Interferometric vs Spectral IASI Radiances: Effective Data-Reduction Approaches for the Satellite Sounding of Atmospheric Thermodynamical Parameters

Abstract: Two data-reduction approaches for the Infrared Atmospheric Sounder Interferometer satellite instrument are discussed and compared. The approaches are intended for the purpose of devising and implementing fast near real time retrievals of atmospheric thermodynamical parameters. One approach is based on the usual selection of sparse channels or portions of the spectrum. This approach may preserve the spectral resolution, but at the expense of the spectral coverage. The second approach considers a suitable truncation of the interferogram (the Fourier transform of the spectrum) at points below the nominal maximum optical path difference. This second approach is consistent with the Shannon-Whittaker sampling theorem, preserves the full spectral coverage, but at the expense of the spectral resolution. While the first data-reduction acts within the spectraldomain, the second can be performed within the interferogram domain and without any specific need to go back to the spectral domain for the purpose of retrieval. To assess the impact of these two different data-reduction strategies on retrieval of atmospheric parameters, we have used a statistical retrieval algorithm for skin temperature, temperature, water vapour and ozone profiles. The use of this retrieval algorithm is mostly intended for illustrative purposes and the user could choose a different inverse strategy. In fact, the interferogram-based data-reduction strategy is generic and independent of any inverse algorithm. It will be also shown that this strategy yields subset of interferometric radiances, which are less sensitive to potential interfering effects such as those possibly introduced by the day-night cycle (e.g., the solar component, and spectroscopic effect induced by sun energy) and unknown trace gases variability

NO2 pollution over selected cities in the Po valley in 2018-2021 and its possible effects on boosting COVID-19 deaths

This work analyzes nitrogen dioxide (NO2) pollution over a set of cities in the Po Valley in northern Italy, using satellite and in situ observations. The cities include Milan, Bergamo, and Brescia, the first area of the COVID-19 outbreak and diffusion in Italy, with a higher mortality rate than in other parts of Italy and Europe. The analysis was performed for three years, from May 2018 to April 2021, including the period of first-wave diffusion of COVID-19 over the Po Valley, that is, January 2020-April 2020. The study aimed at giving a more general picture of the NO2 temporal and spatial variation, possibly due to the lockdown adopted for the pandemic crisis containment and other factors, such as the meteorological conditions and the seasonal cycle. We have mainly investigated two effects: first, the correlation of NO2 pollution with atmospheric parameters such as air and dew point temperature, and second the possible correlation between air quality and COVID-19 deaths, which could explain the high mortality rate. We have found a good relationship between air quality and temperature. In light of this relationship, we can conclude that the air quality improvement in March 2020 was primarily because of the lockdown adopted to prevent and limit virus diffusion. We also report a good correlation between NO2 pollution and COVID-19 deaths, which is not seen when considering a reference city in the South of Italy. The critical factor in explaining the difference is the persistence of air pollution in the Po Valley in wintertime. We found that NO2 pollution shows a seasonal cycle, yielding a non-causal correlation with the COVID-19 deaths. However, causality comes in once we read the correlation in the context of current and recent epidemiological evidence and leads us to conclude that air pollution may have acted as a significant risk factor in boosting COVID-19 fatalities

Partially scanned interferogram methodology applied to IASI for the retrieval of CO, CO_2, CH_4 and N_2O

The technique of partially scanned interferograms is applied to the retrieval of trace gases from Infrared Atmospheric Sounding Interferometer (IASI) observations. For the specific case of CO, CO2, CH4 and N2O, we show that this methodology allows us to retrieve trace gases column abundances at an unprecedented accuracy at the level of the single IASI footprint. The technique consists in transforming the IASI spectra back to the interferogram domain where we identify small regions that are mostly sensitive to single gas species. The retrieval is then performed by directly applying Least Squares estimation to these small segments of interferometric radiances. One of the main advantages of the technique is that it allows the efficient use of the information contained in all the IASI channels that are available in the absorption bands of a specific gas species. The retrieval technique has been applied to IASI radiances measured over the Mediterranean sea during the month of July 2010, one of the hottest months on record. Results have been validated against ground-based measurements. We have also carried out a comparison with Atmospheric Infrared Radiometer Sounder data and IASI retrievals obtained with usual variational approaches in the spectral domain

ASSESSING THE IMPACT OF INCORRECT OBSERVATIONAL COVARIANCE MATRIX OVER RETRIEVAL: METHODS AND APPLICATION TO IASI DATA

The paper addresses the effect of an incorrect specified observational covariance matrix on the retrieval of atmospheric parameters from high spectral resolution infrared radiances. The case of a non-diagonal covariance matrix approximated with its diagonal is dealt with. The problem is of interest to IASI (Infrared Atmospheric Sounding Interferometer) which, because of apodization, is characterized by a non-diagonal covariance matrix. However, the diagonal alone rather than the full matrix is normally used in data assimilation and retrieval schemes because of numerical and computational efficiency. The problem will be analysed in its formal, mathematical aspects through the help of the linear approximation. In addition, a series of retrieval exercises will help to draw more general conclusions. We have found that the incorrect use of the diagonal instead of the full covariance matrix can affect the spatial vertical resolution of the retrieval and can lead to instability in the final solution, especially in the lower troposphere

Using the full IASI spectrum for the physical retrieval of temperature, H2O, HDO, O3, minor and trace gases

