scholarly journals Design and Implementation of Aircraft Measurement Data Monitoring and Analysis Software under Linux

2019 ◽  
Vol 677 ◽  
pp. 052116
Author(s):  
Zebo Zhu
Keyword(s):  
Measurement Data ◽  
Data Monitoring ◽  
Analysis Software ◽  
Aircraft Measurement ◽  
Design And Implementation

Cybersecurity From Field to Host

2016 ◽  
Author(s):  
Lee A. Cysouw ◽  
Douglas C. Osburn ◽  
Nader M. Rabadi
Keyword(s):  
Best Practices ◽  
Control Systems ◽  
Cyber Security ◽  
Measurement Data ◽  
Cyber Attacks ◽  
Operating Costs ◽  
Data Monitoring ◽  
Industry Standards ◽  
And Control ◽  
Remote Communications

Remote communications to field devices for data monitoring and controls has greatly reduced operating costs, reduced downtime, and helped to optimize our industry. With the ever growing threat of cyber-attacks, the need for securing that data is becoming a more common topic of discussion. Whether collecting SCADA or Measurement data from the field, doing remote configuration, or even sitting dormant, it is important to keep the line of communication to your devices secure. This presentation will discuss potential threats and examples of cyber-attacks. It will cover industry standards, types of cyber security, and the importance and best practices for securing data for Measurement and/or SCADA and control systems.

scholarly journals Bias corrections of GOSAT SWIR XCO<sub>2</sub> and XCH<sub>4</sub> with TCCON data and their evaluation using aircraft measurement data

2016 ◽  
Author(s):  
M. Inoue ◽  
I. Morino ◽  
O. Uchino ◽  
T. Nakatsuru ◽  
Y. Yoshida ◽  
...  
Keyword(s):  
Measurement Data ◽  
Correction Method ◽  
Temporal Variations ◽  
Department Of Energy ◽  
Total Carbon ◽  
Japan Meteorological Agency ◽  
Aircraft Measurement ◽  
Observation Network ◽  
Systematic Biases ◽  
Aircraft Data

Abstract. We describe a method for removing systematic biases of column-averaged dry air mole fractions of CO2 (XCO2) and CH4 (XCH4) derived from short-wavelength infrared (SWIR) spectra of the Greenhouse gases Observing SATellite (GOSAT). We conduct correlation analyses between the GOSAT biases and simultaneously-retrieved auxiliary parameters. We use these correlations to bias correct the GOSAT data, removing these spurious correlations. Data from Total Carbon Column Observing Network (TCCON) were used as reference values for this regression analysis. To evaluate the effectiveness of this correction method, the uncorrected/corrected GOSAT data were compared to independent XCO2 and XCH4 data derived from aircraft measurements taken for the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the U.S. Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the Japan Meteorological Agency (JMA), the HIAPER Pole- to-Pole observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. These comparisons demonstrate that the empirically-derived bias correction improves the agreement between GOSAT XCO2/XCH4 and the aircraft data. Finally, we present latitudinal distributions and temporal variations of the derived GOSAT biases.

scholarly journals Validation of XCO<sub>2</sub> derived from SWIR spectra of GOSAT TANSO-FTS with aircraft measurement data

2013 ◽  
Vol 13 (19) ◽  
pp. 9771-9788 ◽  
Author(s):  
M. Inoue ◽  
I. Morino ◽  
O. Uchino ◽  
Y. Miyamoto ◽  
Y. Yoshida ◽  
...  
Keyword(s):  
Standard Deviation ◽  
A Priori ◽  
Measurement Data ◽  
Department Of Energy ◽  
Minor Effect ◽  
Aircraft Measurements ◽  
Aircraft Measurement ◽  
Additional Information ◽  
Observation Network ◽  
A Minor

Abstract. Column-averaged dry air mole fractions of carbon dioxide (XCO2) retrieved from Greenhouse gases Observing SATellite (GOSAT) Short-Wavelength InfraRed (SWIR) observations were validated with aircraft measurements by the Comprehensive Observation Network for TRace gases by AIrLiner (CONTRAIL) project, the National Oceanic and Atmospheric Administration (NOAA), the US Department of Energy (DOE), the National Institute for Environmental Studies (NIES), the HIAPER Pole-to-Pole Observations (HIPPO) program, and the GOSAT validation aircraft observation campaign over Japan. To calculate XCO2 based on aircraft measurements (aircraft-based XCO2), tower measurements and model outputs were used for additional information near the surface and above the tropopause, respectively. Before validation, we investigated the impacts of GOSAT SWIR column averaging kernels (CAKs) and the shape of a priori profiles on the aircraft-based XCO2 calculation. The differences between aircraft-based XCO2 with and without the application of GOSAT CAK were evaluated to be less than ±0.4 ppm at most, and less than ±0.1 ppm on average. Therefore, we concluded that the GOSAT CAK produces only a minor effect on the aircraft-based XCO2 calculation in terms of the overall uncertainty of GOSAT XCO2. We compared GOSAT data retrieved within ±2 or ±5° latitude/longitude boxes centered at each aircraft measurement site to aircraft-based data measured on a GOSAT overpass day. The results indicated that GOSAT XCO2 over land regions agreed with aircraft-based XCO2, except that the former is biased by −0.68 ppm (−0.99 ppm) with a standard deviation of 2.56 ppm (2.51 ppm), whereas the averages of the differences between the GOSAT XCO2 over ocean and the aircraft-based XCO2 were −1.82 ppm (−2.27 ppm) with a standard deviation of 1.04 ppm (1.79 ppm) for ±2° (±5°) boxes.

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