Andrew M. Sayer

Cloud retrievals from satellite data using optimal estimation: evaluation and application to ATSR

Clouds play an important role in balancing the Earth’s radiation budget. Hence, it is vital that cloud climatologies are produced that quantify cloud macro and micro physical parameters and the associated uncertainty. In this paper, we present an algorithm ORAC (Oxford-RAL retrieval of Aerosol and Cloud) which is based on fitting a physically consistent cloud model to satellite observations simultaneously from the visible to the mid-infrared, thereby ensuring that the resulting cloud properties provide both a good representation of the short-wave and long-wave radiative effects of the observed cloud. The advantages of the optimal estimation method are that it enables rigorous error propagation and the inclusion of all measurements and any a priori information and associated errors in a rigorous mathematical framework. The algorithm provides a measure of the consistency between retrieval representation of cloud and satellite radiances. The cloud parameters retrieved are the cloud top pressure, cloud optical depth, cloud effective radius, cloud fraction and cloud phase. The algorithm can be applied to most visible/infrared satellite instruments. In this paper, we demonstrate the applicability to the Along-Track Scanning Radiometers ATSR-2 and AATSR. Examples of applying the algorithm to ATSR-2 flight data are presented and the sensitivity of the retrievals assessed, in particular the algorithm is evaluated for a number of simulated single-layer and multi-layer conditions. The algorithm was found to perform well for single-layer cloud except when the cloud was very thin; i.e., less than 1 optical depths. For the multi-layer cloud, the algorithm was robust except when the upper ice cloud layer is less than five optical depths. In these cases the retrieved cloud top pressure and cloud effective radius become a weighted average of the 2 layers. The sum of optical depth of multi-layer cloud is retrieved well until the cloud becomes thick, greater than 50 optical depths, where the cloud begins to saturate. The cost proved a good indicator of multi-layer scenarios. Both the retrieval cost and the error need to be considered together in order to evaluate the quality of the retrieval. This algorithm in the configuration described here has been applied to both ATSR-2 and AATSR visible and infrared measurements in the context of the GRAPE (Global Retrieval and cloud Product Evaluation) project to produce a 14 yr consistent record for climate research.

Can Asian dust trigger phytoplankton blooms in the oligotrophic northern South China Sea

Satellite data estimate a high dust deposition flux (approximately 18 g m(exp-2 a(exp-1) into the northern South China Sea (SCS). However, observational evidence concerning any biological response to dust fertilization is sparse. In this study, we combined long-term aerosol and chlorophyll-a (Chl-a) measurements from satellite sensors (MODIS and SeaWiFS) with a 16-year record of dust events from surface PM10 observations to investigate dust transport, flux, and the changes in Chl-a concentration over the northern SCS. Our result revealed that readily identifiable strong dust events over this region, although relatively rare (6 cases since 1994) and accounting for only a small proportion of the total dust deposition (approximately 0.28 g m(exp-2 a(exp-1), do occur and could significantly enhance phytoplankton blooms. Following such events, the Chl-a concentration increased up to 4-fold, and generally doubled the springtime background value (0.15 mg m(exp-3). We suggest these heavy dust events contain readily bioavailable iron and enhance the phytoplankton growth in the oligotrophic northern SCS.

Oxford-RAL Aerosol and Cloud (ORAC): aerosol retrievals from satellite radiometers

This chapter describes an optimal estimation retrieval scheme for the derivation of the properties of atmospheric aerosol from top-of-atmosphere (TOA) radiances measured by satellite-borne visible-IR radiometers. The algorithm makes up part of the Oxford-RAL Aerosol and Cloud (ORAC) retrieval scheme (the other part of the algorithm performs cloud retrievals and is described in detail elsewhere [by Watts et al.] [37]).

