Radar rainfall data quality control by the Influence Function Method

1987 ◽  
Vol 23 (5) ◽  
pp. 837-844 ◽  
Author(s):  
Witold F. Krajewski
2018 ◽  
Vol 13 (2) ◽  
pp. 131-146
Author(s):  
Mirwan Rofiq Ginanjar ◽  
Sri Mulat Yuningsih

Planning and management of water resources are dependent on the quality of hydrological data. Hydrological data plays an important role in hydrological analysis. The availability of good and qualified hydrological data is one of the determinants of the results of hydrological analysis. However, the facts indicate that many of the available data do not fit their ideal state. To solve this problem, a hydrological data quality control model should be established in order to improve the quality of national hydrological data. The scope includes quality control of rainfall and discharge data. Analysis of the quality control of rainfall data was conducted on 58 rainfall stations spread on the island of Java. The analysis shows that 41 stations are good categorized, 14 stations are in moderate category and 3 stations are badly categorized. Based on these results, a light improvement scenario was performed, good category Station increased to 46 stations, moderate category decreased to 11 stations and bad category reduced to 1 Stations. Quality control of discharge data analysis was conducted on 14 discharge stations spread on Java Island. Analyzes were performed for QC1, QC2 and QC3 then got final QC value. The results on the final QC show no stations for good category, 2 stations for moderate categories and 12 stations for bad category. Based on the results of the analysis, a light improvement scenario was performed with the result of bad category increased to good category 5 stations, bad category increased to moderate 7 stations, and moderate category 1 stations.


1999 ◽  
Vol 35 (8) ◽  
pp. 2487-2503 ◽  
Author(s):  
Matthias Steiner ◽  
James A. Smith ◽  
Stephen J. Burges ◽  
Carlos V. Alonso ◽  
Robert W. Darden

Author(s):  
Antonella D. Pontoriero ◽  
Giovanna Nordio ◽  
Rubaida Easmin ◽  
Alessio Giacomel ◽  
Barbara Santangelo ◽  
...  

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

2014 ◽  
Vol 926-930 ◽  
pp. 4254-4257 ◽  
Author(s):  
Jin Xu ◽  
Da Tao Yu ◽  
Zhong Jie Yuan ◽  
Bo Li ◽  
Zi Zhou Xu

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.


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

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