Estimating snow microphysical properties using collocated multisensor observations

Wood, Norman B.; L’Ecuyer, Tristan; Heymsfield, Andrew J.; Stephens, Graeme L.; Hudak, David; Rodriguez, Peter

doi:10.1002/2013jd021303

Cited by 93 publications

(93 citation statements)

References 40 publications

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“…Indeed, CPR does not provide enough independent information on snow microphysics to be able to connect measured reflectivity to a unique snowfall rate. Thus, the inversion problem is not fully constrained, which necessitates the use of parameterizations in estimating the snowfall rate from measured reflectivity, implying uncertainties in snowfall estimates [57]. Another difficulty comes from melting snow events.…”

Section: Discussionmentioning

confidence: 99%

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%

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

et al. 2018

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“…In addition, the CloudSat 2C-SNOW-PROFILE (hereafter, 2C-SNOW), used extensively in this study, provides profiles of instantaneous liquid equivalent surface snowfall rate retrievals based on CloudSat orbital swath data. The 2C-SNOW product uses an optimal estimation procedure to extract snowfall rate from W-band radar reflectivity values, using a priori snow microphysical properties to constrain snowfall rate retrievals [36,37]. It is worth noting that 2C-SNOW surface snowfall rate (and associated SWC profile) is computed only for CPR profiles where the 2C-PRECIP CPR product indicates surface snow probable or certain, or if estimated liquid fraction is <10% (dry snow).…”

Section: Methodology: Gmi-cpr Dataset Descriptionmentioning

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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“…The CPR on board CloudSat is proving to be a useful tool for mapping the vertical distribution of IWC and S globally, in part because of its high sensitivity to light precipitation and its ability to provide near-global data (Liu 2008;Wood et al 2014). The radar-only (2B-CWC-RO) and radaroptical depth (2B-CWC-RVOD) products are the standard products used to retrieve the IWC.…”

Section: Introductionmentioning

confidence: 99%

“…The IWC from the CloudSat Ice Cloud Property Product (2C-ICE; Deng et al 2015) is intended to incorporate additional observations (i.e., CALIPSO) that improve the sensitivity to small ice particles relative to radar-only retrievals. The product 2C-SNOW-PROFILE (hereinafter 2C-SP; Wood et al 2013) is intended to retrieve snowfall rate. Although the CloudSat radar is about an order of magnitude more sensitive to very light precipitation than any other existing space-based radar (Skofronick-Jackson et al 2013), CloudSat reflectivities attenuate in deep, higher-rate snowfall events (Cao et al 2014).…”

Section: Introductionmentioning

confidence: 99%

“…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 ). For lower snowfall rates, both the Swedish radars and CloudSat seem to suffer from limitations.…”

Section: Introductionmentioning

confidence: 99%

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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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Estimating snow microphysical properties using collocated multisensor observations

Cited by 93 publications

References 40 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

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

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