Ground-based validation of the EOS Multi-angle Imaging SpectroRadiometer (MISR) aerosol retrieval algorithms and science data products

Abstract. The Orbiting Carbon Observatory-2 (OCO-2) is the first National Aeronautics and Space Administration (NASA) satellite designed to measure atmospheric carbon dioxide (CO2) with the accuracy, resolution, and coverage needed to quantify CO2 fluxes (sources and sinks) on regional scales. OCO-2 was successfully launched on 2 July 2014 and has gathered more than 2 years of observations. The v7/v7r operational data products from September 2014 to January 2016 are discussed here. On monthly timescales, 7 to 12 % of these measurements are sufficiently cloud and aerosol free to yield estimates of the column-averaged atmospheric CO2 dry air mole fraction, XCO2, that pass all quality tests. During the first year of operations, the observing strategy, instrument calibration, and retrieval algorithm were optimized to improve both the data yield and the accuracy of the products. With these changes, global maps of XCO2 derived from the OCO-2 data are revealing some of the most robust features of the atmospheric carbon cycle. This includes XCO2 enhancements co-located with intense fossil fuel emissions in eastern US and eastern China, which are most obvious between October and December, when the north–south XCO2 gradient is small. Enhanced XCO2 coincident with biomass burning in the Amazon, central Africa, and Indonesia is also evident in this season. In May and June, when the north–south XCO2 gradient is largest, these sources are less apparent in global maps. During this part of the year, OCO-2 maps show a more than 10 ppm reduction in XCO2 across the Northern Hemisphere, as photosynthesis by the land biosphere rapidly absorbs CO2. As the carbon cycle science community continues to analyze these OCO-2 data, information on regional-scale sources (emitters) and sinks (absorbers) which impart XCO2 changes on the order of 1 ppm, as well as far more subtle features, will emerge from this high-resolution global dataset.

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An Overview of the Science Performances and Calibration/Validation of Joint Polar Satellite System Operational Products

Remote Sensing ◽

10.3390/rs11060698 ◽

2019 ◽

Vol 11 (6) ◽

pp. 698 ◽

Cited By ~ 7

Author(s):

Lihang Zhou ◽

Murty Divakarla ◽

Xingpin Liu ◽

Arron Layns ◽

Mitch Goldberg

Keyword(s):

Performance Monitoring ◽

Performance Metrics ◽

Satellite System ◽

Quality Data ◽

Direct Result ◽

Science Data ◽

Successful Operation ◽

Data Products ◽

The Status ◽

Polar Satellite

The Suomi National Polar-orbiting Partnership (S-NPP) satellite, launched in October 2011, initiated a series of the next-generation weather satellites for the National Oceanic and Atmospheric Administration (NOAA) Joint Polar Satellite System (JPSS) program. The JPSS program at the Center for Satellite Applications and Research (JSTAR) leads the development of the algorithms, the calibration and validation of the products to meet the specified requirements, and long-term science performance monitoring and maintenance. All of the S-NPP products have been validated and are in successful operation. The recently launched JPSS-1 (renamed as NOAA-20) satellite is producing high-quality data products that have been available from S-NPP, along with additional products, as a direct result of the instrument upgrades and science improvements. This paper presents an overview of the JPSS product suite, the performance metrics achieved for the S-NPP, and the utilization of the products by NOAA stakeholders and user agencies worldwide. The status of NOAA-20 science data products and ongoing calibration/validation (Cal/Val) efforts are discussed for user awareness. In addition, operational implementation statuses of JPSS enterprise (multisensor and multiplatform) science algorithms for product generation and science product reprocessing efforts for the S-NPP mission are discussed.

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Extinction and optical depth retrievals for CALIPSO's Version 4 data release

Atmospheric Measurement Techniques ◽

10.5194/amt-11-5701-2018 ◽

2018 ◽

Vol 11 (10) ◽

pp. 5701-5727 ◽

Cited By ~ 39

Author(s):

Stuart A. Young ◽

Mark A. Vaughan ◽

Anne Garnier ◽

Jason L. Tackett ◽

James D. Lambeth ◽

...

