ESSDEarth System Science DataESSDEarth Syst. Sci. Data1866-3516Copernicus PublicationsGöttingen, Germany10.5194/essd-10-583-2018The International Satellite Cloud Climatology Project
H-Series climate data record productISCCP H-series CDR productYoungAlisa H.alisa.young@noaa.govKnappKenneth R.InamdarAnandHankinsWilliamRossowWilliam B.NOAA's National Centers for Environmental Information, 325 S. Broadway, Boulder, CO 80305, USANOAA's National Centers for Environmental Information, 151 Patton Ave, Asheville, NC 28801, USACooperative Institute for Climate and Satellites, North Carolina State University, Asheville, NC 28801, USAERT, Inc., Asheville, NC 28801, USANOAA/CREST, City College of the City University of New York, New York, NY 10031, USAAlisa H. Young (alisa.young@noaa.gov)16March201810158359312July20179August201713December201713December2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://essd.copernicus.org/articles/10/583/2018/essd-10-583-2018.htmlThe full text article is available as a PDF file from https://essd.copernicus.org/articles/10/583/2018/essd-10-583-2018.pdf
This paper describes the new global long-term International Satellite Cloud
Climatology Project (ISCCP) H-series climate data record (CDR). The H-series
data contain a suite of level 2 and 3 products for monitoring the
distribution and variation of cloud and surface properties to better
understand the effects of clouds on climate, the radiation budget, and the
global hydrologic cycle. This product is currently available for public use
and is derived from both geostationary and polar-orbiting satellite imaging
radiometers with common visible and infrared (IR) channels. The H-series data
currently span July 1983 to December 2009 with plans for continued
production to extend the record to the present with regular updates. The
H-series data are the longest combined geostationary and polar orbiter
satellite-based CDR of cloud properties. Access to the data is provided in
network common data form (netCDF) and archived by NOAA's National Centers for
Environmental Information (NCEI) under the satellite Climate Data Record
Program (https://doi.org/10.7289/V5QZ281S). The basic
characteristics, history, and evolution of the dataset are presented herein
with particular emphasis on and discussion of product changes between the
H-series and the widely used predecessor D-series product which also spans
from July 1983 through December 2009. Key refinements included in the ISCCP
H-series CDR are based on improved quality control measures, modified
ancillary inputs, higher spatial resolution input and output products,
calibration refinements, and updated documentation and metadata to bring the
H-series product into compliance with existing standards for climate data
records.
ISCCP ten most cited papers that have contributed to the dataset's
more than 15 000 citations. The number of citations given here is based on
Google Analytics, accessed February 10, 2017.
Introduction
The International Satellite and Cloud Climatology Project
(ISCCP) was established in 1982. Its intent was to produce a global, reduced-resolution, calibrated infrared and visible radiance dataset with basic
information on surface and atmospheric radiative properties and to derive
global cloud characteristics from satellite data (Schiffer and Rossow, 1983).
Today, ISCCP is the longest-running international satellite-based global
environmental data project. It delivers a record spanning over 25 years of global cloud
and surface radiative properties obtained from radiance images from
geostationary and polar-orbiting satellites. As a mark of the dataset's
value, it has been cited in more than 15 000 articles, with Rossow and
Schiffer (1999) receiving over 1800 citations (Fig. 1) and continuing. This
achievement can be attributed to the precedent set by the World Climate
Research Program that established ISCCP and utilized international
collaborations to obtain, process, distribute, and archive data from US- and
non-US-operated geostationary and polar imaging meteorological satellites.
The collection of ISCCP applications and analyses demonstrate that ISCCP has
made a significant contribution to advancing climate science and assessment.
However, the widely used ISCCP D-series product has not been updated since
December 2009. Moreover, several studies have evaluated the product to
highlight specific opportunities to advance the dataset (Rossow and Ferrier,
2015; Evan et al., 2007; Norris, 2000; Rossow and Schiffer, 1999; Stubenrauch
et al., 2013) and take further advantage of its record, spanning over 25 years, to improve
its capability to estimate long-term trends in global cloudiness. This detail
is relevant considering newer cloud datasets that have shorter records but
improved capabilities for cloud detection and retrieval due to technological
advancements that include active spaceborne sensors (e.g.,
Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations
– CALIPSO – and CloudSat) and cloud datasets that rely on newer passive imagers
with higher spectral, spatial, radiometric, and temporal resolutions
(Platnick et al., 2003; Hutchison et al., 2005; Stengel et al., 2017).
