NOAA极轨气象卫星数据格式详解——质量标志与定标系数

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"此资源主要涉及的是NOAA极轨气象卫星的数据格式规范,特别是与质量标志相关的部分,包括软件工程中的事实和误区。" 在NOAA极轨气象卫星数据格式中,质量标志是一个重要的组成部分,它提供了关于数据质量和可靠性的关键信息。质量标志通常由一系列的bit位组成,这些位具有特定的含义,可以用来评估和解析卫星数据的准确性和完整性。例如,描述中提到的"25~28 4 I*4"这部分代表质量标识符的bit域,其具体意义需要参考附录A来解读。同样的,"29~32 4 I*4"是扫描线质量标记的bit域,以及"33~38 2x3 I*4"为IR通道的定标质量标记符,这些都关乎数据的定标质量和错误检测。 定标系数是另一个核心概念,它们用于校正卫星传感器收集到的数据,以减少环境因素和设备偏差的影响。如描述中所示,定标系数包括可见光定标系数和红外(IR)定标系数。可见光定标系数由5个部分组成,针对3个不同通道(通道1、2、3A),每个通道有两个斜率和一个截距,以及一个选择交叉点的记数值。这些系数以10的幂次表示,确保了数据处理的精度。同样,IR定标系数有3个系数,用于IR通道的校准。 这些数据格式是国家卫星气象中心(NSMC)处理和分析NOAA系列卫星数据的基础,包括NOAA-1B数据格式的不同版本,如压缩形式的1B格式和NOAA-K/L/M/N等后续卫星的1B数据格式。这些卫星的数据文件格式通常包含详细的元数据,如卫星状态信息、传感器参数以及扫描线的质量信息,以支持气象学研究和天气预报。 NOAA系列卫星,尤其是第五代的NOAA-15至NOAA-17,配备了改进的探测器和更先进的仪器,如AVHRR/3和ATOVS,提供了更高分辨率的观测数据。它们的轨道参数,如高度、倾角和周期,确保了全球覆盖,并且有固定的回归周期,便于长期的数据比较和分析。 总结来说,NOAA极轨气象卫星数据格式是复杂而精密的,它融合了软件工程的质量控制原理,以及气象学的定标技术,目的是提供可靠、一致的全球气候和天气监测数据。理解和解析这些格式对于气象学家、遥感专家以及相关领域的研究者至关重要。

Compared with homogeneous network-based methods, het- erogeneous network-based treatment is closer to reality, due to the different kinds of entities with various kinds of relations [22– 24]. In recent years, knowledge graph (KG) has been utilized for data integration and federation [11, 17]. It allows the knowledge graph embedding (KGE) model to excel in the link prediction tasks [18, 19]. For example, Dai et al. provided a method using Wasser- stein adversarial autoencoder-based KGE, which can solve the problem of vanishing gradient on the discrete representation and exploit autoencoder to generate high-quality negative samples [20]. The SumGNN model proposed by Yu et al. succeeds in inte- grating external information of KG by combining high-quality fea- tures and multi-channel knowledge of the sub-graph [21]. Lin et al. proposed KGNN to predict DDI only based on triple facts of KG [66]. Although these methods have used KG information, only focusing on the triple facts or simple data fusion can limit performance and inductive capability [69]. Su et al. successively proposed two DDIs prediction methods [55, 56]. The first one is an end-to-end model called KG2ECapsule based on the biomedical knowledge graph (BKG), which can generate high-quality negative samples and make predictions through feature recursively propagating. Another one learns both drug attributes and triple facts based on attention to extract global representation and obtains good performance. However, these methods also have limited ability or ignore the merging of information from multiple perspectives. Apart from the above, the single perspective has many limitations, such as the need to ensure the integrity of related descriptions, just as network-based methods cannot process new nodes [65]. So, the methods only based on network are not inductive, causing limited generalization [69]. However, it can be alleviated by fully using the intrinsic property of the drug seen as local information, such as chemical structure (CS) [40]. And a handful of existing frameworks can effectively integrate multi-information without losing induction [69]. Thus, there is a necessity for us to propose an effective model to fully learn and fuse the local and global infor- mation for improving performance of DDI identification through multiple information complementing.是什么意思

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