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Abstract. Radar observations of winter storms often exhibit locally enhanced linear features in reflectivity, sometimes labeled as snow bands. We have developed a new, objective method for detecting locally enhanced echo features in radar data from winter storms. In comparison to convective cells in warm season precipitation, these features are usually less distinct from the background echo and often have more fuzzy or feathered edges. This technique identifies both prominent, strong features and more subtle, faint features. A key difference from previous radar reflectivity feature detection algorithms is the combined use of two adaptive differential thresholds, one that decreases with increasing background values and one that increases with increasing background values. The algorithm detects features within a snow rate field rather than reflectivity and incorporates an underestimate and overestimate of feature areas to account for uncertainties in the detection. We demonstrate the technique on several examples from the US National Weather Service operational radar network. The feature detection algorithm is highly customizable and can be tuned for a variety of data sets and applications. 

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 Limited knowledge exists about ∼100-m-scale precipitation processes within U.S. northeast coastal snowstorms because of a lack of high-resolution observations. We investigate characteristics of microscale updraft regions within the cyclone comma head and their relationships with snowbands, wind shear, frontogenesis, and vertical mass flux using high-spatiotemporal-resolution vertically pointing Ka-band radar measurements, soundings, and reanalysis data for four snowstorms observed at Stony Brook, New York. Updraft regions are defined as contiguous time–height plotted areas with upward Doppler velocity without hydrometeor sedimentation that is equal to or greater than 0.4 m s⁻¹. Most updraft regions in the time–height data occur on a time scale of seconds (<20 s), which is equivalent to spatial scales < 500 m. These small updraft regions within cloud echo occur more than 30% of the time for three of the four cases and 18% for the other case. They are found at all altitudes and can occur with or without frontogenesis and with or without snowbands. The updraft regions with relatively large Doppler spectrum width (>0.4 m s⁻¹) occur more frequently within midlevels of the storms, where there are strong wind shear layers and moist shear instability layers. This suggests that the dominant forcing for the updrafts appears to be turbulence associated with the vertical shear instability. The updraft regions can be responsible for upward mass flux when they are closer together in space and time. The higher values of column mean upward mass flux often occur during snowband periods. Significance StatementSmall-scale (<500 m) upward motions within four snowstorms along the U.S. northeast coast are analyzed for the first time using high-spatiotemporal-resolution millimeter-wavelength cloud radar pointed vertically. The analysis reveals that updrafts appear in the storms regardless of whether snowbands are present or whether there is larger-scale forcing for ascent. The more turbulent and stronger updrafts frequently occur in midlevels of storms associated with instability from vertical shear and contribute to upward mass flux during snowband periods when they are closer together in space and time. 

Abstract Large, abrupt clearing events have been documented in the marine stratocumulus cloud deck over the subtropical Southeast Atlantic Ocean. In these events, clouds are rapidly eroded along a line hundreds–to–thousands of kilometers in length that generally moves westward away from the coast. Because marine stratocumulus clouds exert a strong cooling effect on the planet, any phenomenon that acts to erode large areas of low clouds may be climatically important. Previous satellite-based research suggests that the cloud-eroding boundaries may be caused by westward-propagating atmospheric gravity waves rather than simple advection of the cloud. The behavior of the coastal offshore flow, which is proposed as a fundamental physical mechanism associated with the clearing events, is explored using the Weather Research and Forecasting model. Results are presented from several week-long simulations in the month of May when cloud-eroding boundaries exhibit maximum frequency. Two simulations cover periods containing multiple cloud-eroding boundaries (active periods), and two other simulations cover periods without any cloud-eroding boundaries (null periods). Passive tracers and an analysis of mass flux are used to assess the character of the diurnal west-African coastal circulation. Results indicate that the active periods containing cloud-eroding boundaries regularly experience stronger and deeper nocturnal offshore flow from the continent above the marine boundary layer, compared to the null periods. Additionally, we find that the boundary layer height is higher in the null periods than in the active periods, suggesting that the active periods are associated with areas of thinner clouds that may be more susceptible to cloud erosion.