scholarly journals Cross-verification of simulated GEMS tropospheric ozone retrievals and ozonesonde measurements over Northeast Asia

2019 ◽  
Author(s):  
Juseon Bak ◽  
Kang-Hyeon Baek ◽  
Jae-Hwan Kim ◽  
Xiong Liu ◽  
Jhoon Kim ◽  
...  
Keyword(s):  
Air Quality ◽  
Tropospheric Ozone ◽  
Northeast Asia ◽  
Optimal Estimation ◽  
New Delhi ◽  
Air Quality Monitoring ◽  
Environment Monitoring ◽  
Satellite Mission ◽  
Ozone Profile ◽  
Column Ozone

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be launched in 2019 on board the GEO-KOMPSAT (GEOstationary KOrea Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global geostationary constellation of air quality monitoring. To support this air quality satellite mission, we perform the cross-verification of simulated GEMS ozone profile retrievals based on the Optimal Estimation and ozonesonde measurements within the GEMS domain, covering from 5° S (Indonesia) to 45° N (south of the Russian border) and from 75° E to 145° E. The comparison between ozonesonde and GEMS shows a significant dependence on ozonesonde types. Ozonesonde data measured by Modified Brewer-Master (MB-M) at Trivandrum and New Delhi show inconsistent seasonal-variabilities in the tropospheric ozone, compared to latitudinally adjacent stations with Carbon Iodine (CI) and Electrochemical Condensation Cell (ECC). CI ozonesonde measurements are biased relative to ECC measurements by 2–4 DU; a better agreement with GEMS simulations is achieved with ECC measurements. ECC ozone data at Hanoi, Kuala Lump, and Singapore show abnormally worse agreements with simulated GEMS retrievals among ECC measurements. Therefore, ECC ozonesonde measurements at Hong Kong, Pohang, Naha, Sapporo, and Tsukuba are finally identified as an optimal reference. The accuracy of simulated GEMS retrievals is estimated to be ~ 5.0 % for both tropospheric and stratospheric column ozone with the precision of 15 % and 5 %, which meet the GEMS ozone requirements.

scholarly journals Cross-evaluation of GEMS tropospheric ozone retrieval performance using OMI data and the use of an ozonesonde dataset over East Asia for validation

2019 ◽  
Vol 12 (9) ◽  
pp. 5201-5215 ◽  
Author(s):  
Juseon Bak ◽  
Kang-Hyeon Baek ◽  
Jae-Hwan Kim ◽  
Xiong Liu ◽  
Jhoon Kim ◽  
...  
Keyword(s):  
Air Quality ◽  
Tropospheric Ozone ◽  
Optimal Estimation ◽  
New Delhi ◽  
Kuala Lumpur ◽  
Retrieval Performance ◽  
Ozone Monitoring Instrument ◽  
Satellite Mission ◽  
Ozone Profile ◽  
Column Ozone

Abstract. The Geostationary Environment Monitoring Spectrometer (GEMS) is scheduled to be launched in 2019–2020 on board the GEO-KOMPSAT (GEOstationary KOrea Multi-Purpose SATellite)-2B, contributing as the Asian partner of the global geostationary constellation of air quality monitoring. To support this air quality satellite mission, we perform a cross-evaluation of simulated GEMS ozone profile retrievals from OMI (Ozone Monitoring Instrument) data based on the optimal estimation and ozonesonde measurements within the GEMS domain, covering from 5∘ S (Indonesia) to 45∘ N (south of the Russian border) and from 75 to 145∘ E. The comparison between ozonesonde and GEMS shows a significant dependence on ozonesonde types. Ozonesonde data measured by modified Brewer–Mast (MBM) at Trivandrum and New Delhi show inconsistent seasonal variabilities in tropospheric ozone compared to carbon–iodine (CI) and electrochemical condensation cell (ECC) ozonesondes at other stations in a similar latitude regime. CI ozonesonde measurements are negatively biased relative to ECC measurements by 2–4 DU; better agreement is achieved when simulated GEMS ozone retrievals are compared to ECC measurements. ECC ozone data at Hanoi, Kuala Lumpur, and Singapore show abnormally worse agreements with simulated GEMS retrievals than other ECC measurements. Therefore, ECC ozonesonde measurements at Hong Kong, Pohang, Naha, Sapporo, and Tsukuba are finally identified as an optimal reference dataset. The accuracy of simulated GEMS retrievals is estimated to be ∼5.0 % for both tropospheric and stratospheric column ozone with the precision of 15 % and 5 %, which meets the GEMS ozone requirements.

