The Precipitation Imaging Package: Phase Partitioning Capabilities

remote sensing

Article The Precipitation Imaging Package: Phase Partitioning Capabilities

Claire Pettersen 1,* , Larry F. Bliven 2, Mark S. Kulie 3, Norman B. Wood 1, Julia A. Shates 4, Jaclyn Anderson 5, Marian E. Mateling 4, Walter A. Petersen 6 , Annakaisa von Lerber 7 and David B. Wolff 2

1 Space Science and Engineering Center, University of Wisconsin–Madison, Madison, WI 53706, USA; [email protected] 2 NASA Goddard Space Flight Center, Wallops Flight Facility, Wallops Island, VA 23337, USA; [email protected] (L.F.B.); [email protected] (D.B.W.) 3 Advanced Satellite Products Branch, NOAA/NESDIS/Center for Satellite Applications and Research, Madison, WI 53706, USA; [email protected] 4 Department of Atmospheric and Oceanic Sciences, University of Wisconsin–Madison, Madison, WI 53706, USA; [email protected] (J.A.S.); [email protected] (M.E.M.) 5 National Weather Service, NOAA, Milwaukee, WI 53118, USA; [email protected] 6 NASA Marshall Space Flight Center, Huntsville, AL 35808, USA; [email protected] 7 Finnish Meteorological Institute, 00560 Helsinki, Finland; Annakaisa.von.Lerber@fmi.ﬁ * Correspondence: [email protected]

Abstract: Surface precipitation phase is a fundamental meteorological property with immense importance. Accurate classification of phase from satellite remotely sensed observations is difficult. This study demonstrates the ability of the Precipitation Imaging Package (PIP), a ground-based, in situ precipitation imager, to distinguish precipitation phase. The PIP precipitation phase identification capabilities are compared to observer records from the National Weather Service (NWS) office Citation: Pettersen, C.; Bliven, L.F.; in Marquette, Michigan, as well as co-located observations from profiling and scanning radars, Kulie, M.S.; Wood, N.B.; Shates, J.A.; disdrometer data, and surface meteorological measurements. Examined are 13 events with at least one Anderson, J.; Mateling, M.E.; precipitation phase transition. The PIP-determined onsets and endings of the respective precipitation Petersen, W.A.; von Lerber, A.; Wolff, phase periods agree to within 15 min of NWS observer records for the vast majority of the events. D.B. The Precipitation Imaging Additionally, the PIP and NWS liquid water equivalent accumulations for 12 of the 13 events were Package: Phase Partitioning within 10%. Co-located observations from scanning and profiling radars, as well as reanalysis- Capabilities. Remote Sens. 2021, 13, 2183. https://doi.org/10.3390/rs derived synoptic and thermodynamic conditions, support the accuracy of the precipitation phases 13112183 identified by the PIP. PIP observations for the phase transition events are compared to output from a parameterization based on wet bulb and near-surface lapse rates to produce a probability of solid Academic Editor: Gareth Rees precipitation. The PIP phase identification and the parameterization output are consistent. This work highlights the ability of the PIP to properly characterize hydrometeor phase and provide Received: 28 April 2021 dependable precipitation accumulations under complicated mixed-phase and rain and snow (or vice Accepted: 31 May 2021 versa) transition events. Published: 3 June 2021 Keywords: precipitation; mixed-phase precipitation; rain rate; snowfall rate; snow mass retrieval; Publisher’s Note: MDPI stays neutral video disdrometers with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction The phase of surface precipitation is a fundamental meteorological property with immense importance. Transitions between liquid, solid, and mixed-phase precipitation Copyright: © 2021 by the authors. at a given location impact daily life in both mundane (e.g., clothing choice, impacts to Licensee MDPI, Basel, Switzerland. recreational activities) and societally disruptive (e.g., transportation delays, accidents due This article is an open access article to hazardous conditions) ways. Accurately forecasting and monitoring transitions in pre- distributed under the terms and cipitation type is also an extremely important, yet challenging, task. For instance, extreme conditions of the Creative Commons cold season weather events such as United States East Coast “Nor’easter” storms typically Attribution (CC BY) license (https:// have distinct rain–snow transition zones that separate warm and cold storm sectors [1–4]. creativecommons.org/licenses/by/ The rain–snow line in such storms demarcates locations that receive potentially copious 4.0/).

Remote Sens. 2021, 13, 2183. https://doi.org/10.3390/rs13112183 https://www.mdpi.com/journal/remotesensing Remote Sens. 2021, 13, 2183 2 of 25

amounts of snow versus rain accumulations. Similar surface precipitation phase-related complications arise in the United States East Coast and Midwest during cold air damming events that produce an array of precipitation types, including debilitating freezing rain, and frequent phase transitions that are intimately linked to near-surface thermodynamic composition [5]. Short-term surface precipitation phase monitoring is also important for flooding events that are exacerbated by rain falling on existing snowpack [6–9], while long- term surface rain versus snow observations are crucial for understanding climate-scale snowline trends in mountainous regions [10,11]. Remote sensing instruments are vital hydrologic monitoring tools and provide valuable global precipitation datasets, yet accurately classifying surface precipitation type solely through remote sensing observations is rife with complications, particularly for spaceborne instruments. Radar observations provide valuable information about surface precipitation type by identifying “bright bands” (abrupt, high values of reflectivity) aloft that indicate melting zones and likely surface rain [12], and/or dual-polarization signals that distinguish liquid versus solid hydrometeors [13,14]. Radar precipitation phase detection capabilities are diminished by blind zone complications in both spaceborne (e.g., first few clutter-affect range gates) and ground-based scanning (e.g., radar beam height located too high above the surface at long ranges) radar observations, especially when melting layers are located very close to the surface [15–17]. Spaceborne passive microwave sensors also provide precipitation estimates, but are sensitive to column-averaged properties and cannot easily distinguish surface precipitation phase. Therefore, passive microwave algorithms of precipitation commonly rely on model-derived thermodynamic fields to characterize the atmospheric profile and provide surface precipitation phase guidance [18]. Parameter- izations have been developed that statistically determine surface phase type using key thermodynamic variables and thresholds, such as two meter and/or wet bulb temperature and thermodynamic lapse rates, which are determined from aggregated surface observer datasets [19–21]. Automated instruments can obtain precipitation information without the need for human intervention. Weighing gauges and tipping buckets are precipitation sensors that provide rate and accumulation measurements on varying time scales, but do not provide ex- plicit phase identification [22]. The Automated Service Observing System (ASOS) network contains over 900 sensors deployed throughout the United States that provide precipitation rate (tipping bucket), precipitation identification (light emitting diode), and freezing rain identification (magnetostriction) capabilities. However, the ASOS observational suites provide no microphysical information, and tipping buckets may not effectively measure solid precipitation [23]. The Particle Size Velocity (Parsivel) laser-optical disdrometer and present weather sensor obtains precipitation phase information [24,25] and provides physically reasonable rainfall rates and raindrop size distributions [26,27], but struggles to resolve larger raindrops associated with intense rainfall rates [28] and snow microphysical properties [29]. This study describes the Precipitation Imaging Package PIP; [30,31], an in situ precipitation imager that skillfully discerns precipitation phase and provides robust microphysical information (e.g., particle size distribution, particle fall speeds, particle effective density, and precipitation rates) at high temporal sampling rates under solid, liquid, and mixed surface precipitation conditions. The PIP, along with a precipitation profiling radar, were deployed in the northern Great Lakes region at the Marquette, Michigan (MQT), National Weather Service (NWS) office in 2014 [32,33]. This enhanced observational suite focuses on cold season precipitation observations with direct applications to improving remote sensing quantitative precipitation retrievals, including surface precipitation rate and phase. The unique observing capabilities for surface precipitation are highlighted, including the ability of the PIP to properly characterize hydrometeor phase and provide reasonable precipitation accumulations under complicated mixed-phase and transitional events. Remote Sens. 2021, 13, 2183 3 of 25

2. Data and Methods 2.1. Location This study leverages observations from the MQT NWS ofﬁce from January 2014 through the present [32,33]. This observational dataset consists of coincident remotely sensed and in situ observations of precipitation. Additionally, observations from co-located NWS instrumentation, surface measurements, and weather forecaster records are utilized. This region receives approximately 91 cm of rainfall and 200–500 cm of snowfall per year (geometric accumulation) per NWS observer records.

2.2. Instrumentation 2.2.1. Precipitation Imaging Package The Precipitation Imaging Package (PIP) is a NASA-developed video disdrometer [30,31]. The PIP plays a major role in NASA Global Precipitation Measurement (GPM) mission ground validation (GV) campaigns as an inexpensive and easily deployable instrument [34]. The PIP consists of a high-speed video camera (380 frames per s) aimed at a bright halogen lamp at 2 m distance. The PIP has a ﬁeld of view of 64 by 48 mm and image resolution of 0.1 by 0.1 mm. The PIP camera records videos of hydrometeors as they fall through the observation volume and produces hydrometeor characteristics, such as the particle equivalent area diameter and vertical and horizontal velocities. Higher order products from the PIP include particle size distributions (PSDs) and fall speed distributions at a 1 min resolution. The development of an empirically derived particle size, fall speed, and effective density parameterization [31] allows for retrievals of precipitation rates and precipitation phase discrimination capabilities [32,33,35]. The phase discrimination methodology is further described in Section 2.4.

2.2.2. Micro Rain Radar The METEK Micro Rain Radar 2 (MRR) is a vertically profiling, 24-GHz frequency- modulated, continuous-wave Doppler radar [36]. The MRR has been used in field campaigns for observations of rain (e.g., [37]) and snow (e.g., [32,33,38,39]). The MRR at the MQT site is configured to profile the atmosphere to 3000 m above ground level (AGL) with a vertical resolution of 100 m and temporal resolution of 1 min.

2.2.3. Weather Radar The NWS MQT office has an S-band Weather Surveillance Radar-1988 (WSR-88D), which is part of the Next Generation Weather Radar (NEXRAD) network. Level II data products for the NEXRAD system are available through the National Oceanic and Atmo- spheric Administration (NOAA) National Center for Environmental Information (NCEI) archive. In this work, we use WSR-88D observations of reflectivity, differential reflectivity, and cross correlation ratio from scans at 1.5◦ elevation angle.

2.2.4. Parsivel An OTT Hydromet Particle Size Velocity 2 (Parsivel) laser disdrometer was added to the NWS MQT site in February 2017. The Parsivel is a laser-optical disdrometer that measures the diameter and fall speeds of hydrometeors. The Parsivel also produces hydrometeor phase identification for each particle [24]. The Parsivel identifies hydrometeor phase using the determined volume equivalent diameter and the fall speed of the particles. The Parsivel has been used successfully to examine the characteristics and precipitation rates of rain [26,27], while it is more limited in observing snowfall [29]. For this work, we are only using the Parsivel hydrometeor identification capabilities and we are limiting these to only identification of “rain” or “snow” (we are omitting the “mixed-phase”, “rimed”, and “ice pellet” categories). Remote Sens. 2021, 13, 2183 4 of 25

2.2.5. Surface Meteorological Measurements The surface meteorological data are obtained from the automated Davis Vantage Pro2 weather station, which is mounted on a tower (10 m AGL) adjacent to the NWS MQT office. Observations of surface temperature, dew point, and wind speed are recorded at one-minute intervals. The surface meteorological data are used to calculate the wet bulb temperatures in combination with an empirically derived formula from Stull et al. [40]. Precipitation observations are obtained five times per day by NWS MQT meteorologists at 0000, 0459, 0600, 1200, and 1800 UTC during ongoing precipitation events. Measure- ments of rain accumulation are collected using a 20.32 cm diameter standard rain gauge. Measurements of geometric snow accumulation are collected using a plastic, white snow board that is placed adjacent to the open snow stake field and cleared off between each measurement. Liquid water equivalent (LWE) measurements of snow are obtained from the melted snow in the rain gauge.

2.2.6. Weather Forecaster Records The weather forecasters at the NWS MQT ofﬁce keep daily records of precipitation. The 6-h accumulation is recorded for both rain and snow (geometric and LWE) at 0000, 0600, 1200, and 1800 UTC. In addition, start and stop times for observed precipitation type (e.g., rain showers, snowfall, fog) are recorded when these transitions occur (these are not restricted only to the 6-h observations). These records are archived and distributed by the NWS MQT ofﬁce.

