scholarly journals A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor

2019 ◽  
Vol 12 (12) ◽  
pp. 6771-6802 ◽  
Author(s):  
Oliver Schneising ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
Heinrich Bovensmann ◽  
John P. Burrows ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Air Quality ◽  
Systematic Error ◽  
Random Error ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Validation Data ◽  
Data Set ◽  
Airborne Measurements ◽  
Global Coverage

Abstract. Carbon monoxide (CO) is an important atmospheric constituent affecting air quality, and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir-viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR), combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with an unprecedented level of detail on a global scale, introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 µm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified Differential Optical Absorption Spectroscopy (WFM-DOAS). This algorithm is intended to be used with the operational algorithms for mutual verification and to provide new geophysical insights. We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine-learning-based quality filter, and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier transform spectrometer (FTS) measurements providing realistic error estimates of the satellite data: the XCO data set is characterised by a random error of 5.1 ppb (5.8 %) and a systematic error of 1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb (0.8 %) and a systematic error of 4.3 ppb (0.2 %). The natural XCO and XCH4 variations are well-captured by the satellite retrievals, which is demonstrated by a high correlation with the validation data (R=0.97 for XCO and R=0.91 for XCH4 based on daily averages). We also present selected results from the mission start until the end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4 emissions from the energy sector, which potentially allows for the advance of emission monitoring and air quality assessments to an entirely new level.

scholarly journals A scientific algorithm to simultaneously retrieve carbon monoxide and methane from TROPOMI onboard Sentinel-5 Precursor

2019 ◽  
Author(s):  
Oliver Schneising ◽  
Michael Buchwitz ◽  
Maximilian Reuter ◽  
Heinrich Bovensmann ◽  
John P. Burrows ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Systematic Error ◽  
Random Error ◽  
Weighting Function ◽  
Synthetic Data ◽  
Solar Spectrum ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Airborne Measurements ◽  
Global Coverage

Abstract. Carbon monoxide (CO) is an important atmospheric constituent affecting air quality and methane (CH4) is the second most important greenhouse gas contributing to human-induced climate change. Detailed and continuous observations of these gases are necessary to better assess their impact on climate and atmospheric pollution. While surface and airborne measurements are able to accurately determine atmospheric abundances on local scales, global coverage can only be achieved using satellite instruments. The TROPOspheric Monitoring Instrument (TROPOMI) onboard the Sentinel-5 Precursor satellite, which was successfully launched in October 2017, is a spaceborne nadir viewing imaging spectrometer measuring solar radiation reflected by the Earth in a push-broom configuration. It has a wide swath on the terrestrial surface and covers wavelength bands between the ultraviolet (UV) and the shortwave infrared (SWIR) combining a high spatial resolution with daily global coverage. These characteristics enable the determination of both gases with unprecedented level of detail on a global scale introducing new areas of application. Abundances of the atmospheric column-averaged dry air mole fractions XCO and XCH4 are simultaneously retrieved from TROPOMI's radiance measurements in the 2.3 μm spectral range of the SWIR part of the solar spectrum using the scientific retrieval algorithm Weighting Function Modified DOAS (WFM-DOAS). We introduce the algorithm in detail, including expected error characteristics based on synthetic data, a machine learning-based quality filter and a shallow learning calibration procedure applied in the post-processing of the XCH4 data. The quality of the results based on real TROPOMI data is assessed by validation with ground-based Fourier Transform Spectrometer (FTS) measurements providing realistic error estimates of the satellite data: The XCO data set is characterised by a random error of 5.1 ppb (5.7 %) and a systematic error of 1.9 ppb (2.1 %); the XCH4 data set exhibits a random error of 14.0 ppb (0.8 %) and a systematic error of 4.4 ppb (0.2 %). The natural XCO and XCH4 variations are well captured by the satellite retrievals, which is demonstrated by a high correlation to the reference data (R = 0.97 for XCO and R = 0.91 for XCH4 based on daily averages). We also present selected results from mission start until end of 2018, including a first comparison to the operational products and examples of the detection of emission sources in a single satellite overpass, such as CO emissions from the steel industry and CH4 emissions from the energy sector.