Some implications of sampling choices on comparisons between satellite and model aerosol optical depth fields

Abstract. The comparison of satellite and model aerosol optical depth (AOD) fields provides useful information on the strengths and weaknesses of both. However, the sampling of satellite and models is very different and some subjective decisions about data selection and aggregation must be made in order to perform such comparisons. This work examines some implications of these decisions, using GlobAerosol AOD retrievals at 550 nm from Advanced Along-Track Scanning Radiometer (AATSR) measurements, and aerosol fields from the GEOS-Chem chemistry transport model. It is recommended to sample the model only where the satellite flies over on a particular day; neglecting this can cause regional differences in model AOD of up to 0.1 on monthly and annual timescales. The comparison is observed to depend strongly upon thresholds for sparsity of satellite retrievals in the model grid cells. Requiring at least 25% coverage of the model grid cell by satellite data decreases the observed difference between the two by approximately half over land. The impact over ocean is smaller. In both model and satellite datasets, there is an anticorrelation between the proportion p of a model grid cell covered by satellite retrievals and the AOD. This is attributed to small p typically occuring due to high cloud cover and lower AODs being found in large clear-sky regions. Daily median AATSR AODs were found to be closer to GEOS-Chem AODs than daily means (with the root mean squared difference being approximately 0.05 smaller). This is due to the decreased sensitivity of medians to outliers such as cloud-contaminated retrievals, or aerosol point sources not included in the model.

Cross-Calibration of S-NPP VIIRS Moderate Resolution Reflective Solar Bands Against MODIS Aqua over Dark Water Scenes

The Visible Infrared Imaging Radiometer Suite (VIIRS) is being used to continue the record of Earth Science observations and data products produced routinely from National Aeronautics and Space Administration (NASA) Moderate Resolution Imaging Spectroradiometer (MODIS) measurements. However, the absolute calibration of VIIRSs reflected solar bands is thought to be biased, leading to offsets in derived data products such as aerosol optical depth (AOD) as compared to when similar algorithms are applied to different sensors. This study presents a cross-calibration of these VIIRS bands against MODIS Aqua over dark water scenes, finding corrections to the NASA VIIRS Level 1 (version 2) reflectances between approximately +1 % and -7 % (dependent on band) are needed to bring the two into alignment (after accounting for expected differences resulting from different band spectral response functions), and indications of relative trending of up to ^0.35 % per year in some bands. The derived calibration gain corrections are also applied to the VIIRS reflectance and then used in an AOD retrieval, and are shown to decrease the bias and total error in AOD across the midvisible spectral region compared to the standard VIIRS NASA reflectance calibration. The resulting AOD bias characteristics are similar to those of NASA MODIS AOD data products, which is encouraging in terms of multisensor data continuity.

Retrieving the height of smoke and dust aerosols by synergistic use of VIIRS, OMPS, and CALIOP observations

This study extends the application of the previously developed Aerosol Single-scattering albedo and layer Height Estimation (ASHE) algorithm, which was originally applied to smoke aerosols only, to both smoke and dust aerosols by including nonspherical dust properties in the retrieval process. The main purpose of the algorithm is to derive aerosol height information over wide areas using aerosol products from multiple satellite sensors simultaneously: aerosol optical depth (AOD) and Angstrom exponent from the Visible Infrared Imaging Radiometer Suite (VIIRS), UV aerosol index from the Ozone Mapping and Profiler Suite (OMPS), and total backscatter coefficient profile from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP). The case studies suggest that the ASHE algorithm performs well for both smoke and dust aerosols, showing root-mean-square error of the retrieved aerosol height as compared to CALIOP observations from 0.58 to 1.31 km and mean bias from −0.70 to 1.13 km. In addition, the algorithm shows the ability to retrieve single-scattering albedo to within 0.03 of Aerosol Robotic Network inversion data for moderate to thick aerosol loadings (AOD of ~1.0). For typical single-layered aerosol cases, the estimated uncertainty in the retrieved height ranges from 1.20 to 1.80 km over land and from 1.15 to 1.58 km over ocean when favorable conditions are met. Larger errors are observed for multilayered aerosol events, due to the limited sensitivities of the passive sensors to such cases.