2.3. Reanalysis Products This work utilizes the ERA5 reanalysis dataset [41] to examine the spatial surface values of mean sea level pressure (MSLP) and winds, and the 850 hPa pressure level temperature and winds in the region surrounding the NWS MQT. ERA5 temperature and relative humidity profiles are examined at specific time steps during precipitation phase transitions. Note that the ERA5 relative humidity is calculated by interpolating between the ice and water values using a quadratic function. The ERA5 products are available hourly from 1979 through 2020 and have a horizontal resolution of 31 km (globally) and 137 vertical layers to 0.01 hPa. This work uses reanalysis data in the region of 100◦ W to 70◦ W and 35◦ N to 55◦ N, and the grid point closest to the site (46.58◦ N, 87.58◦ W) for the profiles. The ERA5 profiles of temperature and relative humidity were used to calculate near surface lapse rates where pressure coordinates were converted to height coordinates using hydrostatic balance.

2.4. PIP Method for Determining Preipitation Phase The PIP instrument uses observations of particle size and fall speed distributions in conjunction with an empirically derived parameterization to produce estimates of the bulk density of precipitation at 1 min resolution. This bulk effective density, hereafter ρe, is described in detail in Pettersen et al. [31]. The relationship of ρe is shown in Equation (1):

π R ρ (D , V(D ))D3N(D )dD = 6 e e e e e e ρe π R 3 (1) 6 De N(De)dDe

where De is the particle equivalent diameter (i.e., the diameter of a spherical particle with the same projected area as that of the observed particle), V(De) is the fall speed distribution, and N(De) is the size distribution. A single ρe value represents the mean density of all the −3 precipitation particles that fall during the 1 min period. The units of ρe are g cm and range from 0.015 to 1 (the density of liquid water). The parameterization produces maximum ρe values for particles with fallspeeds reaching those for raindrops as deﬁned by Atlas and Ulbrich [42]. Further details on the ρe parameterization are found in Pettersen et al. [31]. Comparisons of PIP-observed particle fall speeds to the terminal velocity limits from Atlas and Ulbrich [42] are shown in Figure1 for a rain-to-snow transition event from Remote Sens. 2021, 13, x FOR PEER REVIEW 5 of 25

Remote Sens. 2021, 13, 2183 5 of 25 Comparisons of PIP-observed particle fall speeds to the terminal velocity limits from Atlas and Ulbrich [42] are shown in Figure 1 for a rain-to-snow transition event from 24 to24 25 to October 25 October 2017. 2017. At 1100 At 1100UTC UTCon 24 on Octobe 24 Octoberr 2017 (Figure 2017 (Figure 1a), the1a), precipitation the precipitation is rain is phaserain phaseonly and only the and mean the meanobserved observed fall speeds fall speeds as a function as a function of De ofareD roughlye are roughly along along the theoreticalthe theoretical terminal terminal velocity velocity of rain. of At rain. 1730 At UTC 1730 (Figure UTC (Figure1b) the fall1b) speeds the fall for speeds the smaller for the particlessmaller ( particlesDe < 1 mm) (De are< 1 mm)along are the along theoretical the theoretical terminal terminal velocity velocity of rain, of while rain, larger while largerpar- ticlesparticles (De > (1D mm)e > 1 have mm) lower have lowermean meanobserved observed fall speeds fall speeds than the than theory the theory predicts predicts for rain. for Therain. PIP The observations PIP observations of precipitation of precipitation at this time at this indicate time a indicate mix of rain a mix and of snow. rain and At 2230 snow. UTCAt 2230on 24 UTC October on 24 2017, October the PIP-observed 2017, the PIP-observed mean fall speeds mean fallfor speedsparticles for at particlesall sizes at(De all) aresizes much (De )lower are much than lowerwhat thanis predicted what is from predicted the terminal from the velocity terminal of velocity rain theory of rain (Figure theory 1c),(Figure therefore1c), therefore designating designating that this thatevent this fully event transitioned fully transitioned to snowfall. to snowfall.

FigureFigure 1. 1.PIPPIP observations observations of of mean mean fall fall speed speed as as a afunction function of of DDe fore for the the example example transition transition event event on on 2424 October October 2017. 2017. At At 1100 1100 UTC UTC (a ()a all) all the the precipitation precipitation is isclassified classiﬁed as as rain rain (red (red circles) circles) and and particle particle fallfall speed speed is isalong along the the theorized theorized terminal terminal velocity velocity of rain of rain [42]. [ 42At]. 1730 At 1730UTC UTC(b) a (mixtureb) a mixture of rain of and rain snowand snowexists exists(blue stars), (blue stars),as the aslarger thelarger particles particles have slower have slower mean meanfall speeds fall speeds (and depart (and depart from fromthe theorized terminal velocity [42]). Finally, at 2330 UTC (c) only snow is present, as the mean fall the theorized terminal velocity [42]). Finally, at 2330 UTC (c) only snow is present, as the mean fall speeds are much slower that the Atlas–Ulbrich terminal velocity for all De. speeds are much slower that the Atlas–Ulbrich terminal velocity for all De.

TheThe example example transitional transitional event event in in Figure Figure 11 demonstrates demonstrates that that observed observed precipitation precipitation microphysical characteristics can be used to elucidate precipitation phase. The param- microphysical characteristics can be used to elucidate precipitation phase. The ρe parameterization uses both fall speed and De and can therefore distinguish liquid and solid par- eterization uses both fall speed and De and can therefore distinguish liquid and solid particles. In addition to bulk values of ρe, the PIP also produces an effective density distribution as a function of particle size. The PIP PSDs and effective density distribution as a

Remote Sens. 2021, 13, x FOR PEER REVIEW 6 of 25

ticles. In addition to bulk values of , the PIP also produces an effective density distribution as a function of particle size. The PIP PSDs and effective density distribution as a function of De for the 24 to 25 October 2017 transition event are shown in Figure 2 (panels a and b, respectively). The PSD broadens as the precipitation transitions from predominately rain to mixed-phase (~1600 UTC) to snow (~2300 UTC). Through examination of events at the NWS MQT site and in Wallops, Virginia, an empirically derived effective density value of ≥0.85 g cm−3 indicates rain, while values <0.85 g cm−3 are classified as Remote Sens. 2021, 13, 2183 6 of 25 “non-rain”. Non-rain can mean either mixed-phase precipitation or snowfall. This thresh- old was determined through examination of multiple independent events with co-located complementary instrumentation for assessing precipitation phase. The 0.85 g cm−3 thresh- functionold also accounts of De for thefor 24the to one 25 Octoberstandard 2017 deviati transitionon spread event that are is shown observed in Figure for the2 (panelsrain par- a andticle b,fall respectively). speeds, particularly The PSD at broadens the larger as diameters the precipitation (e.g., Figure transitions 1a). from predominately rain toAt mixed-phasethe NWS MQT (~1600 site, UTC)we find to snowthat snowfall (~2300 UTC).tends Throughto have effective examination density of eventsvalues atbetween the NWS 0.015 MQT and site 0.25 and g cm in−3 Wallops,. Mixed-phase Virginia, precipitation an empirically effective derived density effective is >0.25 density g cm−3 valueand <0.85 of ≥ 0.85g cm g−3 cm, but−3 isindicates rarely observed rain, while compared values <0.85 to rain g cm and−3 aresnow. classiﬁed The range as “non-rain”. of values Non-rainobserved during can mean snowfa eitherll events, mixed-phase as confirmed precipitation by the NW orS snowfall.MQT office This and thresholdancillary obser- was determinedvations is consistent through with examination previous in of situ multiple studie independents of ice and snow events particles with from co-located aircraft comple- probes − mentary[43]. Note instrumentation that the PIP cannot for currently assessing verify precipitation the presence/absence phase. The 0.85 of riming. g cm 3 Rimedthreshold particles also accountswill increase for the the mass one standardand therefore deviation the fall spread speed thatof the is particles observed and for may the raincontribute particle to fallthe speeds,uncertainty particularly of the partitioning at the larger method diameters (see Pettersen (e.g., Figure et al.1 [31]a). for details).

Figure 2. PIP observations during an event from 24 October 2017 at 0600 UTC to 25 October 2017 atat 0600 UTC. The top panel (a) illustratesillustrates thethe PIPPIP DSDDSD valuesvalues andand thethe bottombottom panelpanel ((bb)) showsshows thethe PIPPIP determined effective density. The dashed magentamagenta lineslines highlighthighlight 1100,1100, 1730,1730, andand 23302330 UTC,UTC, whichwhich are the times used to highlight precipitation characteristics in Figure 1. are the times used to highlight precipitation characteristics in Figure1.

3. ResultsAt the NWS MQT site, we find that snowfall tends to have effective density values betweenWe 0.015identified and 0.2513 independent g cm−3. Mixed-phase events at precipitation the NWS MQT effective site between density is1 >0.25January g cm 2017−3 and <0.851 January g cm 2020−3, but with is rarelyat least observed one precipit comparedation phase to rain transition. and snow. These The range events of include values observedobservations during of coincident snowfall PI events,P, MRR, as WSR-88D, confirmed Parsivel, by the NWSsurface MQT meteorological office and ancillaryobserva- observationstions, and weather is consistent forecaster with records. previous We in fir situst assess studies the of precipitation ice and snow phase particles transition from aircrafttimes determined probes [43 by]. Notethe PIP that and the compare PIP cannot to the currently NWS MQT verify weather the presence/absence forecaster records. of riming.We also Rimedcompare particles the PIP-produced will increase LWE the mass accumulation and therefore to the the NWS fall speedMQT measurements. of the particles andFinally, may we contribute examine to in the depth uncertainty four precipitation of the partitioning events: method two rain-to-snow (see Pettersen transition et al. [31] forevents, details). a multiple transition event, and a prolonged mixed-phased event.

3.3.1. Results Phase Transision Event Timelines and Characteristics We identiﬁed 13 independent events at the NWS MQT site between 1 January 2017 Table 1 outlines the observations from the PIP and the NWS MQT forecaster records and 1 January 2020 with at least one precipitation phase transition. These events include for 11 events with a single precipitation phase transition. We found that rain-to-snow pre- observations of coincident PIP, MRR, WSR-88D, Parsivel, surface meteorological observa- cipitation phase transition events are more common than snow-to-rain events during the tions, and weather forecaster records. We ﬁrst assess the precipitation phase transition 2017 to 2020 seasons. Three of the events transition from snowfall to rain, while eight times determined by the PIP and compare to the NWS MQT weather forecaster records. We also compare the PIP-produced LWE accumulation to the NWS MQT measurements. Finally, we examine in depth four precipitation events: two rain-to-snow transition events, a multiple transition event, and a prolonged mixed-phased event.

3.1. Phase Transision Event Timelines and Characteristics Table1 outlines the observations from the PIP and the NWS MQT forecaster records for 11 events with a single precipitation phase transition. We found that rain-to-snow precipitation phase transition events are more common than snow-to-rain events during the 2017 to 2020 seasons. Three of the events transition from snowfall to rain, while eight Remote Sens. 2021, 13, 2183 7 of 25

events transition from rain to snowfall. All events examined were required to be a minimum of 6-h duration and range from 7 to 20 h.

Table 1. Events with a single precipitation phase transition with observations from the PIP and the NWS MQT forecaster records. * Denotes +1 day UTC from start date.