scholarly journals Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3 μm reflectance measurements

2017 ◽  
Author(s):  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Philippe Nédélec ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Processing ◽  
Pearson Correlation ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Validation Data ◽  
Data Set ◽  
Airborne Measurements ◽  
Clear Sky ◽  
Infrared Measurements

Abstract. We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311–2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data was processed with the Shortwave Infrared CO Retrieval algorithm SICOR that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA’s Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites. For Ny-Ålesund, Lauder, Mauna Loa, and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. This improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6.0 ppb and a strong correlation between the validation data set and the SCIAMACHY data sets with a mean Pearson correlation coefficient r = 0.7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set, which is less sensitive to the spatial representativeness of the satellite and validation measurement. For example, at the cities Teheran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171.2 ppb and 57.9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. The validation improves significantly for cloudy sky retrievals with b = 52.3 ppb and 5.0 ppb, respectively. This is due to a reduced retrieval sensitivity to CO below the cloud and so to the altitude range, which is mostly affected by strong local surface emissions. At the less urbanized region around the airportWindhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15.5 ppb, but can be even further improved considering cloudy SCIAMACHY observations with a mean CO bias of b = 0.2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short wave infrared measurements present a valuable addition to the clear-sky only data set. Moreover, the study represents the first application of the S5P algorithm for operational CO data processing on cloudy observations prior to the launch of the S5P mission.

scholarly journals Carbon monoxide column retrieval for clear-sky and cloudy atmospheres: a full-mission data set from SCIAMACHY 2.3  µm reflectance measurements

2017 ◽  
Vol 10 (5) ◽  
pp. 1769-1782 ◽  
Author(s):  
Tobias Borsdorff ◽  
Joost aan de Brugh ◽  
Haili Hu ◽  
Philippe Nédélec ◽  
Ilse Aben ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Data Processing ◽  
Pearson Correlation ◽  
Short Wave ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Data Set ◽  
Airborne Measurements ◽  
Clear Sky ◽  
Infrared Measurements

Abstract. We discuss the retrieval of carbon monoxide (CO) vertical column densities from clear-sky and cloud contaminated 2311–2338 nm reflectance spectra measured by the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) from January 2003 until the end of the mission in April 2012. These data were processed with the Shortwave Infrared CO Retrieval algorithm (SICOR) that we developed for the operational data processing of the Tropospheric Monitoring Instrument (TROPOMI) that will be launched on ESA's Sentinel-5 Precursor (S5P) mission. This study complements previous work that was limited to clear-sky observations over land. Over the oceans, CO is estimated from cloudy-sky measurements only, which is an important addition to the SCIAMACHY clear-sky CO data set as shown by NDACC and TCCON measurements at coastal sites. For Ny-Ålesund, Lauder, Mauna Loa and Reunion, a validation of SCIAMACHY clear-sky retrievals is not meaningful because of the high retrieval noise and the few collocations at these sites. The situation improves significantly when considering cloudy-sky observations, where we find a low mean bias b = ±6. 0 ppb and a strong correlation between the validation and the SCIAMACHY results with a mean Pearson correlation coefficient r = 0. 7. Also for land observations, cloudy-sky CO retrievals present an interesting complement to the clear-sky data set. For example, at the cities Tehran and Beijing the agreement of SCIAMACHY clear-sky CO observations with MOZAIC/IAGOS airborne measurements is poor with a mean bias of b = 171. 2 ppb and 57.9 ppb because of local CO pollution, which cannot be captured by SCIAMACHY. For cloudy-sky retrievals, the validation improves significantly. Here the retrieved column is mainly sensitive to CO above the cloud and so not affected by the strong local surface emissions. Adjusting the MOZAIC/IAGOS measurements to the vertical sensitivity of the retrieval, the mean bias adds up to b = 52. 3 ppb and 5.0 ppb for Tehran and Beijing. At the less urbanised region around the airport Windhoek, local CO pollution is less prominent and so MOZAIC/IAGOS measurements agree well with SCIAMACHY clear-sky retrievals with a mean bias of b = 15. 5 ppb, but can be even further improved for cloudy SCIAMACHY observations with a mean bias of b = 0. 2 ppb. Overall the cloudy-sky CO retrievals from SCIAMACHY short-wave infrared measurements present a major extension of the clear-sky-only data set, which more than triples the amount of data and adds unique observations over the oceans. Moreover, the study represents the first application of the S5P algorithm for operational CO data processing on cloudy observations prior to the launch of the S5P mission.

