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>

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.

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.

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 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 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 Methanol from TES global observations: retrieval algorithm and seasonal and spatial variability

2012 ◽  
Vol 12 (5) ◽  
pp. 11823-11859 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
M. W. Shephard ◽  
D. B. Millet ◽  
M. Luo ◽  
K. C. Wells ◽  
...  
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
A Priori ◽  
Lower Troposphere ◽  
Initial Guess ◽  
Retrieval Algorithm ◽  
Atmospheric Conditions ◽  
Chemical Transport Model ◽  
Model Retrieval ◽  
Peak Sensitivity

Abstract. We present a detailed description of the TES methanol (CH3OH) retrieval algorithm, along with initial global results showing the seasonal and spatial distribution of methanol in the lower troposphere. The full development of the TES methanol retrieval is described, including microwindow selection, error analysis, and the utilization of a priori and initial guess information provided by the GEOS-Chem chemical transport model. Retrieval simulations and a sensitivity analysis using the developed retrieval strategy show that TES: (i) generally provides between 0.5 and 1.0 pieces of information, (ii) is sensitive in the lower troposphere with peak sensitivity typically occurring between ~900–700 hPa (~1–3 km) at a vertical resolution of ~5 km, (iii) has a limit of detectability between 0.5 and 1.0 ppbv Representative Volume Mixing Ratio (RVMR) depending on the atmospheric conditions, corresponding roughly to a profile with a maximum concentration of at least 1 to 2 ppbv, and (iv) in a simulation environment has a mean bias of 0.16 ppbv and a standard deviation of 0.34 ppbv. Applying the newly-derived TES retrieval globally and comparing the results with corresponding GEOS-Chem output, we find generally consistent large-scale patterns between the two. However, TES often reveals higher methanol concentrations than predicted in the Northern Hemisphere spring, summer and fall. In the Southern Hemisphere, the TES methanol observations indicate a model overestimate over the bulk of South America from December through July, and a model underestimate during the biomass burning season.

scholarly journals Methanol from TES global observations: retrieval algorithm and seasonal and spatial variability

2012 ◽  
Vol 12 (17) ◽  
pp. 8189-8203 ◽  
Author(s):  
K. E. Cady-Pereira ◽  
M. W. Shephard ◽  
D. B. Millet ◽  
M. Luo ◽  
K. C. Wells ◽  
...  
Keyword(s):  
Large Scale ◽  
Transport Model ◽  
A Priori ◽  
Lower Troposphere ◽  
Initial Guess ◽  
Retrieval Algorithm ◽  
Atmospheric Conditions ◽  
Chemical Transport Model ◽  
Model Retrieval ◽  
Peak Sensitivity

Abstract. We present a detailed description of the TES methanol (CH3OH) retrieval algorithm, along with initial global results showing the seasonal and spatial distribution of methanol in the lower troposphere. The full development of the TES methanol retrieval is described, including microwindow selection, error analysis, and the utilization of a priori and initial guess information provided by the GEOS-Chem chemical transport model. Retrieval simulations and a sensitivity analysis using the developed retrieval strategy show that TES: (i) generally provides less than 1.0 piece of information, (ii) is sensitive in the lower troposphere with peak sensitivity typically occurring between ~900–700 hPa (~1–3 km) at a vertical resolution of ~5 km, (iii) has a limit of detectability between 0.5 and 1.0 ppbv Representative Volume Mixing Ratio (RVMR) depending on the atmospheric conditions, corresponding roughly to a profile with a maximum concentration of at least 1 to 2 ppbv, and (iv) in a simulation environment has a mean bias of 0.16 ppbv with a standard deviation of 0.34 ppbv. Applying the newly derived TES retrieval globally and comparing the results with corresponding GEOS-Chem output, we find generally consistent large-scale patterns between the two. However, TES often reveals higher methanol concentrations than simulated in the Northern Hemisphere spring, summer and fall. In the Southern Hemisphere, the TES methanol observations indicate a model overestimate over the bulk of South America from December through July, and a model underestimate during the biomass burning season.

scholarly journals Glyoxal vertical columns from GOME-2 backscattered light measurements and comparisons with a global model

2010 ◽  
Vol 10 (24) ◽  
pp. 12059-12072 ◽  
Author(s):  
C. Lerot ◽  
T. Stavrakou ◽  
I. De Smedt ◽  
J.-F. Müller ◽  
M. Van Roozendael
Keyword(s):  
Transport Model ◽  
Total Error ◽  
A Priori ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Chemical Transport Model ◽  
Vertical Column ◽  
Air Mass ◽  
Tropical Regions