Keyword(s):

Optical Depth ◽

Satellite Observations ◽

Orthogonal Polarization ◽

Ice Clouds ◽

Extinction Coefficients ◽

Retrieval Algorithms ◽

Data Release ◽

Data Products ◽

Lidar Ratio ◽

Level 2

Abstract. The Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) on board the Cloud–Aerosol Lidar Infrared Pathfinder Satellite Observations (CALIPSO) satellite has been making near-global height-resolved measurements of cloud and aerosol layers since mid-June 2006. Version 4.10 (V4) of the CALIOP data products, released in November 2016, introduces extensive upgrades to the algorithms used to retrieve the spatial and optical properties of these layers, and thus there are both obvious and subtle differences between V4 and previous data releases. This paper describes the improvements made to the extinction retrieval algorithms and illustrates the impacts of these changes on the extinction and optical depth estimates reported in the CALIPSO lidar level 2 data products. The lidar ratios for both aerosols and ice clouds are generally higher than in previous data releases, resulting in generally higher extinction coefficients and optical depths in V4. A newly implemented algorithm for retrieving extinction coefficients in opaque layers is described and its impact examined. Precise lidar ratio estimates are also retrieved in these opaque layers. For semi-transparent cirrus clouds, comparisons between CALIOP V4 optical depths and the optical depths reported by MODIS collection 6 show substantial improvements relative to earlier comparisons between CALIOP version 3 and MODIS collection 5.

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Streamlining Oceanic Biogeochemical Dataset Assembly in Support of Global Data Products

10.5194/egusphere-egu2020-11709 ◽

2020 ◽

Author(s):

Eugene Burger ◽

Benjamin Pfeil ◽

Kevin O'Brien ◽

Linus Kamb ◽

Steve Jones ◽

...

Keyword(s):

Quality Control ◽

Data Integration ◽

Scientific Data ◽

Climate Data ◽

Science Data ◽

Metadata Record ◽

Data Products ◽

Analysis And Synthesis ◽

Data Integration System ◽

Global Data

<p>Data assembly in support of global data products, such as GLODAP, and submission of data to national data centers to support long-term preservation, demands significant effort. This is in addition to the effort required to perform quality control on the data prior to submission. Delays in data assembly can negatively affect the timely production of scientific indicators that are dependent upon these datasets, including products such as GLODAP. What if data submission, metadata assembly and quality control can all be rolled into a single application? To support more streamlined data management processes in the NOAA Ocean Acidification Program (OAP) we are developing such an application.This application has the potential for application towards a broader community.</p><p>This application addresses the need that data contributing to analysis and synthesis products are high quality, well documented, and accessible from the applications scientists prefer to use. The Scientific Data Integration System (SDIS) application developed by the PMEL Science Data Integration Group, allows scientists to submit their data in a number of formats. Submitted data are checked for common errors. Metadata are extracted from the data that can then be complemented with a complete metadata record using the integrated metadata entry tool that collects rich metadata that meets the Carbon science community requirements. Still being developed, quality control for standard biogeochemical parameters will be integrated into the application. The quality control routines will be implemented in close collaboration with colleagues from the Bjerknes Climate Data Centre (BCDC) within the Bjerknes Centre for Climate Research (BCCR).&#160; This presentation will highlight the capabilities that are now available as well as the implementation of the archive automation workflow, and it&#8217;s potential use in support of GLODAP data assembly efforts.</p>

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Model-enforced post-process correction of satellite aerosol retrievals

Atmospheric Measurement Techniques ◽

10.5194/amt-14-2981-2021 ◽

2021 ◽

Vol 14 (4) ◽

pp. 2981-2992

Author(s):

Antti Lipponen ◽

Ville Kolehmainen ◽

Pekka Kolmonen ◽

Antti Kukkurainen ◽

Tero Mielonen ◽

...

