DOI: 10.1109/TGRS.2019.2941682
论文题名: Metric Learning for Approximation of Microwave Channel Error Covariance: Application for Satellite Retrieval of Drizzle and Light Snowfall
作者: Ebtehaj A. ; Kummerow C.D. ; Turk F.J.
刊名: IEEE Transactions on Geoscience and Remote Sensing
ISSN: 1962892
出版年: 2020
卷: 58, 期: 2 语种: 英语
英文关键词: Channel error covariance
; metric learning
; precipitation passive microwave retrievals
; precipitation phase
; satellite snowfall detection
Scopus关键词: Covariance matrix
; Microwaves
; Nearest neighbor search
; Rain
; Satellites
; Sea ice
; Snow
; Channel error
; Classification approach
; Error covariance matrix
; Global precipitation measurements
; Metric learning
; Passive microwaves
; Precipitation phase
; Radiative transfer equations
; Error detection
; CloudSat
; detection method
; drizzle
; error analysis
; frequency analysis
; learning
; measurement method
; microwave imagery
; precipitation (climatology)
; precision
; satellite data
英文摘要: Improved microwave retrieval of land and atmospheric state variables requires proper weighting of the information content of radiometric channels through their error covariance matrix. Inspired by recent advances in metric learning techniques, a new framework is proposed for a formal approximation of the channel error covariance. The idea is tested for the detection of precipitation and its phase over oceans, using coincidences of passive/active data from the Global Precipitation Measurement (GPM) and CloudSat satellites. The initial results demonstrate that the presented approach cannot only capture the known laws of radiative transfer equations, but also the surrogate signatures that can arise due to the co-occurrence of precipitation and other radiometrically active land-atmospheric state variables. In particular, the results demonstrate high precision (low error) for the low-frequency channels of 10-37 GHz in the detection of both rain and snowfall over oceans. Using the optimal estimate of the channel error covariance through the multi-frequency {k} -nearest neighbor (kNN) classification approach, without any ancillary data, it is demonstrated that the probability of passive microwave detection of snowfall (0.97) can be higher than that of the rainfall (0.88), when drizzle and light snowfall are the dominant form of precipitation. This improvement is hypothesized to be largely related to the information content of the low-frequency channels of 10-37 GHz that can capture the co-occurrence of snowfall with an increased cloud liquid water content, sea ice, and wind-induced changes of surface emissivity. © 1980-2012 IEEE.
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资源类型: 期刊论文
标识符: http://119.78.100.158/handle/2HF3EXSE/159770
Appears in Collections: 气候变化与战略
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作者单位: Department of Civil Environmental and Geo-Engineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Atmospheric Science, Colorado State University, Fort Collins, CO 80523, United States; Radar Science Group, Jet Propulsion Laboratory, Pasadena, CA 91109, United States
Recommended Citation:
Ebtehaj A.,Kummerow C.D.,Turk F.J.. Metric Learning for Approximation of Microwave Channel Error Covariance: Application for Satellite Retrieval of Drizzle and Light Snowfall[J]. IEEE Transactions on Geoscience and Remote Sensing,2020-01-01,58(2)