Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective

Kulie, Mark S.; Milani, Lisa

doi:10.1002/qj.3222

Cited by 43 publications

(39 citation statements)

References 49 publications

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“…Remarkably, this method nearly entirely eliminates the occurrence of snow over the Gulf of Alaska, northern Atlantic south and east of Iceland, and Southern Ocean north of about 50°S. The land-ocean disparities among various classification methods make sense in that cold air outbreaks over ocean produce steeper lapse rates than over land (because of the larger heat capacity of water), which coupled with the surface moisture flux are more likely to produce shallow convection (Kulie et al 2016;Kulie and Milani 2018). Thus, over-ocean retrievals are much more susceptible than those over land because of classification errors in the radar-based methods that do not use information about the temperature structure near the surface.…”

Section: A Classification-induced Differencesmentioning

confidence: 99%

Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective

Skofronick-Jackson

Kulie

Milani

et al. 2019

Journal of Applied Meteorology and Climatology

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Retrievals of falling snow from space-based observations represent key inputs for understanding and linking Earth’s atmospheric, hydrological, and energy cycles. This work quantifies and investigates causes of differences among the first stable falling snow retrieval products from the Global Precipitation Measurement (GPM) Core Observatory satellite and CloudSat’s Cloud Profiling Radar (CPR) falling snow product. An important part of this analysis details the challenges associated with comparing the various GPM and CloudSat snow estimates arising from different snow–rain classification methods, orbits, resolutions, sampling, instrument specifications, and algorithm assumptions. After equalizing snow–rain classification methodologies and limiting latitudinal extent, CPR observes nearly 10 (3) times the occurrence (accumulation) of falling snow as GPM’s Dual-Frequency Precipitation Radar (DPR). The occurrence disparity is substantially reduced if CloudSat pixels are averaged to simulate DPR radar pixels and CPR observations are truncated below the 8-dBZ reflectivity threshold. However, even though the truncated CPR- and DPR-based data have similar falling snow occurrences, average snowfall rate from the truncated CPR record remains significantly higher (43%) than the DPR, indicating that retrieval assumptions (microphysics and snow scattering properties) are quite different. Diagnostic reflectivity (Z)–snow rate (S) relationships were therefore developed at Ku and W band using the same snow scattering properties and particle size distributions in a final effort to minimize algorithm differences. CPR–DPR snowfall amount differences were reduced to ~16% after adopting this diagnostic Z–S approach.

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Section: A Classification-induced Differencesmentioning

confidence: 99%

Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective

Skofronick-Jackson

Kulie

Milani

et al. 2019

Journal of Applied Meteorology and Climatology

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“…Therefore, the algorithm is only used for dry snowfall. More details about the algorithm have been reported previously [14][15][16][17]39].…”

Section: Cloudsat Datamentioning

confidence: 99%

“…The cloud profiling radar (CPR) system onboard CloudSat enables the direct observation of precipitation throughout the atmosphere and can measure light precipitation [10,11]. The CloudSat snowfall retrieval products have been used to reconstruct annual and seasonal snowfall on the AIS [12][13][14][15][16]. Monthly CloudSat snowfall data have been examined by snow accumulation measurements from automatic weather stations (AWSs) [17].…”

mentioning

confidence: 99%

Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets

Liu

Hao

et al. 2019

Remote Sensing

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Snowfall data are vital in calculating the surface mass balance of the Antarctic Ice Sheet (AIS), where in-situ and satellite measurements are sparse at synoptic timescales. CloudSat data are used to construct Antarctic snowfall data at synoptic timescales to compensate for the sparseness of synoptic snowfall data on the AIS and to better understand its surface mass balance. Synoptic CloudSat snowfall data are evaluated by comparison with daily snow accumulation measurements from ten automatic weather stations (AWSs) and the fifth generation of the European Centre for Medium-Range Weather Forecasts climate reanalysis (ERA5) snowfall. Synoptic snowfall data were constructed based on the CloudSat measurements within a radius of 1.41 • . The results show that reconstructed CloudSat snowfall at daily and two-day resolutions cover about 28% and 29% of the area of the AIS, respectively. Daily CloudSat snowfall and AWS snow accumulation have similar trends at all stations. While influenced by stronger winds, >73.3% of extreme snow accumulation events correspond to snowfall at eight stations. Even if the CloudSat snowfall data have not been assimilated into the ERA5 dataset, the synoptic CloudSat snowfall data are almost identical to the daily ERA5 snowfall with only small biases (average root mean square error and mean absolute error < 3.9 mm/day). Agreement among the three datasets suggests that the CloudSat data can provide reliable synoptic snowfall data in most areas of the AIS. The ERA5 dataset captures a large number of extreme snowfall events at all AWSs, with capture rates varying from 56% to 88%. There are still high uncertainties in ERA5. Nevertheless, the result suggests that ERA5 can be used to represent actual snowfall events on the AIS at synoptic timescale.CloudSat has recently become the most useful tool for directly measuring solid precipitation (i.e., snowfall) on the AIS [9]. The cloud profiling radar (CPR) system onboard CloudSat enables the direct observation of precipitation throughout the atmosphere and can measure light precipitation [10,11]. The CloudSat snowfall retrieval products have been used to reconstruct annual and seasonal snowfall on the AIS [12][13][14][15][16]. Monthly CloudSat snowfall data have been examined by snow accumulation measurements from automatic weather stations (AWSs) [17]. However, there are few attempts to use ground-based measurements (e.g., micro-rain radar (MRR)) to examine the CloudSat snowfall over the AIS at shorter timescales [5,18,19] because there is currently no precipitation gauge network and few continuous snowfall measurements for the AIS [5,20]. CloudSat provides a possible way to solve these problems.CloudSat has a 16-day repeat period covering regions from 82 • N to 82 • S. The density of radar footprints increases with the rise of latitude. The daily overpass frequency of radar measurements for each grid box is between 0.3 and 1.3 days over continental Antarctica at a resolution of 1 • latitude × 1.5 • longitude [17]. Such a temporal resolution is enha...

