Verification of convection-allowing model ensemble analyses of near-storm environments using MPEX upsonde observations

Christopher A. Kerr, David J. Stensrud, Xuguang Wang

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

The Mesoscale Predictability Experiment (MPEX) conducted during the spring of 2013 included frequent coordinated sampling of near-storm environments via upsondes. These unique observations were taken to better understand the upscale effects of deep convection on the environment, are used to validate the accuracy of convection-allowing (Dx 5 3 km) model ensemble analyses. A 36-member ensemble was created with physics diversity using the Weather Research Forecasting Model, observations were assimilated via the Data Assimilation Research Testbed using an ensemble adjustment Kalman filter. A 4-day sequence of convective events from 28 to 31 May 2013 in the south-central United States was analyzed by assimilating Doppler radar conventional observations. No MPEX upsonde observations were assimilated. Since the ensemble mean analyses produce an accurate depiction of the storms, the MPEX observations are used to verify the accuracy of the analyses of the near-storm environment. A total of 81 upsondes were released over the 4-day period, sampling different regions of near-storm environments including storm inflow, outflow, anvil. The MPEX observations reveal modest analysis errors overall when considering all samples, although specific environmental regions reveal larger errors in some state fields. The ensemble analyses underestimate cold pool depth, storm inflow meridional winds have a pronounced northerly bias that results from an underprediction of inflow wind speed magnitude. Most bias distributions are Gaussian-like, with a few being bimodal owing to systematic biases of certain state fields environmental regions.

Original languageEnglish (US)
Pages (from-to)857-875
Number of pages19
JournalMonthly Weather Review
Volume145
Issue number3
DOIs
StatePublished - Jan 1 2017

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convection
inflow
experiment
cold pool
Doppler radar
error analysis
sampling
Kalman filter
data assimilation
outflow
physics
wind velocity
weather

All Science Journal Classification (ASJC) codes

  • Atmospheric Science

Cite this

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abstract = "The Mesoscale Predictability Experiment (MPEX) conducted during the spring of 2013 included frequent coordinated sampling of near-storm environments via upsondes. These unique observations were taken to better understand the upscale effects of deep convection on the environment, are used to validate the accuracy of convection-allowing (Dx 5 3 km) model ensemble analyses. A 36-member ensemble was created with physics diversity using the Weather Research Forecasting Model, observations were assimilated via the Data Assimilation Research Testbed using an ensemble adjustment Kalman filter. A 4-day sequence of convective events from 28 to 31 May 2013 in the south-central United States was analyzed by assimilating Doppler radar conventional observations. No MPEX upsonde observations were assimilated. Since the ensemble mean analyses produce an accurate depiction of the storms, the MPEX observations are used to verify the accuracy of the analyses of the near-storm environment. A total of 81 upsondes were released over the 4-day period, sampling different regions of near-storm environments including storm inflow, outflow, anvil. The MPEX observations reveal modest analysis errors overall when considering all samples, although specific environmental regions reveal larger errors in some state fields. The ensemble analyses underestimate cold pool depth, storm inflow meridional winds have a pronounced northerly bias that results from an underprediction of inflow wind speed magnitude. Most bias distributions are Gaussian-like, with a few being bimodal owing to systematic biases of certain state fields environmental regions.",
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Verification of convection-allowing model ensemble analyses of near-storm environments using MPEX upsonde observations. / Kerr, Christopher A.; Stensrud, David J.; Wang, Xuguang.

In: Monthly Weather Review, Vol. 145, No. 3, 01.01.2017, p. 857-875.

Research output: Contribution to journalArticle

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