scholarly journals Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterization, and trends

2018 ◽  
Author(s):  
Marina Zara ◽  
K. Folkert Boersma ◽  
Isabelle De Smedt ◽  
Andreas Richter ◽  
Enno Peters ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Random Noise ◽  
Uncertainty Budget ◽  
Optical Absorption Spectroscopy ◽  
Statistical Uncertainty ◽  
Climate Variables ◽  
Climate Data ◽  
Uncertainty Estimates ◽  
Climate Data Record

Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone and aerosol Essential Climate Variables, and it is essential to quantify and characterize their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from 4 different research groups (BIRA-IASB, IUP, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in Differential Optical Absorption Spectroscopy (DOAS) fitting techniques, and by the desire to provide a fully traceable uncertainty budget for climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement, but with substantial differences in the reported uncertainties between groups and instruments. As a check of the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8 × 1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12 × 1015 molec. cm−2 ), but the closure between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite’s level-1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 %/yr related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase

scholarly journals Improved slant column density retrieval of nitrogen dioxide and formaldehyde for OMI and GOME-2A from QA4ECV: intercomparison, uncertainty characterisation, and trends

2018 ◽  
Vol 11 (7) ◽  
pp. 4033-4058 ◽  
Author(s):  
Marina Zara ◽  
K. Folkert Boersma ◽  
Isabelle De Smedt ◽  
Andreas Richter ◽  
Enno Peters ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Trend Detection ◽  
Optical Absorption Spectroscopy ◽  
Statistical Uncertainty ◽  
Random Component ◽  
Climate Variables ◽  
Total Uncertainty ◽  
Uncertainty Estimates ◽  
Calculation Step

Abstract. Nitrogen dioxide (NO2) and formaldehyde (HCHO) column data from satellite instruments are used for air quality and climate studies. Both NO2 and HCHO have been identified as precursors to the ozone (O3) and aerosol essential climate variables, and it is essential to quantify and characterise their uncertainties. Here we present an intercomparison of NO2 and HCHO slant column density (SCD) retrievals from four different research groups (BIRA-IASB, IUP Bremen, and KNMI as part of the Quality Assurance for Essential Climate Variables (QA4ECV) project consortium, and NASA) and from the OMI and GOME-2A instruments. Our evaluation is motivated by recent improvements in differential optical absorption spectroscopy (DOAS) fitting techniques and by the desire to provide a fully traceable uncertainty budget for the climate data record generated within QA4ECV. The improved NO2 and HCHO SCD values are in close agreement but with substantial differences in the reported uncertainties between groups and instruments. To check the DOAS uncertainties, we use an independent estimate based on the spatial variability of the SCDs within a remote region. For NO2, we find the smallest uncertainties from the new QA4ECV retrieval (0.8  ×  1015 molec. cm−2 for both instruments over their mission lifetimes). Relative to earlier approaches, the QA4ECV NO2 retrieval shows better agreement between DOAS and statistical uncertainty estimates, suggesting that the improved QA4ECV NO2 retrieval has reduced but not altogether eliminated systematic errors in the fitting approach. For HCHO, we reach similar conclusions (QA4ECV uncertainties of 8–12  ×  1015 molec. cm−2), but the closeness between the DOAS and statistical uncertainty estimates suggests that HCHO uncertainties are indeed dominated by random noise from the satellite's level 1 data. We find that SCD uncertainties are smallest for high top-of-atmosphere reflectance levels with high measurement signal-to-noise ratios. From 2005 to 2015, OMI NO2 SCD uncertainties increase by 1–2 % year−1, which is related to detector degradation and stripes, but OMI HCHO SCD uncertainties are remarkably stable (increase  <  1 % year−1) and this is related to the use of Earth radiance reference spectra which reduces stripes. For GOME-2A, NO2 and HCHO SCD uncertainties increased by 7–9 and 11–15 % year−1 respectively up until September 2009, when heating of the instrument markedly reduced further throughput loss, stabilising the degradation of SCD uncertainty to  <  3 % year−1 for 2009–2015. Our work suggests that the NO2 SCD uncertainty largely consists of a random component ( ∼  65 % of the total uncertainty) as a result of the propagation of measurement noise but also of a substantial systematic component ( ∼  35 % of the total uncertainty) mainly from stripe effects. Averaging over multiple pixels in space and/or time can significantly reduce the SCD uncertainties. This suggests that trend detection in OMI, GOME-2 NO2, and HCHO time series is not limited by the spectral fitting but rather by the adequacy of assumptions on the atmospheric state in the later air mass factor (AMF) calculation step.

