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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
NASA NTRS1990

Cirrus cloud statistics: Temperatures and optical depths

Measurements of the upwelling infrared radiance at 10.5 and 6.5 microns were obtained during the FIRE cirrus Intensive Field Observations using a radiometer with a 15 deg cone nadir field-of-view flown on the NASA Ames ER-2. Data are recorded at a frequency of 1 Hz and the radiometer is continuously calibrated with a liquid nitrogen blackbody source, thereby providing a large number of very accurate radiance values during the course of a several hour flight. For this study, the focus is on the statistical properties of the cirrus deck as deduced from the radiance data. The data acquired on 28 October 1986 is stressed, but some data from the other flights are also shown for comparison purposes.

Valero, Francisco P. J.↗
NASA NTRS2002

Sensitivity of Stratocumulus Optical Depths to Droplet Concentrations: Satellite Observations and Large-Eddy Simulations

A number of observations and simulations have shown that increased droplet concentrations in ship tracks increase their total cross-sectional area, thereby enhancing cloud albedo and providing a negative (cooling) radiative forcing at the surface and the top of the atmosphere. In some cases cloud water has been found to be enhanced in ship tracks, which has been attributed to suppression of drizzle and implies an enhanced susceptibility of cloud albedo to droplet concentrations. However, observations from aircraft and satellite indicate that on average cloud water is instead reduced in daytime ship tracks. Such a reduction in liquid water may be attributable to cloud-burning caused by solar heating by soot within the ship exhaust, or by increased precipitation resulting from giant nuclei in the ship exhaust. We will summarize the observational evidence and present results from large-eddy simulations that evaluate these mechanisms. Along the way we will present our insights into the interpretation of satellite retrievals of cloud microphysical properties.

Ackerman, A. S.↗
NASA NTRS2010

Global Estimates of Average Ground-Level Fine Particulate Matter Concentrations from Satellite-Based Aerosol Optical Depth

Exposure to airborne particles can cause acute or chronic respiratory disease and can exacerbate heart disease, some cancers, and other conditions in susceptible populations. Ground stations that monitor fine particulate matter in the air (smaller than 2.5 microns, called PM2.5) are positioned primarily to observe severe pollution events in areas of high population density; coverage is very limited, even in developed countries, and is not well designed to capture long-term, lower-level exposure that is increasingly linked to chronic health effects. In many parts of the developing world, air quality observation is absent entirely. Instruments aboard NASA Earth Observing System satellites, such as the MODerate resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR), monitor aerosols from space, providing once daily and about once-weekly coverage, respectively. However, these data are only rarely used for health applications, in part because the can retrieve the amount of aerosols only summed over the entire atmospheric column, rather than focusing just on the near-surface component, in the airspace humans actually breathe. In addition, air quality monitoring often includes detailed analysis of particle chemical composition, impossible from space. In this paper, near-surface aerosol concentrations are derived globally from the total-column aerosol amounts retrieved by MODIS and MISR. Here a computer aerosol simulation is used to determine how much of the satellite-retrieved total column aerosol amount is near the surface. The five-year average (2001-2006) global near-surface aerosol concentration shows that World Health Organization Air Quality standards are exceeded over parts of central and eastern Asia for nearly half the year.

Van Donkelaar, A.↗
NASA NTRS2011

Validation of MODIS Aerosol Optical Depth Retrievals over a Tropical Urban Site, Pune, India

In the present paper, MODIS (Terra and Aqua; level 2, collection 5) derived aerosoloptical depths (AODs) are compared with the ground-based measurements obtained from AERONET (level 2.0) and Microtops - II sun-photometer over a tropical urban station, Pune (18 deg 32'N; 73 deg 49'E, 559 m amsl). This is the first ever systematic validation of the MODIS aerosol products over Pune. Analysis of the data indicates that the Terra and Aqua MODIS AOD retrievals at 550 nm have good correlations with the AERONET and Microtops - II sun-photometer AOD measurements. During winter the linear regression correlation coefficients for MODIS products against AERONET measurements are 0.79 for Terra and 0.62 for Aqua; however for premonsoon, the corresponding coefficients are 0.78 and 0.74. Similarly, the linear regression correlation coefficients for Microtops measurements against MODIS products are 0.72 and 0.93 for Terra and Aqua data respectively during winter and are 0.78 and 0.75 during pre-monsoon. On yearly basis in 2008-2009, correlation coefficients for MODIS products against AERONET measurements are 0.80 and 0.78 for Terra and Aqua respectively while the corresponding coefficients are 0.70 and 0.73 during 2009-2010. The regressed intercepts with MODIS vs. AERONET are 0.09 for Terra and 0.05 for Aqua during winter whereas their values are 0.04 and 0.07 during pre-monsoon. However, MODIS AODs are found to underestimate during winter and overestimate during pre-monsoon with respect to AERONET and Microtops measurements having slopes 0.63 (Terra) and 0.74 (Aqua) during winter and 0.97 (Terra) and 0.94 (Aqua) during pre-monsoon. Wavelength dependency of Single Scattering Albedo (SSA) shows presence of absorbing and scattering aerosol particles. For winter, SSA decreases with wavelength with the values 0.86 +/- 0.03 at 440 nm and 0.82 +/- 0.04 at 1020nm. In pre-monsoon, it increases with wavelength (SSA is 0.87 +/- 0.02 at 440nm; and 0.88 +/-0.04 at 1020 nm).

