DOI: 10.1002/2016GL070621
论文题名: Identifying errors in dust models from data assimilation
作者: Pope R.J. ; Marsham J.H. ; Knippertz P. ; Brooks M.E. ; Roberts A.J.
刊名: Geophysical Research Letters
ISSN: 0094-8673
EISSN: 1944-8404
出版年: 2016
卷: 43, 期: 17 起始页码: 9270
结束页码: 9279
语种: 英语
英文关键词: aerosol optical depth
; data assimilation increments
; dust forecasts
; haboobs
Scopus关键词: Aerosols
; Atmospheric aerosols
; Dust
; Earth (planet)
; Image reconstruction
; Optical properties
; Radiometers
; Aerosol optical depths
; Data assimilation
; Global forecast models
; Haboobs
; Mesoscale process
; Moderate resolution imaging spectroradiometer
; Moist convection
; Weather and climate models
; Climate models
; aerosol
; atmospheric convection
; climate prediction
; cold pool
; data assimilation
; dust
; error analysis
; forecasting method
; MODIS
; optical depth
; remote sensing
; wind forcing
; Sahara
; Sahel [Sub-Saharan Africa]
英文摘要: Airborne mineral dust is an important component of the Earth system and is increasingly predicted prognostically in weather and climate models. The recent development of data assimilation for remotely sensed aerosol optical depths (AODs) into models offers a new opportunity to better understand the characteristics and sources of model error. Here we examine assimilation increments from Moderate Resolution Imaging Spectroradiometer AODs over northern Africa in the Met Office global forecast model. The model underpredicts (overpredicts) dust in light (strong) winds, consistent with (submesoscale) mesoscale processes lifting dust in reality but being missed by the model. Dust is overpredicted in the Sahara and underpredicted in the Sahel. Using observations of lighting and rain, we show that haboobs (cold pool outflows from moist convection) are an important dust source in reality but are badly handled by the model's convection scheme. The approach shows promise to serve as a useful framework for future model development. ©2016. The Authors. URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-84985961576&doi=10.1002%2f2016GL070621&partnerID=40&md5=00bf3cbcf404c0ad27662918db3de278
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/9636
Appears in Collections: 科学计划与规划 气候变化与战略
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作者单位: Institute for Atmospheric and Climate Science, University of Leeds, Leeds, United Kingdom
Recommended Citation:
Pope R.J.,Marsham J.H.,Knippertz P.,et al. Identifying errors in dust models from data assimilation[J]. Geophysical Research Letters,2016-01-01,43(17).