scholarly journals Explicit and consistent aerosol correction for visible wavelength satellite cloud and nitrogen dioxide retrievals based on optical properties from a global aerosol analysis

2020 ◽  
Author(s):  
Alexander Vasilkov ◽  
Nickolay Krotkov ◽  
Eun-Su Yang ◽  
Lok Lamsal ◽  
Joanna Joiner ◽  
...  
Keyword(s):  
Optical Properties ◽  
Cloud Fraction ◽  
Ozone Monitoring Instrument ◽  
Current Standard ◽  
Bidirectional Reflectance ◽  
Aerosol Analysis ◽  
Ozone Monitoring ◽  
Implicit And Explicit ◽  
Aerosol Effects

Abstract. We discuss an explicit and consistent aerosol correction for cloud and NO2 retrievals that are based on the mixed Lambertian-equivalent reflectivity (MLER) concept. We apply the approach to data from the Ozone Monitoring Instrument (OMI) for a case study over norththeast Asia. The cloud algorithm reports an effective cloud pressure, also known as cloud optical centroid pressure (OCP), from oxygen dimer (O2–O2) absorption at 477 nm after determining an effective cloud fraction (ECF) at 466 nm. The retrieved cloud products are then used as inputs to the standard OMI NO2 algorithm. A geometry-dependent Lambertian-equivalent reflectivity (GLER), which is a proxy of surface bidirectional reflectance, is used for the ground reflectivity in our implementation of the MLER approach. The current standard OMI cloud and NO2 algorithms implicitly account for aerosols by treating them as non-absorbing particulate scatters within the cloud retrieval. To explicitly account for aerosol effects, we use a model of aerosol optical properties from a global aerosol assimilation system and radiative transfer computations. This approach allows us to account for aerosols within the OMI cloud and NO2 algorithms with relatively small changes. We compare the OMI cloud and NO2 retrievals with implicit and explicit aerosol corrections over our study area.

scholarly journals Explicit and consistent aerosol correction for visible wavelength satellite cloud and nitrogen dioxide retrievals based on optical properties from a global aerosol analysis

2021 ◽  
Vol 14 (4) ◽  
pp. 2857-2871
Author(s):  
Alexander Vasilkov ◽  
Nickolay Krotkov ◽  
Eun-Su Yang ◽  
Lok Lamsal ◽  
Joanna Joiner ◽  
...  
Keyword(s):  
Optical Properties ◽  
Northeastern China ◽  
Ozone Monitoring Instrument ◽  
Current Standard ◽  
Bidirectional Reflectance ◽  
Aerosol Analysis ◽  
Ozone Monitoring ◽  
Implicit And Explicit ◽  
Aerosol Effects

Abstract. We discuss an explicit and consistent aerosol correction for cloud and NO2 retrievals that are based on the mixed Lambertian-equivalent reflectivity (MLER) concept. We apply the approach to data from the Ozone Monitoring Instrument (OMI) for a case study over northeastern China. The cloud algorithm reports an effective cloud pressure, also known as cloud optical centroid pressure (OCP), from oxygen dimer (O2−O2) absorption at 477 nm after determining an effective cloud fraction (ECF) at 466 nm. The retrieved cloud products are then used as inputs to the standard OMI NO2 algorithm. A geometry-dependent Lambertian-equivalent reflectivity (GLER), which is a proxy of surface bidirectional reflectance, is used for the ground reflectivity in our implementation of the MLER approach. The current standard OMI cloud and NO2 algorithms implicitly account for aerosols by treating them as nonabsorbing particulate scatters within the cloud retrieval. To explicitly account for aerosol effects, we use a model of aerosol optical properties from a global aerosol assimilation system and radiative transfer computations. This approach allows us to account for aerosols within the OMI cloud and NO2 algorithms with relatively small changes. We compare the OMI cloud and NO2 retrievals with implicit and explicit aerosol corrections over our study area.

scholarly journals Validation of OMI, GOME-2A and GOME-2B tropospheric NO<sub>2</sub>, SO<sub>2</sub> and HCHO products using MAX-DOAS observations from 2011 to 2014 in Wuxi, China: investigation of the effects of priori profiles and aerosols on the satellite products

2017 ◽  
Vol 17 (8) ◽  
pp. 5007-5033 ◽  
Author(s):  
Yang Wang ◽  
Steffen Beirle ◽  
Johannes Lampel ◽  
Mariliza Koukouli ◽  
Isabelle De Smedt ◽  
...  
Keyword(s):  
Cloud Fraction ◽  
Satellite Observations ◽  
Data Sets ◽  
Ozone Monitoring Instrument ◽  
Vertical Column ◽  
Air Mass ◽  
Clear Sky ◽  
Satellite Retrievals ◽  
Mass Factor ◽  
Ozone Monitoring

