Climatic change ; Climatic data ; Climatology ; Fitting ; Multidimensional scaling ; Precipitation ; Statisticalbias ; Temperature
The majority of documented climatic data set biases can be divided into two categories : physical biases and calendar biases. Previous homogenization efforts for prominent temperature and precipitation data sets are detailed in this review. Numerous
physical biases are accounted for in these homogenization efforts and a debate exists in the literature regarding the effectiveness of these bias adjustment methods, leading some investigators to question the suitability of these adjusted data sets
for the identification of large-scale climate change signals. Calendar biases are not addressed in previous homogenization efforts. After a brief analysis, a calendar bias known to exist in homogenized data sets (the leap year bias) is identified in the CRUTEM3v
Consumer behaviour ; Distance ; Error ; Model ; Simulation ; Spatial choice ; Statistics ; Trip ; Utility fonction
In the presence of correlation between error terms in set delineation and choice selection, statistical estimators are biased (selection bias). An alternative two-stage method is proposed to estimate parameters of set delineation and choice
selection. Monte Carlo simulation is used to explore the properties of these estimators, and to show the magnitude of bias inherent in traditional methods of estimation.
The sources of electoral bias in Jamaican elections results, 1967-1980
of manipulation of constituency boundaries to produce this bias is provided by statistical analysis of the election results. (MPM).
Jamaican election results for the four General Elections between 1976 and 1980 show considerable electoral bias, with substantial mismatch between a party's percentage of votes cast and its percentage of seats. No circumstantial evidence
Investigation of elevation bias of the SRTM C- and X-band digital elevation models
Altitude ; Australia ; Digital elevation model ; Land utilisation ; Plant canopy ; Queensland ; Radar imagery ; Remote sensing ; Sar imagery ; Statisticalbias ; Vegetation
This paper presents results of a comparative study of the vegetation-caused elevation bias of the space shuttle topographic mission data product, both C- and X-band (SRTM.C/X). The SRTM.C/X bands data were compared against a high-resolution digital
terrain model and discussed. As a test site, an area on the Gold Coast, Queensland, Australia, was selected. This area is covered by vegetation varying from grassland and shrubs to forest. The study method allowed the development of a statistical model
relating the elevation bias to the percentage of the vegetation cover of a given land parcel. This model, once verified on varieties of vegetation types, could be utilised to estimate and eliminate the elevation bias from the InSAR elevation model.
Frequency and magnitude biases in the Fryberger model, with implications for characterizing geomorphically effective winds
Aeolian features ; Aeolian transport ; Canada ; Dune ; Maritime Province ; Model ; Prince Edward Island ; Statisticalbias ; Wind speed
biases in the model that result from : variations in wind direction sector range, and use of wind speed class mid-point values over more statistically representative values. The significance of these biases is tested using unclassified data from Stanhope
An analysis of seasonal biases in satellite and reanalysis rainfall products in the Savannah River basin
Data processing ; Hydrology ; Model ; Precipitation ; Remote sensing ; Satellite ; Season ; Southeastern United States ; Statisticalbias ; United States of America ; Watershed
Biases in satellite-based precipitation data are often region-specific and such information is important for quantifying input errors in hydrological models. Therefore, this study examines biases in daily precipitation data for a watershed
in the southeastern United States. The AA. observed biases that occur seasonally and by magnitude. Seasonally, precipitation correlates well in most seasons but summer, likely due to the sporadic nature of convective precipitation that is a common precipitation
mechanism in this region during the summer. Daily precipitation biases are around 5 mm, but the sign of the bias varies by season, with positive biases in all seasons but fall. Additionally, the AA. found that satellite-based data tend to overestimate light
Stone cover on desert hillslopes: extent of bias in diameters estimated from grid samples and procedures for bias correction
Arid area ; Grain size distribution ; Methodology ; Sampling ; Slope ; Slope dynamics ; Statistics
must be surface-based. Grid sampling thus appears attractive, but results presented here suggest that the method is only suitable if adequate bias correction is applied.
Canada ; Cold area ; Comparative study ; Core sampling ; Freezing ; Gaspé ; Quebec ; Research technique ; River bed ; Sampling ; Statisticalbias
The aims of this paper are to quantify the relative biases of the freeze-core and bulk sampling techniques against more accurate composite samples and to examine whether systematic losses of fines can be predicted. At 6 riffles the spawning
with the original freeze-core and bulk samples to assess the relative precision and biases of the 2 techniques.
Distance from city centre ; Econometric model ; Error ; Estimation ; Land value ; Model ; Spatial analysis ; Spatial autocorrelation ; Spatial choice ; Statisticalbias
Climatology ; Methodology ; Potential evapotranspiration ; Precipitation ; Statisticalbias ; Temperature ; Water balance
The AA. re-evaluate the results of Legates and Mather using bias-adjusted precipitation estimates and an alternative method of estimating potential evapotranspiration (i.e., the Hamon, 1963, method). These new results are compared with those from
previous studies and are considered to be an improvement over the Legates and Mather values, as they are based on bias-adjusted precipitation estimates.
This paper aims firstly to investigate various biases which spatial correlation, in cross-sectional data takes carry into the statistical test and inferential result and secondly to review some methods to remove the biases. - (SGA)
The enrichment of quartz in regolith, and the resulting bias in cosmogenic erosion rate estimates, can be quantified using concentrations of immobile elements (such as zirconium) in bedrock and regolith. Here the AA. show that the erosion rate bias
introduced by regolith dissolution is less than 12%, across 22 granitic catchments that span a wide range of temperate climates. Except in extreme weathering environments, biases due to regolith dissolution will be a small component of the overall uncertainty