基于ERA5的快速辐射传输模式与FY-4A成像仪观测结果的偏差分析

Characterization of bias in FY-4A advanced geostationary radiation imager observations from ERA5 background simulations using RTTOV

中文关键词:  辐射传输模式  静止轨道卫星  成像仪

英文关键词:Radiative transfer model  Geostationary satellite  Radiation imager

中文摘要:

      2016年12月发射升空的FY-4A是中国第二代静止轨道气象卫星，该星上搭载了可提供东半球近实时高分辨率卫星观测数据的扫描辐射成像仪——AGRI （Advanced Geostationary Radiation Imager）。在其观测数据应用于大气参数反演或同化前，数据偏差的定量化分析是一个必要环节。采用快速辐射传输模式RTTOV （Radiative Transfer for the TIROS Operational Vertical Sounder），基于欧洲中期天气预报中心（ECMWF）第5个全球再分析数据产品（ERA5）对AGRI的7个红外通道（通道08—14）进行了模拟，并利用MODIS云检测产品对模拟结果进行了晴空筛选，以期得到一些对AGRI的定量应用有价值的偏差分析结果。观测-模拟（O-B）的偏差分析结果显示：海洋和陆地上，通道10（7.1 μm）存在明显大于其他红外通道的系统性偏差，这很可能来源于ERA5在对流层中层对水汽的高估。通道08为近红外短波通道，地表反射作用影响强烈，陆地上存在较大的平均偏差，而海洋上平均偏差小于0.4 K。通道14在ERA5近地层气温偏高及定标偏差的影响下，海洋存在接近1 K的平均偏差；陆地上存在2 K左右的平均偏差。其余各红外通道在海洋和陆地上的平均偏差分别在0.6和1.3 K以下。偏差影响因子分析结果显示：地表海拔高度、观测天顶角对偏差也存在一定程度的影响；海洋上偏差分布存在季节变化可能来源于再分析资料中海表温度估算的季节性误差。

英文摘要:

      The next-generation geostationary satellite FY-4A equipped with Advanced Geostationary Radiation Imager (AGRI) was launched on 11 December 2016. In this study, the biases of seven infrared channels (08-14 channel) of AGRI from model simulations are characterized by using the Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). The Moderate-Resolution Imaging Spectroradiometer (MODIS) cloud mask is used for selecting clear-sky data. The significant systematic deviations at channel 10 (7.1 μm) are most possibly caused by the overestimation of water vapor in ERA5 (the fifth ECMWF reanalysis) data. The calibration error and higher air temperature of surface layer in ERA5 data are possible causes for deviations at channel 14 (13.5 μm). Since the brightness temperature of near-infrared channel (channel 08, 3.72 μm) is strongly affected by surface reflection, there is a large average bias on the land at channel 08. The bias of this channel over the ocean is less than 0.4 K. The average biases at all other infrared channels over the ocean and land are below 0.6 K and 1.3 K respectively. In addition, surface elevation and sensor zenith also have a certain degree of influence on the simulation and subsequently lead to corresponding bias characteristics. The seasonal variation of bias distribution over the ocean existing at channels 11 (8.5 μm), 12 (10.8 μm) and 13 (12.0 μm) probably comes from the seasonal error of sea surface temperature estimate in the reanalysis data.