TY - JOUR
T1 - Influence of a portable near-surface observing network on experimental ensemble forecasts of deep convection hazards during VORTEX-SE
AU - Hill, Aaron J.
AU - Weiss, Christopher C.
AU - Dowell, David C.
N1 - Funding Information:
Acknowledgments. We would like to acknowledge the individuals that contributed to the deployment, testing, and maintenance of the StickNet platforms during VORTEX-SE: Dr. Eric Bruning, Dr. Vanna Chmielewski, Abby Hutson, Alex Schueth, and Jessica McDonald. We are also indebted to the many landowners who supported our science objectives for over nine cumulative months. We greatly appreciate the sharing of analysis and plotting code by Dr. Patrick Skinner. The authors also wish to acknowledge the efforts of Dr. Matthew Bunkers and three anonymous reviewers for their thoughtful suggestions and comments, which greatly improved the quality of the manuscript. Computing support was generously provided by the National Oceanic Atmospheric Administration (NOAA). This work is supported by NOAA Awards NA17OAR4590206 and NA18OAR4590318.
Publisher Copyright:
© 2021 American Meteorological Society.
PY - 2021/8
Y1 - 2021/8
N2 - Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the Southeast United States. StickNet observations are assimilated with an experimental version of the High-Resolution Rapid Refresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis. SIGNIFICANCE STATEMENT: In this work, observations from a portable observation network during a large-scale field campaign are incorporated into numerical weather prediction models to improve forecasts of severe storms and their attendant hazards: tornadoes, hail, and severe wind. Observations are gathered from StickNet platforms (developed at Texas Tech University), which were placed throughout northern Alabama and southern Tennessee during the project. Over four cases examined in this manuscript, simulations that include StickNet observations are improved at earlier times, but forecast impacts at later times are varied. The observations improve near-surface temperature and moisture forecasts, but do not routinely influence forecasts of the actual storms, likely because the most sensitive regions that would improve forecasts were not well sampled by the StickNets. Future work should evaluate how more frequent observations could improve forecasts, beyond what was considered here (i.e., one observation per hour).
AB - Ensemble forecasts are generated with and without the assimilation of near-surface observations from a portable, mesoscale network of StickNet platforms during the Verification of the Origins of Rotation in Tornadoes Experiment-Southeast (VORTEX-SE). Four VORTEX-SE intensive observing periods are selected to evaluate the impact of StickNet observations on forecasts and predictability of deep convection within the Southeast United States. StickNet observations are assimilated with an experimental version of the High-Resolution Rapid Refresh Ensemble (HRRRE) in one experiment, and withheld in a control forecast experiment. Overall, StickNet observations are found to effectively reduce mesoscale analysis and forecast errors of temperature and dewpoint. Differences in ensemble analyses between the two parallel experiments are maximized near the StickNet array and then either propagate away with the mean low-level flow through the forecast period or remain quasi-stationary, reducing local analysis biases. Forecast errors of temperature and dewpoint exhibit periods of improvement and degradation relative to the control forecast, and error increases are largely driven on the storm scale. Convection predictability, measured through subjective evaluation and objective verification of forecast updraft helicity, is driven more by when forecasts are initialized (i.e., more data assimilation cycles with conventional observations) rather than the inclusion of StickNet observations in data assimilation. It is hypothesized that the full impact of assimilating these data is not realized in part due to poor sampling of forecast sensitive regions by the StickNet platforms, as identified through ensemble sensitivity analysis. SIGNIFICANCE STATEMENT: In this work, observations from a portable observation network during a large-scale field campaign are incorporated into numerical weather prediction models to improve forecasts of severe storms and their attendant hazards: tornadoes, hail, and severe wind. Observations are gathered from StickNet platforms (developed at Texas Tech University), which were placed throughout northern Alabama and southern Tennessee during the project. Over four cases examined in this manuscript, simulations that include StickNet observations are improved at earlier times, but forecast impacts at later times are varied. The observations improve near-surface temperature and moisture forecasts, but do not routinely influence forecasts of the actual storms, likely because the most sensitive regions that would improve forecasts were not well sampled by the StickNets. Future work should evaluate how more frequent observations could improve forecasts, beyond what was considered here (i.e., one observation per hour).
KW - Data assimilation
KW - Ensembles
KW - Numerical weather prediction/forecasting
KW - Operational forecasting
KW - Severe storms
UR - http://www.scopus.com/inward/record.url?scp=85109725305&partnerID=8YFLogxK
U2 - 10.1175/WAF-D-20-0237.1
DO - 10.1175/WAF-D-20-0237.1
M3 - Article
AN - SCOPUS:85109725305
VL - 36
SP - 1141
EP - 1167
JO - Weather and Forecasting
JF - Weather and Forecasting
SN - 0882-8156
IS - 4
ER -