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

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 <i>p</i> of a model grid cell covered by satellite retrievals and the AOD. This is attributed to small <i>p</i> 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

Response to "Toward Unified Satellite Climatology of Aerosol Properties. 3. MODIS Versus MISR Versus AERONET"

A recent paper by Mishchenko et al. compares near-coincident MISR, MODIS, and AERONET aerosol optical depth (AOD) products, and reports much poorer agreement than that obtained by the instrument teams and others. We trace the reasons for the discrepancies primarily to differences in (1) the treatment of outliers, (2) the application of absolute vs. relative criteria for testing agreement, and (3) the ways in which seasonally varying spatial distributions of coincident retrievals are taken into account

Surface radiation budget for climate applications

The Surface Radiation Budget (SRB) consists of the upwelling and downwelling radiation fluxes at the surface, separately determined for the broadband shortwave (SW) (0 to 5 micron) and longwave (LW) (greater than 5 microns) spectral regions plus certain key parameters that control these fluxes, specifically, SW albedo, LW emissivity, and surface temperature. The uses and requirements for SRB data, critical assessment of current capabilities for producing these data, and directions for future research are presented

Addressing and Presenting Quality of Satellite Data via Web-Based Services

With the recent attention to climate change and proliferation of remote-sensing data utilization, climate model and various environmental monitoring and protection applications have begun to increasingly rely on satellite measurements. Research application users seek good quality satellite data, with uncertainties and biases provided for each data point. However, different communities address remote sensing quality issues rather inconsistently and differently. We describe our attempt to systematically characterize, capture, and provision quality and uncertainty information as it applies to the NASA MODIS Aerosol Optical Depth data product. In particular, we note the semantic differences in quality/bias/uncertainty at the pixel, granule, product, and record levels. We outline various factors contributing to uncertainty or error budget; errors. Web-based science analysis and processing tools allow users to access, analyze, and generate visualizations of data while alleviating users from having directly managing complex data processing operations. These tools provide value by streamlining the data analysis process, but usually shield users from details of the data processing steps, algorithm assumptions, caveats, etc. Correct interpretation of the final analysis requires user understanding of how data has been generated and processed and what potential biases, anomalies, or errors may have been introduced. By providing services that leverage data lineage provenance and domain-expertise, expert systems can be built to aid the user in understanding data sources, processing, and the suitability for use of products generated by the tools. We describe our experiences developing a semantic, provenance-aware, expert-knowledge advisory system applied to NASA Giovanni web-based Earth science data analysis tool as part of the ESTO AIST-funded Multi-sensor Data Synergy Advisor project

Investigating organic aerosol loading in the remote marine environment

Aerosol loading in the marine environment is investigated using aerosol composition measurements from several research ship campaigns (ICEALOT, MAP, RHaMBLe, VOCALS and OOMPH), observations of total AOD column from satellite (MODIS) and ship-based instruments (Maritime Aerosol Network, MAN), and a global chemical transport model (GEOS-Chem). This work represents the most comprehensive evaluation of oceanic OM emission inventories to date, by employing aerosol composition measurements obtained from campaigns with wide spatial and temporal coverage. The model underestimates AOD over the remote ocean on average by 0.02 (21 %), compared to satellite observations, but provides an unbiased simulation of ground-based Maritime Aerosol Network (MAN) observations. Comparison with cruise data demonstrates that the GEOS-Chem simulation of marine sulfate, with the mean observed values ranging between 0.22 μg m−3 and 1.34 μg m−3, is generally unbiased, however surface organic matter (OM) concentrations, with the mean observed concentrations between 0.07 μg m−3 and 0.77 μg m−3, are underestimated by a factor of 2–5 for the standard model run. Addition of a sub-micron marine OM source of approximately 9 TgC yr−1 brings the model into agreement with the ship-based measurements, however this additional OM source does not explain the model underestimate of marine AOD. The model underestimate of marine AOD is therefore likely the result of a combination of satellite retrieval bias and a missing marine aerosol source (which exhibits a different spatial pattern than existing aerosol in the model)

Earth observations from DSCOVR EPIC instrument

The National Oceanic and Atmospheric Administration (NOAA) Deep Space Climate Observatory (DSCOVR) spacecraft was launched on 11 February 2015 and in June 2015 achieved its orbit at the first Lagrange point (L1), 1.5 million km from Earth toward the sun. There are two National Aeronautics and Space Administration (NASA) Earth-observing instruments on board: the Earth Polychromatic Imaging Camera (EPIC) and the National Institute of Standards and Technology Advanced Radiometer (NISTAR). The purpose of this paper is to describe various capabilities of the DSCOVR EPIC instrument. EPIC views the entire sunlit Earth from sunrise to sunset at the backscattering direction (scattering angles between 168.5° and 175.5°) with 10 narrowband filters: 317, 325, 340, 388, 443, 552, 680, 688, 764, and 779 nm. We discuss a number of preprocessing steps necessary for EPIC calibration including the geolocation algorithm and the radiometric calibration for each wavelength channel in terms of EPIC counts per second for conversion to reflectance units. The principal EPIC products are total ozone (O3) amount, scene reflectivity, erythemal irradiance, ultraviolet (UV) aerosol properties, sulfur dioxide (SO2) for volcanic eruptions, surface spectral reflectance, vegetation properties, and cloud products including cloud height. Finally, we describe the observation of horizontally oriented ice crystals in clouds and the unexpected use of the O2 B-band absorption for vegetation properties.The NASA GSFC DSCOVR project is funded by NASA Earth Science Division. We gratefully acknowledge the work by S. Taylor and B. Fisher for help with the SO2 retrievals and Marshall Sutton, Carl Hostetter, and the EPIC NISTAR project for help with EPIC data. We also would like to thank the EPIC Cloud Algorithm team, especially Dr. Gala Wind, for the contribution to the EPIC cloud products. (NASA Earth Science Division)Accepted manuscrip

