Inter-comparison of wind vectors derived from geostationary satellites with the Aeolus/ALADIN

2021 ◽  
Author(s):  
Hyemin Shin ◽  
Myoung-Hwan Ahn ◽  
Jisoo Kim ◽  
Jae-Gwan Kim ◽  
Joon-Tae Choi
Keyword(s):  
Quality Control ◽  
Lower Layer ◽  
Weather Prediction ◽  
Motion Vector ◽  
Radiosonde Data ◽  
Data Quality Control ◽  
Geostationary Satellites ◽  
Time Space ◽  
Comparison Results ◽  
Hemisphere Winter

<p>Wind information obtained from various means ​​play an important role in data assimilation of numerical weather prediction. Atmospheric Motion Vector (AMV) obtained from the geostationary satellites provide a high spatio-temporal resolution wind information over the whole glove. An accurate quality control is one of the key factor that needs for a better utilization of AMV. Here, we use Aeolus/Atmospheric Laser Doppler Instrument (ALADIN) data to analyze the error characteristics of AMV derived from a newly commissioned geostationary satellite, Geostationary Korea Multi Purpose Satellite-2A (GK2A), stationed over 128.2<sup>o </sup>E. As majority of the GK2A AMV data are obtained over the ocean where the radiosonde data (used for the reference wind measurement for the error analysis of AMV) is sparse, the ALADIN data could play an important contribution. Data obtained from December 2019 to February 2020 (northern hemisphere winter) are collocated with time, space, and altitude criteria of ±15 min, 0.9<sup> o</sup>, and 50hPa. For the quality control data, only AMV data with a Quality Index (QI) of 0.85 or higher are used. In case of the ALADIN data, quality control is performed using the observation type (clear and cloudy) and error estimation value of the ALADIN data. The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 39971 which gives the root mean square difference (RMSD) of 3.88 m/s. The lower layer (lower than 700 hPa altitude) RMSD shows slightly better comparison, 3.35 m/s vs. 4.17 m/s, while the correlation coefficient is better for the upper and middle layers of 0.98 compared to the 0.94 of the lower layer. In the conference, detailed analysis of the comparison results and additional AMV data, including visible channel and water vapor channel along with the extended time period are going to be presented.</p>

scholarly journals Diagnostic Analysis of Various Observation Impacts in the 3DVAR Assimilation System of Global GRAPES

2018 ◽  
Vol 146 (10) ◽  
pp. 3125-3142 ◽  
Author(s):  
Lihong Zhang ◽  
Jiandong Gong ◽  
Ruichun Wang
Keyword(s):  
Northern Hemisphere ◽  
Weather Prediction ◽  
Motion Vector ◽  
Diagnostic Technique ◽  
Diagnostic Techniques ◽  
Equatorial Region ◽  
Radiosonde Data ◽  
Prediction System ◽  
Research Attention ◽  
Proper Mass

Abstract Observation impact studies have received increasing amounts of research attention. The impacts of observations on numerical weather prediction (NWP) are highly dependent on assimilation algorithm, prediction system, and observation source. Therefore, the major NWP centers worldwide have each developed their own diagnostic techniques to assess observation impacts. However, similar diagnostic techniques have not yet been developed in China. In this study, a diagnostic technique was exploited with the randomized perturbation method in the Global/Regional Assimilation and Prediction System (GRAPES) 3DVAR system, and then applied to evaluate observation impacts for various regions of the world. It was found that a reasonable and stable estimation could be obtained when the number of perturbations was greater than 15. Because of differences in observations in the Northern and Southern Hemispheres, refractivity data from GNSS radio occultation (GNSS-RO), satellite radiance, and atmospheric motion vector data had more impact in the Southern Hemisphere than in the Northern Hemisphere. However, radiosonde data, aircraft, and surface data were more important in the Northern Hemisphere. Low-impact observation points were located in data-rich areas, whereas high-impact observation points were located in data-poor areas. In the equatorial region, the contributions of observations to the analysis were smaller than those in the nonequatorial regions because of the lack of proper mass–wind balance relationship. Radiosondes contributed the largest impact in China and its surrounding regions, with contributions of radiosondes and GNSS-RO data exceeding 60% of the total contributions, except for wind speed below 700 hPa.

