scholarly journals Long-Term Variations in the Pixel-to-Pixel Variability of NOAA AVHRR SST Fields from 1982 to 2015

2019 ◽  
Vol 11 (7) ◽  
pp. 844 ◽  
Author(s):  
Fan Wu ◽  
Peter Cornillon ◽  
Lei Guan ◽  
Katherine Kilpatrick
Keyword(s):  
Quality Data ◽  
Retrieval Algorithm ◽  
Ocean Fronts ◽  
Noaa Avhrr ◽  
Data Record ◽  
Along Track ◽  
Level 2 ◽  
Very High ◽  
Significant Effort

Sea surface temperature (SST) fields obtained from the series of space-borne five-channel Advanced Very High Resolution Radiometers (AVHRRs) provide the longest continuous time series of global SST available to date (1981–present). As a result, these data have been used for many studies and significant effort has been devoted to their careful calibration in an effort to provide a climate quality data record. However, little attention has been given to the local precision of the SST retrievals obtained from these instruments, which we refer to as the pixel-to-pixel (p2p) variability, a characteristic important in the ability to resolve structures such as ocean fronts characterized by small gradients in the SST field. In this study, the p2p variability is estimated for Level-2 SST fields obtained with the Pathfinder retrieval algorithm for AVHRRs on NOAA-07, 9, 11, 12 and 14-19. These estimates are stratified by year, season, day/night and along-scan/along-track. The overall variability ranges from 0.10 K to 0.21 K. For each satellite, the along-scan variability is between 10 and 20% smaller than the along-track variability (except for NOAA-16 nighttime for which it is approximately 30% smaller) and the summer and fall σ s are between 10 and 15% smaller than the winter and spring σ s. The differences between along-track and along-scan are attributed to the way in which the instrument has been calibrated. The seasonal differences result from the T 4 − T 5 term in the Pathfinder retrieval algorithm. This term is shown to be a major contributor to the p2p variability and it is shown that its impact could be substantially reduced without a deleterious effect on the overall p2p σ of the resulting products by spatially averaging it as part of the retrieval process. The AVHRR/3s (NOAA-15 through 19) were found to be relatively stable with trends in the p2p variability of at most 0.015 K/decade.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals AerGOM, an improved algorithm for stratospheric aerosol extinction retrieval from GOMOS observations. Part 1: Algorithm development

2016 ◽  
Author(s):  
Filip Vanhellemont ◽  
Nina Mateshvili ◽  
Laurent Blanot ◽  
Charles E. Robert ◽  
Christine Bingen ◽  
...  
Keyword(s):  
Stratospheric Aerosol ◽  
Quality Data ◽  
Retrieval Algorithm ◽  
Aerosol Extinction ◽  
Data Inversion ◽  
Aerosol Retrieval ◽  
Stellar Occultation ◽  
Algorithmic Problems ◽  
The Subject ◽  
Level 2

Abstract. The GOMOS instrument on EnviSat has succesfully demonstrated that a UV/Vis/NIR spaceborne stellar occultation instrument is capable of delivering quality data on the gaseous and particulate composition of Earth's atmosphere. Still, some problems related to data inversion remained to be treated. In the past, it was found that the aerosol extinction profile retrievals in the upper troposphere and stratosphere are of good quality at a reference wavelength of 500 nm, but suffer from anomalous, retrieval-related perturbations at other wavelengths. Identification of algorithmic problems and subsequent improvement was therefore necessary. This work has been carried out; the resulting AerGOM Level 2 retrieval algorithm together with the first data version AerGOMv1.0 forms the subject of this paper. First, a brief overview of the operational IPFv6.01 GOMOS algorithm is given, since the AerGOM algorithm is to a certain extent similar. Then, the discussion on the AerGOM algorithm specifically focuses on the new aspects that were implemented to tackle the aerosol retrieval problems. Finally, a first assess- ment of the obtained aerosol extinction data quality is presented, clearly showing significant improvement of aerosol profile shape, spectral behaviour and similarity to SAGE II data.