To build on ISCCP's legacy and further advance the dataset in light of these
advancements, in 2004, a large data stewardship effort by the National
Climatic Data Center (now known as the National Centers for Environmental
Information – NCEI) led to the rescue of ISCCP B1 data with ∼ 10 km
and 3-hourly spatial and temporal resolution (Knapp, 2008). This effort set
the stage for ISCCP B1U (uniformly formatted B1) data to serve as the new
geostationary satellite data input to ISCCP processing. The NASA MEaSUREs
(Making Earth Science Data Records for Use in Research Environments) and NOAA
climate data record programs have served as resources for implementing
product updates that exploit the higher resolution B1U and global area
coverage (GAC) AVHRR data and more recent research results. The latter
includes results from the Global Energy and Water Cycle Experiment (GEWEX)
cloud assessment in which a special version of the ISCCP D-series level 3
monthly product with 1∘ spatial resolution was compared with 11 other
“state-of-the-art” cloud datasets from active and passive remote sensors
(Stubenrauch et al., 2013). Relative geographical and seasonal variations in
the cloud properties agree very well (with only a few exceptions, like
deserts and snow-covered regions). Discrepancies among the various products
for detection and retrieval of cloud properties were mainly due to the use of
different spectral domains and instrument performance. However, some of the
results from these and other evaluations (e.g., Evan et al., 2007;
Jiménez, et al., 2012) have led to algorithmic changes for production of
ISCCP H-series data described herein.
To document these updates, this paper gives a description of the new ISCCP
H-series product with specific emphasis on the changes in the algorithm and
products in transitioning from the D-series (Rossow and Schiffer, 1999) to
the H-series. The more complete version of all the product updates are
contained in the Climate-Algorithm Theoretical Basis Document (Rossow,
2017). The sections below provide a description of the newly developed
H-series collection, comparison with its predecessor D-series product,
details for data access, caveats, and plans for future development under the
stewardship of NOAA's NCEI.
ISCCP H-series processing
Like the ISCCP D-series products, the primary instruments that serve as
inputs to the ISCCP H-series analysis are the imaging radiometers on
operational weather satellites. These include the Advanced Very High
Resolution Radiometer (AVHRR) on the polar-orbiting satellites and a variety
of imagers (Rossow, 2017) that fly onboard the geostationary meteorological
satellites. ISCCP handles these data using seven data-processing streams.
Both the geostationary and polar orbiter (AVHRR GAC) data have been sampled
to ∼ 10km spatial resolution. The ISCCP data-processing streams are
labeled by the originating satellites and are provided in Fig. 2, in which the
ISCCP general processing for pixel-level cloud detection and retrievals is
illustrated. The seven data-processing streams are given by the following.
GMS: Japanese Geostationary Meteorological Satellite with a subsatellite longitude of
∼ 140∘ E;
INS: Indian ocean sector coverage with a subsatellite longitude at
∼ 63∘ E;
MET: European and African sector coverage with a subsatellite longitude of
∼ 0∘.
Illustration of ISCCP production with satellite processing streams
defined for five geostationary data streams (GMS at 140∘ E, MET at
0∘, GOE at 75∘ W, GOW at 135∘ W and INS at
∼ 63∘ E) and two polar orbiter streams (NOM and NOA). The left
side of the image shows important steps in ISCCP H-series data processing
that feed into the various H-series products.
GOE: Eastern United States and South American coverage with a subsatellite longitude of
75∘ W;
GOW: Pacific Ocean and western United States coverage with a subsatellite longitude of
135∘ W;
NOA: afternoon polar-orbiting satellite stream;
NOM: morning polar-orbiting satellite stream.
In the mapping step, data are
mapped to a 10 km grid. Geostationary data are preferred between
55∘ N and 55∘ S. If more than one geostationary satellite is available, the
geostationary satellite with the larger cosine of the satellite view zenith
angle is preferred. The afternoon polar orbiter results are used if no
geostationary results are available and, finally, the morning polar orbiter is
used if no geostationary or afternoon polar orbiter data are available.
