Intercomparison of the Vaisala RS92 and RS41 Radiosonde GRUAN Data Products (GDP) in the Troposphere and Lower Stratosphere

2021 ◽  
Author(s):  
Tzvetan Simeonov ◽  
Ruud Dirksen ◽  
Christoph von Rohden ◽  
Michael Sommer
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Lower Stratosphere ◽  
Time Lag ◽  
Measurement Data ◽  
Uncertainty Estimates ◽  
Data Products ◽  
Humidity Measurements ◽  
Special Challenge ◽  
Distributed Measurement

<p><span>The GCOS Reference Upper Air Network (GRUAN) consists of 30 globally distributed measurement sites that provide reference observations of essential climate variables such as temperature and water vapour for climate monitoring. At these sites, radiosondes provide in-situ profiles of temperature, humidity and pressure at high vertical resolution. However, data products from commercial radiosondes often rely on black-box or proprietary algorithms, which are not disclosed to the scientific user. Furthermore, long-term time-series from these products are frequently hampered by changes in the hardware and/or the data processing. Therefore, GRUAN data products (GDP) are developed, that employ open-source and well-documented corrections to the measured data, thereby complying with the requirements for reference data, which include measurement traceability and the availability of measurement uncertainties. The GRUAN data processing is applied to the raw measurement data of temperature, humidity, pressure, altitude, and wind, and includes corrections of errors from known sources, such as for example solar radiation error for temperature and sensor time lag for humidity measurements. The vertically resolved uncertainty estimates include the uncertainty of the applied corrections and the calibration uncertainty of the sensors.</span></p><p><span>A substantial number of GRUAN sites employ the Vaisala RS41 radiosonde, and its predecessor, the RS92, before that. This large-scale change of instrumentation poses a special challenge to the network, and great care is taken to characterize the differences between these instruments in order to prevent inhomogeneities in the data records. As part of this effort, the GRUAN data products for both radiosonde types are compared. In this study we used data from approximately 1000 RS92+RS41 twin-soundings (two sondes on a rig attached to one balloon) </span><span><!-- A short explanation what a twin sounding is (two sondes on the same rig) might be appropriate. --></span><span>that were performed at 11 GRUAN sites, covering the main climate zones.</span></p><p><span>The first analysis shows that daytime temperature differences in the stratosphere increase steadily with altitude, with RS92-GDP up to 0.5 K warmer than RS41-GDP above 25 km. In addition, at daytime the RS41-GDP is 0.2 K warmer than the manufacturer-processed RS41-EDT product above 15 km. Analysis of the humidity profiles shows a slight moist bias of the RS41 compared to the RS92 for both GDP and manufacturer-processed data. Differences between the RS41-EDT and GDP humidity products are most pronounced in the upper troposphere - lower stratosphere region and are attributed to the time lagcorrection. The analysis of the temperature differences will be refined by investigating the influence of the solar </span><span>radiation in conjunction with sonde orientation and ventilation</span><span>. Furthermore, the uncertainty of the humidity data will be assessed by comparing with coincident measurements of the water vapor profile by the Cryogenic Frostpoint Hygrometer (CFH).</span></p><p><span>Key words: </span>Radiosonde, RS41, RS92, humidity, temperature, uncertainty, GRUAN, troposphere, lower stratosphere</p>

scholarly journals Intercomparison of the Vaisala RS92 and RS41 Radiosonde GRUAN Data Products (GDP) in the Troposphere and Lower Stratosphere

2021 ◽  
Author(s):  
Tzvetan Simeonov ◽  
Ruud Dirksen ◽  
Christoph von Rohden ◽  
Michael Sommer
Keyword(s):  
Data Processing ◽  
Large Scale ◽  
Lower Stratosphere ◽  
Time Lag ◽  
Measurement Data ◽  
Uncertainty Estimates ◽  
Data Products ◽  
Humidity Measurements ◽  
Special Challenge ◽  
Distributed Measurement

