N. C. Hsu

Comparisons of the TOMS aerosol index with Sun‐photometer aerosol optical thickness: Results and applications

A nearly 20-year global data set (1979–1994 and 1996 to the present) of tropospheric absorbing aerosols has been developed from total ozone mapping spectrometer (TOMS) backscattered radiance measurements in the range from 331 to 380 nm. The occurrence of aerosols is derived directly from measured backscattered radiances and is represented by a quantity known as the aerosol index. Previous theoretical model simulations have demonstrated that the aerosol index depends on aerosol optical thickness (AOT), single scattering albedo, and aerosol height and that the AOT can be determined provided that the microphysical properties and height of aerosols are known. In this paper we show that the TOMS aerosol index measurements are linearly proportional to the AOT derived independently from ground-based Sun-photometer instruments over regions of biomass burning and regions covered by African dust. We also show how this linear relationship can be used to directly convert the aerosol index into AOT for smoke and dust aerosols for the regions near the Sun-photometer sites and how information about aerosol height can be inferred from the results. Finally, we apply this method to the TOMS data over the last two decades and find a significant increase in the amount of biomass burning smoke in the African savanna regions during the 1990s in addition to the more obvious increase in South America.

Detection of mineral dust over the North Atlantic Ocean and Africa with the Nimbus 7 TOMS

It has recently been found that the ultraviolet measurements obtained with the Nimbus 7 total ozone mapping spectrometer (TOMS) instrument can be used to retrieve information on the distribution of aerosols over oceanic and continental surfaces. Here we examine the use of the derived TOMS aerosol index (AI) for the detection of absorbing aerosol in terms of mineral dust aerosol over the North Atlantic Ocean and North Africa. Specifically, we compare the TOMS AI with the time series of daily aerosol measurements made in the boundary layer at Sal Island (Cape Verde), Barbados, and Miami and in the free troposphere on Tenerife (Canary Islands); these sites are frequently impacted by African dust events. At Tenerife, over the time period 1988–1992, TOMS detected 80% of the African dust events that yielded daily average dust concentrations greater than 20 μg m−3; at Barbados and Miami, TOMS detected 65% and 44% respectively of the events over the period 1979–1992. If we exclude events during which some of the TOMS data are missing and also short (1-day) dust events, TOMS detected 99% of the events at Tenerife, 97% at Barbados, and 81% at Miami. TOMS was also successful in detecting the “low altitude” African dust events recorded at Sal during the winter season. Over Africa we compare the TOMS AI data with ground-based measurements of aerosol optical thickness (AOT) obtained during field experiments in Senegal and Niger; these yield a nearly linear relationship between the TOMS AI and the AOT. Discrepancies between ground-based measurements (in terms of dust concentrations or AOT) and TOMS AI can be attributed to a number of factors: variations in the physical properties of the aerosol; the sensitivity of the TOMS response to the altitude of the aerosol layer; or the coarse spatial resolution of the TOMS pixel. Nonetheless, our results clearly show that the TOMS AI provides a remarkably accurate picture of mineral dust distributions in the atmosphere over both continental and oceanic regions.

MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and “merged” data sets, and usage recommendations

The Moderate Resolution Imaging Spectroradiometer (MODIS) Atmospheres data product suite includes three algorithms applied to retrieve midvisible aerosol optical depth (AOD): the Enhanced Deep Blue (DB) and Dark Target (DT) algorithms over land, and a DT over-water algorithm. All three have been refined in the recent “Collection 6” (C6) MODIS reprocessing. In particular, DB has been expanded to cover vegetated land surfaces as well as brighter desert/urban areas. Additionally, a new “merged” data set which draws from all three algorithms is included in the C6 products. This study is intended to act as a point of reference for new and experienced MODIS data users with which to understand the global and regional characteristics of the C6 DB, DT, and merged data sets, based on MODIS Aqua data. This includes validation against Aerosol Robotic Network (AERONET) observations at 111 sites, focused toward regional and categorical (surface/aerosol type) analysis. Neither algorithm consistently outperforms the other, although in many cases the retrieved AOD and the level of its agreement with AERONET are very similar. In many regions the DB, DT, and merged data sets are all suitable for quantitative applications, bearing in mind that they cannot be considered independent, while in other cases one algorithm does consistently outperform the other. Usage recommendations and caveats are thus somewhat complicated and regionally dependent.