Event Date Precip. Phase PIP Times NWS Times Precip. Phase PIP Times NWS Times 1035 UTC 1045 UTC 1648 UTC 1645 UTC 10 February 2017 Snow Rain 1700 UTC 1715 UTC 1833 UTC 1830 UTC 0300 UTC 0300 UTC 0425 UTC 0430 UTC 23 February 2017 Rain Snow 0425 UTC 0430 UTC 1108 UTC 1115 UTC 0745 UTC 0750 UTC 1753 UTC 1750 UTC 27 April 2017 Rain Snow 1755 UTC 1750 UTC 2350 UTC 2345 UTC 0550 UTC 0600 UTC 1515 UTC 1525 UTC 24 October 2017 Rain Snow/Mixed 2130 UTC 2000 UTC 0720 * UTC 0730 * UTC 0325 UTC 0250 UTC 0723 UTC 0730 UTC 2 November 2017 Snow Rain 0820 UTC 0845 UTC 1035 UTC 1055 UTC 0250 UTC 0235 UTC 1000 UTC 1015 UTC 5 December 2017 Rain Snow 0945 UTC 0950 UTC 1230 UTC 1215 UTC 1440 UTC 1445 UTC 1630 UTC 1625 UTC 11 January 2018 Rain Snow 1655 UTC 1645 UTC 0000 * UTC 0000 * UTC 0035 UTC 0050 UTC 0615 UTC 0635 UTC 7 November 2018 Rain Snow 0835 UTC 0835 UTC 2125 UTC 2110 UTC 1945 UTC 1945 UTC 1930 UTC 1945 UTC 11 April 2019 Snow Rain/Mixed 0255 * UTC 0300 UTC 0820 * UTC 0815 * UTC 0940 UTC 0940 UTC 1650 UTC 1650 UTC 21 November 2019 Rain Snow 1700 UTC 1650 UTC 0000 * UTC 0000 * UTC 0855 UTC 0900 UTC 1105 UTC 1110 UTC 30 December 2019 Rain Snow 1120 UTC 1110 UTC 2100 UTC 2100 UTC

For the snow-to-rain transition events, the observed onsets of snowfall from the PIP and the NWS MQT are within 15 min, and the identified ends of the snowfall are within 25 min. The onsets of the rain in the second periods of the events are within 15 min, and the ends of the rain are within 20 min, as observed by the PIP and NWS MQT. For the eight rain-to-snow transition events, we see similar agreement between the observing methods. The PIP and NWS MQT observed that the onsets of the rain are within 15 min of each other and within 10 min for observed ends, with the exception of 24 October 2017 where the end times differed by 90 min. The second part of the rain-to-snow transition events had the onsets of snowfall within 20 min and the ending of snowfall within 15 min between the PIP and NWS MQT observations. The 24 October 2017 event had an unusually long transition between rain to snowfall and much of the precipitation between 1800 and 2200 UTC was identified as mixed by the PIP instrument (Figure2b). Events with prolonged periods of mixed-phase precipitation are harder to characterize for the PIP and are potentially more subjective for the NWS forecaster, so there will be more inherit variability in classifying these cases and they will be more likely to exhibit differences between observing methods. Table2 outlines the observations from the PIP and the NWS MQT forecaster records for the two events with multiple precipitation phase transitions. Both of these events started as snowfall, transitioned to rain, and then transitioned back to snowfall. The event beginning on 17 November 2017 was 15 h duration and the event beginning on 27 December 2018 was 30 h duration. The onsets of the first snowfall period for both events agreed within 10 min between the PIP and NWS MQT observations, and the endings within 20 min. The respective onsets of rain for the 17 November 2017 event were within 15 min, but a much larger 65 min difference in rain onsets was reported for the 27 December 2018 event. The endings of the rain periods were within 15 min between the PIP and NWS MQT observations. The onsets of the second snowfall period for both events were within 10 min and the observed endings within 5 min between the observations. Remote Sens. 2021, 13, 2183 8 of 25

Table 2. Events with a multiple precipitation phase transitions with observations from the PIP and the NWS MQT forecaster records. * Denotes +1 day and ** denotes +2 UTC from start date.

Precip. Precip. Precip. Event Date PIP Times NWS Times PIP Times NWS Times PIP Times NWS Times Phase Phase Phase 1255 UTC 1300 UTC 1645 UTC 1630 UTC 2345 UTC 2350 UTC 17 November 2017 Snow Rain Snow 1450 UTC 1435 UTC 2345 UTC 2350 UTC 0300 * UTC 0300 * UTC 0930 UTC 0925 UTC 1955 UTC 2100 UTC 1005 * UTC 1015 * UTC 27 December 2018 Snow Rain Snow 1855 UTC 1910 UTC 1030 * UTC 1015 * UTC 0125 ** UTC 0130 ** UTC

In addition to examining observed precipitation onset and termination and phase, we compare the measured accumulation between the PIP and the NWS MQT methods. Accumulations (in LWE) for the events are shown in Table3, as well as differences in PIP accumulations relative to the NWS measurements and wind speeds. The PIP and NWS MQT estimates of accumulation are within 10% for 11 of the 12 of the events. The 24 October 2017 event had a much larger difference with the PIP underestimating the accumulation relative to the NWS MQT by 34.3%. Notably, this event had an unusually prolonged period (>6 h) of mixed phase precipitation (identiﬁed by both of the PIP and the NWS MQT). In addition, this event experienced the highest mean and maximum wind speeds of all the events examined, which would impact accurate retrieval of precipitation totals from both methods, particularly during the snowfall. Interestingly, the two events with multiple transitions (17 November 2017 and 27 December 2018) displayed mostly consistent measurements, with differences of only 7.7% and 2.7%, respectively.

Table 3. Accumulations in LWE from the PIP and NWS observations and percentage difference for all the precipitation transition events. Differences are calculated by subtracting the NWS from the PIP accumulation and then dividing by the PIP accumulation. Mean and maximum wind speeds during the events are included. * Denotes +1 day and ** denotes +2 UTC from start date.

Diff. % Event Date PIP LWE (mm) NWS LWE (mm) Mean WS (m s−1) Max WS (m s−1) PIP–NWS 10 February 2017 2.32 2.54 −9.4 3.94 5.81 1000–1800 UTC 23 February 2017 11.01 10.16 7.7 3.57 5.36 0200–1200 UTC 27 April 2017 17.58 17.78 −1.1 0.33 0.83 0800–0000 * UTC 24 October 2017 39.71 53.34 −34.3 6.78 12.07 0600–0800 * UTC 2 November 2017 3.23 3.55 −9.9 1.33 2.68 0300–1200 UTC 17 November 2017 8.53 7.87 7.7 2.66 5.36 1200–0600 * UTC 5 December 2017 4.74 4.57 3.6 6.05 11.17 0200–1200 UTC 11 January 2018 9.90 9.65 2.5 2.76 6.23 1400–0000 * UTC 7 November 2018 3.74 3.56 4.8 3.62 5.81 1000–0000 ** UTC 27 December 2018 16.97 16.51 2.7 3.41 6.71 0000–0000 ** UTC 11 April 2019 34.59 32.26 6.7 3.29 6.71 1800–1200 * UTC 21 November 2019 9.31 10.16 −9.1 3.29 6.23 1000–0000 * UTC 30 December 2019 24.03 23.11 3.8 2.32 4.02 0800–2100 UTC

3.2. Detailed Observations of Example Cases Here we present detailed observations of four cases: two rain-to-snow transition events (11 January 2018 and 21 November 2019), one multiple precipitation phase transition Remote Sens. 2021, 13, 2183 9 of 25

event (27 December 2018), and a prolonged mixed-phase precipitation event (24 October 2017). We focus on highlighting the ability of the PIP to accurately identify the precipitation phase during these complex events and use ancillary data from the MRR, Parsivel, surface observations, WSR-88D, and ERA5 reanalysis data products to support the PIP phase identiﬁcation.

3.2.1. Rain-to-Snow Transition Events The first event examined in detail is a rain-to-snow transition event on 11 January 2018 lasting a total of nine hours. High-temporal resolution profile and surface observations for this event are shown in Figure3. The PIP and NWS MQT office identified rain beginning at approximately 1445 UTC and snow beginning at approximately 1630 UTC (Table1). The precipitation transition is seen in the PIP estimates of density in Figure3d where the red (rain) transitions to mostly blue (snow). Additionally, the PIP observes generally small Remote Sens. 2021, 13, x FOR PEER REVIEWparticles and narrow PSDs during the rain period, whereas after the transition to snowfall 10 of 25 the PIP PSDs are much broader with larger particles. The PIP and the Parsivel (Figure3d, dots) also show a similar rain to snowfall transition near 1630 UTC.

Figure 3. Cont.

Figure 3. Observations during the 11 January 2018 rain-to-snow transition event. Shown are MRR reflectivity (a) and Doppler velocity (b), PIP PSDs (c) and density estimates (d), Parsivel hydrometeor identification (d, red and blue dots on the upper portion of the panel), PIP and NWS LWE accumulation (e), ERA temperature profiles (red >0 °C) (f), and surface temperature, dew point, and winds (g). The dashed lines indicate time periods examined in the next figure.

Examining the spatial distribution of the ERA5 surface mean sea level pressure (MSLP) and winds (Figure 4a,b) and the 850 hPa temperature and winds (Figure 4c,d) helps illuminate the synoptic conditions occurring during the rain and snow periods of this event. At 1500 UTC, during the rain period of the event, a front is moving through the region with low pressure and calm surface winds located at the NWS MQT site (Figure 4a). The 850 hPa temperatures are above freezing at this time with light winds out of the southwest (Figure 4c). At 2100 UTC, during the snowfall period of the event, the low pressure moved east of the NWS MQT site (Figure 4b) and the cold front arrived with 850 hPa temperature below freezing and a steep temperature gradient to the west of the site (Figure 4d). The synoptic conditions depict the movement of a cold front into the region, which is again consistent with the PIP observations showing rain transitioning to snowfall during the event. Remote Sens. 2021, 13, x FOR PEER REVIEW 10 of 25

Remote Sens. 2021, 13, 2183 10 of 25

FigureFigure 3. 3.Observations Observations during during the 11 January the 11 2018January rain-to-snow 2018 rain transition-to-snow event. transition Shown are event. Shown are MRR MRRreflectivity reflectivity (a ()a )and and Doppler velocity velocity (b), PIP(b), PSDs PIP (PSDsc) and ( densityc) and estimates density ( destimates), Parsivel (d), Parsivel hydrome- hydrometeorteor identification identification (d, (d, red red andand blue blue dots dots on the on upper the portion upper of theportion panel), of PIP the and NWSpanel), PIP and NWS LWE LWE accumulation (e), ERA temperature profiles (red >0 ◦C) (f), and surface temperature, dew point, andaccumulation winds (g). The dashed(e), ERA lines temperature indicate time periods profiles examined (red >0 in the°C) next (f), figure. and surface temperature, dew point, and winds (g). The dashed lines indicate time periods examined in the next figure. There is also evidence of the precipitation phase transition in the MRR profile observations. Enhanced values of reflectivity (>25 dBZ) exist in the lowest 800–1000 m above groundExamining level (AGL) at the onsetspatial of the distribution rain at ~1445 UTC of to th thee ERA5 transition surface to snowfall mean at sea level pressure 1630(MSLP) UTC, when and the winds enhanced (Figure low-level 4a,b) reflectivity and the values 850 abruptly hPa endtemperature (Figure3a). Addi- and winds (Figure 4c,d) tionally,helps theilluminate profiles of the MRR synoptic Doppler velocities conditions are much occu largerrring near during the surface the during rain and snow periods of thethis rain event. period At of the 1500 event UTC, than duringduring the the snow rain period, period indicating of the higher event, particle a front fall is moving through speeds consistent with the PIP-classified rain phase. The profiles of temperature from the ERA5the region reanalysis with denote low a deep pressure layer of and temperatures calm surfac >0 ◦Ce extending winds located to ~850 hPa at (topthe ofNWS MQT site (Figure the4a). boundary The 850 layer) hPa until temperatures about 1700 UTC. are Afterwards,above freezing the relatively at this warm time air with diminishes light winds out of the south- ◦ andwest colder (Figure air (<0 4c).C) movesAt 2100 into UTC, the lower during levels ofthe the snow atmospherefall period (Figure 3off ). the Again, event, the low pressure the progression of the profile temperatures is consistent with the timing of the transition of rainmoved to snowfall east of identified the NWS by PIP MQT observations. site (Figure 4b) and the cold front arrived with 850 hPa temper- atureThe below precipitation freezing accumulation and a steep estimates temperature from the PIP gradie agree wellnt to with the the west 6-h obser- of the site (Figure 4d). The vationssynoptic from conditions the NWS MQT depict both during the movement the rain-dominated of a cold and snow-dominated front into the periods region, which is again con- (Figure3e), with about 3 mm rain and ~7.5 mm snow (LWE). The surface temperatures aresistent around with +1 ◦C the from PIP the observations onset of the precipitation showing to rain ~1630 transitioning UTC where they to dip snowfall slightly during the event. to just above freezing (Figure3g). The temperature drops rapidly below freezing at approximately 1900 UTC, well after the observed onset of the snowfall. The wind speeds also increase at 1900 UTC. The trend of the surface temperatures throughout the event is consistent with rain transitioning to snowfall. Examining the spatial distribution of the ERA5 surface mean sea level pressure (MSLP) and winds (Figure4a,b) and the 850 hPa temperature and winds (Figure4c,d) helps illuminate the synoptic conditions occurring during the rain and snow periods of this event. At 1500 UTC, during the rain period of the event, a front is moving through the region with low pressure and calm surface winds located at the NWS MQT site (Figure4a ). The 850 hPa temperatures are above freezing at this time with light winds out of the southwest (Figure4c ). At 2100 UTC, during the snowfall period of the event, the low pressure moved east of the NWS MQT site (Figure4b) and the cold front arrived with 850 hPa temperature below freezing and a steep temperature gradient to the west of the site (Figure4d). The synoptic conditions depict the movement of a cold front into the region, which is again consistent with the PIP observations showing rain transitioning to snowfall during the event. RemoteRemote Sens. Sens. 20212021, 13, 13, x, 2183FOR PEER REVIEW 11 of 25 11 of 25

FigureFigure 4.4. ERA5ERA5 MSLP MSLP and and surface surface winds winds and 850and hPa 850 temperatures hPa temperatures and winds and for winds 11 January for 11 2018 January at 2018 at1500 1500 (a, c()a and,c) and 2100 2100 UTC UTC (b,d). (b The,d). purple The purple star denotes star denotes the location the location of NWS MQT. of NWS MQT.