IASI (Infrared Atmospheric Sounder Interferometer) is flying on the European MetOp series of weather satellites. Besides acquiring temperature and humidity data, IASI also observes the infrared emission of the main minor and trace atmospheric components with high precision. The retrieval of these gases would be highly beneficial to the efforts of scientists monitoring Earths climate. IASI retrieval capability and algorithms have been mostly driven by Numerical Weather Prediction centers, whose limited resources for data transmission and computing is hampering the full exploitation of IASI information content. The quest for real or nearly real time processing has affected the precision of the estimation of minor and trace gases, which are normally retrieved on a very coarse spatial grid. The paper presents the very first retrieval of the complete suite of IASI target parameters by exploiting all its 8461 channels. The analysis has been exemplified for sea surface and the target parameters will include sea surface temperature, temperature profile, water vapour and HDO profiles, ozone profile, total column amount of CO, CO2, CH4, N2O, SO2, HNO3, NH3, OCS and CF4. Concerning CO2, CH4 and N2O, it will be shown that their colum amount can be obtained for each single IASI IFOV (Instantaneous Field of View) with a precision better than 1-2%, which opens the possibility to analyze, e.g., the formation of regional patterns of greenhouse gases. To assess the quality of the retrieval, a case study has been set up which considers two years of IASI soundings over the Hawaii, Manua Loa validation station

KALMAN FILTER RETRIEVAL OF SEA SKIN TEMPERATURE FROM SEVIRI: A COMPARISON CASE STUDY

The high temporal resolution of data acquisition by geostationary satellites and their capability to resolve the diurnal cycle allow for the retrieval of a valuable source of information about geophysical parameters. To exploit this information we have developed a Kalman filter methodology for the retrieval of surface emissivity and temperature from radiance measurements made from geostationary platforms. The application of the retrieval methodology to SEVIRI (Spinning Enhanced Visible and Infrared Imager) infrared channels shows that we can simultaneously retrieve surface emissivity and temperature with an accuracy of ± 0.005 and ± 0.2 K, respectively. This performance is exemplified in this paper with a case study, which considers the retrieval of sea skin temperature for a target area of the Naples Gulf. Retrieval for temperature has been intercompared with similar products derived from AVHRR (Advanced Very High Resolution Radiometer) and MODIS (Moderate Resolution Imaging Spectroradiometer) satellite sensors

Hyper fast radiative transfer for the physical retrieval of surface parameters from SEVIRI observations

This paper describes the theoretical aspects of a fast scheme for the physical retrieval of surface temperature and emissivity from SEVIRI data, their implementation and some sample results obtained. The scheme is based on a Kalman Filter approach, which effectively exploits the temporal continuity in the observations of the geostationary Meteosat Second Generation (MSG) platform, on which SEVIRI (Spinning Enhanced Visible and InfraRed Imager) operates. Such scheme embodies in its core a physical retrieval algorithm, which employs an hyper fast radiative transfer code highly customized for this retrieval task. Radiative transfer and its customizations are described in detail. Fastness, accuracy and stability of the code are fully documented for a variety of surface features, showing a peculiar application to the massive Greek forest fires in August 2007

Spatio-temporal constraints for emissivity and surface temperature retrieval: Preliminary results and comparisons for SEVIRI and IASI observation

Infrared instrumentation on geostationary satellites is now rapidly approaching the spectral quality and accuracy of modern sensors flying on polar platforms. Currently, the core of EUMETSAT geostationary meteorological programme is the Meteosat Second Generation (MSG). However, EUMETSAT is preparing for the Meteosat Third Generation (MTG). The capability of geostationary satellites to resolve the diurnal cycle and hence to provide time-resolved sequences or times series of observations is a source of information which could suitably constrain the derivation of geophysical parameters. Nowadays, also because of lack of time continuity, when dealing with observations from polar platforms, the problem of deriving geophysical parameters is normally solved by considering each single observation as independent of past and future events. For historical reason, the same approach is currently pursued with geostationary observations, which are still being dealt with as they were with polar observations. In this study we show some preliminary results on emissivity and surface temperature retrieval for SEVIRI observations, using the Kalman filter methodology (KF) and compare the retrievals with those obtained using IASI observations co-localized with SEVIRI ones using the times accumulation approach (Optimal Estimation OE). The Sahara desert was chosen as target area, and both SEVIRI and IASI data (infrared radiances and cloud mask) were acquired. The time period considered is that of July 2010 (the whole month). ECMWF analyses for the same date and target area have also been acquired, which comprise Ts, T(p), O(p), Q(p) for the canonical hours 0:00, 6:00, 12:00 and 18:00. Moreover, for the purpose of developing a suitable background for emissivity, the Global Infrared Land Surface Emissivity database developed at CIMSS, University of Wisconsin, derived by MODIS observations was used and was available from the year 2003 till 2011. Concerning the performance of the two methodologies, the retrieval of skin temperature is almost equivalent. The agreement between OE and KF is fairly good if compared with ECMWF analysis for sea surface, while for land surface, OE and KF agree fairly well with ECMWF during the night, but at midday ECMWF shows a cold bias of 10 K and more. For emissivity the comparison with the UW/BFEMIS database for the same date and location is fairly good for both methods

Technical note: Functional sliced inverse regression to infer temperature, water vapour and ozone from IASI data.

A retrieval algorithm that uses a statistical strategy based on dimension reduction is proposed. The methodology and details of the implementation of the new algorithm are presented and discussed. The algorithm has been applied to high resolution spectra measured by the Infrared Atmospheric Sounding Interferometer instrument to retrieve atmospheric profiles of temperature, water vapour and ozone. The performance of the inversion strategy has been assessed by comparing the retrieved profiles to the ones obtained by co-locating in space and time profiles from the European Centre for Medium-Range Weather Forecasts analysi