Details
| Original language | English |
|---|---|
| Pages (from-to) | 544-557 |
| Number of pages | 14 |
| Journal | Journal of hydrology |
| Volume | 522 |
| Early online date | 13 Jan 2015 |
| Publication status | Published - Mar 2015 |
Abstract
Weather radar provides areal rainfall information with very high temporal and spatial resolution. Radar data has been implemented in several hydrological applications despite the fact that the data suffers from varying sources of error. Several studies have attempted to propose methods for solving these problems. Additionally, weather radar usually underestimates or overestimates the rainfall amount. In this study, a new method is proposed for correcting radar data by implementing the quantile mapping bias correction method. Then, the radar data is merged with observed rainfall by conditional merging and kriging with external drift interpolation techniques. The merging product is analysed regarding the sensitivity of the two investigated methods to the radar data quality. After implementing bias correction, not only did the quality of the radar data improve, but also the performance of the interpolation techniques using radar data as additional information. In general, conditional merging showed greater sensitivity to radar data quality, but performed better than all the other interpolation techniques when using bias corrected radar data. Furthermore, a seasonal variation of interpolation performances has in general been observed. A practical example of using radar data for disaggregating stations from daily to hourly temporal resolution is also proposed in this study.
Keywords
- Bias correction, Geostatistics, Merging, Radar, Rainfall
ASJC Scopus subject areas
- Environmental Science(all)
- Water Science and Technology
Cite this
- Standard
- Harvard
- Apa
- Vancouver
- BibTeX
- RIS
In: Journal of hydrology, Vol. 522, 03.2015, p. 544-557.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Applying bias correction for merging rain gauge and radar data
AU - Rabiei, E.
AU - Haberlandt, U.
N1 - Funding Information: The study was funded by the German Research Foundation (DFG, 3504/5-2). The authors wish to thank Sven van der Heijden and Ross Pidoto for their comments on an earlier draft of the paper. Radar data and rain gauge data are provided by the German Weather Service (DWD). We would also like to thank G. Pegram, the anonymous referee as well as the associate editor for their constructive reviews.
PY - 2015/3
Y1 - 2015/3
N2 - Weather radar provides areal rainfall information with very high temporal and spatial resolution. Radar data has been implemented in several hydrological applications despite the fact that the data suffers from varying sources of error. Several studies have attempted to propose methods for solving these problems. Additionally, weather radar usually underestimates or overestimates the rainfall amount. In this study, a new method is proposed for correcting radar data by implementing the quantile mapping bias correction method. Then, the radar data is merged with observed rainfall by conditional merging and kriging with external drift interpolation techniques. The merging product is analysed regarding the sensitivity of the two investigated methods to the radar data quality. After implementing bias correction, not only did the quality of the radar data improve, but also the performance of the interpolation techniques using radar data as additional information. In general, conditional merging showed greater sensitivity to radar data quality, but performed better than all the other interpolation techniques when using bias corrected radar data. Furthermore, a seasonal variation of interpolation performances has in general been observed. A practical example of using radar data for disaggregating stations from daily to hourly temporal resolution is also proposed in this study.
AB - Weather radar provides areal rainfall information with very high temporal and spatial resolution. Radar data has been implemented in several hydrological applications despite the fact that the data suffers from varying sources of error. Several studies have attempted to propose methods for solving these problems. Additionally, weather radar usually underestimates or overestimates the rainfall amount. In this study, a new method is proposed for correcting radar data by implementing the quantile mapping bias correction method. Then, the radar data is merged with observed rainfall by conditional merging and kriging with external drift interpolation techniques. The merging product is analysed regarding the sensitivity of the two investigated methods to the radar data quality. After implementing bias correction, not only did the quality of the radar data improve, but also the performance of the interpolation techniques using radar data as additional information. In general, conditional merging showed greater sensitivity to radar data quality, but performed better than all the other interpolation techniques when using bias corrected radar data. Furthermore, a seasonal variation of interpolation performances has in general been observed. A practical example of using radar data for disaggregating stations from daily to hourly temporal resolution is also proposed in this study.
KW - Bias correction
KW - Geostatistics
KW - Merging
KW - Radar
KW - Rainfall
UR - http://www.scopus.com/inward/record.url?scp=84929496870&partnerID=8YFLogxK
U2 - 10.1016/j.jhydrol.2015.01.020
DO - 10.1016/j.jhydrol.2015.01.020
M3 - Article
AN - SCOPUS:84929496870
VL - 522
SP - 544
EP - 557
JO - Journal of hydrology
JF - Journal of hydrology
SN - 0022-1694
ER -