scholarly journals Evaluation of ozone profile and tropospheric ozone retrievals from GEMS and OMI spectra

2013 ◽  
Vol 6 (2) ◽  
pp. 239-249 ◽  
Author(s):  
J. Bak ◽  
J. H. Kim ◽  
X. Liu ◽  
K. Chance ◽  
J. Kim
Keyword(s):  
Spectral Resolution ◽  
Tropospheric Ozone ◽  
Stratospheric Ozone ◽  
A Priori ◽  
Ozone Monitoring Instrument ◽  
Satellite Platform ◽  
Ozone Profile ◽  
Retrieval Errors ◽  
Standard Deviations ◽  
Column Ozone

Abstract. South Korea is planning to launch the GEMS (Geostationary Environment Monitoring Spectrometer) instrument into the GeoKOMPSAT (Geostationary Korea Multi-Purpose SATellite) platform in 2018 to monitor tropospheric air pollutants on an hourly basis over East Asia. GEMS will measure backscattered UV radiances covering the 300–500 nm wavelength range with a spectral resolution of 0.6 nm. The main objective of this study is to evaluate ozone profiles and stratospheric column ozone amounts retrieved from simulated GEMS measurements. Ozone Monitoring Instrument (OMI) Level 1B radiances, which have the spectral range 270–500 nm at spectral resolution of 0.42–0.63 nm, are used to simulate the GEMS radiances. An optimal estimation-based ozone profile algorithm is used to retrieve ozone profiles from simulated GEMS radiances. Firstly, we compare the retrieval characteristics (including averaging kernels, degrees of freedom for signal, and retrieval error) derived from the 270–330 nm (OMI) and 300–330 nm (GEMS) wavelength ranges. This comparison shows that the effect of not using measurements below 300 nm on retrieval characteristics in the troposphere is insignificant. However, the stratospheric ozone information in terms of DFS decreases greatly from OMI to GEMS, by a factor of ∼2. The number of the independent pieces of information available from GEMS measurements is estimated to 3 on average in the stratosphere, with associated retrieval errors of ~1% in stratospheric column ozone. The difference between OMI and GEMS retrieval characteristics is apparent for retrieving ozone layers above ~20 km, with a reduction in the sensitivity and an increase in the retrieval errors for GEMS. We further investigate whether GEMS can resolve the stratospheric ozone variation observed from high vertical resolution Earth Observing System (EOS) Microwave Limb Sounder (MLS). The differences in stratospheric ozone profiles between GEMS and MLS are comparable to those between OMI and MLS below ~3 hPa (~40 km), except with slightly larger biases and larger standard deviations by up to 5%. At pressure altitudes above ~3 hPa, GEMS retrievals show strong influence of a priori and large differences with MLS, which, however, can be sufficiently improved by using better a priori information. The GEMS-MLS differences show negative biases of less than 4% for stratospheric column ozone, with standard deviations of 1–3%, while OMI retrievals show similar agreements with MLS except for 1% smaller biases at middle and high latitudes. Based on the comparisons, we conclude that GEMS will measure tropospheric ozone and stratospheric ozone columns with accuracy comparable to that of OMI and ozone profiles with slightly worse performance than that of OMI below ~3 hPa.

scholarly journals Characterization and Correction of OMPS Nadir Mapper Measurements for Ozone Profile Retrievals

2017 ◽  
Author(s):  
Juseon Bak ◽  
Xiong Liu ◽  
Jae-Hwan Kim ◽  
David P. Haffner ◽  
Kelly Chance ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Degrees Of Freedom ◽  
Optimal Estimation ◽  
Inversion Method ◽  
Radiometric Calibration ◽  
Ozone Monitoring Instrument ◽  
Ozone Profile ◽  
Noise Error ◽  
Significant Wavelength ◽  
Cross Track