scholarly journals Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

2014 ◽  
Vol 7 (3) ◽  
pp. 3021-3073 ◽  
Author(s):  
M. Grossi ◽  
P. Valks ◽  
D. Loyola ◽  
B. Aberle ◽  
S. Slijkhuis ◽  
...  
Keyword(s):  
Water Vapour ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Vertical Column Density ◽  
Data Processor ◽  
Climate Analysis ◽  
Total Column

Abstract. The knowledge of the total column water vapour (TCWV) global distribution is fundamental for climate analysis and weather monitoring. In this work, we present the retrieval algorithm used to derive the operational TCWV from the GOME-2 sensors and perform an extensive inter-comparison and validation in order to estimate their absolute accuracy and long-term stability. We use the recently reprocessed data sets retrieved by the GOME-2 instruments aboard EUMETSAT's MetOp-A and MetOp-B satellites and generated by DLR in the framework of the O3M-SAF using the GOME Data Processor (GDP) version 4.7. The retrieval algorithm is based on a classical Differential Optical Absorption Spectroscopy (DOAS) method and combines H2O/O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. We evaluate the overall consistency between about 8 months measurements from the newer GOME-2 instrument on the MetOp-B platform with the GOME-2/MetOp-A data in the overlap period. Furthermore, we compare GOME-2 results with independent TCWV data from ECMWF and with SSMIS satellite measurements during the full period January 2007–August 2013 and we perform a validation against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project. We find global mean biases as small as ± 0.03 g cm−2 between GOME-2A and all other data sets. The combined SSM/I-MERIS sample is typically drier than the GOME-2 retrievals (−0.005 g cm−2), while on average GOME-2 data overestimate the SSMIS measurements by only 0.028 g cm−2. However, the size of some of these biases are seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, but are not as evident in the validation with the ECMWF and the SSM/I + MERIS data. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three independent data sets, especially for land areas, although some discrepancies over ocean and over land areas with high humidity and a relatively large surface albedo are also present.

scholarly journals Orbiting Carbon Observatory-2 (OCO-2) cloud screening algorithms; validation against collocated MODIS and CALIOP data

2015 ◽  
Vol 8 (12) ◽  
pp. 12663-12707 ◽  
Author(s):  
T. E. Taylor ◽  
C. W. O'Dell ◽  
C. Frankenberg ◽  
P. Partain ◽  
H. Q. Cronk ◽  
...  
Keyword(s):  
Surface Pressure ◽  
Near Infrared ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Orthogonal Polarization ◽  
Screening Methods ◽  
Data Set ◽  
Atmospheric Scattering ◽  
Carbon Dioxide Co2 ◽  
Cloud Screening