The secondsecond event event also also exhibits exhibits a transition a transition from from rain torain snowfall, to snowfall, however however with a with a longerlonger periodperiod of of rain rain and and several several key key profile profile changes changes as the rainas the evolves rain beforeevolves switching before switching to snowfall. This rain-to-snow event occurred on 21 November 2019 and was approximately to snowfall. This rain-to-snow event occurred on 21 November 2019 and was approxi- 14 h in duration (Figure5). The PIP and the NWS MQT observed rain beginning at 0940 UTC matelyand ending 14 h around in duration 1650–1700 (Figure UTC, 5). with The snowfall PIP and onset the NWS at 1650 MQT UTC. observed The precipitation rain beginning attransition 0940 UTC is identified and ending in the around PIP estimates 1650–1700 of density UTC, inwith Figure snowfall5d where onset the at red 1650 (rain) UTC. The precipitationtransitions to bluetransition (snow). is identified The PIP PSDs in the exhibit PIP estimates generally smallof density particles in Figure and narrow 5d where the redwidth (rain) during transitions the rain period, to blue while (snow). after The the transition PIP PSDs to exhibit snow, the generally PIP PSDs small are broader particles and narrowand contain width larger during particles. the rain There pe isriod, again while very goodafter agreementthe transition between to snow, the PIP the and PIP the PSDs are broaderParsivel (Figureand contain5d, dots) larger for the particles. precipitation There phase is again transition very good at 1650 agreement UTC. between the PIP and theSimilar Parsivel to the (Figure previous 5d, event, dots) there for are the distinct precipitation melting layer phase and transition rain signatures at 1650 in theUTC. MRR profile observations. There is evidence of a bright band and enhanced reflectivities (>25 dBZ) in the lowest ~2 km AGL from the onset of the rain to ~1300 UTC, at which point the layer of enhanced reflectivities becomes very shallow (<1 km) and continues to taper until disappearing entirely at 1650 UTC (Figure5a). The profiles of MRR Doppler velocity show higher fall speeds in the same period as the enhanced reflectivities (Figure5b ), indicating that the precipitation is rain. This rain period coincides with the timing of rainfall indicated by the PIP. The profiles of temperature from the ERA5 reanalysis denote a much deeper layer of warm temperatures (>0 ◦C) extending to almost 700 hPa through 1400 UTC. The 0 ◦C isotherm then starts to slowly descend reaching 925 hPa at 1800 UTC (Figure5f ), after the onset of the snowfall, at which point the temperatures above the 925 hPa level are below freezing. The profile observations from the MRR and the ERA5 reanalysis are consistent with the observations and timing of the transition of rain to snowfall seen by the PIP.

RemoteRemote Sens. Sens. 20212021, ,1313, ,x 2183 FOR PEER REVIEW 12 of 25 12 of 25

FigureFigure 5. 5.Observations Observations during during the the 21 to21 22 to November 22 November 2019 event2019 rain-to-snowevent rain-to-snow transition transition event. event. ShownShown are are MRR MRR reflectivity reflectivity (a) and(a) and Doppler Doppler velocity velocity (b), PIP (b), PSDs PIP (cPSDs) and ( densityc) and estimatesdensity estimates (d), (d), ParsivelParsivel hydrometeor hydrometeor identification identification (d, red (d and, red blue and dots blue on thedots upper on the portion upper of theportion panel), of PIP the and panel), PIP NWSand NWS LWE accumulationLWE accumulation (e), ERA ( temperaturee), ERA temperature profiles (red profiles > 0 ◦C) (red (f), and > 0 surface°C) (f), temperature,and surface dewtemperature, point,dew point, and winds and (windsg). The ( dashedg). The linesdashed indicate lines time indicate periods time examined periods in examined the next figure. in the next figure.

TheSimilar surface to observationsthe previous of event, LWE accumulation there are dist frominct themelting PIP agree layer with and the rain 6-h signatures mea- in surementsthe MRR fromprofile the observations. NWS MQT, particularly There is duringeviden thece of rain-dominated a bright band periods and enhanced (Figure5e ). reflectiv- Theities total (>25 accumulation dBZ) in the lowest in LWE ~2 was km ~6.5 AGL mm from rain th ande onset 2 mm of snow. the rain The to surface ~1300 temper- UTC, at which ature is around +4 ◦C from the onset of the precipitation to ~1600 UTC, at which time it point the layer of enhanced reflectivities becomes very shallow (<1 km) and continues to decreases, reaching 0 ◦C at 1650 UTC and stabilizing until 2100 UTC when it again slowly coolstaper to until below disappearing freezing (Figure entirely3g). It isat notable1650 UTC that the(Figure snowfall 5a). startsThe profiles while the of surface MRR Doppler temperaturesvelocity show are higher still several fall speeds degrees in above the same freezing. period Wind as the speeds enhanced increase reflectivities from 2.5 to (Figure 55b), m s indicating−1 at 1800 UTC. that Again,the precipitation the patterns is of rain. the surface This rain observations period coincides throughout with the eventthe timing of arerainfall consistent indicated with rainby the transitioning PIP. The profiles to snowfall. of temperature from the ERA5 reanalysis denote a muchWSR-88D deeper observations layer of warm are also temperatures used to determine (>0 °C) the extending precipitation to phasealmost during 700 hPa the through transition1400 UTC. events. The 0 The°C isotherm spatial patterns then starts of reﬂectivity, to slowly differential descend reaching reﬂectivity 925 (Z hPaDR), at and 1800 UTC (Figure 5f), after the onset of the snowfall, at which point the temperatures above the 925 hPa level are below freezing. The profile observations from the MRR and the ERA5 reanalysis are consistent with the observations and timing of the transition of rain to snowfall seen by the PIP.

Remote Sens. 2021, 13, x FOR PEER REVIEW 13 of 25

The surface observations of LWE accumulation from the PIP agree with the 6-h measurements from the NWS MQT, particularly during the rain-dominated periods (Figure 5e). The total accumulation in LWE was ~6.5 mm rain and 2 mm snow. The surface temperature is around +4 °C from the onset of the precipitation to ~1600 UTC, at which time it decreases, reaching 0 °C at 1650 UTC and stabilizing until 2100 UTC when it again slowly cools to below freezing (Figure 3g). It is notable that the snowfall starts while the surface temperatures are still several degrees above freezing. Wind speeds increase from 2.5 to 5 m s−1 at 1800 UTC. Again, the patterns of the surface observations throughout the event are consistent with rain transitioning to snowfall. WSR-88D observations are also used to determine the precipitation phase during the Remote Sens. 2021, 13, 2183 13 of 25 transition events. The spatial patterns of reflectivity, differential reflectivity (ZDR), and cross correlation ratio (ρHV) can illuminate characteristics of the precipitation, as these observations contain information about hydrometeor type and can be used to identify the locationcross correlationof the melting ratio level (ρHV )[44–46]. can illuminate Figure characteristics6 illustrates the of thespatial precipitation, extent of asthe these reflectivity,observations ZDR, and ρHV contain during information the rain period about at hydrometeor 1203 UTC type(a,b,c) and and can during be used the to identifysnowfall the period at 1804location UTC of the(d,e,f). melting At 1203 level UTC [44–46 on]. Figure21 November6 illustrates 2019, the spatialthe WSR-88D extent of theshows reflectivity, widespread ρ reflectivitiesZDR, and HVexceedingduring the 25 raindBZ period in the atNWS 1203 MQ UTCT (a,b,c)region and (Figure during 6a). the There snowfall are period also larger at 1804 UTC (d,e,f). At 1203 UTC on 21 November 2019, the WSR-88D shows widespread values of ZDR (Figure 6b) and relatively low values of ρHV (Figure 6c) in the northwest reflectivities exceeding 25 dBZ in the NWS MQT region (Figure6a). There are also larger quadrant of the observational area. This region of higher reflectivity, larger ZDR, and low values of ZDR (Figure6b) and relatively low values of ρHV (Figure6c) in the northwest HV ρ quadrant values indicate of the observational that there is area. likely This a regionmix of of rain higher and reflectivity, ice/snow largerprecipitation. ZDR, and This low area is ~30ρHV tovalues 40 km indicate from that thethere WSR-88D is likely and a mix therefore of rain and this ice/snow layer of precipitation. mixed precipitation This area is is between~30 to0.75 40 kmand from 1.05 the km WSR-88D AGL (assuming and therefore standard this layer atmospheric of mixed precipitation refraction). is between This height range0.75 is and slightly 1.05 km higher AGL than (assuming the MRR-indicated standard atmospheric melting refraction).level (Figure This 5a,b), height but range it does is indi- cateslightly that the higher surface than precipitation the MRR-indicated phase meltingis rain, levelwhich (Figure agrees5a,b), with but the it does PIP indicateand other that surface andthe profile surface observations precipitation at phase1203 UTC. is rain, At which 1804 UTC agrees the with WSR-88D the PIP reflectivities and other surface are more and vari- profile observations at 1203 UTC. At 1804 UTC the WSR-88D reflectivities are more variable, able, with intensities ranging from 10 to 35 dBZ (Figure 6d). The values of ZDR are generally with intensities ranging from 10 to 35 dBZ (Figure6d). The values of Z DR are generally small and ρHV generally high (Figure 6e,f, respectively), and there are no indications of melting small and ρHV generally high (Figure6e,f, respectively), and there are no indications of or hydrometeormelting or hydrometeor phase changes. phase The changes. observations The observations from the from WSR-88D the WSR-88D indicate indicate that the that precip- itationthe precipitationat this time is at snowfall, this time iswhich snowfall, is consistent which is with consistent PIP observations. with PIP observations.

FigureFigure 6. WSR-88D 6. WSR-88D values values of reflectivity of reﬂectivity,, differential differential reﬂectivityreflectivity (Z (ZDRDR),), and and cross cross correlation correlation ratio ratio (ρHV ()ρ duringHV) during the rain the rain periodperiod (1203 (1203 UTC, UTC, panels panels (a (–ac–)c )and and the the snow periodperiod (1804 (1804 UTC, UTC, panels panels (d–f ))(d for–f)) the for event the on event 21 November on 21 November 2019. The rings2019. The rings denote 10 10 km km radial radial distance distance from from the radar.the radar.

