The paper uses Level 2 IASI (Infrared Atmospheric Sounder Interferometer) products to analyse long-standing heatwaves and related droughts. The paper is mostly interested in studying and assessing the effect of drought on vegetation. To this end, we have devised a series of indices sensitive to the water deficit. IASI retrievals are used to derive indices from the surface temperature, emissivity, and temperature/humidity atmospheric profiles. We define the emissivity contrast index, which is sensitive to the land cover and type, and the water deficit index, which combines the surface and air dew point temperatures. These two indices are assessed by considering the heatwave, which hit most of Europe and the Mediterranean basin in 2017. The application of the methodology will be shown by considering a target area in Southern Italy, where woodlands are suffering from climate change. It will be shown that the two indices are sensitive to the water deficit caused by long-lasting droughts.
In this work, a nonlinear statistical regressor method based on deep learning feed-forward neural network (NN) for the retrieval of atmospheric CH4 is proposed. The methodology has been trained and validated on a simulated dataset of observations by the processing of the Monitoring Atmospheric Composition and Climate (MACC) Reanalysis dataset with the state-of-the-art transfer model (RTM) σ-IASI-as. Global data related to one day of the 12 months of 2012 and four synoptic hours (00-06-12-18 UTC) have been processed to catch typical seasonal and diurnal cycles, corresponding to a fairly large number (168.000) of simulated IASI-L1 spectral radiances. CH4 profiles have been predicted on 60 pressure layers. A regression framework based on the principal components analysis (PCA) of the IASI radiances and CH4 profiles has been implemented. The choice of the number of principal components has been addressed by the study of their eigenvalues, to filter redundant information from IASI channels and extract the most significant information from the CH4 profiles. The analysis of the NN retrieval, shows agreement with the reference MACC CH4 contents, allowing to obtain unbiased profile estimates, with accuracy on the total content of about 1.55%. The same accuracy has been obtained for the tropospheric column while for the stratosphere atmospheric column the accuracy is about 3%. Finally, an additional analysis of the CH4 total content shows a correlation between the reference and predicted values of about 0.97.
An analysis of the air quality over the Po valley has been performed by using both satellite and in situ observations of NO2 for the COVID-19 years, 2019-2021. To match satellite observations to those in situ, we have used a geostatistical re-gridding technique. The tools allow us to scale the satellite NO2 retrievals to a finer spatial resolution, which helps us to perform a better spatial colocation with in situ observations. The satellite data consist of Level 2 (L2) NO2 retrievals from TROPOMI (the TROPOspheric Monitoring Instrument), whereas in situ observtaions are taken at eleven diverse stations, which are spread over the Po valley. The Po Valley, in the winter 2019/20, has been the first region in Europe to be severely hit by the COVID-19 pandemic. The Italian government introduced severe restriction measures from March to May 2020 (lockdown). We compared TROPOMI NO2 concentration during winters 2018-19 (no-COVID-19) and the following 2 winters. The observations of TROPOMI, in agreement with the in-situ measurements, saw a significant decrease in the NO2 concentration in March 2020 after the introduction of the lockdown. But they also found a general decrease in lower tropospheric NO2 in winter 2019/2020, the warmest winter ever observed that has limited the use of power for residential and commercial heating. NO2 concentrations raise almost to the pre-COVID-19 values in the 2020/21 winter.
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