Geophysical validation of two years of Sentinel-5p tropical tropospheric ozone columns

2020 ◽  
Author(s):  
Daan Hubert ◽  
Tijl Verhoelst ◽  
Steven Compernolle ◽  
Arno Keppens ◽  
José Granville ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Convective Cloud ◽  
Spatial And Temporal Variability ◽  
Data Set ◽  
Mean Values ◽  
Clear Sky ◽  
Ozone Data ◽  
Sky Conditions ◽  
Ozone Column

<p>Tropospheric ozone damages ecosystems and causes human health problems. The high spatial and temporal variability of ozone concentrations in the troposphere challenges global observing systems to monitor ozone at all relevant scales. TROPOMI is a nadir-viewing UV-Vis-NIR-SWIR sensor that combines a high spatial resolution, a large swath width and the spectral measurement characteristics required to deliver trace gas data records at unprecedented detail. The first tropospheric data product was publicly released in Fall 2018, a year after launch on the Sentinel-5p platform (S5p). It is based on the convective-cloud differential technique (CCD) to infer 0.5°x1° resolved daily maps of 3-day moving mean values of the tropospheric ozone column (surface to 270 hPa) between 20°S and 20°N in clear-sky conditions. This makes it the highest resolved tropospheric ozone data set currently available for the tropical belt. About two years of data have been collected since the end of the commissioning phase in April 2018.</p><p>We present an assessment of the quality of the Sentinel-5p TROPOMI convective-cloud differential tropospheric ozone column data products (O3_TCL OFFL v01.01.05-01.01.07), carried out within the context of ESA’s Sentinel-5p Mission Performance Center (MPC) and the S5PVT AO project CHEOPS-5p. Our assessment of the first two years of TROPOMI data is based on comparisons with (a) quality-assured co-located in-situ measurements by the SHADOZ ozonesonde network, and, (b) satellite data by the GOME-2 and OMI sensors. These well-characterized observational data records serve as references to evaluate the bias and the dispersion of S5p data, and their dependence on influence quantities. Additional visual inspections of the S5p tropospheric ozone maps unveiled non-geophysical structures introduced by the sampling pattern of sensor and clouds. We conclude by assessing the compliance of S5p tropospheric ozone data with respect to mission and user requirements for key data applications.</p>

scholarly journals TROPOMI tropospheric ozone column data: Geophysical assessment and comparison to ozonesondes, GOME-2B and OMI

2020 ◽  
Author(s):  
Daan Hubert ◽  
Klaus-Peter Heue ◽  
Jean-Christopher Lambert ◽  
Tijl Verhoelst ◽  
Marc Allaart ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Convective Cloud ◽  
Horizontal Resolution ◽  
Measurement Systems ◽  
Clear Sky ◽  
International Regulations ◽  
Spatio Temporal ◽  
Sky Conditions ◽  
Ozone Column

Abstract. Ozone in the troposphere affects humans and ecosystems as a pollutant and as a greenhouse gas. Observing, understanding and modelling this dual role, as well as monitoring effects of international regulations on air quality and climate change, however, challenge measurement systems to operate at opposite ends of the spatio-temporal scale ladder. On board of the ESA/EU Copernicus Sentinel-5 Precursor (S5P) satellite launched in October 2017, TROPOspheric Monitoring Instrument (TROPOMI) aspires to take the next leap forward by measuring ozone and its precursors at unprecedented horizontal resolution until at least the mid 2020s. In this work, we assess the quality of TROPOMI's first release (V01.01.05–08) of tropical tropospheric ozone column data (TrOC). Derived with the Convective Cloud Differential (CCD) method, TROPOMI daily TrOC data represent the three-day moving mean ozone column between surface and 270 hpa under clear sky conditions gridded at 0.5° latitude by 1° longitude resolution. Comparisons to almost two years of co-located SHADOZ ozonesonde and satellite data (Aura OMI and MetOp-B GOME-2) conclude to TROPOMI biases between −0.1 and +2.3 DU (

scholarly journals Using MODIS land surface temperatures and the Crocus snow model to understand the warm bias of ERA-Interim reanalyses at the surface in Antarctica

2014 ◽  
Vol 8 (1) ◽  
pp. 55-84 ◽  
Author(s):  
H. Fréville ◽  
E. Brun ◽  
G. Picard ◽  
N. Tatarinova ◽  
L. Arnaud ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface ◽  
Data Set ◽  
Warm Bias ◽  
Clear Sky ◽  
Surface Temperatures ◽  
Stable Conditions ◽  
Land Surface Temperatures ◽  
Sky Conditions

