Global Distribution of Snow Precipitation Features and Their Properties from 3 Years of GPM Observations

Adhikari, Abishek; Liu, Chuntao; Kulie, Mark S.

doi:10.1175/jcli-d-17-0012.1

Cited by 41 publications

(47 citation statements)

References 53 publications

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“…Finally, mountain ranges, such as the Himalayas, the Andes, the Alps and the Rocky Mountains show occurrences that can exceed 20%. These patterns match very well those identified at a lower resolution by Kulie et al [11] and Behrangi et al [4], using CloudSat CPR observations, and Adhikari et al [56], using a GPM Dual-frequency Precipitation Radar. show an occurrence between 2 and 20%, with local variability.…”

Section: Climatology Of Snowfall Occurrencesupporting

confidence: 87%

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

et al. 2018

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This paper describes a new algorithm that is able to detect snowfall and retrieve the associated snow water path (SWP), for any surface type, using the Global Precipitation Measurement (GPM) Microwave Imager (GMI). The algorithm is tuned and evaluated against coincident observations of the Cloud Profiling Radar (CPR) onboard CloudSat. It is composed of three modules for (i) snowfall detection, (ii) supercooled droplet detection and (iii) SWP retrieval. This algorithm takes into account environmental conditions to retrieve SWP and does not rely on any surface classification scheme. The snowfall detection module is able to detect 83% of snowfall events including light SWP (down to 1 × 10 −3 kg·m −2 ) with a false alarm ratio of 0.12. The supercooled detection module detects 97% of events, with a false alarm ratio of 0.05. The SWP estimates show a relative bias of −11%, a correlation of 0.84 and a root mean square error of 0.04 kg·m −2 . Several applications of the algorithm are highlighted: Three case studies of snowfall events are investigated, and a 2-year high resolution 70 • S-70 • N snowfall occurrence distribution is presented. These results illustrate the high potential of this algorithm for snowfall detection and SWP retrieval using GMI.

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Section: Climatology Of Snowfall Occurrencesupporting

confidence: 87%

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

et al. 2018

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“…Pettegrew (2008) observed a diurnal peak of thundersnow events during 00Z to 04Z. The early morning peak of snowfall (Adhikari et al, 2018) and lake effect snow (Grim et al, 2004;Kristovich & Spinar, 2005) over land in the Northern Hemisphere was also reported. We speculate that the evening time peak might be related to surface heating (Chronis & Koshak, 2017), and the nighttime maximum is because of the low-level jet, which also reaches a maximum during these hours (Whiteman et al, 1997) and leads to the development of elevated convection.…”

Section: Seasonal and Diurnal Variationsmentioning

confidence: 94%

“…The uncertainty and error analysis while using GPM KuPR precipitation retrievals and collocation process should be considered. The KuPR precipitation retrieval related issues such as detectability, single-frequency retrieval, ground clutter contamination, and precipitation phase at surface are well documented in Adhikari et al (2018), and some of the relevant issues are highlighted here. The KuPR misses most of the light snow (reflectivity <12 dBZ) but provides better estimation of heavy snow (reflectivity >20 dBZ).…”

Section: Source Of Uncertainties and Errormentioning

confidence: 99%

“…Because of the increased radar sensitivity (~12 dBZ) (Hamada & Takayabu, 2016) and high inclined orbit (65°), GPM can be used to detect snowfall in the midlatitude and high latitude. The GPM Ku-band radar has already collected millions of snapshots of precipitation systems (Liu & Zipser, 2015) and snow systems (Adhikari et al, 2018). The near surface Ku-band radar reflectivity is used to retrieve precipitation types, which is reliant on inputs from ancillary environmental data from Japan Meteorological Agency Global Analysis as described in Seto et al (2013) and the GPM data product file specification (Precipitation Processing System Team, 2014).…”

Section: Gpm Observationsmentioning

confidence: 99%

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Geographical Distribution of Thundersnow Events and Their Properties From GPM Ku‐Band Radar

