scholarly journals Fully automated analysis of space-based lidar data: an overview of the CALIPSO retrieval algorithms and data products

2004 ◽  
Author(s):  
Mark A. Vaughan ◽  
Stuart A. Young ◽  
David M. Winker ◽  
Kathleen A. Powell ◽  
Ali H. Omar ◽  
...  
Keyword(s):  
Automated Analysis ◽  
Lidar Data ◽  
Retrieval Algorithms ◽  
Data Products

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

2018 ◽  
Vol 11 (10) ◽  
pp. 5701-5727 ◽  
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.

scholarly journals Model-enforced post-process correction of satellite aerosol retrievals

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.

scholarly journals Overview of SCIAMACHY validation: 2002–2004

2006 ◽  
Vol 6 (1) ◽  
pp. 127-148 ◽  
Author(s):  
A. J. M. Piters ◽  
K. Bramstedt ◽  
J.-C. Lambert ◽  
B. Kirchhoff
Keyword(s):  
Near Infrared ◽  
Data Sets ◽  
Limited Data ◽  
Spatial And Temporal Scales ◽  
Retrieval Algorithms ◽  
Data Products ◽  
Uv Visible ◽  
Aerosol Parameters ◽  
Face Complex ◽  
Level 2

Abstract. SCIAMACHY, on board Envisat, has been in operation now for almost three years. This UV/visible/NIR spectrometer measures the solar irradiance, the earthshine radiance scattered at nadir and from the limb, and the attenuation of solar radiation by the atmosphere during sunrise and sunset, from 240 to 2380 nm and at moderate spectral resolution. Vertical columns and profiles of a variety of atmospheric constituents are inferred from the SCIAMACHY radiometric measurements by dedicated retrieval algorithms. With the support of ESA and several international partners, a methodical SCIAMACHY validation programme has been developed jointly by Germany, the Netherlands and Belgium (the three instrument providing countries) to face complex requirements in terms of measured species, altitude range, spatial and temporal scales, geophysical states and intended scientific applications. This summary paper describes the approach adopted to address those requirements. Since provisional releases of limited data sets in summer 2002, operational SCIAMACHY processors established at DLR on behalf of ESA were upgraded regularly and some data products – level-1b spectra, level-2 O3, NO2, BrO and clouds data – have improved significantly. Validation results summarised in this paper and also reported in this special issue conclude that for limited periods and geographical domains they can already be used for atmospheric research. Nevertheless, current processor versions still experience known limitations that hamper scientific usability in other periods and domains. Free from the constraints of operational processing, seven scientific institutes (BIRA-IASB, IFE/IUP-Bremen, IUP-Heidelberg, KNMI, MPI, SAO and SRON) have developed their own retrieval algorithms and generated SCIAMACHY data products, together addressing nearly all targeted constituents. Most of the UV-visible data products – O3, NO2, SO2, H2O total columns; BrO, OClO slant columns; O3, NO2, BrO profiles – already have acceptable, if not excellent, quality. Provisional near-infrared column products – CO, CH4, N2O and CO2 – have already demonstrated their potential for a variety of applications. Cloud and aerosol parameters are retrieved, suffering from calibration with the exception of cloud cover. In any case, scientific users are advised to read carefully validation reports before using the data. It is required and anticipated that SCIAMACHY validation will continue throughout instrument lifetime and beyond and will accompany regular processor upgrades.

scholarly journals Aerosol observation using multi-wavelength Mie-Raman lidars of the Ad-Net and aerosol component analysis

2018 ◽  
Vol 176 ◽  
pp. 09005
Author(s):  
Tomoaki Nishizawa ◽  
Nobuo Sugimoto ◽  
Atsushi Shimizu ◽  
Itsushi Uno ◽  
Yukari Hara ◽  
...  
Keyword(s):  
Sea Salt ◽  
The Other ◽  
Lidar Data ◽  
Network Data ◽  
Vertical Profiles ◽  
Component Retrieval ◽  
Aerosol Component ◽  
Retrieval Algorithms ◽  
Multi Wavelength ◽  
Ground Based Lidar

We deployed multi-wavelength Mie-Raman lidars (MMRL) at three sites of the AD-Net and have conducted continuous measurements using them since 2013. To analyze the MMRL data and better understand the externally mixing state of main aerosol components (e.g., dust, sea-salt, and black carbon) in the atmosphere, we developed an integrated package of aerosol component retrieval algorithms, which have already been developed or are being developed, to estimate vertical profiles of the aerosol components. This package applies to the other ground-based lidar network data (e.g., EARLINET) and satellite-borne lidar data (e.g., CALIOP/CALIPSO and ATLID/EarthCARE) as well as the other lidar data of the AD-Net.

