scholarly journals Validation and update of OMI Total Column Water Vapor product

2016 ◽  
Vol 16 (17) ◽  
pp. 11379-11393 ◽  
Author(s):  
Huiqun Wang ◽  
Gonzalo Gonzalez Abad ◽  
Xiong Liu ◽  
Kelly Chance
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Validation Data ◽  
Data Set ◽  
Wide Range ◽  
Key Factor ◽  
The Mean ◽  
Total Column

Abstract. The collection 3 Ozone Monitoring Instrument (OMI) Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory's (SAO) algorithm version 1.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR's ground-based GPS data, AERONET's sun-photometer data, and Remote Sensing System's (RSS) SSMIS data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean OMI−GPS over land is −0.3 mm and the mean OMI−AERONET over land is 0 mm. For July 2005, the mean OMI−SSMIS over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the version 1.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter optimized retrieval window of 427.7–465 nm. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land. We have also made several updates to the air mass factor (AMF) calculation. The updated version 2.1 retrieval algorithm improves the land/ocean consistency and the overall quality of the OMI TCWV data set. The version 2.1 OMI data largely eliminate the low bias of the version 1.0 OMI data over the ocean and are 1.5 mm higher than RSS's “clear” sky SSMIS data in July 2005. Over the ocean, the mean of version 2.1 OMI−GlobVapour is 1 mm for July 2005 and 0 mm for January 2005. Over land, the version 2.1 OMI data are about 1 mm higher than GlobVapour when TCWV  <  15 mm and about 1 mm lower when TCWV  >  15 mm.

scholarly journals Validation of OMI Total Column Water Vapor Product

2016 ◽  
Author(s):  
Huiqun Wang ◽  
Gonzalo Gonzalez Abad ◽  
Xiong Liu ◽  
Kelly Chance
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Data Center ◽  
Retrieval Algorithm ◽  
Validation Data ◽  
Wide Range ◽  
Key Factor ◽  
The Mean ◽  
Total Column

Abstract. The Collection 3 OMI Total Column Water Vapor (TCWV) data generated by the Smithsonian Astrophysical Observatory (SAO)’s algorithm Version 1.0.0 and archived at the Aura Validation Data Center (AVDC) are compared with NCAR’s ground-based GPS data, AERONET sunphotometer data and Remote Sensing System’s SSM/I data. Results show that the OMI data track the seasonal and interannual variability of TCWV for a wide range of climate regimes. During the period from 2005 to 2009, the mean (OMI – GPS) over land is −0.3 mm, the mean (OMI – AERONET) over land is 0 mm, and the mean (OMI – SSM/I) over the ocean is −4.3 mm. The better agreement over land than over the ocean is corroborated by the smaller fitting residuals over land and suggests that liquid water is a key factor for the fitting quality over the ocean in the Version 1.0.0 retrieval algorithm. We find that the influence of liquid water is reduced using a shorter retrieval window. As a result, the TCWV retrieved with the new algorithm increases significantly over the ocean and only slightly over land, improving the land/ocean consistency and the overall quality of whole OMI TCWV dataset.

scholarly journals Satellite and Numerical Model Investigation of Two Heavy Rain Events over the Central Mediterranean

2011 ◽  
Vol 12 (4) ◽  
pp. 634-649 ◽  
Author(s):  
Sante Laviola ◽  
Agata Moscatello ◽  
Mario Marcello Miglietta ◽  
Elsa Cattani ◽  
Vincenzo Levizzani
Keyword(s):  
Water Vapor ◽  
Numerical Model ◽  
Liquid Water ◽  
Heavy Rain ◽  
Sensor Data ◽  
Retrieval Algorithm ◽  
Central Mediterranean ◽  
Model Simulations ◽  
Wide Range ◽  
Rain Events

