Quantification of uncertainty in aerosol optical thickness retrieval arising from aerosol microphysical model and other sources, applied to Ozone Monitoring Instrument (OMI) measurements

Satellite instruments are nowadays successfully utilised for measuring atmospheric aerosol in many applications as well as in research. Therefore, there is a growing need for rigorous error characterisation of the measurements. Here, we introduce a methodology for quantifying the uncertainty in the retrieval of aerosol optical thickness (AOT). In particular, we concentrate on two aspects: uncertainty due to aerosol microphysical model selection and uncertainty due to imperfect forward modelling. We apply the introduced methodology for aerosol optical thickness retrieval of the Ozone Monitoring Instrument (OMI) on board NASA's Earth Observing System (EOS) Aura satellite, launched in 2004. We apply statistical methodologies that improve the uncertainty estimates of the aerosol optical thickness retrieval by propagating aerosol microphysical model selection and forward model error more realistically. For the microphysical model selection problem, we utilise Bayesian model selection and model averaging methods. Gaussian processes are utilised to characterise the smooth systematic discrepancies between the measured and modelled reflectances (i.e. residuals). The spectral correlation is composed empirically by exploring a set of residuals. The operational OMI multi-wavelength aerosol retrieval algorithm OMAERO is used for cloud-free, over-land pixels of the OMI instrument with the additional Bayesian model selection and model discrepancy techniques introduced here. The method and improved uncertainty characterisation is demonstrated by several examples with different aerosol properties: weakly absorbing aerosols, forest fires over Greece and Russia, and Sahara desert dust. The statistical methodology presented is general; it is not restricted to this particular satellite retrieval application

Fast Simulators for Satellite Cloud Optical Centroid Pressure Retrievals, 1. Evaluation of OMI Cloud Retrievals

The cloud Optical Centroid Pressure (OCP), also known as the effective cloud pressure, is a satellite-derived parameter that is commonly used in trace-gas retrievals to account for the effects of clouds on near-infrared through ultraviolet radiance measurements. Fast simulators are desirable to further expand the use of cloud OCP retrievals into the operational and climate communities for applications such as data assimilation and evaluation of cloud vertical structure in general circulation models. In this paper, we develop and validate fast simulators that provide estimates of the cloud OCP given a vertical profile of optical extinction. We use a pressure-weighting scheme where the weights depend upon optical parameters of clouds and/or aerosol. A cloud weighting function is easily extracted using this formulation. We then use fast simulators to compare two different satellite cloud OCP retrievals from the Ozone Monitoring Instrument (OMI) with estimates based on collocated cloud extinction profiles from a combination of CloudS at radar and MODIS visible radiance data. These comparisons are made over a wide range of conditions to provide a comprehensive validation of the OMI cloud OCP retrievals. We find generally good agreement between OMI cloud OCPs and those predicted by CloudSat. However, the OMI cloud OCPs from the two independent algorithms agree better with each other than either does with the estimates from CloudSat/MODIS. Differences between OMI cloud OCPs and those based on CloudSat/MODIS may result from undetected snow/ice at the surface, cloud 3-D effects, low altitude clouds missed by CloudSat, and the fact that CloudSat only observes a relatively small fraction of an OMI field-of-view

Consistency and trends of technological innovations: a network approach to the international patent classification data

Classifying patents by the technology areas they pertain is important to enable information search and facilitate policy analysis and socio-economic studies. Based on the OECD Triadic Patent Family database, this study constructs a cohort network based on the grouping of IPC subclasses in the same patent families, and a citation network based on citations between subclasses of patent families citing each other. This paper presents a systematic analysis approach which obtains naturally formed network clusters identified using a Lumped Markov Chain method, extracts community keys traceable over time, and investigates two important community characteristics: consistency and changing trends. The results are verified against several other methods, including a recent research measuring patent text similarity. The proposed method contributes to the literature a network-based approach to study the endogenous community properties of an exogenously devised classification system. The application of this method may improve accuracy and efficiency of the IPC search platform and help detect the emergence of new technologies

Retrieval and validation of ozone columns derived from measurements of SCIAMACHY on Envisat