Keyword(s):

Machine Learning ◽

Satellite Data ◽

Data Retrieval ◽

Post Processing ◽

Correction Model ◽

Post Process ◽

Spectral Bands ◽

Processing Step ◽

Retrieval Algorithms ◽

Data Products

Abstract. Satellite-based aerosol retrievals provide a timely view of atmospheric aerosol properties, having a crucial role in the subsequent estimation of air quality indicators, atmospherically corrected satellite data products, and climate applications. However, current aerosol data products based on satellite data often have relatively large biases compared to accurate ground-based measurements and distinct uncertainty levels associated with them. These biases and uncertainties are often caused by oversimplified assumptions and approximations used in the retrieval algorithms due to unknown surface reflectance or fixed aerosol models. Moreover, the retrieval algorithms do not usually take advantage of all the possible observational data collected by the satellite instruments and may, for example, leave some spectral bands unused. The improvement and the re-processing of the past and current operational satellite data retrieval algorithms would become tedious and computationally expensive. To overcome this burden, we have developed a model-enforced post-process correction approach to correct the existing operational satellite aerosol data products. Our approach combines the existing satellite aerosol retrievals and a post-processing step carried out with a machine-learning-based correction model for the approximation error in the retrieval. The developed approach allows for the utilization of auxiliary data sources, such as meteorological information, or additional observations such as spectral bands unused by the original retrieval algorithm. The post-process correction model can learn to correct for the biases and uncertainties in the original retrieval algorithms. As the correction is carried out as a post-processing step, it allows for computationally efficient re-processing of existing satellite aerosol datasets without fully re-processing the much larger original radiance data. We demonstrate with over-land aerosol optical depth (AOD) and Ångström exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of the Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. For instance, in our evaluation, the number of AOD samples within the MODIS Dark Target expected error envelope increased from 63 % to 85 % when the post-process correction was applied. In addition to method description and accuracy results, we also give recommendations for validating machine-learning-based satellite data products.

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General Server for Rapid Publishing of OGC-Compliant Earth Science Data Products

10.1002/essoar.10509722.1 ◽

2021 ◽

Author(s):

Mattheus Ueckermann ◽

Jerry Bieszczad

Keyword(s):

Earth Science ◽

Science Data ◽

Data Products ◽

Earth Science Data

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Multi-sensor cloud and aerosol retrieval simulator and remote sensing from model parameters – Part 2: Aerosols

Geoscientific Model Development ◽

10.5194/gmd-9-2377-2016 ◽

2016 ◽

Vol 9 (7) ◽

pp. 2377-2389 ◽

Cited By ~ 4

Author(s):

Galina Wind ◽

Arlindo M. da Silva ◽

Peter M. Norris ◽

Steven Platnick ◽

Shana Mattoo ◽

...

Keyword(s):

