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Saharan dust transport over Gran Canaria islands & Cabo Verde

On 2019.02.05, a remarkable dust outbreak issued from the Western Sahara coast spread over Gran Canaria islands. This thick plume, with heavy load of particles, and larger than 1.000 km width, was well observed via a series of satellite images:

RGB Composite image from OLCI Sentinel-3 of the Saharan dust outbreak of 2019.02.05. Credit SentinelHub.. Source: https://apps.sentinel-hub.com/eo-browser
Aerosol Index UVAI retried from TROPOMI Sentinel-5 Precursor over the Saharan dust outbreak of 2019.02.05. Credit SentinelHub.. Source: https://apps.sentinel-hub.com/eo-browser

 

 

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Tweet from the AC SAF showing images from Meteosat Second Generation (MSG) SEVIRI (left) and GOME2 Metop UVAI index (right) of the Saharan dust outbreak of 2019.02.05. Source: https://twitter.com/Atmospheric_SAF/status/1092847004844208128

Several days later, on 2019.03.02, another Saharan dust was transported over Cabo Verde. Similarly, a very large and thick plume was captured in the images from the NASA SUOMI VIIRS sensor, and measured by the aerosol index UVAI from Tropomi.

RGB composite image from SUOMI VIIRS of the Saharan dust transport of 2019.03.02. Credit: NASA EODIS Worldview Source: https://worldview.earthdata.nasa.gov
Aerosol Index UVAI retried from TROPOMI Sentinel-5 Precursor over the Saharan dust transport of 2019.03.02. Credit SentinelHub.. Source: https://apps.sentinel-hub.com/eo-browser

Such aerosol dust events regularly occur in these areas. I always find the related satellite images not only impressive but also informative regarding their spatial scales, intensity and transport.

 

More information?

  • The EU Copernicus programme here
  • Overview of the NASA & NOAA SUOMI mission here
  • The EUMETSAT AC SAF here
  • EUMETSAT agency here
  • TROPOMI WebPage here
  • Current Earth observation satellites with Sentinel-3 & Sentinel-5 Precursor WebPage here
  • Aerosol webPage here

 

Biomass burning in Central Africa: NOx, CO & aerosol animations from Sentinel-5 P & SUOMI observations

Biomass burning is a major source of trace gases & aerosol particles on a regional and a global scale (Seiler and Crutzen, 1980; Logan et al., 1981; Crutzen and Andreae, 1990; Andreae, 1991). Interannual variations in biomass burning within specific regions of the world can be dramatic, depending on factors such as rainfall and political incentives to clear land. The forest fires in Indonesia during 1997–1998 and those in Mexico during 1998, both related to the El Nino Southern Oscillation (ENSO) induced drought, are well known examples of extreme fire events (e.g. Levine, 1999; Nakajima et al., 1999; Peppler et al., 2000; Cheng and Lin, 2001).

The principal biomass burning areas can be observed in the Amazonian region and in central Africa. Among the trace gases released, NO2 – nitrogen dioxide & CO – carbon monoxide abundances can be very high. Satellite observations are a helpful tool for the identification of these sources in the troposphere and to follow their transport. In addition, these intensive biomass burning episodes release a large quantity of aerosol particles, at fine size and with absorbing properties.

Below are the animations of NO2 and CO columns as observed by the TROPOMI sensor, on-board the Sentinel-5 Precursor mission from the European Copernicus program. These animations cover ~1 month of biomass burning over Central Africa. They are extracted from the SentinelHub Earth Observation (EO) browser.

Animation Tropospheric NO2 vertical column density from TROPOMI Sentinel-5 Precursor from 2019.01.01 to 2019.02.07. Credit SentinelHub. Source: https://apps.sentinel-hub.com/eo-browser/
Animation CO total column from TROPOMI Sentinel-5 Precursor from 2019.01.01 to 2019.02.07. Credit SentinelHub. Source: https://apps.sentinel-hub.com/eo-browser/

Additionally, you can visualise here animations based on the NASA SUOMI VIIRS observations showing the fire detected pixels (in red) and the detection of fine absorbing particles in large concentrations. Note that SUOMI and Sentinel-5 P are flying together on the same orbit / same track with only a few minutes apart.

