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

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.

Download Full-text

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

Atmospheric Measurement Techniques ◽

10.5194/amt-8-1111-2015 ◽

2015 ◽

Vol 8 (3) ◽

pp. 1111-1133 ◽

Cited By ~ 25

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.

Download Full-text

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

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-4249-2013 ◽

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.

Download Full-text

Retrieval of Daytime Total Column Water Vapour from OLCI Measurements over Land Surfaces

Remote Sensing ◽

10.3390/rs13050932 ◽

2021 ◽

Vol 13 (5) ◽

pp. 932

Author(s):

René Preusker ◽

Cintia Carbajal Henken ◽

Jürgen Fischer

Keyword(s):

Water Vapour ◽

Near Infrared ◽

Retrieval Algorithm ◽

Data Sets ◽

Data Set ◽

Uncertainty Estimates ◽

Resolution Imaging ◽

Retrieval Errors ◽

Mean Square Errors ◽

Total Column

A new retrieval of total column water vapour (TCWV) from daytime measurements over land of the Ocean and Land Colour Instrument (OLCI) on-board the Copernicus Sentinel-3 missions is presented. The Copernicus Sentinel-3 OLCI Water Vapour product (COWa) retrieval algorithm is based on the differential absorption technique, relating TCWV to the radiance ratio of non-absorbing band and nearby water vapour absorbing band and was previously also successfully applied to other passive imagers Medium Resolution Imaging Spectrometer (MERIS) and Moderate Resolution Imaging Spectroradiometer (MODIS). One of the main advantages of the OLCI instrument regarding improved TCWV retrievals lies in the use of more than one absorbing band. Furthermore, the COWa retrieval algorithm is based on the full Optimal Estimation (OE) method, providing pixel-based uncertainty estimates, and transferable to other Near-Infrared (NIR) based TCWV observations. Three independent global TCWV data sets, i.e., Aerosol Robotic Network (AERONET), Atmospheric Radiation Measurement (ARM) and U.S. SuomiNet, and a German Global Navigation Satellite System (GNSS) TCWV data set, all obtained from ground-based observations, serve as reference data sets for the validation. Comparisons show an overall good agreement, with absolute biases between 0.07 and 1.31 kg/m2 and root mean square errors (RMSE) between 1.35 and 3.26 kg/m2. This is a clear improvement in comparison to the operational OLCI TCWV Level 2 product, for which the bias and RMSEs range between 1.10 and 2.55 kg/m2 and 2.08 and 3.70 kg/m2, respectively. A first evaluation of pixel-based uncertainties indicates good estimated uncertainties for lower retrieval errors, while the uncertainties seem to be overestimated for higher retrieval errors.

Download Full-text

A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range

10.5194/essd-2021-319 ◽

2021 ◽

Author(s):

Christian Borger ◽

Steffen Beirle ◽

Thomas Wagner

Keyword(s):

Water Vapour ◽

Reference Data ◽

Retrieval Algorithm ◽

Time Range ◽

Data Sets ◽

Climate Data ◽

Data Set ◽

Data Record ◽

Climate Data Record ◽

Total Column

Abstract. We present a long-term data set of 1° × 1° monthly mean total column water vapour (TCWV) based on global measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. In comparison to the retrieval algorithm of Borger et al. (2020) several modifications and filters have been applied accounting for instrumental issues (such as OMI's "row-anomaly") or the inferior quality of solar reference spectra. For instance, to overcome the problems of low quality reference spectra, the daily solar irradiance spectrum is replaced by an annually varying mean Earthshine radiance obtained in December over Antarctica. For the TCWV data set only measurements are taken into account for which the effective cloud fraction < 20 %, the AMF > 0.1, the ground pixel is snow- and ice-free, and the OMI row is not affected by the "row-anomaly" over the complete time range of the data set. The individual TCWV measurements are then gridded to a regular 1° × 1° lattice, from which the monthly means are calculated. In a comprehensive validation study we demonstrate that the OMI TCWV data set is in good agreement to reference data sets of ERA5, RSS SSM/I, and ESA CCI Water Vapour CDR-2: over ocean ordinary least squares (OLS) as well as orthogonal distance regressions (ODR) indicate slopes close to unity with very small offsets and high correlation coefficients of around 0.98. However, over land, distinctive positive deviations are obtained especially within the tropics with relative deviations of approximately +10 % likely caused by uncertainties in the retrieval input data (surface albedo, cloud information) due to frequent cloud contamination in these regions. Nevertheless, a temporal stability analysis proves that the OMI TCWV data set is consistent with the temporal changes of the reference data sets and shows no significant deviation trends. Since the TCWV retrieval can be easily applied to further satellite missions, additional TCWV data sets can be created from past missions such as GOME-1 or SCIAMACHY, which under consideration of systematic differences (e.g. due to different observation times) can be combined with the OMI TCWV data set in order to create a data record that would cover a time span from 1995 to the present. Moreover, the TCWV retrieval will also work for all missions dedicated to NO2 in future such as Sentinel-5 on MetOp-SG. The MPIC OMI total column water vapour (TCWV) climate data record is available at https://doi.org/10.5281/zenodo.5776718 (Borger et al., 2021b).

