CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms

Minnis, Patrick; Sun-Mack, S.; Young, D. F.; Heck, Patrick W.; Garber, Donald P.; Chen, Yan; Spangenberg, Douglas A.; Arduini, Robert F.; Trepte, Qing Z.; Smith, William L.; Ayers, J. Kirk; Gibson, Sharon; Miller, Walter F.; Hong, Gang; Chakrapani, V.; Takano, Y.; Liou, Kuo‐Nan; Xie, Yu; Yang, Ping

doi:10.1109/tgrs.2011.2144601

Cited by 454 publications

(457 citation statements)

References 38 publications

Supporting

Mentioning

446

Contrasting

Order By: Relevance

“…Gan et al (2014) found close agreement between AOD measurements at the Bondville SURFRAD site and nearby AERONET sites, so we assume that AERONET aerosol properties represent those at SURFRAD. For information on thin cirrus clouds, we rely on cloud fraction, cloud water path, and ice and liquid radius data retrieved from the CERES instrument onboard the Terra and Aqua satellites (Minnis et al, 2011). CERES thin cirrus cloud optical depths have been shown to those retrieved by the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (launched in 2006) over land (r = 0.65).…”

Section: Methodsmentioning

confidence: 99%

Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

et al. 2017

View full text Add to dashboard Cite

Abstract. In situ surface observations show that downward surface solar radiation (SWdn) over the central and southeastern United States (US) has increased by 0.58-1.0 Wm −2 a −1 over the 2000-2014 time frame, simultaneously with reductions in US aerosol optical depth (AOD) of 3.3-5.0 × 10 −3 a −1 . Establishing a link between these two trends, however, is challenging due to complex interactions between aerosols, clouds, and radiation. Here we investigate the clear-sky aerosol-radiation effects of decreasing US aerosols on SWdn and other surface variables by applying a one-dimensional radiative transfer to 2000-2014 measurements of AOD at two Surface Radiation Budget Network (SURFRAD) sites in the central and southeastern United States. Observations characterized as "clear-sky" may in fact include the effects of thin cirrus clouds, and we consider these effects by imposing satellite data from the Clouds and Earth's Radiant Energy System (CERES) into the radiative transfer model. The model predicts that 2000-2014 trends in aerosols may have driven clear-sky SWdn trends of +1.35 Wm −2 a −1 at Goodwin Creek, MS, and +0.93 Wm −2 a −1 at Bondville, IL. While these results are consistent in sign with observed trends, a cross-validated multivariate regression analysis shows that AOD reproduces 20-26 % of the seasonal (June-September, JJAS) variability in clear-sky direct and diffuse SWdn at Bondville, IL, but none of the JJAS variability at Goodwin Creek, MS. Using in situ soil and surface flux measurements from the Ameriflux network and Illinois Climate Network (ICN) together with assimilated meteorology from the North American Land Data Assimilation System (NLDAS), we find that sunnier summers tend to coincide with increased surface air temperature and soil moisture deficits in the central US. The 1990-2015 trends in the NLDAS SWdn over the central US are also of a similar magnitude to our modeled 2000-2014 clear-sky trends. Taken together, these results suggest that climate and regional hydrology in the central US are sensitive to the recent reductions in aerosol concentrations. Our work has implications for severely polluted regions outside the US, where improvements in air quality due to reductions in the aerosol burden could inadvertently pose an enhanced climate risk.

show abstract

Section: Methodsmentioning

confidence: 99%

Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

et al. 2017

View full text Add to dashboard Cite

show abstract

“…For each RXS MODIS pixel, effective visible optical depth τ eff , effective particle size r eff and particle phase were retrieved based on inversion of a 1D radiative transfer model (Minnis et al, 2010a). If CloudSat's columnar classification (Sassen and Wang, 2008) is cirrus, altostratus, altocumulus or deep convection, those cells in the column identified as cloud with temperatures < 273 K are classed as ice and r eff is set uniformly to the value inferred from MODIS data (Minnis et al, 2008(Minnis et al, , 2010a(Minnis et al, , 2010b.…”

Section: Merged A-train Datamentioning

confidence: 99%

“…If CloudSat's columnar classification (Sassen and Wang, 2008) is cirrus, altostratus, altocumulus or deep convection, those cells in the column identified as cloud with temperatures < 273 K are classed as ice and r eff is set uniformly to the value inferred from MODIS data (Minnis et al, 2008(Minnis et al, , 2010a(Minnis et al, , 2010b. Warmer cells in the column are set to liquid with r eff = 10 µm.…”

