Artificial neural networks to retrieve land and sea skin temperature from IASI
Safieddine, S.; Parracho, A.C.; George, M.; Aires, F.; Pellet, V.; Clarisse, L.; Whitburn, S.; Lezeaux, O.; Thépaut, J.-N.; Hersbach, H.; Radnoti, G.; Goettsche, F.; Martin, M.; Doutriaux-Boucher, M.; Coppens, D.; August, T.; Zhou, D.K.; Clerbaux, C. (2020). Artificial neural networks to retrieve land and sea skin temperature from IASI. Remote Sens. 12(17): 2777. https://hdl.handle.net/10.3390/rs12172777
In: Remote Sensing. MDPI: Basel. ISSN 2072-4292; e-ISSN 2072-4292, more
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| Author keywords |
skin temperature; IASI; neural networks; entropy reduction; ERA5;EUMETSAT; SURFRAD |
| Authors | | Top |
- Safieddine, S.
- Parracho, A.C.
- George, M.
- Aires, F.
- Pellet, V.
- Clarisse, L., more
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- Whitburn, S.
- Lezeaux, O.
- Thépaut, J.-N.
- Hersbach, H.
- Radnoti, G.
- Goettsche, F.
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- Martin, M.
- Doutriaux-Boucher, M.
- Coppens, D.
- August, T.
- Zhou, D.K.
- Clerbaux, C., more
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| Abstract |
Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis. |
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