Likewise, polar orbiter data are preferred poleward of 55∘ N/S but
may rely on geostationary results in the absence of PO data. The combination
of the geostationary and polar-orbiting satellites allows ISCCP to establish
an intercalibration procedure in which radiances from imagers onboard the
geostationary satellites are normalized to the low-earth-orbit AVHRR
radiances from the afternoon polar orbiter satellite series. In this
approach, NOAA-9 acts as the absolute reference through 2009 (Rossow and
Ferrier, 2015). As the H-series dataset is processed forward in time, NOAA-18
will serve this function. Although most of the imaging radiometers make
measurements of radiation emitted from earth at multiple spectral
wavelengths, the H-series product uses only one visible
(VIS ≈0.65±0.05–0.20 µm) and infrared
(IR ≈10.5±0.5–0.75 µm) “window” channel to
derive cloud and surface properties. In previous versions of the ISCCP, data
products have relied on B3 data with 3-hourly and 30 km temporal and spatial
resolution (Schiffer and Rossow, 1985). However, the
primary geostationary input to ISCCP H-series is B1U data, which have 3-hourly and
∼ 10 km temporal and spatial resolutions. ISCCP ancillary products
have also undergone modifications following recommendations from Raschke et
al. (2006). Table 1 shows the details of D- to H-series ancillary product
changes. In general, the updated input and ancillary data products yield a
more consistent record for the reprocessing of higher resolution cloud
products.
ISCCP H-series cloud detection
The ISCCP H-series cloud detection algorithm and retrievals are generally
minor revisions of the D-series algorithm and retrievals that mostly serve to
reduce uncertainties. The algorithm is largely described by four steps
following the re-mapping step shown in Fig. 2. First, tests of the space and
time variations of the observed radiances on several scales are used to
estimate cloud-free radiances (B4). Results of the space–time tests are used
in conjunction with the ancillary products to obtain a global composite of
clear-sky radiances for each image pixel location and time (CLRSKY). Second,
cloudy conditions are diagnosed when IR- or VIS-observed satellite radiances
sufficiently deviate from estimated values using various combinations of VIS
and IR thresholds (BX) (Rossow and Garder, 1993a, b; Rossow et al., 1993).
From here, the composite clear-sky radiances are revised based on the prior
detection threshold results and application of revised threshold tests of
each image's pixels against the revised composite clear-sky radiance values
using the ancillary products (CY). Then finally, cloud and surface properties
are retrieved producing the HXS product (see Rossow and Schiffer, 1991,
1999). These steps summarize the ISCCP processing system subroutines (B4PROD
(B4), CLRSKY, BX, and CY) referenced in Fig. 2.
List of H-series and D-series ancillary data products including in
producing ISCCP cloud and surface products; n/a: not
applicable.
VersionProductDescriptionProduct referenceProductresolutionAtmosphericHnnHIRSNeural network analysis of High-resolutionShi et al. (2016)3-hourly globalprofilesInfrared Radiometer Sounder (HIRS)1∘ equivalentand stratospheric water and ozoneequal-areasatellite homogenized data. Data are reported at 16 vertical levels.gridDTOVSAtmosphere and surface data includingDaily 280 kmtemperature structure, water, and ozoneequivalentabundances obtained from the TIROSequal-areaOperational Vertical Sounding (TOVS)gridProduct and supplemented by twoclimatologies Sounding (TOVS) Productand supplemented by the NOAA GFDLtemperature–humidity climatology (Oort, 1983)and an ozone climatology from the NIMBUS-4BUV data (Hilsenrath and Schlesinger, 1981).Data are reported at 10 different levels.AEROSOLHMAC-v1Merges surface-based aerosol emissionKinne et al. (2013)Monthlydata from AERONET and satellite1∘ equivalentproducts from MODIS and MISR, withequal-areathe median results from an ensemblegridof emission-transport models.Dn/aOZONEHTOMS,Daily variations of the global distributionStolarski et al. (1991),Daily 1∘abundanceTOVS,of total column ozone abundance fromKroon et al. (2011),equivalentSMOBA,a