari‐8 improve the analysis and forecast of isolated convective cells and sudden local severe rainfall induced by weak large‐scale forcing. The results imply that satellite data assimilation can contribute to better forecasting severe weather events in smaller spatiotemporal scales than the previous studies.

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Section: Introductionmentioning

confidence: 99%

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

et al. 2019

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“…This research uses the ensemble data assimilation system-described in detail by Wheatley et al (2015) and Jones et al (2016) (Benjamin et al 2016). The NEWS-e system was initialized at 1800 UTC each day using 3-h forecasts generated from the first 18 members of the HRRR-e.…”

Section: Warn-on-forecast Systemmentioning

confidence: 99%

“…Different sets of WRF model physics options are applied to each ensemble member to introduce the required model spread (e.g., Stensrud et al 2000). All members use the Thompson cloud microphysics (Thompson et al 2004a(Thompson et al , 2008; no cumulus parameterization is applied on the storm-scale grid (Wheatley et al 2015). The NEWS-e system assimilates Weather Surveillance Radar-1988 Doppler radial velocity and reflectivity observations, Geostationary Operational Environmental Satellite (GOES)-13 satellite cloud water path (CWP) retrievals , and Oklahoma mesonet data.…”

Section: Warn-on-forecast Systemmentioning

confidence: 99%

“…Forecast output is generated at 5-min intervals over the duration of the forecast period. Additional details concerning the NEWS-e system (including localization radii, additive noise, adaptive inflation, observation errors, and model physics) can be found in Wheatley et al (2015) and Jones et al (2016). This system was successfully tested in real-time operations as part of the 2016 Hazardous Weather Testbed during May 2016, including the 8 May event discussed here (e.g., Jirak et al 2014).…”

Section: Warn-on-forecast Systemmentioning

confidence: 99%

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Short-term Forecasts of Left-Moving Supercells from an Experimental Warn-on-Forecast System

Jones¹,

Nixon²

2017

J. Operational Meteor.

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Most research in storm-scale numerical weather prediction has been focused on right-moving supercells as they typically lend themselves to all forms of high-impact weather, including tornadoes. As the dynamics behind splitting updrafts and storm motion have become better understood, differentiating between atmospheric conditions that encourage right-and left-moving supercells has become the subject of increasing study because of its implication for these weather forecasts. Despite still often producing large hail and damaging winds, leftmoving (anticyclonically rotating in the Northern Hemisphere) supercells have received much less attention.During the 2016, NOAA Hazardous Weather Testbed, the NSSL Experimental Warn-on-Forecast System for ensembles (NEWS-e) was run in real-time. One event in particular occurred on 8 May 2016 during which multiple left-and right-moving supercells developed in western Oklahoma and Kansas-producing many severe weather reports. The goal of this study was to analyze the near storm environment created by NEWS-e using wind shear and other severe weather parameters. Then, we sought to determine the ability of the NEWS-e system to forecast storm splits and the persistence of left-and right-moving supercells through qualitatively analyzing tracks of forecast updraft helicity. Initial results show that the NEWS-e system analyzed several areas favorable for left-and right-moving supercells during which the system forecasted storm splitting and the persistence of both storm types. In particular, hail-producing left-moving supercells were accurately forecast in central and northern Oklahoma while a tornado-producing right-moving supercell was forecast in southern Oklahoma. ABSTRACT (Manuscript

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“…Data assimilation (DA) of Doppler radar observations using the ensemble Kalman filter (EnKF) has been shown to be a feasible method to produce initial states for storm‐scale forecasts (Snyder and Zhang, ; Stensrud et al , ) and is close to the operational phase at various weather centres (Wheatley et al , ; Bick et al , ; Jones et al , ). Most difficulties in connecting underlying non‐hydrostatic weather models like the Weather Research and Forecasting Model (WRF) and the Consortium for Small‐scale Modelling (COSMO) model to EnKF algorithms appear to be solved.…”

Section: Introductionmentioning

confidence: 99%

Characterizing noise and spurious convection in convective data assimilation

Lange¹,

Craig²,

Janjić³

2017

Quart J Royal Meteoro Soc

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The occurrence of noise and spurious convective cells in analyses of convective‐scale data assimilation (DA) is a problem that is not quantified sufficiently to develop reliable set‐ups for the DA of radar observations in an ensemble Kalman filter. This article presents two approaches to quantifying these phenomena using a local ensemble transform Kalman filter testbed, where simulated radar observations are assimilated with varying spatial and temporal DA parameters. Firstly, the dynamical coupling of cold pools and emerging cells of organized deep convection is quantified in terms of spatio‐temporal correlations, so that spurious perturbations can be identified. Secondly, the abundance of gravity‐wave noise is characterized by the variance of the vertical velocity field during DA cycling and the ensemble forecasts. This evaluation is performed separately for subsets of model points inside observed storms, in their vicinity and in the surrounding clear‐air regions. Varying horizontal localization lengths, observation averaging scales and the length of the assimilation cycling interval are compared, with respect to the two diagnostic approaches. It is concluded that the cold‐pool coupling method and the partitioned variance of vertical velocity fields are viable measures to quantify the influence of DA parameters and algorithms on the balance of analysis states that are used to initiate forecasts of convection.

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Storm-Scale Data Assimilation and Ensemble Forecasting with the NSSL Experimental Warn-on-Forecast System. Part I: Radar Data Experiments

Cited by 126 publications

References 45 publications

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

Assimilating Every‐10‐minute Himawari‐8 Infrared Radiances to Improve Convective Predictability

Short-term Forecasts of Left-Moving Supercells from an Experimental Warn-on-Forecast System

Characterizing noise and spurious convection in convective data assimilation

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