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Abstract. Mesoscale pressure waves, including atmospheric gravity waves, outflow and frontal passages, and wake lows, are outputs of and can potentially modify clouds and precipitation. The vertical motions associated with these waves can modify the temperature and relative humidity of air parcels and thus yield potentially irreversible changes to the cloud and precipitation content of those parcels. A wavelet-based method for identifying and tracking these types of wave signals in time series data from networks of low-cost, high-precision (0.8 Pa noise floor, 1 Hz recording frequency) pressure sensors is demonstrated. Strong wavelet signals are identified using a wave-period-dependent (i.e., frequency-dependent) threshold, and then those signals are extracted by inverting the wavelet transform. Wave periods between 1 and 120 min were analyzed – a range which could capture acoustic, acoustic-gravity, and gravity wave modes. After extracting the signals from a network of pressure sensors, the cross-correlation function is used to estimate the time difference between the wave passage at each pressure sensor. From those time differences, the wave phase velocity vector is calculated using a least-squares fit. If the fitting error is sufficiently small (thresholds of RMSE < 90 s and NRMSE < 0.1 were used), then a wave event is considered robust and trackable. We present examples of tracked wave events, including a Lamb wave caused by the Hunga Tonga volcanic eruption in January 2020, a gravity wave train, an outflow boundary passage, a frontal passage, and a cold front passage. The data and processing techniques presented here can have research applications in wave climatology and testing associations between waves and atmospheric phenomena. 

Abstract. In winter storms, enhanced radar reflectivity is often associated with heavy snow. However, some higher reflectivities are the result of mixed precipitation including melting snow. The correlation coefficient (a dual-polarization radar variable) can identify regions of mixed precipitation, but this information is usually presented separately from reflectivity. Especially under time pressure, radar data users can mistake regions of mixed precipitation for heavy snow because of the high cognitive load associated with comparing data in two fields while simultaneously attempting to discount a portion of the high reflectivity values. We developed an image muting method for regional radar maps that visually de-emphasizes the high reflectivity values associated with mixed precipitation. These image muted depictions of winter storm precipitation structures are useful for analyzing regions of heavy snow and monitoring real-time weather conditions. 

Abstract The current study identifies and quantifies various mechanisms of entrainment, and their diluting effects, in the developing and mature stages of a simulated supercell thunderstorm. The two stages, differentiated by the lack or presence of a rotating updraft, are shown to entrain air by different, but related mechanisms that result from the strong vertical wind shear of the environment. The greatest entrainment rates in the developing stage result from the asymmetric overturning of large eddies near cloud top on the down-shear side. These rates are greater than those published in the literature for cumuli developing in environments lacking strong shear. Although the entrainment rate increases exponentially in time throughout the developing stage, successive cloud turrets help to replenish some of the lost buoyancy and condensate, allowing the nascent storm to develop further. During the mature stage, the greatest entrainment rates occur via “ribbons” of horizontal vorticity wrapping around the rotating updraft that ascend in time. The smaller width of the ribbons in comparison to the wider storm core limits their dilutive effects. Passive tracers placed in the low-level air ingested by the mature storm indicate that on average 20% of the core contains some undiluted air ingested from below the storm base, unaffected by any entrainment mechanism.