Matching of Observation Footprints in the FY-3G MWRI-RM Using BGI

There is often a lack of consistency in the footprints between satellite observation data, as well as between satellite observations and numerical models. This mismatch is typically caused by the different spatial resolutions of observed data in multiple frequency channels. The Backus-Gilbert invers...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	IEEE journal of selected topics in applied earth observations and remote sensing Ročník 17; s. 17794 - 17805
Hlavní autori:	Chen, Ke, Cai, Bowen, Han, Wei, Suo, Zihao
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Piscataway IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	Algorithms Backus–Gilbert inversion Brightness temperature brightness temperature remapping Channels Footprints Frequency dependence Hurricanes Imaging Matching Mathematical models Microwave communication Microwave filters Microwave imaging Microwave measurement Microwave radiation microwave radiation imager for the rainfall mission (MWRI-RM) Microwave radiometry Microwaves Misalignment Noise Numerical models Payloads Precipitation Rainfall Satellite observation Satellites Sounding Spatial data Spatial discrimination Spatial resolution Surface radiation temperature Typhoons
ISSN:	1939-1404, 2151-1535
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	There is often a lack of consistency in the footprints between satellite observation data, as well as between satellite observations and numerical models. This mismatch is typically caused by the different spatial resolutions of observed data in multiple frequency channels. The Backus-Gilbert inversion (BGI) algorithm is widely used for brightness temperature remapping in various microwave payloads to make the observation footprints more consistent. The microwave radiation imager for the rainfall mission onboard FengYun-3G introduces a novel integration of imaging and sounding channels for precipitation monitoring. However, this integration presents challenges in the matching of observation footprints, as they exhibit not only the conventional discrepancy in footprint sizes but also a misalignment between the center locations of the footprints of the sounding and imaging channels. This article proposes an improved BGI algorithm to match the observation footprints from sounding channels with those from imaging channels in terms of both spatial resolution and location. This study uses a distance-based fixed window for the filtering of source observations and employs an automatic noise parameter selection scheme to strike a balance between the fit error of footprints and noise. A coefficient reuse method is applied to improve computational efficiency. The experimental results using simulated data demonstrate that our improved BGI algorithm leads to a significant decrease in the overall root mean square error by 80% and 50% in the clear-sky and typhoon cases, respectively, compared to the raw <inline-formula><tex-math notation="LaTeX">{{T}_A}</tex-math></inline-formula>. The remapping results of real observations demonstrate that our algorithm effectively suppresses noise in each channel.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14
ISSN:	1939-1404 2151-1535
DOI:	10.1109/JSTARS.2024.3468437