Details
| Original language | English |
|---|---|
| Pages (from-to) | 1668-1693 |
| Number of pages | 26 |
| Journal | International Journal of Geographical Information Science |
| Volume | 29 |
| Issue number | 9 |
| Publication status | Published - 19 Jun 2015 |
Abstract
Accurate estimations of spatio-temporal fields at unsampled locations are important in a number of applications. Often, spatio-temporal fields are advected, which means the change in field values over time at a particular point in space stems to a large extent from motion of a more or less constant spatial field. For such dynamic fields, interpolation methods including information on the motion behaviour of the field are promising extensions of solely spatial (snapshot) and symmetric spatio-temporal methods. In this paper, the performance of different deterministic and geostatistical interpolation methods is compared for precipitation estimation from 1-minute time series of spatially distributed rain gauges. The focus is on spatio-temporal methods that include information on the motion behaviour of the rainfield, estimated from weather radar using optical flow. The different interpolation methods are introduced and evaluated using rain gauge measurements of a 15-day period and cross-validation. The results show that including information on the motion behaviour significantly improves interpolation quality in terms of RMSE.
Keywords
- interpolation, motion field, precipitation, spatio-temporal
ASJC Scopus subject areas
- Computer Science(all)
- Information Systems
- Social Sciences(all)
- Geography, Planning and Development
- Social Sciences(all)
- Library and Information Sciences
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In: International Journal of Geographical Information Science, Vol. 29, No. 9, 19.06.2015, p. 1668-1693.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Estimation of precipitation fields from 1-minute rain gauge time series
T2 - comparison of spatial and spatio-temporal interpolation methods
AU - Fitzner, Daniel
AU - Sester, Monika
PY - 2015/6/19
Y1 - 2015/6/19
N2 - Accurate estimations of spatio-temporal fields at unsampled locations are important in a number of applications. Often, spatio-temporal fields are advected, which means the change in field values over time at a particular point in space stems to a large extent from motion of a more or less constant spatial field. For such dynamic fields, interpolation methods including information on the motion behaviour of the field are promising extensions of solely spatial (snapshot) and symmetric spatio-temporal methods. In this paper, the performance of different deterministic and geostatistical interpolation methods is compared for precipitation estimation from 1-minute time series of spatially distributed rain gauges. The focus is on spatio-temporal methods that include information on the motion behaviour of the rainfield, estimated from weather radar using optical flow. The different interpolation methods are introduced and evaluated using rain gauge measurements of a 15-day period and cross-validation. The results show that including information on the motion behaviour significantly improves interpolation quality in terms of RMSE.
AB - Accurate estimations of spatio-temporal fields at unsampled locations are important in a number of applications. Often, spatio-temporal fields are advected, which means the change in field values over time at a particular point in space stems to a large extent from motion of a more or less constant spatial field. For such dynamic fields, interpolation methods including information on the motion behaviour of the field are promising extensions of solely spatial (snapshot) and symmetric spatio-temporal methods. In this paper, the performance of different deterministic and geostatistical interpolation methods is compared for precipitation estimation from 1-minute time series of spatially distributed rain gauges. The focus is on spatio-temporal methods that include information on the motion behaviour of the rainfield, estimated from weather radar using optical flow. The different interpolation methods are introduced and evaluated using rain gauge measurements of a 15-day period and cross-validation. The results show that including information on the motion behaviour significantly improves interpolation quality in terms of RMSE.
KW - interpolation
KW - motion field
KW - precipitation
KW - spatio-temporal
UR - http://www.scopus.com/inward/record.url?scp=84941259313&partnerID=8YFLogxK
U2 - 10.1080/13658816.2015.1040022
DO - 10.1080/13658816.2015.1040022
M3 - Article
AN - SCOPUS:84941259313
VL - 29
SP - 1668
EP - 1693
JO - International Journal of Geographical Information Science
JF - International Journal of Geographical Information Science
SN - 1365-8816
IS - 9
ER -