scholarly journals The MOPITT Version 9 CO Product: Sampling Enhancements and Validation

2021 ◽  
Author(s):  
Merritt Deeter ◽  
Gene Francis ◽  
John Gille ◽  
Debbie Mao ◽  
Sara Martínez-Alonso ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Near Infrared ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Temporal Consistency ◽  
Cloud Detection ◽  
Geographical Variability ◽  
Satellite Retrieval ◽  
Source Regions

Abstract. Characteristics of the Version 9 (V9) MOPITT ("Measurements of Pollution in the Troposphere") satellite retrieval product for tropospheric carbon monoxide (CO) are described. The new V9 product includes many CO retrievals over land which, in previous MOPITT product versions, would have been discarded by the cloud detection algorithm. Globally, the number of daytime MOPITT retrievals over land has increased by 30–40 % relative to the Version 8 product, although the increase in retrieval coverage exhibits significant geographical variability. Areas benefiting from the improved cloud detection performance include (but are not limited to) source regions often characterized by high aerosol concentrations. The V9 MOPITT product also incorporates a modified calibration strategy for the MOPITT near-infrared (NIR) CO channels, resulting in greater temporal consistency for the NIR-only and thermal infrared-near infrared (TIR-NIR) retrieval variants. Validation results based on in-situ CO profiles acquired from aircraft in a variety of contexts indicate that retrieval biases for V9 are typically within the range of ±5 % and are generally comparable to results for the V8 product.

scholarly journals Near-Ultraviolet to Near-Infrared Band Thresholds Cloud Detection Algorithm for TANSAT-CAPI

2021 ◽  
Vol 13 (10) ◽  
pp. 1906
Author(s):  
Ning Ding ◽  
Jianbing Shao ◽  
Changxiang Yan ◽  
Junqiang Zhang ◽  
Yanfeng Qiao ◽  
...  
Keyword(s):  
Near Infrared ◽  
Lightweight Design ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Main Function ◽  
Cloud Detection ◽  
Infrared Band ◽  
Band Ratio ◽  
Near Ultraviolet ◽  
Imaging Detector

Cloud and aerosol polarization imaging detector (CAPI) is one of the important payloads on the China Carbon Dioxide Observation Satellite (TANSAT), which can realize multispectral polarization detection and accurate on-orbit calibration. The main function of the instrument is to identify the interference of clouds and aerosols in the atmospheric detection path and to improve the retrieval accuracy of greenhouse gases. Therefore, it is of great significance to accurately identify the clouds in remote sensing images. However, in order to meet the requirement of lightweight design, CAPI is only equipped with channels in the near-ultraviolet to near-infrared bands. It is difficult to achieve effective cloud recognition using traditional visible light to thermal infrared band spectral threshold cloud detection algorithms. In order to solve the above problem, this paper innovatively proposes a cloud detection method based on different threshold tests from near ultraviolet to near infrared (NNDT). This algorithm first introduces the 0.38 μm band and the ratio of 0.38 μm band to 1.64 μm band, to realize the separation of cloud pixels and clear sky pixels, which can take advantage of the obvious difference in radiation characteristics between clouds and ground objects in the near-ultraviolet band and the advantages of the band ratio in identifying clouds on the snow surface. The experimental results show that the cloud recognition hit rate (PODcloud) reaches 0.94 (ocean), 0.98 (vegetation), 0.99 (desert), and 0.86 (polar), which therefore achieve the application standard of CAPI data cloud detection The research shows that the NNDT algorithm replaces the demand for thermal infrared bands for cloud detection, gets rid of the dependence on the minimum surface reflectance database that is embodied in traditional cloud recognition algorithms, and lays the foundation for aerosol and CO2 parameter inversion.

scholarly journals Multilayer Cloud Detection with the MODIS Near-Infrared Water Vapor Absorption Band

2010 ◽  
Vol 49 (11) ◽  
pp. 2315-2333 ◽  
Author(s):  
Galina Wind ◽  
Steven Platnick ◽  
Michael D. King ◽  
Paul A. Hubanks ◽  
Michael J. Pavolonis ◽  
...  
Keyword(s):  
Water Vapor ◽  
Near Infrared ◽  
Single Layer ◽  
Precipitable Water ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Water Vapor Absorption ◽  
Cloud Properties ◽  
Cloud Models ◽  
Moderate Resolution Imaging Spectroradiometer