Section: Merged A-train Datamentioning

confidence: 99%

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Barker

Jerg

Wehr

et al. 2011

Quart J Royal Meteoro Soc

144

View full text Add to dashboard Cite

† The contribution of H. W. Barker to this article was prepared as part of his official duties as a Canadian government employee This article presents and assesses an algorithm that constructs 3D distributions of cloud from passive satellite imagery and collocated 2D nadir profiles of cloud properties inferred synergistically from lidar, cloud radar and imager data. It effectively widens the active-passive retrieved cross-section (RXS) of cloud properties, thereby enabling computation of radiative fluxes and radiances that can be compared with measured values in an attempt to perform radiative closure experiments that aim to assess the RXS. For this introductory study, A-train data were used to verify the scene-construction algorithm and only 1D radiative transfer calculations were performed.The construction algorithm fills off-RXS recipient pixels by computing sums of squared differences (a cost function F) between their spectral radiances and those of potential donor pixels/columns on the RXS. Of the RXS pixels with F lower than a certain value, the one with the smallest Euclidean distance to the recipient pixel is designated as the donor, and its retrieved cloud properties and other attributes such as 1D radiative heating rates are consigned to the recipient. It is shown that both the RXS itself and Moderate Resolution Imaging Spectroradiometer (MODIS) imagery can be reconstructed extremely well using just visible and thermal infrared channels. Suitable donors usually lie within 10 km of the recipient. RXSs and their associated radiative heating profiles are reconstructed best for extensive planar clouds and less reliably for broken convective clouds. Domain-average 1D broadband radiative fluxes at the top of the atmosphere (TOA) for (21 km) 2 domains constructed from MODIS, CloudSat and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) data agree well with coincidental values derived from Clouds and the Earth's Radiant Energy System (CERES) radiances: differences between modelled and measured reflected shortwave fluxes are within ±10 W m −2 for ∼35% of the several hundred domains constructed for eight orbits. Correspondingly, for outgoing longwave radiation ∼65% are within ±10 W m −2 .

show abstract

“…The same properties can be retrieved for optically thin cirrus clouds during night as well using only thermal observations (e.g. Prabhakara et al, 1988;Ackerman et al, 1990;Yue and Liou, 2009;Minnis et al, 2011;Heidinger et al, 2015;Wang et al, 2016), but with a limited accuracy due to the low sensitivity to large ice crystal sizes and large optical thicknesses.…”

Section: Introductionmentioning

confidence: 99%

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

et al. 2017

View full text Add to dashboard Cite

Abstract. Cirrus clouds play an important role in climate as they tend to warm the Earth-atmosphere system. Nevertheless their physical properties remain one of the largest sources of uncertainty in atmospheric research. To better understand the physical processes of cirrus clouds and their climate impact, enhanced satellite observations are necessary. In this paper we present a new algorithm, CiPS (Cirrus Properties from SEVIRI), that detects cirrus clouds and retrieves the corresponding cloud top height, ice optical thickness and ice water path using the SEVIRI imager aboard the geostationary Meteosat Second Generation satellites. CiPS utilises a set of artificial neural networks trained with SE-VIRI thermal observations, CALIOP backscatter products, the ECMWF surface temperature and auxiliary data.CiPS detects 71 and 95 % of all cirrus clouds with an optical thickness of 0.1 and 1.0, respectively, that are retrieved by CALIOP. Among the cirrus-free pixels, CiPS classifies 96 % correctly. With respect to CALIOP, the cloud top height retrieved by CiPS has a mean absolute percentage error of 10 % or less for cirrus clouds with a top height greater than 8 km. For the ice optical thickness, CiPS has a mean absolute percentage error of 50 % or less for cirrus clouds with an optical thickness between 0.35 and 1.8 and of 100 % or less for cirrus clouds with an optical thickness down to 0.07 with respect to the optical thickness retrieved by CALIOP. The ice water path retrieved by CiPS shows a similar performance, with mean absolute percentage errors of 100 % or less for cirrus clouds with an ice water path down to 1.7 g m −2 . Since the training reference data from CALIOP only include ice water path and optical thickness for comparably thin clouds, CiPS also retrieves an opacity flag, which tells us whether a retrieved cirrus is likely to be too thick for CiPS to accurately derive the ice water path and optical thickness.By retrieving CALIOP-like cirrus properties with the large spatial coverage and high temporal resolution of SEVIRI during both day and night, CiPS is a powerful tool for analysing the temporal evolution of cirrus clouds including their optical and physical properties. To demonstrate this, the life cycle of a thin cirrus cloud is analysed.

show abstract

CERES Edition-2 Cloud Property Retrievals Using TRMM VIRS and Terra and Aqua MODIS Data—Part I: Algorithms

Cited by 454 publications

References 38 publications

Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

Aerosol trends as a potential driver of regional climate in the central United States: evidence from observations

A 3D cloud‐construction algorithm for the EarthCARE satellite mission

Cirrus cloud retrieval with MSG/SEVIRI using artificial neural networks

Contact Info

Product

Resources

About