TY - JOUR
T1 - Assessing the ensemble predictability of precipitation forecasts for the January 2015 and 2016 East Coast winter storms
AU - Greybush, Steven J.
AU - Saslo, Seth
AU - Grumm, Richard
N1 - Funding Information:
Portions of this work were funded by NSF Award 1450488. The authors thank Craig Schwartz for providing access to a special run of the NCAR ensemble, thank three anonymous peer reviewers for helpful comments on this manuscript, and acknowledge NOAA for access to operational model output and observation products. The authors are grateful to the Penn State Department of Meteorology and Atmospheric Science and the Institute for CyberScience for computational resources used in this study.
Publisher Copyright:
© 2017 American Meteorological Society.
PY - 2017/6/1
Y1 - 2017/6/1
N2 - The ensemble predictability of the January 2015 and 2016 East Coast winter storms is assessed, with model precipitation forecasts verified against observational datasets. Skill scores and reliability diagrams indicate that the large ensemble spread produced by operational forecasts was warranted given the actual forecast errors imposed by practical predictability limits. For the 2015 storm, uncertainties along the western edge's sharp precipitation gradient are linked to position errors of the coastal low, which are traced to the positioning of the preceding 500-hPa wave pattern using the ensemble sensitivity technique. Predictability horizon diagrams indicate the forecast lead time in terms of initial detection, emergence of a signal, and convergence of solutions for an event. For the 2016 storm, the synoptic setup was detected at least 6 days in advance by global ensembles, whereas the predictability of mesoscale features is limited to hours. Convection-permitting WRF ensemble forecasts downscaled from the GEFS resolve mesoscale snowbands and demonstrate sensitivity to synoptic and mesoscale ensemble perturbations, as evidenced by changes in location and timing. Several perturbation techniques are compared, with stochastic techniques [the stochastic kinetic energy backscatter scheme (SKEBS) and stochastically perturbed parameterization tendency (SPPT)] and multiphysics configurations improving performance of both the ensemble mean and spread over the baseline initial conditions/boundary conditions (IC/BC) perturbation run. This study demonstrates the importance of ensembles and convective-allowing models for forecasting and decision support for east coast winter storms.
AB - The ensemble predictability of the January 2015 and 2016 East Coast winter storms is assessed, with model precipitation forecasts verified against observational datasets. Skill scores and reliability diagrams indicate that the large ensemble spread produced by operational forecasts was warranted given the actual forecast errors imposed by practical predictability limits. For the 2015 storm, uncertainties along the western edge's sharp precipitation gradient are linked to position errors of the coastal low, which are traced to the positioning of the preceding 500-hPa wave pattern using the ensemble sensitivity technique. Predictability horizon diagrams indicate the forecast lead time in terms of initial detection, emergence of a signal, and convergence of solutions for an event. For the 2016 storm, the synoptic setup was detected at least 6 days in advance by global ensembles, whereas the predictability of mesoscale features is limited to hours. Convection-permitting WRF ensemble forecasts downscaled from the GEFS resolve mesoscale snowbands and demonstrate sensitivity to synoptic and mesoscale ensemble perturbations, as evidenced by changes in location and timing. Several perturbation techniques are compared, with stochastic techniques [the stochastic kinetic energy backscatter scheme (SKEBS) and stochastically perturbed parameterization tendency (SPPT)] and multiphysics configurations improving performance of both the ensemble mean and spread over the baseline initial conditions/boundary conditions (IC/BC) perturbation run. This study demonstrates the importance of ensembles and convective-allowing models for forecasting and decision support for east coast winter storms.
UR - http://www.scopus.com/inward/record.url?scp=85020011666&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85020011666&partnerID=8YFLogxK
U2 - 10.1175/WAF-D-16-0153.1
DO - 10.1175/WAF-D-16-0153.1
M3 - Article
AN - SCOPUS:85020011666
SN - 0882-8156
VL - 32
SP - 1057
EP - 1078
JO - Weather and Forecasting
JF - Weather and Forecasting
IS - 3
ER -