<p>The GCOS Reference Upper Air Network (GRUAN) consists of 30 globally distributed measurement sites that provide reference observations of essential climate variables such as temperature and water vapour for climate monitoring. At these sites, radiosondes provide in-situ profiles of temperature, humidity and pressure at high vertical resolution. However, data products from commercial radiosondes often rely on black-box or proprietary algorithms, which are not disclosed to the scientific user. Furthermore, long-term time-series from these products are frequently hampered by changes in the hardware and/or the data processing. Therefore, GRUAN data products (GDP) are developed, that employ open-source and well-documented corrections to the measured data, thereby complying with the requirements for reference data, which include measurement traceability and the availability of measurement uncertainties. The GRUAN data processing is applied to the raw measurement data of temperature, humidity, pressure, altitude, and wind, and includes corrections of errors from known sources, such as for example solar radiation error for temperature and sensor time lag for humidity measurements. The vertically resolved uncertainty estimates include the uncertainty of the applied corrections and the calibration uncertainty of the sensors.</p><p>A substantial number of GRUAN sites employ the Vaisala RS41 radiosonde, and its predecessor, the RS92, before that. This large-scale change of instrumentation poses a special challenge to the network, and great care is taken to characterize the differences between these instruments in order to prevent inhomogeneities in the data records. As part of this effort, the GRUAN data products for both radiosonde types are compared. In this study we used data from approximately 1000 RS92+RS41 twin-soundings (two sondes on a rig attached to one balloon) that were performed at 11 GRUAN sites, covering the main climate zones.</p><p>The first analysis shows that daytime temperature differences in the stratosphere increase steadily with altitude, with RS92-GDP up to 0.5 K warmer than RS41-GDP above 25 km. In addition, at daytime the RS41-GDP is 0.2 K warmer than the manufacturer-processed RS41-EDT product above 15 km. Analysis of the humidity profiles shows a slight moist bias of the RS41 compared to the RS92 for both GDP and manufacturer-processed data. Differences between the RS41-EDT and GDP humidity products are most pronounced in the upper troposphere - lower stratosphere region and are attributed to the time lagcorrection. The analysis of the temperature differences will be refined by investigating the influence of the solar radiation in conjunction with sonde orientation and ventilation. Furthermore, the uncertainty of the humidity data will be assessed by comparing with coincident measurements of the water vapor profile by the Cryogenic Frostpoint Hygrometer (CFH).</p><p>Key words: Radiosonde, RS41, RS92, humidity, temperature, uncertainty, GRUAN, troposphere, lower stratosphere</p>

RS41 GRUAN Data Product Version 1 (RS41-GDP.1) - Reference Radiosonde Data for the Troposphere and Lower Stratosphere

2021 ◽  
Author(s):  
Michael Sommer ◽  
Christoph von Rohden ◽  
Tzvetan Simeonov ◽  
Ruud Dirksen
Keyword(s):  
Humidity Sensor ◽  
Measurement Techniques ◽  
Time Lag ◽  
Measurement Data ◽  
Atmospheric Temperature ◽  
Radiosonde Data ◽  
Error Sources ◽  
Data Product ◽  
Uncertainty Estimates ◽  
Data Products

<p>One of the main goals of the GCOS Reference Upper Air Network (GRUAN) is to perform reference observations of profiles of atmospheric temperature, humidity and wind for monitoring climate change. Two essential criteria for establishing a reference observation are measurement-traceability and the availability of measurement uncertainties. Radiosoundings have proven valuable in providing in-situ profiles of temperature, humidity, pressure and wind at unmatched vertical resolution. Data products from commercial radiosondes often rely on black-box or proprietary algorithms, which are not disclosed to the scientific user. Furthermore, long-term time-series from these products are frequently hampered by changes in the hardware and/or the data processing.</p><p>The GRUAN data products (GDP’s) comply with the above-mentioned criteria for a reference product. Correction algorithms are open-source and well documented and the data include vertically resolved best estimates of the uncertainties. Another major advantage of a GRUAN data product is that it includes the radiosonde’s raw measurement data, which allows for reprocessing when new or improved corrections become available. Currently, GDP’s are available for the Vaisala RS92 and Meisei RS-11G radiosondes. Data products for additional radiosonde models, as well as for other measurement techniques are in the making. The GDP’s are used to determine trends, constrain and calibrate data from more spatially‐comprehensive observing systems (including satellites and current radiosonde networks), and provide appropriate data for studying atmospheric processes.</p><p>This presentation introduces the GRUAN processing of Vaisala RS41 radiosoundings, the correction algorithms that are applied, and the derivation of the vertically resolved uncertainty estimates. Well-known, dominant error sources for the RS41 profiles are related to solar radiation, causing a temperature error, and time-lag of the humidity sensor at low temperatures. The corrections for these error sources are based on dedicated experiments that were performed at Lindenberg observatory to measure the response of the RS41 temperature sensor to solar irradiance and to determine the time-lag of the humidity sensor at temperatures down to -70 °C. The RS41-GDP.1 is planned to become available in 2021. The majority of the 30, globally distributed, GRUAN sites employ the RS41, and its predecessor the RS92 before, establishing a continuous data record of more than 10 years of reference climate observations.</p>

scholarly journals Reference quality upper-air measurements: GRUAN data processing for the Vaisala RS92 radiosonde

