Quality control of surface station temperature data with non-Gaussian observation-minus-background distributions

2010 ◽  
Vol 115 (D16) ◽  
Author(s):  
Z.-K. Qin ◽  
X. Zou ◽  
G. Li ◽  
X.-L. Ma
Keyword(s):  
Quality Control ◽  
Temperature Data ◽  
Surface Station ◽  
Non Gaussian

Quality control and homogenization of the Belgian historical temperature data

2018 ◽  
Vol 39 (1) ◽  
pp. 157-171 ◽  
Author(s):  
Charles Delvaux ◽  
Romain Ingels ◽  
Vladimír Vrábeĺ ◽  
Michel Journée ◽  
Cédric Bertrand
Keyword(s):  
Quality Control ◽  
Temperature Data ◽  
Historical Temperature

Bayesian Inference of Drag Parameters Using AXBT Data from Typhoon Fanapi

2013 ◽  
Vol 141 (7) ◽  
pp. 2347-2367 ◽  
Author(s):  
Ihab Sraj ◽  
Mohamed Iskandarani ◽  
Ashwanth Srinivasan ◽  
W. Carlisle Thacker ◽  
Justin Winokur ◽  
...  
Keyword(s):  
Bayesian Inference ◽  
Wind Speed ◽  
Drag Coefficient ◽  
Monte Carlo Sampling ◽  
Temperature Data ◽  
Rate Of Change ◽  
Wind Speeds ◽  
Gaussian Probability Distribution ◽  
Non Gaussian ◽  
Polynomial Expansions

Abstract The authors introduce a three-parameter characterization of the wind speed dependence of the drag coefficient and apply a Bayesian formalism to infer values for these parameters from airborne expendable bathythermograph (AXBT) temperature data obtained during Typhoon Fanapi. One parameter is a multiplicative factor that amplifies or attenuates the drag coefficient for all wind speeds, the second is the maximum wind speed at which drag coefficient saturation occurs, and the third is the drag coefficient's rate of change with increasing wind speed after saturation. Bayesian inference provides optimal estimates of the parameters as well as a non-Gaussian probability distribution characterizing the uncertainty of these estimates. The efficiency of this approach stems from the use of adaptive polynomial expansions to build an inexpensive surrogate for the high-resolution numerical model that couples simulated winds to the oceanic temperature data, dramatically reducing the computational burden of the Markov chain Monte Carlo sampling. These results indicate that the most likely values for the drag coefficient saturation and the corresponding wind speed are about 2.3 × 10−3 and 34 m s−1, respectively; the data were not informative regarding the drag coefficient behavior at higher wind speeds.

scholarly journals Quality control of 10-min air temperature data at RMI

2013 ◽  
Vol 10 (1) ◽  
pp. 1-5 ◽  
Author(s):  
C. Bertrand ◽  
L. Gonzalez Sotelino ◽  
M. Journée
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
Temperature Data ◽  
Temporal Consistency ◽  
Decision Algorithm ◽  
A Value ◽  
Spatial Consistency ◽  
Meteorological Measurement ◽  
Human Decision ◽  
Human Operators

Abstract. In the '90s, the Royal Meteorological Institute (RMI) of Belgium started to replace its conventional ''manual'' meteorological network by automated weather stations (AWSs). The meteorological measurement network is now fully automated. RMI counts 18 AWSs that made automated observations centrally available in our headquarters in Uccle, Brussels to internal as well as external users. Due to the large increase in the data amount associated with the automation, quality assurance (QA) procedures are being automated. However, human operators continue to play an essential role in the data validation processes. This contribution describes our newly developed semi-automatic quality control (QC) of 10-min air temperature data. After an existence test, the data are checked for limits consistency, temporal consistency and spatial consistency. At the end of these automated checks, a decision algorithm attributes a flag to each particular data. Each day the QC staff analyzes the preceding day observations in the light of the quality flags assigned by automated QA procedures during the night. It is the human decision whether or not a value is accepted.

scholarly journals The Framework for Assimilating All-Sky GPM Microwave Imager Brightness Temperature Data in the NASA GEOS Data Assimilation System

2020 ◽  
Vol 148 (6) ◽  
pp. 2433-2455
Author(s):  
Min-Jeong Kim ◽  
Jianjun Jin ◽  
Amal El Akkraoui ◽  
Will McCarty ◽  
Ricardo Todling ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Brightness Temperature ◽  
Heavy Precipitation ◽  
Temperature Data ◽  
Background Error ◽  
Single Observation ◽  
Microwave Brightness Temperature ◽  
Earth Observing System ◽  
Microwave Imager ◽  
Non Gaussian