Aerosol retrieval based on 10 years of passive remote sensing satellite measurements over the Arctic – validation and trend analysis

2020 ◽  
Author(s):  
Soheila Jafariserajehlou ◽  
Marco Vountas ◽  
Larysa Istomina ◽  
John P. Burrows
Keyword(s):  
Remote Sensing ◽  
Trend Analysis ◽  
Optical Thickness ◽  
Arctic Region ◽  
The Arctic ◽  
Retrieval Algorithm ◽  
Identification Algorithm ◽  
Aerosol Retrieval ◽  
Along Track

<p>The Aerosol Optical Thickness (AOT) retrieval over the Arctic region is a challenging task due to uncertainties and difficulties in its prerequisites, mainly (i) cloud masking methods and (ii) modeling the underlying snow/ice surface. In the past this led to a large data gap over the Arctic which hampered our understanding of the direct/indirect aerosol effect on Arctic and global climate change. For the purpose of improving our knowledge, we present, for the first time, long-term AOT maps of snow and ice covered areas based on satellite remote sensing.</p><p>In this study, a previously developed aerosol retrieval algorithm over snow/ice, (Istomina et al., 2012; in IUP, University of Bremen) is used to retrieve AOT for a period of 10 years, 2002-2012, over the Arctic and to analyze its spatial and temporal changes. This algorithm is based on a multi-angle approach and uses pre-computed look-up tables to retrieve AOT.</p><p>The algorithm has been improved with respect to cloud masking (based on clear snow spectral shape) using the ASCIA cloud identification algorithm (Jafariserajehlou et al., 2019). The modified AOT retrieval algorithm is applied to observations from Advanced Along-Track Scanning Radiometer (AATSR) on European Space Agency’s (ESA) measurements. The retrieved dataset provides long-term AOT at a spatial resolution of 1 km<sup>2</sup> over snow/ice covered surface in the extended Arctic region (60<sup>°</sup>- 90<sup>°</sup>) during polar day. The results show that Arctic haze events appearing every late-winter and early spring are very well captured in AATSR derived AOTs. To validate the retrieved AOTs, results are compared with ground-based AERONET data. The comparisons revealed partially excellent agreement but also limits of the retrieval algorithm are discussed. In addition, some preliminary results of a trend analysis of the long-term record will be presented. It is foreseen to use the results in the trans-regional research project (AC)³ investigating Arctic amplification.</p><p><em><strong>References</strong></em></p><p>[1] Istomina, L.: Retrieval of aerosol optical thickness over snow and ice surfaces in the Arctic using Advanced Along Track Scanning Radiometer, PhD thesis, University of Bremen, Bremen, Germany, 2012.</p><p>[2] Jafariserajehlou, S. and Mei, L. and Vountas, M. and Rozanov, V. and Burrows, J. P. and Hollmann, R., A cloud identification algorithm over the Arctic for use with AATSR/SLSTR measurements, Atmos. Meas. Tech., 12, 1059-1076, doi:10.5194/amt-12-1059-2019, 2019.</p><p> </p>

The FDR4ATMOS Project

2020 ◽  
Author(s):  
Günter Lichtenberg ◽  
Sander Slijkhuis ◽  
Mourad Hamidouche ◽  
Melanie Coldewey-Egbers ◽  
Bernd Aberle ◽  
...  
Keyword(s):  
Time Series ◽  
Phase 1 ◽  
Atmospheric Composition ◽  
Data Preservation ◽  
Uncertainty Estimates ◽  
Data Record ◽  
Level 1 ◽  
Level 2 ◽  
Total Column