2014 ◽  
Vol 7 (4) ◽  
pp. 3727-3800 ◽  
Author(s):  
R. J. Dirksen ◽  
M. Sommer ◽  
F. J. Immler ◽  
D. F. Hurst ◽  
R. Kivi ◽  
...  
Keyword(s):  
Data Processing ◽  
Laboratory Experiments ◽  
Temperature Profiles ◽  
Error Sources ◽  
Uncertainty Estimates ◽  
Humidity Measurements ◽  
Reference Quality ◽  
Reference Measurements ◽  
Radiation Laboratory ◽  
Humidity Profiles

Abstract. The GCOS Reference Upper Air Network (GRUAN) data processing for the Vaisala RS92 radiosonde was developed to meet the criteria for reference measurements. These criteria stipulate the collection of metadata, the use of well-documented correction algorithms, and estimates of the measurement uncertainty. An important and novel aspect of the GRUAN processing is that the uncertainty estimates are vertically resolved. This paper describes the algorithms that are applied in version 2 of the GRUAN processing to correct for systematic errors in radiosonde measurements of pressure, temperature, humidity, and wind, as well as how the uncertainties related to these error sources are derived. An additional GRUAN requirement for performing reference measurements with the RS92 is that the manufacturer-prescribed procedure for the radiosonde's preparation, i.e., heated reconditioning of the sensors and recalibration during ground check, is followed. In the GRUAN processing however, the recalibration of the humidity sensors that is applied during ground check is removed. For the dominant error source, solar radiation, laboratory experiments were performed to investigate and model its effect on the RS92's temperature and humidity measurements. Daytime temperature profiles for GRUAN and Vaisala processing are comparable and consistent within the estimated uncertainty. GRUAN daytime humidity profiles are up to 15% moister than Vaisala processed profiles, of which two-thirds is due to the radiation dry bias correction, and one-third due to an additional calibration correction. GRUAN humidity profiles and those measured by frost point hygrometers (CFH and NOAA FPH) agree to within 15% in the troposphere.

scholarly journals Statistically-estimated tree biomass, stem density, and basal area for the upper Midwestern United States at the time of Euro-American settlement

2019 ◽  
Author(s):  
Christopher J. Paciorek ◽  
Charles V. Cogbill ◽  
Jody A. Peters ◽  
Simon J. Goring ◽  
John W. Williams ◽  
...  
Keyword(s):  
United States ◽  
Large Scale ◽  
Basal Area ◽  
Stem Density ◽  
Midwestern United States ◽  
Data Product ◽  
Uncertainty Estimates ◽  
Data Products ◽  
Additive Modeling ◽  
Approximate Bayesian

AbstractWe present gridded 8 km-resolution data products of the estimated biomass, basal area, and stem density of tree taxa at the time of Euro-American settlement of the midwestern United States for the states of Minnesota, Wisconsin, Michigan, Illinois, and Indiana. The data come from settlement-era Public Land Survey (PLS) data (ca. 0.8-km resolution) of trees recorded by land surveyors. The surveyor notes have been transcribed, cleaned, and processed to estimate biomass, basal area, and stem density at individual points on the landscape. The point-level data are then aggregated within grid cells and statistically smoothed using a statistical model that accounts for zero-inflated continuous data with smoothing based on generalized additive modeling techniques and approximate Bayesian uncertainty estimates. We expect this data product to be useful for understanding the state of vegetation in the midwestern United States prior to large-scale Euro-American settlement. In addition to specific regional questions, the data product can serve as a baseline against which to investigate how forests and ecosystems change after intensive settlement. The data products (including both raw and statistically smoothed estimates at the 8-km scale) are being made available at the LTER network data portal as version 1.0.

Soil/Tire Interaction Analysis Using FEM and FVM

2005 ◽  
Vol 33 (1) ◽  
pp. 38-62 ◽  
Author(s):  
S. Oida ◽  
E. Seta ◽  
H. Heguri ◽  
K. Kato
Keyword(s):  
Large Scale ◽  
Interaction Analysis ◽  
Yield Criterion ◽  
Surrounding Medium ◽  
Flow Analysis ◽  
Measurement Data ◽  
Traction Force ◽  
Elastoplastic Material ◽  
The Road ◽  
Soil Flow

Abstract Vehicles, such as an agricultural tractor, construction vehicle, mobile machinery, and 4-wheel drive vehicle, are often operated on unpaved ground. In many cases, the ground is deformable; therefore, the deformation should be taken into consideration in order to assess the off-the-road performance of a tire. Recent progress in computational mechanics enabled us to simulate the large scale coupling problem, in which the deformation of tire structure and of surrounding medium can be interactively considered. Using this technology, hydroplaning phenomena and tire traction on snow have been predicted. In this paper, the simulation methodology of tire/soil coupling problems is developed for pneumatic tires of arbitrary tread patterns. The Finite Element Method (FEM) and the Finite Volume Method (FVM) are used for structural and for soil-flow analysis, respectively. The soil is modeled as an elastoplastic material with a specified yield criterion and a nonlinear elasticity. The material constants are referred to measurement data, so that the cone penetration resistance and the shear resistance are represented. Finally, the traction force of the tire in a cultivated field is predicted, and a good correlation with experiments is obtained.