Abstract. Tropospheric vertical column densities (VCDs) of NO2, SO2 and HCHO derived from the Ozone Monitoring Instrument (OMI) on AURA and the Global Ozone Monitoring Experiment 2 aboard METOP-A (GOME-2A) and METOP-B (GOME-2B) are widely used to characterize the global distributions, trends and dominating sources of these trace gases. They are also useful for the comparison with chemical transport models (CTMs). We use tropospheric VCDs and vertical profiles of NO2, SO2 and HCHO derived from MAX-DOAS measurements from 2011 to 2014 in Wuxi, China, to validate the corresponding products (daily and bi-monthly-averaged data) derived from OMI and GOME-2A/B by different scientific teams. Prior to the comparison, the spatial and temporal coincidence criteria for MAX-DOAS and satellite data are determined by a sensitivity study using different spatial and temporal averaging conditions. Cloud effects on both MAX-DOAS and satellite observations are also investigated. Our results indicate that the discrepancies between satellite and MAX-DOAS results increase with increasing effective cloud fraction and are dominated by the effects of clouds on the satellite products. In comparison with MAX-DOAS, we found a systematic underestimation of all SO2 (40 to 57 %) and HCHO products (about 20 %), and an overestimation of the GOME-2A/B NO2 products (about 30 %), but good consistency with the DOMINO version 2 NO2 product. To better understand the reasons for these differences, we evaluated the a priori profile shapes used in the OMI retrievals (derived from CTM) by comparison with those derived from the MAX-DOAS observations. Significant differences are found for the SO2 and HCHO profile shapes derived from the IMAGES model, whereas on average good agreement is found for the NO2 profile shapes derived from the TM4 model. We also applied the MAX-DOAS profile shapes to the satellite retrievals and found that these modified satellite VCDs agree better with the MAX-DOAS VCDs than the VCDs from the original data sets by up to 10, 47 and 35 % for NO2, SO2 and HCHO, respectively. Furthermore, we investigated the effect of aerosols on the satellite retrievals. For OMI observations of NO2, a systematic underestimation is found for large AOD, which is mainly attributed to effect of the aerosols on the cloud retrieval and the subsequent application of a cloud correction scheme (implicit aerosol correction). In contrast, the effect of aerosols on the clear-sky air mass factor (explicit aerosol correction) has a smaller effect. For SO2 and HCHO observations selected in the same way, no clear aerosol effect is found, probably because for the considered data sets no cloud correction is applied (and also because of the larger scatter). From our findings we conclude that for satellite observations with cloud top pressure (CTP) > 900 hPa and effective cloud fraction (eCF) < 10 % the application of a clear-sky air mass factor might be a good option if accurate aerosol information is not available. Another finding of our study is that the ratio of morning-to-afternoon NO2 VCDs can be considerably overestimated if results from different sensors and/or retrievals (e.g. OMI and GOME-2) are used, whereas fewer deviations for HCHO and SO2 VCDs are found.

scholarly journals Updated Smithsonian Astrophysical Observatory Ozone Monitoring Instrument (SAO OMI) formaldehyde retrieval

2015 ◽  
Vol 8 (1) ◽  
pp. 19-32 ◽  
Author(s):  
G. González Abad ◽  
X. Liu ◽  
K. Chance ◽  
H. Wang ◽  
T. P. Kurosu ◽  
...  
Keyword(s):  
Temporal Stability ◽  
Cloud Fraction ◽  
Retrieval Algorithm ◽  
Reference Spectrum ◽  
Ozone Monitoring Instrument ◽  
High Concentration ◽  
Monitoring Instrument ◽  
Mass Factor ◽  
Ozone Monitoring ◽  
Level 2

Abstract. We present and discuss the Smithsonian Astrophysical Observatory (SAO) formaldehyde (H2CO) retrieval algorithm for the Ozone Monitoring Instrument (OMI) which is the operational retrieval for NASA OMI H2CO. The version of the algorithm described here includes relevant changes with respect to the operational one, including differences in the reference spectra for H2CO, the fit of O2–O2 collisional complex, updates in the high-resolution solar reference spectrum, the use of a model reference sector over the remote Pacific Ocean to normalize the retrievals, an updated air mass factor (AMF) calculation scheme, and the inclusion of scattering weights and vertical H2CO profile in the level 2 products. The setup of the retrieval is discussed in detail. We compare the results of the updated retrieval with the results from the previous SAO H2CO retrieval. The improvement in the slant column fit increases the temporal stability of the retrieval and slightly reduces the noise. The change in the AMF calculation has increased the AMFs by 20%, mainly due to the consideration of the radiative cloud fraction. Typical values for retrieved vertical columns are between 4 × 1015 and 4 × 1016 molecules cm−2, with typical fitting uncertainties ranging between 45 and 100%. In high-concentration regions the errors are usually reduced to 30%. The detection limit is estimated at 1 × 1016 molecules cm−2.