Abstract Data Collection 5 processing for the Moderate Resolution Imaging Spectroradiometer (MODIS) on board the NASA Earth Observing System (EOS) Terra and Aqua spacecraft includes an algorithm for detecting multilayered clouds in daytime. The main objective of this algorithm is to detect multilayered cloud scenes, specifically optically thin ice cloud overlying a lower-level water cloud, that present difficulties for retrieving cloud effective radius using single-layer plane-parallel cloud models. The algorithm uses the MODIS 0.94-μm water vapor band along with CO2 bands to obtain two above-cloud precipitable water retrievals, the difference of which, in conjunction with additional tests, provides a map of where multilayered clouds might potentially exist. The presence of a multilayered cloud results in a large difference in retrievals of above-cloud properties between the CO2 and the 0.94-μm methods. In this paper the MODIS multilayered cloud algorithm is described, results of using the algorithm over example scenes are shown, and global statistics for multilayered clouds as observed by MODIS are discussed. A theoretical study of the algorithm behavior for simulated multilayered clouds is also given. Results are compared to two other comparable passive imager methods. A set of standard cloudy atmospheric profiles developed during the course of this investigation is also presented. The results lead to the conclusion that the MODIS multilayer cloud detection algorithm has some skill in identifying multilayered clouds with different thermodynamic phases.

Role of Satellite Technology in Oceanic Study

2018 ◽  
pp. 80-103
Author(s):  
Thomas Mathew
Keyword(s):  
Remote Sensing ◽  
Electromagnetic Radiation ◽  
Crucial Role ◽  
Near Infrared ◽  
Thermal Infrared ◽  
Microwave Region ◽  
Satellite Technology ◽  
The Earth

The three-fourth surface of the earth is covered with ocean. The study of the ocean is important for sustainable overall development of a nation and world at large in view of it being rich in resources and playing a crucial role in the climate of the region and changes associated with it. The space-based observations assume significance, as it provides synoptic and repetitive coverage of the ocean in contrast to the sparse and isolated in-situ buoy or ship observations. The remote sensing of the ocean with the help of satellite or satellite oceanography has many other applications also. The electromagnetic radiation in the visible, near infrared, thermal infrared, and microwave regions are used by the sensors on-board space platforms to measure the diverse physical, biological, and geological parameters of the ocean. Amongst the various electromagnetic regions, the microwave region plays an important role in the study of the ocean.

scholarly journals Validation of MOPITT Version 5 thermal-infrared, near-infrared, and multispectral carbon monoxide profile retrievals for 2000-2011

2013 ◽  
Vol 118 (12) ◽  
pp. 6710-6725 ◽  
Author(s):  
M. N. Deeter ◽  
S. Martínez-Alonso ◽  
D. P. Edwards ◽  
L. K. Emmons ◽  
J. C. Gille ◽  
...  
Keyword(s):  
Carbon Monoxide ◽  
Near Infrared ◽  
Thermal Infrared

scholarly journals A statistical approach to quantify uncertainty in carbon monoxide measurements at the Izaña global GAW station: 2008–2011

2013 ◽  
Vol 6 (3) ◽  
pp. 787-799 ◽  
Author(s):  
A. J. Gomez-Pelaez ◽  
R. Ramos ◽  
V. Gomez-Trueba ◽  
P. C. Novelli ◽  
R. Campo-Hernandez
Keyword(s):  
Carbon Monoxide ◽  
Response Function ◽  
Measurement System ◽  
Minimum Variance ◽  
Standard Uncertainty ◽  
In Situ Measurements ◽  
Temporal Consistency ◽  
The Mean ◽  
Mean Differences

Abstract. Atmospheric CO in situ measurements are carried out at the Izaña (Tenerife) global GAW (Global Atmosphere Watch Programme of the World Meteorological Organization – WMO) mountain station using a Reduction Gas Analyser (RGA). In situ measurements at Izaña are representative of the subtropical Northeast Atlantic free troposphere, especially during nighttime. We present the measurement system configuration, the response function, the calibration scheme, the data processing, the Izaña 2008–2011 CO nocturnal time series, and the mean diurnal cycle by months. We have developed a rigorous uncertainty analysis for carbon monoxide measurements carried out at the Izaña station, which could be applied to other GAW stations. We determine the combined standard measurement uncertainty taking into consideration four contributing components: uncertainty of the WMO standard gases interpolated over the range of measurement, the uncertainty that takes into account the agreement between the standard gases and the response function used, the uncertainty due to the repeatability of the injections, and the propagated uncertainty related to the temporal consistency of the response function parameters (which also takes into account the covariance between the parameters). The mean value of the combined standard uncertainty decreased significantly after March 2009, from 2.37 nmol mol−1 to 1.66 nmol mol−1, due to improvements in the measurement system. A fifth type of uncertainty we call representation uncertainty is considered when some of the data necessary to compute the temporal mean are absent. Any computed mean has also a propagated uncertainty arising from the uncertainties of the data used to compute the mean. The law of propagation depends on the type of uncertainty component (random or systematic). In situ hourly means are compared with simultaneous and collocated NOAA flask samples. The uncertainty of the differences is computed and used to determine whether the differences are significant. For 2009–2011, only 24.5% of the differences are significant, and 68% of the differences are between −2.39 and 2.5 nmol mol−1. Total and annual mean differences are computed using conventional expressions but also expressions with weights based on the minimum variance method. The annual mean differences for 2009–2011 are well within the ±2 nmol mol−1 compatibility goal of GAW.