THE WALTHAM SYSTEM AND EARLY AMERICAN TEXTILE COST ACCOUNTING 1813–1848

1980 ◽  
Vol 7 (1) ◽  
pp. 1-15 ◽  
Author(s):  
David M. Porter
Keyword(s):  
Large Scale ◽  
Time Lag ◽  
Cost Accounting ◽  
Industrial Enterprise ◽  
Early American

This study of the original accounting records of a pioneering American industrial enterprise narrows by one half the time lag between the earliest known English and American applications of industrial cost accounting. The research indicates that the precursors of the costing systems now considered essential tools of management were in use virtually from the beginning of large scale industry in America.

scholarly journals Uncertainty-Aware Deep Learning-Based Cardiac Arrhythmias Classification Model of Electrocardiogram Signals

Computers ◽  
2021 ◽  
Vol 10 (6) ◽  
pp. 82
Author(s):  
Ahmad O. Aseeri
Keyword(s):  
Deep Learning ◽  
Cardiac Arrhythmias ◽  
Large Scale ◽  
Clinical Decision Making ◽  
Probabilistic Approach ◽  
Classification Model ◽  
Gating Mechanism ◽  
Uncertainty Estimates ◽  
Wide Range

Deep Learning-based methods have emerged to be one of the most effective and practical solutions in a wide range of medical problems, including the diagnosis of cardiac arrhythmias. A critical step to a precocious diagnosis in many heart dysfunctions diseases starts with the accurate detection and classification of cardiac arrhythmias, which can be achieved via electrocardiograms (ECGs). Motivated by the desire to enhance conventional clinical methods in diagnosing cardiac arrhythmias, we introduce an uncertainty-aware deep learning-based predictive model design for accurate large-scale classification of cardiac arrhythmias successfully trained and evaluated using three benchmark medical datasets. In addition, considering that the quantification of uncertainty estimates is vital for clinical decision-making, our method incorporates a probabilistic approach to capture the model’s uncertainty using a Bayesian-based approximation method without introducing additional parameters or significant changes to the network’s architecture. Although many arrhythmias classification solutions with various ECG feature engineering techniques have been reported in the literature, the introduced AI-based probabilistic-enabled method in this paper outperforms the results of existing methods in outstanding multiclass classification results that manifest F1 scores of 98.62% and 96.73% with (MIT-BIH) dataset of 20 annotations, and 99.23% and 96.94% with (INCART) dataset of eight annotations, and 97.25% and 96.73% with (BIDMC) dataset of six annotations, for the deep ensemble and probabilistic mode, respectively. We demonstrate our method’s high-performing and statistical reliability results in numerical experiments on the language modeling using the gating mechanism of Recurrent Neural Networks.

scholarly journals Inverse Infiltration Modeling of Dike Covers Made of Dredged Material Using PEST and AMALGAM

Geosciences ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 41
Author(s):  
Tim Jurisch ◽  
Stefan Cantré ◽  
Fokke Saathoff
Keyword(s):  
Large Scale ◽  
Measurement Data ◽  
Fine Grained ◽  
Dredged Materials ◽  
Measurement Results ◽  
Large Scale Field ◽  
Eigenvalue Ratio ◽  
Ill Posed ◽  
Materials Used ◽  
Installation Technology

A variety of studies recently proved the applicability of different dried, fine-grained dredged materials as replacement material for erosion-resistant sea dike covers. In Rostock, Germany, a large-scale field experiment was conducted, in which different dredged materials were tested with regard to installation technology, stability, turf development, infiltration, and erosion resistance. The infiltration experiments to study the development of a seepage line in the dike body showed unexpected measurement results. Due to the high complexity of the problem, standard geo-hydraulic models proved to be unable to analyze these results. Therefore, different methods of inverse infiltration modeling were applied, such as the parameter estimation tool (PEST) and the AMALGAM algorithm. In the paper, the two approaches are compared and discussed. A sensitivity analysis proved the presumption of a non-linear model behavior for the infiltration problem and the Eigenvalue ratio indicates that the dike infiltration is an ill-posed problem. Although this complicates the inverse modeling (e.g., termination in local minima), parameter sets close to an optimum were found with both the PEST and the AMALGAM algorithms. Together with the field measurement data, this information supports the rating of the effective material properties of the applied dredged materials used as dike cover material.

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