combination of satellite-based instruments.Chesters and Neuendorffer (1991),equal-areaOMINeuendorffer et al. (1996),gridYan et al. (2006)DTOVSThe main dataset used to produce a daily,ISCCP Science TeamDaily 280 kmglobal description of the ozone, temperature,10.5067/ISCCP/TOVS_NATequivalentand humidity distributions is that obtainedequal-areafrom the analysis of datagridfrom the TIROS Operational VerticalSounder (TOVS) System.SNOW/ICEHNorthern Hemisphere EASE-Grid Weekly Snow CoverBrown and Robinson (2011),Daily 0.25∘coverand Sea Ice Extent (Version3), NOAA NSIDC IMSArmstrong and Brodzik (2005)equivalentfractionDaily Northern Hemisphere Snow and Ice Analysis,equal-areaOSI-SAF Global Sea Ice Concentration ReprocessinggridDataset, GLIMS permanent glacier cover product.DAverages of snow and sea ice fractional coverageRossow et al. (1996)1∘ equaldeduced from ship or shore station reports and satellitearea grid,visible, infrared, and microwave imagery data.5-day, globalTOPOHUSGS Earth Resources Observation and ScienceThese data are available fromFixed 0.1∘(EROS) GTOPO30 product reconciled withthe US Geological Surveyequivalentthe USGS Global Land 1 km AVHRR Projectequal-arealand–water mask. The data were modified togridproduce a reconciled product for use in ISCCP.Dn/aSURFACETYPEHMODIS International Geosphere–BiosphereLoveland et al. (2000)Fixed 0.10∘Programme (IGBP) surfaceequivalenttype classification.equal-area gridDGlobal Vegetation Types, 1971–1982;Matthews (1983)Fixed 1.0∘A global digital database of vegetation was10.3334/ORNLDAAC/419compiled at 1∘ latitude by 1∘ longitude resolution,drawing on approximately 100 published sources.
High-level summary of differences between ISCCP D-series and
H-series products and their impacts. Other details on differences are
provided in the C-ATBD; n/a: not applicable.
D-seriesH-seriesImpactsInput resolution30 km, B3/GAC10 km, B1U/GACHigher spatial resolution output productsCloud algorithmCombination of IR and VIS, andOnly IR and VIS forIncreases low-level cloud sensitivityNIR channels in polar regionsentire period of recordover snow and ice in polar regionsMetadatan/aCF 1.6Improved documentation forproduct reproducibilityImproved/addedExample: Mac-v1 AerosolsReduces cloud optical thickness inancillary dataProductregions with relatively high aerosolabundances.Period of07/1983–12/2009 with no07/1983–12/2009Restores research application ofrecordadditional productionwith additional productionISCCP data post-2009planned to extend PORplanned to extend PORFormatBinarynetCDF for all productsSupported by netCDF software applicationsexcept HXSand tools.Available productsDX (∼ 30 km, 3 hourly)HXS (∼ 10 km, 3 hourly)n/aHXG (0.1∘, 3 hourly)DS (not released)HGS (1.0∘, 3 hourly)D1 (2.5∘, 3 hourly)HGG (1.0∘, 3 hourly)D2 (2.5∘,HGH (1.0∘,3-hourly monthly avg.)3-hourly monthly avg.)D3 (2.5∘, monthly avg.)HGM (1.0∘, monthly avg.)
Differences between the D- and H-series cloud detection algorithms include the
following modifications: (1) a new radiance space contrast test inside
regions of land–water mixtures, (2) updated surface type categories for
algorithm tests to improve cloud tests in rough topography, (3) revised
daytime cloud detection over snow and ice by eliminating 3.7 µm
tests since this channel is not available for all AVHRR datasets over the
whole period of record and implemented simpler test for reversed VIS radiance
contrast situations to improve homogeneity of record, (4) improved summertime
polar cloud detection by reducing VIS thresholds over snow and ice, and
(5) improved wintertime polar cloud detection by changing marginally cloudy
to clear and marginally clear to cloudy. Otherwise, the current H version
(v01r00) of the ISCCP cloud detection algorithm is the same as the D version
which is a modification of the C version. Hence, all publications regarding
the first two versions of ISCCP products are also relevant to the H-series
algorithm. Likewise, the differences in the D- and H-series surface and cloud
retrievals are generally due to small changes in the assumptions in the
radiative transfer calculations on which they are based. The most notable
changes are listed in the next section.
