Assessment of present-day estimates of AOD from global reanalyses against different satellite products and multi-model ensembles

<p>Aerosols are known to affect atmospheric processes on a wide range of spatio-temporal scales, from dust storms reducing incoming solar radiation to aerosol-climate feedbacks. Although plenty of studies address aerosol radiative forcing, there are persistent differences in current aerosol estimates from both, observations and models. Global reanalyses are able to provide consistent estimates of aerosol distributions by combining these two data sources. However, continuous assimilation of single satellite products forces the analyses towards the satellites climatology including possible inaccuracies. This study investigates differences between current estimates of aerosol optical depth (AOD) by addressing two questions: (1.) How well do we know the large-scale spatio-temporal pattern of present-day AOD across state-of-the-art data? (2.) How does current global aerosol reanalyses perform in comparison to other model- and observation-based data sets? To answer these questions, AOD from the global CAMS and MERRA-2 reanalyses is compared to 8 satellite products, 1 established climatology and 4 multi-model ensembles. The comprehensive data set used in this study allows to evaluate the performance of individual products concerning different spatial and temporal aspects. The evaluation covers results from 1998 to 2019, including most recently available products like the climate model inter-comparison project CMIP6.</p><p>Spatially and temporally averaged AOD from MERRA-2 agrees well with the mean satellite climatology, while the CAMS climatology is higher than most other products. With relative standard deviations of about 11%, temporal variations of CAMS and MERRA-2 agree well with the mean satellite variation. However, averaged AOD from the individual satellites show large differences, ranging from 0.124 for MISR to 0.164 for MODIS. In addition to average differences, spatial patterns vary significantly between the individual data sets. Because the CAMS reanalysis only assimilates AOD from MODIS, it remains close to the MODIS climatology which overestimates AOD in most regions in comparison to other products. This overestimation is considerably increased over eastern China were CAMS simulates regional values of more than 1.2 during summer. By assimilating both, MODIS and MISR data, the MERRA-2 reanalysis is closer to the satellite mean under most conditions. Although annual deviations remain small compared to other models, MERRA-2 tends to underestimate AOD at the equator and overestimates AOD at higher latitudes especially during the winter-season. The spatio-temporal differences between individual aerosol data sets underline the need for further research on both satellite retrievals and model simulations for aerosols. For example, integrating multiple observations in a reanalysis system would allow to compensate for inaccuracies of the individual products. Further developing the multi-scale coupled ICON-ART system at the German Weather Service provides a promising environment to achieve accurate aerosol climatologies on high spatial resolution.</p>

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Internal consistency of the Regional Brewer Calibration Centre for Europe triad during the period 2005–2016

Atmospheric Measurement Techniques ◽

10.5194/amt-11-4059-2018 ◽

2018 ◽

Vol 11 (7) ◽

pp. 4059-4072 ◽

Cited By ~ 2

Author(s):

Sergio Fabián León-Luis ◽

Alberto Redondas ◽

Virgilio Carreño ◽

Javier López-Solano ◽

Alberto Berjón ◽

...

Keyword(s):

Standard Deviation ◽

Relative Standard Deviation ◽

Data Sets ◽

Individual Values ◽

Relative Standard ◽

The World ◽

The Mean ◽

The Individual ◽

Ozone Column

Abstract. Total ozone column measurements can be made using Brewer spectrophotometers, which are calibrated periodically in intercomparison campaigns with respect to a reference instrument. In 2003, the Regional Brewer Calibration Centre for Europe (RBCC-E) was established at the Izaña Atmospheric Research Center (Canary Islands, Spain), and since 2011 the RBCC-E has transferred its calibration based on the Langley method using travelling standard(s) that are wholly and independently calibrated at Izaña. This work is focused on reporting the consistency of the measurements of the RBCC-E triad (Brewer instruments #157, #183 and #185) made at the Izaña Atmospheric Observatory during the period 2005–2016. In order to study the long-term precision of the RBCC-E triad, it must be taken into account that each Brewer takes a large number of measurements every day and, hence, it becomes necessary to calculate a representative value of all of them. This value was calculated from two different methods previously used to study the long-term behaviour of the world reference triad (Toronto triad) and Arosa triad. Applying their procedures to the data from the RBCC-E triad allows the comparison of the three instruments. In daily averages, applying the procedure used for the world reference triad, the RBCC-E triad presents a relative standard deviation equal to σ = 0.41 %, which is calculated as the mean of the individual values for each Brewer (σ157 = 0.362 %, σ183 = 0.453 % and σ185 = 0.428 %). Alternatively, using the procedure used to analyse the Arosa triad, the RBCC-E presents a relative standard deviation of about σ = 0.5 %. In monthly averages, the method used for the data from the world reference triad gives a relative standard deviation mean equal to σ = 0.3 % (σ157 = 0.33 %, σ183 = 0.34 % and σ185 = 0.23 %). However, the procedure of the Arosa triad gives monthly values of σ = 0.5 %. In this work, two ozone data sets are analysed: the first includes all the ozone measurements available, while the second only includes the simultaneous measurements of all three instruments. Furthermore, this paper also describes the Langley method used to determine the extraterrestrial constant (ETC) for the RBCC-E triad, the necessary first step toward accurate ozone calculation. Finally, the short-term or intraday consistency is also studied to identify the effect of the solar zenith angle on the precision of the RBCC-E triad.