Abstract. MODIS land surface temperatures in Antarctica were processed in order to produce a gridded data set at 25 km resolution, spanning the period 2000–2011 at an hourly time-step. The AQUA and TERRA orbits and MODIS swath width, combined with frequent clear-sky conditions, lead to very high availability of quality-controlled observations: on average, hourly data are available 14 h per day at the grid points around the South Pole and more than 9 over a large area of the Antarctic Plateau. Processed MODIS land surface temperatures, referred to hereinafter as MODIS Ts, were compared with in situ hourly measurements of surface temperature collected over the entire year 2009 by 7 stations from the BSRN and AWS networks. In spite of an occasional failure in the detection of clouds, MODIS Ts exhibit a good performance, with a bias ranging from −1.8 to 0.1 °C and errors ranging from 2.2 to 4.8 °C root mean square at the 5 stations located on the plateau. These results show that MODIS Ts can be used as a precise and accurate reference to test other surface temperature data sets. Here, we evaluate the performance of surface temperature in the ERA-Interim reanalysis. During conditions detected as cloud-free by MODIS, ERA-Interim shows a widespread warm bias in Antarctica in every season, ranging from +3 to +6 °C on the plateau. This confirms a recent study which showed that the largest discrepancies in 2 m air temperature between ERA-Interim and the HadCRUT4 data set occur in Antarctica. A comparison with in situ surface temperature shows that this bias is not strictly limited to clear-sky conditions. A detailed comparison with stand-alone simulations by the Crocus snowpack model, forced by ERA-Interim, and with the ERA-Interim/land simulations, shows that the warm bias may be due primarily to an overestimation of the surface turbulent fluxes in very stable conditions. Numerical experiments with Crocus show this is likely due to an overestimation of the surface exchange coefficients under very stable conditions.

scholarly journals Estimating Surface Soil Heat Flux in Permafrost Regions Using Remote Sensing-Based Models on the Northern Qinghai-Tibetan Plateau under Clear-Sky Conditions

2019 ◽  
Vol 11 (4) ◽  
pp. 416 ◽  
Author(s):  
Cheng Yang ◽  
Tonghua Wu ◽  
Jiemin Wang ◽  
Jimin Yao ◽  
Ren Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Surface Soil ◽  
Ground Surface ◽  
Soil Heat Flux ◽  
Parameterization Scheme ◽  
Clear Sky ◽  
Sky Conditions ◽  
Permafrost Regions

The ground surface soil heat flux (G0) quantifies the energy transfer between the atmosphere and the ground through the land surface. However; it is difficult to obtain the spatial distribution of G0 in permafrost regions because of the limitation of in situ observation and complication of ground surface conditions. This study aims at developing an improved G0 parameterization scheme applicable to permafrost regions of the Qinghai-Tibet Plateau under clear-sky conditions. We validated several existing remote sensing-based models to estimate G0 by analyzing in situ measurement data. Based on the validation of previous models on G0; we added the solar time angle to the G0 parameterization scheme; which considered the phase difference problem. The maximum values of RMSE and MAE between “measured G0” and simulated G0 using the improved parameterization scheme and in situ data were calculated to be 6.102 W/m2 and 5.382 W/m2; respectively. When the error of the remotely sensed land surface temperature is less than 1 K and the surface albedo measured is less than 0.02; the accuracy of estimates based on remote sensing data for G0 will be less than 5%. MODIS data (surface reflectance; land surface temperature; and emissivity) were used to calculate G0 in a 10 x 10 km region around Tanggula site; which is located in the continuous permafrost region with long-term records of meteorological and permafrost parameters. The results obtained by the improved scheme and MODIS data were consistent with the observation. This study enhances our understanding of the impacts of climate change on the ground thermal regime of permafrost and the land surface processes between atmosphere and ground surface in cold regions.

scholarly journals Trends of tropical tropospheric ozone from 20 years of European satellite measurements and perspectives for the Sentinel-5 Precursor

2016 ◽  
Vol 9 (10) ◽  
pp. 5037-5051 ◽  
Author(s):  
Klaus-Peter Heue ◽  
Melanie Coldewey-Egbers ◽  
Andy Delcloo ◽  
Christophe Lerot ◽  
Diego Loyola ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Total Ozone ◽  
Atlantic Coast ◽  
Convective Cloud ◽  
Differential Method ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Cloud Data ◽  
The Individual ◽  
Column Ozone