<p>The Fundamental Data Record for ATMOSpheric Composition (FDR4ATMOS) project is part of the ESA Long Term Data Preservation (LTDP) programme aimed at the preservation and valorization of data assets from ESA’s Earth Observation (EO) Heritage Missions. It has two objectives. The first one is to update the SCIAMACHY processing chain for better Ozone total column data. After the full re-processing of the SCIAMACHY mission with the updated processor versions 9 (Level 1) and version 7 (Level 2), ground-based validation showed that the total Ozone column drifted downward by nearly 2% over the mission lifetime. This drift is likely caused by changes in the degradation correction in the Level 1 processor, that led to subtle changes in the spectral structures. These are misinterpreted as an atmospheric signature. FDR4ATMOS will update the Level 0-1 processor accordingly with the final aim of a mission re-processing.</p><p>The main objective of the FDR4ATMOS project is to develop a cross-instrument Level 1 product for GOME-1 and SCIAMACHY for the UV, VIS and NIR spectral range, with focus on the spectral windows used for O3, SO2, NO2 total column retrieval and the determination of cloud properties. Contrary to other projects, FDR4ATMOS does not aim to build harmonised time series based on Level 2 products (geophysical parameters) but to build a Fundamental Data Record (FDR) of Level 1 products, i.e. radiances and reflectances. The GOME-1 and SCIAMACHY instruments together span 17 years of spectrally highly resolved data essential for air quality, climate, ozone trend and UV radiation applications. The goal of the FDR4ATMOS project is to generate harmonised data sets that allow the user to use it directly in long-term trend analysis, independently of the instrument. Since this was never done for highly resolved spectrometers, new methods have to be developed that e.g. take into account the different observation geometries and observation times of the instrument without impacting the spectral structures that are used for the retrieval of the atmospheric species. The resulting algorithms and the processor should also be as generic as possible to be able to easily transfer the methodology to other instruments (e.g. GOME-2 and Sentinel-5p) for a future extension of the time series. The project will support new applications and services and will enhance traceability of satellite-derived data with improved uncertainty estimates based on rigorous metrological principles.</p><p>FDR4ATMOS started in October 2019 and is currently in phase 1. We will present the motivation, goals and first results of the project.</p><p><br><br></p>

scholarly journals Retrieval of daytime mesospheric ozone using OSIRIS observation of O<sub>2</sub>(a<sup>1</sup>∆<sub>g</sub>) emission

2020 ◽  
Author(s):  
Anqi Li ◽  
Chris Roth ◽  
Kristell Pérot ◽  
Ole Martin Christensen ◽  
Adam M. Bourassa ◽  
...  
Keyword(s):  
Middle Atmosphere ◽  
Lower Thermosphere ◽  
High Sensitivity ◽  
Retrieval Technique ◽  
Volume Emission ◽  
Hartley Band ◽  
One Year ◽  
Along Track ◽  
Very High

Abstract. Improving knowledge of the ozone global distributions in the mesosphere-lower thermosphere (MLT) is a crucial step in understanding the behaviour of the middle atmosphere. However, the ozone concentration under sunlit conditions in the MLT is often so low that its measurement requires instruments with very high sensitivity. Fortunately, the bright oxygen airglow can serve as a proxy to retrieve the daytime ozone density indirectly, due to the strong connection to ozone photolysis in the Hartley band. The OSIRIS IR imager (hereafter IRI), one of the instruments on the Odin satellite, routinely measures the oxygen infrared atmospheric band (IRA band) at 1.27 μm. In this paper, we will describe the detailed steps of retrieving the calibrated IRA band limb radiance, the volume emission rate of O2(a1∆g) and, finally, the ozone number density. This retrieval technique is applied to a one-year-sample IRI dataset. The resulting product is a completely new ozone dataset with very high along-track resolution. The performance of the retrieval technique is demonstrated by a comparison of the coincident ozone measurements from the same spacecraft, as well as zonal mean and monthly average comparisons between OS, SMR, MIPAS and ACE-FTS. The consistency of this IRI ozone dataset implies that such a retrieval technique can be further applied to all the measurements made throughout the 19 years-long mission, leading to a long-term, high resolution dataset in the middle atmosphere.

scholarly journals The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record from the ESA Climate Change Initiative

2015 ◽  
Vol 8 (5) ◽  
pp. 4607-4652 ◽  
Author(s):  
M. Coldewey-Egbers ◽  
D. G. Loyola ◽  
M. Koukouli ◽  
D. Balis ◽  
J.-C. Lambert ◽  
...  
Keyword(s):  
Climate Change ◽  
Total Ozone ◽  
Climate Model ◽  
Change Initiative ◽  
Climate Variable ◽  
Individual Level ◽  
Data Record ◽  
Level 3 ◽  
Level 2