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The SPARC water vapour assessment II: Profile-to-profile comparisons of stratospheric and lower mesospheric water vapour data sets obtained from satellites

10.5194/amt-2018-380 ◽

2018 ◽

Cited By ~ 1

Author(s):

Stefan Lossow ◽

Farahnaz Khosrawi ◽

Michael Kiefer ◽

Kaley A. Walker ◽

Jean-Loup Bertaux ◽

...

Keyword(s):

Time Series ◽

Water Vapour ◽

Data Sets ◽

General Behaviour ◽

Data Set ◽

Observational Database ◽

Comparison Results ◽

Wide Range ◽

The Individual ◽

Mean Time

Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesospheric water vapour were performed considering 33 data sets derived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data set specific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 hPa and 5 hPa. Typically, they range from 0.25 ppmv to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases are overall increasing with altitude but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 ppmv and 1 ppmv (4 % to 20 %). Obvious data set specific bias issues are found for a number of data sets. In our work we performed a drift analysis for data sets overlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data sets that are statistically significant at the 2σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 hPa to 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 ppmv decade−1 and 0.3 ppmv decade−1. Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. Like for the biases, a number of data sets exhibit prominent drift issues. In our analyses we found that the large number of MIPAS data sets included in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data sets exhibit a relative similarity with respect to the remaining data sets, despite that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database.

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The SPARC water vapour assessment II: profile-to-profile comparisons of stratospheric and lower mesospheric water vapour data sets obtained from satellites

Atmospheric Measurement Techniques ◽

10.5194/amt-12-2693-2019 ◽

2019 ◽

Vol 12 (5) ◽

pp. 2693-2732 ◽

Cited By ~ 6

Author(s):

Stefan Lossow ◽

Farahnaz Khosrawi ◽

Michael Kiefer ◽

Kaley A. Walker ◽

Jean-Loup Bertaux ◽

...

Keyword(s):

Time Series ◽

Water Vapour ◽

Data Sets ◽

General Behaviour ◽

Data Set ◽

Observational Database ◽

Comparison Results ◽

Wide Range ◽

The Individual ◽

Mean Time

Abstract. Within the framework of the second SPARC (Stratosphere-troposphere Processes And their Role in Climate) water vapour assessment (WAVAS-II), profile-to-profile comparisons of stratospheric and lower mesospheric water vapour were performed by considering 33 data sets derived from satellite observations of 15 different instruments. These comparisons aimed to provide a picture of the typical biases and drifts in the observational database and to identify data-set-specific problems. The observational database typically exhibits the largest biases below 70 hPa, both in absolute and relative terms. The smallest biases are often found between 50 and 5 hPa. Typically, they range from 0.25 to 0.5 ppmv (5 % to 10 %) in this altitude region, based on the 50 % percentile over the different comparison results. Higher up, the biases increase with altitude overall but this general behaviour is accompanied by considerable variations. Characteristic values vary between 0.3 and 1 ppmv (4 % to 20 %). Obvious data-set-specific bias issues are found for a number of data sets. In our work we performed a drift analysis for data sets overlapping for a period of at least 36 months. This assessment shows a wide range of drifts among the different data sets that are statistically significant at the 2σ uncertainty level. In general, the smallest drifts are found in the altitude range between about 30 and 10 hPa. Histograms considering results from all altitudes indicate the largest occurrence for drifts between 0.05 and 0.3 ppmv decade−1. Comparisons of our drift estimates to those derived from comparisons of zonal mean time series only exhibit statistically significant differences in slightly more than 3 % of the comparisons. Hence, drift estimates from profile-to-profile and zonal mean time series comparisons are largely interchangeable. As for the biases, a number of data sets exhibit prominent drift issues. In our analyses we found that the large number of MIPAS data sets included in the assessment affects our general results as well as the bias summaries we provide for the individual data sets. This is because these data sets exhibit a relative similarity with respect to the remaining data sets, despite the fact that they are based on different measurement modes and different processors implementing different retrieval choices. Because of that, we have by default considered an aggregation of the comparison results obtained from MIPAS data sets. Results without this aggregation are provided on multiple occasions to characterise the effects due to the numerous MIPAS data sets. Among other effects, they cause a reduction of the typical biases in the observational database.

