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利用静止气象卫星红外通道遥感监测中国沙尘暴
Remote Sensing and Detection of Dust Storm in China Using the Thermal Bands of Geostationary Meteorological Satellite
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摘要: 气象卫星的红外窗区通道 (8~12 μm) 对于通常大气气溶胶几乎没有响应, 但对于较大颗粒且浓度较强的沙尘气溶胶, 尤其是沙尘暴有明显的信号反应。空气中的沙尘在红外分裂窗通道表现出两个特征:一是对地表发射到空间的红外信号产生衰减, 造成卫星探测到的地气系统亮温降低, 这就是所谓的红外差值沙尘指数IDDI; 二是沙尘粒子在红外分裂窗两个通道的比辐射率不同, 11 μm比12 μm的比辐射率低, 从而造成这两个通道的亮温差是负值。基于这两个特征和沙尘多通道光谱聚类法, 针对静止气象卫星观测数据进行了沙尘暴卫星遥感监测业务算法开发, 输出沙尘暴监测产品和红外差值沙尘指数产品, 这一算法不仅用于已经退役的GMS-5卫星, 而且应用于正在运行的静止气象卫星FY-2C, 它还为沙尘暴的定量或半定量遥感提供参考借鉴。Abstract: The earth observation system from space includes two kinds of platform:Polar orbit and geostationary satellite. Optical sensors onboard polar satellite have the advantage of high spatial resolution and more spectral bands in visible to infrared regions such as AVHRR/NOAA, MODIS/EOS, MVIS/FY-1C/1D etc. But it can conduct only twice a day. It is not enough for hazardous dust weather whose time scale is very short and moves quickly. It is difficult to understand dust moving and evolution as a whole using polar sensors' observation. Geostationary Meteorological Satellite such as GMS-5, FY-2, GOES and Meteosat can observe the earth continuously all daytime and night at high temporal resolution. Developing an algorithm for remote sensing and monitoring dust event will be very useful for forecast model, environment and climate monitoring and scientific research. Dust cloud is not easy to be discriminated like other strong weather phenomena such as typhoon. It is to be understood of the optical and radiative mechanism of airborne dust. The base theories of remote sensing of airborne dust will be introduced using the thermal and other bands of geostationary sensors. Observation signal of thermal infrared window (8—12 μm) bands have almost no sensitivity to general aerosols with small and thin particle sizes. There are some sensitivity to large and strong dust particles, especially in dust storm or heavy dust storm. The airborne dust can exert two kinds of features on thermal infrared observation signals. Firstly the infrared radiance of ground target into space will be reduced by dust layer and the brightness temperature of the observed underlying target be decreased. This kind of temperature reduction is called infrared difference dust index (IDDI). Secondly the emissivities of airborne dust are different in these two split window bands and produce the negative brightness temperature difference for dust targets. Based on these above theories and the traditional sophisticated multispectral classification technique, a set of algorithms for automatically detecting dust storm is developed using observation data of geostationary meteorological satellite. The first step of this algorithm is to extract the data of all bands from normalized disk image of observation and to conduct the interested region projection and calibration processing. The data reading is not only from present time but also from previous ten days for integrating background brightness temperature image. It is ready for IDDI image integration. The second step is cloud mask processing which is very important for dust discrimination. And then dust determination is conducted using above mentioned theories and methods. It is the key part of this algorithm. The last step is the production output including several types of dust remote sensing. This algorithm can obtain ideal result of dust storm detection and the product of IDDI. This algorithm has already been experimentally run in National Satellite Meteorological Center since 2001. It is not only used for dust detection from data of Japanese GMS-5, but also becomes a useful operational production of the new geostationary meteorological satellite FY-2C in orbit in 2005. In addition, it provides extended potential of quantitative or semi-quantitative remote sensing of dust storm.
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图 2 中纬度冬季标准大气条件下不同强度的沙尘分裂窗亮温差随地面能见度的变化
Fig. 2 Brightness temperature difference (BTD) in split window dependence of dust loading in several winter surface temperatures in mid-latitudes
图 3 地面能见度和红外差值沙尘指数对应关系[6]
(其中能见度对应7个等级[0, 2.5], [2.5, 5], [5, 7.5], [7.5, 10], [10, 15], [15, 20], [20, 30] km; 括号内数字为样本数)
Fig. 3 Climatological relation between IDDI and visibility[6]
(visibility measurements being classified into seven categories defined by the intervals [0, 2.5], [2.5, 5], [5, 7.5], [7.5, 10], [10, 15], [15, 20], [20, 30] km; the number inside bracket denotes the sample number)
图 4 GMS-5 10 d合成红外背景亮温图像
Fig. 4 GMS-5 integrated brightness temperature image of background during ten days
图 5 GSM-5沙尘暴监测结果格点图
（图中红点代表地面有沙尘报告的气象观测站）
Fig. 5 GSM-5 dust detection grid image
(red points denote the weather station with the report of dust occurrence)
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