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

<div> <p>Atmospheric water plays a key role for the Earth’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.  </p> </div><div> <p>We apply this novel retrieval to measurements of the TROPOspheric Monitoring Instrument (TROPOMI) onboard ESA‘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.  </p> </div>

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 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 ◽  
Input Data ◽  
Reference Data ◽  
Boreal Summer ◽  
Data Sets ◽  
Clear Sky ◽  
Sky Conditions ◽  
Total Column ◽  
Good Agreement

Abstract. Total column water vapour has been retrieved from TROPOMI measurements in the visible blue spectral range and compared to a variety of different reference data sets for clear-sky conditions during boreal summer and winter. The retrieval consists of the common two-step DOAS approach: first the spectral analysis is performed within a linearized scheme and then the retrieved slant column densities are converted to vertical columns 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. Moreover, a modified albedo map was used combining the OMI LER albedo and scaled MODIS albedo map. The use of the alternative albedo is especially important over regions with very low albedo and high probability of clouds like the Amazon region. The errors of the TCWV retrieval have been theoretically estimated considering the contribution of a variety of different uncertainty sources. For observations during clear-sky conditions, over ocean surface, and at low solar zenith angles the error typically is around values of 10–20 % and during cloudy-sky conditions, over land surface, and at high solar zenith angles it reaches values around 20–50 %. In the framework of a validation study the retrieval demonstrates that it can well capture the global water vapour distribution: the retrieved H2O VCDs show very good agreement to the reference data sets over ocean for boreal summer and winter whereby the modified albedo map substantially improves the retrieval's consistency to the reference data sets in particular over tropical landmasses. However over land the retrieval underestimates the VCD by about 10 %, particularly during summertime. Our investigations show that this underestimation is likely caused by uncertainties within the surface albedo and the cloud input data: Low level clouds cause an underestimation but for mid to high level clouds good agreement is found. In addition, our investigations indicate that these biases can probably be further reduced by the use of updated cloud input data. The TCWV retrieval can be easily applied to further satellite sensors (e.g. GOME-2 or OMI) for creating uniform measurement data sets on longterm which is particularly interesting for climate and trend studies of water vapour.

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.

Analysis of global trends of total column water vapour from multiple years of OMI observations

2021 ◽  
Author(s):  
Christian Borger ◽  
Steffen Beirle ◽  
Thomas Wagner
Keyword(s):  
Water Vapour ◽  
Spectral Range ◽  
Global Analysis ◽  
Weather Prediction ◽  
Optical Absorption Spectroscopy ◽  
Climate Modelling ◽  
Climate Data ◽  
Ozone Monitoring Instrument ◽  
Additional Reference ◽  
Total Column

&lt;p&gt;&lt;span&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 spatiotemporal scales. In this context, global monitoring of the water vapour distribution is essential for numerical weather prediction, climate modelling, and a better understanding of climate feedbacks.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Total column water vapour (TCWV), or integrated water vapour, can be retrieved from satellite spectra in the visible &amp;#8220;blue&amp;#8221; spectral range (430-450nm) using Differential Optical Absorption Spectroscopy (DOAS). The UV-vis spectral range offers several advantages for monitoring the global water vapour distribution: for instance it allows for accurate, straightforward retrievals over ocean and land even under partly-cloudy conditions.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;To investigate changes in the TCWV distribution from space, the Ozone Monitoring Instrument (OMI) on board NASA&amp;#8217;s Aura satellite is particularly promising as it provides long-term measurements (late 2004-ongoing) with daily global coverage.&lt;/span&gt;&lt;/p&gt;&lt;p&gt;&lt;span&gt;Here, we present a global analysis of trends of total column water vapour retrieved from multiple years of OMI observations (2005-2020). Furthermore, we put our results in context to trends of other climate data records and validate the OMI TCWV data by comparisons to additional reference data sets.&lt;/span&gt;&lt;/p&gt;

scholarly journals Total column water vapour retrieval from S-5P/TROPOMI in the visible blue spectral range

2020 ◽  
Vol 13 (5) ◽  
pp. 2751-2783 ◽  
Author(s):  
Christian Borger ◽  
Steffen Beirle ◽  
Steffen Dörner ◽  
Holger Sihler ◽  
Thomas Wagner
Keyword(s):  
Water Vapour ◽  
Spectral Range ◽  
Input Data ◽  
Reference Data ◽  
Boreal Summer ◽  
Data Sets ◽  
Clear Sky ◽  
Sky Conditions ◽  
Total Column ◽  
Good Agreement