International audienceThis paper describes a new ozone column retrieval algorithm and its application to SCIAMACHY measurements. The TOSOMI algorithm is based on the Differential Optical Absorption Spectroscopy (DOAS) technique and implements several improvements over older algorithms. These improvements include aspects like (i) the explicit treatment of rotational Raman scattering, (ii) an improved air-mass factor formulation which is based on a simulation of the reflectivity spectrum and a subsequent DOAS fit of this simulated spectrum, (iii) the use of an improved ozone climatology and a column dependent air-mass factor, (iv) the use of daily varying ECMWF temperature profile analyses. The results of three validation exercises are reported. The TOSOMI columns are compared with an extensive set of ground-based observations (Brewer, Dobson) for the years 2003 and 2004. Secondly, a direct comparison for January?June 2003 with two new GOME retrievals, GDP Version 4 and TOGOMI, is presented. Third, data assimilation is used to study the dependence of the TOSOMI columns with retrieval parameters such as the viewing angle, cloud fraction and geographical location. These comparisons show a good consistency on the percent level between the GOME and SCIAMACHY algorithms. The present TOSOMI implementation (v0.32) shows an offset of about ?1.5% with respect to ground-based observations and the GOME retrievals

Total ozone column derived from GOME and SCIAMACHY using KNMI retrieval algorithms: Validation against Brewer measurements at the Iberian Peninsula

This article focuses on the validation of the total ozone column (TOC) data set acquired by the Global Ozone Monitoring Experiment (GOME) and the Scanning Imaging Absorption Spectrometer for Atmospheric Chartography (SCIAMACHY) satellite remote sensing instruments using the Total Ozone Retrieval Scheme for the GOME Instrument Based on the Ozone Monitoring Instrument (TOGOMI) and Total Ozone Retrieval Scheme for the SCIAMACHY Instrument Based on the Ozone Monitoring Instrument (TOSOMI) retrieval algorithms developed by the Royal Netherlands Meteorological Institute. In this analysis, spatially colocated, daily averaged ground-based observations performed by five well-calibrated Brewer spectrophotometers at the Iberian Peninsula are used. The period of study runs from January 2004 to December 2009. The agreement between satellite and ground-based TOC data is excellent (R2 higher than 0.94). Nevertheless, the TOC data derived from both satellite instruments underestimate the ground-based data. On average, this underestimation is 1.1% for GOME and 1.3% for SCIAMACHY. The SCIAMACHY-Brewer TOC differences show a significant solar zenith angle (SZA) dependence which causes a systematic seasonal dependence. By contrast, GOME-Brewer TOC differences show no significant SZA dependence and hence no seasonality although processed with exactly the same algorithm. The satellite-Brewer TOC differences for the two satellite instruments show a clear and similar dependence on the viewing zenith angle under cloudy conditions. In addition, both the GOME-Brewer and SCIAMACHY-Brewer TOC differences reveal a very similar behavior with respect to the satellite cloud properties, being cloud fraction and cloud top pressure, which originate from the same cloud algorithm (Fast Retrieval Scheme for Clouds from the Oxygen A-Band (FRESCO+)) in both the TOSOMI and TOGOMI retrieval algorithms.This work was partially supported by the Andalusian Regional Government through projects P08‐RNM ‐3568 andP10‐RNM‐6299, the Spanish Ministry of Science and Technology throughprojects CGL2010–18782 and CSD2007–00067, and the European Unionthrough ACTRIS project (EU INFRA‐2010‐1.1.16‐262254)

Mapping Patent Classifications: Portfolio and Statistical Analysis, and the Comparison of Strengths and Weaknesses

The Cooperative Patent Classifications (CPC) jointly developed by the European and US Patent Offices provide a new basis for mapping and portfolio analysis. This update provides an occasion for rethinking the parameter choices. The new maps are significantly different from previous ones, although this may not always be obvious on visual inspection. Since these maps are statistical constructs based on index terms, their quality--as different from utility--can only be controlled discursively. We provide nested maps online and a routine for portfolio overlays and further statistical analysis. We add a new tool for "difference maps" which is illustrated by comparing the portfolios of patents granted to Novartis and MSD in 2016.Comment: Scientometrics 112(3) (2017) 1573-1591; http://link.springer.com/article/10.1007/s11192-017-2449-

Comparison of aerosol optical depths from the Ozone Monitoring Instrument (OMI) on Aura with results from airborne sunphotometry, other space and ground measurements during MILAGRO/INTEX-B

