A42A-04: Determination of Cloud Thermodynamic Phase with Ground Based, Polarimetrically Sensitive, Passive Sky Radiometers

When observed from the ground, optically thick clouds minimally polarize light, while the linear polarization direction (angle) of optically thin clouds contains information about thermodynamic phase. For instruments such at the Cimel radiometers that comprise the AErosol RObotic NEtwork (AERONET), these properties can also be exploited to aid cloud optical property retrievals. Using vector radiative transfer simulations, we explore the conditions most favorable to cloud thermodynamic phase determination, then test with actual AERONET data. Results indicate that this technique may be appropriate for some, but not all, conditions, and motivate a deeper investigation about the polarization direction measurement capability of Cimel instruments, which to date have been primarily used to determine degree of polarization. Recent work explores these measurement issues using a newly installed instrument at the NASA Ames Research Center in Moffett Field, California

The impact of neglecting ice phase on cloud optical depth retrievals from AERONET cloud mode observations

Clouds present many challenges to climate modelling. To develop and verify the parameterisations needed to allow climate models to represent cloud structure and processes, there is a need for high-quality observations of cloud optical depth from locations around the world. Retrievals of cloud optical depth are obtainable from radiances measured by Aerosol Robotic Network (AERONET) radiometers in “cloud mode” using a two-wavelength retrieval method. However, the method is unable to detect cloud phase, and hence assumes that all of the cloud in a profile is liquid. This assumption has the potential to introduce errors into long-term statistics of retrieved optical depth for clouds that also contain ice. Using a set of idealised cloud profiles we find that, for optical depths above 20, the fractional error in retrieved optical depth is a linear function of the fraction of the optical depth that is due to the presence of ice cloud (“ice fraction”). Clouds that are entirely ice have positive errors with magnitudes of the order of 55 % to 70 %. We derive a simple linear equation that can be used as a correction at AERONET sites where ice fraction can be independently estimated. Using this linear equation, we estimate the magnitude of the error for a set of cloud profiles from five sites of the Atmospheric Radiation Measurement programme. The dataset contains separate retrievals of ice and liquid retrievals; hence ice fraction can be estimated. The magnitude of the error at each location was related to the relative frequencies of occurrence in thick frontal cloud at the mid-latitude sites and of deep convection at the tropical sites – that is, of deep cloud containing both ice and liquid particles. The long-term mean optical depth error at the five locations spans the range 2–4, which we show to be small enough to allow calculation of top-of-atmosphere flux to within 10 % and surface flux to about 15 %

An Analysis of AERONET Aerosol Absorption Properties and Classifications Representative of Aerosol Source Regions

Partitioning of mineral dust, pollution, smoke, and mixtures using remote sensing techniques can help improve accuracy of satellite retrievals and assessments of the aerosol radiative impact on climate. Spectral aerosol optical depth (tau) and single scattering albedo (omega (sub 0) ) from Aerosol Robotic Network (AERONET) measurements are used to form absorption [i.e., omega (sub 0) and absorption Angstrom exponent (alpha(sub abs))] and size [i.e., extinction Angstrom exponent (alpha(sub ext)) and fine mode fraction of tau] relationships to infer dominant aerosol types. Using the long-term AERONET data set (1999-2010), 19 sites are grouped by aerosol type based on known source regions to: (1) determine the average omega (sub 0) and alpha(sub abs) at each site (expanding upon previous work); (2) perform a sensitivity study on alpha(sub abs) by varying the spectral omega (sub 0); and (3) test the ability of each absorption and size relationship to distinguish aerosol types. The spectral omega (sub 0) averages indicate slightly more aerosol absorption (i.e., a 0.0 < delta omega (sub 0) <= 0.02 decrease) than in previous work and optical mixtures of pollution and smoke with dust show stronger absorption than dust alone. Frequency distributions of alpha(sub abs) show significant overlap among aerosol type categories and at least 10% of the alpha(sub abs) retrievals in each category are below 1.0. Perturbing the spectral omega (sub 0) by +/- 0.03 induces significant alpha(sub abs) changes from the unperturbed value by at least approx. +/- 0.6 for Dust, approx. +/-0.2 for Mixed, and approx. +/-0.1 for Urban/Industrial and Biomass Burning. The omega (sub 0)440nm and alpha(sub ext) 440-870nm relationship shows the best separation among aerosol type clusters, providing a simple technique for determining aerosol type from surface- and future space-based instrumentation

Cloud optical depth retrievals from the Aerosol Robotic Network (AERONET) cloud mode observations

Cloud optical depth is one of the most poorly observed climate variables. The new “cloud mode” capability in the Aerosol Robotic Network (AERONET) will inexpensively yet dramatically increase cloud optical depth observations in both number and accuracy. Cloud mode optical depth retrievals from AERONET were evaluated at the Atmospheric Radiation Measurement program’s Oklahoma site in sky conditions ranging from broken clouds to overcast. For overcast cases, the 1.5 min average AERONET cloud mode optical depths agreed to within 15% of those from a standard ground‐based flux method. For broken cloud cases, AERONET retrievals also captured rapid variations detected by the microwave radiometer. For 3 year climatology derived from all nonprecipitating clouds, AERONET monthly mean cloud optical depths are generally larger than cloud radar retrievals because of the current cloud mode observation strategy that is biased toward measurements of optically thick clouds. This study has demonstrated a new way to enhance the existing AERONET infrastructure to observe cloud optical properties on a global scale

Lidar-Radiometer Inversion Code (LIRIC) for the retrieval of vertical aerosol properties from combined lidar/radiometer data: development and distribution in EARLINET

