DOI: 10.1007/s00382-017-3668-z
Scopus记录号: 2-s2.0-85017184315
论文题名: Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?
作者: Manzanas R. ; Lucero A. ; Weisheimer A. ; Gutiérrez J.M.
刊名: Climate Dynamics
ISSN: 9307575
出版年: 2018
卷: 50, 期: 2018-03-04 起始页码: 1161
结束页码: 1176
语种: 英语
英文关键词: Bias correction
; Correlation
; Perfect prognosis
; Precipitation
; Reliability categories
; Seasonal forecasting
; Skill
; Statistical downscaling
Scopus关键词: climate change
; correlation
; downscaling
; error correction
; geostatistics
; global climate
; precipitation (climatology)
; regional climate
; reliability analysis
; seasonal variation
; weather forecasting
英文摘要: Statistical downscaling methods are popular post-processing tools which are widely used in many sectors to adapt the coarse-resolution biased outputs from global climate simulations to the regional-to-local scale typically required by users. They range from simple and pragmatic Bias Correction (BC) methods, which directly adjust the model outputs of interest (e.g. precipitation) according to the available local observations, to more complex Perfect Prognosis (PP) ones, which indirectly derive local predictions (e.g. precipitation) from appropriate upper-air large-scale model variables (predictors). Statistical downscaling methods have been extensively used and critically assessed in climate change applications; however, their advantages and limitations in seasonal forecasting are not well understood yet. In particular, a key problem in this context is whether they serve to improve the forecast quality/skill of raw model outputs beyond the adjustment of their systematic biases. In this paper we analyze this issue by applying two state-of-the-art BC and two PP methods to downscale precipitation from a multimodel seasonal hindcast in a challenging tropical region, the Philippines. To properly assess the potential added value beyond the reduction of model biases, we consider two validation scores which are not sensitive to changes in the mean (correlation and reliability categories). Our results show that, whereas BC methods maintain or worsen the skill of the raw model forecasts, PP methods can yield significant skill improvement (worsening) in cases for which the large-scale predictor variables considered are better (worse) predicted by the model than precipitation. For instance, PP methods are found to increase (decrease) model reliability in nearly 40% of the stations considered in boreal summer (autumn). Therefore, the choice of a convenient downscaling approach (either BC or PP) depends on the region and the season. © 2017, Springer-Verlag Berlin Heidelberg. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/109441
Appears in Collections: 影响、适应和脆弱性 气候变化事实与影响
There are no files associated with this item.
作者单位: Meteorology Group. Institute of Physics of Cantabria (IFCA), CSIC-University of Cantabria, Santander, 39005, Spain; Philippine Atmospheric, Geophysical and Astronomical Services Administration (PAGASA), Quezon City, Philippines; Department of Physics, National Centre for Atmospheric Science (NCAS), University of Oxford, Oxford, OX1 3PU, United Kingdom; European Centre for Medium-Range Weather Forecasts (ECMWF), Reading, RG2 9AX, United Kingdom
Recommended Citation:
Manzanas R.,Lucero A.,Weisheimer A.,et al. Can bias correction and statistical downscaling methods improve the skill of seasonal precipitation forecasts?[J]. Climate Dynamics,2018-01-01,50(2018-03-04)