3.2.2. Snow-to-Rain-to-Snow Transition Event A multiple precipitation phase transition event is shown in Figure7. This event lasted

30 h starting on 27 December 2018 at 0930 UTC and began as snowfall, then changed to rain at ~2000–2100 UTC for 13 h before transitioning back to snowfall at 1000 UTC on 28 December 2018. The multiple transitions are clearly discernable as the sections of blue (snow), red (rain), and blue (snow) in the PIP density estimates during the event (Figure7d), and these transitions generally agree with the Parsivel observations ( Figure7d , Remote Sens. 2021, 13, x FOR PEER REVIEW 14 of 25

3.2.2. Snow-to-Rain-to-Snow Transition Event A multiple precipitation phase transition event is shown in Figure 7. This event lasted 30 h starting on 27 December 2018 at 0930 UTC and began as snowfall, then changed to Remote Sens. 2021, 13, 2183 rain at ~2000–2100 UTC for 13 h before transitioning back to snowfall at 1000 UTC14 on of 28 25 December 2018. The multiple transitions are clearly discernable as the sections of blue (snow), red (rain), and blue (snow) in the PIP density estimates during the event (Figure 7d), and these transitions generally agree with the Parsivel observations (Figure 7d, dots). dots).However However the Parsivel the Parsivel has missing has missing data during data the during rain theperiod, rain likely period, due likely to the due sporadic to the sporadicand light andnature light of naturethe precipitation of the precipitation during this during time. thisThe time.PIP PSDs The PIP(Figure PSDs 7c) (Figure are fairly7c) arebroad fairly with broad an abundance with an abundance of small ofparticles small particlesduring the during first thesnowfall ﬁrst snowfall period, period,with indica- with indicationstions of even of larger even largerparticles particles during during the second the second snowfall snowfall period. period. During During the rainfall the rainfall period periodof this ofevent this the event PIP the PSDs PIP are PSDs narrow are narrow and there and thereare periodic are periodic gaps, gaps, providing providing more more evi- evidencedence of very of very light light precipitation precipitation rates. rates.

Figure 7. Observations during the 27 to 28 December 2018 event rain-to-snow transition event. Figure 7. Observations during the 27 to 28 December 2018 event rain-to-snow transition event. Shown are MRR reflectivity (a) and Doppler velocity (b), PIP PSDs (c) and density estimates (d), Shown are MRR reflectivity (a) and Doppler velocity (b), PIP PSDs (c) and density estimates (d), Parsivel hydrometeor identification (d, red and blue dots on the upper portion of the panel), PIP Parsiveland NWS hydrometeor LWE accumulation identification (e), ERA (d, redtemperature and blue dotsprofiles on the (red upper >0 °C) portion (f), and of surface the panel), tempera- PIP and ◦ NWSture, dew LWE point, accumulation and winds (e), (g ERA). The temperature dashed lines profiles indicate (red time >0 periodsC) (f), and examined surface temperature,in the next fig- dew point,ure. and winds (g). The dashed lines indicate time periods examined in the next figure.

The beginning of the precipitation event is dominated by a deep layer of falling snow, as seen by the MRR profiles of reflectivity and Doppler velocities (Figure7a,b). Melting layers are clearly indicated in the MRR profile observations, as there is a noticeable bright band in the reflectivity and an abrupt increase in Doppler velocities beginning at ~2000 UTC 27 December (Figure7a,b). The melt layer starts out at ~1.5 km AGL and is continuous, but becomes shallower (1 km AGL) and sporadic starting at ~0100 UTC on 28 December. There is some indication of a short period of mixed-phase precipitation, both Remote Sens. 2021, 13, 2183 15 of 25

from the MRR and PIP, at 0000 UTC on 28 December. The second transition, starting at 1015 UTC on 28 December, is dominated by shallow, lake-effect snowfall with MRR echo top heights <1 km AGL (Figure7a). The profiles of ERA5 reanalysis temperatures illustrate a shallow layer of >0 ◦C air starting at 1200 UTC and growing to a much deeper layer (up to 850 hPa) between 0000 and 1200 UTC on 28 December (Figure7f), during which time the precipitation is generally rain, after which the profile cools considerably. The evolution of the profile temperatures is consistent with the timing of the transition of snowfall to rain to snowfall identified by PIP observations. The LWE accumulation estimates between the PIP and the 6-h NWS MQT measurements are remarkably consistent throughout the entire 30 h event (Figure7e). The total accumulation was ~17 mm LWE, with snowfall accounting for 12 mm (LWE) and rain the remaining 5 mm. The surface temperatures started out below freezing (−4 ◦C) and then slowly warmed to a maximum of +2 ◦C at ~0700 UTC on 28 December, when they then rapidly cooled to −10 ◦C (Figure7g). The winds speeds are light (2.5 m s −1) for the first snowfall and rain periods of the event, and abruptly increase as surface temperatures fell. Profiles of temperature and relative humidity from the ERA5 reanalysis can be used to further understand the precipitation phase transitions. Figure8a shows the temperature profiles for 27 December at 1200 UTC, and 28 December at 0600 and 1800 UTC. During the first snow period of the event, the near-surface temperatures are about 0 ◦C and slowly cool with height (solid line). During the rain period, the temperatures are above 0 ◦C until about 800 hPa (dashed line). In the second snow period of the event the temperatures are much colder (<−5 ◦C) and a pronounced inversion exists around 850 hPa (dotted line). The relative humidity profiles (Figure8b) show larger values throughout the profile at the beginning of the event during the deep snowfall period (solid line), large values to ~750 hPa during the rain period (dashed line), and a drier, oscillating profile during the shallow, lake-effect snowfall period (dotted line). The profiles of the temperature and relative humidity for the deep and shallow (lake-effect) snowfall periods are consistent with previous studies at the NWS MQT site [32,33]. The progression of the temperature Remote Sens. 2021, 13, x FOR PEER REVIEW 16 of 25 profiles is consistent with the PIP observations of snowfall transitioning to rain and then back to snowfall.

FigureFigure 8. 8. ProfilesProﬁles of of temperature temperature ( (aa)) and and relative relative humidity humidity ( (bb)) from from the the ERA5 ERA5 reanalysis reanalysis for for 27 27 De- De- cembercember 2018 2018 at at 1200 1200 UTC UTC (solid (solid line), line), 28 28 December December 2018 2018 at at 0600 0600 UTC (dashed line) line) and 1800 UTC (dotted(dotted line). line). The The black black dots dots in in panel panel b b signify signify the the 0 0 °C◦C level level for for the the 1200 1200 and and 0600 0600 UTC UTC profiles. proﬁles.

3.2.3. Mixed-Phase Precipitation Event An event with a remarkably long period of mixed-phase precipitation is featured in Figure 9. This event lasted approximately 26 h starting on 24 October 2017 at 0550 UTC and ending on 25 October at 0730 UTC. There was a prolonged period of mixed-phase precipitation starting on 24 October at 1700 UTC and ending at 2300 UTC. During this period, the PIP indicated both rain (red) and snow (blue) precipitation, while the Parsivel toggled back and forth between rain and snow identification (Figure 9d, dots). The predominately rainfall period is between 0600 UTC and 1700 UTC, and the raindrops are noticeably large, with some >2 mm effective diameter (Figure 9c). Large snow particles (>10 mm) emerge at ~2200 UTC, as seen in both the PIP PSDs (Figure 9c) and the density distribution estimates (Figure 9d), particularly around 0000 UTC on 25 October. Radar reflectivities and Doppler velocities increase appreciably in the lowest 1 km AGL at the beginning of the event, indicating the melting level of the precipitation (Figure 9a,b). The melted layer persists until 1400 UTC when it starts to diminish. Higher Doppler velocities are also observed in the near-surface bin of the MRR profile until ~2200 UTC (Figure 9b), coinciding with most of the extended mixed-phase precipitation period, as indicated by both the PIP and Parsivel (Figure 9d). The profiles of ERA5 reanalysis temperatures illustrate a layer of >0 °C air up to 850 hPa at the onset of the precipitation, and the layer gradually becomes increasingly shallow throughout the event, descending to ~925 hPa (Figure 9f). Notably, the ERA5 reanalysis temperature profiles indicate that the surface temperatures were >0 °C for the entirety of the event. This agrees with the observed surface temperatures (Figure 9g), which were 8 °C at the beginning of the event and slowly cooled to 0 °C at ~2330 UTC and dropped to just below freezing (–1 °C) at 0300 UTC on 25 October. This very slow decline in temperatures and progressive cooling of the profile is consistent with the prolonged period of mixed-phase precipitation observed by the PIP. The LWE accumulation estimates from the PIP and the 6-h NWS MQT measurements are consistent for the first part of the rain period of the event, between 0600 and 1200 UTC (Figure 9e). However, after 1200 UTC, these estimates diverge with the PIP LWE accumulation 34% lower than the NWS MQT measurements by the end of the event. Two significant features of this event differ from the others: (1) this event exhibits a period of 6-h

Remote Sens. 2021, 13, 2183 16 of 25

Remote Sens. 2021, 13, x FOR PEER REVIEW3.2.3. Mixed-Phase Precipitation Event 17 of 25

An event with a remarkably long period of mixed-phase precipitation is featured in Figure9. This event lasted approximately 26 h starting on 24 October 2017 at 0550 UTC andperiod ending of mixed on 25 phase October precipitation, at 0730 UTC. and There (2) wasthe wind a prolonged speeds periodare by far ofmixed-phase the highest of pre- all cipitationthe events starting examined. on 24 The October PIP uses at 1700 an UTCempiri andcally ending determined at 2300 UTC.parameterization During this period,to esti- themate PIP the indicated densities both of particle rain (red) and and glean snow prec (blue)ipitation precipitation, rates, which while does the not Parsivel have observa- toggled backtions and from forth mixed-phase between rain or high and snowsnow identiﬁcationdensity events (Figure (see details9d, dots). in Pettersen The predominately et al. [31]). rainfallThe lack period of parameterization is between 0600 constraints UTC and 1700 may UTC, impact and retrievals the raindrops of mixed-phase are noticeably precipita- large, withtion. someAdditionally, >2 mm effective the wind diameter speeds (Figureincrease9c). to Large>10 m snows−1 at particles1200 UTC (>10 on mm)24 October emerge (Fig- at ~2200ure 9g), UTC, which as seen impact in both the theaccumulation PIP PSDs (Figure measurements9c) and the from density both distribution the PIP and estimates the NWS (FigureMQT snow9d), particularly board and precipitation around 0000 gauges. UTC on 25 October.

FigureFigure 9.9. ObservationsObservationsduring during the the 24 24 to to 25 25 October October 2017 2017 event event rain-to-snow rain-to-snow transition transition event. event. Shown areShown MRR are reflectivity MRR reflectivity (a) and Doppler(a) and Doppler velocity velocity (b), PIP ( PSDsb), PIP (c PSDs) and ( densityc) and density estimates estimates (d), Parsivel (d), Parsivel hydrometeor identification (d, red and blue dots on the upper portion of the panel), PIP hydrometeor identification (d, red and blue dots on the upper portion of the panel), PIP and NWS and NWS LWE accumulation (e), ERA temperature profiles (red >0 °C) (f), and surface tempera- LWE accumulation (e), ERA temperature profiles (red >0 ◦C) (f), and surface temperature, dew point, ture, dew point, and winds (g). The dashed lines indicate time periods examined in the next fig- andure. winds (g). The dashed lines indicate time periods examined in the next figure. Radar reflectivities and Doppler velocities increase appreciably in the lowest 1 km AGL Investigation of the spatial distribution of the ERA5 reanalysis surface MSLP and at the beginning of the event, indicating the melting level of the precipitation (Figure9a,b ). winds (Figure 10a–c) and the 850 hPa temperature and winds (Figure 10d–f) illuminate the synoptic conditions occurring before, during, and after the prolonged mixed-phase period during the event. At 0800 UTC, during the onset of the rain only period, we see that there is a low-pressure center to the east of the NWS MQT site with surface winds

