A Multisensor Perspective on the Radiative Impacts of Clouds and Aerosols
By David S. Henderson, Tristan L'Ecuyer, Graeme L. Stephens, Philip Partain, M. Sekiguchi. 2013
209 citations recorded by the source corpus.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/e3086a807be843518f352f3fea9a217e8e753fa2
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Atmospheric Science
- Seiji Kato
- Fred G. Rose
- David R. Doelling
- A. Matus
- David A. Rutan
- Tristan L'Ecuyer
- George J. Huffman
- Eric J. Fetzer
In Global and Planetary Change
- Seiji Kato
- Fred G. Rose
- David R. Doelling
- David A. Rutan
- A. Matus
- Thomas E. Caldwell
- Justin Sheffield
- Tristan L'Ecuyer
Citations in the corpus
- The role of cloud phase in Earth's radiation budget (2017, methodology): “To evaluate the performance of the FLXHR-LIDAR flux product, estimates of SW and LW fluxes at the TOA are compared with CERES single scanner footprint (SSF) fluxes reported in the CALIPSO, CloudSat, CERES, and MODIS (C3M) product [Kato et al., 2010].”
- A global survey of cloud overlap based on CALIPSO and CloudSat measurements (2015, methodology): “…and lidar-based aerosol retrievals as inputs for a broadband, two-stream, 187 plane-parallel, adding-and-doubling radiative transfer model, the 2B-FLXHR-LIDAR 188 product provides calculated radiative fluxes and atmospheric heating rates at 240 m 189 vertical increments (Henderson et al., 2013).”
- The Observed State of the Energy Budget in the Early Twenty-First Century (2015, methodology): “…2B-FLXHR-lidar algorithm, for example, blends information from CloudSat, CALIPSO, Unauthenticated | Downloaded 06/05/22 05:26 AM UTC MODIS, and AMSR-E to generate profiles of atmospheric radiative fluxes at high vertical and spatial resolutions (L’Ecuyer et al. 2008; Henderson et al. 2013).”
- CERES Synoptic Product: Methodology and Validation of Surface Radiant Flux (2015, methodology): “Stephens et al. (2012), Kato et al. (2011), and Henderson et al. (2013) use combined CERES, MODIS, CloudSat, and Cloud–Aerosol Lidar and InfraredPathfinder SatelliteObservations (CALIPSO) data to provide improved instantaneous surface radiative fluxes.”
- Cloud feedback mechanisms and their representation in global climate models (2017, unknown): “Clouds reflect solar radiation (negative SW CRE, global-mean effect of −45 W m) and reduce outgoing terrestrial radiation (positive LW CRE, 27 W m), with an overall cooling effect estimated at −18 W m (numbers from Henderson et al.(33)).”