Download Full-text

Validation of SCIAMACHY AMC-DOAS water vapour columns

Atmospheric Chemistry and Physics ◽

10.5194/acp-5-1835-2005 ◽

2005 ◽

Vol 5 (7) ◽

pp. 1835-1841 ◽

Cited By ~ 31

Author(s):

S. Noël ◽

M. Buchwitz ◽

H. Bovensmann ◽

J. P. Burrows

Keyword(s):

Water Vapour ◽

Visible Spectral Region ◽

Optical Absorption Spectroscopy ◽

Data Sets ◽

Monthly Means ◽

Data Set ◽

Weather Forecasts ◽

Microwave Imager ◽

Scanning Imaging

Abstract. A first validation of water vapour total column amounts derived from measurements of the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY) in the visible spectral region has been performed. For this purpose, SCIAMACHY water vapour data have been determined for the year 2003 using an extended version of the Differential Optical Absorption Spectroscopy (DOAS) method, called Air Mass Corrected (AMC-DOAS). The SCIAMACHY results are compared with corresponding water vapour measurements by the Special Sensor Microwave Imager (SSM/I) and with model data from the European Centre for Medium-Range Weather Forecasts (ECMWF). In confirmation of previous results it could be shown that SCIAMACHY derived water vapour columns are typically slightly lower than both SSM/I and ECMWF data, especially over ocean areas. However, these deviations are much smaller than the observed scatter of the data which is caused by the different temporal and spatial sampling and resolution of the data sets. For example, the overall difference with ECMWF data is only -0.05 g/cm2 whereas the typical scatter is in the order of 0.5 g/cm2. Both values show almost no variation over the year. In addition, first monthly means of SCIAMACHY water vapour data have been computed. The quality of these monthly means is currently limited by the availability of calibrated SCIAMACHY spectra. Nevertheless, first comparisons with ECMWF data show that SCIAMACHY (and similar instruments) are able to provide a new independent global water vapour data set.

Download Full-text

Tropospheric nitrogen dioxide column retrieval from ground-based zenith–sky DOAS observations

Atmospheric Measurement Techniques ◽

10.5194/amt-8-2417-2015 ◽

2015 ◽

Vol 8 (6) ◽

pp. 2417-2435 ◽

Cited By ~ 10

Author(s):

F. Tack ◽

F. Hendrick ◽

F. Goutail ◽

C. Fayt ◽

A. Merlaud ◽

...

Keyword(s):

Nitrogen Dioxide ◽

Atmospheric Composition ◽

Optical Absorption Spectroscopy ◽

Retrieval Algorithm ◽

Measuring Instruments ◽

Reference Spectrum ◽

Vertical Column ◽

Data Set ◽

Residual Amount

Abstract. We present an algorithm for retrieving tropospheric nitrogen dioxide (NO2) vertical column densities (VCDs) from ground-based zenith–sky (ZS) measurements of scattered sunlight. The method is based on a four-step approach consisting of (1) the differential optical absorption spectroscopy (DOAS) analysis of ZS radiance spectra using a fixed reference spectrum corresponding to low NO2 absorption, (2) the determination of the residual amount in the reference spectrum using a Langley-plot-type method, (3) the removal of the stratospheric content from the daytime total measured slant column based on stratospheric VCDs measured at sunrise and sunset, and simulation of the rapid NO2 diurnal variation, (4) the retrieval of tropospheric VCDs by dividing the resulting tropospheric slant columns by appropriate air mass factors (AMFs). These steps are fully characterized and recommendations are given for each of them. The retrieval algorithm is applied on a ZS data set acquired with a multi-axis (MAX-) DOAS instrument during the Cabauw (51.97° N, 4.93° E, sea level) Intercomparison campaign for Nitrogen Dioxide measuring Instruments (CINDI) held from 10 June to 21 July 2009 in the Netherlands. A median value of 7.9 × 1015 molec cm−2 is found for the retrieved tropospheric NO2 VCDs, with maxima up to 6.0 × 1016 molec cm−2. The error budget assessment indicates that the overall error σTVCD on the column values is less than 28%. In the case of low tropospheric contribution, σTVCD is estimated to be around 39% and is dominated by uncertainties in the determination of the residual amount in the reference spectrum. For strong tropospheric pollution events, σTVCD drops to approximately 22% with the largest uncertainties on the determination of the stratospheric NO2 abundance and tropospheric AMFs. The tropospheric VCD amounts derived from ZS observations are compared to VCDs retrieved from off-axis and direct-sun measurements of the same MAX-DOAS instrument as well as to data from a co-located Système d'Analyse par Observations Zénithales (SAOZ) spectrometer. The retrieved tropospheric VCDs are in good agreement with the different data sets with correlation coefficients and slopes close to or larger than 0.9. The potential of the presented ZS retrieval algorithm is further demonstrated by its successful application on a 2-year data set, acquired at the NDACC (Network for the Detection of Atmospheric Composition Change) station Observatoire de Haute Provence (OHP; Southern France).