Remote Sens. 2021, 13, 2183 17 of 25

The melted layer persists until 1400 UTC when it starts to diminish. Higher Doppler velocities are also observed in the near-surface bin of the MRR profile until ~2200 UTC (Figure9b ), coinciding with most of the extended mixed-phase precipitation period, as indicated by both the PIP and Parsivel (Figure9d). The profiles of ERA5 reanalysis temperatures illustrate a layer of >0 ◦C air up to 850 hPa at the onset of the precipitation, and the layer gradually becomes increasingly shallow throughout the event, descending to ~925 hPa (Figure9f). Notably, the ERA5 reanalysis temperature profiles indicate that the surface temperatures were >0 ◦C for the entirety of the event. This agrees with the observed surface temperatures (Figure9g), which were 8 ◦C at the beginning of the event and slowly cooled to 0 ◦C at ~2330 UTC and dropped to just below freezing (−1 ◦C) at 0300 UTC on 25 October. This very slow decline in temperatures and progressive cooling of the profile is consistent with the prolonged period of mixed-phase precipitation observed by the PIP. The LWE accumulation estimates from the PIP and the 6-h NWS MQT measurements are consistent for the first part of the rain period of the event, between 0600 and 1200 UTC (Figure9e). However, after 1200 UTC, these estimates diverge with the PIP LWE accumulation 34% lower than the NWS MQT measurements by the end of the event. Two significant features of this event differ from the others: (1) this event exhibits a period of 6-h period of mixed phase precipitation, and (2) the wind speeds are by far the highest of all the events examined. The PIP uses an empirically determined parameterization to estimate the densities of particle and glean precipitation rates, which does not have observations from mixed-phase or high snow density events (see details in Pettersen et al. [31]). The lack of parameterization constraints may impact retrievals of mixed-phase precipitation. Additionally, the wind speeds increase to >10 m s−1 at 1200 UTC on 24 October (Figure9g), which impact the accumulation measurements from both the PIP and the NWS MQT snow board and precipitation gauges. Investigation of the spatial distribution of the ERA5 reanalysis surface MSLP and winds (Figure 10a–c) and the 850 hPa temperature and winds (Figure 10d–f) illuminate the synoptic conditions occurring before, during, and after the prolonged mixed-phase period during the event. At 0800 UTC, during the onset of the rain only period, we see that there is a low-pressure center to the east of the NWS MQT site with surface winds from the north (Figure 10a), while the 850 hPa temperatures are slightly above freezing (~2 ◦C) with winds from the northeast (Figure 10d). At the beginning of the mixed-phase precipitation period (1600 UTC), the low-pressure center moved slightly east and extended northeast (Figure 10b), and the 850 hPa temperatures cooled to below freezing (−2 ◦C) with winds continuing out of the northeast (Figure 10e). During the snow period starting at 0200 UTC on 25 October, the gradient of MSLP weakened and the low pressure is extended further to the northeast (Figure 10c), the 850 hPa temperatures continued to cool and are now −6 ◦C near the NWS MQT site (Figure 10f), and both the surface and 850 hPa winds shifted, originating from northwest of the site (Figure 10c,f). The slow progression of the low-pressure center eastward and cooling of the 850 hPa temperatures are consistent with the rain to mixed-phase to snowfall transition of the precipitation observed by the PIP. Additionally, this system appears to be occluded at the surface, with weak cold air advection in the lower levels (Figure 10d–f) that may have helped to promote the prolonged mixed-phase precipitation period seen during this event. Remote Sens. 2021, 13, x FOR PEER REVIEW 18 of 25

from the north (Figure 10a), while the 850 hPa temperatures are slightly above freezing (~2 °C) with winds from the northeast (Figure 10d). At the beginning of the mixed-phase precipitation period (1600 UTC), the low-pressure center moved slightly east and extended northeast (Figure 10b), and the 850 hPa temperatures cooled to below freezing (–2 °C) with winds continuing out of the northeast (Figure 10e). During the snow period starting at 0200 UTC on 25 October, the gradient of MSLP weakened and the low pressure is extended further to the northeast (Figure 10c), the 850 hPa temperatures continued to cool and are now –6 °C near the NWS MQT site (Figure 10f), and both the surface and 850 hPa winds shifted, originating from northwest of the site (Figure 10c,f). The slow progression of the low-pressure center eastward and cooling of the 850 hPa temperatures are consistent with the rain to mixed-phase to snowfall transition of the precipitation observed

Remote Sens. 2021, 13, 2183 by the PIP. Additionally, this system appears to be occluded at the surface, with 18weak of 25 cold air advection in the lower levels (Figure 10d–f) that may have helped to promote the prolonged mixed-phase precipitation period seen during this event.

FigureFigure 10. 10. ERA5ERA5 MSLP MSLP and and surface surface winds winds and and 850 850 hPa hPa temperatures temperatures and and winds winds for for 24 October 24 October 2017 2017 0800 0800 UTC UTC (a,d) (anda,d) 1600 and UTC1600 ( UTCb,e), and (b,e ),25 and October 25 October 2017 0200 2017 UTC 0200 (c, UTCf). (c,f).

TheThe observations observations from from the the NWS NWS MQT MQT office office WSR-88D WSR-88D yield yield additional additional insight insight into into thethe precipitation precipitation characteristics. characteristics. Figure Figure 1111 shows radar reflectivity,reflectivity, ZZDRDR, and ρρHVHV duringduring thethe event, event, to to highlight highlight precipitation precipitation features features during during the the rain, rain, mixed-phase, mixed-phase, and and snowfall snowfall periodsperiods of of the the event. event. At At 0805 0805 UTC UTC on 24 on Oc 24tober October 2017, 2017, the WSR-88D the WSR-88D shows shows extensive extensive large reflectivitieslarge reflectivities (30 to 40 (30 dBZ) to 40 in dBZ) the NWS in the MQT NWS region MQT (Figure region 11a). (Figure There 11 a).are Therealso relatively are also largerelatively values large of Z valuesDR (Figure of Z11b)DR (Figure and low 11 ρb)HV and(Figure low 11c)ρHV concentrically(Figure 11c) concentricallyat distances >20 at kmdistances from the >20 NWS km fromMQT the site. NWS These MQT values site. indi Thesecate values the approximate indicate the region approximate of the mixed region iceof theandmixed rain precipitation ice and rain and precipitation melting level, and which melting correspond level, which to heights correspond >0.5 tokm heights AGL. This>0.5 reasonably km AGL. This agrees reasonably with the agrees MRR withprofile the indication MRR profile of melting indication levels of meltingaround levels1 km AGLaround (Figure 1 km 9a,b) AGL and (Figure indicates9a,b) andthat indicatesthe precipitation that the precipitationis rain phase isat rainthe surface, phase at con- the sistentsurface, with consistent the PIP with precipitation the PIP precipitation phase identification phase identification (Figure 9d). (Figure At 16009d). UTC, At 1600the WSR- UTC, 88Dthe WSR-88Dreflectivities reflectivities are very large are very(>40 dBZ) large within (>40 dBZ) the 15 within km adjacent the 15 km to the adjacent NWS toMQT the ρ (FigureNWS MQT 11d). (FigureAdditionally, 11d). there Additionally, are large Z thereDR (Figure are large 11e) ZandDR low(Figure ρHV (Figure11e) and 11f) low valuesHV (Figure 11f) values in the same region. This implies that the mixed-phase precipitation layer is very close to the surface, matching the observations seen at the surface by the PIP. At 0204 UTC on 25 October 2017, the WSR-88D observations suggest the presence of convective, possibly lake-effect snowfall at the NWS MQT site, with relatively small ZDR and high ρHV values throughout the observing area (Figure 11g–i). The observations of precipitation characteristics from the WSR-88D agree with the progression of rain, to mixed-phase precipitation, to snowfall seen by the PIP and ancillary observations. Remote Sens. 2021, 13, x FOR PEER REVIEW 19 of 25

in the same region. This implies that the mixed-phase precipitation layer is very close to the surface, matching the observations seen at the surface by the PIP. At 0204 UTC on 25 October 2017, the WSR-88D observations suggest the presence of convective, possibly lake-effect snowfall at the NWS MQT site, with relatively small ZDR and high ρHV values throughout the observing area (Figure 11g–i). The observations of precipitation character- Remote Sens. 2021, 13, 2183 istics from the WSR-88D agree with the progression of rain, to mixed-phase precipitation,19 of 25 to snowfall seen by the PIP and ancillary observations.

Figure 11. WSR-88D values of reflectivity, differential reflectivity (ZDR), and cross correlation ratio (ρHV) for 24 October 2017 0805 UTC (a–c) and 1600 UTC (d–f), and 25 October 2017 0204 UTC (g–i). Figure 11. WSR-88D values of reflectivity, differential reflectivity (ZDR), and cross correlation ratio (ρHV) for 24 October 2017 0805 UTC (a–c) and 1600 UTC (d–f), and 25 October 2017 0204 UTC (g–i). Finally, profiles of temperature and relative humidity from the ERA5 reanalysis are examined for the rain, mixed-phase precipitation, and snowfall periods of the event. Finally, profiles of temperature and relative humidity from the ERA5 reanalysis are Figure 12a shows the temperature profiles for 24 October 2017 at 0800 UTC and 1600 UTC, examined for the rain, mixed-phase precipitation, and snowfall periods of the event. Fig- and 25 October at 0200 UTC. During the rain period (solid line), profile temperatures ure 12a shows the temperature profiles for 24 October 2017 at 0800 UTC and 1600 UTC, throughout the boundary layer are quite warm and the near-surface temperature is >10 ◦C. and 25 October at 0200 UTC. During the rain period (solid line), profile temperatures During both the mixed-phase (dashed) and the snowfall (dotted) periods the near-surface throughout the boundary layer are quite warm and the near-surface temperature is >10 temperatures are >0 ◦C. All three temperature profiles indicate an inversion around 800 hPa. °C. During both the mixed-phase (dashed) and the snowfall (dotted) periods the near- The profiles of relative humidity (Figure 12b) do not vary much between the times, with surface temperatures are >0 °C. All three temperature profiles indicate an inversion the exception that at 24 October 0800 UTC the boundary layer is relatively dry, and at around 800 hPa. The profiles of relative humidity (Figure 12b) do not vary much between 25 October 0200 UTC that the free atmosphere is relatively dry. Again, the progression the times, with the exception that at 24 October 0800 UTC the boundary layer is relatively of the temperature profiles is consistent with the PIP observations of rain transitioning to mixed-phase precipitation and then finally to snowfall.

Remote Sens. 2021, 13, x FOR PEER REVIEW 20 of 25

dry, and at 25 October 0200 UTC that the free atmosphere is relatively dry. Again, the Remote Sens. 2021, 13, 2183 20 of 25 progression of the temperature profiles is consistent with the PIP observations of rain transitioning to mixed-phase precipitation and then finally to snowfall.

FigureFigure 12. 12. ProfilesProﬁles of of temperature temperature (a ()a and) and relative relative humidity humidity (b) ( bfrom) from the the ERA5 ERA5 reanalysis reanalysis for for24 October24 October 2017 2017 0800 0800 UTC UTC (solid (solid lines) lines) and and 1600 1600 UTC UTC (dashed (dashed lines), lines), and and 25 25 October October 2017 2017 0200 0200 UTC UTC (dotted lines). The black dots in panel b signify the 0 °C level for the profiles. (dotted lines). The black dots in panel b signify the 0 ◦C level for the proﬁles.