scholarly journals Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning

2021 ◽  
pp. 106239
Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  
Keyword(s):  
Quality Control ◽  
Deep Learning ◽  
Data Quality ◽  
Pet Imaging ◽  
Data Quality Control ◽  
Brain Pet

scholarly journals An interactive graphical system for XBT data quality control and visualization

2001 ◽  
Vol 27 (7) ◽  
pp. 867-876 ◽  
Author(s):  
Pankajakshan Thadathil ◽  
Aravind K Ghosh ◽  
J.S Sarupria ◽  
V.V Gopalakrishna
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Graphical System

Implementation of Marine Environment Monitoring Data Quality Control System

2014 ◽  
Vol 926-930 ◽  
pp. 4254-4257 ◽  
Author(s):  
Jin Xu ◽  
Da Tao Yu ◽  
Zhong Jie Yuan ◽  
Bo Li ◽  
Zi Zhou Xu
Keyword(s):  
Quality Control ◽  
Control System ◽  
Data Quality ◽  
Marine Environment ◽  
Quality Control System ◽  
Monitoring Data ◽  
Environment Monitoring ◽  
Data Quality Control ◽  
Labor Costs ◽  
Marine Environment Monitoring

Traditional artificial perception quality control methods of marine environment monitoring data have many disadvantages, including high labor costs and mistakes of data review. Based on GIS spatial analysis technology, Marine Environment Monitoring Data Quality Control System is established according to the Bohai Sea monitoring regulation. In the practical application process, it plays the role of improving efficiency of quality control, saving the manpower and financial resources. It also provides an important guarantee for the comprehensive analysis and management of marine environment data.

Data quality control in a multi-insitutional cancer trial

1980 ◽  
Vol 1 (2) ◽  
pp. 171-172
Author(s):  
M.M. Koretz ◽  
M. Kohler ◽  
E. McGuigan ◽  
J.F. Hannigan ◽  
B.W. Brown
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Data Quality Control ◽  
Cancer Trial

Proactive Quality Control: Observing System Simulation Experiments with the Lorenz ’96 Model

2019 ◽  
Vol 147 (1) ◽  
pp. 53-67 ◽  
Author(s):  
Tse-Chun Chen ◽  
Eugenia Kalnay
Keyword(s):  
Quality Control ◽  
Weather Prediction ◽  
Computational Cost ◽  
Network Size ◽  
Simulation Experiments ◽  
Systematic Testing ◽  
Kalman Gain ◽  
Lorenz 96 ◽  
First Time ◽  
Shed Light

Proactive quality control (PQC) is a fully flow-dependent QC for observations based on the ensemble forecast sensitivity to observations technique (EFSO). It aims at reducing the forecast skill dropout events suffered in operational numerical weather prediction by rejecting observations identified as detrimental by EFSO. Past studies show that individual dropout cases from the Global Forecast System (GFS) were significantly improved by noncycling PQC. In this paper, we perform for the first time cycling PQC experiments in a controlled environment with the Lorenz model to provide a systematic testing of the new method and possibly shed light on the optimal configuration of operational implementation. We compare several configurations and PQC update methods. It is found that PQC improvement is insensitive to the suboptimal configurations in DA, including ensemble size, observing network size, model error, and the length of DA window, but the improvements increase with the flaws in observations. More importantly, we show that PQC improves the analysis and forecast even in the absence of flawed observations. The study reveals that reusing the exact same Kalman gain matrix for PQC update not only provides the best result but requires the lowest computational cost among all the tested methods.

scholarly journals Monitoring the impacts of weather radar data quality control for quantitative application at the continental scale

2020 ◽  
Vol 27 (4) ◽  
Author(s):  
Daniel Michelson ◽  
Bjarne Hansen ◽  
Dominik Jacques ◽  
François Lemay ◽  
Peter Rodriguez
Keyword(s):  
Quality Control ◽  
Data Quality ◽  
Weather Radar ◽  
Radar Data ◽  
Data Quality Control ◽  
Continental Scale
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