Abstract. We present the new GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record which has been created within the framework of the European Space Agency's Climate Change Initiative (ESA-CCI). Total ozone column observations – based on the GOME-type Direct Fitting version 3 algorithm – from GOME (Global Ozone Monitoring Experiment), SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY), and GOME-2 have been combined into one homogeneous time series, thereby taking advantage of the high inter-sensor consistency. The data record spans the 15-year period from March 1996 to June 2011 and it contains global monthly mean total ozone columns on a 1° × 1° grid. Geophysical ground-based validation using Brewer, Dobson, and UV-visible instruments has shown that the GTO-ECV level 3 data record is of the same high quality as the equivalent individual level 2 data products that constitute it. Both absolute agreement and long-term stability are excellent with respect to the ground-based data, for almost all latitudes apart from a few outliers which are mostly due to sampling differences between the level 2 and level 3 data. We conclude that the GTO-ECV data record is valuable for a variety of climate applications such as the long-term monitoring of the past evolution of the ozone layer, trend analysis and the evaluation of Chemistry–Climate Model simulations.

scholarly journals Quality assessment of the Ozone_cci Climate Research Data Package (release 2017) – Part 1: Ground-based validation of total ozone column data products

2018 ◽  
Vol 11 (3) ◽  
pp. 1385-1402 ◽  
Author(s):  
Katerina Garane ◽  
Christophe Lerot ◽  
Melanie Coldewey-Egbers ◽  
Tijl Verhoelst ◽  
Maria Elissavet Koukouli ◽  
...  
Keyword(s):  
Total Ozone ◽  
Data Sets ◽  
Short Term ◽  
Climate Research ◽  
Data Record ◽  
Level 3 ◽  
Mean Differences ◽  
Ozone Column ◽  
Level 2

Abstract. The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) is a level-3 data record, which combines individual sensor products into one single cohesive record covering the 22-year period from 1995 to 2016, generated in the frame of the European Space Agency's Climate Change Initiative Phase II. It is based on level-2 total ozone data produced by the GODFIT (GOME-type Direct FITting) v4 algorithm as applied to the GOME/ERS-2, OMI/Aura, SCIAMACHY/Envisat and GOME-2/Metop-A and Metop-B observations. In this paper we examine whether GTO-ECV meets the specific requirements set by the international climate–chemistry modelling community for decadal stability long-term and short-term accuracy. In the following, we present the validation of the 2017 release of the Climate Research Data Package Total Ozone Column (CRDP TOC) at both level 2 and level 3. The inter-sensor consistency of the individual level-2 data sets has mean differences generally within 0.5 % at moderate latitudes (±50°), whereas the level-3 data sets show mean differences with respect to the OMI reference data record that span between −0.2 ± 0.9 % (for GOME-2B) and 1.0 ± 1.4 % (for SCIAMACHY). Very similar findings are reported for the level-2 validation against independent ground-based TOC observations reported by Brewer, Dobson and SAOZ instruments: the mean bias between GODFIT v4 satellite TOC and the ground instrument is well within 1.0 ± 1.0 % for all sensors, the drift per decade spans between −0.5 % and 1.0 ± 1.0 % depending on the sensor, and the peak-to-peak seasonality of the differences ranges from ∼ 1 % for GOME and OMI to  ∼ 2 % for SCIAMACHY. For the level-3 validation, our first goal was to show that the level-3 CRDP produces findings consistent with the level-2 individual sensor comparisons. We show a very good agreement with 0.5 to 2 % peak-to-peak amplitude for the monthly mean difference time series and a negligible drift per decade of the differences in the Northern Hemisphere of −0.11 ± 0.10 % decade−1 for Dobson and +0.22 ± 0.08 % decade−1 for Brewer collocations. The exceptional quality of the level-3 GTO-ECV v3 TOC record temporal stability satisfies well the requirements for the total ozone measurement decadal stability of 1–3 % and the short-term and long-term accuracy requirements of 2 and 3 %, respectively, showing a remarkable inter-sensor consistency, both in the level-2 GODFIT v4 and in the level-3 GTO-ECV v3 datasets, and thus can be used for longer-term analysis of the ozone layer, such as decadal trend studies, chemistry–climate model evaluation and data assimilation applications.

