scholarly journals Total columns of H<sub>2</sub>O measured from the ground and from space at Observatoire de Haute-Provence in France (44° N)

2013 ◽  
Vol 6 (3) ◽  
pp. 4249-4277
Author(s):  
S. Alkasm ◽  
A. Sarkissian ◽  
P. Keckhut ◽  
A. Pazmino ◽  
F. Goutail ◽  
...  
Keyword(s):  
Water Vapour ◽  
Column Density ◽  
Data Sets ◽  
Satellite Measurements ◽  
Vertical Column ◽  
Vertical Column Density ◽  
Satellite Sensors ◽  
Global Agreement ◽  
Density Of Water ◽  
Total Column

Abstract. In this work, we compare vertical column density of water vapour measured at Observatoire de Haute-Provence, Southern France (5° 42' E, +43° 55' N). Data were obtained by three satellite sensors, GOME, GOME 2 and SCIAMACHY, and by two ground-based spectrometers, Elodie and SAOZ. These five instruments are able to measure total column density of water vapour in the visible and have different principles of observation. All these instruments reproduce the total column water vapour with good accuracy. The mean difference between the satellite measurements, ground-based measurements, and between both types, are quantified. The diurnal cycle of water vapour above the station and its variability with latitude have been investigated. The differences between these data sets are due sometimes to the differences in the time of the measurements, or to the differences in the geometry of observations, or also due to both effects. The effect of land and sea and the effect of the season on the total column water vapour has been analysed. The global agreement between our data sets range from 10% in summer to 25% in winter, improved significantly when observations are closer in time and location.

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 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.

Total Column Water Vapour Retrieval from S-5P/TROPOMI in the Visible Blue Spectral Range

2020 ◽  
Author(s):  
Christian Borger ◽  
Steffen Beirle ◽  
Steffen Dörner ◽  
Holger Sihler ◽  
Thomas Wagner
Keyword(s):  
Water Vapour ◽  
Spectral Range ◽  
Climate Modeling ◽  
Weather Prediction ◽  
A Priori ◽  
Data Sets ◽  
Vertical Column ◽  
Linearized Scheme ◽  
The Impact ◽  
Total Column

&lt;div&gt; &lt;p&gt;Atmospheric water plays a key role for the Earth&amp;#8217;s energy budget and temperature distribution via radiative effects (clouds and vapour) and latent heat transport. Thus, the distribution and transport of water vapour are closely linked to atmospheric dynamics on different spatio-temporal scales. In this context, global monitoring of the water vapour distribution is essential for numerical weather prediction, climate modeling and a better understanding of climate feedbacks.&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;Here, we present a total column water vapour (TCWV) retrieval using the absorption structures of water vapour in the visible blue spectral range. The retrieval consists of the common two-step DOAS approach: first the spectral analysis is performed within a linearized scheme. Then, the retrieved slant column densities are converted to vertical column densities (VCDs) using an iterative scheme for the water vapour a priori profile shape which is based on an empirical parameterization of the water vapour scale height.&amp;#160;&amp;#160;&lt;/p&gt; &lt;/div&gt;&lt;div&gt; &lt;p&gt;We apply this novel retrieval to measurements of the TROPOspheric Monitoring Instrument (TROPOMI) onboard ESA&amp;#8216;s Sentinel-5P satellite and compare our retrieved H&lt;sub&gt;2&lt;/sub&gt;O VCDs to a variety of different reference data sets. Furthermore we present a detailed characterization of this retrieval including theoretical error estimations for different observation conditions. In addition we investigate the impact of different input data sets (e.g. surface albedo) on the retrieved H&lt;sub&gt;2&lt;/sub&gt;O VCDs.&amp;#160;&amp;#160;&lt;/p&gt; &lt;/div&gt;

scholarly journals Quantitative bias estimates for tropospheric NO<sub>2</sub> columns retrieved from SCIAMACHY, OMI, and GOME-2 using a common standard for East Asia

2012 ◽  
Vol 5 (10) ◽  
pp. 2403-2411 ◽  
Author(s):  
H. Irie ◽  
K. F. Boersma ◽  
Y. Kanaya ◽  
H. Takashima ◽  
X. Pan ◽  
...  
Keyword(s):  
Regression Line ◽  
Correlation Coefficients ◽  
Satellite Observation ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Vertical Column ◽  
Vertical Column Density ◽  
Satellite Sensors ◽  
Single Set ◽  
Japan And China

