| contributor author | Hemri, Stephan | |
| contributor author | Haiden, Thomas | |
| contributor author | Pappenberger, Florian | |
| date accessioned | 2017-06-09T17:33:42Z | |
| date available | 2017-06-09T17:33:42Z | |
| date copyright | 2016/07/01 | |
| date issued | 2016 | |
| identifier issn | 0027-0644 | |
| identifier other | ams-87236.pdf | |
| identifier uri | http://onlinelibrary.yabesh.ir/handle/yetl/4230883 | |
| description abstract | his paper presents an approach to postprocess ensemble forecasts for the discrete and bounded weather variable of total cloud cover. Two methods for discrete statistical postprocessing of ensemble predictions are tested: the first approach is based on multinomial logistic regression and the second involves a proportional odds logistic regression model. Applying them to total cloud cover raw ensemble forecasts from the European Centre for Medium-Range Weather Forecasts improves forecast skill significantly. Based on stationwise postprocessing of raw ensemble total cloud cover forecasts for a global set of 3330 stations over the period from 2007 to early 2014, the more parsimonious proportional odds logistic regression model proved to slightly outperform the multinomial logistic regression model. | |
| publisher | American Meteorological Society | |
| title | Discrete Postprocessing of Total Cloud Cover Ensemble Forecasts | |
| type | Journal Paper | |
| journal volume | 144 | |
| journal issue | 7 | |
| journal title | Monthly Weather Review | |
| identifier doi | 10.1175/MWR-D-15-0426.1 | |
| journal fristpage | 2565 | |
| journal lastpage | 2577 | |
| tree | Monthly Weather Review:;2016:;volume( 144 ):;issue: 007 | |
| contenttype | Fulltext | |