Abstract. This paper verifies and corrects the Ozone Mapping and Profiler Suite (OMPS) Nadir Mapper (NM) Level 1B v2.0 measurements with the aim of producing accurate ozone profile retrievals using an optimal estimation based inversion method to fit measurements in the spectral range 302.5–340 nm. The evaluation of available slit functions demonstrates that preflight-measured slit functions well represent OMPS measurements compared to derived Gaussian slit functions. Our initial OMPS fitting residuals contain significant wavelength and cross-track dependent biases, resulting into serious cross-track striping errors in the tropospheric ozone retrievals. To eliminate the systematic component of the fitting residuals, we apply “soft calibration” to OMPS radiances. With the soft calibration the amplitude of fitting residuals decreases from ~ 1 % to 0.2 % over low/mid latitudes, and thereby the consistency of tropospheric ozone retrievals between OMPS and the Ozone Monitoring Instrument (OMI) is substantially improved. A common mode correction is also implemented for additional radiometric calibration; it improves retrievals especially at high latitudes where the amplitude of fitting residuals decreases by a factor of ~ 2. We estimate the floor noise error of OMPS measurements from standard deviations of the fitting residuals. The derived error in the Huggins band (~ 0.1 %) is twice the OMPS L1B measurement error. OMPS floor noise errors better constrains our retrievals, leading to improving information content of ozone and reducing fitting residuals. The final precision of the fitting residuals is less than 0.1 % in the low/mid latitude, with ~ 1 degrees of freedom for signal for the tropospheric ozone, meeting the general requirements for successful tropospheric ozone retrievals.

scholarly journals Characterization and correction of OMPS nadir mapper measurements for ozone profile retrievals

2017 ◽  
Vol 10 (11) ◽  
pp. 4373-4388 ◽  
Author(s):  
Juseon Bak ◽  
Xiong Liu ◽  
Jae-Hwan Kim ◽  
David P. Haffner ◽  
Kelly Chance ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Degrees Of Freedom ◽  
Optimal Estimation ◽  
Inversion Method ◽  
Radiometric Calibration ◽  
Ozone Monitoring Instrument ◽  
Noise Floor ◽  
Middle Latitudes ◽  
Ozone Profile ◽  
Cross Track

Abstract. This paper verifies and corrects the Ozone Mapping and Profiler Suite (OMPS) nadir mapper (NM) level 1B v2.0 measurements with the aim of producing accurate ozone profile retrievals using an optimal-estimation-based inversion method to fit measurements in the spectral range 302.5–340 nm. The evaluation of available slit functions demonstrates that preflight-measured slit functions represent OMPS measurements well compared to derived Gaussian slit functions. Our initial OMPS fitting residuals contain significant wavelength and cross-track-dependent biases, resulting in serious cross-track striping errors in the tropospheric ozone retrievals. To eliminate the systematic component of the fitting residuals, we apply soft calibration to OMPS radiances. With the soft calibration the amplitude of fitting residuals decreases from  ∼  1 to 0.2 % over low and middle latitudes, and thereby the consistency of tropospheric ozone retrievals between OMPS and the Ozone Monitoring Instrument (OMI) is substantially improved. A common mode correction is also implemented for additional radiometric calibration; it improves retrievals especially at high latitudes where the amplitude of fitting residuals decreases by a factor of  ∼  2. We estimate the noise floor error of OMPS measurements from standard deviations of the fitting residuals. The derived error in the Huggins band ( ∼  0.1 %) is twice the OMPS L1B measurement error. OMPS noise floor errors constrain our retrievals better, leading to improving information content of ozone and reducing fitting residuals. The final precision of the fitting residuals is less than 0.1 % in the low and middle latitudes, with  ∼  1 degrees of freedom for signal for the tropospheric ozone, meeting the general requirements for successful tropospheric ozone retrievals.

scholarly journals Evaluation of ozone profile and tropospheric ozone retrievals from GEMS and OMI spectra