ISCCP H-series productsH-series products
Table 2 provides a summary of the differences between the ISCCP D- and
H-series products. The ISCCP D-series algorithm relied on ISCCP Stage B3 data
with spatial and temporal resolutions of 30 km and 3 h for geostationary
satellites. Thus, the highest resolution D-series data produced the
30 km 3-hourly product for individual satellites known as DX. Downstream
level 3 products included D1 (global and 3 hourly) and D2 (monthly mean)
products on an equal area grid with a spatial interval of 280 km
(2.5∘ equivalent). In comparison, the ISCCP H-series products rely on
∼ 10 km and 3-hourly B1U data and polar orbiter data sampled to
∼ 10 km intervals. The level 2 products are HXS and HXG and level 3
products are HGS, HGG, HGH, and HGM. The products have the following
descriptions:
HXS (H-series pixel level by satellite) provides pixel-level results of
cloud and surface properties retrieved or used in the retrieval for each
individual satellite image in nearly the original projection for
geostationary satellites and for groupings of orbit swaths for polar orbiter
data in six midlatitude (ascending and descending swaths in 120∘
longitude sectors) and two polar sectors.
HXG (H-series pixel-level global) is a global merger of the information from
HXS common to all satellites and is mapped and provided every 3 h on a
0.10∘ equal angle grid (∼ 240 files per month).
HGS (H-series gridded by satellite) reduces the HXS Product to the
1∘ equal-angle grid with additional statistical and cloud type
information and combines these results with the information from the
ancillary data products prior to the global merger.
HGG (H-series gridded global) is the global merger of the HGS products from
all available satellites (e.g., all HGS files), in which overlapping coverage is
resolved in favor of the satellite with the best viewing geometry, with a
preference for geostationary results at lower latitudes and polar orbiter
results in the polar regions. The time interval is 3 h and the map grid is
1∘ equal-area grid. The HGG product is the H-series analogue to the
D1 product and should be regarded as the main ISCCP Cloud Product.
HGH (high-resolution global hourly) is the monthly 1∘ equal-area
gridded average of the HGG product at each of the eight 3-hourly
times of day (00Z, 03Z, 06Z, etc.) used in the ISCCP algorithm.
HGM (high-resolution global monthly) is the average of the eight HGH
products for each month.
All H-series products, except HXS, are formatted in netCDF-4. Other
differences in the D- and H-series products include (1) revisions in the
counts-to-physical conversion tables to remove special values for underflow
and overflow; (2) increased uncertainty estimate information; and (3) missing
observations are filled in the global, 3-hourly product (HGG) instead of the
monthly product (the HXG product is also filled). A subset of the HGG, HGH,
and HGM products are also available in a Climate and Forecast compliant equal angle format
known as ISCCP Basic, which has fewer variables and a total volume of
305 GB. Other changes between the D-series and H-series products include the
following.
Radiance calibrations from D version to H version:
anchor for VIS calibration extended to combine results for NOAA-9 (through 2009) and NOAA-18 (post-2009), spanning the whole
record;
overall IR calibration adjusted for small gain error in AVHRR calibrations compared to MODIS for all AVHRRs on NOAA-15 and onward (Cao and Heidinger, 2002).
geostationary normalization procedure changed to use all of the radiance data directly instead of a small number of special samples – manual
procedures eliminated (similar to that used by Inamdar and Knapp, 2015) and corrected the AVHRR KLM calibration error after 2001 (Evan et al., 2007).
VIS and IR Radiance Models from D version to H version:
replaced ocean VIS reflectance model with more accurate version that includes a better glint treatment.
added water vapor above 300 mb level in atmospheric ancillary
data;
added treatment of stratospheric and tropospheric aerosol scattering and
absorption;
improved surface temperature retrieval by accounting for variations of surface IR emissivity by surface
type;
introduced more explicit atmospheric and cloud vertical structures for cloud
retrievals;
changed specified liquid cloud droplet effective radius from 10 µm everywhere to 13 and 15 µm over land and ocean,
respectively;
changed cloud-top temperature value separating ice and liquid phase clouds from 260 to
253 K;
updated ice cloud scattering phase function to empirically based model from satellite polarimetry observations and revised specified ice particle effective radius
from 30 µm for all clouds to 20 and 34 µm for clouds with TAU < 3.55 and TAU ≥ 3.55,
respectively;
corrected placement of thin clouds from just above the tropopause to at the
tropopause;
added treatment of cloud-top location when surface temperature inversions are present.
updated solar ephemeris.
Product variables
Beginning with the original C-Series product, ISCCP has delivered an
extensive set of product variables. The cloud properties include (but are
not limited to) the following:
cloud amount
cloud-top temperature, TC (in Kelvins)
cloud-top pressure, PC (in mb)
cloud optical thickness, TAU (unitless)
cloud water path, CWP (in g m-2)
cloud phase
cloud type.
Surface properties include the following:
surface temperature, TS (in Kelvins)
surface reflectance, RS (unitless).
