Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia

Takafuji, Eduardo Henrique de Moraes; Rocha, Marcelo Monteiro da; Manzione, Rodrigo Lilla

doi:10.1007/s42452-020-2814-0

Cited by 5 publications

(2 citation statements)

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“…In this study, the covariates were the post-processed PERSIANN-CCS satellite precipitation data and geographical features. Space–time kriging and STRK have been successfully applied to different datasets and rainfall networks to analyze precipitation variations in space and time [ 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ]. These works endorse the applicability of the methodology on precipitation data.…”

Section: Introductionmentioning

confidence: 99%

Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Varouchakis

Kamińska-Chuchmała

Kowalik

et al. 2021

Sensors

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The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area.

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Section: Introductionmentioning

confidence: 99%

Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Varouchakis

Kamińska-Chuchmała

Kowalik

et al. 2021

Sensors

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show abstract

“…Spatio-temporal Kriging to Predict Water (Manzione et al, 2019) iii. Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia (Takafuji et al, 2020) Além disso, foram publicados dois trabalhos completos em anais de congresso:…”

Section: Publicações Científicasunclassified

Simulação geoestatística espaçotemporal com deriva externa temporal local

Takafuji¹

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Exploring 20-year applications of geostatistics in precision agriculture in Brazil: what’s next?

2023

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Spatiotemporal forecast with local temporal drift applied to weather patterns in Patagonia

Cited by 5 publications

References 83 publications

Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

Simulação geoestatística espaçotemporal com deriva externa temporal local

Exploring 20-year applications of geostatistics in precision agriculture in Brazil: what’s next?

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