Abstract Two heavy rain events over the Central Mediterranean basin, which are markedly different by genesis, dimensions, duration, and intensity, are analyzed. Given the relative low frequency of this type of severe storms in the area, a synoptic analysis describing their development is included. A multispectral analysis based on geostationary multifrequency satellite images is applied to identify cloud type, hydrometeor phase, and cloud vertical extension. Precipitation intensity is retrieved from (i) surface rain gauges, (ii) satellite data, and (iii) numerical model simulations. The satellite precipitation retrieval algorithm 183-Water vapor Strong Lines (183-WSL) is used to retrieve rain rates and cloud hydrometeor type, classify stratiform and convective rainfall, and identify liquid water clouds and snow cover from the Advanced Microwave Sounding Unit-B (AMSU-B) sensor data. Rainfall intensity is also simulated with the Weather Research and Forecasting (WRF) numerical model over two nested domains with horizontal resolutions of 16 km (comparable to that of the satellite sensor AMSU-B) and 4 km. The statistical analysis of the comparison between satellite retrievals and model simulations demonstrates the skills of both methods for the identification of the main characteristics of the cloud systems with a suggested overall bias of the model toward very low rain intensities. WRF (in the version used for the experiment) seems to classify as low rain intensity regions those areas where the 183-WSL retrieves no precipitation while sensing a mixture of freshly nucleated cloud droplets and a large amount of water vapor; in these areas, especially adjacent to the rain clouds, large amounts of cloud liquid water are detected. The satellite method performs reasonably well in reproducing the wide range of gauge-detected precipitation intensities. A comparison of the 183-WSL retrievals with gauge measurements demonstrates the skills of the algorithm in discriminating between convective and stratiform precipitation using the scattering and absorption of radiation by the hydrometeors.

scholarly journals Cloud<i>_</i>cci ATSR-2 and AATSR data set version 3: a 17-year climatology of global cloud and radiation properties

2020 ◽  
Vol 12 (3) ◽  
pp. 2121-2135
Author(s):  
Caroline A. Poulsen ◽  
Gregory R. McGarragh ◽  
Gareth E. Thomas ◽  
Martin Stengel ◽  
Matthew W. Christensen ◽  
...  
Keyword(s):  
Liquid Water ◽  
European Space Agency ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Liquid Water Path ◽  
Data Set ◽  
Water Path ◽  
Cloud Properties ◽  
Wide Range ◽  
Good Agreement

Abstract. We present version 3 (V3) of the Cloud_cci Along-Track Scanning Radiometer (ATSR) and Advanced ATSR (AATSR) data set. The data set was created for the European Space Agency (ESA) Cloud_cci (Climate Change Initiative) programme. The cloud properties were retrieved from the second ATSR (ATSR-2) on board the second European Remote Sensing Satellite (ERS-2) spanning 1995–2003 and the AATSR on board Envisat, which spanned 2002–2012. The data are comprised of a comprehensive set of cloud properties: cloud top height, temperature, pressure, spectral albedo, cloud effective emissivity, effective radius, and optical thickness, alongside derived liquid and ice water path. Each retrieval is provided with its associated uncertainty. The cloud property retrievals are accompanied by high-resolution top- and bottom-of-atmosphere shortwave and longwave fluxes that have been derived from the retrieved cloud properties using a radiative transfer model. The fluxes were generated for all-sky and clear-sky conditions. V3 differs from the previous version 2 (V2) through development of the retrieval algorithm and attention to the consistency between the ATSR-2 and AATSR instruments. The cloud properties show improved accuracy in validation and better consistency between the two instruments, as demonstrated by a comparison of cloud mask and cloud height with co-located CALIPSO data. The cloud masking has improved significantly, particularly in its ability to detect clear pixels. The Kuiper Skill score has increased from 0.49 to 0.66. The cloud top height accuracy is relatively unchanged. The AATSR liquid water path was compared with the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) in regions of stratocumulus cloud and shown to have very good agreement and improved consistency between ATSR-2 and AATSR instruments. The correlation with MAC-LWP increased from 0.4 to over 0.8 for these cloud regions. The flux products are compared with NASA Clouds and the Earth's Radiant Energy System (CERES) data, showing good agreement within the uncertainty. The new data set is well suited to a wide range of climate applications, such as comparison with climate models, investigation of trends in cloud properties, understanding aerosol–cloud interactions, and providing contextual information for co-located ATSR-2/AATSR surface temperature and aerosol products. The following new digital identifier has been issued for the Cloud_cci ATSR-2/AATSRv3 data set: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V003 (Poulsen et al., 2019).

scholarly journals Total column water vapor retrieval for GOME-2 visible blue observations