2012 ◽  
Vol 5 (5) ◽  
pp. 6733-6762 ◽  
Author(s):  
J. Bak ◽  
J. H. Kim ◽  
X. Liu ◽  
K. Chance ◽  
J. Kim
Keyword(s):  
Spectral Resolution ◽  
Tropospheric Ozone ◽  
Degrees Of Freedom ◽  
Stratospheric Ozone ◽  
A Priori ◽  
Ozone Monitoring Instrument ◽  
Ozone Profile ◽  
Retrieval Errors ◽  
Standard Deviations ◽  
Column Ozone

Abstract. Korea is planning to launch the GEMS (Geostationary Environment Monitoring Spectrometer) instrument into a Geostationary (GEO) platform in 2018 to monitor tropospheric air pollutants on an hourly basis over East Asia. GEMS will measure backscattered UV radiances covering the 300–500 nm wavelength range with a spectral resolution of 0.6 nm. The main objective of this study is to evaluate ozone profiles and stratospheric column ozone amounts retrieved from simulated GEMS measurements. Ozone Monitoring Instrument (OMI) Level 1B radiances, which have the spectral range 270–500 nm at spectral resolution of 0.42–0.63 nm, are used to simulate the GEMS radiances. An optimal estimation-based ozone profile algorithm is used to retrieve ozone profiles from simulated GEMS radiances. Firstly, we compare the retrieval characteristics (including averaging kernels, degrees of freedom for signal, and retrieval error) derived from the 270–330 nm (OMI) and 300–330 nm (GEMS) wavelength ranges. This comparison shows that the effect of not using measurements below 300 nm on tropospheric ozone retrievals is insignificant. However, the stratospheric ozone information decreases greatly from OMI to GEMS, by a factor of ∼2. The number of the independent pieces of information available from GEMS measurements is estimated to 3 on average in the stratosphere, with associated retrieval errors of ∼1% in stratospheric column ozone. The difference between OMI and GEMS retrieval characteristics is apparent for retrieving ozone layers above ∼20 km, with a reduction in the sensitivity and an increase in the retrieval errors for GEMS. We further investigate whether GEMS can resolve the stratospheric ozone variation observed from high vertical resolution EOS Microwave Limb Sounder (MLS). The differences in stratospheric ozone profiles between GEMS and MLS are comparable to those between OMI and MLS above ∼3 hPa (∼40 km) except with slightly larger biases and larger standard deviations by up to 5%. At pressure altitudes above ∼3 hPa, GEMS retrievals show strong influence of a priori and large differences with MLS, which, however, can be sufficiently improved by using better a priori information. The GEMS-MLS differences show negative biases of less than 4% for stratospheric column ozone, with standard deviations of 1–3%, while OMI retrievals show similar agreements with MLS except for 1% smaller biases at mid and high latitudes. Based on the comparisons, we conclude that GEMS will measure tropospheric ozone and stratospheric ozone columns with accuracy comparable to that of OMI and ozone profiles with slightly worse performance than that of OMI below ∼3 hPa.

scholarly journals An Observing System Simulation Experiment Framework for Air Quality Forecasts in Northeast Asia: A Case Study Utilizing Virtual Geostationary Environment Monitoring Spectrometer and Surface Monitored Aerosol Data

2022 ◽  
Vol 14 (2) ◽  
pp. 389
Author(s):  
Hyeon-Kook Kim ◽  
Seunghee Lee ◽  
Kang-Ho Bae ◽  
Kwonho Jeon ◽  
Myong-In Lee ◽  
...  
Keyword(s):  
Air Quality ◽  
South Korea ◽  
System Simulation ◽  
Northeast Asia ◽  
Gobi Desert ◽  
Observation Data ◽  
Environment Monitoring ◽  
Study Results ◽  
Current Monitoring