More, Sanjay↗
NASA NTRS2011

MODIS Aerosol Optical Depth Bias Adjustment Using Machine Learning Algorithms

To monitor the earth atmosphere and its surface changes, satellite based instruments collect continuous data. While some of the data is directly used, some others such as aerosol properties are indirectly retrieved from the observation data. While retrieved variables (RV) form very powerful products, they don't come without obstacles. Different satellite viewing geometries, calibration issues, dynamically changing atmospheric and earth surface conditions, together with complex interactions between observed entities and their environment affect them greatly. This results in random and systematic errors in the final products.

Albayrak, Arif↗
NASA NTRS2014

Application of Spectral Analysis Techniques in the Intercomparison of Aerosol Data. Part II: Using Maximum Covariance Analysis to Effectively Compare Spatiotemporal Variability of Satellite and AERONET Measured Aerosol Optical Depth

Moderate Resolution Imaging SpectroRadiometer (MODIS) and Multi-angle Imaging Spectroradiomater (MISR) provide regular aerosol observations with global coverage. It is essential to examine the coherency between space- and ground-measured aerosol parameters in representing aerosol spatial and temporal variability, especially in the climate forcing and model validation context. In this paper, we introduce Maximum Covariance Analysis (MCA), also known as Singular Value Decomposition analysis as an effective way to compare correlated aerosol spatial and temporal patterns between satellite measurements and AERONET data. This technique not only successfully extracts the variability of major aerosol regimes but also allows the simultaneous examination of the aerosol variability both spatially and temporally. More importantly, it well accommodates the sparsely distributed AERONET data, for which other spectral decomposition methods, such as Principal Component Analysis, do not yield satisfactory results. The comparison shows overall good agreement between MODIS/MISR and AERONET AOD variability. The correlations between the first three modes of MCA results for both MODIS/AERONET and MISR/ AERONET are above 0.8 for the full data set and above 0.75 for the AOD anomaly data. The correlations between MODIS and MISR modes are also quite high (greater than 0.9). We also examine the extent of spatial agreement between satellite and AERONET AOD data at the selected stations. Some sites with disagreements in the MCA results, such as Kanpur, also have low spatial coherency. This should be associated partly with high AOD spatial variability and partly with uncertainties in satellite retrievals due to the seasonally varying aerosol types and surface properties.

aerosols↗
NASA NTRS2015

Nighttime Aerosol Optical Depth Measurements Using a Ground-based Lunar Photometer

In recent years it was proposed to combine AERONET network photometer capabilities with a high precision lunar model used for satellite calibration to retrieve columnar nighttime AODs. The USGS lunar model can continuously provide pre-atmosphere high precision lunar irradiance determinations for multiple wavelengths at ground sensor locations. When combined with measured irradiances from a ground-based AERONET photometer, atmospheric column transmissions can determined yielding nighttime column aerosol AOD and Angstrom coefficients. Additional demonstrations have utilized this approach to further develop calibration methods and to obtain data in polar regions where extended periods of darkness occur. This new capability enables more complete studies of the diurnal behavior of aerosols, and feedback for models and satellite retrievals for the nighttime behavior of aerosols. It is anticipated that the nighttime capability of these sensors will be useful for comparisons with satellite lidars such as CALIOP and CATS in additional to ground-based lidars in MPLNET at night, when the signal-to-noise ratio is higher than daytime and more precise AOD comparisons can be made.

Berkoff, Tim↗
NASA NTRS2016

Identification of Columnar Aerosol Types Under High Aerosol Optical Depth Conditions for a Single AERONET Site in Korea

Dominant aerosol types were classified using level 2 inversion products for the Anmyon Aerosol Robotic Network (AERONET) site in Korea for the period 19992007. The aerosol types were mineral dust (MD), MD mixed with carbon, and black carbonmixed coarse particles (BCCP) for coarsemode aerosols, black carbon (BC), organic carbon (OC), and secondary inorganic ions (SII) for fine mode aerosols, and mixed particles between. The classification was carried out using a clustering method based on parameters, including single scattering albedo (SSA), absorption Angstrom exponent (AAE), and fine mode volume fraction (FMVF). Among the seven aerosol types, MD was distinct, with the highest AAE and a very low FMVF and SII with the highest SSA and FMVF. BCCP was introduced to designate coarse particles mixed with BC, of which the AAE was lower than 1, despite a low FMVF. In addition to a large difference in AAE between BC andOC, the SSA ofOC was larger than that of BC, indicating the effects of the white smoke produced from the smoldering phase of biomass burning. Monthly variations of the aerosol types were well interpreted by meteorology and emissions and coincided with those in the previous studies. Applying our results to well-characterized global AERONET sites, we confirmed that the aerosol types at Anmyon were valid at other sites. However, the results also showed that the mean properties for aerosol types were influenced by the specific aerosols prevalent at the study sites.

Choi, Yongjoo↗
NASA NTRS2018

Using Multi-Sensor Aerosol Optical Depth Retrievals to Improve Infrared Radiance Assimilation

Coarse dust aerosols absorb terrestrial radiation leading to significant longwave heating/cooling rates. Modules accounting for aerosol impacts on radiation have been implemented into CRTM framework, but operational centers continue to assume aerosol-free conditions when assimilating infrared radiances into NWP models. This assumption can introduce significant biases in analysis fields (temp, moisture, etc.), which can reduce forecast skill.

Naeger, Aaron↗