Influence of a portable near-surface observing network on experimental ensemble forecasts of deep convection hazards during VORTEX-SE

Aaron J. Hill, Christopher C. Weiss, David C. Dowell

Research output: Contribution to journalArticlepeer-review

Abstract

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).

Original languageEnglish
Pages (from-to)1141-1167
Number of pages27
JournalWeather and Forecasting
Volume36
Issue number4
DOIs
StatePublished - Aug 2021

Keywords

  • Data assimilation
  • Ensembles
  • Numerical weather prediction/forecasting
  • Operational forecasting
  • Severe storms

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