2020 ◽  
Author(s):  
Ka Lok Chan ◽  
Pieter Valks ◽  
Sander Slijkhuis ◽  
Claas Köhler ◽  
Diego Loyola
Keyword(s):  
Water Vapor ◽  
Transport Model ◽  
A Priori ◽  
Spectral Band ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Blue Band ◽  
Data Set ◽  
Ozone Monitoring ◽  
Total Column

Abstract. We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the EUMETSAT MetOp satellites. The blue band algorithm allows retrieval of water vapor from sensors which do not cover longer wavelengths, such as Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel-5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME-2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing to the operational product retrieved in the red spectral band, sun-photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results, and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5.

scholarly journals OMI total column ozone: extending the long term data record

2015 ◽  
Vol 8 (7) ◽  
pp. 7491-7510 ◽  
Author(s):  
R. D. McPeters ◽  
S. Frith ◽  
G. J. Labow
Keyword(s):  
Cross Sections ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Data Set ◽  
Total Column Ozone ◽  
Ozone Data ◽  
Combining Data ◽  
Data Record ◽  
Column Ozone ◽  
Total Column

Abstract. The ozone data record from the Ozone Monitoring Instrument (OMI) onboard the NASA EOS-Aura satellite has proven to be very stable over the ten plus years of operation. The OMI total column ozone processed through the TOMS ozone retrieval algorithm (version 8.5) has been compared with ground based measurements and with ozone from a series of SBUV/2 instruments. Comparison with an ensemble of Brewer and Dobson sites shows an absolute offset of about 1.5 % but stability over the ten years to better than half a percent. Comparison with a merged ozone (MOD) data set created by combining data from a series of SBUV/2 instruments again shows an offset, of about 1 %, and a relative trend of less than half a percent over ten years. The offset is mostly due to the use of the old Bass and Paur ozone cross sections in the OMI retrievals rather than the Brion/Daumont/Malicet cross sections that are now recommended. The bias in the Southern Hemisphere is smaller than that in the Northern Hemisphere, 1 vs. 1.5 %, for reasons that are not completely understood. When OMI was compared with the European realization of a multi-instrument ozone time series, the GTO (GOME type ozone) dataset, there was a small trend of about −0.85 % decade−1. Since all the comparisons of OMI relative to other ozone measuring systems show relative trends that are less than 1 % decade−1, we conclude that the OMI total column ozone data are sufficiently stable that they can be used in studies of ozone trends.

scholarly journals Total column water vapor retrieval for Global Ozone Monitoring Experience-2 (GOME-2) visible blue observations

2020 ◽  
Vol 13 (8) ◽  
pp. 4169-4193
Author(s):  
Ka Lok Chan ◽  
Pieter Valks ◽  
Sander Slijkhuis ◽  
Claas Köhler ◽  
Diego Loyola
Keyword(s):  
Water Vapor ◽  
Transport Model ◽  
A Priori ◽  
Spectral Band ◽  
Atmospheric Composition ◽  
Retrieval Algorithm ◽  
Blue Band ◽  
Data Set ◽  
Ozone Monitoring ◽  
Total Column

Abstract. We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue spectral band for the Global Ozone Monitoring Experience 2 (GOME-2) instruments on board the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Metop satellites. The blue band algorithm allows the retrieval of water vapor from sensors which do not cover longer wavelengths, such as the Ozone Monitoring Instrument (OMI) and the Copernicus atmospheric composition missions Sentinel-5 Precursor (S5P), Sentinel-4 (S4) and Sentinel-5 (S5). The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical columns using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from the chemistry transport model (CTM). The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. The new algorithm is applied to GOME-2A and GOME-2B observations to retrieve TCWV. The data set is validated by comparing it to the operational product retrieved in the red spectral band, sun photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results and can be used for the current and forthcoming Copernicus Sentinel missions S4 and S5.

scholarly journals OMI Total Column Water Vapor Version 4 Validation and Applications

2019 ◽  
Author(s):  
Huiqun Wang ◽  
Amir Hossein Souri ◽  
Gonzalo Gonzalez Abad ◽  
Xiong Liu ◽  
Kelly Chance
Keyword(s):  
Water Vapor ◽  
Relative Difference ◽  
Cloud Fraction ◽  
Retrieval Algorithm ◽  
Spatial And Temporal Scales ◽  
Trade Offs ◽  
Range Check ◽  
The Mean ◽  
Cloud Top Pressure ◽  
Total Column