Abstract. For the intercomparison of tropospheric nitrogen dioxide (NO2) vertical column density (VCD) data from three different satellite sensors (SCIAMACHY, OMI, and GOME-2), we use a common standard to quantitatively evaluate the biases for the respective data sets. As the standard, a regression analysis using a single set of collocated ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations at several sites in Japan and China from 2006–2011 is adopted. Examinations of various spatial coincidence criteria indicates that the slope of the regression line can be influenced by the spatial distribution of NO2 over the area considered. While the slope varies systematically with the distance between the MAX-DOAS and satellite observation points around Tokyo in Japan, such a systematic dependence is not clearly seen and correlation coefficients are generally higher in comparisons at sites in China. On the basis of these results, we focus mainly on comparisons over China and estimate the biases in SCIAMACHY, OMI, and GOME-2 data (TM4NO2A and DOMINO version 2 products) against the MAX-DOAS observations to be −5 ± 14%, −10 ± 14%, and +1 ± 14%, respectively, which are all small and insignificant. We suggest that these small biases now allow for analyses combining these satellite data for air quality studies, which are more systematic and quantitative than previously possible.

scholarly journals First quantitative bias estimates for tropospheric NO<sub>2</sub> columns retrieved from SCIAMACHY, OMI, and GOME-2 using a common standard

2012 ◽  
Vol 5 (3) ◽  
pp. 3953-3971 ◽  
Author(s):  
H. Irie ◽  
K. F. Boersma ◽  
Y. Kanaya ◽  
H. Takashima ◽  
X. Pan ◽  
...  
Keyword(s):  
Regression Line ◽  
Correlation Coefficients ◽  
Satellite Observation ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Vertical Column ◽  
Vertical Column Density ◽  
Satellite Sensors ◽  
Single Set ◽  
Japan And China

Abstract. For the intercomparison of tropospheric nitrogen dioxide NO2 vertical column density (VCD) data from three different satellite sensors (SCIAMACHY, OMI, and GOME-2), we use a common standard to quantitatively evaluate the biases for the respective data sets. As the standard, a regression analysis using a single set of collocated ground-based Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) observations at several sites in Japan and China in 2006–2011 is adopted. Examination of various spatial coincidence criteria indicates that the slope of the regression line can be influenced by the spatial distribution of NO2 over the area considered. While the slope varies systematically with the distance between the MAX-DOAS and satellite observation points around Tokyo in Japan, such a systematic dependence is not clearly seen and correlation coefficients are generally higher in comparisons at sites in China. On the basis of these results, we focus mainly on comparisons over China and best estimate the biases in SCIAMACHY, OMI, and GOME-2 data (TM4NO2A and DOMINO version 2 products) against the MAX-DOAS observations to be −5±14 %, −10±14 %, and +1±14 %, respectively, which are all small and insignificant. We suggest that these small biases now allow analyses combining these satellite data for air quality studies that are more systematic and quantitative than previously possible.

Latitudinal variation of HNO3, HCl and HF vertical column density above 11.5 km

1982 ◽  
Vol 9 (2) ◽  
pp. 135-138 ◽  
Author(s):  
André Girard ◽  
Louis Gramont ◽  
Nicole Louisnard ◽  
Sylvie Le Boiteux ◽  
Gilbert Fergant
Keyword(s):  
Column Density ◽  
Latitudinal Variation ◽  
Vertical Column ◽  
Vertical Column Density

scholarly journals Intercomparison of Pandora Stratospheric NO<sub>2</sub> Slant Column Product with the NIWA M07 NDACC Standard

2017 ◽  
Author(s):  
Travis N. Knepp ◽  
Richard Querel ◽  
Paul Johnston ◽  
Larry Thomason ◽  
David Flittner ◽  
...  
Keyword(s):  
Column Density ◽  
Atmospheric Composition ◽  
Reference Spectrum ◽  
Vertical Column ◽  
Common Reference ◽  
Retrieval Algorithms ◽  
Vertical Column Density ◽  
Mass Factor ◽  
Validation Tool ◽  
Good Agreement

Abstract. In September 2014 a Pandora multi-spectral photometer operated by the SAGE-III project was sent to Lauder, New Zealand to operate side-by-side with the National Institute of Water and Atmospheric Research's (NIWA) Network for Detection of atmospheric Composition Change (NDACC) standard zenith slant column NO2 instrument to allow intercomparison between the two instruments, and evaluation of the Pandora unit as a potential SAGE-III validation tool for stratospheric NO2. This intercomparison spanned a full year, from September 2014–September 2015. Both datasets were produced using their respective native algorithms using a common reference spectrum (i.e. 12:00 on 26 February 2015). Throughout the entire deployment period both instruments operated in a zenith-only observation configuration. Though conversion from slant column density (SCD) to vertical-column density is routine (by application of an air mass factor), we limit the current analysis to SCD only. This omission is beneficial in that it provides a strict intercomparison of the two instruments and the retrieval algorithms as opposed to introducing an AMF-dependence in the intercomparison as well. It was observed that the current hardware configurations and retrieval algorithms are in good agreement (R > 0.95). The detailed results of this investigation are presented herein.

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