Download Full-text

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

10.5194/egusphere-egu2020-3408 ◽

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

<div> <p>Atmospheric water plays a key role for the Earth&#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.</p> </div><div> <p>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.&#160;&#160;</p> </div><div> <p>We apply this novel retrieval to measurements of the TROPOspheric Monitoring Instrument (TROPOMI) onboard ESA&#8216;s Sentinel-5P satellite and compare our retrieved H<sub>2</sub>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<sub>2</sub>O VCDs.&#160;&#160;</p> </div>

Download Full-text

Rapid methods for inversion of MAXDOAS elevation profiles to surface-associated box concentrations, visibility, and heights: application to analysis of Arctic BrO events

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-3-4645-2010 ◽

2010 ◽

Vol 3 (6) ◽

pp. 4645-4674 ◽

Cited By ~ 3

Author(s):

D. Donohoue ◽

D. Carlson ◽

W. R. Simpson

Keyword(s):

Column Density ◽

Surface Concentration ◽

Trace Gas ◽

Optical Absorption Spectroscopy ◽

Vertical Column ◽

Data Set ◽

Profile Method ◽

Remote Sensing Technique ◽

Vertical Column Density ◽

Gas Layer

Abstract. Multiple Axis Differential Optical Absorption Spectroscopy (MAXDOAS) is a remote sensing technique that measures surface-associated trace gas profiles using simple automated instrumentation that requires very low power and is deployable at remote sites. However, the analysis of MAXDOAS data is complex and often cannot be applied rapidly or consistently over long measurement periods. Here we present three transparent methods to analyze MAXDOAS data. The box profile method finds the best trace gas layer height and surface-associated vertical column density (VCD) to simultaneously fit oxygen collisional dimer (O4) and trace gas differential slant column density (dSCD) observations. The elevated viewing method estimates the surface-associated VCD from observations at high view elevations, such as 10° and 20°. The horizon viewing method estimates the surface concentration of a trace gas by using near-horizon view trace gas and O4 data. We apply these methods to a two-month data set and show that the methods retrieve information 80% of the time and provides a consistent time series. Surface-associated trace gas VCD observations by the elevated viewing method correlate (r2 > 0.93) with the box profile method with slopes within 15% of unity. Surface-associated concentration observations from the horizon viewing method correlate well (r2 > 0.90) with the box profile method and a slope within 4% of unity. Application of these retrieval methods to UV-absorbing trace gases other than BrO is straightforward, and application in other spectral regions is discussed. These methods provide rapid and comprehensive inversions of MAXDOAS spectral data that are useful during field campaigns, as well as, verification of more complex (e.g. optimal estimate inversion) methods.

Download Full-text

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

Atmospheric Measurement Techniques ◽

10.5194/amt-5-2403-2012 ◽

2012 ◽

Vol 5 (10) ◽

pp. 2403-2411 ◽

Cited By ~ 81

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.

Download Full-text

Application of SCIAMACHY and MOPITT CO total column measurements to evaluate model results over biomass burning regions and Eastern China

Atmospheric Chemistry and Physics ◽

10.5194/acp-11-6083-2011 ◽

2011 ◽

Vol 11 (12) ◽

pp. 6083-6114 ◽

Cited By ~ 28

Author(s):

C. Liu ◽

S. Beirle ◽

T. Butler ◽

J. Liu ◽

P. Hoor ◽

...

Keyword(s):

Biomass Burning ◽

Atmospheric Chemistry ◽

Eastern China ◽

Central Africa ◽

Satellite Observations ◽

Anthropogenic Sources ◽

Data Sets ◽

Vertical Column ◽

Data Set ◽

Vertical Column Density

Abstract. We developed a new CO vertical column density product from near IR observations of the SCIAMACHY instrument onboard ENVISAT. For the correction of a temporally and spatially variable offset of the CO vertical column densities we apply a normalisation procedure based on coincident MOPITT (version 4) observations over the oceans. The resulting normalised SCIAMACHY CO data is well suited for the investigation of the CO distribution over continents, where important emission sources are located. We use only SCIAMACHY observations for effective cloud fractions below 20 %. Since the remaining effects of clouds can still be large (up to 100 %), we applied a cloud correction scheme which explicitly considers the cloud fraction, cloud top height and surface albedo of individual observations. The normalisation procedure using MOPITT data and the cloud correction substantially improve the agreement with independent data sets. We compared our new SCIAMACHY CO data set, and also observations from the MOPITT instrument, to the results from three global atmospheric chemistry models (MATCH, EMAC at low and high resolution, and GEOS-Chem); the focus of this comparison is on regions with strong CO emissions (from biomass burning or anthropogenic sources). The comparison indicates that over most of these regions the seasonal cycle is generally captured well but the simulated CO vertical column densities are systematically smaller than those from the satellite observations, in particular with respect to SCIAMACHY observations. Because SCIAMACHY is more sensitive to the lowest part of the atmosphere compared to MOPITT, this indicates that especially close to the surface the model simulations systematically underestimate the true atmospheric CO concentrations, probably caused by an underestimation of CO emissions by current emission inventories. For some biomass burning regions, however, such as Central Africa in July–August, model results are also found to be higher than the satellite observations.

Download Full-text