scholarly journals Total column water vapor retrieval for Global Ozone Monitoring Experience-2 (GOME-2) visible blue observations

2020 ◽  
Vol 13 (8) ◽  
pp. 4169-4193
Author(s):  
Ka Lok Chan ◽  
Pieter Valks ◽  
Sander Slijkhuis ◽  
Claas Köhler ◽  
Diego Loyola
Keyword(s):  
Water Vapor ◽  
Transport Model ◽  
A Priori ◽  
Spectral Band ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Blue Band ◽  
Data Set ◽  
Ozone Monitoring ◽  
Total Column

Abstract. We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Metop satellites. The blue band algorithm allows the retrieval of water vapor from sensors which do not cover longer wavelengths, such as the Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel-5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical columns using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from the chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME-2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing it to the operational product retrieved in the red spectral band, sun photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5.

scholarly journals Total column water vapor retrieval for GOME-2 visible blue observations

2020 ◽  
Author(s):  
Ka Lok Chan ◽  
Pieter Valks ◽  
Sander Slijkhuis ◽  
Claas Köhler ◽  
Diego Loyola
Keyword(s):  
Water Vapor ◽  
Transport Model ◽  
A Priori ◽  
Spectral Band ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Blue Band ◽  
Data Set ◽  
Ozone Monitoring ◽  
Total Column

Abstract. We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the EUMETSAT MetOp satellites. The blue band algorithm allows retrieval of water vapor from sensors which do not cover longer wavelengths, such as Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel-5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME-2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing to the operational product retrieved in the red spectral band, sun-photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results, and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5.

Total column water vapor retrieval for TROPOMI/S5P observations in the visible blue band

2020 ◽  
Author(s):  
Ka Lok Chan ◽  
Sander Slijkhuis ◽  
Pieter Valks ◽  
Claas Köhler ◽  
Diego Loyola
Keyword(s):  
Water Vapor ◽  
Transport Model ◽  
A Priori ◽  
Spectral Band ◽  
Lower Troposphere ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Blue Band ◽  
Data Set ◽  
Total Column

<p>We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue band for the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5P) satellite. Retrieving water vapor columns in the blue band has numerous advantages over longer wavelengths. Measurements in the blue band are more sensitive at lower troposphere over oceans due to higher surface albedo at this wavelength band. In addition, no correction for spectral saturation effects is required as water vapor is optically thin in this spectral band. The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM).</p><p>The new algorithm is applied to TROPOMI observations to retrieve TCWV. Due to the long measurement record of GOME-2, the new algorithm is also used to retrieve TCWV from GOME-2. The TCWV data set is validated by comparing to the GOME-2 TCWV operational product retrieved in the red spectral band, MODIS and SSMIS satellite observations. In addition, the new TCWV data set is also compared to ground based sun-photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results. Therefore, it was selected for the S5P Processor Algorithm Laboratory (PAL) project as a future operational product. This algorithm can also be used for the forthcoming Copernicus Sentinel S4 and S5 missions.</p>

TROPOMI observations of total column water vapor

2021 ◽  
Author(s):  
Ka Lok Chan ◽  
Sander Slijkhuis ◽  
Katerina Garane ◽  
Pieter Valks ◽  
Diego Loyola
Keyword(s):  
Water Vapor ◽  
Pearson Correlation ◽  
A Priori ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Blue Band ◽  
Monitoring Instrument ◽  
Ozone Monitoring ◽  
Typical Satellite ◽  
Total Column