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A Visual and VAE Based Hierarchical Indoor Localization Method

Sensors ◽

10.3390/s21103406 ◽

2021 ◽

Vol 21 (10) ◽

pp. 3406

Author(s):

Jie Jiang ◽

Yin Zou ◽

Lidong Chen ◽

Yujie Fang

Keyword(s):

Image Retrieval ◽

Indoor Localization ◽

Data Sets ◽

Indoor Environments ◽

Global Features ◽

Data Set ◽

Data Annotation ◽

Wide Range ◽

Annotation Costs ◽

Global And Local

Precise localization and pose estimation in indoor environments are commonly employed in a wide range of applications, including robotics, augmented reality, and navigation and positioning services. Such applications can be solved via visual-based localization using a pre-built 3D model. The increase in searching space associated with large scenes can be overcome by retrieving images in advance and subsequently estimating the pose. The majority of current deep learning-based image retrieval methods require labeled data, which increase data annotation costs and complicate the acquisition of data. In this paper, we propose an unsupervised hierarchical indoor localization framework that integrates an unsupervised network variational autoencoder (VAE) with a visual-based Structure-from-Motion (SfM) approach in order to extract global and local features. During the localization process, global features are applied for the image retrieval at the level of the scene map in order to obtain candidate images, and are subsequently used to estimate the pose from 2D-3D matches between query and candidate images. RGB images only are used as the input of the proposed localization system, which is both convenient and challenging. Experimental results reveal that the proposed method can localize images within 0.16 m and 4° in the 7-Scenes data sets and 32.8% within 5 m and 20° in the Baidu data set. Furthermore, our proposed method achieves a higher precision compared to advanced methods.

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Global whole-rock geochemical database compilation

10.5194/essd-2019-50 ◽

2019 ◽

Cited By ~ 2

Author(s):

Matthew Gard ◽

Derrick Hasterok ◽

Jacqueline Halpin

Keyword(s):

Database Management ◽

Temporal Trends ◽

Physical Property ◽

Database Management System ◽

Geochemical Data ◽

Data Sets ◽

Unique Contribution ◽

Data Set ◽

Wide Range ◽

Geochemical Indices

Abstract. Dissemination and collation of geochemical data are critical to promote rapid, creative and accurate research and place new results in an appropriate global context. To this end, we have assembled a global whole-rock geochemical database, with other associated sample information and properties, sourced from various existing databases and supplemented with numerous individual publications and corrections. Currently the database stands at 1,023,490 samples with varying amounts of associated information including major and trace element concentrations, isotopic ratios, and location data. The distribution both spatially and temporally is quite heterogeneous, however temporal distributions are enhanced over some previous database compilations, particularly in terms of ages older than ~ 1000 Ma. Also included are a wide range of computed geochemical indices, physical property estimates and naming schema on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated by sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data is published in .csv format for the purposes of simple distribution but exists in a format acceptable for database management systems (e.g. SQL). One can either manipulate this data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload to a relational database management system for easy querying and management of the data as unique keys already exist. This data set will continue to grow, and we encourage readers to contact us or other database compilations contained within about any data that is yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592823 (Gard et al., 2019).

