Abstract. 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.

[1] Land surface temperature (LST) and its diurnal variation are important when evaluating climate change, land-atmosphere energy exchange, and the global hydrological cycle. These characteristics are observable from satellites using thermal infrared measurements, but doing so at both high spatial and temporal resolutions has been difficult. Accurate temporal and spatial knowledge of LST is critical in global-scale hydrological assimilation to improve estimates of soil moisture and evapotranspiration. Historically, satellite retrieval of global LST at high spatial resolutions (1 km) has relied on NOAA polar-orbiting satellites recently augmented by Moderate Resolution Imaging Spectroradiometer (MODIS) data on board the Earth Observing System (EOS). Each satellite instrument in a polar orbit typically provides one to two observations per day. High temporal sampling of LST is achievable with geostationary satellites but at spatial resolutions is too coarse to distinguish different land surface types (4-5 km) and with lower accuracy. We describe an approach which employs MODIS data as a calibration source for Geostationary Environmental Satellite (GOES) data, then uses both data sets to yield half-hourly LST values, at 1 km spatial resolution, and returns LST with an accuracy better than 2°C. The approach requires good cloud clearing, atmospheric correction, and an underlying LST model to propagate values between observations. Retrieved LST against ground truth data indicate the approach is accurate to about 2°C.

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[1] Accurate temporal and spatial estimation of land surface temperatures (LST) is important for modeling the hydrological cycle at field to global scales because LSTs can improve estimates of soil moisture and evapotranspiration. Using remote sensing satellites, accurate LSTs could be routine, but unfortunately the only instruments available to provide diurnal cycle observations have coarse spatial resolution (4 km). One approach that may help overcome the spatial resolution constraint is to disaggregate geostationary LST data using visible to thermal infrared information provided by single time of day MODIS 1 km observations. These higher-resolution observations are correlative with observations at 4-km scales, and thus can be used to estimate 1-km LST values throughout a day. Inamdar et al 2008, for example, showed how GOES 10 imager and MODIS data could be combined to produce accurate half-hourly, 1-km LST values. However, the method disaggregated coarse LST values using Normalized Difference Vegetation Index (NDVI) data and was sometimes highly inaccurate when considering heterogeneous terrain. This problem can be greatly reduced with an alternative approach, whereby MODIS land cover emissivity data sets supply the needed 1-km information. In a study of LST estimation over the US Southwest, diurnal disaggregation models using emissivity data were significantly more accurate than a comparable NDVI-based model. This alternative approach which directly employs 8-day composites of MODIS 1 km emissivity is a simple and fast method. Citation: Inamdar, A. K., and A. French (2009), Disaggregation of GOES land surface temperatures using surface emissivity, Geophys. Res. Lett., 36, L02408,