Adhikari

Liu

2019

JGR Atmospheres

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Six‐years (2010–2015) of snow lightning characteristics and climatology, including seasonal, diurnal, and surface temperature distribution, are generated. The World Wide Lightning Location Network (WWLLN) and the National Lightning Detection Network lightning observations are collocated with Modern‐Era Retrospective Analysis for Research and Application (MERRA‐2) temperatures. Cold season lightning events are identified as lightning with the MERRA‐2 two‐meter surface temperature colder than 0 °C and then further classified as snow lightning or thundersnow, when the entire vertical temperature profile is below 2 °C, and as freezing rain lightning when there is a temperature warmer than 2 °C somewhere in the column above the freezing surface. The statistics of snow lightning events from WWLLN and National Lightning Detection Network are well matched and are consistent with the climatology of thundersnow days reported at ground‐based stations over the United States. Using 4 years of observations from the Global Precipitation Measuring Mission Ku band radar, 443 Thunder Snow Features (TSFs) are defined, having a contiguous area of nonzero near surface snow precipitation derived from the Ku band radar and MERRA‐2 data, along with collocated WWLLN lightning flashes. The majority (about 394) are found over high mountainous regions such the Himalayas, Tibet, the Andes, and the Zagros mountain regions. Low‐elevation TSFs (45) are observed over the continental and coastal regions. Though only a small number of TSFs are identified with 4 years of Global Precipitation Mission data, most TSFs have maximum radar reflectivity above 30 dBZ at temperature colder than −10 °C, which indicates the importance of the noninductive charging process in these events.

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“…For this study, precipitation features (PFs) have been grouped together using the observations from the PR instrument. A PF is defined as contiguous raining pixels of greater than 75 km 2 observed by the TRMM PR that are grouped together to create a raining feature (Adhikari et al, 2018;Liu & Liu, 2016;Seto et al, 2013). The threshold of 75 km 2 was chosen because the contribution of total rainfall by PFs less than 75 km 2 , based on the TRMM 2A25 algorithm, is less than 5% (Liu et al, 2010).…”

Section: Tropical Rainfall Measuring Mission Precipitation Featuresmentioning

confidence: 99%

How Does the Trend in Thunder Days Relate to the Variation of Lightning Flash Density?

Lavigne

Liu

2019

JGR Atmospheres

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A longstanding question for scientists has been whether or not any observable trends or shifts in global lightning activity have occurred since the Industrial Revolution. This study utilized over 8,000 certified ground‐based stations over a 43‐year period, as well as 16 years of Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) data, to provide a better understanding of the processes behind these trends. Ground station results show that many global regions have observed significant increases or decreases in thunder day occurrence. The Amazon, Maritime Continent, India, Congo, Central America, and Argentina display increases in annual thunder days since the 1970s, whereas China, Australia, and the Sahel among others observe decreases in the number of thunder days. The corresponding change in lightning flash density from the TRMM‐LIS, as well as the number of thunderstorm features and lightning flashes per thunderstorm feature, is compared to the thunder day trends during the TRMM lifespan. Results show a positive correlation between the changes of thunder day occurrence and flash density over most regions of the TRMM domain, including the Maritime Continent, China, South Africa, and Argentina. However, there are several regions with disagreements between the flash density and thunder day trends, such as India and Western Africa. The disagreements are related to the changes in the number of flashes per thunderstorm, which suggest other reasons to interpret the long term trends in thunder day occurrence over various regions. Understanding these regional trends in lightning activity is important in understanding the changes of precipitation systems under a varying climate.

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Global Distribution of Snow Precipitation Features and Their Properties from 3 Years of GPM Observations

Cited by 41 publications

References 53 publications

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

SLALOM: An All-Surface Snow Water Path Retrieval Algorithm for the GPM Microwave Imager

Geographical Distribution of Thundersnow Events and Their Properties From GPM Ku‐Band Radar

How Does the Trend in Thunder Days Relate to the Variation of Lightning Flash Density?

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