Gateway to Think Tanks
来源类型 | Technical notes |
规范类型 | 报告 |
来源ID | RP0269 |
RP0269 – A New Approach to Extract Monthly High Resolution Information for Precipitation from GCM Scenarios and Predictions | |
Neil M. Ward; Stefano Materia; Silvio Gualdi; Antonio Navarra | |
发表日期 | 2016-04 |
出版年 | 2016 |
语种 | 英语 |
摘要 | Statistical downscaling to 0.25° spatial resolution TRMM monthly precipitation explores the potential and limitations of using the relatively short (here 15 years) but spatially extensive and complete precipitation archive, that is satellite-based and merged with station information. Downscaling models relate reanalysis circulation to TRMM, utilizing principal component regression (PCR) and canonical correlation analysis (CCA). Results are demonstrated for two contrasting regions: Northeastern Brazil (NEB, tropical, distinct wet and dry season) and Central Italy (CIT, mid-latitude/Mediterranean, complex terrain). Models are constructed for individual months (M1) and by pooling three months (M3) to increase model-training sample size when downscaling to the central month of the three. Cross-validated skill with M1 is promising and is noticeably more consistent and higher with M3, e.g., mean skill at the grid-box scale rises from r=0.44 to r=0.59 for CIT (averaged over all months), and from r=0.58 to r=0.68 for the NEB wet season months. Spatial structure of the downscaling models (as revealed by CCA modes) supports a clear expression of orography in the precipitation anomaly fields. Application to a global coupled model climate change scenario (2012-2050) generates plausible downscaled time-series and fields. For CIT, results (skill, spatial structure) are consistent with those produced using a station-only gridded (0.25°) dataset for the extended period 1979- 2012. The overall impression gained is that TRMM data enable estimation of skilful downscaling relationships, at least for some locations. Developments drawing on longer datasets to adjust the downscaled fields will likely further increase the utility of a record like TRMM. |
URL | https://www.cmcc.it/publications/rp0269-a-new-approach-to-extract-monthly-high-resolution-information-for-precipitation-from-gcm-scenarios-and-predictions |
来源智库 | Centro Euro-Mediterraneo sui Cambiamenti Climatici (Italy) |
资源类型 | 智库出版物 |
条目标识符 | http://119.78.100.153/handle/2XGU8XDN/200463 |
推荐引用方式 GB/T 7714 | Neil M. Ward,Stefano Materia,Silvio Gualdi,等. RP0269 – A New Approach to Extract Monthly High Resolution Information for Precipitation from GCM Scenarios and Predictions. 2016. |
条目包含的文件 | ||||||
文件名称/大小 | 资源类型 | 版本类型 | 开放类型 | 使用许可 | ||
rp0269-csp-04-2016.p(2912KB) | 智库出版物 | 限制开放 | CC BY-NC-SA | 浏览 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。