Abstract. The objective of the National Aeronautics and Space Administration's (NASA) Orbiting Carbon Observatory-2 (OCO-2) mission is to retrieve the column-averaged carbon dioxide (CO2) dry air mole fraction (XCO2) from satellite measurements of reflected sunlight in the near-infrared. These estimates can be biased by clouds and aerosols within the instrument's field of view (FOV). Screening of the most contaminated soundings minimizes unnecessary calls to the computationally expensive Level 2 (L2) XCO2 retrieval algorithm. Hence, robust cloud screening methods have been an important focus of the OCO-2 algorithm development team. Two distinct, computationally inexpensive cloud screening algorithms have been developed for this application. The A-Band Preprocessor (ABP) retrieves the surface pressure using measurements in the 0.76 μm O2 A-band, neglecting scattering by clouds and aerosols, which introduce photon path-length (PPL) differences that can cause large deviations between the expected and retrieved surface pressure. The Iterative Maximum A-Posteriori (IMAP) Differential Optical Absorption Spectroscopy (DOAS) Preprocessor (IDP) retrieves independent estimates of the CO2 and H2O column abundances using observations taken at 1.61 μm (weak CO2 band) and 2.06 μm (strong CO2 band), while neglecting atmospheric scattering. The CO2 and H2O column abundances retrieved in these two spectral regions differ significantly in the presence of cloud and scattering aerosols. The combination of these two algorithms, which key off of different features in the spectra, provides the basis for cloud screening of the OCO-2 data set. To validate the OCO-2 cloud screening approach, collocated measurements from NASA's Moderate Resolution Imaging Spectrometer (MODIS), aboard the Aqua platform, were compared to results from the two OCO-2 cloud screening algorithms. With tuning to allow throughputs of ≃ 30 %, agreement between the OCO-2 and MODIS cloud screening methods is found to be ≃ 85 % over four 16-day orbit repeat cycles in both the winter (December) and spring (April–May) for OCO-2 nadir-land, glint-land and glint-water observations. No major, systematic, spatial or temporal dependencies were found, although slight differences in the seasonal data sets do exist and validation is more problematic with increasing solar zenith angle and when surfaces are covered in snow and ice and have complex topography. To further analyze the performance of the cloud screening algorithms, an initial comparison of OCO-2 observations was made to collocated measurements from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) aboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO). These comparisons highlight the strength of the OCO-2 cloud screening algorithms in identifying high, thin clouds but suggest some difficulty in identifying some clouds near the surface, even when the optical thicknesses are greater than 1.

scholarly journals Tropospheric nitrogen dioxide column retrieval from ground-based zenith–sky DOAS observations

2015 ◽  
Vol 8 (6) ◽  
pp. 2417-2435 ◽  
Author(s):  
F. Tack ◽  
F. Hendrick ◽  
F. Goutail ◽  
C. Fayt ◽  
A. Merlaud ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Atmospheric Composition ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Measuring Instruments ◽  
Reference Spectrum ◽  
Vertical Column ◽  
Data Set ◽  
Residual Amount

Abstract. We present an algorithm for retrieving tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) from ground-based zenith–sky (ZS) measurements of scattered sunlight. The method is based on a four-step approach consisting of (1) the differential optical absorption spectroscopy (DOAS) analysis of ZS radiance spectra using a fixed reference spectrum corresponding to low NO2 absorption, (2) the determination of the residual amount in the reference spectrum using a Langley-plot-type method, (3) the removal of the stratospheric content from the daytime total measured slant column based on stratospheric VCDs measured at sunrise and sunset, and simulation of the rapid NO2 diurnal variation, (4) the retrieval of tropospheric VCDs by dividing the resulting tropospheric slant columns by appropriate air mass factors (AMFs). These steps are fully characterized and recommendations are given for each of them. The retrieval algorithm is applied on a ZS data set acquired with a multi-axis (MAX-) DOAS instrument during the Cabauw (51.97° N, 4.93° E, sea level) Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI) held from 10 June to 21 July 2009 in the Netherlands. A median value of 7.9 × 1015 molec cm−2 is found for the retrieved tropospheric NO2 VCDs, with maxima up to 6.0 × 1016 molec cm−2. The error budget assessment indicates that the overall error σTVCD on the column values is less than 28%. In the case of low tropospheric contribution, σTVCD is estimated to be around 39% and is dominated by uncertainties in the determination of the residual amount in the reference spectrum. For strong tropospheric pollution events, σTVCD drops to approximately 22% with the largest uncertainties on the determination of the stratospheric NO2 abundance and tropospheric AMFs. The tropospheric VCD amounts derived from ZS observations are compared to VCDs retrieved from off-axis and direct-sun measurements of the same MAX-DOAS instrument as well as to data from a co-located Système d'Analyse par Observations Zénithales (SAOZ) spectrometer. The retrieved tropospheric VCDs are in good agreement with the different data sets with correlation coefficients and slopes close to or larger than 0.9. The potential of the presented ZS retrieval algorithm is further demonstrated by its successful application on a 2-year data set, acquired at the NDACC (Network for the Detection of Atmospheric Composition Change) station Observatoire de Haute Provence (OHP; Southern France).

