scholarly journals A climate-scale satellite record for carbon monoxide: the MOPITT Version 7 product

2017 ◽  
Vol 10 (7) ◽  
pp. 2533-2555 ◽  
Author(s):  
Merritt N. Deeter ◽  
David P. Edwards ◽  
Gene L. Francis ◽  
John C. Gille ◽  
Sara Martínez-Alonso ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Near Infrared ◽  
Retrieval Algorithm ◽  
Bias Drift ◽  
New Strategy ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Atmospheric Carbon Monoxide ◽  
Climate Scale ◽  
Cloud Mask

Abstract. The MOPITT (Measurements of Pollution in the Troposphere) satellite instrument has been making observations of atmospheric carbon monoxide since 2000. Recent enhancements to the MOPITT retrieval algorithm have resulted in the release of the version 7 (V7) product. Improvements include (1) representation of growing atmospheric concentrations of N2O, (2) use of meteorological fields from the MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications) reanalysis for the entire MOPITT mission (instead of MERRA), (3) use of the MODIS (Moderate-Resolution Imaging Spectroradiometer) Collection 6 cloud mask product (instead of Collection 5), (4) a new strategy for radiance-bias correction and (5) an improved method for calibrating MOPITT's near-infrared (NIR) radiances. Statistical comparisons of V7 validation results with corresponding V6 results are presented, using aircraft in situ measurements as the reference. Clear improvements are demonstrated for V7 products with respect to overall retrieval biases, bias variability and bias drift uncertainty.

scholarly journals A Climate-scale Satellite Record for Carbon Monoxide: The MOPITT Version 7 Product

2017 ◽  
Author(s):  
Merritt N. Deeter ◽  
David P. Edwards ◽  
Gene L. Francis ◽  
John C. Gille ◽  
Sara Martinez-Alonso ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Retrieval Algorithm ◽  
Bias Drift ◽  
New Strategy ◽  
Atmospheric Carbon Monoxide ◽  
Climate Scale ◽  
Cloud Mask ◽  
Atmospheric Concentrations ◽  
Drift Uncertainty

Abstract. The MOPITT (“Measurements of Pollution in the Troposphere”) satellite instrument has been making observations of atmospheric carbon monoxide since 2000. Recent enhancements to the MOPITT retrieval algorithm have resulted in the release of the Version 7 (V7) product. Improvements include (1) representation of growing atmospheric concentrations of N2O, (2) use of meteorological fields from the MERRA-2 reanalysis for the entire MOPITT mission (instead of MERRA), (3) use of the MODIS Collection 6 cloud mask product (instead of Collection 5), (4) a new strategy for radiance bias correction, and (5) an improved method for calibrating MOPITT’s NIR radiances. Statistical comparisons of V7 validation results with corresponding V6 results are presented, using aircraft in-situ measurements as the reference. Clear improvements are demonstrated for V7 products with respect to overall retrieval biases, bias variability, and bias drift uncertainty.

scholarly journals Tropospheric carbon monoxide variability from AIRS and IASI under clear and cloudy conditions

2013 ◽  
Vol 13 (6) ◽  
pp. 16337-16366 ◽  
Author(s):  
J. Warner ◽  
F. Carminati ◽  
Z. Wei ◽  
W. Lahoz ◽  
J.-L. Attié
Keyword(s):  
Carbon Monoxide ◽  
Emission Inventory ◽  
Correlation Coefficients ◽  
Retrieval Algorithm ◽  
Atmospheric Infrared Sounder ◽  
Fire Emissions ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask

Abstract. We study the Carbon Monoxide (CO) variability in the last decade measured by NASA's Atmospheric InfraRed Sounder (AIRS) on the Earth Observing Systems (EOS)/Aqua satellite and Europe's Infrared Atmospheric Sounder Interferometer (IASI) on MetOp platform. The focus of this study is to analyze CO variability and short-term trends separately for background CO and new emissions based on a new statistical approach. The AIRS Level 2 (L2) retrieval algorithm, as well as the IASI products from NOAA, utilizes cloud clearing to treat cloud contaminations in the signals; and this increases the data coverage significantly to a yield of more than 50% of the total measurements (Susskind et al., 2003). We first study if the cloud clearing affects CO retrievals and the subsequent trend studies by using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) (Ackerman et al., 1998) cloud mask to identify AIRS clear sky scenes. We then separate AIRS CO data into clear and cloud-cleared scenes and into background and new emissions, respectively. Furthermore, we carry out a similar study for the IASI CO and discuss the consistency with AIRS. We validate the CO variability of the emissions developed from AIRS against other emission inventory databases (i.e., Global Fire Emissions Database – GFED3 and the MACC/CityZEN UE – MACCity) and calculate that the correlation coefficients between the AIRS CO emissions and the emission inventory databases are 0.726 for the Northern Hemisphere (NH) and 0.915 for the Southern Hemisphere (SH).

Test of a Modified Infrared-Only ABI Cloud Mask Algorithm for AHI Radiance Observations

2016 ◽  
Vol 55 (11) ◽  
pp. 2529-2546 ◽  
Author(s):  
X. Zhuge ◽  
X. Zou
Keyword(s):  
Near Infrared ◽  
Extended System ◽  
Clear Sky ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
Very High ◽  
Lower False Alarm Rate ◽  
Infrared Channel

AbstractAssimilation of infrared channel radiances from geostationary imagers requires an algorithm that can separate cloudy radiances from clear-sky ones. An infrared-only cloud mask (CM) algorithm has been developed using the Advanced Himawari Imager (AHI) radiance observations. It consists of a new CM test for optically thin clouds, two modified Advanced Baseline Imager (ABI) CM tests, and seven other ABI CM tests. These 10 CM tests are used to generate composite CMs for AHI data, which are validated by using the Moderate Resolution Imaging Spectroradiometer (MODIS) CMs. It is shown that the probability of correct typing (PCT) of the new CM algorithm over ocean and over land is 89.73% and 90.30%, respectively and that the corresponding leakage rates (LR) are 6.11% and 4.21%, respectively. The new infrared-only CM algorithm achieves a higher PCT and a lower false-alarm rate (FAR) over ocean than does the Clouds from the Advanced Very High Resolution Radiometer (AVHRR) Extended System (CLAVR-x), which uses not only the infrared channels but also visible and near-infrared channels. A slightly higher FAR of 7.92% and LR of 6.18% occurred over land during daytime. This result requires further investigation.

scholarly journals Critical evaluation of cloud contamination in the MISR aerosol products using MODIS cloud mask products

2014 ◽  
Vol 7 (6) ◽  
pp. 1791-1801 ◽  
Author(s):  
Y. Shi ◽  
J. Zhang ◽  
J. S. Reid ◽  
B. Liu ◽  
E. J. Hyer
Keyword(s):  
High Latitude ◽  
Near Infrared ◽  
Critical Evaluation ◽  
Mean Value ◽  
Screening Methods ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
One Year ◽  
Cloud Mask ◽  
Cloud Screening

Abstract. Using Terra Moderate Resolution Imaging Spectroradiometer (MODIS)-based cloud screening methods, the impacts of cloud contamination on the Terra Multi-angle Imaging Spectroradiometer (MISR) aerosol optical depth (AOD) product are evaluated. Based on one year of collocated MISR and MODIS data, this study suggests that cloud contamination exists in both over-water and over-land MISR AOD data, with heavier cloud contamination occurring over the high latitude southern hemispheric oceans. On average globally, this study shows that thin cirrus cloud contamination introduces a possible ~ 0.01 high bias for the over-water MISR AOD retrievals. Over the mid- to high-latitude oceans and Southeast Asia, this number increases to 0.015–0.02. However, biases much larger than this mean value are found in individual retrievals, especially in retrievals that are near cloud edges. This study suggests that cloud-clearing methods using observations from MISR alone, which has only visible and near-infrared channels, may not be sufficient for all scenarios. Measurements from MODIS can be applied to assist cloud-clearing of the MISR aerosol retrievals. Cloud screening algorithms based on multi-sensor approaches are feasible and should be considered for current and future satellite aerosol studies.

scholarly journals Comparison of Cloud Properties from Himawari-8 and FengYun-4A Geostationary Satellite Radiometers with MODIS Cloud Retrievals