scholarly journals OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

2015 ◽  
Vol 8 (12) ◽  
pp. 13471-13524 ◽  
Author(s):  
R. Lutz ◽  
D. Loyola ◽  
S. Gimeno García ◽  
F. Romahn
Keyword(s):  
A Priori ◽  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Polarization Measurement ◽  
Point Of View ◽  
Computational Time ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
Priori Information

Abstract. This paper describes an approach for cloud parameter retrieval (radiometric cloud fraction estimation) using the polarization measurements of the Global Ozone Monitoring Experiment-2 (GOME-2) on-board the MetOp-A/B satellites. The core component of the Optical Cloud Recognition Algorithm (OCRA) is the calculation of monthly cloud-free reflectances for a global grid (resolution of 0.2° in longitude and 0.2° in latitude) and to derive radiometric cloud fractions. These cloud fractions will serve as a priori information for the retrieval of cloud top height (CTH), cloud top pressure (CTP), cloud top albedo (CTA) and cloud optical thickness (COT) with the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. This approach is already being implemented operationally for the GOME/ERS-2 and SCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRA algorithm applied to the GOME-2 sensors. Based on more than six years of GOME-2A data (February 2007–June 2013), reflectances are calculated for &amp;approx; 35 000 orbits. For each measurement a degradation correction as well as a viewing angle dependent and latitude dependent correction is applied. In addition, an empirical correction scheme is introduced in order to remove the effect of oceanic sun glint. A comparison of the GOME-2A/B OCRA cloud fractions with co-located AVHRR geometrical cloud fractions shows a general good agreement with a mean difference of −0.15±0.20. From operational point of view, an advantage of the OCRA algorithm is its extremely fast computational time and its straightforward transferability to similar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 and Sentinel 5. In conclusion, it is shown that a robust, accurate and fast radiometric cloud fraction estimation for GOME-2 can be achieved with OCRA by using the polarization measurement devices (PMDs).

scholarly journals OCRA radiometric cloud fractions for GOME-2 on MetOp-A/B

2016 ◽  
Vol 9 (5) ◽  
pp. 2357-2379 ◽  
Author(s):  
Ronny Lutz ◽  
Diego Loyola ◽  
Sebastián Gimeno García ◽  
Fabian Romahn
Keyword(s):  
A Priori ◽  
Recognition Algorithm ◽  
Cloud Fraction ◽  
Polarization Measurement ◽  
Point Of View ◽  
Computational Time ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
Priori Information

Abstract. This paper describes an approach for cloud parameter retrieval (radiometric cloud-fraction estimation) using the polarization measurements of the Global Ozone Monitoring Experiment-2 (GOME-2) onboard the MetOp-A/B satellites. The core component of the Optical Cloud Recognition Algorithm (OCRA) is the calculation of monthly cloud-free reflectances for a global grid (resolution of 0.2° in longitude and 0.2° in latitude) to derive radiometric cloud fractions. These cloud fractions will serve as a priori information for the retrieval of cloud-top height (CTH), cloud-top pressure (CTP), cloud-top albedo (CTA) and cloud optical thickness (COT) with the Retrieval Of Cloud Information using Neural Networks (ROCINN) algorithm. This approach is already being implemented operationally for the GOME/ERS-2 and SCIAMACHY/ENVISAT sensors and here we present version 3.0 of the OCRA algorithm applied to the GOME-2 sensors. Based on more than five years of GOME-2A data (April 2008 to June 2013), reflectances are calculated for  ≈  35 000 orbits. For each measurement a degradation correction as well as a viewing-angle-dependent and latitude-dependent correction is applied. In addition, an empirical correction scheme is introduced in order to remove the effect of oceanic sun glint. A comparison of the GOME-2A/B OCRA cloud fractions with colocated AVHRR (Advanced Very High Resolution Radiometer) geometrical cloud fractions shows a general good agreement with a mean difference of −0.15 ± 0.20. From an operational point of view, an advantage of the OCRA algorithm is its very fast computational time and its straightforward transferability to similar sensors like OMI (Ozone Monitoring Instrument), TROPOMI (TROPOspheric Monitoring Instrument) on Sentinel 5 Precursor, as well as Sentinel 4 and Sentinel 5. In conclusion, it is shown that a robust, accurate and fast radiometric cloud-fraction estimation for GOME-2 can be achieved with OCRA using polarization measurement devices (PMDs).