scholarly journals Validation and update of OMI Total Column Water Vapor product

2016 ◽  
Vol 16 (17) ◽  
pp. 11379-11393 ◽  
Author(s):  
Huiqun Wang ◽  
Gonzalo Gonzalez Abad ◽  
Xiong Liu ◽  
Kelly Chance
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Validation Data ◽  
Data Set ◽  
Wide Range ◽  
Key Factor ◽  
The Mean ◽  
Total Column

Abstract. The collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory's (SAO) algorithm version 1.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR's ground-based GPS data, AERONET's sun-photometer data, and Remote Sensing System's (RSS) SSMIS data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean OMI−GPS over land is −0.3 mm and the mean OMI−AERONET over land is 0 mm. For July 2005, the mean OMI−SSMIS over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the version 1.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter optimized retrieval window of 427.7–465 nm. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land. We have also made several updates to the air mass factor (AMF) calculation. The updated version 2.1 retrieval algorithm improves the land/ocean consistency and the overall quality of the OMI TCWV data set. The version 2.1 OMI data largely eliminate the low bias of the version 1.0 OMI data over the ocean and are 1.5 mm higher than RSS's “clear” sky SSMIS data in July 2005. Over the ocean, the mean of version 2.1 OMI−GlobVapour is 1 mm for July 2005 and 0 mm for January 2005. Over land, the version 2.1 OMI data are about 1 mm higher than GlobVapour when TCWV  <  15 mm and about 1 mm lower when TCWV  >  15 mm.

scholarly journals Three years of global carbon monoxide from SCIAMACHY: comparison with MOPITT and first results related to the detection of enhanced CO over cities

2007 ◽  
Vol 7 (9) ◽  
pp. 2399-2411 ◽  
Author(s):  
M. Buchwitz ◽  
I. Khlystova ◽  
H. Bovensmann ◽  
J. P. Burrows
Keyword(s):  
Carbon Monoxide ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Data Set ◽  
Retrieval Method ◽  
First Results ◽  
Long Time ◽  
Global Carbon ◽  
Good Agreement

Abstract. Carbon monoxide (CO) is an important atmospheric constituent affecting air quality and climate. SCIAMACHY on ENVISAT is currently the only satellite instrument that can measure the vertical column of CO with nearly equal sensitivity at all altitudes down to the Earth's surface because of its near-infrared nadir observations of reflected solar radiation. Here we present three years' (2003–2005) of SCIAMACHY CO columns consistently retrieved with the latest version of our retrieval algorithm (WFMDv0.6). We describe the retrieval method and discuss the multi-year global CO data set focusing on a comparison with the operational CO column data product of MOPITT. We found reasonable to good agreement (~20%) with MOPITT, with the best agreement for 2004. We present detailed results for various regions (Europe, Middle East, India, China) and discuss to what extent enhanced levels of CO can be detected over populated areas including individual cities. The expected CO signal from cities is close to or even below the detection limit of individual measurements. We show that cities can be identified when averaging long time series.

