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GHRSST NOAA/STAR GOES-17 ABI L2P America Region SST v2.71 dataset in GDS2

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doi.org2025-03-22 收录
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http://doi.org/10.5067/GHG17-2PO71
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GOES-17 (G17) is the second satellite in the US NOAA's GOES-R series. It was launched on 1 Mar 2018 in an interim position at 89.5-deg W for initial Cal/Val, moved to its nominal position at 137.2-deg W in Nov 2018, and declared NOAA operational GOES-West satellite on 12 Feb 2019. Advanced Baseline Imager (ABI) is a 16 channel sensor, of which five (3.9, 8.4, 10.3, 11.2, 12.3 um) are suitable for SST. From altitude 35,800km, G17/ABI maps SST in a Full Disk (FD) area from 163E-77W and 60S-60N, with spatial resolution 2km/nadir to 15km/VZA 67-deg, and 10-min temporal sampling. The ABI L2P SST is derived at the native sensor resolution using NOAA ACSPO system. ACSPO processes every 10-min FD, identifies good-quality ocean pixels (Petrenko et al., 2010) and derives SST using Non-Linear SST (NLSST) algorithm (Petrenko et al., 2014). Unfortunately, the G17 ABI loop heat pipe (LHP) that should maintain the ABI at its intended temperature, is not operating at its designed capacity, which required mitigations to the ACSPO algorithms and releasing an updated ACSPO version 2.71 (Pennybacker et al, 2019). In particular, band 11.2um, most subject to calibration problems, is not used leading to a 3-band (8.4, 10.3, and 12.3um) NLSST, and increased calibration problems prevent SST retrievals at night. As a result, the G17 SST is only reported for 13 out of 24hrs/day, from 20UTC to 08UTC. The 10-min FD data are subsequently collated in time, to produce 1-hr product, with improved coverage and reduced cloud leakages and image noise. The collation algorithm also reduces G17 excessive sensor noise and striping to levels similar to G16. The collated SSTs are only reported over clear-sky water pixels. All pixels with valid SSTs are recommended for use. The L2P is reported in NetCDF4 GDS2 format, 13 granules per day, with a total data volume 0.3GB/day. ACSPO files also report sun-sensor geometry, wind speed and l2p_flags (day/night, land, ice, twilight, glint flags). Per GDS2 specifications, two Sensor-Specific Error Statistics (bias and standard deviation) are reported in each pixel (Petrenko et al., 2016). Pixel earth locations are not reported in the granules, as they remain unchanged from granule to granule. Those can be obtained using a flat lat/lon file or a Python script (see Documentation page). The ACSPO G17 ABI SSTs are continuously validated in SQUAM (Dash et al, 2010). A reduced size (0.1GB/day), 0.02-deg equal-angle gridded L3C product is available at https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71.

GOES-17(G17)是美国NOAA GOES-R系列中的第二颗卫星。该卫星于2018年3月1日发射,起初处于89.5度西经的临时位置进行初始校准和验证,并于2018年11月迁移至137.2度西经的预定位置。2019年2月12日,G17被正式宣布为NOAA的GOES-West运行卫星。高级基础成像仪(ABI)是一个16通道的传感器,其中五个通道(3.9、8.4、10.3、11.2、12.3微米)适用于海表温度(SST)的测量。从35,800公里的高度,G17/ABI在全圆盘(FD)区域内绘制SST,覆盖范围从163度东经至77度西经,以及60度南至60度北,空间分辨率为2公里(视场中心)至15公里(视场角67度),时间采样为10分钟。ABI的第二级产品(L2P)SST是在原始传感器分辨率下使用NOAA ACSPO系统衍生的。ACSPO系统每10分钟处理一次全圆盘数据,识别高质量海洋像素(Petrenko等,2010年),并利用非线性SST(NLSST)算法(Petrenko等,2014年)计算SST。遗憾的是,G17 ABI回路热管(LHP),该设备应维持ABI在其设计温度,并未达到设计容量,这需要调整ACSPO算法并发布更新的ACSPO版本2.71(Pennybacker等,2019年)。特别是11.2微米波段,最容易受到校准问题的影响,因此未使用该波段,导致3波段(8.4、10.3和12.3微米)的NLSST,以及增加的校准问题阻止了夜间SST的提取。因此,G17的SST仅在每天24小时中的13小时内报告,从20UTC至08UTC。10分钟FD数据随后在时间上进行汇总,以生成1小时产品,该产品具有更广泛的覆盖范围和减少的云层泄露和图像噪声。汇总算法还降低了G17过度的传感器噪声和条纹,使其与G16相似。汇总的SST仅在晴朗天空的水域像素中报告。所有具有有效SST的像素都建议使用。L2P数据以NetCDF4 GDS2格式报告,每天13个数据块,总数据量为每天0.3GB。ACSPO文件还报告了太阳传感器几何形状、风速和2p_flags(白天/夜晚、陆地、冰、晨昏、反光标志)。根据GDS2规范,每个像素报告两种传感器特定误差统计(偏置和标准偏差)(Petrenko等,2016年)。像素的地球位置未在数据块中报告,因为它们在数据块之间保持不变。这些信息可以通过平坦的经纬度文件或Python脚本获取(参见文档页面)。ACSPO G17 ABI的SST在SQUAM(Dash等,2010年)中进行持续验证。一个缩小尺寸(0.1GB/天)、0.02度等角网格的L3C产品可在https://podaac.jpl.nasa.gov/dataset/ABI_G17-STAR-L3C-v2.71处获取。
提供机构:
doi.org
搜集汇总
背景与挑战
背景概述
该数据集基于NOAA GOES-17卫星ABI传感器,提供美洲区域的海面温度(SST)数据,采用ACSPO v2.71系统处理。由于传感器冷却问题,算法调整为仅使用3个红外波段,且数据报告时间限制在每天13小时(20UTC至08UTC),以10分钟全盘观测整合为1小时产品,有效减少云干扰和噪声。数据以NetCDF4 GDS2格式存储,每天13个文件,总计0.3GB,包含SST值、误差统计及辅助标志,适用于海洋监测和气候研究。
以上内容由遇见数据集搜集并总结生成
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