Satellite for Estimating Aquatic Salinity and Temperature (SEASALT) - A Scientific Overview

SEASALT is a small satellite mission designed to explore the estimation of salinity in coastal environments using ocean color. A SEASALT constellation would fill the coastal gap by providing coastal SSS observations with much higher spatial resolution (30m) and much shorter revisit times (less than 1 day) on a global scale. Planet’s nanosatellites currently provide daily monitoring of the earth’s surface, as well as coastal locations, at 3-meter resolution. However, they do not have the required bands needed in the near infrared (NIR) for atmospheric correction (they only possess 1 NIR band), thus making atmospheric correction over water very challenging. Accurate atmospheric corrections are fundamental to reliably retrieving salinity from ocean color. SEASALT has these required bands by design. Planet’s nanosatellites also do not have a 412nm band to monitor CDOM and create optimized salinity products. SEASALT has bands centered at 412nm, 470nm, 540nm, 625nm, 746nm, 865nm, and 12013nm. A SEASALT constellation has the potential to monitor coastal regions consistently on a global scale as locally-optimized salinity retrieval algorithms can be developed. Besides retrieving SSS with a high temporal and spatial resolution, SEASALT will retrieve concurrent sea surface temperature (SST)

Eliminating bias in satellite retrievals of sea surface temperature

Sea surface temperature (SST) is a critical parameter for climate research, and needs to be measured with an absolute accuracy of ~0.3 K (average over ~100 km scale on a weekly to monthly time scale) and with a long term stability of 0.1 K per decade. These stringent requirements present a formidable challenge to satellite based SST measurement. The most promising satellite radiometer is the ATSR (and successors), but bias and spurious trends have arisen in the ATSR SST retrieval process. Eliminating such retrieval bias is the focus of this thesis. SSTs derived from the ATSR using the prelaunch retrieval scheme are biased by up to -1.5 K by stratospheric aerosol from the eruption of Mount Pinatubo shortly before launch. An "aerosol-robust" retrieval scheme is derived which has no detectable aerosol- related bias. Another bias of up to 0.5 K arising from a deficiency of the radiative transfer model used to develop the prelaunch retrieval scheme is resolved by implementing an updated parameterisation of water vapour continuum absorption. The new SSTs are shown to have an accuracy better than 0.3 K (error in a single retrieval over a -20 km spatial scale) and to be robust to aerosol effects, by a validation exercise against buoys measuring SST in situ. The validation data consist of 620 satellite-buoy coincidences in the tropical Pacific between September 1991 and May 1992, a region and period associated with high loadings of stratospheric aerosol and tropospheric water vapour. This is the first validation exercise to correct for the effects of the difference between bulk SSTs (measured by buoys) and skin SSTs (measured radiometrically). The factor now limiting accuracy is residual cloud contamination. The new retrieval scheme has been adopted for the reprocessing of all archived ATSR data to SST

The HelioMont method for assessing solar irradiance over complex terrain: Validation and improvements

n/

Developing and Diagnosing Climate Change Indicators of Regional Aerosol Optical Properties

Given the importance of aerosol particles to radiative transfer via aerosol-radiation interactions, a methodology for tracking and diagnosing causes of temporal changes in regional-scale aerosol populations is illustrated. The aerosol optical properties tracked include estimates of total columnar burden (aerosol optical depth, AOD), dominant size mode (ngstrm exponent, AE), and relative magnitude of radiation scattering versus absorption (single scattering albedo, SSA), along with metrics of the structure of the spatial field of these properties. Over well-defined regions of North America, there are generally negative temporal trends in mean and extreme AOD, and SSA. These are consistent with lower aerosol burdens and transition towards a relatively absorbing aerosol, driven primarily by declining sulfur dioxide emissions. Conversely, more remote regions are characterized by increasing mean and extreme AOD that is attributed to increased local wildfire emissions and long-range (transcontinental) transport. Regional and national reductions in anthropogenic emissions of aerosol precursors are leading to declining spatial autocorrelation in the aerosol fields and increased importance of local anthropogenic emissions in dictating aerosol burdens. However, synoptic types associated with high aerosol burdens are intensifying (becoming more warm and humid), and thus changes in synoptic meteorology may be offsetting aerosol burden reductions associated with emissions legislation