Abstract. Total Column Water Vapor (TCWV) is important for the weather and climate. TCWV is derived from the OMI visible spectra using the Version 4 retrieval algorithm developed at the Smithsonian Astrophysical Observatory. The algorithm uses a retrieval window between 432.0 and 466.5 nm and includes various updates. The retrieval window optimization results from the trade-offs among competing factors. The OMI product is characterized by comparing against commonly used reference datasets – GPS network data over land and SSMIS data over the oceans. We examine how cloud fraction and cloud top pressure affect the comparisons. The results lead us to recommend filtering OMI data with cloud fraction < 5–15 % and cloud top pressure > 750 mb or stricter criteria, in addition to the main data quality, fitting RMS and TCWV range check. The mean of OMI-GPS is 0.85 mm with a standard deviation (σ) of 5.2 mm. Smaller differences between OMI and GPS (0.2 mm) occur when TCWV is within 10–20 mm. The bias is much smaller than the previous version. The mean of OMI-SSMIS is 1.2–1.9 mm (σ = 6.5–6.8 mm), with better agreement for January than for July. Smaller differences between OMI and SSMIS (0.3–1.6 mm) occur when TCWV is within 10–30 mm. However, the relative difference between OMI and the reference datasets is large when TCWV is less than 10 mm. As test applications of the Version 4 OMI TCWV over a range of spatial and temporal scales, we find prominent signals of the patterns associated with El Niño and La Niña, the high humidity associated with a corn sweat event and the strong moisture band of an Atmospheric River (AR). A data assimilation experiment demonstrates that the OMI data can help improve WRF’s skill at simulating the structure and intensity of the AR and the precipitation at the AR landfall.

Total column water vapor retrieval for TROPOMI/S5P observations in the visible blue band

2020 ◽  
Author(s):  
Ka Lok Chan ◽  
Sander Slijkhuis ◽  
Pieter Valks ◽  
Claas Köhler ◽  
Diego Loyola
Keyword(s):  
Water Vapor ◽  
Transport Model ◽  
A Priori ◽  
Spectral Band ◽  
Lower Troposphere ◽  
Retrieval Algorithm ◽  
Vertical Column ◽  
Blue Band ◽  
Data Set ◽  
Total Column

&lt;p&gt;We present a new total column water vapor (TCWV) retrieval algorithm in the visible blue band for the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel 5 Precursor (S5P) satellite. Retrieving water vapor columns in the blue band has numerous advantages over longer wavelengths. Measurements in the blue band are more sensitive at lower troposphere over oceans due to higher surface albedo at this wavelength band. In addition, no correction for spectral saturation effects is required as water vapor is optically thin in this spectral band. The blue band algorithm uses the differential optical absorption spectroscopic (DOAS) technique to retrieve water vapor slant columns. The measured water vapor slant columns are converted to vertical column using air mass factors (AMFs). The new algorithm has an iterative optimization module to dynamically find the optimal a priori water vapor profile. The dynamic a priori algorithm makes use of the fact that the vertical distribution of water vapor is strongly correlated to the total column. This makes it better suited for climate studies than usual satellite retrievals with static a priori or vertical profile information from chemistry transport model (CTM).&lt;/p&gt;&lt;p&gt;The new algorithm is applied to TROPOMI observations to retrieve TCWV. Due to the long measurement record of GOME-2, the new algorithm is also used to retrieve TCWV from GOME-2. The TCWV data set is validated by comparing to the GOME-2 TCWV operational product retrieved in the red spectral band, MODIS and SSMIS satellite observations. In addition, the new TCWV data set is also compared to ground based sun-photometer and radiosonde measurements. Water vapor columns retrieved in the blue band are in good agreement with the other data sets, indicating that the new algorithm derives precise results. Therefore, it was selected for the S5P Processor Algorithm Laboratory (PAL) project as a future operational product. This algorithm can also be used for the forthcoming Copernicus Sentinel S4 and S5 missions.&lt;/p&gt;

scholarly journals Integrated water vapor and liquid water path retrieval using a single-channel radiometer