Abstract. Glyoxal vertical column densities have been retrieved from nadir backscattered radiances measured from 2007 to 2009 by the spaceborne GOME-2/METOP-A sensor. The retrieval algorithm is based on the DOAS technique and optimized settings have been used to determine glyoxal slant columns. The liquid water absorption is accounted for using a two-step DOAS approach, leading to a drastic improvement of the fit quality over remote clear water oceans. Air mass factors are calculated by means of look-up tables of weighting functions pre-calculated with the LIDORT v3.3 radiative transfer model and using a priori glyoxal vertical distributions provided by the IMAGESv2 chemical transport model. The total error estimate comprises random and systematic errors associated to the DOAS fit, the air mass factor calculation and the cloud correction. The highest glyoxal vertical column densities are mainly observed in continental tropical regions, while the mid-latitude columns strongly depend on the season with maximum values during warm months. An anthropogenic signature is also observed in highly populated regions of Asia. Comparisons with glyoxal columns simulated with IMAGESv2 in different regions of the world generally point to a missing glyoxal source, most probably of biogenic origin.

scholarly journals Glyoxal vertical columns from GOME-2 backscattered light measurements and comparisons with a global model

2010 ◽  
Vol 10 (9) ◽  
pp. 21147-21188 ◽  
Author(s):  
C. Lerot ◽  
T. Stavrakou ◽  
I. De Smedt ◽  
J.-F. Müller ◽  
M. Van Roozendael
Keyword(s):  
Transport Model ◽  
Total Error ◽  
A Priori ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Chemical Transport Model ◽  
Vertical Column ◽  
Air Mass ◽  
Tropical Regions

Abstract. Glyoxal vertical column densities have been retrieved from nadir backscattered radiances measured from 2007 to 2009 by the spaceborne GOME-2/METOP-A sensor. The retrieval algorithm is based on the DOAS technique and optimized settings have been used to determine glyoxal slant columns. The liquid water absorption is accounted for using a two-step DOAS approach, leading to a drastic improvement of the fit quality over remote clear water oceans. Air mass factors are calculated by means of look-up tables of weighting functions pre-calculated with the LIDORT v3.3 radiative transfer model and using a priori glyoxal vertical distributions provided by the IMAGESv2 chemical transport model. The total error estimate comprises random and systematic errors associated to the DOAS fit, the air mass factor calculation and the cloud correction. The highest glyoxal vertical column densities are mainly observed in continental tropical regions, while the mid-latitude columns strongly depend on the season with maximum values during warm months. An anthropogenic signature is also observed in highly populated regions of Asia. Comparisons with glyoxal columns simulated with IMAGESv2 in different regions of the world generally point to a missing glyoxal source, most probably of biogenic origin.

scholarly journals Total column water vapour measurements from GOME-2 MetOp-A and MetOp-B

2015 ◽  
Vol 8 (3) ◽  
pp. 1111-1133 ◽  
Author(s):  
M. Grossi ◽  
P. Valks ◽  
D. Loyola ◽  
B. Aberle ◽  
S. Slijkhuis ◽  
...  
Keyword(s):  
Water Vapour ◽  
Temporal Stability ◽  
Optical Absorption Spectroscopy ◽  
Retrieval Algorithm ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Vertical Column Density ◽  
Climate Analysis ◽  
Total Column

Abstract. 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 aboard EUMETSAT's MetOp-A and MetOp-B satellites and perform an extensive inter-comparison in order to evaluate their consistency and temporal stability. For the analysis, the GOME-2 data sets are generated by DLR in the framework of the EUMETSAT O3M-SAF project 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 a H2O and O2 retrieval for the computation of the trace gas vertical column density. We introduce a further enhancement in the quality of the H2O total column by optimizing the cloud screening and developing an empirical correction in order to eliminate the instrument scan angle dependencies. The overall consistency between measurements from the newer GOME-2 instrument on board of the MetOp-B platform and the GOME-2/MetOp-A data is evaluated in the overlap period (December 2012–June 2014). Furthermore, we compare GOME-2 results with independent TCWV data from the ECMWF ERA-Interim reanalysis, with SSMIS satellite measurements during the full period January 2007–June 2014 and against the combined SSM/I + MERIS satellite data set developed in the framework of the ESA DUE GlobVapour project (January 2007–December 2008). Global mean biases as small as ±0.035 g cm−2 are found between GOME-2A and all other data sets. The combined SSM/I-MERIS sample and the ECMWF ERA-Interim data set are typically drier than the GOME-2 retrievals, while on average GOME-2 data overestimate the SSMIS measurements by only 0.006 g cm−2. However, the size of these biases is seasonally dependent. Monthly average differences can be as large as 0.1 g cm−2, based on the analysis against SSMIS measurements, which include only data over ocean. The seasonal behaviour is not as evident when comparing GOME-2 TCWV to the ECMWF ERA-Interim and the SSM/I+MERIS data sets, since the different biases over land and ocean surfaces partly compensate each other. Studying two exemplary months, we estimate regional differences and identify a very good agreement between GOME-2 total columns and all three data sets, especially for land areas, although some discrepancies (bias larger than ±0.5 g cm−2) over ocean and over land areas with high humidity or a relatively large surface albedo are observed.

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