Abstract. In preparation of the TROPOMI/S5P launch in early 2017, a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellite instruments GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995–2015) of tropospheric column ozone was generated. To have a consistent total ozone data set for all sensors, one common retrieval algorithm, namely GODFITv3, was applied and the L1 reflectances were also soft calibrated. The total ozone columns and the cloud data were input into the tropospheric ozone retrieval. However, the tropical tropospheric column ozone (TCO) for the individual instruments still showed small differences and, therefore, we harmonised the data set. For this purpose, a multilinear function was fitted to the averaged difference between SCIAMACHY's TCO and those from the other sensors. The original TCO was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also, a direct comparison of the TCO in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small. Based on the harmonised observations, we created a merged data product, containing the TCO from July 1995 to December 2015. A first application of this 20-year record is a trend analysis. The tropical trend is 0.7 ± 0.12 DU decade−1. Regionally the trends reach up to 1.8 DU decade−1 like on the African Atlantic coast, while over the western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade−1. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TCO is part of the operational products.

scholarly journals Verification of a Clear-Sky Mesoscale Simulation Using Satellite-Derived Surface Temperatures

2008 ◽  
Vol 136 (12) ◽  
pp. 5148-5161 ◽  
Author(s):  
M. A. Jiménez ◽  
A. Mira ◽  
J. Cuxart ◽  
A. Luque ◽  
S. Alonso ◽  
...  
Keyword(s):  
High Temporal Resolution ◽  
Local Cooling ◽  
Satellite Measurements ◽  
Mesoscale Simulation ◽  
Clear Sky ◽  
Surface Temperatures ◽  
Satellite Sensors ◽  
Sky Conditions ◽  
Mesoscale Simulations

Abstract A mesoscale simulation for Majorca Island is made using the Méso-NH model for a spring night, under a slack synoptic pressure gradient with weak general winds and clear skies. The circulations over and around the island are driven mostly by the locally generated flows, due to the topography and the land–sea thermal contrast. The verification of mesoscale simulations in clear-sky conditions is difficult, especially if the network of stations is not very dense. The main objective of this work is to try to verify the mesoscale simulation using measurements from automatic weather stations and satellite measurements. The model outputs are compared with the available instrumental data and the representativeness of the stations is discussed. Furthermore, complete two-dimensional comparisons are made between the radiative surface temperatures produced by the model and those processed from the National Oceanic and Atmospheric Administration and Meteosat Second Generation (MSG) satellite sensors. The high temporal resolution of the MSG images also allows comparison of the temporal evolutions of the surface temperature between satellite pixels and model grid cells. The procedure permits assessment of the closeness of the simulation to in situ and remote sensing observations. The results of the comparison show that the model is able to reproduce most of the observed patterns, such as intense local cooling or persistent outflows at the largest basins.

scholarly journals Satellite-Based Estimation of Temporally Resolved Dust Radiative Forcing in Snow Cover

2016 ◽  
Vol 17 (7) ◽  
pp. 1999-2011 ◽  
Author(s):  
Steven D. Miller ◽  
Fang Wang ◽  
Ann B. Burgess ◽  
S. McKenzie Skiles ◽  
Matthew Rogers ◽  
...  
Keyword(s):  
Solar Irradiance ◽  
Radiative Forcing ◽  
Hydrological Cycle ◽  
Clear Sky ◽  
Operational Management ◽  
Moderate Resolution ◽  
Resolution Imaging ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Sky Conditions

Abstract Runoff from mountain snowpack is an important freshwater supply for many parts of the world. The deposition of aeolian dust on snow decreases snow albedo and increases the absorption of solar irradiance. This absorption accelerates melting, impacting the regional hydrological cycle in terms of timing and magnitude of runoff. The Moderate Resolution Imaging Spectroradiometer (MODIS) Dust Radiative Forcing in Snow (MODDRFS) satellite product allows estimation of the instantaneous (at time of satellite overpass) surface radiative forcing caused by dust. While such snapshots are useful, energy balance modeling requires temporally resolved radiative forcing to represent energy fluxes to the snowpack, as modulated primarily by varying cloud cover. Here, the instantaneous MODDRFS estimate is used as a tie point to calculate temporally resolved surface radiative forcing. Dust radiative forcing scenarios were considered for 1) clear-sky conditions and 2) all-sky conditions using satellite-based cloud observations. Comparisons against in situ stations in the Rocky Mountains show that accounting for the temporally resolved all-sky solar irradiance via satellite retrievals yields a more representative time series of dust radiative effects compared to the clear-sky assumption. The modeled impact of dust on enhanced snowmelt was found to be significant, accounting for nearly 50% of the total melt at the more contaminated station sites. The algorithm is applicable to regional basins worldwide, bearing relevance to both climate process research and the operational management of water resources.

