Intercomparison of snowfall estimates derived from the CloudSat  Cloud Profiling Radar and the ground-based weather radar network over Sweden

Norin, Lars; Devasthale, Abhay; L’Ecuyer, Tristan; Wood, Norman B.; Smalley, Mark

doi:10.5194/amt-8-5009-2015

Cited by 50 publications

(51 citation statements)

References 30 publications

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“…Both Cao et al () and Chen et al () show that NEXRAD‐based blended QPE datasets might be optimal for high snowfall rates compared to the 2CSP product (due to possible CPR attenuation effects), but CloudSat is conversely superior at detecting lighter snowfall events. Norin, Devasthale, L'Ecuyer, Wood, and Smalley () compare 2CSP with ground‐based scanning radar data in Sweden and show excellent snowfall rate distribution agreement between 0.1 and 1.0 mm/hr snowfall rates, also indicating good agreement between the two datasets when CloudSat overpasses are between 46 and 82 km from the radar sites, with degrading correlations at further distances. Possible CloudSat underestimation is also highlighted at the highest snowfall rates.…”

Section: Methodsmentioning

confidence: 97%

Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective

Kulie

Milani

2018

Quart J Royal Meteoro Soc

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Cumuliform snowfall seasonal variability is studied using a multi‐year CloudSat snowfall rate and cloud classification retrieval dataset. Microwave radiometer sea ice concentration datasets are also utilized to illustrate the intimate link between oceanic cumuliform snowfall production and decreased sea ice coverage. Three metrics are calculated to illustrate seasonal cumuliform snowfall signatures: (a) cumuliform snowfall frequency of occurrence, (b) mean cumuliform snowfall rate, and (c) fraction of snowfall attributed to cumuliform snowfall events. Distinct seasonal cumuliform snowfall cycles are observed over the Northern Hemispheric oceans. Cumuliform snowfall frequency of occurrence (mean snowfall rate) peaks in months SON (DJF) at most latitudes. Maximum mean cumuliform snowfall rates exceed 300 mm/year in various North Atlantic Ocean locations, with DJF exhibiting the largest areal extent of higher snowfall rates. Cumuliform snow occurrence fraction frequently exceeds 0.5, but regional seasonal sensitivity is observed where transient sea ice coverage exists. Annual snowfall rate fraction attributed to cumuliform snow does not vary appreciably during SON, DJF and MAM north of ∼70°N, but seasonal zonal variability is evident south of this latitudinal threshold. Land cumuliform snowfall features do not universally display strong seasonal signals. Southern Hemisphere seasonal results indicate a strong mean cumuliform snowfall rate maximum (minimum) in JJA (DJF) accompanied by a seasonal latitudinal shift in the snowfall rate peak. Maximum regional snowfall rates exceed 300 mm/year over a broader area compared to the Northern Hemisphere. Cumuliform snowfall production is again strongly linked to seasonal sea ice coverage. Southern Hemispheric cumuliform snowfall occurrence and snowfall rate fraction seasonality is not as obvious as in the Northern Hemisphere, but some latitudinal zones experience ∼5–20% seasonal variability in these quantities. Typical cumuliform snowfall fractions range from 0.4 to 0.6 in the prominent cumuliform snowfall belt covering most of the Southern Ocean. Some subtle seasonal cumuliform snowfall signatures are observed over Antarctica.

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Section: Methodsmentioning

confidence: 97%

Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective

Kulie

Milani

2018

Quart J Royal Meteoro Soc

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“…They found that the mean vertical profile of IWC is overestimated below 10-km heights, with peak values off by a factor of 2. In a second evaluation, Norin et al (2015) quantitatively intercompared snowfall estimates from a ground-based polarized C-band Doppler radar in Sweden to CloudSat estimates when the satellite passed overhead in the vicinity of the radar. Taking only those comparison cases where the radar and CloudSat measurements were relatively collocated (;30 km), they concluded that the 2C-SP retrieval algorithm (Wood et al 2013) has limited ability to retrieve at the higher end of the snowfall intensity distribution (.1mmh 21 ).…”