Prior knowledge of the effectiveness of new observation instruments or new data streams for air quality can contribute significantly to shaping the policy and budget planning related to those instruments and data. In view of this, one of the main purposes of the development and application of the Observing System Simulation Experiments (OSSE) is to assess the potential impact of new observations on the quality of the current monitoring or forecasting systems, thereby making this framework valuable. This study introduces the overall OSSE framework established to support air quality forecasting and the details of its individual components. Furthermore, it shows case study results from Northeast Asia and the potential benefits of the new observation data scenarios on the PM2.5 forecasting skills, including the PM data from 200 virtual monitoring sites in the Gobi Desert and North Korean non-forest areas (NEWPM) and the aerosol optical depths (AOD) data from South Korea’s Geostationary Environment Monitoring Spectrometer (GEMS AOD). Performance statistics suggest that the concurrent assimilation of the NEWPM and the PM data from current monitoring sites in China and South Korea can improve the PM2.5 concentration forecasts in South Korea by 66.4% on average for October 2017 and 95.1% on average for February 2018. Assimilating the GEMS AOD improved the performance of the PM2.5 forecasts in South Korea for October 2017 by approximately 68.4% (~78.9% for February 2018). This OSSE framework is expected to be continuously implemented to verify its utilization potential for various air quality observation systems and data scenarios. Hopefully, this kind of application result will aid environmental researchers and decision-makers in performing additional in-depth studies for the improvement of PM air quality forecasts.

scholarly journals Robust Inferential Techniques Applied to the Analysis of the Tropospheric Ozone Concentration in an Urban Area

Sensors ◽  
2021 ◽  
Vol 21 (1) ◽  
pp. 277
Author(s):  
Wilmar Hernandez ◽  
Alfredo Mendez ◽  
Vicente González-Posadas ◽  
José Luis Jiménez-Martín ◽  
Iván Menes Camejo
Keyword(s):  
Air Quality ◽  
Confidence Intervals ◽  
Tropospheric Ozone ◽  
Air Quality Monitoring ◽  
Robust Methods ◽  
Time Period ◽  
Measurement Results ◽  
Robust Techniques ◽  
The City ◽  
Better Than

This paper analyzes 12 years of tropospheric ozone (O3) concentration measurements using robust techniques. The measurements were taken at an air quality monitoring station called Belisario, which is in Quito, Ecuador; the data collection time period was 1 January 2008 to 31 December 2019, and the measurements were carried out using photometric O3 analyzers. Here, the measurement results were used to build variables that represented hours, days, months, and years, and were then classified and categorized. The index of air quality (IAQ) of the city was used to make the classifications, and robust and nonrobust confidence intervals were used to make the categorizations. Furthermore, robust analysis methods were compared with classical methods, nonparametric methods, and bootstrap-based methods. The results showed that the analysis using robust methods is better than the analysis using nonrobust methods, which are not immune to the influence of extreme observations. Using all of the aforementioned methods, confidence intervals were used to both establish and quantify differences between categories of the groups of variables under study. In addition, the central tendency and variability of the O3 concentration at Belisario station were exhaustively analyzed, concluding that said concentration was stable for years, highly variable for months and hours, and slightly changing between the days of the week. Additionally, according to the criteria established by the IAQ, it was shown that in Quito, the O3 concentration levels during the study period were not harmful to human health.

scholarly journals IOT based Air Quality Monitoring System Using MQ135 and MQ7 with Machine Learning Analysis

2019 ◽  
Vol 20 (4) ◽  
pp. 599-606 ◽  
Author(s):  
Kinnera Bharath Kumar Sai ◽  
Somula Rama Subbareddy ◽  
Ashish Kumar Luhach
Keyword(s):  
Machine Learning ◽  
Air Quality ◽  
State Of The Art ◽  
New Delhi ◽  
Air Quality Monitoring ◽  
High Pollution ◽  
Iot Platforms ◽  
The Cost ◽  
Learning Analysis ◽  
Stroke Engine

This paper deals with measuring the Air Quality using MQ135 sensor along with Carbon Monoxide CO using MQ7 sensor. Measuring Air Quality is an important element for bringing awareness to take care of the future generations and for a healthier life. Based on this, Government of India has already taken certain measures to ban Single Stroke and Two Stroke Engine based motorcycles which are emitting high pollution. We are trying to implement a system using IoT platforms like Thingspeak or Cayenne in order to bring awareness to every individual about the harm we are doing to our environment. Already, New Delhi is remarked as the most pollution city in the world recording Air Quality above 300 PPM. We have used easiest platform like Thingspeak and set the dashboard to public such that everyone can come to know the Air Quality at the location where the system is installed. Machine Learning analysis brings us a lot of depth in understanding the information that we obtained from the data. Moreover, we are proviing a reducement of the cost of components versus the state of the art.

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