Animation RGB image composite from SUOMI VIIRS from 2019.01.01 to 2019.02.07. Credit NASA EODIS WorldView. Source: https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day,OMPS_Aerosol_Index(hidden),AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Terra_Thermal_Anomalies_All(hidden),MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2019-01-25-T00%3A00%3A00Z&z=3&v=-80.65378411193339,-57.40841307238665,84.29515205827931,42.15408692761331&ab=on&as=2019-01-01T00%3A00%3A00Z&ae=2019-02-07T00%3A00%3A00Z&av=3&al=false
Animation UV Absorbing Aerosol Index (UVAI – Red = absorbing aerosols detected in large amounts) from SUOMI VIIRS from 2019.01.01 to 2019.02.07. Credit NASA EODIS WorldView. Source: https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor,MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor(hidden),VIIRS_SNPP_Thermal_Anomalies_375m_Day,OMPS_Aerosol_Index(hidden),AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Terra_Thermal_Anomalies_All(hidden),MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2019-01-25-T00%3A00%3A00Z&z=3&v=-80.65378411193339,-57.40841307238665,84.29515205827931,42.15408692761331&ab=on&as=2019-01-01T00%3A00%3A00Z&ae=2019-02-07T00%3A00%3A00Z&av=3&al=false

 

More information?

  • TROPOMI, on-board the Copernicus Sentinel-5 Precursor satellite, here
  • NO2 – nitrogen dioxide here
  • CO – carbon monoxide here
  • Aerosol particles here
  • Trace gases in the atmospheric composition here

Suffocating air in China during wintertime – How does it look like from space?

In winter time, cold temperature leads to an increase in using heaters of course. And when the electricity source is notably based on coal power plants, then gas emissions (and particles) increase as well leading to higher pollutant concentration.

Air pollution in China is well known. Satellite observations as evidence show a strong increasing trend of NO2 column concentrations since 1995 in China (Irie et al., 2005; Richter et al., 2005; van der A et al., 2006). The main anthropogenic emissions of NOx in China are from transport and coal-fired power plants (Liu et al., 2015; Saikawa et al., 2017; Li et al., 2017). Because of the rapid implementation of new technologies and air quality control regulations for power plants and vehicles in China, their emission factors and activities are also changing with time. In spite of major reductions the last 7 years thanks to very supportive governmental decisions, it still remains an issue.

Pictures below, from NASA MODIS Aqua let us imagine how suffocating this air may remain these days. TROPOMI Sentinel-5 Precursor observations indeed show high concentrations of not only NO2 – nitrogen dioxide, and CO – carbon monoxide. Efforts in reducing emissions due to the electricity generation and vehicles must continue to ensure a better health for the whole population.

2019.01.17 NASA MODIS Aqua false RGB composite image. Credit NASA EOS WorldView. Source: https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor,MODIS_Terra_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_Angstrom_Exponent_Ocean(hidden),MODIS_Aqua_AOD_Deep_Blue_Land(hidden),MODIS_Aqua_Aerosol_Optical_Depth_3km(hidden),MODIS_Aqua_AOD_Deep_Blue_Combined(hidden),AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Terra_Thermal_Anomalies_All,MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2019-01-17-T00%3A00%3A00Z&z=3&v=97.78198716466228,19.823136129740995,139.01922120721545,44.71376112974098
2019.01.17 Tropospheric NO2 from TROPOMI Sentinel-5 Precursor. Credit Copernicus SentinelHub. Source: https://apps.sentinel-hub.com/eo-browser/?lat=31.63&lng=110.26&zoom=5&time=2019-01-17&preset=CO_VISUALIZED&datasource=Sentinel-5P%20CO
2019.01.17 CO Total column from TROPOMI Sentinel-5 Precursor. Credit Copernicus SentinelHub. Source: https://apps.sentinel-hub.com/eo-browser/?lat=31.63&lng=110.26&zoom=5&time=2019-01-17&preset=CO_VISUALIZED&datasource=Sentinel-5P%20CO

