Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases
Validation of a weather forecast model at radiance level against satellite observations allowing quantification of temperature, humidity, and cloud‐related biases
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根据卫星观测验证辐射级天气预报模型，从而量化温度、湿度和云相关偏差
DOI：
10.1002/2016ms000751
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发表时间：
2016
影响因子：
6.8
通讯作者：
P. Yang
中科院分区：
文献类型：
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作者：
M. Bani Shahabadi;Yi Huang;L. Garand;S. Heilliette;P. Yang
为了验证加拿大全球环境多尺度（GEM）模式对大气红外探测器（AIRS）观测辐射水平的预测，采用了一个已建立的辐射传输模式（RTM）来模拟全天空红外辐射光谱。合成光谱由短期（3-9小时）GEM预测生成2个月。RTM使用每月气候地表发射率/反射率图集。介绍了一个用于云辐射计算的冰粒子光学特性库。正演模型的亮度温度（BT）偏差被评估为在晴天和阴天条件下都是101 K的量级。为了量化GEM预测气象变量偏差，生成光谱灵敏度内核，并用于将辐射偏差归因于表面和大气温度、大气湿度和云偏差。核方法，补充检索配置文件的基础上AIRS观测搭配微波探测器，实现了很好的关闭在解释晴空辐射偏差，这主要是由于地面温度和对流层上层水汽偏差。多云天空的辐射偏差主要是由云引起的辐射偏差。主要的GEM偏差被认为是：（1）陆地表面温度过低，导致大气窗口区的偏差约为-5 K;（2）对流层上层水汽含量过高，导致水汽吸收带的偏差约为-3 K;（3）对流层上层云太少，导致窗口带的偏差约为+10 K，水汽带的偏差约为+6 K。
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.