scholarly journals Ozone Monitoring Instrument (OMI) Total Column Water Vapor version 4 validation and applications

2019 ◽  
Vol 12 (9) ◽  
pp. 5183-5199 ◽  
Author(s):  
Huiqun Wang ◽  
Amir Hossein Souri ◽  
Gonzalo González Abad ◽  
Xiong Liu ◽  
Kelly Chance
Keyword(s):  
Water Vapor ◽  
Regression Line ◽  
Cloud Fraction ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Trade Offs ◽  
Ozone Monitoring ◽  
Cloud Top Pressure ◽  
Total Column

Abstract. Total column water vapor (TCWV) is important for the weather and climate. TCWV is derived from the Ozone Monitoring Instrument (OMI) visible spectra using the version 4.0 retrieval algorithm developed at the Smithsonian Astrophysical Observatory. The algorithm uses a retrieval window between 432.0 and 466.5 nm and includes updates to reference spectra and water vapor profiles. The retrieval window optimization results from the trade-offs among competing factors. The OMI product is characterized by comparing against commonly used reference datasets – global positioning system (GPS) network data over land and Special Sensor Microwave Imager/Sounder (SSMIS) data over the oceans. We examine how cloud fraction and cloud-top pressure affect the comparisons. The results lead us to recommend filtering OMI data with a cloud fraction less than f=0.05–0.25 and cloud-top pressure greater than 750 mb (or stricter), in addition to the data quality flag, fitting root mean square (RMS) and TCWV range check. Over land, for f=0.05, the overall mean of OMI–GPS is 0.32 mm with a standard deviation (σ) of 5.2 mm; the smallest bias occurs when TCWV = 10–20 mm, and the best regression line corresponds to f=0.25. Over the oceans, for f=0.05, the overall mean of OMI–SSMIS is 0.4 mm (1.1 mm) with σ=6.5 mm (6.8 mm) for January (July); the smallest bias occurs when TCWV = 20–30 mm, and the best regression line corresponds to f=0.15. For both land and the oceans, the difference between OMI and the reference datasets is relatively large when TCWV is less than 10 mm. The bias for the version 4.0 OMI TCWV is much smaller than that for version 3.0. As test applications of the version 4.0 OMI TCWV over a range of spatial and temporal scales, we find prominent signals of the patterns associated with El Niño and La Niña, the high humidity associated with a corn sweat event, and the strong moisture band of an atmospheric river (AR). A data assimilation experiment demonstrates that the OMI data can help improve the Weather Research and Forecasting (WRF) model skill at simulating the structure and intensity of the AR and the precipitation at the AR landfall.

Science objectives of the ozone monitoring instrument

2006 ◽  
Vol 44 (5) ◽  
pp. 1199-1208 ◽  
Author(s):  
P.F. Levelt ◽  
E. Hilsenrath ◽  
G.W. Leppelmeier ◽  
G.H.J. van den Oord ◽  
P.K. Bhartia ◽  
...  
Keyword(s):  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Ozone Monitoring

scholarly journals Opportunistic validation of sulfur dioxide in the Sarychev Peak volcanic eruption cloud

2011 ◽  
Vol 4 (9) ◽  
pp. 1705-1712 ◽  
Author(s):  
S. A. Carn ◽  
T. M. Lopez
Keyword(s):  
Sulfur Dioxide ◽  
Limited Data ◽  
Ozone Monitoring Instrument ◽  
Spatial And Temporal Scales ◽  
Eruption Cloud ◽  
Kurile Islands ◽  
Volcanic Cloud ◽  
Ozone Monitoring ◽  
Lidar Observations ◽  
Sarychev Peak

Abstract. We report attempted validation of Ozone Monitoring Instrument (OMI) sulfur dioxide (SO2) retrievals in the stratospheric volcanic cloud from Sarychev Peak (Kurile Islands) in June 2009, through opportunistic deployment of a ground-based ultraviolet (UV) spectrometer (FLYSPEC) as the volcanic cloud drifted over central Alaska. The volcanic cloud altitude (~12–14 km) was constrained using coincident CALIPSO lidar observations. By invoking some assumptions about the spatial distribution of SO2, we derive averages of FLYSPEC vertical SO2 columns for comparison with OMI SO2 measurements. Despite limited data, we find minimum OMI-FLYSPEC differences within measurement uncertainties, which support the validity of the operational OMI SO2 algorithm. However, our analysis also highlights the challenges involved in comparing datasets representing markedly different spatial and temporal scales. This effort represents the first attempt to validate SO2 in a stratospheric volcanic cloud using a mobile ground-based instrument, and demonstrates the need for a network of rapidly deployable instruments for validation of space-based volcanic SO2 measurements.

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