Abstract. Total column water vapour has been retrieved from TROPOMI measurements in the visible blue spectral range and compared to a variety of different reference data sets for clear-sky conditions during boreal summer and winter. The retrieval consists of the common two-step DOAS approach: first the spectral analysis is performed within a linearized scheme and then the retrieved slant column densities are converted to vertical columns 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. Moreover, a modified albedo map was used combining the OMI LER albedo and scaled MODIS albedo map. The use of the alternative albedo is especially important over regions with very low albedo and high probability of clouds like the Amazon region. The errors of the total column water vapour (TCWV) retrieval have been theoretically estimated considering the contribution of a variety of different uncertainty sources. For observations during clear-sky conditions, over ocean surface, and at low solar zenith angles the error typically is around values of 10 %–20 %, and during cloudy-sky conditions, over land surface, and at high solar zenith angles it reaches values around 20 %–50 %. In the framework of a validation study the retrieval demonstrates that it can well capture the global water vapour distribution: the retrieved H2O vertical column densities (VCDs) show very good agreement with the reference data sets over ocean for boreal summer and winter whereby the modified albedo map substantially improves the retrieval's consistency to the reference data sets, in particular over tropical land masses. However, over land the retrieval underestimates the VCD by about 10 %, particularly during summertime. Our investigations show that this underestimation is likely caused by uncertainties within the surface albedo and the cloud input data: low-level clouds cause an underestimation, but for mid- to high-level clouds good agreement is found. In addition, our investigations indicate that these biases can probably be further reduced by the use of improved cloud input data. For the general purpose we recommend only using VCDs with cloud fraction <20 % and AMF >0.1, which represents a good compromise between spatial coverage and retrieval accuracy. The TCWV retrieval can be easily applied to further satellite sensors (e.g. GOME-2 or OMI) for creating uniform, long-term measurement data sets, which is particularly interesting for climate and trend studies of water vapour.

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.

Survey Data Quality in Analyzing Harmonized Indicators of Protest Behavior: A Survey Data Recycling Approach

2021 ◽  
pp. 000276422110216
Author(s):  
Kazimierz M. Slomczynski ◽  
Irina Tomescu-Dubrow ◽  
Ilona Wysmulek
Keyword(s):  
Data Processing ◽  
Data Quality ◽  
Survey Data ◽  
A Priori ◽  
Data Sets ◽  
New Approach ◽  
Survey Quality ◽  
Survey Error ◽  
Ex Post ◽  
The Impact

This article proposes a new approach to analyze protest participation measured in surveys of uneven quality. Because single international survey projects cover only a fraction of the world’s nations in specific periods, researchers increasingly turn to ex-post harmonization of different survey data sets not a priori designed as comparable. However, very few scholars systematically examine the impact of the survey data quality on substantive results. We argue that the variation in source data, especially deviations from standards of survey documentation, data processing, and computer files—proposed by methodologists of Total Survey Error, Survey Quality Monitoring, and Fitness for Intended Use—is important for analyzing protest behavior. In particular, we apply the Survey Data Recycling framework to investigate the extent to which indicators of attending demonstrations and signing petitions in 1,184 national survey projects are associated with measures of data quality, controlling for variability in the questionnaire items. We demonstrate that the null hypothesis of no impact of measures of survey quality on indicators of protest participation must be rejected. Measures of survey documentation, data processing, and computer records, taken together, explain over 5% of the intersurvey variance in the proportions of the populations attending demonstrations or signing petitions.

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.

Validation of Sentinel-5P TROPOMI tropospheric NO2 with airborne imaging, ground-based stationary, and mobile DOAS measurements from the S5P-VAL-DE-Ruhr campaign

2021 ◽  
Author(s):  
Kezia Lange ◽  
Andreas C. Meier ◽  
Michel Van Roozendael ◽  
Thomas Wagner ◽  
Thomas Ruhtz ◽  
...  
Keyword(s):  
A Priori ◽  
Data Sets ◽  
Vertical Column ◽  
Data Set ◽  
Ruhr Area ◽  
Spatial And Temporal Heterogeneity ◽  
Western Germany ◽  
Airborne Imaging ◽  
Urban Character ◽  
One Year