Publication in AMT journal of “Minimizing aerosol effects on the OMI tropospheric NO2 retrieval – An improved use of the 477 nm O2-O2 band and an estimation of the aerosol correction uncertainty”

Our last research paper focused on the challenging topics of aerosol layer height (ALH) retrieval from hyperspectral visible measurements, aerosol correction, and tropospheric NO2 retrieval from satellite sensors like OMI was published in the Atmospheric Measurement Techniques (AMT) journal. This work relies on the activities achieved during the last months of my thesis research with my colleagues of the Geoscience and Remote Sensing (GRS) department of TU Delft and KNMI: Dr. J. Pepijn Veefkind, Dr. Johan de Haan, Dr. Piet Stammess, and Prof. Dr. Pieternel  F. Levelt.

This paper is based on the last developments we published during 2016, 2017, and 2018. During these years, not only the OMI cloud algorithm was improved (Veefkind et al., 2016), but also an OMI aerosol layer height (and optical thickness) neural network algorithm was developed (Chimot et al., 2017, 2018). This time, we directly evaluate the impacts of these developments to correct of aerosol absorption and scattering effects in the visible spectral range in view of retrieving tropospheric NO2, an important trace gas affecting air quality in urban and industrialised areas.

What are the main conclusions? Aerosol correction on tropospheric NO2 retrieval from OMI has been greatly improving the last 2-3 years for UV-Vis air quality satellites. Notably thanks to the updated effective cloud retrievals. But also there is a clear potential from the ALH based on the 477 nm O2-O2 band. However, the decision for the future processors is not necessarily easy to take: accuracy vs. radiance closure budget are clearly competing.

Gotten curious? See more information here.

I greatly thank my co-authors from the Netherlands for this very interesting work! This paper closes the loop of my whole research work achieved during the last 4 years with the Geoscience and Remote Sensing (GRS) department of TU Delft and KNMI.

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Average maps of MODIS AOD(550nm), OMI DOMINO NNvO2 and differences after applying the implicit (with OMCLDO2-New) or explicit (with NNMODIS,SSA=0.95) aerosol correction over China in summertime (June–July–August) 2006–2007. (a) MODIS AOD(550nm), (b) OMI DOMINO NNvO2, (c) OMI NNvO2 differences due to changes between OMCLDO2-New and DOMINO implicit aerosol corrections, (d) NNvO2 differences between explicit aerosol correction based on the NNMODIS,SSA=0.95 aerosol parameters (i.e. aerosol forward model assuming SSA = 0.95, MODIS AOD(550 nm) and retrieved ALH) and implicit aerosol correction implemented in DOMINO.

How air pollution may look like from space?

Air pollution may have various looks and shapes depending on our eyes and used space-borne sensors to visualise it.

An example of India on 2019.01.18 which allows to observe a dark smoke, shielding the surface, heavy abundance of tiny particles aerosols retrieved from MODIS Terra, and high concentration of nitrogen dioxide – NO2 toxic gas from the Copernicus Sentinel-5 Precursor (S5P) TROPOMI sensor.

MODIS Terra RGB False colour composite, India 2019.01.18. Source NASA EODIS WorldView https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Fires_Terra,MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2016-01-16&z=3&v=-144.91024116847828,-98.2219769021739,179.4795346467391,100.9030230978261
MODIS Terra Aerosol Optical Depth (AOD) (550 nm). Source: NASA EODIS WorldView https://worldview.earthdata.nasa.gov/?p=geographic&l=VIIRS_SNPP_CorrectedReflectance_TrueColor(hidden),MODIS_Aqua_CorrectedReflectance_TrueColor(hidden),MODIS_Terra_CorrectedReflectance_TrueColor,AIRS_CO_Total_Column_Day(hidden),OMI_Nitrogen_Dioxide_Tropo_Column(hidden),MODIS_Fires_Terra,MODIS_Terra_Aerosol_Optical_Depth_3km(hidden),Reference_Labels(hidden),Reference_Features(hidden),Coastlines&t=2016-01-16&z=3&v=-144.91024116847828,-98.2219769021739,179.4795346467391,100.9030230978261
Tropospheric NO2 vertical column density from Sentinel-5 Precursor TROPOMI, India 2019.01.18. Source: Copernicus SentinelHub https://sentinel-hub.com