scholarly journals Retrievals of tropospheric ozone profiles from the synergism of AIRS and OMI: methodology and validation

2018 ◽  
Vol 11 (10) ◽  
pp. 5587-5605 ◽  
Author(s):  
Dejian Fu ◽  
Susan S. Kulawik ◽  
Kazuyuki Miyazaki ◽  
Kevin W. Bowman ◽  
John R. Worden ◽  
...  
Keyword(s):  
Measurement Error ◽  
Tropospheric Ozone ◽  
Retrieval Algorithm ◽  
Vertical Resolution ◽  
Ozone Monitoring Instrument ◽  
Atmospheric Infrared Sounder ◽  
Data Set ◽  
Global Coverage ◽  
Ozone Monitoring ◽  
Wide Swath

Abstract. The Tropospheric Emission Spectrometer (TES) on the A-Train Aura satellite was designed to profile tropospheric ozone and its precursors, taking measurements from 2004 to 2018. Starting in 2008, TES global sampling of tropospheric ozone was gradually reduced in latitude, with global coverage stopping in 2011. To extend the record of TES, this work presents a multispectral approach that will provide O3 data products with vertical resolution and measurement error similar to TES by combining the single-footprint thermal infrared (TIR) hyperspectral radiances from the Aqua Atmospheric Infrared Sounder (AIRS) instrument and the ultraviolet (UV) channels from the Aura Ozone Monitoring Instrument (OMI). The joint AIRS+OMI O3 retrievals are processed through the MUlti-SpEctra, MUlti-SpEcies, MUlti-SEnsors (MUSES) retrieval algorithm. Comparisons of collocated joint AIRS+OMI and TES to ozonesonde measurements show that both systems have similar errors, with mean and standard deviation of the differences well within the estimated measurement error. AIRS+OMI and TES have slightly different biases (within 5 parts per billion) vs. the sondes. Both AIRS and OMI have wide swath widths (∼1650 km for AIRS; ∼2600 km for OMI) across satellite ground tracks. Consequently, the joint AIRS+OMI measurements have the potential to maintain TES vertical sensitivity while increasing coverage by 2 orders of magnitude, thus providing an unprecedented new data set with which to quantify the evolution of tropospheric ozone.

scholarly journals SMOS derived sea ice thickness: algorithm baseline, product specifications and initial verification

2013 ◽  
Vol 7 (6) ◽  
pp. 5735-5792 ◽  
Author(s):  
X. Tian-Kunze ◽  
L. Kaleschke ◽  
N. Maaß ◽  
M. Mäkynen ◽  
N. Serra ◽  
...  
Keyword(s):  
Sea Ice ◽  
Thickness Distribution ◽  
Remote Sensing Data ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Ice Thickness ◽  
Transfer Model ◽  
Validation Data ◽  
Data Set ◽  
Sea Ice Thickness

Abstract. Following the launch of ESA's Soil Moisture and Ocean salinity (SMOS) mission it has been shown that brightness temperatures at a low microwave frequency of 1.4 GHz (L-band) are sensitive to sea ice properties. In a first demonstration study, sea ice thickness has been derived using a semi-empirical algorithm with constant tie-points. Here we introduce a novel iterative retrieval algorithm that is based on a sea ice thermodynamic model and a three-layer radiative transfer model, which explicitly takes variations of ice temperature and ice salinity into account. In addition, ice thickness variations within a SMOS footprint are considered through a statistical thickness distribution function derived from high-resolution ice thickness measurements from NASA's Operation IceBridge campaign. This new algorithm has been used for the continuous operational production of a SMOS based sea ice thickness data set from 2010 on. This data set is compared and validated with estimates from assimilation systems, remote sensing data, and airborne electromagnetic sounding data. The comparisons show that the new retrieval algorithm has a considerably better agreement with the validation data and delivers a more realistic Arctic-wide ice thickness distribution than the algorithm used in the previous study.

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