scholarly journals Earlinet single calculus chain: new products overview

2018 ◽  
Vol 176 ◽  
pp. 09014 ◽  
Author(s):  
Giuseppe D’Amico ◽  
Ina Mattis ◽  
Ioannis Binietoglou ◽  
Holger Baars ◽  
Lucia Mona ◽  
...  
Keyword(s):  
Extinction Coefficient ◽  
Atmospheric Aerosol ◽  
New Products ◽  
Lidar Data ◽  
Depolarization Ratio ◽  
Flexible Tool ◽  
Retrieval Algorithms ◽  
Linear Depolarization Ratio ◽  
Aerosol Backscatter ◽  
Aerosol Characterization

The Single Calculus Chain (SCC) is an automatic and flexible tool to analyze raw lidar data using EARLINET quality assured retrieval algorithms. It has been already demonstrated the SCC can retrieve reliable aerosol backscatter and extinction coefficient profiles for different lidar systems. In this paper we provide an overview of new SCC products like particle linear depolarization ratio, cloud masking, aerosol layering allowing relevant improvements in the atmospheric aerosol characterization.

scholarly journals Model Enforced Post-Process Correction of Satellite Aerosol Retrievals

2020 ◽  
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 global view of atmospheric aerosol properties for air quality, atmospheric characterization, and correction of satellite data products and climate applications. Current aerosol data products based on satellite data, however, often have relatively large biases relative to accurate ground-based measurements and distinct levels of uncertainty 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 a tedious and computationally expensive task. To overcome this burden, we have developed a model enforced post-process correction approach that can be used to correct the existing and 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 with no need to fully reprocess the much larger original radiance data. We demonstrate with over land aerosol optical depth (AOD) and Angstrom exponent (AE) data from the Moderate Imaging Spectroradiometer (MODIS) of Aqua satellite that our approach can significantly improve the accuracy of the satellite aerosol data products and reduce the associated uncertainties. We also give recommendations for the validation of satellite data products that are constructed using machine learning based models.

scholarly journals Extinction and Optical Depth Retrievals for CALIPSO's Version 4 Data Release

2018 ◽  
Author(s):  
Stuart A. Young ◽  
Mark A. Vaughan ◽  
Anne Garnier ◽  
Jason L. Tackett ◽  
James B. 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.

scholarly journals Overview of SCIAMACHY validation: 2002–2004

2005 ◽  
Vol 5 (4) ◽  
pp. 7769-7828 ◽  
Author(s):  
A. J. M. Piters ◽  
K. Bramstedt ◽  
J.-C. Lambert ◽  
B. Kirchhoff
Keyword(s):  
Near Infrared ◽  
Data Distribution ◽  
Spatial And Temporal Scales ◽  
Atmospheric Research ◽  
Summary Paper ◽  
Retrieval Algorithms ◽  
Data Products ◽  
Uv Visible ◽  
Face Complex ◽  
Level 2

Abstract. SCIAMACHY, on board Envisat, is now in operation for almost three years. This UV/visible/NIR spectrometer measures the solar irradiance, the earthshine radiance scattered at nadir and from the limb, and the attenuation of solar radiation by the atmosphere during sunrise and sunset, from 240 to 2380 nm and at moderate spectral resolution. Vertical columns and profiles of a variety of atmospheric constituents are inferred from the SCIAMACHY radiometric measurements by dedicated retrieval algorithms. With the support of ESA and several international partners, a methodical SCIAMACHY validation programme has been developed jointly by Germany, the Netherlands and Belgium (the three instrument providing countries) to face complex requirements in terms of measured species, altitude range, spatial and temporal scales, geophysical states and intended scientific applications. This summary paper describes the approach adopted to address those requirements. The actual validation of the operational SCIAMACHY processors established at DLR on behalf of ESA has been hampered by data distribution and processor problems. Since first data releases in summer 2002, operational processors were upgraded regularly and some data products – level-1b spectra, level-2 O3, NO2, BrO and clouds data – have improved significantly. Validation results summarised in this paper conclude that for limited periods and geographical domains they can already be used for atmospheric research. Nevertheless, remaining processor problems cause major errors preventing from scientific usability in other periods and domains. Untied to the constraints of operational processing, seven scientific institutes (BIRA-IASB, IFE, IUP-Heidelberg, KNMI, MPI, SAO and SRON) have developed their own retrieval algorithms and generated SCIAMACHY data products, together addressing nearly all targeted constituents. Most of the UV-visible data products (both columns and profiles) already have acceptable, if not excellent, quality. Several near-infrared column products are still in development but they have already demonstrated their potential for a variety of applications. In any case, scientific users are advised to read carefully validation reports before using the data. It is required and anticipated that SCIAMACHY validation will continue throughout instrument lifetime and beyond. The actual amount of work will obviously depend on funding considerations.

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