A method for generating daily surfaces of temperature, precipitation, humidity, and radiation over large regions of complex terrain is presented. Required inputs include digital elevation data and observations of maximum temperature, minimum temperature and precipitation from ground-based meteorological stations. Our method is based on the spatial convolution of a truncated Gaussian weighting filter with the set of station locations. Sensitivity to the typical heterogeneous distribution of stations in complex terrain is accomplished with an iterative station density algorithm. Spatially and temporally explicit empirical analyses of the relationships of temperature and precipitation to elevation were performed, and the characteristic spatial and temporal scales of these relationships were explored. A daily precipitation occurrence algorithm is introduced, as a precursor to the prediction of daily precipitation amount. Surfaces of humidity (vapor pressure deficit) are generated as a function of the predicted daily minimum temperature and the predicted daily average daylight temperature. Daily surfaces of incident solar radiation are generated as a function of Sun-slope geometry and interpolated diurnal temperature range. The application of these methods is demonstrated over an area of approximately 400 000 detailed illustration of the parameterization process. A cross-validation analysis was performed, comparing predicted and observed daily and annual average values. Mean absolute errors (MAE) for predicted annual average maximum and minimum temperature were 0.7°C and 1.2°C, with biases of +0.1°C and −0.1°C, respectively. MAE for predicted annual total precipitation was 13.4 cm, or, expressed as a percentage of the observed annual totals, 19.3%. The success rate for predictions of daily precipitation occurrence was 83.3%. Particular attention was given to the predicted and observed relationships between precipitation frequency and intensity, and they were shown to be similar. We tested the sensitivity of these methods to prediction grid-point spacing, and found that areal averages were unchanged for grids ranging in spacing from 500 m to 32 km. We tested the dependence of the results on timestep, and found that the temperature prediction algorithms scale perfectly in this respect. Temporal scaling of precipitation predictions was complicated by the daily occurrence predictions, but very nearly the same predictions were obtained at daily and annual timesteps.
Journal of Hydrology – Elsevier
Published: Mar 15, 1997
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera