气候变化领域集成服务门户(CCPortal): A Community Error Inventory for Satellite Microwave Observation Error Representation and Quantification

CCPortal

DOI	10.1175/BAMS-D-22-0207.1
	A Community Error Inventory for Satellite Microwave Observation Error Representation and Quantification
	Yang, John Xun; You, Yalei; Blackwell, William; Da, Cheng; Kalnay, Eugenia; Grassotti, Christopher; Liu, Quanhua (Mark); Ferraro, Ralph; Meng, Huan; Zou, Cheng-Zhi; Ho, Shu-Peng; Yin, Jifu; Petkovic, Veljko; Hewison, Timothy; Posselt, Derek; Gambacorta, Antonia; Draper, David; Misra, Sidharth; Kroodsma, Rachael; Chen, Min
发表日期	2024
ISSN	0003-0007
EISSN	1520-0477
起始页码	105
结束页码	1
卷号	105 期号:1
英文摘要	Satellite observations are indispensable for weather forecasting, climate change monitoring, and environmental studies. Understanding and quantifying errors and uncertainties associated with satellite observations are essential for hardware calibration, data assimilation, and developing environmental and climate data records. Satellite observation errors can be classified into four categories: measurement, observation operator, representativeness, and preprocessing errors. Current methods for diagnosing observation errors still yield large uncertainties due to these complex errors. When simulating satellite errors, empirical errors are usually used, which do not always accurately represent the truth. We address these challenges by developing an error inventory simulator, the Satellite Error Representation and Realization (SatERR). SatERR can simulate a wide range of observation errors, from instrument measurement errors to model assimilation errors. Most of these errors are based on physical models, including existing and newly developed algorithms. SatERR takes a bottom-up approach: errors are generated from root sources and forward propagate through radiance and science products. This is different from, but complementary to, the top-down approach of current diagnostics, which inversely solves unknown errors. The impact of different errors can be quantified and partitioned, and a ground-truth testbed can be produced to test and refine diagnostic methods. SatERR is a community error inventory, open-source on GitHub, which can be expanded and refined with input from engineers, scientists, and modelers. This debut version of SatERR is centered on microwave sensors, covering traditional large satellites and small satellites operated by NOAA, NASA, and EUMETSAT.
英文关键词	Satellite observations; Climate records; Databases; Numerical weather prediction/ forecasting; Data assimilation; Model errors
语种	英语
WOS研究方向	Meteorology & Atmospheric Sciences
WOS类目	Meteorology & Atmospheric Sciences
WOS记录号	WOS:001147724700001
来源期刊	BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/299161
作者单位	University System of Maryland; University of Maryland College Park; National Oceanic Atmospheric Admin (NOAA) - USA; University of North Carolina; University of North Carolina Wilmington; Massachusetts Institute of Technology (MIT); Lincoln Laboratory; University System of Maryland; University of Maryland College Park; California Institute of Technology; National Aeronautics & Space Administration (NASA); NASA Jet Propulsion Laboratory (JPL); National Aeronautics & Space Administration (NASA); NASA Goddard Space Flight Center; Ball Aerospace & Technologies; University of Wisconsin System; University of Wisconsin Madison
推荐引用方式 GB/T 7714	Yang, John Xun,You, Yalei,Blackwell, William,et al. A Community Error Inventory for Satellite Microwave Observation Error Representation and Quantification[J],2024,105(1).
APA	Yang, John Xun.,You, Yalei.,Blackwell, William.,Da, Cheng.,Kalnay, Eugenia.,...&Chen, Min.(2024).A Community Error Inventory for Satellite Microwave Observation Error Representation and Quantification.BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY,105(1).
MLA	Yang, John Xun,et al."A Community Error Inventory for Satellite Microwave Observation Error Representation and Quantification".BULLETIN OF THE AMERICAN METEOROLOGICAL SOCIETY 105.1(2024).