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Global Attributes:
NC_GLOBAL.title: Multisensor Advanced Climatology of LWP (MAC-LWP) Cloud+Rain LWP (TotalLWP) NC_GLOBAL.Identifier_Product_DOI_Authority: http://dx.doi.org/ NC_GLOBAL.Identifier_Product_DOI: 10.5067/MEASURES/MACTWPM NC_GLOBAL.institution: Columbia University/NASA GISS, New York, NY; and Colorado State University, Fort Collins, CO NC_GLOBAL.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. NC_GLOBAL.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. NC_GLOBAL.references: MAC-LWP product and data fields are described in Elsaesser et al. 2017 (J. Climate). NC_GLOBAL.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. NC_GLOBAL.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. NC_GLOBAL.Conventions: CF-1.6 DODS_EXTRA.Unlimited_Dimension: time
Variables:
lon
: Array of 32 bit Reals [lon = 0..359]
lon:
long_name: longitude units: degrees_east axis: X bounds: lon_bnds
lon_bnds
: Array of 32 bit Reals [lon = 0..359][bnds = 0..1]
lon:
bnds:
lat
: Array of 32 bit Reals [lat = 0..179]
lat:
long_name: latitude units: degrees_north axis: Y bounds: lat_bnds
lat_bnds
: Array of 32 bit Reals [lat = 0..179][bnds = 0..1]
lat:
bnds:
time
: Array of 16 bit Integers [time = 0..11]
time:
long_name: Month index (0-11); 0=January, ..., 11=December units: months since 2001-01-01 00:00:00 axis: T
totallwp
: Grid of Array of 32 bit Reals [time = 0..11][lat = 0..179][lon = 0..359]
time:
lat:
lon:
totallwp.long_name: Monthly Average Total Cloud+Rain Liquid Water path totallwp.units: g/m^2 totallwp.missing_value: -999.000000 totallwp._FillValue: -999.000000 totallwp.comment: Corrected for diurnal cycle. Contains both liquid cloud and rain components. time.long_name: Month index (0-11); 0=January, ..., 11=December time.units: months since 2001-01-01 00:00:00 time.axis: T lat.long_name: latitude lat.units: degrees_north lat.axis: Y lat.bounds: lat_bnds lon.long_name: longitude lon.units: degrees_east lon.axis: X lon.bounds: lon_bnds
totallwp_error
: Grid of Array of 32 bit Reals [time = 0..11][lat = 0..179][lon = 0..359]
time:
lat:
lon:
totallwp_error.long_name: 1-sigma Error on Monthly Average Total Cloud+Rain Liquid Water path totallwp_error.units: g/m^2 totallwp_error.missing_value: -999.000000 totallwp_error._FillValue: -999.000000 totallwp_error.comment: Approximate statistical error only. Does not contain systematic errors, which are not fully known. time.long_name: Month index (0-11); 0=January, ..., 11=December time.units: months since 2001-01-01 00:00:00 time.axis: T lat.long_name: latitude lat.units: degrees_north lat.axis: Y lat.bounds: lat_bnds lon.long_name: longitude lon.units: degrees_east lon.axis: X lon.bounds: lon_bnds
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