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INTERESTING SPATIO-TEMPORAL REGION DISCOVERY COMPUTATIONS OVER GPU AND MAPREDUCE PLATFORMS

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprsannals-ii-4-w2-35-2015 ◽

2015 ◽

Vol II-4/W2 ◽

pp. 35-41 ◽

Cited By ~ 1

Author(s):

M. McDermott ◽

S. K. Prasad ◽

S. Shekhar ◽

X. Zhou

Keyword(s):

Parallel Architecture ◽

Computation Time ◽

Data Sets ◽

Temporal Region ◽

Data Set ◽

Atmospheric Sciences ◽

3 Dimensional ◽

Linear Speedup ◽

Spatio Temporal ◽

Mapreduce Model

Discovery of interesting paths and regions in spatio-temporal data sets is important to many fields such as the earth and atmospheric sciences, GIS, public safety and public health both as a goal and as a preliminary step in a larger series of computations. This discovery is usually an exhaustive procedure that quickly becomes extremely time consuming to perform using traditional paradigms and hardware and given the rapidly growing sizes of today’s data sets is quickly outpacing the speed at which computational capacity is growing. In our previous work (Prasad et al., 2013a) we achieved a 50 times speedup over sequential using a single GPU. We were able to achieve near linear speedup over this result on interesting path discovery by using Apache Hadoop to distribute the workload across multiple GPU nodes. Leveraging the parallel architecture of GPUs we were able to drastically reduce the computation time of a 3-dimensional spatio-temporal interest region search on a single tile of normalized difference vegetative index for Saudi Arabia. We were further able to see an almost linear speedup in compute performance by distributing this workload across several GPUs with a simple MapReduce model. This increases the speed of processing 10 fold over the comparable sequential while simultaneously increasing the amount of data being processed by 384 fold. This allowed us to process the entirety of the selected data set instead of a constrained window.

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Panoramic stitching of heterogeneous single-cell transcriptomic data

10.1101/371179 ◽

2018 ◽

Cited By ~ 17

Author(s):

Brian Hie ◽

Bryan Bryson ◽

Bonnie Berger

Keyword(s):

Single Cell ◽

Cell Types ◽

Data Sets ◽

Cell Type ◽

Data Set ◽

Wide Range ◽

Data Set Integration ◽

Biological Patterns ◽

Insight Into ◽

Comprehensive Reference

AbstractResearchers are generating single-cell RNA sequencing (scRNA-seq) profiles of diverse biological systems1–4 and every cell type in the human body.5 Leveraging this data to gain unprecedented insight into biology and disease will require assembling heterogeneous cell populations across multiple experiments, laboratories, and technologies. Although methods for scRNA-seq data integration exist6,7, they often naively merge data sets together even when the data sets have no cell types in common, leading to results that do not correspond to real biological patterns. Here we present Scanorama, inspired by algorithms for panorama stitching, that overcomes the limitations of existing methods to enable accurate, heterogeneous scRNA-seq data set integration. Our strategy identifies and merges the shared cell types among all pairs of data sets and is orders of magnitude faster than existing techniques. We use Scanorama to combine 105,476 cells from 26 diverse scRNA-seq experiments across 9 different technologies into a single comprehensive reference, demonstrating how Scanorama can be used to obtain a more complete picture of cellular function across a wide range of scRNA-seq experiments.

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Characterising RDF data sets

Journal of Information Science ◽

10.1177/0165551516677945 ◽

2017 ◽

Vol 44 (2) ◽

pp. 203-229 ◽

Cited By ~ 6

Author(s):

Javier D Fernández ◽

Miguel A Martínez-Prieto ◽

Pablo de la Fuente Redondo ◽

Claudio Gutiérrez

Keyword(s):

Data Structures ◽

Large Scale ◽

Open Data ◽

Structural Features ◽

Data Sets ◽

Data Set ◽

Wide Range ◽

Rdf Data ◽

Description Framework ◽

Resource Description

The publication of semantic web data, commonly represented in Resource Description Framework (RDF), has experienced outstanding growth over the last few years. Data from all fields of knowledge are shared publicly and interconnected in active initiatives such as Linked Open Data. However, despite the increasing availability of applications managing large-scale RDF information such as RDF stores and reasoning tools, little attention has been given to the structural features emerging in real-world RDF data. Our work addresses this issue by proposing specific metrics to characterise RDF data. We specifically focus on revealing the redundancy of each data set, as well as common structural patterns. We evaluate the proposed metrics on several data sets, which cover a wide range of designs and models. Our findings provide a basis for more efficient RDF data structures, indexes and compressors.

