A kriging approach to the analysis of climate model experiments

Drignei, Dorin

doi:10.1198/jabes.2009.0006

Cited by 23 publications

(13 citation statements)

References 17 publications

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“…Linear least squares estimation algorithms in the form of the GeoStatistical Kriging methods [7] are probably the most popular for geospatial interpolation because they enable the prediction of unknown data point conditions (values) determined from a known set of values from neighboring data points. This way we can model change across a plane with a high degree of value expectation certainty.…”

Section: Interpolation Methodsmentioning

confidence: 99%

Ensemble Interpolation Methods for Spatio-temporal Data Modelling

Sallis

Hernández

2010

2010 Fourth UKSim European Symposium on Computer Modeling and Simulation

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Abstract-Real time weather forecasting is a highly influential tool in decision making for agriculture. Geographic Information Systems (GIS) can be built to provide information about topographic data such as elevation and distance to oceans or water reservoirs. This data has begun to have increased availability, providing easier access for developing new applications. By using geographic information together with terrestrial measurements from weather stations, the spatial and temporal scales of the climatic variables can be analyzed by interpolation and forecasting. Most of the interpolation methods provided in common GIS tools are only related to the spatial domain, limiting its use in numerical modelling and prediction of climatic states. However, by adopting a Bayesian approach, it appears possible to estimate the dynamic behaviour of the unobserved climate pattern using a state-space representation. Using this framework, the ensemble Kalman filter or a more general sequential Monte Carlo method could be used for the estimation procedure. A wireless sensor network providing continuous data to populate such a model is described here for potential application of this approach.

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Section: Interpolation Methodsmentioning

confidence: 99%

Ensemble Interpolation Methods for Spatio-temporal Data Modelling

Sallis

Hernández

2010

2010 Fourth UKSim European Symposium on Computer Modeling and Simulation

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“…Therefore, the results are expected to be more precise. Correlation models for temporal (and spatial) temperature data have been discussed, for example, in Drignei et al (Drignei et al 2008) and Drignei (Drignei 2009), although not in the context of change-point analysis. The model that includes serial correlation error will still be Y ¼ Xg þ z but the errors vector z will have a multivariate normal distribution with mean vector zero and covariance matrix W. The errors vector will be modeled by a time series ARMA(p,q) model (e.g., (Brockwell and Davis 2002)).…”

Section: Datamentioning

confidence: 97%

Change-point analysis for serially correlated summit temperatures in the Romanian Carpathians

Croitoru

Drignei

Holobâcă

et al. 2011

Theor Appl Climatol

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Characterizing climatic changes in the highaltitude mountain regions helps scientists and policy makers understand the effects of such changes on water resources, economic development, and the health of ecosystems. This paper proposes a change-point analysis to determine the time and magnitude of summer temperature changes in the summit areas of Romanian Carpathians between 1961 and 2007. Due to their altitude, massiveness, and position, Romanian Carpathians are an important barrier for different types of air masses between Western and Southeastern Europe. The results show that the change in summer temperatures occurred shortly after 1980. The average magnitude of this change is consistent with changes occurring in other parts of Southern Europe in the same time period although the magnitude of changes at individual weather stations may differ substantially. We aided our analysis by a statistical method based on regression models with serially correlated ARMA errors.

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“…The aggregation process first performs spatial averaging over each GCM cell and then performs temporal averaging to monthly time scales. This process can be carried out using other spatial averaging schemes such as kriging (Drignei 2009). The concept of spatial upscaling is illustrated in Fig.…”

Section: Bias Correctionmentioning

confidence: 99%

Joint variable spatial downscaling

Feng

Georgakakos

2011

Climatic Change

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Joint Variable Spatial Downscaling (JVSD), a new statistical technique for downscaling gridded climatic variables, is developed to generate high resolution gridded datasets for regional watershed modeling and assessments. The proposed approach differs from previous statistical downscaling methods in that multiple climatic variables are downscaled simultaneously and consistently to produce realistic climate projections. In the bias correction step, JVSD uses a differencing process to create stationary joint cumulative frequency statistics of the variables being downscaled. The functional relationship between these statistics and those of the historical observation period is subsequently used to remove GCM bias. The original variables are recovered through summation of bias corrected differenced sequences. In the spatial disaggregation step, JVSD uses a historical analogue approach, with historical analogues identified simultaneously for all atmospheric fields and over all areas of the basin under study. Analysis and comparisons are performed for 20th Century Climate in Coupled Models (20C3M), broadly available for most GCMs. The results show that the proposed downscaling method is able to reproduce the sub-grid climatic features as well as their temporal/spatial variability in the historical periods. Comparisons are also performed for precipitation and temperature with other statistical and dynamic downscaling methods over the southeastern US and show that JVSD performs favorably. The downscaled sequences are used to assess the implications of GCM scenarios for the Apalachicola-Chattahoochee-Flint river basin as part of a comprehensive climate change impact assessment.

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A kriging approach to the analysis of climate model experiments

Cited by 23 publications

References 17 publications

Ensemble Interpolation Methods for Spatio-temporal Data Modelling

Ensemble Interpolation Methods for Spatio-temporal Data Modelling

Change-point analysis for serially correlated summit temperatures in the Romanian Carpathians

Joint variable spatial downscaling

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