OPeNDAP Server Dataset Access Form
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Global Attributes:
NC_GLOBAL.title: Multisensor Advanced Climatology of LWP (MAC-LWP) Cloud-Component Only LWP (CloudLWP) NC_GLOBAL.Identifier_Product_DOI_Authority: http://dx.doi.org/ NC_GLOBAL.Identifier_Product_DOI: 10.5067/MEASURES/MACLWPM 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. 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 was created on Feb 2017. 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 2015-01-01 00:00:00 axis: T
cloudlwp
: Grid of Array of 32 bit Reals [time = 0..11][lat = 0..179][lon = 0..359]
time:
lat:
lon:
cloudlwp.long_name: Monthly Average Cloud Liquid Water Path cloudlwp.units: g/m^2 cloudlwp.missing_value: -999.000000 cloudlwp._FillValue: -999.000000 cloudlwp.comment: Corrected for diurnal cycle. Contains cloud only; no rain component. time.long_name: Month index (0-11); 0=January, ..., 11=December time.units: months since 2015-01-01 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
cloudlwp_error
: Grid of Array of 32 bit Reals [time = 0..11][lat = 0..179][lon = 0..359]
time:
lat:
lon:
cloudlwp_error.long_name: 1-sigma Error on Monthly Average Cloud Liquid Water Path cloudlwp_error.units: g/m^2 cloudlwp_error.missing_value: -999.000000 cloudlwp_error._FillValue: -999.000000 cloudlwp_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 2015-01-01 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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