<p>We present the total column water vapor (TCWV) retrieval for the TROPOspheric Monitoring Instrument (TROPOMI) observations in the blue band. The retrieval was first developed to retrieve TCWV from Global Ozone Monitoring Experience 2 (GOME-2). We have modified the settings of the retrieval to adapt it for TROPOMI observations. The TROPOMI TCWV retrieval algorithm consists of two major steps. The first step is the retrieval of water vapor slant columns by applying the differential optical absorption spectroscopy (DOAS) technique to TROPOMI observations in the blue band. The retrieved water vapor slant columns are then converted to vertical columns using air mass factors (AMFs). An iterative optimization has been developed to dynamically find the optimal a priori water vapor profile for AMF calculation. The dynamic search algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column amount. This makes the algorithm better suited for climate studies compared to typical satellite retrievals with static a priori or vertical profile information from the chemistry transport model (CTM). Details of the TCWV retrieval are presented. The TCWV retrieval algorithm is applied to TROPOMI observations. The results are validated by comparing to Ozone Monitoring Instrument (OMI), GOME-2 and Special Sensor Microwave Imager Sounder (SSMIS) satellite observations. TCWV derived from TROPOMI observations agree well with the other data sets with Pearson correlation coefficient (R) ranging from 0.94 to 0.99. The correlation is slight better during winter time of the northern hemisphere. Small discrepancies are found among TROPOMI, OMI, GOME-2 and SSMIS observations. The discrepancies are mainly due to differences in measurement time and cloud filtering. More detailed validation against ground based sun-photometer observations are presented separately in this session*.</p><p> </p><p>*see the respective abstract by Katerina Garane.</p>

scholarly journals Extending methane profiles from aircraft into the stratosphere for satellite total column validation using the ECMWF C-IFS and TOMCAT/SLIMCAT 3-D model

2017 ◽  
Vol 17 (11) ◽  
pp. 6663-6678 ◽  
Author(s):  
Shreeya Verma ◽  
Julia Marshall ◽  
Mark Parrington ◽  
Anna Agustí-Panareda ◽  
Sebastien Massart ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Transport Model ◽  
A Priori ◽  
Atmospheric Composition ◽  
Random Errors ◽  
Chemical Transport Model ◽  
Spatial Coverage ◽  
Forecasting System ◽  
The Impact ◽  
Total Column

Abstract. Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 and 34 ppb, respectively). In comparison, the a priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

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 NDACC harmonized formaldehyde time series from 21 FTIR stations covering a wide range of column abundances

2018 ◽  
Vol 11 (9) ◽  
pp. 5049-5073 ◽  
Author(s):  
Corinne Vigouroux ◽  
Carlos Augusto Bauer Aquino ◽  
Maite Bauwens ◽  
Cornelis Becker ◽  
Thomas Blumenstock ◽  
...  
Keyword(s):  
Time Series ◽  
Transport Model ◽  
Limit Of Detection ◽  
Atmospheric Composition ◽  
Good Precision ◽  
Data Set ◽  
Spatial Coverage ◽  
Combining Data ◽  
Wide Range ◽  
Total Column

Abstract. Among the more than 20 ground-based FTIR (Fourier transform infrared) stations currently operating around the globe, only a few have provided formaldehyde (HCHO) total column time series until now. Although several independent studies have shown that the FTIR measurements can provide formaldehyde total columns with good precision, the spatial coverage has not been optimal for providing good diagnostics for satellite or model validation. Furthermore, these past studies used different retrieval settings, and biases as large as 50 % can be observed in the HCHO total columns depending on these retrieval choices, which is also a weakness for validation studies combining data from different ground-based stations.For the present work, the HCHO retrieval settings have been optimized based on experience gained from past studies and have been applied consistently at the 21 participating stations. Most of them are either part of the Network for the Detection of Atmospheric Composition Change (NDACC) or under consideration for membership. We provide the harmonized settings and a characterization of the HCHO FTIR products. Depending on the station, the total systematic and random uncertainties of an individual HCHO total column measurement lie between 12 % and 27 % and between 1 and 11×1014 molec cm−2, respectively. The median values among all stations are 13 % and 2.9×1014 molec cm−2 for the total systematic and random uncertainties.This unprecedented harmonized formaldehyde data set from 21 ground-based FTIR stations is presented and its comparison with a global chemistry transport model shows consistency in absolute values as well as in seasonal cycles. The network covers very different concentration levels of formaldehyde, from very clean levels at the limit of detection (few 1013 molec cm−2) to highly polluted levels (7×1016 molec cm−2). Because the measurements can be made at any time during daylight, the diurnal cycle can be observed and is found to be significant at many stations. These HCHO time series, some of them starting in the 1990s, are crucial for past and present satellite validation and will be extended in the coming years for the next generation of satellite missions.

