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NC_GLOBAL
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title:
Multisensor Advanced Climatology of LWP (MAC-LWP) Cloud+Rain LWP (TotalLWP)
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Identifier_Product_DOI_Authority:
http://dx.doi.org/
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Identifier_Product_DOI:
10.5067/MEASURES/MACTWPM
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institution:
Columbia University/NASA GISS, New York, NY; and Colorado State University, Fort Collins, CO
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source:
Based upon Remote Sensing Systems (RSS) V7 SSM/I, V7 SSMIS, V7 WindSat, V7 AMSR-E, V7 AMSR-2, V7 TMI, and V8.1 GMI 0.25 Degree Cloud LWP Retrievals; and NOAA Optimum Interpolation (OI) SST V2 dataset to invert cloud-only LWP to Total (cloud+rain) LWP based on cloud/rainwater partitioning strictly varying with SST. Co-located AMSR-E/MODIS data have revealed the presence of a bias in Cloud LWP (positive Cloud LWP when there should be none). Thus, all sensor and scene 0.25 Degree Cloud LWP data are bias-corrected to account for this discrepancy.
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history:
This MAC-LWP Version 1 field, created on Feb 2017, uses a cloud/rainfall partitioning equation to back-out TotalLWP from CloudLWP (based on SST). The MAC-LWP product, further described in Elsaesser et al. (2017, J. Climate), is an extension of the heritage UWisc CloudLWP climatology (O'Dell et al. 2008, J. Climate). The use of the latest Remote Sensing Systems (RSS) input datasets, several new satellite sensors, a total (cloud+rain) liquid water path field, and a cloud water bias correction are new relative to the UWisc product.
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references:
MAC-LWP product and data fields are described in Elsaesser et al. 2017 (J. Climate).
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comments:
Negative numbers should be treated as 0.0 for monthly cloud or total LWP. However, they must be taken into account (i.e. averaged) in climate studies that perform spatial/temporal averaging of these data to avoid biasing.
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contact:
Greg Elsaesser: gregory.elsaesser@columbia.edu; Chris O'Dell: odell@atmos.colostate.edu; Matt Lebsock: matthew.d.lebsock@jpl.nasa.gov; Frank Wentz: frank.wentz@remss.com.
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Conventions:
CF-1.6
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