scholarly journals An Uncertainty Quantified Fundamental Climate Data Record for Microwave Humidity Sounders

2019 ◽  
Vol 11 (5) ◽  
pp. 548 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan Buehler ◽  
Marc Prange ◽  
Theresa Lang ◽  
...  
Keyword(s):  
Brightness Temperature ◽  
Uncertainty Propagation ◽  
Uncertainty Budget ◽  
Intermediate Step ◽  
Climate Data ◽  
Data Record ◽  
Upper Tropospheric Humidity ◽  
Uncertainty Information ◽  
Climate Data Record

To date, there is no long-term, stable, and uncertainty-quantified dataset of upper tropospheric humidity (UTH) that can be used for climate research. As intermediate step towards the overall goal of constructing such a climate data record (CDR) of UTH, we produced a new fundamental climate data record (FCDR) on the level of brightness temperature for microwave humidity sounders that will serve as basis for the CDR of UTH. Based on metrological principles, we constructed and implemented the measurement equation and the uncertainty propagation in the processing chain for the microwave humidity sounders. We reprocessed the level 1b data to obtain newly calibrated uncertainty quantified level 1c data in brightness temperature. Three aspects set apart this FCDR from previous attempts: (1) the data come in a ready-to-use NetCDF format; (2) the dataset provides extensive uncertainty information taking into account the different correlation behaviour of the underlying errors; and (3) inter-satellite biases have been understood and reduced by an improved calibration. Providing a detailed uncertainty budget on these data, this new FCDR provides valuable information for a climate scientist and also for the construction of the CDR.

scholarly journals Long-Term Variation in the Tropospheric Nitrogen Dioxide Vertical Column Density over Korea and Japan from the MAX-DOAS Network, 2007–2017

2021 ◽  
Vol 13 (10) ◽  
pp. 1937
Author(s):  
Yongjoo Choi ◽  
Yugo Kanaya ◽  
Hisahiro Takashima ◽  
Hitoshi Irie ◽  
Kihong Park ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Column Density ◽  
Optical Absorption Spectroscopy ◽  
Industrial Complex ◽  
Vertical Column ◽  
Source Regions ◽  
Potential Source ◽  
Vertical Column Density ◽  
Good Agreement

We investigated long-term observations of the tropospheric nitrogen dioxide vertical column density (NO2 TropVCD) from the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) network in Russia and ASia (MADRAS) from 2007 to 2017 at urban (Yokosuka and Gwangju) and remote (Fukue and Cape Hedo) sites in East Asia. The monthly mean in the NO2 TropVCD from MAX-DOAS measured at ~13:30 local time, which is the Ozone Monitoring Instrument (OMI) overpass time, shows good agreement with OMI data during summer, but differences between the two datasets increase in winter. The Theil-Sen slope of the long-term trend indicate a relatively rapid and gradual reduction in NO2 at Yokosuka and two remote sites (Fukue and Cape Hedo), respectively, regardless of the season except for fall at Fukue, but significant changes in NO2 are not observed at Gwangju, Korea. In contrast, OMI satellite data reveal an increase in the NO2 TropVCD at all sites except for Yokosuka, where a decreasing trend common to MAX-DOAS is found, suggesting that the results from satellites need to be cautiously used for investigating long-term trends in less polluted or remote areas. Using backward trajectories, potential source regions are identified for the two urban sites. The spatial distribution from OMI data shows good agreement with the potential source regions at Yokosuka. The potential source regions in Gwangju are identified as the National Industrial Complex in Yeosu and Gwangyang, while the transport route is not clearly visible with OMI data because of their low sensitivity in less polluted areas. The proposed approach is suitable for identifying potential source areas that might not be recognized by satellite observations.

scholarly journals Detection of pollution transport events southeast of Mexico City using ground-based visible spectroscopy measurements of nitrogen dioxide

