Cloud top

Abstract: The Moderate Resolution Imaging Spectroradiometer (MODIS) is one of five instruments aboard the Terra Earth Observing System (EOS) platform launched in December 1999. After achieving final orbit, MODIS began Earth observations in late February 2000 and has been acquiring data since that time. The instrument is also being flown on the Aqua spacecraft, launched in May 2002. A comprehensive set of remote sensing algorithms for cloud detection and the retrieval of cloud physical and optical properties have been developed by members of the MODIS atmosphere science team. The archived products from these algorithms have applications in climate change studies, climate modeling, numerical weather prediction, as well as fundamental atmospheric research. In addition to an extensive cloud mask, products include cloud-top properties (temperature, pressure, effective emissivity), cloud thermodynamic phase, cloud optical and microphysical parameters (optical thickness, effective particle radius, water path), as well as derived statistics. We will describe the various algorithms being used for the remote sensing of cloud properties from MODIS data with an emphasis on the pixel-level retrievals (referred to as Level-2 products), with 1-km or 5-km spatial resolution at nadir. An example of each Level-2 cloud product from a common data granule (5 min of data) off the coast of South America will be discussed. Future efforts will also be mentioned. Relevant points related to the global gridded statistics products (Level-3) are highlighted though additional details are given in an accompanying paper in this issue.

Abstract: The Moderate-Resolution Imaging Spectroradiometer (MODIS) level-2 (L2) cloud product (earth science data set names MOD06 and MYD06 for Terra and Aqua MODIS, respectively) provides pixel-level retrievals of cloud top properties (day and night pressure, temperature, and height) and cloud optical properties (optical thickness, effective particle radius, and water path for both liquid water and ice cloud thermodynamic phases—daytime only) Collection 6 (C6) reprocessing of the product was completed in May 2014 and March 2015 for MODIS Aqua and Terra, respectively Here we provide an overview of major C6 optical property algorithm changes relative to the previous Collection 5 (C5) product Notable C6 optical and microphysical algorithm changes include: 1) new ice cloud optical property models and a more extensive cloud radiative transfer code lookup table (LUT) approach; 2) improvement in the skill of the shortwave-derived cloud thermodynamic phase; 3) separate cloud effective radius retrieval data sets for each spectral combination used in previous collections; 4) separate retrievals for partly cloudy pixels and those associated with cloud edges; 5) failure metrics that provide diagnostic information for pixels having observations that fall outside the LUT solution space; and 6) enhanced pixel-level retrieval uncertainty calculations The C6 algorithm changes can collectively result in significant changes relative to C5, though the magnitude depends on the data set and the pixel’s retrieval location in the cloud parameter space Example L2 granule and level-3 gridded data set differences between the two collections are shown While the emphasis is on the suite of cloud optical property data sets, other MODIS cloud data sets are discussed when relevant

Abstract: An annual cycle of cloud and radiation measurements made as part of the Surface Heat Budget of the Arctic (SHEBA) program are utilized to determine which properties of Arctic clouds control the surface radiation balance. Surface cloud radiative forcing (CF), defined as the difference between the all-sky and clear-sky net surface radiative fluxes, was calculated from ground-based measurements of broadband fluxes and results from a clear-sky model. Longwave cloud forcing (CFLW) is shown to be a function of cloud temperature, height, and emissivity (i.e., microphysics). Shortwave cloud forcing (CFSW) is a function of cloud transmittance, surface albedo, and the solar zenith angle. The annual cycle of Arctic CF reveals cloud-induced surface warming through most of the year and a short period of surface cooling in the middle of summer, when cloud shading effects overwhelm cloud greenhouse effects. The sensitivity of CFLW to cloud fraction is about 0.65 W m−2 per percent cloudiness. The sensitivity of ...

Abstract: The role of fractional area coverage by cloud types in the energy balance of the earth is investigated through joint use of International Satellite Cloud Climatology Project (ISCCP) C1 cloud data and Earth Radiation Budget Experiment (ERBE) broadband energy flux data for the one-year period March 1985 through February 1986. Multiple linear regression is used to relate the radiation budget data to the cloud data. Comparing cloud forcing estimates obtained from the ISCCP-ERBE regression with those derived from the ERBE scene identification shows generally good agreement except over snow, in tropical convective regions, and in regions that are either nearly cloudless or always overcast. It is suggested that a substantial fraction of the disagreement in longwave cloud forcing in tropical convective regions is associated with the fact that the ERBE scene identification does not take into account variations in upper-tropospheric water vapor. On a global average basis, low clouds make the largest contri...