DOI: 10.1002/2016MS000751
Scopus记录号: 2-s2.0-84988568690
论文题名: Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud-related biases
作者: Bani Shahabadi M ; , Huang Y ; , Garand L ; , Heilliette S ; , Yang P
刊名: Journal of Advances in Modeling Earth Systems
ISSN: 19422466
出版年: 2016
卷: 8, 期: 3 起始页码: 1453
结束页码: 1467
语种: 英语
英文关键词: Acoustic devices
; Atmospheric humidity
; Atmospheric temperature
; Forecasting
; Infrared devices
; Infrared instruments
; Optical properties
; Radiative transfer
; Sounding apparatus
; Spectroscopy
; Surface properties
; Troposphere
; Water absorption
; Water vapor
; Atmospheric infrared sounders
; Global environmental multiscale models
; Meteorological variables
; Model validation
; Radiative transfer model
; Satellite observations
; Upper tropospheric water vapors
; Water vapor absorption bands
; Weather forecasting
; AIRS
; brightness temperature
; clear sky
; cloud cover
; emissivity
; humidity
; infrared radiation
; model validation
; quantitative analysis
; radiative transfer
; reflectivity
; satellite data
; temperature
; weather forecasting
英文摘要: An established radiative transfer model (RTM) is adapted for simulating all-sky infrared radiance spectra from the Canadian Global Environmental Multiscale (GEM) model in order to validate its forecasts at the radiance level against Atmospheric InfraRed Sounder (AIRS) observations. Synthetic spectra are generated for 2 months from short-term (3–9 h) GEM forecasts. The RTM uses a monthly climatological land surface emissivity/reflectivity atlas. An updated ice particle optical property library was introduced for cloudy radiance calculations. Forward model brightness temperature (BT) biases are assessed to be of the order of ∼1 K for both clear-sky and overcast conditions. To quantify GEM forecast meteorological variables biases, spectral sensitivity kernels are generated and used to attribute radiance biases to surface and atmospheric temperatures, atmospheric humidity, and clouds biases. The kernel method, supplemented with retrieved profiles based on AIRS observations in collocation with a microwave sounder, achieves good closure in explaining clear-sky radiance biases, which are attributed mostly to surface temperature and upper tropospheric water vapor biases. Cloudy-sky radiance biases are dominated by cloud-induced radiance biases. Prominent GEM biases are identified as: (1) too low surface temperature over land, causing about −5 K bias in the atmospheric window region; (2) too high upper tropospheric water vapor, inducing about −3 K bias in the water vapor absorption band; (3) too few high clouds in the convective regions, generating about +10 K bias in window band and about +6 K bias in the water vapor band. © 2016. The Authors. Citation statistics:
资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/75866
Appears in Collections: 影响、适应和脆弱性 气候变化与战略
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作者单位: Department of Atmospheric and Oceanic Sciences, McGill University, Montreal, QC, Canada; Environment Canada, Dorval, QC, Canada; Department of Atmospheric Sciences, Texas A&M University, College StationTX, United States
Recommended Citation:
Bani Shahabadi M,, Huang Y,, Garand L,et al. Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud-related biases[J]. Journal of Advances in Modeling Earth Systems,2016-01-01,8(3)