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The GTC exoplanet transit spectroscopy survey

Astronomy and Astrophysics ◽

10.1051/0004-6361/201834063 ◽

2019 ◽

Vol 622 ◽

pp. A172 ◽

Cited By ~ 7

Author(s):

F. Murgas ◽

G. Chen ◽

E. Pallé ◽

L. Nortmann ◽

G. Nowak

Keyword(s):

Transmission Spectrum ◽

Rayleigh Scattering ◽

Extrasolar Planets ◽

Light Curves ◽

Data Sets ◽

Transmission Spectra ◽

Data Set ◽

Systematic Effects ◽

The Individual ◽

Spectrum Slope

Context. Rayleigh scattering in a hydrogen-dominated exoplanet atmosphere can be detected using ground- or space-based telescopes. However, stellar activity in the form of spots can mimic Rayleigh scattering in the observed transmission spectrum. Quantifying this phenomena is key to our correct interpretation of exoplanet atmospheric properties. Aims. We use the ten-meter Gran Telescopio Canarias (GTC) telescope to carry out a ground-based transmission spectra survey of extrasolar planets to characterize their atmospheres. In this paper we investigate the exoplanet HAT-P-11b, a Neptune-sized planet orbiting an active K-type star. Methods. We obtained long-slit optical spectroscopy of two transits of HAT-P-11b with the Optical System for Imaging and low-Intermediate-Resolution Integrated Spectroscopy (OSIRIS) on August 30, 2016 and September 25, 2017. We integrated the spectrum of HAT-P-11 and one reference star in several spectroscopic channels across the λ ~ 400–785 nm region, creating numerous light curves of the transits. We fit analytic transit curves to the data taking into account the systematic effects and red noise present in the time series in an effort to measure the change of the planet-to-star radius ratio (Rp∕Rs) across wavelength. Results. By fitting both transits together, we find a slope in the transmission spectrum showing an increase of the planetary radius towards blue wavelengths. Closer inspection of the transmission spectrum of the individual data sets reveals that the first transit presents this slope while the transmission spectrum of the second data set is flat. Additionally, we detect hints of Na absorption on the first night, but not on the second. We conclude that the transmission spectrum slope and Na absorption excess found in the first transit observation are caused by unocculted stellar spots. Modeling the contribution of unocculted spots to reproduce the results of the first night we find a spot filling factor of δ = 0.62−0.17+0.20 and a spot-to-photosphere temperature difference of ΔT = 429−299+184 K.

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THE RELIABILITY OF THE INTERNATIONAL NORMALISED RATIO (INR) DURING SHORT TERM ORAL ANTICOAGULANT TREATMENT

10.1055/s-0038-1643260 ◽

1987 ◽

Author(s):

A McKernan ◽

J M Thomson ◽

L Poller

Keyword(s):

Induction Phase ◽

Clotting Factors ◽

Oral Anticoagulant Treatment ◽

Sensitivity Indices ◽

Freeze Dried ◽

Wide Range ◽

Reference Preparation ◽

The Mean ◽

The Individual ◽

A Prospective Study

A prospective study has been performed to assess INR values obtained with a variety of thromboplastins during the early days of coumarin treatment. The reagents were BCT/253 (the primary International Reference Preparation), Diagen Activated, Diagen Freeze Dried, Manchester Reagent and Dade Thromboplastin FS. Prothrombin times were performed before the start of treatment and at regular intervals on fifteen patients who were given a slow induction regime. In theory INR should be the same irrespective of the thromboplastin. A wide range of values was however observed with the different thromboplastins on the same plasma samples. The mean deviations of the individual reagents from the mean INR obtained with the primary IRP were: Diagen Freeze-Dried 26%, Diagen Activated 13%, Dade FS 17%, Manchester Reagent 3%.There are two possible explanations for the discrepant findings. 1) In the induction phase Vitamin K dependent clotting factors are depressed at varying rates and thromboplastins differ in their sensitivity to the depression of these factors. The International Sensitivity Indices from which INR are derived are based on results from long-term stabilised patients. 2) The manufacturers' calibrations may be incorrect as demonstrated by the consistent differences of the results with some reagents from the IRP. The findings indicate therefore that INR values may not be dependable in the early days of oral anticoagulation with some thromboplastin reagents and that manufacturers' calibrations require independent assessment preferably by national control laboratories.

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