4.4. Discussion Discussion ThereThere is considerable uncertaintyuncertainty in in the the ability ability of of satellite satellite observations observations to discriminateto discrimi- naterain, rain, varying varying degrees degrees of mixed-phase, of mixed-phase, and snowand snow precipitation. precipitation. Current Current satellite-based satellite-based radar radarprofiles profiles detect detect reflectivities reflectivities from precipitation, from precipitation, but there but is there no inherent is no inherent measurement measure- that mentyields that precipitation yields precipitation phase with phase high certainty. with high Similar certainty. to the Similar profiles to from the profiles the MRR from and spa-the MRRtial distribution and spatial from distribution the WSR-88D, from the the WSR-88D, abrupt transition the abrupt to higher transition values to of higher reflectivity values (i.e., of reflectivity“bright band”) (i.e., is “bright often associated band”) is often with theassociated melting with of ice the and melting snow of within ice and the snow atmosphere, within theand atmosphere, can reflect theand presence can reflect of the rain presence at the surface of rain [ 45at –the47]. surface However, [45–47]. the satelliteHowever, radar the satellite“blind zone” radar affects “blind the zone” ability affects to distinguish the ability precipitation to distinguish phase precipitation when melting phase layers when are meltinglocated withinlayers are 1 to located 2 km AGL within [15– 117 to]. 2 Additionally, km AGL [15–17]. algorithms Additionally, that use algorithms satellite microwave that use satellitesensors providemicrowave precipitation sensors provide estimates precipitation and rely on estimates ancillary and information, rely on ancillary typically infor- from mation,model reanalyses, typically from to classify model phasereanalyses, [18]. Toto cl helpassify improve phase estimates[18]. To help of precipitationimprove estimates phase ofat precipitation the surface, Simsphase and at the Liu surface, [20] developed Sims and a Liu parameterization [20] developed using a parameterization reanalysis values us- ingof2 reanalysis m temperature, values relativeof 2 m temperature, humidity, and relative surface humidity, pressure and to calculate surface pressure the surface to wetcal- culatebulb temperature the surface wet (WBT) bulb and temperature the near-surface (WBT) (500 and mthe AGL) near-surface vertical lapse(500 m rate AGL) (LR). vertical These lapsevariables rate are(LR). used These to assign variables conditional are used probabilities to assign conditional of solid precipitation probabilities at of the solid surface. pre- cipitationHere, we useat the the surface. surface Here, observations we use andthe su reanalysisrface observations profiles to obtainand reanalysis values of profiles WBT and to obtainnear-surface values LRof WBT (both and 500 andnear-surface 1000 m AGL), LR (both respectively, 500 and 1000 for the m phaseAGL), transitionrespectively, events for theexamined phase transition in Section events3.2, and examined assess the in probability Section 3.2, of and solid assess precipitation the probability using theof solid Sims precipitationand Liu [20] parameterization.using the Sims and Liu [20] parameterization. TheThe event onon 1111 January January 2018 2018 transitioned transitioned from from rain rain to mostlyto mostly snow snow around around 1630 UTC,1630 at which point surface temperatures steadily decrease from 0 to −10 ◦C over the next 8 h. At UTC, at which point surface temperatures steadily decrease from 0 to –10 °C over the next 1500 UTC the PIP observes rain precipitation at the surface (Figure3d) and the WBT is 1.2 ◦C 8 h. At 1500 UTC the PIP observes rain precipitation at the surface (Figure 3d) and the and near-surface (500 m AGL) LR is 5 ◦C km−1 (positive LR values indicates temperature WBT is 1.2 °C and near-surface (500 m AGL) LR is 5 °C km−1 (positive LR values indicates decreasing with height), which correspond to a probability of solid precipitation of 30% temperature decreasing with height), which correspond to a probability of solid precipi- using the Sims and Liu [20] parameterization (see Table4). There is a steady decrease in tation of 30% using the Sims and Liu [20] parameterization (see Table 4). There is a steady WBTs and increase in LRs between 1500 and 1900 UTC, leading to increased probability of snowfall (30% to 90%) during this period. The WBT drops to −2 ◦C at 1900 UTC at which point the LR is 6.4 ◦C km−1 and indicates a probability of solid precipitation >90%, while simultaneously the PIP observes mostly snowfall with trace amounts of mixed-phase Remote Sens. 2021, 13, 2183 21 of 25

precipitation. At 2100 UTC, the PIP observed only snowfall for several hours and the WBT is constant at −6.5 ◦C, which is associated with a 100% probability of solid precipitation (effectively regardless of the near-surface LR). Notably, the LR values for 1000 m AGL were 1.5 to 8 ◦C km−1 less than those for 500 m AGL, which would generally correspond to a reduction in the likelihood of solid precipitation at the surface.

Table 4. Select times within each event with calculated wet bulb temperature (WBT) from surface observations and lapse rate (LR) from ERA5 reanalysis proﬁles. The WBT and LR are used to ﬁnd a probability of solid precipitation via Sims and Liu [20] and compare to the precipitation phase as determined by the PIP. * Denotes +1 day UTC from start date.

Lapse Rate Probability of Precipitation Time Wet Bulb (◦C) (◦C km−1) Solid Precipitation Phase (PIP) 11 January 2018 1500 UTC 1.2 5.0 30% Rain 1700 UTC 0.8 5.5 40% Snow 1900 UTC −2.0 6.4 >90% Snow 2100 UTC −6.5 8.8 100% Snow 21 November 2019 1500 UTC 3.0 5.2 <10% Rain 1800 UTC 0.2 6.2 >80% Snow 27 December 2018 1200 UTC −2.8 2.5 >90% Snow 1800 UTC −1.0 6.8 >90% Rain 0000 UTC* 0.0 4.8 80% Rain 0600 UTC* 1.0 3.1 30% Rain 1800 UTC* −7.0 10.0 100% Snow 24 October 2017 0800 UTC 6.0 7.2 <10% Rain 1600 UTC 2.0 6.5 10% Rain 1800 UTC 1.8 6.5 10% Mixed 2000 UTC 1.2 7.0 40% Mixed 2200 UTC 0.8 7.2 60% Mixed 0200 UTC * 0.0 7.5 80% Snow

The 21 November 2019 precipitation event transitioned abruptly from rain to snowfall at 1650 UTC, corresponding to a rapid decline in observed surface temperatures from 3.5 to 0.5 ◦C starting at 1600 UTC (Figure5g). During the rain period of the event, the WBTs were constant at 3 ◦C with near-surface LRs of ~5.2 ◦C km−1, which correspond to <10% likelihood of solid precipitation (Table4). At 1800 UTC the PIP observations indicated snowfall for >1 h and the WBT is 0.2 ◦C and LR 6.2 ◦C km−1, which correspond to a probability of solid precipitation >80%. Again, there is a difference between the LRs calculated at 500 versus 1000 m AGL, with the latter having values 2 ◦C km−1 lower, which would reduce the probability of solid precipitation to ~70% during the snowfall period of the event. The precipitation event on 27 December 2018 featured two phase transitions: snow- to-rain at approximately 2000 UTC, and rain-to-snow on 28 December at 1015 UTC (Figure7d). At 1200 UTC on 27 December, the WBT was −2.8 ◦C and the near-surface LR was 2.5 ◦C km−1, predicting a >90% likelihood of solid precipitation, which corresponds to the PIP indicated snowfall at that time (Table4). The PIP indicated sporadic rain between 2000 UTC and 1000 UTC (28 December), with trends of WBT from 0 and 2 ◦C and LRs from 4.8 to 3 ◦C km−1. The probability of solid precipitation was therefore 80% at the onset of the rain, <10% at the highest WBT, and 30% towards the end of the rainfall period. The surface temperature rapidly decreased starting at 0800 UTC (28 December) and continued to drop. The decrease coincided with reduced WBTs and probabilities of solid precipitation Remote Sens. 2021, 13, 2183 22 of 25

of >90% by 1200 UTC, which were consistent with the PIP observations of snowfall from 1015 UTC onward. As with the two previously discussed events, the LRs calculated 1000 m AGL have values 2 to 4 ◦C km−1 smaller than those at 500 m AGL, which would reduce the probabilities of solid precipitation during the rain period of the event by about 20%. Finally, we examine the prolonged mixed-phase precipitation event on 24 October 2017, with a period of transitioning precipitation from approximately 1600 to 2200 UTC (Figure9d). During the rain period of the event, the WBTs decreased from 7 ◦C at 0600 UTC to 2 ◦C at 1600 UTC, while LRs over the same period ranged from 7.2 to 6.50 ◦C km−1, indicating 10% likelihood of solid precipitation (Table4). Simultaneous with the onset of the mixed-precipitation at 1600 UTC, the WBTs slowly decreased from 2 to 0.8 ◦C (at 2200 UTC) and LRs increased from 6.5 to 7.2 ◦C km−1. The mixed-phase period corresponded to a probability of solid precipitation of 10% at the beginning and steadily increased to 60% by 2200 UTC. The PIP observations indicate mostly snowfall by 0200 UTC on 28 October, at which time the WBT is 0 ◦C and LR is 7.5 ◦C km−1, which corresponds to a probability of solid precipitation of ~80%. Unlike the other events examined, there were little to no differences in the LR values at 500 m and 1000 m AGL. The probabilities of solid precipitation per the Sims and Liu [20] parameterization largely agree with the PIP observations of surface precipitation phase during the example events. Additionally, the trends in probabilities (increase/decrease) align with the observations, even during complicated multiple phase transition and prolonged mixed-phase precipitation events.

5. Conclusions The 13 events examined illustrate the ability of the PIP instrument to accurately distinguish precipitation phases, including mixed-phase conditions with simultaneous liquid and non-liquid hydrometeors. The PIP has excellent agreement with the NWS MQT forecaster reports of the timing of precipitation phase changes, with the majority of event onsets and endings within 15 min. The PIP and NWS MQT event LWE accumulation were within 10%, with the exception of the 24 October 2017, which contained an unusually long period of mixed-phase precipitation, was by far the highest wind speeds of any event. Additionally, the PIP observations are at very high-temporal resolution (1 min) and can therefore resolve abrupt changes in precipitation phase. The four precipitation transition events that were investigated in detail indicate that the PIP phase identification is accurate. The PIP-determined precipitation phase observations are supported through investigation of co-located observations from the Parsivel, MRR, and WSR-88D, and from ERA5 reanalysis products such as profiles of temperature and relative humidity and spatial information from MSLP, 850 hPa temperatures, and winds. The PIP precipitation phase identification compared well to the Sims and Liu [20] parameterization output of the probability of solid precipitation. The PIP precipitation phase information can be used to assess and validate parameterization schemes such as demonstrated here. Further, the PIP observations could be leveraged to improve and refine parameterizations, which would subsequently improve satellite remote sensing products that utilize the outputs to inform their retrievals of precipitation. Additionally, deployment of the PIP instrument with co-located ground-based scanning (e.g., WSR-88D) or profiling (e.g., MRR) radars could help assess precipitation phase gleaned from remote sensing observations. This would be especially useful for phase transitions occurring very close to the surface or during mixed-phase precipitation. Remote Sens. 2021, 13, 2183 23 of 25

Author Contributions: Conceptualization, C.P., L.F.B., and M.S.K.; methodology, C.P., M.S.K., and N.B.W.; software, L.F.B., C.P., J.A.S., and M.E.M.; validation, C.P.; formal analysis, C.P., J.A.S., and M.E.M.; investigation, C.P.; resources, C.P., J.A., W.A.P., and D.B.W.; data curation, C.P., M.E.M.; writing—original draft preparation, C.P.; writing—review and editing, C.P., L.F.B., M.S.K., N.B.W., A.v.L., and D.B.W.; visualization, C.P.; supervision, C.P.; project administration, C.P. and M.S.K.; funding acquisition, C.P. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by NASA: grant numbers NNX12AQ76G, 80NSSC18K0701, 80NSSC19K0712, 80NSSC19K0732, 80NSSC20K0982; and NOAA: grant number NA15NES4320001. A.vL. was funded by the Academy of Finland postdoctoral grant (333901). Data Availability Statement: All data used in this study can be acquired from the NASA Global Hydrology Research Center data archive (https://ghrc.nsstc.nasa.gov/home/). Access date (5 Au- gust 2020). Acknowledgments: We acknowledge the longstanding relationship with the NWS MQT office and the efforts from the Meteorologist in Charge, the Science Operations Officer, and the Meteorologists for their efforts in obtaining and sharing the surface observations. Thanks to the National Aeronautics and Space Administration Wallops Flight Facility and Goddard Flight Facility, as well as the Global Precipitation Measurement (GPM) program for providing the MRR, PIP, and Parsivel instruments used in this work. The views, opinions, and findings contained in this report are those of the authors and should not be construed as an official National Oceanic and Atmospheric Administration or U.S. Government position, policy, or decision. Thank you to the three anonymous reviewers for the helpful comments and insight. Conflicts of Interest: The authors declare no conflict of interest.

