Abstract
Thermal infrared remote sensing measurements have greatly improved in terms of spectral, spatial, and temporal resolution. These improvements are producing a clearer picture of the land surface and Earth atmospheric composition than ever before. Nevertheless, the analysis of this big quantity of data presents important challenges due to incomplete temporal and spatial recorded information. The aim of the present paper is to discuss a methodology to retrieve missing values of some interesting geophysical variables on a spatial field retrieved from spatially scattered infrared satellite observations in order to yield level 3, regularly gridded, data. The technique is based on a 2-Dimensional (2D) Optimal Interpolation (OI) scheme and is derived from the broad class of Kalman filter or Bayesian estimation theory. The goodness of the approach has been tested on 15-min temporal resolution Spinning Enhanced Visible and Infrared Imager (SEVIRI) emissivity and surface temperature (ST) products over South Italy (land and sea), on Infrared Atmospheric Sounding Interferometer (IASI) atmospheric ammonia () concentration over North Italy and carbon monoxide (), sulfur dioxide () and concentrations over China. All these gases affect air quality. Moreover, sea surface temperature (SST) retrievals have been compared with gridded data from MODIS (Moderate-resolution Imaging Spectroradiometer) observations. For gases concentration we have considered data from 3 different emission inventories, that is, Emissions Database for Global Atmospheric Research v3.4.2 (EDGARv3.4.2), the Regional Emission inventory in ASiav3.1 (REASv3.1) and MarcoPolov0.1, plus an independent study. The results show the efficacy of the proposed strategy to better capture the daily cycle for surface parameters and to detect hotspots of severe emissions from gas sources affecting air quality such as and and, therefore, to yield valuable information on the variability of gas concentration to complete ground stations measurements.
Highlights
Infrared instrumentation on geostationary and polar orbiting satellites are providing information at a very fine temporal and spatial resolution offering new possibilities for the monitoring of the environment, analysis of trends and patterns, forecasting and air quality studies
Thanks to the high resolution repeat cycle, we could apply Optimal Interpolation (OI) maintaining the same time resolution without averaging data referred to the same instant over more days, which could cause a lost in precision
This paper presented a technique based on Optimal Interpolation to build an L3, regularly gridded, spatial field of geophysical parameters starting from L2 spatially scattered retrievals from infrared satellite observations
Summary
Infrared instrumentation on geostationary and polar orbiting satellites are providing information at a very fine temporal and spatial resolution offering new possibilities for the monitoring of the environment, analysis of trends and patterns, forecasting and air quality studies. The availability of hyperspectral, fine-spatial, and multi-temporal thermal infrared data is introducing more advantages and convenience in terms of retrieval and application for drawing or detecting trend changes. MTG will carry an infrared sounder (IRS) with a hyperspectral resolution of 0.625 cm−1, acting in the long-wave infrared (LWIR) (14.3 − 8.3 μm) and the mid-wave infrared (MWIR) (6.25 − 4.6 μm), with a spatial resolution of 4 km and a repeat cycle of 60 min, which will be able to provide information on horizontally, vertically, and temporally (4-dimensional) resolved water vapour and temperature structures of the atmosphere. It has been designed to provide atmospheric temperature and humidity profiles, as well as monitor ozone and various trace gases
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