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“…Since 2006, CloudSat observations have been used in a series of studies addressing snowfall globally (Liu, 2008;Hiley et al, 2011;Palerme et al, 2014;Kulie et al, 2016;Adhikari et al, 2018;Kulie and Milani, 2018). While some of the global studies include Greenland, a detailed assessment of snowfall over the GrIS using CloudSat has to our knowledge not yet been performed.…”

Section: Introductionmentioning

confidence: 99%

Spatial and temporal variability of snowfall over Greenland from CloudSat observations

Bennartz¹,

Fell²,

Pettersen³

et al. 2019

Preprint

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<p><strong>Abstract.</strong> We use the CloudSat data record 2006&#8211;2016 to estimate snowfall over the Greenland Ice Sheet (GrIS). We first evaluate CloudSat snowfall retrievals with respect to remaining ground-clutter issues. Comparing CloudSat observations to the GrIS topography (obtained from airborne altimetry measurements during IceBridge) we find that at the edges of the GrIS spurious high snowfall retrievals caused by ground clutter occasionally affect the operational snowfall product. After correcting for this effect, the height of the lowest valid CloudSat observation is about 1200 meters above the local topography as defined by IceBridge. We then use ground-based Millimeter Wavelength Cloud Radar (MMCR) observations obtained from the Integrated Characterization of Energy, Clouds, Atmospheric state, and Precipitation at Summit, Greenland (ICECAPS) experiment to devise a simple, empirical correction to account for precipitation processes occurring between the height of the observed CloudSat reflectivities and the snowfall near the surface. Using the height-corrected, clutter-cleared CloudSat reflectivities we next evaluate various Z-S relationships in terms of snowfall accumulation at Summit through comparison with weekly stake field observations of snow accumulation available since 2007. Using a set of three Z-S relationships that best agree with the observed accumulation at Summit, we then calculate the annual cycle snowfall over the entire GrIS as well as over different drainage areas and compare the so-derived mean values and annual cycles of snowfall to ERA-Interim reanalysis. We find the annual mean snowfall over the GrIS inferred from CloudSat to be 34&#8201;&#177;&#8201;7.5&#8201;cm/yr liquid equivalent (where the uncertainty is determined by the range in values between the three different Z-S relationships used). In comparison, the ERA-Interim reanalysis product only yields 30&#8201;cm/yr liquid equivalent snowfall, where the majority of the underestimation in the reanalysis appears to occur in the summer months over the higher GrIS and appears to be related to shallow precipitation events. Comparing all available estimates of snowfall accumulation at Summit Station, we find the annually averaged liquid equivalent snowfall from the stake field to be between 20 and 24&#8201;cm/yr, depending on the assumed snowpack density, and from CloudSat 23&#8201;&#177;&#8201;4.5&#8201;cm/yr. The annual cycle at Summit is generally similar between all data sources, with the exception of ERA-Interim reanalysis, which shows the aforementioned under-estimation during summer months.</p>

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Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective

Cited by 43 publications

References 49 publications

Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective

Satellite Estimation of Falling Snow: A Global Precipitation Measurement (GPM) Core Observatory Perspective

Evaluation of Synoptic Snowfall on the Antarctic Ice Sheet Based on CloudSat, In-Situ Observations and Atmospheric Reanalysis Datasets

Spatial and temporal variability of snowfall over Greenland from CloudSat observations

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