scholarly journals P2C: Understanding Output Data Files via On-the-Fly Transformation from Producer to Consumer Executions

Author(s):  
Yonghwi Kwon ◽  
Fei Peng ◽  
Dohyeong Kim ◽  
Kyungtae Kim ◽  
Xiangyu Zhang ◽  
...  
Keyword(s):  
2021 ◽  
Author(s):  
Matthias Schneider ◽  
Benjamin Ertl ◽  
Christopher J. Diekmann ◽  
Farahnaz Khosrawi ◽  
Andreas Weber ◽  
...  

Abstract. IASI (Infrared Atmospheric Sounding Interferometer) is the core instrument of the currently three Metop (Meteorological operational) satellites of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites). The MUSICA IASI processing has been developed in the framework of the European Research Council project MUSICA (MUlti-platform remote Sensing of Isotopologues for investigating the Cycle of Atmospheric water). The processor performs an optimal estimation of the vertical distributions of water vapour (H2O), the ratio between two water vapour isotopologues (the HDO / H2O ratio), nitrous oxide (N2O), methane (CH4), and nitric acid (HNO3), and works with IASI radiances measured under cloud-free conditions in the spectral window between 1190 and 1400 cm−1. The retrieval of the trace gas profiles is performed on a logarithmic scale, which allows the constraint and the analytic treatment of ln[HDO] – ln[H2O] as proxy for the HDO / H2O ratio. Currently, the MUSICA IASI processing has been applied to all IASI measurements available between October 2014 and April 2020, so more than 1.4 billion individual retrievals have been performed. Here we describe the MUSICA IASI full retrieval product data set. The data set is made available in form of netcdf data files that are compliant with version 1.7 of the CF (Climate and Forecast) metadata convention. For each orbit an individual standard output data file is provided. These files contain for each individual retrieval information on the a priori usage and constraint, the retrieved atmospheric trace gas and temperature profiles, profiles of the leading error components, information on vertical representativeness in form of the averaging kernels as well as averaging kernel metrics, which are more handy than the full kernels. We discuss data filtering options and give examples of the high horizontal and continuous temporal coverage of the MUSICA IASI data products. The standard output data files provide comprehensive information for each individual retrieval resulting in a rather large data volume (about 25 TB for the more than five years of data with global daily coverage). This at a first glance apparent drawback of large data files and data volume is counterbalanced by multiple possibilities of data reusability, which are briefly discussed. In an extended output data file the same variables as in the standard output data files are provided in addition to Jacobians for many different uncertainty sources and Gain matrices (due to this additional variables it is called the extended output). It is limited to 74 observations over a polar, mid-latitudinal and tropical site. We use this additional Jacobian and Gain data for assessing the typical impact of different uncertainty sources – like surface emissivity or spectroscopic parameters – and different cloud types on the retrieval results. We offer two data packages with DOI for free download via the repository RADAR4KIT. The first data package has a data volume of about 17.5 GB and is linked to https://doi.org/10.35097/408 (Schneider, et al., 2021b). It contains example standard output data files for all MUSICA IASI retrievals made for a single day (more than 0.6 million). Furthermore, it includes a ReadMe.pdf file with a description of how to access the total data set (the 25 TB) or parts of it. This data package is for users interested in the typical global daily data coverage and in information about how to download the large data volumes of global daily data for longer periods. The second data package is linked to https://doi.org/10.35097/412 (Schneider et al., 2021a) and contains the extended output data file. Because it provides data for only 74 example retrievals, its data volume is only 73 MB and it is thus recommended to users for having a quick look on the data.


2021 ◽  
Vol 251 ◽  
pp. 02020
Author(s):  
C. Acosta-Silva ◽  
A. Delgado Peris ◽  
J. Flix ◽  
J. Frey ◽  
J.M. Hernández ◽  
...  

CMS is tackling the exploitation of CPU resources at HPC centers where compute nodes do not have network connectivity to the Internet. Pilot agents and payload jobs need to interact with external services from the compute nodes: access to the application software (CernVM-FS) and conditions data (Frontier), management of input and output data files (data management services), and job management (HTCondor). Finding an alternative route to these services is challenging. Seamless integration in the CMS production system without causing any operational overhead is a key goal. The case of the Barcelona Supercomputing Center (BSC), in Spain, is particularly challenging, due to its especially restrictive network setup. We describe in this paper the solutions developed within CMS to overcome these restrictions, and integrate this resource in production. Singularity containers with application software releases are built and pre-placed in the HPC facility shared file system, together with conditions data files. HTCondor has been extended to relay communications between running pilot jobs and HTCondor daemons through the HPC shared file system. This operation mode also allows piping input and output data files through the HPC file system. Results, issues encountered during the integration process, and remaining concerns are discussed.


