notes on climatic indices
data is of bioclimatic indices (1960-90) from GHCN data?
area of interest: 6 tiles 5-7 and 15-17
data come in 30 arc second cells, elevation from SRTM data

what about interpolation? thin plate spline? these useful refs from

Fleming MD, Chapin FS, Cramer W, Hufford GL, Serreze MC (2000) Geographic patterns and dynamics of Alaskan climate interpolated from a sparse station record. GLOBAL CHANGE BIOLOGY 6: 49-58
Data from a sparse network of climate stations in Alaska were interpolated to provide 1-km resolution maps of mean monthly temperature and precipitation-variables that are required at high spatial resolution for input into regional models of ecological processes and resource management. The interpolation model is based on thin-plate smoothing splines, which uses the spatial data along with a digital elevation model to incorporate local topography. The model provides maps that are consistent with regional climatology and with patterns recognized by experienced weather forecasters. The broad patterns of Alaskan climate are well represented and include latitudinal and altitudinal trends in temperature and precipitation and gradients in continentality. Variations within these broad patterns reflect both the weakening and reduction in frequency of low-pressure centres in their eastward movement across southern Alaska during the summer, and the shift of the storm tracks into central and northern Alaska in late summer. Not surprisingly, apparent artifacts of the interpolated climate occur primarily in regions with few or no stations. The interpolation model did not accurately represent low-level winter temperature inversions that occur within large valleys and basins. Along with well-recognized climate patterns, the model captures local topographic effects that would not be depicted using standard interpolation techniques. This suggests that similar procedures could be used to generate high-resolution maps for other high-latitude regions with a sparse density of data.
University of Anchorage, USA

Ninyerola M, Pons X, Roure JM (2000) A methodological approach of climatological modelling of air temperature and precipitation through GIS techniques. INTERNATIONAL JOURNAL OF CLIMATOLOGY 20: 1823-1841
This study proposes an empirical methodology for modelling and mapping the air temperature (mean maximum, mean and mean minimum) and total precipitation, all of which are monthly and annual, using geographical information systems (GIS) techniques. The method can be seen as an alternative to classical interpolation techniques when spatial information is available. The geographical area used to develop and apply this model is Catalonia (32000 km(2), northeast Spain). We have developed a multiple regression analysis between these meteorological variables as the dependent ones, and some geographical variables (altitude (ALT), latitude (LAT), continentality (CON), solar radiation (RAD) and a cloudiness factor (CLO)) as the independent ones. Data for the dependent variables were obtained from meteorological stations, and data for the independent variables were elaborated from a 180 m resolution digital elevation model (DEM). Multiple regression coefficients (b(n)) were used to build final maps, using digital layers for each independent variable, and applying basic GIS techniques. The results are very satisfactory in the case of mean air temperature and mean minimum air temperature, with coefficients of determination (R-2) between 0.79 and 0.97, depending on the month; in the case of mean maximum air temperature, R-2 ranges between 0.70 and 0.89, while in the case of precipitation, it ranges between 0.60 and 0.91. Copyright (C) 2000 Royal Meteorological Society.
Autonomous University of Barcelona, Spain

Dodson R, Marks D (1997) Daily air temperature interpolated at high spatial resolution over a large mountainous region. CLIMATE RESEARCH 8: 1-20
Two methods are investigated for interpolating daily minimum and maximum air temperatures (T-min and T-max) at a 1 km spatial resolution over a large mountainous region (830 000 km(2)) in the U.S. Pacific Northwest. The methods were selected because of their ability to (1) account for the effect of elevation on temperature and (2) efficiently handle large volumes of data. The first method, the neutral stability algorithm (NSA), used the hydrostatic and potential temperature equations to convert measured temperatures and elevations to sea-level potential temperatures. The potential temperatures were spatially interpolated using an inverse-squared-distance algorithm and then mapped to the elevation surface of a digital elevation model (DEM). The second method, linear lapse rate adjustment (LLRA), involved the same basic procedure as the NSA, but used a constant linear lapse rate instead of the potential temperature equation. Cross-validation analyses were performed using the NSA and LLRA methods to interpolate T-min and T-max each day for the 1990 water year, and the methods were evaluated based on mean annual interpolation error (IE). The NSA method showed considerable bias for sites associated with vertical extrapolation. A correction based on climate station/grid cell elevation differences was developed and found to successfully remove the bias. The LLRA method was tested using 3 lapse rates, none of which produced a serious extrapolation bias. The bias-adjusted NSA and the 3 LLRA methods produced almost identical levels of accuracy (mean absolute errors between 1.2 and 1.3 degrees C), and produced very similar temperature surfaces based on image difference statistics. In terms of accuracy, speed, and ease of implementation, LLRA was chosen as the best of the methods tested.

see this book also

more on interpolation

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