Details:

Journal Title:
Monthly Weather Review
Personal Author:
Labriola, Jonathan ; Snook, Nathan ; Jung, Youngsun ; Xue, Ming
Labriola, Jonathan ; Snook, Nathan ; Jung, Youngsun ; Xue, Ming Less -
NOAA Program & Office:
OAR (Oceanic and Atmospheric Research)
Description:
Ensemble Kalman filter (EnKF) analyses of the storms associated with the 8 May 2017 Colorado severe hail event using either the Milbrandt and Yau (MY) or the NSSL double-moment bulk microphysics scheme in the forecast model are evaluated. With each scheme, two experiments are conducted in which the reflectivity (Z) observations update in addition to dynamic and thermodynamic variables: 1) only the hydrometeor mixing ratios or 2) all microphysical variables. With fewer microphysical variables directly constrained by the Z observations, only updating hydrometeor mixing ratios causes the forecast error covariance structure to become unreliable, and results in larger errors in the analysis. Experiments that update all microphysical variables produce analyses with the lowest Z root-mean-square innovations; however, comparing the estimated hail size against hydrometeor classification algorithm output suggests that further constraint from observations is needed to more accurately estimate surface hail size. Ensemble correlation analyses are performed to determine the impact of hail growth assumptions in the MY and NSSL schemes on the forecast error covariance between microphysical and thermodynamic variables. In the MY scheme, Z is negatively correlated with updraft intensity because the strong updrafts produce abundant small hail aloft. The NSSL scheme predicts the growth of large hail aloft; consequently, Z is positively correlated with storm updraft intensity and hail state variables. Hail production processes are also shown to alter the background error covariance for liquid and frozen hydrometeor species. Results in this study suggest that EnKF analyses are sensitive to the choice of MP scheme (e.g., the treatment of hail growth processes).
Keywords:
[+]
Atmospheric Science
Source:
Monthly Weather Review, 148(6), 2365-2389
DOI:
https://doi.org/10.1175/mwr-d-19-0300.1
ISSN:
0027-0644;1520-0493;
Format:
PDF
Publisher:
American Meteorological Society
Document Type:
Journal Article
Funding:
Grant no. NA16OAR4320115 ; Grant no. NA16OAR4590239
Grant no. NA16OAR4320115 ; Grant no. NA16OAR4590239 Less -
Rights Information:
Other
Compliance:
Library
Main Document Checksum:
[+]
urn:sha256:e37dce07cdc1415966b20768643082f205ed6052f9fb52effc193155a9183da4
Download URL:
https://repository.library.noaa.gov/view/noaa/60425/noaa_60425_DS1.pdf
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