Accounting for Non-detects in Satellite Retrievals: Application Using CrIS Ammonia Observations

2021 ◽  
Author(s):  
Evan White ◽  
Mark Shephard ◽  
Karen Cady-Periera ◽  
Shailesh Kharol ◽  
Enrico Dammers ◽  
...  
Keyword(s):  
Detection Limit ◽  
Survival Data ◽  
Infrared Imaging ◽  
Ammonia Concentration ◽  
Detection Algorithm ◽  
Atmospheric Conditions ◽  
Cloud Detection ◽  
Brightness Temperatures ◽  
Source Regions ◽  
Radiometric Signal

<p>For measurements from any instrument there is a minimum detection limit below which the sensor cannot measure (i.e., non-detects). Measurements of trace gases from satellite instruments can also suffer from a significant number of non-detects, especially for species with very low atmospheric concentrations  and that have a very weak or absent signals (signal-to-noise<1) in the spectral region used to detect the species (e.g., ammonia).  For ammonia, these non-signal conditions generally occur when thick clouds obscure the ammonia signal, or atmospheric conditions generates too weak of a radiometric signal to detect (e.g., very low concentrations). Presented is a robust approach to explicitly identify and account for cloud-free satellite observations that are below the detection limit of the sensor (which occur principally in  non-source regions) for the Cross-Track Infrared Sounder (CrIS) Fast Physical Retrieval (CFPR) ammonia (NH<sub>3</sub>) product. This approach uses the newly developed CrIS Ammonia Cloud Detection Algorithm (CACDA) to compute a cloud flag based on the CrIS IMG (CIMG) product . The CIMG product uses coincident Visible Infrared Imaging Radiometer Suite (VIIRS) brightness temperatures and cloud fractions mapped onto CrIS Field of Views (FOV). This cloud flag is used to separate CrIS FOVs without signal due to clouds from FOVs that are below the detection limit due to the atmospheric state (referred to as non-detects).  Survival data is generated from in-situ surface observations from non-emission source regions to produce ammonia concentration values under CrIS non-detect conditions. Accounting for these non-detects can be significant in reducing bias of averaged values (i.e., Level 3 products) in regions or conditions with low concentration amounts (e.g. wintertime, non-agriculture regions, etc.), with little impact on concentrations in emission regions. This presentation will provide examples and evaluations of the CACDA and the inclusion of non-detects in the CFPR generated ammonia product. This will include comparisons of annual and seasonal averages of surface level ammonia concentrations with and without survival data to demonstrate the reduction in bias.</p>

scholarly journals Daytime Cloud Detection Algorithm Based on a Multitemporal Dataset for GK-2A Imagery

2021 ◽  
Vol 13 (16) ◽  
pp. 3215
Author(s):  
Soobong Lee ◽  
Jaewan Choi
Keyword(s):  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Infrared Imaging ◽  
Satellite Observation ◽  
Detection Algorithm ◽  
Cloud Detection ◽  
Surface Information ◽  
Quantitative Results ◽  
Filtering Technique

Cloud detection is an essential and important process in remote sensing when surface information is required for various fields. For this reason, we developed a daytime cloud detection algorithm for GEOstationary KOrea Multi-Purpose SATellite 2A (GEO-KOMPSAT-2A, GK-2A) imagery. For each pixel, the filtering technique using angular variance, which denotes the change in top of atmosphere (TOA) reflectance over time, was applied, and filtering technique by using the minimum TOA reflectance was used to remove remaining cloud pixels. Furthermore, near-infrared (NIR) and normalized difference vegetation index (NDVI) images were applied with dynamic thresholds to improve the accuracy of the cloud detection results. The quantitative results showed that the overall accuracy of proposed cloud detection was 0.88 and 0.92 with Visible Infrared Imaging Radiometer Suite (VIIRS) and Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), respectively, and indicated that the proposed algorithm has good performance in detecting clouds.