2019 ◽  
Vol 11 (14) ◽  
pp. 1703 ◽  
Author(s):  
Ruize Lai ◽  
Shiwen Teng ◽  
Bingqi Yi ◽  
Husi Letu ◽  
Min Min ◽  
...  
Keyword(s):  
Retrieval System ◽  
Geostationary Satellite ◽  
Retrieval Algorithm ◽  
Cloud Properties ◽  
Moderate Resolution ◽  
Cloud Optical Thickness ◽  
Water Cloud ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Relative Differences ◽  
Cloud Mask

With the development and the improvement of meteorological satellites, different instruments have significantly enhanced the ability to observe clouds over large spatial regions. Recent geostationary satellite radiometers, e.g., Advanced Himawari Imager (AHI) and Advanced Geosynchronous Radiation Imager (AGRI) onboard the Himawari-8 and the Fengyun-4A satellite, respectively, provide observations over similar regions at higher spatial and temporal resolutions for cloud and atmosphere studies. To better understand the reliability of AHI and AGRI retrieval products, we compare their cloud products with collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud products, especially in terms of the cloud optical thickness (COT) and cloud effective radius (CER). Our comparison indicates that cloud mask and cloud phase of these instruments are reasonably consistent, while clear differences are noticed for COT and CER results. The average relative differences (RDs) between AHI and AGRI ice COT and that of MODIS are both over 40%, and the RDs of ice CER are less than 20%. The consistency between AHI and MODIS water cloud results is much better, with the RDs of COT and CER being 29% and 9%, respectively, whereas the RDs of AGRI COT and CER are still larger than 30%. Many factors such as observation geometry, cloud horizontal homogeneity, and retrieval system (e.g., retrieval algorithm, forward model, and assumptions) may contribute to these differences. The RDs of COTs from different instruments for homogeneous clouds are about one-third smaller than the corresponding RDs for inhomogeneous clouds. By applying unified retrieval systems based on the forward radiative transfer models designed for each particular band, we find that 30% to 70% of the differences among the results from different instruments are caused by the retrieval system (e.g., different treatments or assumptions for the retrievals), and the rest may be due to sub-pixel inhomogeneity, parallax errors, and calibration.

scholarly journals MISR empirical stray light corrections in high-contrast scenes

2015 ◽  
Vol 8 (7) ◽  
pp. 2927-2943 ◽  
Author(s):  
J. A. Limbacher ◽  
R. A. Kahn
Keyword(s):  
Near Infrared ◽  
Stray Light ◽  
Retrieval Algorithm ◽  
High Contrast ◽  
Validation Data ◽  
Modis Imagery ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Terra Satellite

Abstract. We diagnose the potential causes for the Multi-angle Imaging SpectroRadiometer's (MISR) persistent high aerosol optical depth (AOD) bias at low AOD with the aid of coincident MODerate-resolution Imaging Spectroradiometer (MODIS) imagery from NASA's Terra satellite. Stray light in the MISR instrument is responsible for a large portion of the high AOD bias in high-contrast scenes, such as broken-cloud scenes that are quite common over ocean. Discrepancies among MODIS and MISR nadir-viewing blue, green, red, and near-infrared images are used to optimize seven parameters individually for each wavelength, along with a background reflectance modulation term that is modeled separately, to represent the observed features. Independent surface-based AOD measurements from the AErosol RObotic NETwork (AERONET) and the Marine Aerosol Network (MAN) are compared with MISR research aerosol retrieval algorithm (RA) AOD retrievals for 1118 coincidences to validate the corrections when applied to the nadir and off-nadir cameras. With these corrections, plus the baseline RA corrections and enhanced cloud screening applied, the median AOD bias for all data in the mid-visible (green, 558 nm) band decreases from 0.006 (0.020 for the MISR standard algorithm (SA)) to 0.000, and the RMSE decreases by 5 % (27 % compared to the SA). For AOD558 nm < 0.10, which includes about half the validation data, 68th percentile absolute AOD558 nm errors for the RA have dropped from 0.022 (0.034 for the SA) to < 0.02 (~ 0.018).

scholarly journals Tropospheric carbon monoxide variability from AIRS under clear and cloudy conditions