scholarly journals OPTIMAL EXTRACTION OF TROPOSPHERIC OZONE COLUMN BY SIMULTANEOUS USE OF OMI AND TES DATA AND THE SURFACE TEMPERATURE

2015 ◽  
Vol XL-1-W5 ◽  
pp. 467-474
Author(s):  
M. R. Mobasheri ◽  
H. Shirazi
Keyword(s):  
Satellite Data ◽  
Tropospheric Ozone ◽  
Urban Areas ◽  
Ozone Production ◽  
Vertical Resolution ◽  
The Earth ◽  
Ozone Data ◽  
Ozone Profile ◽  
Mathematical Equations ◽  
Ozone Column

This article aims to increase the accuracy of Ozone data from tropospheric column (TOC) of the OMI and TES satellite instruments. To validate the estimated amount of satellite data, Ozonesonde data is used. The vertical resolution in both instruments in the tropospheric atmosphere decreases so that the degree of freedom signals (DOFS) on the average for TES is reduced to 2 and for OMI is reduced to1. But this decline in accuracy in estimation of tropospheric ozone is more obvious in urban areas so that estimated ozone in both instruments alone in non-urban areas show a high correlation with Ozonesonde. But in urban areas this correlation is significantly reduced, due to the ozone pre-structures and consequently an increase on surface-level ozone in urban areas. In order to improve the accuracy of satellite data, the average tropospheric ozone data from the two instruments were used. The aim is to increase the vertical resolution of ozone profile and the results clearly indicate an increase in correlations, but nevertheless the satellite data have a positive bias towards the earth data. To reduce the bias, with the solar flux and nitrogen dioxide values and surface temperatures are calculated as factors of ozone production on the earth’s surface and formation of mathematical equations based on coefficients for each of the mentioned values and multiplication of these coefficients by satellite data and repeated comparison with the values of Ozonesonde, the results showed that bias in urban areas is greatly reduced.

scholarly journals Trends of tropical tropospheric ozone from twenty years of European satellite measurements and perspectives for Sentinel-5 Precursor

2016 ◽  
Author(s):  
Klaus-Peter Heue ◽  
Melanie Coldewey-Egbers ◽  
Andy Delcloo ◽  
Christophe Lerot ◽  
Diego Loyola ◽  
...  
Keyword(s):  
Tropospheric Ozone ◽  
Total Ozone ◽  
Atlantic Coast ◽  
Convective Cloud ◽  
Differential Method ◽  
Retrieval Algorithm ◽  
Data Set ◽  
Cloud Data ◽  
One Year ◽  
The Individual

Abstract. In preparation of the TROPOMI/S5P launch in autumn 2016 a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellites GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995–2015) of tropospheric ozone columns was retrieved. To have a consistent total ozone data set for all sensors one common retrieval algorithm, namely GODFITv3, has been applied to all sensors and the L1 reflectances have also been soft calibrated. These data were input into the tropospheric ozone retrieval. However, the Tropical Tropospheric Ozone Columns (TTOC) for the individual instruments still showed small differences and therefore we harmonised the data set. For this purpose a multi-variant function was fitted to the averaged difference between SCIAMACHY's TTOC and those from the other sensors. The original TTOC was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also a direct comparison of the TTOCs in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small. The GOME and SCIAMACHY data overlap for one year for the complete tropics, this turned out to be insufficient to extrapolate back until 1995. Based on the harmonised observations, we created a merged data product, containing the TTOC from July 1995 to Dec. 2015. A first application of this 20 years record is a trend analysis. The global tropical trend is 0.75 ± 0.12 DU decade−1. Regionally the trends reaches up to 1.8 DU decade−1 like on the African Atlantic coast, over the Western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade−1. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TTOC is part of the operational products.

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