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ABSTRACT: Interstation observations of daily precipitation are often temporally misaligned due to differences in station time of observation. Several time of observation adjustment methods have historically been applied to improve interstation temporal alignment, but the efficacy of such adjustments has not been fully tested. Here, we examine the ability of several time of observation adjustments to improve observation compatibility under three different adjustment scenarios: adjusting morning observations to midnight, adjusting afternoon observations to midnight, and adjusting afternoon observations to morning. We find that all adjustment methods provide necessary improvements to the temporal alignment of daily precipitation observations, especially with respect to morning versus midnight time of observation totals. However, for a majority of the adjustments, improved temporal alignment comes at the cost of significantly altering observed precipitation intensity, frequency, and extremes. We also find adjustments have the potential to overcorrect and increase both general and extreme event spatio‐temporal coherence. [ABSTRACT FROM AUTHOR]

ABSTRACT Gridded temperature data sets are typically produced at spatial resolutions that cannot fully resolve fine-scale variation in surface air temperature in regions of complex topography. These data limitations have become increasingly important as scientists and managers attempt to understand and plan for potential climate change impacts. Here, we describe the development of a high-resolution (250 m) daily historical (1979-2012) temperature data set for the US Northern Rocky Mountains using observations from both long-term weather stations and a dense network of low-cost temperature sensors. Empirically based models for daily minimum and maximum temperature incorporate lapse rates from regional reanalysis data, modelled daily solar insolation and soil moisture, along with time invariant canopy cover and topographic factors. Daily model predictions demonstrate excellent agreement with independent observations, with mean absolute errors of <1.4 °C for both minimum and maximum temperature. Topographically resolved temperature data may prove useful in a range of applications related to hydrology, fire regimes and fire behaviour, and habitat suitability modelling. The form of the models may provide a means for downscaling future temperature scenarios that account for potential fine-scale topographically mediated changes in near-surface temperature. [ABSTRACT FROM AUTHOR]

ABSTRACT Gridded topoclimatic datasets are increasingly used to drive many ecological and hydrological models and assess climate change impacts. The use of such datasets is ubiquitous, but their inherent limitations are largely unknown or overlooked particularly in regard to spatial uncertainty and climate trends. To address these limitations, we present a statistical framework for producing a 30-arcsec (∼800-m) resolution gridded dataset of daily minimum and maximum temperature and related uncertainty from 1948 to 2012 for the conterminous United States. Like other datasets, we use weather station data and elevation-based predictors of temperature, but also implement a unique spatio-temporal interpolation that incorporates remotely sensed 1-km land skin temperature. The framework is able to capture several complex topoclimatic variations, including minimum temperature inversions, and represent spatial uncertainty in interpolated normal temperatures. Overall mean absolute errors for annual normal minimum and maximum temperature are 0.78 and 0.56 °C, respectively. Homogenization of input station data also allows interpolated temperature trends to be more consistent with US Historical Climate Network trends compared to those of existing interpolated topoclimatic datasets. The framework and resulting temperature data can be an invaluable tool for spatially explicit ecological and hydrological modelling and for facilitating better end-user understanding and community-driven improvement of these widely used datasets. [ABSTRACT FROM AUTHOR]