Effect of MODIS Terra Radiometric Calibration Improvements on Collection 6 Deep Blue Aerosol Products: Validation and Terra/Aqua Consistency

The Deep Blue (DB) algorithms primary data product is midvisible aerosol optical depth (AOD). DB applied to Moderate Resolution Imaging Spectroradiometer (MODIS) measurements provides a data record since early 2000 for MODIS Terra and mid-2002 for MODIS Aqua. In the previous data version (Collection 5, C5), DB production from Terra was halted in 2007 due to sensor degradation; the new Collection 6 (C6) has both improved science algorithms and sensor radiometric calibration. This includes additional calibration corrections developed by the Ocean Biology Processing Group to address MODIS Terras gain, polarization sensitivity, and detector response versus scan angle, meaning DB can now be applied to the whole Terra record. Through validation with Aerosol Robotic Network (AERONET) data, it is shown that the C6 DB Terra AOD quality is stable throughout the mission to date. Compared to the C5 calibration, in recent years the RMS error compared to AERONET is smaller by approximately 0.04 over bright (e.g., desert) and approximately 0.01-0.02 over darker (e.g., vegetated) land surfaces, and the fraction of points in agreement with AERONET within expected retrieval uncertainty higher by approximately 10% and approximately 5%, respectively. Comparisons to the Aqua C6 time series reveal a high level of correspondence between the two MODIS DB data records, with a small positive (Terra-Aqua) average AOD offset <0.01. The analysis demonstrates both the efficacy of the new radiometric calibration efforts and that the C6 MODIS Terra DB AOD data remain stable (to better than 0.01 AOD) throughout the mission to date, suitable for quantitative scientific analyses.

Retrieving near-global aerosol loading over land and ocean from AVHRR

The spaceborne Advanced Very High Resolution Radiometer (AVHRR) sensor data record is approaching 40 years, providing a crucial asset for studying long-term trends of aerosol properties regionally and globally. However, due to limitations of its channels’ information content, aerosol optical depth (AOD) data from AVHRR over land are still largely lacking. In this paper, we describe a new physics-based algorithm to retrieve aerosol loading over both land and ocean from AVHRR for the first time. The over-land algorithm is an extension of our Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue algorithm, while a simplified version of our Satellite Ocean Aerosol Retrieval (SOAR) algorithm is used over ocean. We compare retrieved AVHRR AOD with that from MODIS on a daily and seasonal basis, and find in general good agreement between the two. For the satellites with equatorial crossing times within two hours of solar noon, the spatial coverage of the AVHRR aerosol product is comparable to that of MODIS, except over very bright arid regions (such as the Sahara), where the underlying surface reflectance at 630 nm reaches the critical surface reflectance. Based upon comparisons of the AVHRR AOD against Aerosol Robotic Network (AERONET) data, preliminary results indicate that the expected error confidence interval envelope is around ±(0.03+15%) over ocean and ±(0.05+25%) over land for this first version of the AVHRR aerosol products. Consequently, these new AVHRR aerosol products can contribute important building blocks for constructing a consistent long-term data record for climate studies.

