Susceptibility of aerosol optical thickness retrievals to thin cirrus contamination during the BASE‐ASIA campaign

[1] We used a combination of ground measurements (Aerosol Robotic Network, AERONET; Micro-Pulse Lidar Network, MPLNET) and satellite data (Moderate Resolution Imaging Spectroradiometer, MODIS; Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation, CALIPSO) to examine the susceptibility of ground and satellite aerosol retrievals to thin cirrus contamination at Phimai, Thailand (102.56°E, 15.18°N, also known as Pimai), during the Biomass-burning Aerosols in South East-Asia: Smoke Impact Assessment (BASE-ASIA) campaign (February–May 2006). Using the strengths of spaceborne or ground lidars to detect cirrus clouds, we conducted statistical analyses for four different scenarios: MPLNET versus AERONET, MPLNET versus MODIS, CALIPSO versus AERONET, and CALIPSO versus MODIS. Cirrus identifications from MPLNET or CALIPSO were paired up with concurrent aerosol optical thickness (AOT) measurements from AERONET or MODIS. Results from the BASE-ASIA campaign suggest that current operational AERONET and MODIS AOT products are influenced by thin cirrus contamination featuring strong seasonality. Concurrent AERONET and MPLNET observations indicate that additional thin cirrus screening changes AOT monthly means by 5%, with 20% of the AERONET aerosol data at Phimai being cirrus contaminated in boreal spring. From noncirrus cases to cirrus-contaminated cases, AERONET AOT increases along with larger particle sizes. We further evaluated the performance of eight MODIS-derived cirrus screening parameters for their effectiveness on thin cirrus screening: apparent reflectance at 1.38 μm (R1.38), cirrus reflectance at 0.66 μm (CR0.66), CR0.66 cirrus flag, reflectance ratio between 1.38 μm and 0.66 μm (RR1.38/0.66), reflectance ratio between 1.38 μm and 1.24 μm (RR1.38/1.24), brightness temperature difference between 8.6 μm and 11 μm (BTD8.6–11), brightness temperature difference between 11 μm and 12 μm (BTD11–12), and cloud phase infrared approach. Correlation analysis with the MPLNET cirrus flag indicates that RR1.38/0.66 is slightly preferable for high thin cirrus screening for the AERONET AOT measurements. The quantitative findings from this study suggest particular caution and careful evaluation of thin cirrus contamination in the satellite and ground AOT measurements before they are used for aerosol-related climatic forcing studies.

[1]  Yoram J. Kaufman,et al.  Intercomparison of satellite retrieved aerosol optical depth over ocean during the period September 1997 to December 2000 , 2004 .

[2]  J. Hansen,et al.  Climate Effects of Black Carbon Aerosols in China and India , 2002, Science.

[3]  Lorraine Remer,et al.  A critical examination of the residual cloud contamination and diurnal sampling effects on MODIS estimates of aerosol over ocean , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[4]  K. Sassen,et al.  Global distribution of cirrus clouds from CloudSat/Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO) measurements , 2008 .

[5]  B. Holben,et al.  Preface to special section on East Asian Studies of Tropospheric Aerosols: An International Regional Experiment (EAST‐AIRE) , 2007 .

[6]  Alexander Smirnov,et al.  Cloud-Screening and Quality Control Algorithms for the AERONET Database , 2000 .

[7]  Zhanqing Li,et al.  Quality, compatibility, and synergy analyses of global aerosol products derived from the advanced very high resolution radiometer and Total Ozone Mapping Spectrometer , 2005, Journal of Geophysical Research.

[8]  David M. Winker,et al.  The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance , 2009 .

[9]  T. Berkoff,et al.  Ground-Based Lidar Measurements During the CALIPSO and Twilight Zone (CATZ) Campaign , 2008 .

[10]  A. Smirnov,et al.  AERONET-a federated instrument network and data archive for aerosol Characterization , 1998 .

[11]  S. Emori,et al.  Simulation of climate response to aerosol direct and indirect effects with aerosol transport‐radiation model , 2005 .

[12]  J. Gille,et al.  HIRDLS and CALIPSO observations of tropical cirrus , 2009 .

[13]  B. Gao,et al.  Distribution and Radiative Forcing of Tropical Thin Cirrus Clouds , 2009 .

[14]  Ellsworth J. Welton,et al.  Global monitoring of clouds and aerosols using a network of micropulse lidar systems , 2001, SPIE Asia-Pacific Remote Sensing.

[15]  J. Levine Biomass Burning: Its History, Use, and Distribution and Its Impact on Environmental Quality and Global Climate , 1991 .

[16]  Zhanqing Li,et al.  Quality and compatibility analyses of global aerosol products derived from the advanced very high resolution radiometer and Moderate Resolution Imaging Spectroradiometer , 2005 .

[17]  A global satellite view of aerosol cloud interactions , 2004 .

[18]  Steven Platnick,et al.  Utilizing the MODIS 1.38 μm channel for cirrus cloud optical thickness retrievals: Algorithm and retrieval uncertainties , 2010 .

[19]  David M. Winker,et al.  Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products , 2004, SPIE Remote Sensing.

[20]  Ping Yang,et al.  An algorithm using visible and 1.38-μm channels to retrieve cirrus cloud reflectances from aircraft and satellite data , 2002, IEEE Trans. Geosci. Remote. Sens..

[21]  Y. Kaufman,et al.  Aerosol climatology using a tunable spectral variability cloud screening of AERONET data , 2006 .

[22]  F. Bréon,et al.  Aerosol Effect on Cloud Droplet Size Monitored from Satellite , 2002, Science.

[23]  Kuo-Nan Liou,et al.  Detection of thin cirrus from 1.38 μm/0.65 μm reflectance ratio combined with 8.6–11 μm brightness temperature difference , 2003 .

[24]  E. Vermote,et al.  Operational remote sensing of tropospheric aerosol over land from EOS moderate resolution imaging spectroradiometer , 1997 .

[25]  B. Holben,et al.  Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS) , 2003 .

[26]  Michael D. King,et al.  Deep Blue Retrievals of Asian Aerosol Properties During ACE-Asia , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[27]  K. Liou,et al.  Surface aerosol radiative forcing derived from collocated ground-based radiometric observations during PRIDE, SAFARI, and ACE-Asia. , 2003, Applied optics.

[28]  F. Massey The Kolmogorov-Smirnov Test for Goodness of Fit , 1951 .

[29]  Yoram J. Kaufman,et al.  Distinguishing tropospheric aerosols from thin cirrus clouds for improved aerosol retrievals using the ratio of 1.38‐μm and 1.24‐μm channels , 2002 .

[30]  Y. Kaufman,et al.  Selection of the 1.375-µm MODIS Channel for Remote Sensing of Cirrus Clouds and Stratospheric Aerosols from Space , 1995 .

[31]  D. Koch,et al.  Impacts of aerosol-cloud interactions on past and future changes in tropospheric composition , 2009 .

[32]  G. Leeuw,et al.  Exploring the relation between aerosol optical depth and PM 2.5 at Cabauw, the Netherlands , 2008 .

[33]  P. Bhartia,et al.  Detection of biomass burning smoke from TOMS measurements , 1996 .

[34]  T. Matsui,et al.  Role of atmospheric aerosol concentration on deep convective precipitation: Cloud‐resolving model simulations , 2007 .

[35]  J. Herman,et al.  Radiative impacts from biomass burning in the presence of clouds during boreal spring in southeast Asia , 2003 .