&lt;p&gt;Airborne imaging DOAS and ground-based stationary and mobile DOAS measurements were conducted during the ESA funded S5P-VAL-DE-Ruhr campaign in September 2020 in the Ruhr area. The Ruhr area is located in Western Germany and is a pollution hotspot in Europe with urban character as well as large industrial emitters. The measurements are used to validate data from the Sentinel-5P TROPOspheric Monitoring Instrument (TROPOMI) with focus on the NO&lt;sub&gt;2&lt;/sub&gt; tropospheric vertical column product.&lt;/p&gt;&lt;p&gt;Seven flights were performed with the airborne imaging DOAS instrument, AirMAP, providing continuous maps of NO&lt;sub&gt;2&lt;/sub&gt; in the layers below the aircraft. These flights cover many S5P ground pixels within an area of about 40 km side length and were accompanied by ground-based stationary measurements and three mobile car DOAS instruments. Stationary measurements were conducted by two Pandora, two zenith-sky and two MAX-DOAS instruments distributed over three target areas, partly as long-term measurements over a one-year period.&lt;/p&gt;&lt;p&gt;Airborne and ground-based measurements were compared to evaluate the representativeness of the measurements in time and space. With a resolution of about 100 x 30 m&lt;sup&gt;2&lt;/sup&gt;, the AirMAP data creates a link between the ground-based and the TROPOMI measurements with a resolution of 3.5 x 5.5 km&lt;sup&gt;2&lt;/sup&gt; and is therefore well suited to validate TROPOMI's tropospheric NO&lt;sub&gt;2&lt;/sub&gt; vertical column.&lt;/p&gt;&lt;p&gt;The measurements on the seven flight days show strong variability depending on the different target areas, the weekday and meteorological conditions. We found an overall low bias of the TROPOMI operational NO&lt;sub&gt;2&lt;/sub&gt; data for all three target areas but with varying magnitude for different days. The campaign data set is compared to custom TROPOMI NO&lt;sub&gt;2&lt;/sub&gt; products, using different auxiliary data, such as albedo or a priori vertical profiles to evaluate the influence on the TROPOMI data product. Analyzing and comparing the different data sets provides more insight into the high spatial and temporal heterogeneity in NO&lt;sub&gt;2&lt;/sub&gt; and its impact on satellite observations and their validation.&lt;/p&gt;

scholarly journals Impact Assessment of Daily Satellite-Derived Surface Albedo in a Limited-Area NWP Model

2012 ◽  
Vol 51 (10) ◽  
pp. 1835-1854 ◽  
Author(s):  
Jure Cedilnik ◽  
Dominique Carrer ◽  
Jean-François Mahfouf ◽  
Jean-Louis Roujean
Keyword(s):  
Land Surface ◽  
Short Range ◽  
Surface Albedo ◽  
Weather Prediction ◽  
A Priori ◽  
Limited Area ◽  
Model Biases ◽  
Nwp Model ◽  
The Impact ◽  
Range Forecasts

AbstractThis study examines the impact of daily satellite-derived albedos on short-range forecasts in a limited-area numerical weather prediction (NWP) model over Europe. Contrary to previous studies in which satellite products were used to derive monthly “climatologies,” a daily surface (snow free) albedo is analyzed by a Kalman filter. The filter combines optimally a satellite product derived from the Meteosat Second Generation geostationary satellite [and produced by the Land Surface Analyses–Satellite Application Facility of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT)], an albedo climatology, and a priori information given by “persistence.” The surface albedo analyzed for a given day is used as boundary conditions of the NWP model to run forecasts starting the following day. Results from short-range forecasts over a 1-yr period reveal the capacity of satellite information to reduce model biases and RMSE in screen-level temperature (during daytime and intermediate seasons). The impact on forecast scores is larger when considering the analyzed surface albedo rather than another climatologically based albedo product. From comparisons with measurements from three flux-tower stations over mostly homogeneous French forests, it is seen that the model biases in surface net radiation are significantly reduced. An impact on the whole planetary boundary layer, particularly in summer, results from the use of an observed surface albedo. An unexpected behavior produced in summer by the satellite-derived albedo on surface temperature is also explained. The forecast runs presented here, performed in dynamical adaptation mode, will be complemented later on by data assimilation experiments over typically monthly periods.

Sign in / Sign up

Export Citation Format

Share Document