Evaluation of NASA Deep Blue/SOAR aerosol retrieval algorithms applied to AVHRR measurements

The Deep Blue (DB) and Satellite Ocean Aerosol Retrieval (SOAR) algorithms have previously been applied to observations from sen-sors like the Moderate Resolution Imaging Spectroradiometers (MODIS) and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) to provide records of mid-visible aerosol optical depth (AOD) and related quantities over land and ocean surfaces respectively. Recently, DB and SOAR have also been applied to Ad-vanced Very High Resolution Radiometer (AVHRR) observations from several platforms (NOAA11, NOAA14, and NOAA18), to demonstrate the potential for extending the DB and SOAR AOD records. This study provides an evaluation of the initial version (V001) of the resulting AVHRR-based AOD data set, including validation against Aerosol Robotic Network (AERONET) and ship-borne observations, and comparison against both other AVHRR AOD Research (GESTAR), Universities Space Research Association. records and MODIS/SeaWiFS products at select long-term AERONET sites. Although it is difficult to distil error characteristics into a simple expression, the results suggest that one standard deviation confidence intervals on retrieved AOD of ±(0.03+15%) over water and ±(0.05+25%) over land represent the typical level of uncertainty, with a tendency towards negative biases in high-AOD conditions, caused by a combination of algorithmic assumptions and sensor calibration issues. Most of the available validation data are for NOAA18 AVHRR, although performance appears to be similar for the NOAA11 and NOAA14 sensors as well.

Effect of Thin Cirrus Clouds on Dust Optical Depth Retrievals From MODIS Observations

The effect of thin cirrus clouds in retrieving the dust optical depth from MODIS observations is investigated by using a simplified aerosol retrieval algorithm based on the principles of the Deep Blue aerosol property retrieval method. Specifically, the errors of the retrieved dust optical depth due to thin cirrus contamination are quantified through the comparison of two retrievals by assuming dust-only atmospheres and the counterparts with overlapping mineral dust and thin cirrus clouds. To account for the effect of the polarization state of radiation field on radiance simulation, a vector radiative transfer model is used to generate the lookup tables. In the forward radiative transfer simulations involved in generating the lookup tables, the Rayleigh scattering by atmospheric gaseous molecules and the reflection of the surface assumed to be Lambertian are fully taken into account. Additionally, the spheroid model is utilized to account for the nonsphericity of dust particles in computing their optical properties. For simplicity, the single-scattering albedo, scattering phase matrix, and optical depth are specified a priori for thin cirrus clouds assumed to consist of droxtal ice crystals. The present results indicate that the errors in the retrieved dust optical depths due to the contamination of thin cirrus clouds depend on the scattering angle, underlying surface reflectance, and dust optical depth. Under heavy dusty conditions, the absolute errors are comparable to the predescribed optical depths of thin cirrus clouds.

Satellite Ocean Aerosol Retrieval (SOAR) Algorithm Extension to S‐NPP VIIRS as Part of the “Deep Blue” Aerosol Project

The Suomi National Polar-Orbiting Partnership (S-NPP) satellite, launched in late 2011, carries the Visible Infrared Imaging Radiometer Suite (VIIRS) and several other instruments. VIIRS has similar characteristics to prior satellite sensors used for aerosol optical depth (AOD) retrieval, allowing the continuation of space-based aerosol data records. The Deep Blue algorithm has previously been applied to retrieve AOD from Sea-viewing Wide Field-of-view Sensor (SeaWiFS) and Moderate Resolution Imaging Spectro-radiometer (MODIS) measurements over land. The SeaWiFS Deep Blue data set also included a SeaWiFS Ocean Aerosol Retrieval (SOAR) algorithm to cover water surfaces. As part of NASAs VIIRS data processing, Deep Blue is being applied to VIIRS data over land, and SOAR has been adapted from SeaWiFS to VIIRS for use over water surfaces. This study describes SOAR as applied in version 1 of NASAs S-NPP VIIRS Deep Blue data product suite. Several advances have been made since the SeaWiFS application, as well as changes to make use of the broader spectral range of VIIRS. A preliminary validation against Maritime Aerosol Network (MAN) measurements suggests a typical uncertainty on retrieved 550nm AOD of order ±(0.03+10%), comparable to existing SeaWiFS/MODIS aerosol data products. Retrieved Ångström exponent and fine mode AOD fraction are also well-correlated with MAN data, with small biases and uncertainty similar to or better than SeaWiFS/MODIS products.