 

But a lot of times air pollution is not directly “visible” with our eyes. A special zoom of 2019.01.15 with TROPOMI on two cities that I know well in South West France, Bordeaux and Toulouse. Very clear NO2 pollution can be detected thanks to the fine pixels. Such isolated spots clearly suggest local emissions (mostly cars). Note also the hot-spots in the North of Spain (e.g. Bilbao, SanSebastian , Barcelona)

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Tropospheric NO2 vertical column density from Sentinel-5 Precursor (S5P) TROPOMI, South-West France & North-East Spain 2019.01.15. Source: Copernicus SentinelHub https://sentinel-hub.com

 

More information?

  • Nitrogen dioxide – NO2 here
  • Sentinel-5 Precursor TROPOMI here
  • Visualising Copernicus Sentinel images here
  • NASA MODIS EODIS WorldView False Colour composite images here

Vitamin D dose observed from space

Winter in our latitudes means reduced sunlight & less Vitamin D dose as observed in the UV spectrum by GOME2 on-board Metop EUMETSAT satellite. This daily map from the Atmospheric Composition (AC) Satellite Application Facilities (SAF) shows higher dose in the 0-30 deg S region.

Quite looking forward to Summer time then!

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Daily Vitamin D Dose observed from the UV spectrum measured on 2019.01.14 by GOME2 on-board Metop B. This parameter is part of the off-line surface UV product and is ensured by operational validation and on-line monitoring services provided by the Finish Meteorological Institut (FMI) via the AC SAF. Source: https://acsaf.org/uv_validation/latest_product.php

 

More information?

  • AC SAF: SAFs are dedicated centres of excellence for processing satellite data and form an integral part of the distributed EUMETSAT Application Ground Segment. They are composed of multiple specialist experts representative of the EUMETSAT member states. AC SAF consortium members develop radiative transfer calculation methods and other algorithms for retrieving atmospheric remote sensing variables from the polar-orbiting satellite series Metop. AC SAF also validates the data products and provide associated dissemination and user services.
    • Visit AC SAF here
    • List of all EUMETSAT SAF projects here
  • The European Organisation for the exploitation of Meteorological Satellite (EUMETSAT): it is an intergovernmental organisation, founded in 1986 with the goal to supply weather and climate-related satellite data, images and products – 24 hours a day, 365 days a year – to the National Meteorological Services of the Member States in Europe, and other worldwide users.

Improving aerosol correction of OMI tropospheric NO2 over China, based on a CALIOP climatology – Happy to co-author the work of Liu et al. (2019)!

screenshot 2019-01-03 at 22.25.42
Seasonal spatial distribution of tropospheric NO2 VCD in 2012 for (a) POMINO v1.1, (b) POMINO, and (c) their relative difference.

A very nice work by Mengyao Liu et al. (2019), from Peking University in Beijing, China, has just been published in the Atmospheric Measurement Techniques (AMT) journal, on which I am co-author!

This study evaluated the possibility to improve OMI tropospheric NO2 retrieval over China by creating and exploiting an aerosol vertical profile climatology database from 9 years of CALIOP observations. Among other elements, it shows the potential benefits to use satellite observations in a synergistic way (OMI–MODIS-CALIOP) and how to constrain better aerosol models in view of correcting aerosol scattering and absorption effects in UV-vis satellite measurements.

This notably leads to an update of the POMINO dataset from Lin et al. (2014, 2015).

 

More information?