Airborne sunphotometer measurements are used to evaluate retrievals of extinction aerosol optical depth (AOD) from spatially coincident and temporally near-coincident measurements by the Ozone Monitoring Instrument (OMI) aboard the Aura satellite during the March 2006 Megacity Initiative-Local And Global Research Observations/Phase B of the Intercontinental Chemical Transport Experiment (MILAGRO/INTEX-B). The 14-channel NASA Ames Airborne Tracking Sunphotometer (AATS) flew on nine missions over the Gulf of Mexico and four in or near the Mexico City area. Retrievals of AOD from near-coincident AATS and OMI measurements are compared for three flights over the Gulf of Mexico for flight segments when the aircraft flew at altitudes 60–70 m above sea level, and for one flight over the Mexico City area where the aircraft was restricted to altitudes ~320–800 m above ground level over the rural area and ~550–750 m over the city. OMI-measured top of atmosphere (TOA) reflectances are routinely inverted to yield aerosol products such as AOD and aerosol absorption optical depth (AAOD) using two different retrieval algorithms: a near-UV (OMAERUV) and a multiwavelength (OMAERO) technique. This study uses the archived Collection 3 data products from both algorithms. In particular, AATS and OMI AOD comparisons are presented for AATS data acquired in 20 OMAERUV retrieval pixels (15 over water) and 19 OMAERO pixels (also 15 over water). At least four pixels for one of the over-water coincidences and all pixels for the over-land case were cloud-free. Coincident AOD retrievals from 17 pixels of the Moderate Resolution Imaging Spectroradiometer (MODIS) aboard Aqua are available for two of the over-water flights and are shown to agree with AATS AODs to within root mean square (RMS) differences of 0.00–0.06, depending on wavelength. Near-coincident ground-based AOD measurements from ground-based sun/sky radiometers operated as part of the Aerosol Robotic Network (AERONET) at three sites in and near Mexico City are also shown and are generally consistent with the AATS AODs (which exclude any AOD below the aircraft) both in magnitude and spectral dependence. The OMAERUV algorithm retrieves AODs corresponding to a non-absorbing aerosol model for all three over-water comparisons whereas the OMAERO algorithm retrieves best-fit AODs corresponding to an absorbing biomass-burning aerosol model for two of the three over-water cases. For the four cloud-free pixels in one over-water coincidence (10 March), the OMAERUV retrievals underestimate the AATS AODs by ~0.20, which exceeds the expected retrieval uncertainty, but retrieved AODs agree with AATS values within uncertainties for the other two over-water events. When OMAERO retrieves AODs corresponding to a biomass-burning aerosol over water, the values significantly overestimate the AATS AODs (by up to 0.55). For the Mexico City coincidence, comparisons are presented for a non-urban region ~50–70 km northeast of the city and for a site near the center of the city. OMAERUV retrievals are consistent with AERONET AOD magnitudes for the non-urban site, but are nearly double the AATS and AERONET AODs (with differences of up to 0.29) in the center of the city. Corresponding OMAERO retrievals exceed the AATS and/or AERONET AODs by factors of 3 to 10

Validation of OMI-TOMS and OMI-DOAS total ozone column using five Brewer spectroradiometers at the Iberian peninsula

This article focuses on the comparison of the total ozone column data from the Ozone Monitoring Instrument (OMI) flying aboard the NASA EOS-Aura satellite platform with ground-based measurement recorded by Brewer spectroradiometers located at five Spanish remote sensing ground stations between January 2005 and December 2007. The satellite data are derived from two algorithms: OMI Total Ozone Mapping Spectrometer (OMI-TOMS) and OMI Differential Optical Absorption Spectroscopy (OMI-DOAS). The largest relative differences between these OMI total ozone column estimates reach 5% with a significant seasonal dependence. The agreement between OMI ozone data and Brewer measurements is excellent. Total ozone columns from OMI-TOMS are on average a mere 2.0% lower than Brewer data. For OMI-DOAS data the bias is a mere 1.4%. However, the relative difference between OMI-TOMS and Brewer measurements shows a notably lower seasonal dependence and variability than the differences between OMI-DOAS and ground-based data. For both OMI ozone data products these relative differences show significant dependence on the satellite ground pixel solar zenith angle for cloud-free cases as well as for cloudy conditions. However, the OMI ozone data products are shown to reveal opposite behavior with respect to the two antagonistic sky conditions. No significant dependency of the ground-based to satellite-based differences with respect to the satellite cross-track position is seen for either OMI retrieval algorithm.This work was partially supported by Ministerio de Educación y Ciencia under Project CGL2005-05693-C03-03/CLI and by Ministerio de Ciencia e Innovación under project CGL2008-05939-C03-02/CLI

A Comparative Study of the Electrode Systems of Three pH and Blood Gas Apparatus

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