On the Use of Geostationary Satellites for Remote Sensing in the High Latitudes

1. On the Use of Geostationary Satellites for Remote Sensing in the High Latitudes Yinghui Liu1, Jeffrey R. Key2,Xuanji Wang1, Tim Schmit2, and Jun Li1 1Cooperative Institute for Meteorological Satellite Studies (CIMSS) / Space Science and Engineering Center (SSEC), UW-Madison, Madison, Wisconsin 2Center for Satellite Applications and Research, NOAA/NESDIS, Madison, Wisconsin Introduction Geostationary satellites provide nearly continuous observations of the earth from space over tropical and mid-latitude regions, making them very useful tools for weather analysis and prediction over much of the globe. However, geostationary satellites are not traditionally used in remote sensing of the high latitudes due to larger sensor scanning angles and lower spatial resolution. The next generation of geostationary satellites will provide higher spatial resolution observations and more robust spectral information, so their use at high latitudes needs to be reconsidered. Winds and Ice Motion The minimum detectable motion of clouds (used for wind estimation) and sea ice in imagery is a function of pixel size and the time interval between two consecutive images. It, and the related tracking error, increase with increasing pixel size and with decreasing sampling time, as shown in Figure 6. For example, theoretically, a pixel size of 10 km with time intervals longer than 60 min would detect motions greater than 3 m/s, of marginal use for winds and no use for ice motion that is typically less than 0.1 m/s. For a 5 km pixel and 60 min interval, the minimum detectable motion would be less than 1.5 m/s. So while a higher temporal sampling rate would not improve the retrievals from a geometrical perspective, it would provide a greater possibility of finding appropriate tracking targets. Fig. 3. Left: LZA by latitude. Right: pixel size by latitude along the longitude line of satellite nadir. In addition to a growth in pixel size, an increase in LZA results in an increase in the atmospheric absorption path. This affects the observed radiances at different wavelengths. Figure 4 shows that brightness temperatures at most wavelengths decrease with increasing LZA due to stronger atmospheric absorption. The effect for temperature inversions in the lower troposphere (not shown) or stratosphere would be opposite. Fig. 6. Left: Minimum detectable motion with pixel size and time intervals based on geometry only. Right: tracking error lower limit with image spatial resolution and time intervals (courtesy G. Jedlovec). Issues for the Use of GEO Data at High Latitudes The greatest advantage of geostationary satellites over polar-orbiting satellites is the greater temporal frequency of imagery. The Advanced Baseline Imager (ABI), which is being developed as the future imager on the next generation satellite Geostationary Operational Environmental Satellite (GOES)-R, will scan the full disk in less than every 15 minutes, with 2 km resolution at the sub-point for the infrared bands, and better radiometric performance, image navigation, and registration. This frequency is much higher than that of polar-orbiting satellites, with some exceptions in polar regions (Figure 1). Meanwhile, the greatest disadvantage of geostationary satellites at higher latitudes is their large local zenith angle, which leads to increasing pixel size and atmospheric path length. Atmospheric Profiles of Temperature and Humidity Temperature and humidity profile retrieval accuracy is also a function of viewing geometry. Figure 7 shows the root-mean-squared error of temperature and moisture profile retrievals for local zenith angles of 0, 62.5, 65, and 70o, where GIFTS-like instrument is assumed in the simulation. There is a degradation on the water vapor retrievals but little impact on temperature retrievals.  Large local zenith angles result in a more nonlinear relationship for water vapor retrieval. Fig. 4. Brightness temperature changes with LZA for various channels. At the higher latitudes, observations from geostationary satellites can be used to fill the temporal gaps of polar orbiting satellite observations. However, the effects of larger LZA - pixel size, atmospheric absorption path, and bidirectional reflectance functions - on different retrieval products need be investigated. Effects on Satellite Retrievals Pixel size is an important factor in determining the proportion of pixels in an image that have a characteristic of interest; for example, cloud, snow, or ice fraction. The minimum detectable motion of clouds (for winds) and sea ice is a function pixel size and scan frequency. The accuracy of temperature and moisture profile retrievals is affected by LZA through the atmospheric absorption path. Cloud, Snow, and Ice Cover The spatial coverage of geophysical variables is often determined by categorizing each pixel as either covered by that parameter or not. This binary labeling is strongly dependent upon the pixel size, as the subpixel area fraction distribution is considerably different for small and large pixels. To examine this effect theoretically, the frequency distributions of cloud, snow, or ice cover can be described by the beta distribution, which can be determined based on the mean and variance of the subpixel fractional coverage. Exponential covariance is a reasonable model for many geophysical parameters, where the slope of the exponential describes the dependence of covariance on the separation distance. Given the slope of exponential, the variance of the subpixel area fraction can be derived for a fixed pixel size. Combined with the mean of the subpixel area fraction, a unique beta distribution can be determined, as shown in Figure 5 (left). For a given spectral detection threshold, the area coverage can be estimated, as shown in Figure 5 (right). The area coverage is overestimated for larger pixels and larger slopes of the exponential. Note that this overestimation is much less of a problem if subpixel area fraction is the goal rather than binary labeling. While the distribution of subpixel coverage does change, atmospheric path length and bidirectional reflectance issues may have a greater influence on retrievals. Fig. 7. Left: temperature; right: moisture (right)profile retrieval RMSE with LZA using simulated hyperspectral IR radiance. Latitude Limit Recommendations Pixel size and atmospheric absorption path of geostationary satellites increase dramatically at latitudes over 65 degree. These changes play critical roles in cloud, snow and ice cover determination, atmospheric and ice motion estimates, and atmospheric profile retrievals. Geostationary cloud amount will be overestimated at higher latitudes when a binary approach is taken. Obviously a parallax correction also becomes increasingly important at larger LZA. Minimum detectable wind and ice speeds are sufficient given long enough time intervals used in tracking, but the higher temporal sampling of geostationary satellites is only an advantage in terms of locating tracking features. At higher latitudes, moisture profile retrievals are affected due to the large atmospheric path length, while the temperature profile retrievals do not have the same limitation. The exact limit of usefulness of the geostationary satellite data at high angles is also affected by other factors, such as image registration and navigation.The recommended latitude limits of the current GOES and future GOES-R products are listed in Table 1. These values are subject to discussion and future investigation. Fig. 1. Terra and Aqua satellite overpasses by latitude. While the instruments onboard the geostationary satellite scan the full disk, the instrument scanning angles do not change much due to the very high altitude of the satellite. However, the local zenith angles (LZA) of each pixel (from the ground perspective) have a larger range, from 0 to 90o. As an example, the left panel in Figure 2 shows the LZA distribution of each pixel in a full disk for a geostationary satellite stationed at 75 W longitude. The LZA is 0o at nadir, and increases away from nadir to nearly 90o. Figure 3 provides another view of the relationships between LZA, pixel size, and latitude. Table 1. Recommended latitudinal limitations for GOES (GOES-R in parentheses) Imager products. Fig. 5.Left: Beta distributions of subpixel area fraction for different shape parameters. Right: The estimated cloud cover distribution with cloud spatial structure (slope of exponential) and relative pixel size with fixed threshold. Fig. 2. Left: Local zenith angle (LZA); Right: Theoretical pixels size for 2 km nadir field of view (FOV). This poster does not reflect the views or policy of the GOES-R Program Office. 20 – 24 January 2008 5th GOES Users’ Conference – New Orleans, LA