scholarly journals Evaluation of PROBA-V Collection 1: Refined Radiometry, Geometry, and Cloud Screening

2018 ◽  
Vol 10 (9) ◽  
pp. 1375 ◽  
Author(s):  
Carolien Toté ◽  
Else Swinnen ◽  
Sindy Sterckx ◽  
Stefan Adriaensen ◽  
Iskander Benhadj ◽  
...  
Keyword(s):  
Time Series ◽  
Vegetation Index ◽  
Near Infrared ◽  
Normalized Difference Vegetation Index ◽  
Temporal Stability ◽  
Detection Algorithm ◽  
Short Wave ◽  
Absolute Calibration ◽  
Cloud Detection ◽  
Detection Scheme

PROBA-V (PRoject for On-Board Autonomy–Vegetation) was launched in May-2013 as an operational continuation to the vegetation (VGT) instruments on-board the Système Pour l’Observation de la Terre (SPOT)-4 and -5 satellites. The first reprocessing campaign of the PROBA-V archive from Collection 0 (C0) to Collection 1 (C1) aims at harmonizing the time series, thanks to improved radiometric and geometric calibration and cloud detection. The evaluation of PROBA-V C1 focuses on (i) qualitative and quantitative assessment of the new cloud detection scheme; (ii) quantification of the effect of the reprocessing by comparing C1 to C0; and (iii) evaluation of the spatio-temporal stability of the combined SPOT/VGT and PROBA-V archive through comparison to METOP/advanced very high resolution radiometer (AVHRR). The PROBA-V C1 cloud detection algorithm yields an overall accuracy of 89.0%. Clouds are detected with very few omission errors, but there is an overdetection of clouds over bright surfaces. Stepwise updates to the visible and near infrared (VNIR) absolute calibration in C0 and the application of degradation models to the SWIR calibration in C1 result in sudden changes between C0 and C1 Blue, Red, and NIR TOC reflectance in the first year, and more gradual differences for short-wave infrared (SWIR). Other changes result in some bias between C0 and C1, although the root mean squared difference (RMSD) remains well below 1% for top-of-canopy (TOC) reflectance and below 0.02 for the normalized difference vegetation index (NDVI). Comparison to METOP/AVHRR shows that the recent reprocessing campaigns on SPOT/VGT and PROBA-V have resulted in a more stable combined time series.

scholarly journals Cloud fraction determined by thermal infrared and visible all-sky cameras

2018 ◽  
Author(s):  
Christine Aebi ◽  
Julian Gröbner ◽  
Niklaus Kämpfer
Keyword(s):  
Cloud Amount ◽  
Visible Spectrum ◽  
Thermal Infrared ◽  
Detection Algorithm ◽  
Cloud Fraction ◽  
High Temporal Resolution ◽  
Cloud Detection ◽  
Fractional Cloud ◽  
Significant Difference ◽  
High Level

Abstract. The thermal infrared cloud camera (IRCCAM) is a prototype instrument that determines cloud fraction continuously during day and nighttime with high temporal resolution. It has been developed and tested at Physikalisch-Meteorologisches Observatorium Davos/World Radiation Center (PMOD/WRC) in Davos, Switzerland. The IRCCAM consists of a commercial microbolometer camera sensitive in the 8 μm–14 μm wavelength range. Over a time period of two years, the fractional cloud coverage obtained by the IRCCAM is compared with two other commercial cameras sensitive in the visible spectrum (Mobotix Q24M and Schreder VIS-J1006) as well as with the automated partial cloud amount detection algorithm (APCADA) using pyrgeometer data. In comparison to the visible cloud detection algorithms, the IRCCAM shows median difference values of 0.01 to 0.07 cloud fraction wherein around 90 % of the data are within ±0.25 (±2 oktas) cloud fraction. Thus there is no significant difference in the cloud fraction determination of the IRCCAM in comparison to the other study instruments. Analysis indicates no significant difference in the performance of the IRCCAM during day or nighttime and also not in different seasons. The cloud types where all algorithms are in closest agreement are low-level clouds (with median differences in cloud fraction of −0.01 to 0.02), followed by mid-level (0.00) and high-level clouds (−0.13).

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