The financial support by the European Union's Horizon 2020 research and innovation programme (ACTRIS-2, grant agreement no. 654109) is gratefully acknowledged. The background of LIRIC algorithm and software was developed under the ACTRIS Research Infrastructure project, grant agreement no. 262254, within the European Union Seventh Framework Programme, which financial support is gratefully acknowledged.r I. Binietoglou received funding from the European Union's Seventh Framework Programme for research, technological development and demonstration under the grant agreement no. 289923 - ITARS.This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code) algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar/radiometric input data we use measurements from European Aerosol Research Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data. The algorithm starts with the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode. The LIRIC software package was implemented and tested at a number of EARLINET stations. Intercomparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLINET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed.European Union (EU) 654109ACTRIS Research Infrastructure project within the European Union 262254European Union (EU) 289923 - ITAR

Lidar-Radiometer Inversion Code (LIRIC) for the retrieval of vertical aerosol properties from combined lidar/radiometer data: Development and distribution in EARLINET

This paper presents a detailed description of LIRIC (LIdar-Radiometer Inversion Code) algorithm for simultaneous processing of coincident lidar and radiometric (sun photometric) observations for the retrieval of the aerosol concentration vertical profiles. As the lidar/radiometric input data we use measurements from European Aerosol Research Lidar Network (EARLINET) lidars and collocated sun-photometers of Aerosol Robotic Network (AERONET). The LIRIC data processing provides sequential inversion of the combined lidar and radiometric data. The algorithm starts with the estimations of column-integrated aerosol parameters from radiometric measurements followed by the retrieval of height dependent concentrations of fine and coarse aerosols from lidar signals using integrated column characteristics of aerosol layer as a priori constraints. The use of polarized lidar observations allows us to discriminate between spherical and non-spherical particles of the coarse aerosol mode. The LIRIC software package was implemented and tested at a number of EARLINET stations. Intercomparison of the LIRIC-based aerosol retrievals was performed for the observations by seven EARLINET lidars in Leipzig, Germany on 25 May 2009. We found close agreement between the aerosol parameters derived from different lidars that supports high robustness of the LIRIC algorithm. The sensitivity of the retrieval results to the possible reduction of the available observation data is also discussed

Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations

The water vapor is a relevant greenhouse gas in the Earth's climate system, and satellite products become one of the most effective way to characterize and monitor the columnar water vapor (CWV) content at global scale. Recently, a new product (MCD19) was released as part of MODIS (Moderate Resolution Imaging Spectroradiometer) Collection 6 (C6). This operational product from Multi-Angle Implementation for Atmospheric Correction (MAIAC) algorithm includes a high 1 km resolution CWV retrievals. This study presents the first global validation of MAIAC C6 CWV obtained from MODIS MCD19A2 product. This evaluation was performed using Aerosol Robotic Network (AERONET) observations at 265 sites (2000–2017). Overall, the results show a good agreement between MAIAC/AERONET CWV retrievals, with correlation coefficient higher than 0.95 and RMS error lower than 0.250 cm. The binned error analysis revealed an underestimation (~10%) of Aqua CWV retrievals with negative bias for CWV higher than 3.0 cm. In contrast, Terra CWV retrievals show a slope of regression close to unity and a low mean bias of 0.075 cm. While the accuracy is relatively similar between 1.0 and 5.0 cm for both sensor products, Terra dataset is more reliable for applications in humid tropical areas (>5.0 cm). The expected error was defined as ±15%, with >68% of retrievals falling within this envelope. However, the accuracy is regionally dependent, and lower error should be expected in some regions, such as South America and Oceania. Since MODIS instruments have exceeded their design lifetime, time series analysis was also presented for both sensor products. The temporal analysis revealed a systematic offset of global average between Terra and Aqua CWV records. We also found an upward trend (~0.2 cm/decade) in Terra CWV retrievals, while Aqua CWV retrievals remain stable over time. The sensor degradation influences the ability to detect climate signals, and this study indicates the need for revisiting calibration of the MODIS bands 17–19, mainly for Terra instrument, to assure the quality of the MODIS water vapor product. Finally, this study presents a comprehensive validation analysis of MAIAC CWV over land, raising the understanding of its overall quality.This article is published as Martins, Vitor S., Alexei Lyapustin, Yujie Wang, David M. Giles, Alexander Smirnov, Ilya Slutsker, and Sergey Korkin. "Global validation of columnar water vapor derived from EOS MODIS-MAIAC algorithm against the ground-based AERONET observations." Atmospheric Research 225 (2019): 181-192. DOI: 10.1016/j.atmosres.2019.04.005.</p

Characterizing Aerosols over Southeast Asia using the AERONET Data Synergy Tool

Biomass burning, urban pollution and dust aerosols have significant impacts on the radiative forcing of the atmosphere over Asia. In order to better quanti@ these aerosol characteristics, the Aerosol Robotic Network (AERONET) has established over 200 sites worldwide with an emphasis in recent years on the Asian continent - specifically Southeast Asia. A total of approximately 15 AERONET sun photometer instruments have been deployed to China, India, Pakistan, Thailand, and Vietnam. Sun photometer spectral aerosol optical depth measurements as well as microphysical and optical aerosol retrievals over Southeast Asia will be analyzed and discussed with supporting ground-based instrument, satellite, and model data sets, which are freely available via the AERONET Data Synergy tool at the AERONET web site (http://aeronet.gsfc.nasa.gov). This web-based data tool provides access to groundbased (AERONET and MPLNET), satellite (MODIS, SeaWiFS, TOMS, and OMI) and model (GOCART and back trajectory analyses) databases via one web portal. Future development of the AERONET Data Synergy Tool will include the expansion of current data sets as well as the implementation of other Earth Science data sets pertinent to advancing aerosol research