A geostatistical framework is proposed to downscale daily precipitation and temperature. The methodology is based on multiple-point geostatistics (MPS), where a multivariate training image is used to represent the spatial relationship between daily precipitation and daily temperature over several years. Here the training image consists of daily rainfall and temperature outputs from the Weather ...

OBJECTIVES Orthopaedists often speculate how weather and school schedule may influence pediatric orthopedic trauma volume, but few studies have examined this. This study aims to determine: how do weather patterns, day, month, season and public school schedule influence the daily frequency of pediatric orthopedic trauma consults and admissions? METHODS With IRB approval, orthopedic trauma data...

Satellite-based precipitation data have contributed greatly to quantitatively forecasting precipitation, and provides a potential alternative source for precipitation data allowing researchers to better understand patterns of precipitation over ungauged basins. However, the absence of calibration satellite data creates considerable uncertainties for The Tropical Rainfall Measuring Mission (TRMM...

This study uses a statistical downscaling method based on self-organizing maps (SOMs) to produce highresolution, downscaled precipitation estimates over the state of Pennsylvania in the mid-Atlantic region of the United States. The SOMs approach derives a transfer function between large-scale mean atmospheric states and local meteorological variables (daily point precipitation values) of intere...

Merging satellite and rain gauge data by combining accurate quantitative rainfall from stations with spatial continuous information from remote sensing observations provides a practical method of estimating rainfall. However, generating high spatiotemporal rainfall fields for catchment-distributed hydrological modeling is a problem when only a sparse rain gauge network and coarse spatial resolu...