Abstract Satellite radiance observations combine global coverage with high temporal and spatial resolution, and bring vital information to NWP analyses especially in areas where conventional data are sparse. However, most satellite observations that are actively assimilated have been limited to clear-sky conditions due to difficulties associated with accounting for non-Gaussian error characteristics, nonlinearity, and the development of appropriate observation operators for cloud- and precipitation-affected satellite radiance data. This article provides an overview of the development of the Gridpoint Statistical Interpolation (GSI) configurations to assimilate all-sky data from microwave imagers such as the GPM Microwave Imager (GMI) in the NASA Goddard Earth Observing System (GEOS). Electromagnetic characteristics associated with their wavelengths allow microwave imager data to be highly sensitive to precipitation. Therefore, all-sky data assimilation efforts described in this study are primarily focused on utilizing these data in precipitating regions. To utilize data in cloudy and precipitating regions, state and analysis variables have been added for ice cloud, liquid cloud, rain, and snow. This required enhancing the observation operator to simulate radiances in heavy precipitation, including frozen precipitation. Background error covariances in both the central analysis and EnKF analysis in the GEOS hybrid 4D-EnVar system have been expanded to include hydrometeors. In addition, the bias correction scheme was enhanced to reduce biases associated with thick clouds and precipitation. The results from single observation experiments demonstrate the capability of assimilating all-sky microwave brightness temperature data in GEOS both when the model forecast produces excessive precipitation and too little precipitation. Additional experiments show that hydrometeors and dynamic variables such as winds and pressure are adjusted in physically consistent ways in response to the assimilation.

scholarly journals Determination of Step Check Quality Control Thresholds on Air Temperature Data at South Tangerang Climatological Station

2021 ◽  
Vol 893 (1) ◽  
pp. 012063
Author(s):  
M Halida ◽  
SA Pramono
Keyword(s):  
Quality Control ◽  
Air Temperature ◽  
Temperature Increase ◽  
Data Distribution ◽  
Threshold Value ◽  
Temperature Data ◽  
Temperature Decrease ◽  
Climatological Station ◽  
Check Method ◽  
The Difference

Abstract All data, including air temperature data, must be verified by conducting quality control using the step check method. Step check quality control is carried out by looking at the difference of a parameter in a certain period compared to the threshold value that was already determined. Therefore before carrying out step check quality control, it is necessary to determine the ceiling and floor boundaries of the difference in air temperature data every hour. The data used in this study are hourly air temperature data and hourly present weather data from weather observations at the South Tangerang Climatological Station during 2016 - 2020. In determining the threshold for air temperature step check quality control, the air temperature data is paired with weather condition data to obtain a threshold value according to rain and no rain conditions. The threshold conducted in this study is based on a check for unusual climatological values, where the limits for an unusual and impossible jump in hourly air temperature changes are determined based on a certain percentage of the data distribution. This study uses percentile analysis to determine the threshold, where 5% in the lower and upper part of the data distribution are used as the threshold. The results show various thresholds every hour. The increase in temperature dominates the changes of hourly air temperature in no-rain conditions. The highest threshold for temperature increase occurs at 00.00 – 01.00 UTC at 3.2°C and continues to decrease over time. The highest threshold for temperature decrease occurs at 09.00 UTC - 10.00 UTC at 2.2°C. In rain conditions, the increase in temperature can still occur. However, the decrease in temperature mainly occurs. The highest threshold for temperature increase during rainy conditions is 1.8°C at 01.00 - 02.00 UTC, while the highest threshold for the temperature decrease is 5.8°C at 06.00 UTC – 07.00 UTC. With these results, observers can first carry out quality control with the Step Check method before filling in the data into the system database. Thus, any suspect data either from reading errors or tool errors can be minimized and finally produce a valid dataset.

Probabilistic quality control of daily temperature data

2012 ◽  
Vol 33 (5) ◽  
pp. 1211-1227 ◽  
Author(s):  
G. Sciuto ◽  
B. Bonaccorso ◽  
A. Cancelliere ◽  
G. Rossi
Keyword(s):  
Quality Control ◽  
Temperature Data ◽  
Daily Temperature
Sign in / Sign up

Export Citation Format

Share Document