Improved calibration procedures for the EM27/SUN spectrometers of the COllaborative Carbon Column Observing Network (COCCON)

In this study, an extension on the previously reported status of the COllaborative Carbon Column Observing Network\u27s (COCCON) calibration procedures incorporating refined methods is presented. COCCON is a global network of portable Bruker EM27/SUN FTIR spectrometers for deriving column-averaged atmospheric abundances of greenhouse gases. The original laboratory open-path lamp measurements for deriving the instrumental line shape (ILS) of the spectrometer from water vapour lines have been refined and extended to the secondary detector channel incorporated in the EM27/SUN spectrometer for detection of carbon monoxide (CO). The refinements encompass improved spectroscopic line lists for the relevant water lines and a revision of the laboratory pressure measurements used for the analysis of the spectra. The new results are found to be in good agreement with those reported by Frey et al. (2019) and discussed in detail. In addition, a new calibration cell for ILS measurements was designed, constructed and put into service. Spectrometers calibrated since January 2020 were tested using both methods for ILS characterization, open-path (OP) and cell measurements. We demonstrate that both methods can detect the small variations in ILS characteristics between different spectrometers, but the results of the cell method indicate a systematic bias of the OP method. Finally, a revision and extension of the COCCON network instrument-to-instrument calibration factors for XCO2, XCO and XCH4 is presented, incorporating 47 new spectrometers (of 83 in total by now). This calibration is based on the reference EM27/SUN spectrometer operated by the Karlsruhe Institute of Technology (KIT) and spectra collected by the collocated TCCON station Karlsruhe. Variations in the instrumental characteristics of the reference EM27/SUN from 2014 to 2017 were detected, probably arising from realignment and the dual-channel upgrade performed in early 2018. These variations are considered in the evaluation of the instrument-specific calibration factors in order to keep all tabulated calibration results consistent

Telerilevamento da terra per lo studio della bassa atmosfera in ambiente urbano

Dottorato di ricerca in telerilevamento. 12. ciclo. A.a. 1998-99. Relatore G. Fiocco. Coordinatore G. PicardiConsiglio Nazionale delle Ricerche - Biblioteca Centrale - P.le Aldo Moro, 7, Rome; Biblioteca Nazionale Centrale - P.za Cavalleggeri, 1, Florence / CNR - Consiglio Nazionale delle RichercheSIGLEITItal

Inversion of Electromagnetic Models for Bare Soil Parameter Estimation from Multifrequency Polarimetric SAR Data

The potentiality of polarimetric SAR data for the estimation of bare soil geophysical parameters (i.e., roughness and soil moisture) is investigated in this work. For this purpose, two forward models available in the literature, able to simulate the measurements of a multifrequency radar polarimeter, have been implemented for use within an inversion scheme. A multiplicative noise has been considered in the multidimensional space of the elements of the polarimetric Covariance Matrix, by adopting a complex Wishart distribution to account for speckle effects. An additive error has been also introduced on the simulated measurements to account for calibration and model errors. Maximum a Posteriori Probability and Minimum Variance criteria have been considered to perform the inversion. As for the algorithms to implement the criteria, simple optimization/integration procedures have been used. A Neural Network approach has been adopted as well. A correlation between the roughness parameters has been also supposed in the simulation as a priori information, to evaluate its effect on the estimation accuracy. The methods have been tested on simulated data to compare their performances as function of number of looks, incidence angles and frequency bands, thus identifying the best radar configuration in terms of estimation accuracy. Polarimetric measurements acquired during MAC Europe and SIR-C campaigns, over selected bare soil fields, have been also used as validation data

A physical-statistical approach to match spaceborne microwave radiometric retrieval of rainfall to Mediterranean climatology

A physical-statistical approach to retrieve precipitating cloud parameters from spaceborne microwave radiometric data is described. A Bayesian Maximum A posteriori Probability inversion scheme is trained by a plane-parallel radiative transfer model applied to statistically-generated cloud and precipitation structures. Initial microphysical a priori information on vertical structures of cloud parameters is derived from a mesoscale cloud-resolving model. In order to adapt simulations to the conditions of the area of interest, climatological constraints are derived on a monthly basis from available radiosounding profiles, raingauge network measurements, SSM/I large sets of data over the Mediterranean region, and co-located METEOSAT infrared measurements. Monthly average and variance maps of clear-air surface emissivity at SSM/I frequencies are also includeded using a METEOSAT-based cloud screening. A validation is carried out by comparing SSM/I estimates with rainrates measured by a raingauge network along the Tiber river basin in Italy during 1995

A preliminary evaluation of the absolute backscatter calibration on the ERS-1 altimeter using a passive technique

Sidne

Empirical algorithms to retrieve surface rain-rate from Special Sensor Microwave Imager over a mid-latitude basin

The capability of some empirical algorithms to estimate surface rain-rate at mid-latitude basin scale from the Special Sensor Microwave Imager (SSM/I) data is analyzed. We propose three retrieval techniques based on a multivariate regression, a Bayesian maximum a posteriori inversion and on an artificial feed-forward Neural Network. Three algorithms available in literature are also included as benchmarks. The training data set is derived from coincident SSM/I images and half hourly rain-rate data obtained from a rain-gauge network, placed along the river Tiber basin in Central Italy, during 9 years (from 1992 to 2000). The work points out that an algorithm based on regression or Neural Network is a good estimator of low precipitation, while it tends to underestimate high rain rates. The best results have been achieved with the Bayesian method

Passive calibration of the backscattering coefficient of the ENVISAT RA-2: Evaluation of radiative models for sea and land

The Passive Calibration of the radar altimeter consists in characterising the receiver by observing natural surfaces with known emission in the so-called noise-sensing mode. The paper focuses on the general approach undertaken to simulate the brightness temperature at the top of the atmosphere observed by the Envisat Radar Altimeter (RA-2). It is based on emissivity models for land and sea as well as atmospheric radiation models supported by a continuous flow of on-line data used as model inputs

Monitoring desertification using EO technologies: experience of the ESA DUE DesertWatch project

The DesertWatch ESA DUE project, recently successfully completed, aimed at developing an integrated information system tailored on the specific user needs, built on the technological transfer of the most significant results of the related research projects. The resulting DesertWatch Information System, a user-friendly integrated Software remote sensing tool for monitoring desertification, have being installed and is now used in Italy, Turkey and Portugal