Gap-Filling Mobile Radar Observations of a Snow Squall in the San Luis Valley

Abstract

On 24 January 2017, a convective snow squall developed in the San Luis Valley of Colorado. This squall produced rapidly varying winds at San Luis Valley airport in Alamosa, Colorado, with gusts up to 12 m s−1, and an associated visibility drop to 1.4 km from unlimited in less than 10 min. This snow squall was largely undetected by the operational WSR-88D network because of the Sangre de Cristo Range of the Rocky Mountains lying between the valley and the nearest WSR-88D in Pueblo, Colorado. This study presents observations of the snow squall from the X-band NOAA X-Pol radar, which was deployed in the San Luis Valley during the event. These observations document the squall developing from individual convective cells and growing upscale into a linear squall, with peak radial velocities of 15 m s−1. The environment conducive to the development of this snow squall is examined using data from the High-Resolution Rapid Refresh model, which shows an environment unstable to ascending surface-based parcels, with surface-based convective available potential energy (SBCAPE) values up to 600 J kg−1 in the San Luis Valley. The mobile radar data are integrated into the Multi-Radar Multi-Sensor (MRMS) mosaic to illustrate both the large improvement in detectability of this event gained from a gap-filling radar as well as the capability of MRMS to incorporate data from new radars designed to fill gaps in the current radar network.

A Climatology of Snow Squalls in Southern New England 1994–2018

“…Snow squalls are defined only generally by the NWS and in most of the published literature (Milrad et al 2011(Milrad et al , 2014Rosenow et al 2018), all based on the same criteria. We based our definition of a snow squall on the NWS definition that is noted above; namely, a sudden onset of snow, increased wind speed, and concurrent low visibility.…”

“…The environment was characterized by convective and symmetric instabilities with ascent caused by surface and upper-level frontogenesis. Rosenow et al (2018) documented a similar case of a convective snow squall in Colorado, with an environment characterized by convective instability. Milrad et al (2011) and Milrad et al (2014) each examined two cases of snow squalls (or snow bursts as they are called in these papers).…”

“…The snow squalls discussed in Pettegrew et al (2009) and Milrad et al (2011Milrad et al ( , 2014 were all examples of snow events that produced too little snowfall to qualify for warning, watch, or advisory criteria at the time from NWS or Meteorological Service of Canada forecasters, even though all of these events ultimately caused serious highway crashes. Neither of the events in Colorado was forecast by the operational forecast models or by the local NWS forecasters (Schumacher et al 2010;Rosenow et al 2018). Lundstedt (1993) designed a graphical forecast diagram for snow squalls that is based on the output from the most detailed model then available, the Limited Fine Mesh model.…”

Investigating the Transition from Elevated Multicellular Convection to Surface-Based Supercells during the Tornado Outbreak of 24 August 2016 Using a WRF Model Simulation

“…The trajectory time step is 1 min, and data were linearly interpolated between each time step (Stoelinga et al 2018, section 6a). Several previous mesoscale and synoptic-scale studies have utilized RIP for trajectory analyses (e.g., Schumacher and Johnson 2008;Atkins and St. Laurent 2009;Sippel et al 2011;Rosenow et al 2018). Our methodology for initializing forward and backward trajectories is presented in section 4c.…”

A Method to Update Model Kinematic States by Assimilating Satellite‐Observed Total Lightning Data to Improve Convective Analysis and Forecasting

“…However, the positive impact is highly dependent on the quality of the radar observations and the methodology used to assimilate the convective information obtained from those observations. Although radar networks have been built and are operationally used in many countries, there are still many wide gaps in their spatial coverage (e.g., Rosenow et al, 2018). In addition, it is particularly challenging for weather radar to acquire observations over mountainous regions, where emitted radar beams suffer from full or partial blocking from terrain.…”