2018 ◽  
Vol 1 (1) ◽  
Author(s):  
Alexander Andonov ◽  
◽  
◽  

On the basis of the latest developments, an improved model of underwater communication channel is presented. A set of programs to allow calculation of the basic parameters of the channel over a wide range of parameters has been created. Mathematical models for calculating the spreading factor are developed. A process of creating the model is reviewed, so that the resulting model should become easily expandable. Userfriendly information-transfer interface is set between the programs and input and output data files.


2020 ◽  
Vol 18 ◽  
pp. 1-11
Author(s):  
A. Serrano-Juan ◽  
R. Criollo ◽  
E. Vázquez-Suñé ◽  
M. Alcaraz ◽  
C. Ayora ◽  
...  

Each scientist is specialized in his or her field of research and in the tools that he or she uses during the research in a specified site. Thus, he or she is the most suitable person for improving the tools by overcoming their limitations to realize faster and higher quality analysis. However, most scientists are not software developers. Hence, it is necessary to provide them with an easy approach that enables non-software developers to improve and customize their tools. This paper presents an approach for easily improving and customizing any hydrogeological software. It is the result of experiences with updating several interdisciplinary case studies. The main insights of this approachhave been demonstrated using four examples: MIX (FORTRAN-based), BrineMIX (C++-based), EasyQuim and EasyBal (both spreadsheet-based). The improved software has been proven to be a better tool for enhanced analysis by substantially reducing the computation time and the tedious processing of the input and output data files.


Author(s):  
H. O. Colijn

Many labs today wish to transfer data between their EDS systems and their existing PCs and minicomputers. Our lab has implemented SpectraPlot, a low- cost PC-based system to allow offline examination and plotting of spectra. We adopted this system in order to make more efficient use of our microscopes and EDS consoles, to provide hardcopy output for an older EDS system, and to allow students to access their data after leaving the university.As shown in Fig. 1, we have three EDS systems (one of which is located in another building) which can store data on 8 inch RT-11 floppy disks. We transfer data from these systems to a DEC MINC computer using “SneakerNet”, which consists of putting on a pair of sneakers and running down the hall. We then use the Hermit file transfer program to download the data files with error checking from the MINC to the PC.


Author(s):  
Klaus-Ruediger Peters

Differential hysteresis processing is a new image processing technology that provides a tool for the display of image data information at any level of differential contrast resolution. This includes the maximum contrast resolution of the acquisition system which may be 1,000-times higher than that of the visual system (16 bit versus 6 bit). All microscopes acquire high precision contrasts at a level of <0.01-25% of the acquisition range in 16-bit - 8-bit data, but these contrasts are mostly invisible or only partially visible even in conventionally enhanced images. The processing principle of the differential hysteresis tool is based on hysteresis properties of intensity variations within an image.Differential hysteresis image processing moves a cursor of selected intensity range (hysteresis range) along lines through the image data reading each successive pixel intensity. The midpoint of the cursor provides the output data. If the intensity value of the following pixel falls outside of the actual cursor endpoint values, then the cursor follows the data either with its top or with its bottom, but if the pixels' intensity value falls within the cursor range, then the cursor maintains its intensity value.


1998 ◽  
Vol 10 (1-3) ◽  
pp. 1-9
Author(s):  
Onno Boonstra ◽  
Maarten Panhuysen

Population registers are recognised to be a very important source for demographic research, because it enables us to study the lifecourse of individuals as well as households. A very good technique for lifecourse analysis is event history analysis. Unfortunately, there are marked differences in the way the data are available in population registers and the way event history analysis expects them to be. The source-oriented approach of computing historical data calls for a ‘five-file structure’, whereas event history analysis only can handle fiat files. In this article, we suggest a series of twelve steps with which population register data can be transposed from a five-file structured database into a ‘flat file’ event history analysis dataset.


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