Section: Introductionmentioning

confidence: 99%

Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations

Heymsfield

Krämer

Wood

et al. 2017

Journal of Applied Meteorology and Climatology

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Cloud ice microphysical properties measured or estimated from in situ aircraft observations are compared with global climate models and satellite active remote sensor retrievals. Two large datasets, with direct measurements of the ice water content (IWC) and encompassing data from polar to tropical regions, are combined to yield a large database of in situ measurements. The intention of this study is to identify strengths and weaknesses of the various methods used to derive ice cloud microphysical properties. The in situ data are measured with total water hygrometers, condensed water probes, and particle spectrometers. Data from polar, midlatitude, and tropical locations are included. The satellite data are retrieved from CloudSat/CALIPSO [the CloudSat Ice Cloud Property Product (2C-ICE) and 2C-SNOW-PROFILE] and Global Precipitation Measurement (GPM) Level2A. Although the 2C-ICE retrieval is for IWC, a method to use the IWC to get snowfall rates S is developed. The GPM retrievals are for snowfall rate only. Model results are derived using the Community Atmosphere Model (CAM5) and the Met Office Unified Model [Global Atmosphere 7 (GA7)]. The retrievals and model results are related to the in situ observations using temperature and are partitioned by geographical region. Specific variables compared between the in situ observations, models, and retrievals are the IWC and S. Satellite-retrieved IWCs are reasonably close in value to the in situ observations, whereas the models' values are relatively low by comparison. Differences between the in situ IWCs and those from the other methods are compounded when S is considered, leading to model snowfall rates that are considerably lower than those derived from the in situ data. Anomalous trends with temperature are noted in some instances.

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“…Based on Figure 1, Q snow appears to be the dominant cooling influence in the ocean during the top 20th percentile of snowfall events. Considering that CloudSat has noted limitations in retrieving snowfall rates above 1 mm/hr when compared with surface radar (Norin et al, 2015), it is likely that heavier snowfall rates which could lead to the most powerful cooling fluxes are undercounted. Further considering that calculations of Q snow are based on an uncertain assumption that the satellite retrieved rate of snowfall will equal the rate of snowmelt, the exact threshold when melting snow could become the dominant cooling flux is not confident.…”

Section: Resultsmentioning

confidence: 99%

The Role of Melting Snow in the Ocean Surface Heat Budget

Duffy

Bennartz

2018

Geophysical Research Letters

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We estimate the cooling flux from snow melting in the ocean through CloudSat satellite snowfall retrievals and reanalysis data. For snowfall events with less than 0.01 mm/hr, this flux is inconsequential. Melting snow begins to compete with other ocean surface heat fluxes as snowfall rates increase beyond 0.1 mm/hr, and it may often become the dominant heat flux as snowfall rates approach and exceed 1 mm/hr. The largest monthly average values of the melting snow cooling flux occur in winter months, approaching À10 W/m 2 in both hemispheres. To determine the regional influence of melting snow on a seasonal basis, we calculate an impact metric that gauges the cooling flux of melting snow against the net flux in the ocean. This metric can be between 20% and 30% in the Northern ; and in high-latitude polar oceans during sea ice freeze up seasons.Plain Language Summary Climate scientists are very interested in understanding how much heat is entering and leaving the ocean, as this heat budget has important connections to the weather, ocean, and ice patterns that are crucial to life as we know it. Melting snow cools the ocean surface, but this effect has not been studied as much as heat exchanges from wind or sunlight. Using satellites and weather models, we determine how much melting snow cools the ocean. The cooling from snowmelt is most often inconsequential, since most snowfall is light. During heavy snowstorms, however, the cooling from melting snow can become quite powerful, perhaps becoming the most powerful cooling source in the ocean at times. Melting snow also has a large impact on the ocean heat budget in polar oceans during sea ice freeze up seasons. This is an important result, as the loss of sea ice is one of the most visible and concerning signs of climate change today. Ultimately, we find that melting snow can be a powerful and influential cooling force in the ocean but only under appropriate circumstances.

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Intercomparison of snowfall estimates derived from the CloudSat Cloud Profiling Radar and the ground-based weather radar network over Sweden

Cited by 50 publications

References 30 publications

Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective

Seasonal variability of shallow cumuliform snowfall: A CloudSat perspective

Dependence of the Ice Water Content and Snowfall Rate on Temperature, Globally: Comparison of in Situ Observations, Satellite Active Remote Sensing Retrievals, and Global Climate Model Simulations

The Role of Melting Snow in the Ocean Surface Heat Budget

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