Direct and steady observation of newly fallen snow density is difficult because of the effect of snow compaction. We aimed to evaluate a method for estimation of newly fallen snow density using particle size and fall velocity distribution obtained from disdrometer (Parsivel 2 ) for snowfall cases at temperatures below 0°C. As disdrometer observations cannot easily manage cases of mixed hydrometeor such as graupel and aggregate, we considered only the averaged riming degree of snowfall particles as an index without classifying the hydrometeor types. We observed newly fallen snow density using a snow board for 157 cases of snowfall in the winters of 2020-2021 and 2021-2022 in Niigata Prefecture, Japan. Furthermore, we calculated the riming degree for each case using a fraction of squared fall speed with respect to the unrimed aggregate. The results revealed that the averaged riming degree was correlated with density of newly fallen snow. Based on its relationship with the averaged riming degree investigated herein, the newly fallen snow density can be estimated from the particle size and fall speed distribution, which can be automatically observed using a disdrometer without any manual observations via a snowboard. KEYWORDS newly fallen snow density; riming degree; snow particle size distribution; fall speed
<p>Two products from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) algorithms, a flag of intense solid precipitation above the &#8211;10&#176;C height (&#8220;flagHeavyIcePrecip&#8221;), and a classification of precipitation type (&#8220;typePrecip&#8221;) were validated quantitatively from the viewpoint of microphysics using ground-based in-situ hydrometeor measurements and X-band multi-parameter (X-MP) radar observations of snow clouds that occurred on 4 February 2018. The distribution of the &#8220;flagHeavyIcePrecip&#8221; footprints was in good agreement with that of the graupel-dominant pixels classified by the X-MP radar hydrometeor classification. In addition, the vertical profiles of X-MP radar reflectivity exhibited significant differences between footprints flagged and unflagged by &#8220;flagHeavyPrecip&#8221;. We confirmed the effectiveness of &#8220;flagHeavyIcePrecip&#8221;, which is built into &#8220;typePrecip&#8221; algorithm, for detecting intense ice precipitation and concluded that "flagHeavyIcePrecip" is appropriate to useful for determining convective clouds.</p><p>It is well known that the lightning activity is closely related to the convection. We examined the lightning activity using GPM DPR product flagHeavyIcePrecip that indicates the existence of graupel in the upper cloud. On 20 June 2016, we experienced heavy rain with active lightning during Baiu monsoon rainy season, while the GPM DPR passed over Kyushu region in Japan. The distribution of &#8220;flagHeavyIcePrecip&#8221; obtained from the GPM DPR well corresponded to the CG/IC lightning concentration. On 4 September 2019, isolated thunder clouds observed by the GPM DPR was also similar to the &#8220;flagHeavyIcePrecip&#8221; distribution. However, partially there was IC lightning without &#8220;flagHeavyIcePrecip&#8221;, which was positive lightning. It was suggested to have been produced in the upper clouds in which positive ice crystals were dominant.</p>
Two products from the Global Precipitation Measurement (GPM) Dual-frequency Precipitation Radar (DPR) algorithms, a flag of intense solid precipitation above the −10°C height ("flag HeavyIcePrecip") and a classification of precipitation type ("typePrecip") were validated by ground-based hydrometeor measurements and X-band multi-parameter (X-MP) radar observations of snow clouds on 4 February 2018. Contoured frequency by altitude diagrams of the X-MP radar reflectivity exhibited a significant difference between footprints flagged and unflagged by the "flagHeavyIcePrecip" algorithm, which indicated that the algorithm is reasonable. The hydrometeor classification (HC) by the X-MP radar, which was confirmed by microphysical evidence from ground-based hydrometeor measurements, suggested the existence of graupel in the footprints with "flagHeavyIcePrecip". In addition, according to the information of the GPM DPR, the "flagHeavyIcePrecip" footprints were characterized by not only graupel but also large snowflakes. According to the information of X-MP radar HC, the "typePrecip" algorithm by the detection of "flagHeavyIcePrecip" was effective in classifying precipitation types of snow clouds, whereas it seems that there is room for improvement in the "typePrecip" algorithms based on the extended-DPR m-method and H-method.
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