2009 ◽  
Vol 9 (1) ◽  
pp. 4769-4804 ◽  
Author(s):  
M. L. Melamed ◽  
R. Basaldud ◽  
R. Steinbrecher ◽  
S. Emeis ◽  
L. G. Ruíz-Suárez ◽  
...  
Keyword(s):  
Nitrogen Dioxide ◽  
Mexico City ◽  
Column Density ◽  
Mixing Layer ◽  
Optical Absorption Spectroscopy ◽  
Pollution Transport ◽  
Visible Spectroscopy ◽  
Density Measurements ◽  
Research Site ◽  
Depth Analysis

Abstract. This work presents ground based differential optical absorption spectroscopy (DOAS) measurements of nitrogen dioxide (NO2) during the MILAGRO field campaign in March 2006 at the Tenango del Aire research site located to the southeast of Mexico City. The DOAS NO2 column density measurements are used in conjunction with ceilometer, meteorological and surface nitrogen oxides (NOx) and total reactive nitrogen (NOy) measurements to show a more comprehensive view of air pollution results when a research site has both surface and remote sensing instruments. An in depth analysis of 13 March 2006 demonstrates how DOAS NO2, surface NO2 and ceilometer data can be used to determine the extent of mixing of the pollution layer. In addition, we show the effectiveness of how DOAS measurements can be used to observe pollution sources that may reside above the mixing layer, such as the presence of lightning produced NO2 as seen on 28 March 2006.

scholarly journals PERSIANN-CCS-CDR, a 3-hourly 0.04° global precipitation climate data record for heavy precipitation studies

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Mojtaba Sadeghi ◽  
Phu Nguyen ◽  
Matin Rahnamay Naeini ◽  
Kuolin Hsu ◽  
Dan Braithwaite ◽  
...  
Keyword(s):  
Spatial Resolution ◽  
Heavy Precipitation ◽  
Climate Data ◽  
Spatiotemporal Resolution ◽  
Data Record ◽  
Precipitation Estimation ◽  
Precipitation Estimates ◽  
Global Precipitation ◽  
Climate Data Record

AbstractAccurate long-term global precipitation estimates, especially for heavy precipitation rates, at fine spatial and temporal resolutions is vital for a wide variety of climatological studies. Most of the available operational precipitation estimation datasets provide either high spatial resolution with short-term duration estimates or lower spatial resolution with long-term duration estimates. Furthermore, previous research has stressed that most of the available satellite-based precipitation products show poor performance for capturing extreme events at high temporal resolution. Therefore, there is a need for a precipitation product that reliably detects heavy precipitation rates with fine spatiotemporal resolution and a longer period of record. Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks-Cloud Classification System-Climate Data Record (PERSIANN-CCS-CDR) is designed to address these limitations. This dataset provides precipitation estimates at 0.04° spatial and 3-hourly temporal resolutions from 1983 to present over the global domain of 60°S to 60°N. Evaluations of PERSIANN-CCS-CDR and PERSIANN-CDR against gauge and radar observations show the better performance of PERSIANN-CCS-CDR in representing the spatiotemporal resolution, magnitude, and spatial distribution patterns of precipitation, especially for extreme events.

scholarly journals Recommendations for HCHO and SO2 Retrieval Settings from MAX-DOAS Observations under Different Meteorological Conditions

2021 ◽  
Vol 13 (12) ◽  
pp. 2244
Author(s):  
Zeeshan Javed ◽  
Aimon Tanvir ◽  
Muhammad Bilal ◽  
Wenjing Su ◽  
Congzi Xia ◽  
...  
Keyword(s):  
Column Density ◽  
Trace Gases ◽  
Meteorological Conditions ◽  
Optical Absorption Spectroscopy ◽  
Chemical Production ◽  
Vertical Column ◽  
Phase Oxidation ◽  
Fog Days ◽  
Rms Errors ◽  
Optimal Settings