2020 ◽  
Author(s):  
Anne-Claire Billault-Roux ◽  
Alexis Berne
Keyword(s):  
Water Vapor ◽  
Liquid Water ◽  
Single Channel ◽  
Geographical Information ◽  
Single Frequency ◽  
Retrieval Algorithm ◽  
Liquid Water Path ◽  
Data Set ◽  
Water Path ◽  
The Impact

Abstract. Microwave radiometers are widely used for the retrieval of Liquid Water Path (LWP) and Integrated Water Vapor (IWV) in the context of cloud and precipitation studies. This paper presents a new site-independent retrieval algorithm for LWP and IWV, relying on a single-frequency 89-GHz ground-based radiometer. A statistical approach is used, based on a neural network, which is trained and tested on a synthetic data set constructed from radiosonde profiles worldwide. In addition to 89-GHz brightness temperature, the input features include surface measurements of temperature, pressure and humidity, as well as geographical information and, when available, estimates of IWV and LWP from reanalysis data. An analysis of the algorithm is presented to assess its accuracy, the impact of the various input features, as well as its sensitivity to radiometer calibration and its stability across geographical locations. The new method is then implemented on real data that were collected during a field deployment in Switzerland and during the ICE-POP 2018 campaign in South Korea. The new algorithm is shown to be quite robust, especially in mid-latitude environments with a moderately moist climate, although its accuracy is inevitably lower than that obtained with state-of-the-art multi-channel radiometers.

scholarly journals Ozone Monitoring Instrument (OMI) Total Column Water Vapor version 4 validation and applications

2019 ◽  
Vol 12 (9) ◽  
pp. 5183-5199 ◽  
Author(s):  
Huiqun Wang ◽  
Amir Hossein Souri ◽  
Gonzalo González Abad ◽  
Xiong Liu ◽  
Kelly Chance
Keyword(s):  
Water Vapor ◽  
Regression Line ◽  
Cloud Fraction ◽  
Retrieval Algorithm ◽  
Ozone Monitoring Instrument ◽  
Monitoring Instrument ◽  
Trade Offs ◽  
Ozone Monitoring ◽  
Cloud Top Pressure ◽  
Total Column

Abstract. Total column water vapor (TCWV) is important for the weather and climate. TCWV is derived from the Ozone Monitoring Instrument (OMI) visible spectra using the version 4.0 retrieval algorithm developed at the Smithsonian Astrophysical Observatory. The algorithm uses a retrieval window between 432.0 and 466.5 nm and includes updates to reference spectra and water vapor profiles. The retrieval window optimization results from the trade-offs among competing factors. The OMI product is characterized by comparing against commonly used reference datasets – global positioning system (GPS) network data over land and Special Sensor Microwave Imager/Sounder (SSMIS) data over the oceans. We examine how cloud fraction and cloud-top pressure affect the comparisons. The results lead us to recommend filtering OMI data with a cloud fraction less than f=0.05–0.25 and cloud-top pressure greater than 750 mb (or stricter), in addition to the data quality flag, fitting root mean square (RMS) and TCWV range check. Over land, for f=0.05, the overall mean of OMI–GPS is 0.32 mm with a standard deviation (σ) of 5.2 mm; the smallest bias occurs when TCWV = 10–20 mm, and the best regression line corresponds to f=0.25. Over the oceans, for f=0.05, the overall mean of OMI–SSMIS is 0.4 mm (1.1 mm) with σ=6.5 mm (6.8 mm) for January (July); the smallest bias occurs when TCWV = 20–30 mm, and the best regression line corresponds to f=0.15. For both land and the oceans, the difference between OMI and the reference datasets is relatively large when TCWV is less than 10 mm. The bias for the version 4.0 OMI TCWV is much smaller than that for version 3.0. As test applications of the version 4.0 OMI TCWV over a range of spatial and temporal scales, we find prominent signals of the patterns associated with El Niño and La Niña, the high humidity associated with a corn sweat event, and the strong moisture band of an atmospheric river (AR). A data assimilation experiment demonstrates that the OMI data can help improve the Weather Research and Forecasting (WRF) model skill at simulating the structure and intensity of the AR and the precipitation at the AR landfall.

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