scholarly journals Radiometrically Consistent Climate Fingerprinting Using CrIS and AIRS Hyperspectral Observations

2020 ◽  
Vol 12 (8) ◽  
pp. 1291
Author(s):  
Wan Wu ◽  
Xu Liu ◽  
Qiguang Yang ◽  
Daniel K. Zhou ◽  
Allen M. Larar
Keyword(s):  
Water Vapor ◽  
Atmospheric Temperature ◽  
Satellite Observations ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Climate Data ◽  
Fingerprinting Method ◽  
Level 2

We introduce a novel spectral fingerprinting scheme that can be used to derive long-term atmospheric temperature and water vapor anomalies from hyperspectral infrared sounders such as Cross-track Infrared Sounder (CrIS) and Atmospheric Infrared Sounder (AIRS). It is a challenging task to derive climate trends from real satellite observations due to the difficulty of carrying out accurate cloudy radiance simulations and constructing radiometrically consistent radiative kernels. To address these issues, we use a principal component based radiative transfer model (PCRTM) to perform multiple scattering calculations of clouds and a PCRTM-based physical retrieval algorithm to derive radiometrically consistent radiative kernels from real satellite observations. The capability of including the cloud scattering calculations in the retrieval process allows the establishment of a rigorous radiometric fitting to satellite-observed radiances under all-sky conditions. The fingerprinting solution is directly obtained via an inverse relationship between the atmospheric anomalies and the corresponding spatiotemporally averaged radiance anomalies. Since there is no need to perform Level 2 retrievals on each individual satellite footprint for the fingerprinting approach, it is much more computationally efficient than the traditional way of producing climate data records from spatiotemporally averaged Level 2 products. We have applied the spectral fingerprinting method to six years of CrIS and 16 years of AIRS data to derive long-term anomaly time series for atmospheric temperature and water vapor profiles. The CrIS and AIRS temperature and water vapor anomalies derived from our spectral fingerprinting method have been validated using results from the PCRTM-based physical retrieval algorithm and the AIRS operational retrieval algorithm, respectively.

Identification and correction of systematic error in NOAA AVHRR long-term satellite data record

2012 ◽  
Vol 127 ◽  
pp. 84-97 ◽  
Author(s):  
Rasim Latifovic ◽  
Darren Pouliot ◽  
Craig Dillabaugh
Keyword(s):  
Systematic Error ◽  
Satellite Data ◽  
Noaa Avhrr ◽  
Data Record

scholarly journals The GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record from the ESA Climate Change Initiative

2015 ◽  
Vol 8 (9) ◽  
pp. 3923-3940 ◽  
Author(s):  
M. Coldewey-Egbers ◽  
D. G. Loyola ◽  
M. Koukouli ◽  
D. Balis ◽  
J.-C. Lambert ◽  
...  
Keyword(s):  
Climate Change ◽  
Total Ozone ◽  
Climate Model ◽  
Change Initiative ◽  
Climate Variable ◽  
Individual Level ◽  
Data Record ◽  
Level 3 ◽  
Level 2

Abstract. We present the new GOME-type Total Ozone Essential Climate Variable (GTO-ECV) data record which has been created within the framework of the European Space Agency's Climate Change Initiative (ESA-CCI). Total ozone column observations – based on the GOME-type Direct Fitting version 3 algorithm – from GOME (Global Ozone Monitoring Experiment), SCIAMACHY (SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY), and GOME-2 have been combined into one homogeneous time series, thereby taking advantage of the high inter-sensor consistency. The data record spans the 15-year period from March 1996 to June 2011 and it contains global monthly mean total ozone columns on a 1°× 1° grid. Geophysical ground-based validation using Brewer, Dobson, and UV–visible instruments has shown that the GTO-ECV level 3 data record is of the same high quality as the equivalent individual level 2 data products that constitute it. Both absolute agreement and long-term stability are excellent with respect to the ground-based data, for almost all latitudes apart from a few outliers which are mostly due to sampling differences between the level 2 and level 3 data. We conclude that the GTO-ECV data record is valuable for a variety of climate applications such as the long-term monitoring of the past evolution of the ozone layer, trend analysis and the evaluation of chemistry–climate model simulations.

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