Separate procedures are used to produce these data under daytime versus
nighttime conditions (the nighttime procedure is applied day and night). In
the H-series basic product introduced in Sect. 4.1 these variables are
converted to their physical units. For a more detailed list of all ISCCP
variables, please refer to the ISCCP Climate-Algorithm Theoretical Basis
Document (Rossow, 2017).
January 2009 ISCCP percentage of global cloud amount for
(a) differences between H- and D-series, (b) H-series HGM
product at 1∘ and (c) D-series D3 product at 2.5∘.
As shown, in (a) the differences between the products are greatest
in the polar and coastal regions where for this case the H-series product has
a slightly higher cloud fraction. In general, the H- and D-series
distributions of cloud amount have good agreement.
Comparison of ISCCP H- (blue) and D-series (orange), and differences
between H- and D-series (black) monthly mean cloud fraction ( %) for
(a) total (land and water), (b) land only, and
(c) water only. For the secondary vertical axes, black numbers
represent positive differences and red numbers are negative. Data are for July
1983 through December 2009.
Basic characterization of the ISSCP H-series monthly cloud amount
Given the higher resolution of the B1U/GAC data, the H-series data yield cloud
characteristics with finer spatial detail and more robust spatial
distribution statistics. The improvements to the product take account of
recent research results concerning cloud properties that are assumed in the
retrieval and enhances its capabilities to assess cloud characteristics and
variability that occur on regional to global scales. Some impacts of the
changes are illustrated in Fig. 3, which shows the January 2009 monthly mean
ISCCP cloud amount for (a) percent (%) differences between H- and D-series,
(b) the H-series monthly mean (HGM) product at 1∘, and (c) the
D-series monthly mean (D3) product at 2.5∘. As shown in (a) H- and
D-series differences are greater in polar and coastal regions, mostly due to
the exclusion of the AVHRR NIR channel (3.7 µm) in the H-series
cloud algorithm (see Table 2). Differences are also present due to the higher
resolution input (B1U) data, which impacts the assessment of clear and cloudy
scenes (which increases the number of scenes with no cloud cover or total cloud cover),
to enhanced efforts to gather and/or limit undesirable radiance images from
processing and production via QC, and to changes in the analysis procedure
described in Sect. 2. Based upon these differences, the January 2009 HGM
product has a slightly lower global mean cloud fraction (cf. 65.46 %, H,
and 66.29 %, D). In general, the main cloud properties are very similar
on average. However, the grid-scale distributions have more noticeable
differences in the ratio of ice- and liquid-phase clouds and in the optical
thicknesses of thicker ice clouds in the polar regions.
In addition to the monthly H- and D-series comparison provided in Fig. 3,
which gives users a monthly snapshot of the H- and D-series CF differences
(i.e., H–D), Fig. 4 provides the comparison of ISCCP H- and D-series
monthly mean cloud fraction (%) for July 1983 through December 2009 for
(a) the globe, (b) land, and (c) water. The global mean differences are on
average ∼ 0.21 %. This demonstrates that the H-series product
generally captures a slightly higher cloud fraction compared to D-series
data. However, H- and D-series differences follow a seasonal pattern whereby the
average H-series CF for November through April is slightly lower than the
D-series product, and during May–October, H-series CF is slightly higher than
the D-series product: this difference is due mainly to the impact of the
algorithm changes over the polar regions, more significantly over Antarctica.
As displayed in Fig. 4b and c the monthly mean land cloud fraction for both
H- and D-series is generally less than the CF reported for water. The land CF also
reflects a higher percentage of the mean differences (0.16 %) compared to
water (-0.06 %). Other components of the comparison between H- and
D-series
data (not shown) reveal that the inclusion of MAC-v1 for the treatment of
stratospheric and tropospheric aerosols reduces the cloud optical thickness
in cases of larger aerosol amounts.
Product caveats
There are some caveats that users should be aware of that primarily involve
the absence of some data in the initial release of the product.
The following is a list of issues and caveats users should know.
General notes:
Calibration D to H – ISCCP H series calibration follows the method and
process of the ISCCP D series. Although a correction is applied for the AVHRR
NOAA KLM calibration error, most calibration issues present in ISCCP D are
also present in the H-series product. Users may refer to Brest and Rossow
(1992), Desormeaux et al. (1993), Brest et
al. (1997), Inamdar and Knapp (2015), and Rossow and
Ferrier (2015). All these citations, plus many others, are given in the
Climate Algorithm Theoretical Basis Document (C-ATBD).
