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 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.

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

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).

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 CO<sub>2</sub> measurements at Darwin, Australia – site description and calibration against in situ aircraft profiles

2010 ◽  
Vol 3 (2) ◽  
pp. 989-1021 ◽  
Author(s):  
N. M. Deutscher ◽  
D. W. T. Griffith ◽  
G. W. Bryant ◽  
P. O. Wennberg ◽  
G. C. Toon ◽  
...  
Keyword(s):  
Nir Spectroscopy ◽  
Solar Observatory ◽  
Total Carbon ◽  
Relative Precision ◽  
Mixing Ratios ◽  
Clear Sky ◽  
The World ◽  
Total Column ◽  
Good Agreement

Abstract. An automated Fourier Transform Spectroscopic (FTS) solar observatory was established in Darwin, Australia in August 2005. The laboratory is part of the Total Carbon Column Observing Network, and measures atmospheric column abundances of CO2 and O2 and other gases. Measured CO2 columns were calibrated against integrated aircraft profiles obtained during the TWP-ICE campaign in January–February 2006, and show good agreement with calibrations for a similar instrument in Park Falls, Wisconsin. A clear-sky low airmass relative precision of 0.1% is demonstrated in the CO2 and O2 retrieved column-averaged volume mixing ratios. The 1% negative bias in the FTS XCO2 relative to the World Meteorological Organization (WMO) calibrated in situ scale is within the uncertainties of the NIR spectroscopy and analysis.

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.

Clarity Index Analysis and Modeling Using Probability Distribution Functions in Campo Grande-MS, Brazil

2019 ◽  
Vol 141 (6) ◽  
Author(s):  
Amaury de Souza ◽  
Razika Ihaddadene ◽  
Nabila Ihaddadene ◽  
Pelumi E. Oguntunde
Keyword(s):  
Low Frequency ◽  
Distribution Functions ◽  
Data Sets ◽  
Atmospheric Conditions ◽  
Mato Grosso ◽  
Clear Sky ◽  
Probability Distribution Functions ◽  
Index Analysis ◽  
Sky Conditions ◽  
Mato Grosso Do Sul

The importance of statistical analysis in the field of energy for environmental engineering is shown in this research paper, in which the adequacy of the data sets of clarity index with the model of “best” probability (based on the criteria used) was studied. In Campo Grande which is the capital of the Brazilian state of Mato Grosso do Sul, located in the Center-West region of the country, there is a predominance of the atmospheric conditions of low cloudiness, with a high frequency of days with a clear sky and in consequence a low-frequency of days with cloudy sky. The aerosols resulting from the burning of sugarcane influence the sky conditions in Campo Grande thus reducing the frequency of the clear sky.

scholarly journals Quality assessment of integrated water vapour measurements at the St. Petersburg site, Russia: FTIR vs. MW and GPS techniques

2017 ◽  
Vol 10 (11) ◽  
pp. 4521-4536 ◽  
Author(s):  
Yana A. Virolainen ◽  
Yury M. Timofeyev ◽  
Vladimir S. Kostsov ◽  
Dmitry V. Ionov ◽  
Vladislav V. Kalinnikov ◽  
...  
Keyword(s):  
Quality Assessment ◽  
Water Vapour ◽  
Measurement Errors ◽  
Microwave Radiometer ◽  
Reference Method ◽  
Atmospheric Conditions ◽  
Assessment Protocol ◽  
Clear Sky ◽  
Integrated Water Vapour ◽  
Sky Conditions

Abstract. The cross-comparison of different techniques for atmospheric integrated water vapour (IWV) measurements is the essential part of their quality assessment protocol. We inter-compare the synchronised data sets of IWV values measured by the Bruker 125 HR Fourier-transform infrared spectrometer (FTIR), RPG-HATPRO microwave radiometer (MW), and Novatel ProPak-V3 global navigation satellite system receiver (GPS) at the St. Petersburg site between August 2014 and October 2016. As the result of accurate spatial and temporal matching of different IWV measurements, all three techniques agree well with each other except for small IWV values. We show that GPS and MW data quality depends on the atmospheric conditions; in dry atmosphere (IWV smaller than 6 mm), these techniques are less reliable at the St. Petersburg site than the FTIR method. We evaluate the upper bound of statistical measurement errors for clear-sky conditions as 0.29 ± 0.02 mm (1.6 ± 0.3 %), 0.55 ± 0.02 mm (4.7 ± 0.4 %), and 0.76 ± 0.04 mm (6.3 ± 0.8 %) for FTIR, GPS, and MW methods, respectively. We propose the use of FTIR as a reference method under clear-sky conditions since it is reliable on all scales of IWV variability.

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 Cloud and aerosol classification for 2 1/2 years of MAX-DOAS observations in Wuxi (China) and comparison to independent data sets

2015 ◽  
Vol 8 (5) ◽  
pp. 4653-4709 ◽  
Author(s):  
Y. Wang ◽  
M. Penning de Vries ◽  
P. H. Xie ◽  
S. Beirle ◽  
S. Dörner ◽  
...  
Keyword(s):  
Classification Scheme ◽  
Optical Absorption Spectroscopy ◽  
Data Sets ◽  
Original Algorithm ◽  
Clear Sky ◽  
Cloud Parameters ◽  
Cloud Classification ◽  
Aerosol Classification ◽  
Cloud Optical Thickness ◽  
Sky Conditions

Abstract. Multi-Axis-Differential Optical Absorption Spectroscopy (MAX-DOAS) observations of trace gases can be strongly influenced by clouds and aerosols. Thus it is important to identify clouds and characterise their properties. In a recent study Wagner et al. (2014) developed a cloud classification scheme based on the MAX-DOAS measurements themselves with which different "sky conditions" (e.g. clear sky, continuous clouds, broken clouds) can be distinguished. Here we apply this scheme to long term MAX-DOAS measurements from 2011 to 2013 in Wuxi, China (31.57° N, 120.31° E). The original algorithm has been modified, in particular in order to account for smaller solar zenith angles (SZA). Instrumental degradation is accounted for to avoid artificial trends of the cloud classification. We compared the results of the MAX-DOAS cloud classification scheme to several independent measurements: aerosol optical depth from a nearby AERONET station and from MODIS, visibility derived from a visibility meter; and various cloud parameters from different satellite instruments (MODIS, OMI, and GOME-2). The most important findings from these comparisons are: (1) most cases characterized as clear sky with low or high aerosol load were associated with the respective AOD ranges obtained by AERONET and MODIS, (2) the observed dependences of MAX-DOAS results on cloud optical thickness and effective cloud fraction from satellite indicate that the cloud classification scheme is sensitive to cloud (optical) properties, (3) separation of cloudy scenes by cloud pressure shows that the MAX-DOAS cloud classification scheme is also capable of detecting high clouds, (4) some clear sky conditions, especially with high aerosol load, classified from MAX-DOAS observations corresponding to the optically thin and low clouds derived by satellite observations probably indicate that the satellite cloud products contain valuable information on aerosols.

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