The performance indicators in Dakar site.
Aerosol optical thickness (AOT) was provided by SeaWiFS over oceans from October 1997 to December 2010. Weekly, monthly, and annually maps might help scientifics to better understand climate change and its impacts. Making average of several images to get these maps is not suitable on West African coast. A particularity of this area is that it is constantly traversed by desert dust. The algorithm used by SeaWiFS inverts the reflectance measurements to retrieve the aerosol optical thickness at 865 nm. For the poorly absorbing aerosol optical thickness less than 0.35, the standard algorithm works very well. On the west African coast that is often crossed by desert aerosol plumes characterized by high optical thicknesses. In this paper we study the spatial and temporal variability of aerosols on the West African coast during the period from December 1997 to November 2009 by using neural network inversion. The neural network method we used is mixed method of neuro-variational inversion called SOM-NV. It is an evolution of NeuroVaria that is a combination of a variational inversion and multilayer perceptrons, multilayer perceptrons (MLPs). This work also enables validation of the optical thickness retrieved by SOM-NV with AOT in situ measurements collected at AErosol RObotic NETwork (AERONET) stations.
- West African coast
- absorbing aerosols
- neural networks
The sensor SeaWiFS ocean color provides daily luminance measurements of ocean-atmosphere system in the visible and near infrared since October 1997. Luminances are at wavelengths 412, 443, 490, 510, 555, 670, 765, and 865 nm. For each wavelength
SeaWiFS aerosol products are generated, validated, and made available by NASA. These aerosols from the standard atmospheric correction algorithm can hardly be used for global aerosol studies because of aerosol optical thickness greater than about 0.35.
Plumes of absorbing aerosols are observed regularly on the West African coast near the Sahara, which prevents spatial and temporal operation of atmospheric and oceanic parameters.
This paper presents a new method to retrieve the aerosol parameters from ocean color satellite radiometer and is able to give information on absorbing aerosols.
2. Data and method
For this study, we use daily luminance measurements made by the SeaWiFS sensor off the West African coast in an area between 8°–24°N and 14°–30°W. These measures extend the period of 1997–2009. The TOA reflectance
We used satellite data sets comprising ten-dimensional vectors, whose components are eight wavelengths measured by the radiometer and two viewing angles since the reflectance spectra depend on the geometry of the measurement. These angles are the sun zenith angle
Each vector, whose components correspond to the SeaWiFS wavelengths, represents a
The learning dataset
We also used theoretical database Dataexpert that consists of the computed at eight wavelengths with a 2-layer radiative transfer model  for various optical thickness values, chlorophyll content, and geometry of the measurement and for five aerosol models.
Each Dataexpert vector comprises eight spectral components () and two geometry components which are the sun zenith angle
Dataexpert was used in order to introduce the expertise and to retrieve the aerosol type and the optical thickness values.
In this study, we used SOM-NV , a two successive statistical models for analyzing the
The second statistical model improves the retrieval of the optical thickness. We used a neuro-variational algorithm called NeuroVaria that is able to provide accurate atmospheric corrections for inverting satellite ocean color measurements. The algorithm minimizes a weighted quadratic cost function,
A major advantage of the method is a gain in number of processed pixels from SeaWiFS. This work also enables validation of the optical thickness retrieved by SOM-NV with in situ measurements of optical thicknesses AERONET collected at stations in Dakar and Cabo Verde .
The complete methodology was applied to SeaWiFS images of the ocean off the West African coast from 1997 to 2009 to produce the type of aerosol, the aerosol optical thickness, and the chlorophyll-a concentration.
Monthly mean map aerosols and chlorophyll-a were calculated on 9 km × 9 km used for SeaWiFS GAC product level 3. Seasonality strong τ(865) is characterized by a strong invasion of dust into the months of June, July, and August. Their intensities vary from year to year, depending on aridity conditions in Africa and the wind direction.
In Figure 1, we compare for each pixel of the image (March 10, 2004), the optical thickness and
Maps of average aerosol and chlorophyll-a were computed on grids of 9 km × 9 km GAC used for SeaWiFS level-3 GAC products. The averages obtained by SOM-NV seem statistically more representative than those obtained by SeaWiFS. Monthly averages of optical thickness obtained by the standard processing for the months of March and June of Figure 2 are drastic because only τ values not exceeding 0.35 are taken into account. These failures demonstrate impossibility for SeaWiFS to establish spatial and temporal global maps of aerosol particularly in absorbing area. This is due to the fact that desert aerosols frequently cross the ocean  preventing the standard algorithm to retrieve these aerosols and chlorophyll-a below them.
In Figure 3, average of AOT (top), Chl-a (middle) of the months of January and April 2005 is shown. As seen in Figure 2, we note also in Figure 3 failure for SeaWiFS to establish consistent spatial and temporal global maps of aerosol optical thickness. Fluctuations in the chlorophyll-a observed at Figure 3 are the same in the image for both algorithms. In terms of intensity, the values of Chl-a are higher for the standard algorithm at the continental shelves. An intercomparison with the SeaWiFS standard processing showed that the SOM-NV methodology increased the number of pixels processed of a factor until 10. This is due to the impossibility of the standard algorithm to retrieve the chlorophyll-a in the presence of Saharan dust, which frequently cross the ocean. The low number of pixels processed by SeaWiFS may bias the mean chlorophyll-a maps.
The presence of the case 2 waters explains these high values of Chl-a. The hypothesis of dark ocean is no longer valid in case of water 2. These results confirm that atmospheric corrections on the Saharan dust by SOM-NV are correct because Chl-a retrievals using OC4V4 are satisfactory spatial and in intensity.
Direct measurements of the optical thickness at 865 nm
The two AERONET measurements are found when at least one half of the SeaWiFS, or the SOM-NV retrievals within a 5 × 5 pixels square box containing the AERONET site are valid. The AERONET optical thickness values used for this validation were actually the mean of all the measurements made between 12:00 UT and 14:00 UT, because SeaWiFS images over the area were taken around 13:00 UT. We computed mean value for each month.
We compared the root-mean-square error (RMSE) and the mean relative error (MRE) of
The MRE remains low for SOM-NV (38.8% at Dakar, 40.5% at Cabo Verde), the RMSE are less than 0.03, and the correlations are 80.23% at Dakar station and 82.34% at Cabo Verde. However, for SeaWiFS, the correlation is 13.45% for Dakar and 6.38% for Cabo Verde, and the MRE is 74.54% at Dakar and 60.75% at Cabo Verde. These results are resumed in Tables 1 and 2.
|Mean relative error||38.8%||74.54%|
|Mean relative error||40.5%||74.75%|
SOM-NV, an original and efficient method to retrieve optical properties from TOA reflectance measured by satellite-borne multispectral ocean color sensors, has permitted to study realistic spatial distribution of aerosol optical thickness and chlorophyll-a. The method is based on a combination of a neural network classification and a variational optimization. It makes use of the full spectrum of measurements to perform the aerosol identification. The means obtained by SOM-NV appear statistically more representative than those obtained by SeaWiFS. Monthly mean of aerosol optical thicknesses obtained by the standard processing for March and June are drastic because only τ values not exceeding 0.35 are considered. These temporal and spatial failures demonstrate the impossibility of establishing by SeaWiFS global maps of aerosols especially in absorbing environment. The number of pixels processed by SOM-NV even double or triple those computed by SeaWiFS. This is due to the fact that desert aerosols frequently cross the ocean preventing the standard algorithm to retrieve these aerosols and chlorophyll-a below them.
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