Recently, the occurrence of fog and haze over China has increased. The retrieval of trace gases from the multi-axis differential optical absorption spectroscopy (MAX-DOAS) is challenging under these conditions. In this study, various reported retrieval settings for formaldehyde (HCHO) and sulfur dioxide (SO2) are compared to evaluate the performance of these settings under different meteorological conditions (clear day, haze, and fog). The dataset from 1st December 2019 to 31st March 2020 over Nanjing, China, is used in this study. The results indicated that for HCHO, the optimal settings were in the 324.5–359 nm wavelength window with a polynomial order of five. At these settings, the fitting and root mean squared (RMS) errors for column density were considerably improved for haze and fog conditions, and the differential slant column densities (DSCDs) showed more accurate values compared to the DSCDs between 336.5 and 359 nm. For SO2, the optimal settings for retrieval were found to be at 307–328 nm with a polynomial order of five. Here, root mean square (RMS) and fitting errors were significantly lower under all conditions. The observed HCHO and SO2 vertical column densities were significantly lower on fog days compared to clear days, reflecting a decreased chemical production of HCHO and aqueous phase oxidation of SO2 in fog droplets.

scholarly journals The Passive Microwave Neural Network Precipitation Retrieval Algorithm for Climate Applications (PNPR-CLIM): Design and Verification

2021 ◽  
Vol 13 (9) ◽  
pp. 1701
Author(s):  
Leonardo Bagaglini ◽  
Paolo Sanò ◽  
Daniele Casella ◽  
Elsa Cattani ◽  
Giulia Panegrossi
Keyword(s):  
Neural Network ◽  
Passive Microwave ◽  
Retrieval Algorithm ◽  
Climate Data ◽  
High Quality ◽  
Data Record ◽  
Global Precipitation ◽  
Climate Data Record

This paper describes the Passive microwave Neural network Precipitation Retrieval algorithm for climate applications (PNPR-CLIM), developed with funding from the Copernicus Climate Change Service (C3S), implemented by ECMWF on behalf of the European Union. The algorithm has been designed and developed to exploit the two cross-track scanning microwave radiometers, AMSU-B and MHS, towards the creation of a long-term (2000–2017) global precipitation climate data record (CDR) for the ECMWF Climate Data Store (CDS). The algorithm has been trained on an observational dataset built from one year of MHS and GPM-CO Dual-frequency Precipitation Radar (DPR) coincident observations. The dataset includes the Fundamental Climate Data Record (FCDR) of AMSU-B and MHS brightness temperatures, provided by the Fidelity and Uncertainty in Climate data records from Earth Observation (FIDUCEO) project, and the DPR-based surface precipitation rate estimates used as reference. The combined use of high quality, calibrated and harmonized long-term input data (provided by the FIDUCEO microwave brightness temperature Fundamental Climate Data Record) with the exploitation of the potential of neural networks (ability to learn and generalize) has made it possible to limit the use of ancillary model-derived environmental variables, thus reducing the model uncertainties’ influence on the PNPR-CLIM, which could compromise the accuracy of the estimates. The PNPR-CLIM estimated precipitation distribution is in good agreement with independent DPR-based estimates. A multiscale assessment of the algorithm’s performance is presented against high quality regional ground-based radar products and global precipitation datasets. The regional and global three-year (2015–2017) verification analysis shows that, despite the simplicity of the algorithm in terms of input variables and processing performance, the quality of PNPR-CLIM outperforms NASA GPROF in terms of rainfall detection, while in terms of rainfall quantification they are comparable. The global analysis evidences weaknesses at higher latitudes and in the winter at mid latitudes, mainly linked to the poorer quality of the precipitation retrieval in cold/dry conditions.

scholarly journals Onboard Radio Frequency Interference as the Origin of Inter-Satellite Biases for Microwave Humidity Sounders

2019 ◽  
Vol 11 (7) ◽  
pp. 866 ◽  
Author(s):  
Imke Hans ◽  
Martin Burgdorf ◽  
Stefan A. Buehler
Keyword(s):  
Time Series ◽  
Radio Frequency ◽  
Time Dependent ◽  
Radio Frequency Interference ◽  
Climate Variables ◽  
Compelling Evidence ◽  
Climate Data ◽  
The Earth ◽  
Correction Scheme

Understanding the causes of inter-satellite biases in climate data records from observations of the Earth is crucial for constructing a consistent time series of the essential climate variables. In this article, we analyse the strong scan- and time-dependent biases observed for the microwave humidity sounders on board the NOAA-16 and NOAA-19 satellites. We find compelling evidence that radio frequency interference (RFI) is the cause of the biases. We also devise a correction scheme for the raw count signals for the instruments to mitigate the effect of RFI. Our results show that the RFI-corrected, recalibrated data exhibit distinctly reduced biases and provide consistent time series.

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