2013 ◽  
Vol 13 (24) ◽  
pp. 12469-12479 ◽  
Author(s):  
J. Warner ◽  
F. Carminati ◽  
Z. Wei ◽  
W. Lahoz ◽  
J.-L. Attié
Keyword(s):  
Carbon Monoxide ◽  
Emission Inventory ◽  
Correlation Coefficients ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Fire Emissions ◽  
Earth Observing System ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
A New Technique ◽  
Cloud Mask

Abstract. We study the carbon monoxide (CO) variability in the last decade measured by NASA's Atmospheric InfraRed Sounder (AIRS) on the Earth Observing System (EOS)/Aqua satellite. The focus of this study is to analyze CO variability and short-term trends separately for background CO and fresh CO emissions based on a new statistical approach. The AIRS Level 2 (L2) retrieval algorithm utilizes cloud clearing to treat cloud contaminations in the signals, and this increases the data coverage significantly to a yield of more than 50% of the total measurements. We first study if the cloud clearing affects CO retrievals and the subsequent trend studies by using the collocated Moderate Resolution Imaging Spectroradiometer (MODIS) cloud mask to identify AIRS clear sky scenes. We then carry out a science analysis using AIRS CO data individually for the clear and cloud-cleared scenes to identify any potential effects due to cloud clearing. We also introduce a new technique to separate background and recently emitted CO observations, which aims to constrain emissions using only satellite CO data. We validate the CO variability of the recent emissions estimated from AIRS against other emission inventory databases (i.e., Global Fire Emissions Database – GFED3 and the MACC/CityZEN UE – MACCity) and calculate that the correlation coefficients between the AIRS CO recently emitted and the emission inventory databases are 0.726 for the Northern Hemisphere (NH) and 0.915 for the Southern Hemisphere (SH). The high degree of agreement between emissions identified using only AIRS CO and independent inventory sources demonstrates the validity of this approach to separate recent emissions from the background CO using one satellite data set.

scholarly journals Towards a long-term global aerosol optical depth record: applying a consistent aerosol retrieval algorithm to MODIS and VIIRS-observed reflectance

2015 ◽  
Vol 8 (10) ◽  
pp. 4083-4110 ◽  
Author(s):  
R. C. Levy ◽  
L. A. Munchak ◽  
S. Mattoo ◽  
F. Patadia ◽  
L. A. Remer ◽  
...  
Keyword(s):  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Infrared Imaging ◽  
Quantitative Information ◽  
Environmental Data ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
Data Record ◽  
Moderate Resolution ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To answer fundamental questions about aerosols in our changing climate, we must quantify both the current state of aerosols and how they are changing. Although NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) sensors have provided quantitative information about global aerosol optical depth (AOD) for more than a decade, this period is still too short to create an aerosol climate data record (CDR). The Visible Infrared Imaging Radiometer Suite (VIIRS) was launched on the Suomi-NPP satellite in late 2011, with additional copies planned for future satellites. Can the MODIS aerosol data record be continued with VIIRS to create a consistent CDR? When compared to ground-based AERONET data, the VIIRS Environmental Data Record (V_EDR) has similar validation statistics as the MODIS Collection 6 (M_C6) product. However, the V_EDR and M_C6 are offset in regards to global AOD magnitudes, and tend to provide different maps of 0.55 μm AOD and 0.55/0.86 μm-based Ångström Exponent (AE). One reason is that the retrieval algorithms are different. Using the Intermediate File Format (IFF) for both MODIS and VIIRS data, we have tested whether we can apply a single MODIS-like (ML) dark-target algorithm on both sensors that leads to product convergence. Except for catering the radiative transfer and aerosol lookup tables to each sensor's specific wavelength bands, the ML algorithm is the same for both. We run the ML algorithm on both sensors between March 2012 and May 2014, and compare monthly mean AOD time series with each other and with M_C6 and V_EDR products. Focusing on the March–April–May (MAM) 2013 period, we compared additional statistics that include global and gridded 1° × 1° AOD and AE, histograms, sampling frequencies, and collocations with ground-based AERONET. Over land, use of the ML algorithm clearly reduces the differences between the MODIS and VIIRS-based AOD. However, although global offsets are near zero, some regional biases remain, especially in cloud fields and over brighter surface targets. Over ocean, use of the ML algorithm actually increases the offset between VIIRS and MODIS-based AOD (to ~ 0.025), while reducing the differences between AE. We characterize algorithm retrievability through statistics of retrieval fraction. In spite of differences between retrieved AOD magnitudes, the ML algorithm will lead to similar decisions about "whether to retrieve" on each sensor. Finally, we discuss how issues of calibration, as well as instrument spatial resolution may be contributing to the statistics and the ability to create a consistent MODIS → VIIRS aerosol CDR.