References 1. Uccellini, L.W.; Kocin, P.J. The interaction of jet streak circulations during heavy snow events along the east coast of the United States. Weather Forecast. 1987, 2, 289–308. [CrossRef] 2. Kocin, P.J.; Schumacher, P.N.; Morales, R.F., Jr.; Uccellini, L.W. Overview of the 12–14 March 1993 superstorm. Bull. Am. Meteorol. Soc. 1995, 76, 165–182. [CrossRef] 3. Maglaras, G.J.; Waldstreicher, J.S.; Kocin, P.J.; Gigi, A.F.; Marine, R.A. Winter weather forecasting throughout the eastern United States. Part I: An overview. Weather Forecast. 1995, 10, 5–20. [CrossRef] 4. Uccellini, L.W.; Kocin, P.J.; Schneider, R.S.; Stokols, P.M.; Dorr, R.A. Forecasting the 12–14 March 1993 superstorm. Bull. Am. Meteorol. Soc. 1995, 76, 183–200. [CrossRef] 5. Keeter, K.K.; Businger, S.; Lee, L.G.; Waldstreicher, J.S. Winter weather forecasting throughout the eastern United States. Part III: The effects of topography and the variability of winter weather in the Carolinas and Virginia. Weather Forecast. 1995, 10, 42–60. [CrossRef] 6. Musselman, K.N.; Lehner, F.; Ikeda, K.; Clark, M.P.; Prein, A.F.; Liu, C.; Barlage, M.; Rasmussen, R. Projected increases and shifts in rain-on-snow ﬂood risk over western North America. Nat. Clim. Chang. 2018, 8, 808–812. [CrossRef] 7. Suriano, Z.J.; Leathers, D.J.; Benjamin, A.E. Regionalization of Northeast US moisture conditions: Analysis of synoptic-scale atmospheric drivers. Clim. Res. 2020, 79, 193–206. [CrossRef] 8. Li, Q.; Yang, T.; Zhou, H.; Li, L. Patterns in snow depth maximum and snow cover days during 1961–2015 period in the Tianshan Mountains, Central Asia. Atmos. Res. 2019, 228, 14–22. [CrossRef] 9. Wachowicz, L.J.; Mote, T.L.; Henderson, G.R. A rain on snow climatology and temporal analysis for the eastern United States. Phys. Geogr. 2020, 41, 54–69. [CrossRef] 10. Arigony-Neto, J.; Saurer, H.; Simões, J.C.; Rau, F.; Jaña, R.; Vogt, S.; Gossmann, H. Spatial and temporal changes in dry-snow line altitude on the Antarctic Peninsula. Clim. Chang. 2009, 94, 19–33. [CrossRef] 11. Hantel, M.; Maurer, C.; Mayer, D. The snowline climate of the Alps 1961–2010. Theor. Appl. Climatol. 2012, 110, 517–537. [CrossRef] 12. Fabry, F.; Zawadzki, I. Long-term radar observations of the melting layer of precipitation and their interpretation. J. Atmos. Sci. 1995, 52, 838–851. [CrossRef] 13. Vivekanandan, J.; Zrnic, D.S.; Ellis, S.M.; Oye, R.; Ryzhkov, A.V.; Straka, J. Cloud microphysics retrieval using S-band dual- polarization radar measurements. Bull. Am. Meteorol. Soc. 1999, 80, 381–388. [CrossRef] 14. Lim, J.O.J.; Hong, S.Y. Effects of bulk ice microphysics on the simulated monsoonal precipitation over East Asia. J. Geophys. Res. Atmos. 2005, 110.[CrossRef] 15. Maahn, M.; Burgard, C.; Crewell, S.; Gorodetskaya, I.V.; Kneifel, S.; Lhermitte, S.; Van Tricht, K.; van Lipzig, N.P. How does the spaceborne radar blind zone affect derived surface snowfall statistics in polar regions? J. Geophys. Res. Atmos. 2014, 119, 13–604. [CrossRef] Remote Sens. 2021, 13, 2183 24 of 25

16. Norin, L.; Devasthale, A.; L’Ecuyer, T.S.; Wood, N.B.; Smalley, M. Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden. Atmos. Meas. Tech. 2015, 8, 5009–5021. [CrossRef] 17. Casella, D.; Panegrossi, G.; Sanò, P.; Marra, A.C.; Dietrich, S.; Johnson, B.T.; Kulie, M.S. Evaluation of the GPM-DPR snowfall detection capability: Comparison with CloudSat-CPR. Atmos. Res. 2017, 197, 64–75. [CrossRef] 18. Kummerow, C.D.; Randel, D.L.; Kulie, M.; Wang, N.Y.; Ferraro, R.; Joseph Munchak, S.; Petkovic, V. The evolution of the Goddard profiling algorithm to a fully parametric scheme. J. Atmos. Ocean. Technol. 2015, 32, 2265–2280. [CrossRef] 19. Zhang, J.; Howard, K.; Langston, C.; Vasiloff, S.; Kaney, B.; Arthur, A.; Van Cooten, S.; Kelleher, K.; Kitzmiller, D.; Ding, F.; et al. National Mosaic and Multi-Sensor QPE (NMQ) system: Description, results, and future plans. Bull. Am. Meteorol. Soc. 2011, 92, 1321–1338. [CrossRef] 20. Sims, E.M.; Liu, G. A parameterization of the probability of snow–rain transition. J. Hydrometeorol. 2015, 16, 1466–1477. [CrossRef] 21. Burdanowitz, J.; Klepp, C.; Bakan, S. An automatic precipitation-phase distinction algorithm for optical disdrometer data over the global ocean. Atmos. Meas. Tech. 2016, 9, 1637–1652. [CrossRef] 22. Kochendorfer, J.; Nitu, R.; Wolff, M.; Mekis, E.; Rasmussen, R.; Baker, B.; Earle, M.E.; Reverdin, A.; Wong, K.; Smith, C.D.; et al. Testing and development of transfer functions for weighing precipitation gauges in WMO-SPICE. Hydrol. Earth Syst. Sci. 2018, 22, 1437. [CrossRef] 23. Rasmussen, R.; Baker, B.; Kochendorfer, J.; Meyers, T.; Landolt, S.; Fischer, A.P.; Black, J.; Thériault, J.M.; Kucera, P.; Gochis, D.; et al. How well are we measuring snow: The NOAA/FAA/NCAR winter precipitation test bed. Bull. Am. Meteorol. Soc. 2012, 93, 811–829. [CrossRef] 24. Löffler-Mang, M.; Joss, J. An optical disdrometer for measuring size and velocity of hydrometeors. J. Atmos. Ocean. Technol. 2000, 17, 130–139. [CrossRef] 25. Wayand, N.E.; Stimberis, J.; Zagrodnik, J.P.; Mass, C.F.; Lundquist, J.D. Improving simulations of precipitation phase and snowpack at a site subject to cold air intrusions: Snoqualmie Pass, WA. J. Geophys. Res. Atmos. 2016, 121, 9929–9942. [CrossRef] 26. Tokay, A.; Wolff, D.B.; Petersen, W.A. Evaluation of the new version of the laser-optical disdrometer, OTT Parsivel2. J. Atmos. Ocean. Technol. 2014, 31, 1276–1288. [CrossRef] 27. Dolan, B.; Fuchs, B.; Rutledge, S.A.; Barnes, E.A.; Thompson, E.J. Primary modes of global drop size distributions. J. Atmos. Sci. 2018, 75, 1453–1476. [CrossRef] 28. Park, S.G.; Kim, H.L.; Ham, Y.W.; Jung, S.H. Comparative evaluation of the OTT PARSIVEL2 using a collocated two-dimensional video disdrometer. J. Atmos. Ocean. Technol. 2017, 34, 2059–2082. [CrossRef] 29. Battaglia, A.; Rustemeier, E.; Tokay, A.; Blahak, U.; Simmer, C. PARSIVEL snow observations: A critical assessment. J. Atmos. Ocean. Technol. 2010, 27, 333–344. [CrossRef] 30. Newman, A.J.; Kucera, P.A.; Bliven, L.F. Presenting the snowflake video imager (SVI). J. Atmos. Ocean. Tech. 2009, 26, 167–179. [CrossRef] 31. Pettersen, C.; Bliven, L.F.; von Lerber, A.; Wood, N.B.; Kulie, M.S.; Mateling, M.E.; Moisseev, D.N.; Munchak, S.J.; Petersen, W.A.; Wolff, D.B. The Precipitation Imaging Package: Assessment of Microphysical and Bulk Characteristics of Snow. Atmosphere 2020, 11, 785. [CrossRef] 32. Pettersen, C.; Kulie, M.S.; Bliven, L.F.; Merrelli, A.J.; Petersen, W.A.; Wagner, T.J.; Wolff, D.B.; Wood, N.B. A composite analysis of snowfall modes from four winter seasons in Marquette, Michigan. J. Appl. Meteorol. Climatol. 2020, 59, 103–124. [CrossRef] 33. Kulie, M.S.; Pettersen, C.; Merrelli, A.J.; Wagner, T.J.; Wood, N.B.; Dutter, M.; Beachler, D.; Kluber, T.; Turner, R.; Mateling, M.; et al. Snowfall in the Northern Great Lakes: Lessons Learned from a Multi-Sensor Observatory. Bull. Am. Meteorol. Soc. 2021, 1–61. [CrossRef] 34. Hou, A.Y.; Kakar, R.K.; Neeck, S.; Azarbarzin, A.A.; Kummerow, C.D.; Kojima, M.; Oki, R.; Nakamura, K.; Iguchi, T. The global precipitation measurement mission. Bull. Am. Meteorol. Soc. 2014, 95, 701–722. [CrossRef] 35. von Lerber, A.; Moisseev, D.; Bliven, L.F.; Petersen, W.; Harri, A.M.; Chandrasekar, V. Microphysical properties of snow and their link to Z e–S relations during BAECC 2014. J. Appl. Meteor. Clim. 2017, 56, 1561–1582. [CrossRef] 36. Klugmann, D.; Heinsohn, K.; Kirtzel, H.J. A low cost 24 GHz FM-CW Doppler radar rain profiler. Contrib. Atmos. Phys. 1996, 61, 247–253. 37. Peters, G.; Fischer, B.; Andersson, T. Rain observations with a vertically looking Micro Rain Radar (MRR). Boreal Environ. Res. 2002, 7, 353–362. 38. Kneifel, S.; Maahn, M.; Peters, G.; Simmer, C. Observation of snowfall with a low-power FM-CW K-band radar (Micro Rain Radar). Meteorol. Atmos. Phys. 2011, 113, 75–87. [CrossRef] 39. Maahn, M.; Kollias, P. Improved Micro Rain Radar snow measurements using Doppler spectra post-processing. Atmos. Meas. Tech. 2012, 5, 2661–2673. [CrossRef] 40. Stull, R. Wet-bulb temperature from relative humidity and air temperature. J. Appl. Meteorol. Climatol. 2011, 50, 2267–2269. [CrossRef] 41. Hersbach, H.; Bell, B.; Berrisford, P.; Hirahara, S.; Horányi, A.; Muñoz-Sabater, J.; Nicolas, J.; Peubey, C.; Radu, R.; Schepers, D.; et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 2020, 146, 1999–2049. [CrossRef] 42. Atlas, D.; Ulbrich, C.W. Path-and area-integrated rainfall measurement by microwave attenuation in the 1–3 cm band. J. Appl. Meteorol. 1977, 16, 1322–1331. [CrossRef] Remote Sens. 2021, 13, 2183 25 of 25

43. Heymsﬁeld, A.J.; Bansemer, A.; Schmitt, C.; Twohy, C.; Poellot, M.R. Effective ice particle densities derived from aircraft data. J. Atmos. Sci. 2004, 61, 982–1003. [CrossRef] 44. Herzegh, P.H.; Jameson, A.R. Observing precipitation through dual-polarization radar measurements. Bull. Am. Meteorol. Soc. 1992, 73, 1365–1376. [CrossRef] 45. Ryzhkov, A.V.; Zrnic, D.S. Discrimination between rain and snow with a polarimetric radar. J. Appl. Meteorol. 1998, 37, 1228–1240. [CrossRef] 46. Matrosov, S.Y. Modeling backscatter properties of snowfall at millimeter wavelengths. J. Atmos. Sci. 2007, 64, 1727–1736. [CrossRef] 47. Austin, P.M.; Bemis, A.C. A quantitative study of the “bright band” in radar precipitation echoes. J. Atmos. Sci. 1950, 7, 145–151. [CrossRef]