The Role of Observational Data for Short-Term Severe Weather Forecasting: A Vision of Rapidly Updated, Dynamically Consistent 3-D Analyses for Diagnostic Assessment

The historical focus of short-term severe weather forecasting by the Storm Prediction Center emphasized the analysis and interpretation of observational surface and radiosonde data.  This approach continues to provide a foundational methodology to current operational severe storm products and services.  The addition of newer observing systems such as radar, satellite, total lighting, and high resolution mesonets have helped fill some of the spatiotemporal gaps in traditional data sources.  However, the current integrated observational network is unable to sufficiently resolve details of the pre-convective and near-storm environment, particularly the 4-D distribution of water vapor, that are vitally important for the accurate prediction of convective initiation and evolution.   To address some of these deficiencies, observational-based analysis is now routinely supplemented by frequently updated numerical model analyses.  For example, the SPC Mesoscale Analysis, which is widely used across the operational weather enterprise, blends an objective analysis of surface METAR observations with a RAP model field background to provide an hourly 3-D analysis of atmospheric patterns and structures. 

This approach often provides quite useful information about the mesoscale environment for severe weather forecasters, but it does not necessarily depict accurate representations of storm occurrence and convective interactions/feedback to the larger scales.   Part of this limitation is associated with characteristics of NWP model analyses used as the background field.  The primary goal of NWP models is to provide the “best forecast” possible, and data assimilation schemes/initialization procedures for these models are fine-tuned to meet this goal.  However, this can be inconsistent with producing the “best analysis” for forecaster diagnostic purposes.   It is recommended that modern automated analyses for diagnostic purposes should be the best real-time representation of the current state of the mesoscale and storm-scale atmosphere, with a goal to provide a fully integrated and dynamically consistent rapidly updated view of the mesoscale/storm-scale environment, including storm-attribute fields and feedback from convective storms to the near-storm environment.   When this type of proposed system is mature operationally, it is envisioned that forecasters would look primarily at these integrated mesoscale/stormscale analyses instead of individual observational datasets (including radar), using the analyses for both diagnostic and short-term hazardous weather decision making.