Remote Sensing ◽

Weather Prediction ◽

Retrieval Algorithm ◽

Model Parameters ◽

Aerosol Retrieval ◽

Model Aerosol ◽

Numerical Experimentation ◽

Retrieval Algorithms ◽

Sensor Cloud ◽

Aerosol Properties

Abstract. The Multi-sensor Cloud Retrieval Simulator (MCRS) produces a “simulated radiance” product from any high-resolution general circulation model with interactive aerosol as if a specific sensor such as the Moderate Resolution Imaging Spectroradiometer (MODIS) were viewing a combination of the atmospheric column and land–ocean surface at a specific location. Previously the MCRS code only included contributions from atmosphere and clouds in its radiance calculations and did not incorporate properties of aerosols. In this paper we added a new aerosol properties module to the MCRS code that allows users to insert a mixture of up to 15 different aerosol species in any of 36 vertical layers.This new MCRS code is now known as MCARS (Multi-sensor Cloud and Aerosol Retrieval Simulator). Inclusion of an aerosol module into MCARS not only allows for extensive, tightly controlled testing of various aspects of satellite operational cloud and aerosol properties retrieval algorithms, but also provides a platform for comparing cloud and aerosol models against satellite measurements. This kind of two-way platform can improve the efficacy of model parameterizations of measured satellite radiances, allowing the assessment of model skill consistently with the retrieval algorithm. The MCARS code provides dynamic controls for appearance of cloud and aerosol layers. Thereby detailed quantitative studies of the impacts of various atmospheric components can be controlled.In this paper we illustrate the operation of MCARS by deriving simulated radiances from various data field output by the Goddard Earth Observing System version 5 (GEOS-5) model. The model aerosol fields are prepared for translation to simulated radiance using the same model subgrid variability parameterizations as are used for cloud and atmospheric properties profiles, namely the ICA technique. After MCARS computes modeled sensor radiances equivalent to their observed counterparts, these radiances are presented as input to operational remote-sensing algorithms.Specifically, the MCARS-computed radiances are input into the processing chain used to produce the MODIS Data Collection 6 aerosol product (M{O/Y}D04). The M{O/Y}D04 product is of course normally produced from M{O/Y}D021KM MODIS Level-1B radiance product directly acquired by the MODIS instrument. MCARS matches the format and metadata of a M{O/Y}D021KM product. The resulting MCARS output can be directly provided to MODAPS (MODIS Adaptive Processing System) as input to various operational atmospheric retrieval algorithms. Thus the operational algorithms can be tested directly without needing to make any software changes to accommodate an alternative input source.We show direct application of this synthetic product in analysis of the performance of the MOD04 operational algorithm. We use biomass-burning case studies over Amazonia employed in a recent Working Group on Numerical Experimentation (WGNE)-sponsored study of aerosol impacts on numerical weather prediction (Freitas et al., 2015). We demonstrate that a known low bias in retrieved MODIS aerosol optical depth appears to be due to a disconnect between actual column relative humidity and the value assumed by the MODIS aerosol product.

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Continuity of MODIS and VIIRS Snow Cover Extent Data Products for Development of an Earth Science Data Record

Remote Sensing ◽

10.3390/rs12223781 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3781

Author(s):

George Riggs ◽

Dorothy Hall

Keyword(s):

Snow Cover ◽

Infrared Imaging ◽

Earth Science ◽

Satellite System ◽

Science Data ◽

Snow Cover Extent ◽

Data Record ◽

Data Products ◽

Moderate Resolution ◽

Earth Science Data

An Earth Observing System global snow cover extent data products record at moderate spatial resolution (375–500 m) began in February 2000 with the Moderate-resolution Imaging Spectroradiometer (MODIS) instrument onboard the Terra satellite. The record continued with the Aqua MODIS in July 2002, the Suomi-National Polar Platform (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) in January 2012 and continues with the Joint Polar Satellite System-1 (JPSS-1) VIIRS, launched in November of 2017. The objective of this work is to develop a snow cover extent Earth Science Data Record (ESDR) using different satellites, sensors and algorithms. There are many issues to understand when data from different algorithms and sensors are used over a decade-scale time period to create a continuous dataset. Issues may also arise with sensor degradation and even differences in sensor band locations. In this paper we describe development of an ESDR derived from existing MODIS and VIIRS data products and demonstrate continuity among the products. The MODIS and VIIRS snow cover detection algorithms produce very similar daily snow cover maps, with 90–97% agreement in snow cover extent (SCE) in different landscapes. Differences in SCE between products ranged from 2–15% and are attributable to convolved factors of viewing geometry, pixel spread across a scan and time of observation. Compared at a common grid size of 1 km, there is a mean of 95% agreement in SCE and a difference range of 1–10% between the MODIS and VIIRS SCE maps. Mapping sensor observations to a coarser resolution grid reduces the effect of the factors convolved in the 500 m tile to tile comparisons. We conclude that the MODIS and VIIRS SCE data products are reliable constituents of a moderate-resolution ESDR.

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