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Abstract Ice habit diagrams published prior to 2009—and many since—do not accurately describe in situ observations of ice shapes as a function of temperature and moisture. Laboratory studies and analysis of field observations by Bailey and Hallett in a series of papers in 2002, 2004, and 2009 corrected several errors from earlier studies, but their work has not been widely disseminated. We present a new, simplified diagram based on Bailey and Hallett’s work that focuses on several ice growth forms arising from the underlying surface processes by which mass is added to a crystal: tabular, columnar, branched, side branched, two types of polycrystalline forms, and a multiple growth regime at low ice supersaturations. To aid interpretation for a variety of applications, versions of the ice growth diagram are presented in terms of relative humidity with respect to water as well as the traditional formats of relative humidity with respect to ice and vapor density excess. These diagrams are intended to be understandable and useful in classroom settings at the sophomore undergraduate level and above. The myriad shapes of pristine snow crystals can be described as the result of either a single growth form or a sequence of growth forms. Overlays of data from upper-air soundings on the ice growth diagrams aid interpretation of expected physical properties and processes in conditions of ice growth. 

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. 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. 

This dataset contains over 14,000 hours of regional radar mosaics over the northeast US from 600+ winter storm days between 1996-2023. Winter storm days are defined when at least 2 out of 15 surface stations in the northeast US (see attached map) produced at least 1 inch of snow over the 24 hour period. Sequences of these mosaics aid in analyzing the precipitation area and the structures within winter storms. Radar reflectivity data is combined from the first, lowest (0.5 degree) elevation angle from 12 NEXRAD WSR-88D radars in the northeast US (see attached). The scans occur every 5-10 minutes from each radar depending on the radar scan settings.  The time label of the regional map is based on the scan time central radar, KOKX (Upton, NY). Scans from other radars in the region are used for that time as long as they are within 8 minutes of the KOKX scan. The polar radar data from each radar is interpolated to a regional 1202 km x 1202 km Cartesian grid with 2 km grid spacing covering 35.73-46.8 degN and 66.36-81.85 degW. Where the radar domains overlap, we take the highest reflectivity value. For dates after dual-polarization integration (2012 onwards), files contain the correlation coefficient (RHO_HV) field and a binary field  that can be used to “image mute” the reflectivity which reduces the visual prominence of melting and mixed precipitation commonly mistaken for heavy snow. Image muting is applied where radar reflectivity is ≥ 20 dBZ and RHO_HV is ≤ 0.97. This product is different from other widely used radar mosaics such as the MRMS produced by NOAA since it does not interpolate to a constant altitude and thus preserves the finer scale details in the reflectivity field. Because the data used to create these mosaics are not interpolated to a constant altitude, the altitude varies over the region (altitudes of radar scan used at each grid point are provided as a field for each data file). This data set is specifically designed to analyze fine-scale structures in winter storms. Part 1 contains files pre-dual polarization integration (1996-2012)Part 2 contains files post-dual polarization integration (2012-2023) 

This dataset contains over 14,000 hours of regional radar mosaics over the northeast US from 600+ winter storm days between 1996-2023. Winter storm days are defined when at least 2 out of 15 surface stations in the northeast US (see attached map) produced at least 1 inch of snow over the 24 hour period. Sequences of these mosaics aid in analyzing the precipitation area and the structures within winter storms. Radar reflectivity data is combined from the first, lowest (0.5 degree) elevation angle from 12 NEXRAD WSR-88D radars in the northeast US (see attached). The scans occur every 5-10 minutes from each radar depending on the radar scan settings.  The time label of the regional map is based on the scan time central radar, KOKX (Upton, NY). Scans from other radars in the region are used for that time as long as they are within 8 minutes of the KOKX scan. The polar radar data from each radar is interpolated to a regional 1202 km x 1202 km Cartesian grid with 2 km grid spacing covering 35.73-46.8 degN and 66.36-81.85 degW. Where the radar domains overlap, we take the highest reflectivity value. For dates after dual-polarization integration (2012 onwards), files contain the correlation coefficient (RHO_HV) field and a binary field  that can be used to “image mute” the reflectivity which reduces the visual prominence of melting and mixed precipitation commonly mistaken for heavy snow. Image muting is applied where radar reflectivity is ≥ 20 dBZ and RHO_HV is ≤ 0.97. This product is different from other widely used radar mosaics such as the MRMS produced by NOAA since it does not interpolate to a constant altitude and thus preserves the finer scale details in the reflectivity field. Because the data used to create these mosaics are not interpolated to a constant altitude, the altitude varies over the region (altitudes of radar scan used at each grid point are provided as a field for each data file). This data set is specifically designed to analyze fine-scale structures in winter storms. Part 1 contains files pre-dual polarization integration (1996-2012)Part 2 contains files post-dual polarization integration (2012-2023) 

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.