CloudSat snowfall estimates over Antarctica and the Southern Ocean: An assessment of independent retrieval methodologies and multi-year snowfall analysis

Milani, Lisa; Kulie, Mark S.; Casella, Daniele; Dietrich, Stefano; L’Ecuyer, Tristan; Panegrossi, Giulia; Porcú, Federico; Sanò, Paolo; Wood, Norman B.

doi:10.1016/j.atmosres.2018.05.015

Cited by 56 publications

(103 citation statements)

References 59 publications

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“…In particular, the high sensitivity of snow detection by the SLALOM algorithm, even for light snowfall, is an important asset, as many studies pointed out that most snow events are associated with very light snowfall rates at high latitudes [9,11,13]. In addition, the snowfall and supercooled detection modules can be exploited to document the water phase at the global scale.…”

Section: Discussionmentioning

confidence: 99%

“…Thus, it misses an important layer of snowfall and underestimates the total SWP. On the other hand, ground clutter is sometimes not appropriately corrected in complicated terrain (see [9,13]), thus leading to artificially high snow content. Another important limitation lies in the fact that 2CSP SWP is subjected to high uncertainties due to the difficulties in quantifying SWP using a single frequency instrument, such as CPR.…”

Section: Discussionmentioning

confidence: 99%

“…Spaceborne microwave sensors are particularly suitable for detecting and quantifying snowfall thanks to their unique ability to probe within clouds [5,6]. Active microwave sensors, such as the Cloud Profiling Radar (CPR) onboard CloudSat [7] and the Dual-Frequency Precipitation Radar (DPR) onboard the Global Precipitation Measurement Mission-Core Observatory (GPM-CO, [8]), are particularly valuable for providing detailed vertical profiles of snow [9][10][11][12][13]. However, due to several limitations, such as their reduced swath (especially for CPR) or their limited sensitivity (for DPR), they are not well-suited to comprehensively characterizing snowfall.…”

Section: Introductionmentioning

confidence: 99%

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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: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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“…CloudSat has provided valuable data for the precipitation remote sensing community, especially as the first spaceborne radar to provide observations at higher latitude. The near global coverage (up to~82 • latitude) and high CPR sensitivity (~−28 dBZ) make it very suitable for snowfall-related research [7][8][9]27,[30][31][32]. The CPR nominal footprint size is~1.5 km with~480 m native vertical range gates in each reflectivity profile collected over a 0.16 s integration time interval.…”

Section: Methodology: Gmi-cpr Dataset Descriptionmentioning

confidence: 99%

“…It is worth noting that, as evidenced in the second panel of Figure 2a, 2C-SNOW SWC is available only above the first clutter-free bin which changes depending on the background surface (higher for snow cover and sea ice, and lower over ocean). Therefore, CPR may miss the snowfall in the first 1000-1500 m above the surface (see also Milani et al [32]). For example, for the weaker cloud north of 64 • N (Sector I), the SWC is not available at the lower levels.…”

Section: Case 1: Intense Snowfall Event On 30 April 2014mentioning

confidence: 99%

CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities

Panegrossi

Rysman

Casella³

et al. 2017

Remote Sensing

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Abstract:The sensitivity of Global Precipitation Measurement (GPM) Microwave Imager (GMI) high-frequency channels to snowfall at higher latitudes (around 60 • N/S) is investigated using coincident CloudSat observations. The 166 GHz channel is highlighted throughout the study due to its ice scattering sensitivity and polarization information. The analysis of three case studies evidences the important combined role of total precipitable water (TPW), supercooled cloud water, and background surface composition on the brightness temperature (TB) behavior for different snow-producing clouds. A regression tree statistical analysis applied to the entire GMI-CloudSat snowfall dataset indicates which variables influence the 166 GHz polarization difference (166 ∆TB) and its relation to snowfall. Critical thresholds of various parameters (sea ice concentration (SIC), TPW, ice water path (IWP)) are established for optimal snowfall detection capabilities. The 166 ∆TB can identify snowfall events over land and sea when critical thresholds are exceeded (TPW > 3.6 kg·m −2 , IWP > 0.24 kg·m −2 over land, and SIC > 57%, TPW > 5.1 kg·m −2 over sea). The complex combined 166 ∆TB-TB relationship at higher latitudes and the impact of supercooled water vertical distribution are also investigated. The findings presented in this study can be exploited to improve passive microwave snowfall detection algorithms.

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Southern Ocean precipitation: Toward a process‐level understanding

Siems

Huang

Manton

2022

WIREs Climate Change

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Large differences continue to exist between current precipitation products over the Southern Ocean (SO). This limits our ability to close the hydrological cycle over the SO and Antarctica, as well as limiting our understanding of a range of climatological and meteorological processes. This uncertainty arises from the absence of long-term, high-quality surface observational records of precipitation suitable for evaluation across a range of temporal and spatial scales. We have no "truth" for precipitation across this region that covers $15% of the Earth's surface. These differences extend to spatial and temporal distributions and trends. Precipitation products that have been calibrated and evaluated against established observations in the Northern Hemisphere potentially may be biased due to fundamental differences in the dynamics and microphysics over the remote SO. This review first considers recent advances in our understanding of the precipitation of the SO, including spatial and temporal variability, thermodynamic phase, and response to climate drivers. We then examine several commonly used precipitation products derived from satellite observations (both passive and active), reanalyses, and merged products. Where possible, we examine the skill of these products across a range of precipitation processes that commonly occur across the SO. Finally, we look briefly at the potential of new resources, such as dual-polarized radars and maritime disdrometers, that can be used in field campaigns specifically designed to observe precipitation at the process level, and ultimately used to evaluate precipitation products over the SO.

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CloudSat snowfall estimates over Antarctica and the Southern Ocean: An assessment of independent retrieval methodologies and multi-year snowfall analysis

Cited by 56 publications

References 59 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

CloudSat-Based Assessment of GPM Microwave Imager Snowfall Observation Capabilities

Southern Ocean precipitation: Toward a process‐level understanding

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