scholarly journals Extending methane profiles from aircraft into the stratosphere for satellite total column validation: A comparative analysis of different data sources

2016 ◽  
Author(s):  
Shreeya Verma ◽  
Julia Marshall ◽  
Mark Parrington ◽  
Anna Agusti-Panareda ◽  
Sebastien Massart ◽  
...  
Keyword(s):  
Greenhouse Gases ◽  
Transport Model ◽  
A Priori ◽  
Data Sources ◽  
Atmospheric Composition ◽  
Random Errors ◽  
Chemical Transport Model ◽  
Spatial Coverage ◽  
The Impact ◽  
Total Column

Abstract. Airborne observations of greenhouse gases are a very useful reference for validation of satellite-based column-averaged dry air mole fraction data. However, since the aircraft data are available only up to about 9–13 km altitude, these profiles do not fully represent the depth of the atmosphere observed by satellites and therefore need to be extended synthetically into the stratosphere. In the near future, observations of CO2 and CH4 made from passenger aircraft are expected to be available through the In-Service Aircraft for a Global Observing System (IAGOS) project. In this study, we analyse three different data sources that are available for the stratospheric extension of aircraft profiles by comparing the error introduced by each of them into the total column and provide recommendations regarding the best approach. First, we analyse CH4 fields from two different models of atmospheric composition – the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System for Composition (C-IFS) and the TOMCAT/SLIMCAT 3-D chemical transport model. Secondly, we consider scenarios that simulate the effect of using CH4 climatologies such as those based on balloons or satellite limb soundings. Thirdly, we assess the impact of using a-priori profiles used in the satellite retrievals for the stratospheric part of the total column. We find that the models considered in this study have a better estimation of the stratospheric CH4 as compared to the climatology-based data and the satellite a-priori profiles. Both the C-IFS and TOMCAT models have a bias of about −9 ppb at the locations where tropospheric vertical profiles will be measured by IAGOS. The C-IFS model, however, has a lower random error (6.5 ppb) than TOMCAT (12.8 ppb). These values are well within the minimum desired accuracy and precision of satellite total column XCH4 retrievals (10 ppb and 34 ppb, respectively). In comparison, the a-priori profile from the University of Leicester Greenhouse Gases Observing Satellite (GOSAT) Proxy XCH4 retrieval and climatology-based data introduce larger random errors in the total column, being limited in spatial coverage and temporal variability. Furthermore, we find that the bias in the models varies with latitude and season. Therefore, applying appropriate bias correction to the model fields before using them for profile extension is expected to further decrease the error contributed by the stratospheric part of the profile to the total column.

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.

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 Assimilation of surface NO<sub>2</sub> and O<sub>3</sub> observations into the SILAM chemistry transport model

2015 ◽  
Vol 8 (2) ◽  
pp. 191-203 ◽  
Author(s):  
J. Vira ◽  
M. Sofiev
Keyword(s):  
Transport Model ◽  
Time Of Day ◽  
Covariance Matrices ◽  
Atmospheric Composition ◽  
Integrated Modelling ◽  
Daily Maximum ◽  
Observation Error ◽  
Data Set ◽  
Chemistry Transport Model ◽  
Error Covariance

Abstract. This paper describes the assimilation of trace gas observations into the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition) using the 3D-Var method. Assimilation results for the year 2012 are presented for the prominent photochemical pollutants ozone (O3) and nitrogen dioxide (NO2). Both species are covered by the AirBase observation database, which provides the observational data set used in this study. Attention was paid to the background and observation error covariance matrices, which were obtained primarily by the iterative application of a posteriori diagnostics. The diagnostics were computed separately for 2 months representing summer and winter conditions, and further disaggregated by time of day. This enabled the derivation of background and observation error covariance definitions, which included both seasonal and diurnal variation. The consistency of the obtained covariance matrices was verified using χ2 diagnostics. The analysis scores were computed for a control set of observation stations withheld from assimilation. Compared to a free-running model simulation, the correlation coefficient for daily maximum values was improved from 0.8 to 0.9 for O3 and from 0.53 to 0.63 for NO2.

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