scholarly journals Comparison of the cloud top heights retrieved from MODIS and AHI satellite data with ground-based Ka-band radar

2019 ◽  
Author(s):  
Juan Huo ◽  
Daren Lu ◽  
Shu Duan ◽  
Yongheng Bi ◽  
Bo Liu
Keyword(s):  
Satellite Data ◽  
Radar Data ◽  
Retrieval Algorithm ◽  
Ka Band ◽  
Retrieval Accuracy ◽  
Retrieval Algorithms ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Radar Measurements ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract. To better understand the accuracy of cloud top heights (CTHs) derived from passive satellite data, ground-based Ka-band radar measurements from 2016 and 2017 in Beijing were compared with CTH data inferred from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Advanced Himawari Imager (AHI). Relative to the radar CTHs, the MODIS CTHs were found to be underestimated by −1.10 ± 2.53 km and 49 % of CTH differences were within 1.0 km. Like the MODIS results, the AHI CTHs were underestimated by −1.10 ± 2.27 km and 42 % were within 1.0 km. Both the MODIS and AHI retrieval accuracy depended strongly on the cloud depth (CD). Large differences were mainly occurring for the retrieval of thin clouds of CD  1 km, the CTH difference decreased to −0.48 ± 1.70 km for MODIS and to −0.76 ± 1.63 km for AHI. MODIS CTHs greater than 6 km showed better agreement with the radar data than those less than 4 km. Statistical analysis showed that the average AHI CTHs were lower than the average MODIS CTHs by −0.64 ± 2.36 km. The monthly accuracy of both retrieval algorithms was studied and it was found that the AHI retrieval algorithm had the largest bias in winter while the MODIS retrieval algorithm had the lowest accuracy in spring.

scholarly journals Continuing the MODIS Dark Target Aerosol Time Series with VIIRS

2020 ◽  
Vol 12 (2) ◽  
pp. 308 ◽  
Author(s):  
Virginia Sawyer ◽  
Robert C. Levy ◽  
Shana Mattoo ◽  
Geoff Cureton ◽  
Yingxi Shi ◽  
...  
Keyword(s):  
Near Infrared ◽  
Infrared Imaging ◽  
Retrieval Algorithm ◽  
Spatial And Temporal Variability ◽  
Imaging Spectrometer ◽  
Standard Data ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Infrared Wavelengths ◽  
Good Agreement

For reflected sunlight observed from space at visible and near-infrared wavelengths, particles suspended in Earth’s atmosphere provide contrast with vegetation or dark water at the surface. This is the physical motivation for the Dark Target (DT) aerosol retrieval algorithm developed for the Moderate Resolution Imaging Spectrometer (MODIS). To extend the data record of aerosol optical depth (AOD) beyond the expected 20-year lifespan of the MODIS sensors, DT must be adapted for other sensors. A version of the DT AOD retrieval for the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi-National Polar-Orbiting Partnership (SNPP) is now mature enough to be released as a standard data product, and includes some upgraded features from the MODIS version. Differences between MODIS Aqua and VIIRS SNPP lead to some inevitable disagreement between their respective AOD measurements, but the offset between the VIIRS SNPP and MODIS Aqua records is smaller than the offset between those of MODIS Aqua and MODIS Terra. The VIIRS SNPP retrieval shows good agreement with ground-based measurements. For most purposes, DT for VIIRS SNPP is consistent enough and in close enough agreement with MODIS to continue the record of satellite AOD. The reasons for the offset from MODIS Aqua, and its spatial and temporal variability, are investigated in this study.

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