Spatiotemporal analysis – ISCCP H series cloud algorithm is mostly
unchanged. The examination of the geographic distributions of average ISCCP
cloud amounts continues to show artifacts in association with large changes
in the average value of satellite zenith angle (Rossow and Garder, 1993b).
Satellite coverage – the ISCCP product is limited by the input geostationary
datasets. These have gaps in coverage that are large and small (seen in the
geostationary quilt, Knapp et al. (2011). The larger gaps are caused by
satellite outages, or gaps in the geostationary ring. The smaller gaps can be
up to a week in length and occur more often in the early years.
Specific issues:
MET-3 1995 – Many B1U files are missing the visible channel.
GMS-3 1986 – Many B1U files for February–April are missing the visible channel.
The afternoon Polar Orbiter data (NOM) has a 2-year gap from 2000 to 2002 for
the NOAA-15 to NOAA-17 transition. We have the data and just received status
for the AVHRR instrument for this period. This will be resolved in future
reprocessing.
There are occasional cloud-top pressure errors over the Pacific for May 1994
(and possibly other months). This is caused by large-view zenith angles in
glint regions.
Product access, availability, and future development
ISCCP H-series data are currently available for July 1983–December 2009 with
plans for updates beginning in December 2017 that will extend the record
forward in time to 2015. The record will be operationally maintained with
annual updates beginning in 2018. The NOAA Climate Data Record of the ISCCP
H-series product, version v01r00, is archived and distributed by NCEI's
satellite Climate Data Record Program. The ISCCP H-series products are
maintained by and available from NOAA. The full set of ISCCP CDR products, as
well as the ancillary data, are publicly available with points for access
given at https://www.ncdc.noaa.gov/isccp/isccp-data-access. The
processing code and the Climate Algorithm Theoretical Basis Document
(C-ATBD), which more fully outlines ISCCP H-series production, can be
accessed from
https://www.ncdc.noaa.gov/cdr/atmospheric/cloud-properties-isccp. The
ISCCP H-series Basic CDR product can be downloaded via FTP or from the NCEI
THREDDS data server (https://doi.org/10.7289/V5QZ281S). Users are also
requested to register at https://www.ncdc.noaa.gov/isccp.
The future development of the ISCCP H-series products includes the following:
setting up the ISCCP system to process the newer geostationary and polar
orbiter imagers (e.g., Himawari-8 and GOE S-R) to extend the data record
through the present with operational plans for annual updates;
improvements to satellite calibration particularly to increase the
calibration consistency between adjacent geostationary satellites;
continued efforts for backfilling missing data to develop a more complete
record.
The ISCCP H-Series climate data record, version v01r00, is
archived and distributed by NCEI's satellite Climate Data Record Program. The
ISCCP products are available from several access points that differ based on
the volume of data associated with each product. The complete ISCCP HXS, HXG,
and HGG data, which are considered the larger volume products, can be ordered
from ncei.sat.info@noaa.gov and ISCCP HGM/HGH and ISCCP Basic data can be
downloaded from
https://www.ncdc.noaa.gov/cdr/atmospheric/cloud-properties-isccp
(Rossow et al., 2016).
Conclusions
ISCCP H-series data are now a component of NOAA's suite of
climate data records and will be operationally produced and updated by NOAA
NCEI. Research users are encouraged to use the ISCCP products described
herein to investigate cloud processes in weather and climate. The ISCCP Basic
product is suitable for software applications that allow for ease in viewing
and handling netCDF files (i.e., Weather Climate Toolkit, Panoply, ToolsUI,
etc.). Future improvements and versions will be driven by user requirements.
The authors declare that they have no conflict of
interest.
Acknowledgements
This project received funding from the NOAA/NCEI Climate Data Record Program
and NASA MEaSUREs Program Support. The authors wish to thank Daniel Wunder,
Valerie Toner, Candace Hutchins, Jeff Budai, and the entire ISCCP Integrated
Product Team (IPT) at NCEI for supporting the Research to Operations (R2O)
process. Special appreciation goes to Alison Walker, Violeta Golea, and Cindy
Pearl for their revisions of the code and ancillary data products and the
testing of the revisions from D to H.
Edited by: David Carlson
Reviewed by: two anonymous referees
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