%0 Journal Article %1 Buishand.etal2004 %A Buishand, T. A. %A Shabalova, M. V. %A Brandsma, T. %D 2004 %J Journal of Climate %K GCM Likelihood Likelihood:generalized covariate downscaling logistic:model precipitation relhumidity spechumidity wetday:frequency wetday:intensity Rhine %P 1816-1827 %R 10.1175/1520-0442(2004)017<1816:OTCOTT>2.0.CO;2 %T On the Choice of the Temporal Aggregation Level for Statistical Downscaling of Precipitation. %V 17 %X The merits of daily and monthly downscaling models for precipitation are compared using data from Bern, Switzerland; Deuselbach, Germany; and De Bilt, the Netherlands. For each station, generalized linear models are developed to describe rainfall occurrence, the wet-day precipitation amounts, and the monthly precipitation totals. The predictor dataset includes dynamical variables and atmospheric moisture (relative humidity for rainfall occurrence and specific humidity for rainfall amount).Fitting a generalized linear model to daily rainfall data generally results in larger regression coefficients than fitting the same model to monthly data. For rainfall occurrence this can be attributed mostly to the nonlinearity of the function that links the wet-day probabilities to the predictor variables, whereas for rainfall amounts there is, apart from nonlinearity, also a bias in the estimated regression coefficients of the monthly models caused by averaging predictor variables over both wet and dry days. Because of this bias a monthly rainfall amount model is less sensitive to an increase in the specific humidity than a daily rainfall amount model.Although the squared correlation coefficient r2 between the observed and predicted values of the daily models is low (0.40 for rainfall occurrence and 0.15 for wet-day rainfall), aggregating the results from these models to monthly values gives r2 values comparable to those in the direct fit to the monthly data (0.65 for the number of wet days and 0.50 for rainfall totals). The temporal variations in the predicted annual amounts using monthly relationships are similar to those obtained from daily relationships. Daily models are preferable, however, for the generation of climate change scenarios for impact studies, because the significance of the predictor variables is generally stronger in these models and because the effect of a change in specific humidity is underestimated by the monthly models.

@article{Buishand.etal2004, abstract = {The merits of daily and monthly downscaling models for precipitation are compared using data from Bern, Switzerland; Deuselbach, Germany; and De Bilt, the Netherlands. For each station, generalized linear models are developed to describe rainfall occurrence, the wet-day precipitation amounts, and the monthly precipitation totals. The predictor dataset includes dynamical variables and atmospheric moisture (relative humidity for rainfall occurrence and specific humidity for rainfall amount).Fitting a generalized linear model to daily rainfall data generally results in larger regression coefficients than fitting the same model to monthly data. For rainfall occurrence this can be attributed mostly to the nonlinearity of the function that links the wet-day probabilities to the predictor variables, whereas for rainfall amounts there is, apart from nonlinearity, also a bias in the estimated regression coefficients of the monthly models caused by averaging predictor variables over both wet and dry days. Because of this bias a monthly rainfall amount model is less sensitive to an increase in the specific humidity than a daily rainfall amount model.Although the squared correlation coefficient r2 between the observed and predicted values of the daily models is low (0.40 for rainfall occurrence and 0.15 for wet-day rainfall), aggregating the results from these models to monthly values gives r2 values comparable to those in the direct fit to the monthly data (0.65 for the number of wet days and 0.50 for rainfall totals). The temporal variations in the predicted annual amounts using monthly relationships are similar to those obtained from daily relationships. Daily models are preferable, however, for the generation of climate change scenarios for impact studies, because the significance of the predictor variables is generally stronger in these models and because the effect of a change in specific humidity is underestimated by the monthly models.}, added-at = {2010-12-13T14:39:00.000+0100}, adsnote = {Provided by the SAO/NASA Astrophysics Data System}, adsurl = {http://adsabs.harvard.edu/abs/2004JCli...17.1816B}, author = {Buishand, T. A. and Shabalova, M. V. and Brandsma, T.}, biburl = {https://www.bibsonomy.org/bibtex/2eff6fbf4871ab8e71e72d3713b9f217c/marsianus}, description = {On the Choice of the Temporal Aggregation Level for Statistical Downscaling of P}, doi = {10.1175/1520-0442(2004)017<1816:OTCOTT>2.0.CO;2}, interhash = {f8cbb5b52aede66609860c897fd6f2f3}, intrahash = {eff6fbf4871ab8e71e72d3713b9f217c}, journal = {Journal of Climate}, keywords = {GCM Likelihood Likelihood:generalized covariate downscaling logistic:model precipitation relhumidity spechumidity wetday:frequency wetday:intensity Rhine}, month = may, pages = {1816-1827}, timestamp = {2012-05-01T12:43:32.000+0200}, title = {On the Choice of the Temporal Aggregation Level for Statistical Downscaling of Precipitation.}, volume = 17, year = 2004 }