An Improved Temperature and Emissivity Separation Algorithm for the Advanced Himawari Imager

Zhou, Shugui; Cheng, Jie

doi:10.1109/tgrs.2020.2979846

Cited by 37 publications

(26 citation statements)

References 72 publications

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“…The AGRI data contains many missing timesteps, and hence looks smoother than the more rapidly updating AHI and ABI temperature traces. ABI, Advanced Baseline Imager; AGRI, Advanced Geosynchronous Radiation Imager; AHI, Advanced Himawari Imager; BT, brightness temperature.has a central wavelength closest to 11 μm, an atmospheric window channel in which gaseous absorption is minimal(Zhou & Cheng, 2020). Various LEO sensors also observed this region on a semi-regular basis, and we begin by focusing on the VIIRS instrument's high spatial resolution I5 channel.…”

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confidence: 99%

Record‐Low Cloud Temperatures Associated With a Tropical Deep Convective Event

Proud

Bachmeier

2021

Geophysical Research Letters

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Earth‐orbiting satellites have long been used to examine meteorological processes. In the context of severe weather, brightness temperatures (BTs) at infrared wavelengths allow the determination of convective cloud properties. The anvils of cumulonimbus clouds, for example, typically produce BTs close to the tropopause temperature. Particularly severe storms generate overshoots that penetrate the stratosphere and are cooler than the anvil. In this study, we describe clustered storm overshoots in the tropical West Pacific on December 29, 2018 that resulted in the Visible Infrared Imaging Radiometer Suite (VIIRS) aboard NOAA‐20 measuring a temperature of 161.96K (−111.2°C), which is, to our knowledge, the coldest on record. We describe the local meteorological conditions, examine the VIIRS overpass that produced the cold temperature, compare VIIRS with other sensors that observed the region and, finally, analyze the historical context provided by two other satellite instruments to show that such cold temperatures may be becoming more common.

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confidence: 99%

Record‐Low Cloud Temperatures Associated With a Tropical Deep Convective Event

Proud

Bachmeier

2021

Geophysical Research Letters

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“…The values of slope, aspect, and elevation parameters were obtained by calculating the SRTM DEM by ArcGIS. At the same time, the cloud cover data [20,21], emissivity parameters [22][23][24][25], and land cover types [26][27][28] are obtained by raster calculation.…”

Section: Data Pre-processingmentioning

confidence: 99%

A Multiscale Normalization Method of a Mixed-Effects Model for Monitoring Forest Fires Using Multi-Sensor Data

Feng

Hua-shun

Zhang³

et al. 2022

Sustainability

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This paper points out the shortcomings of existing normalization methods, and proposes a brightness temperature inversion normalization method for multi-source remote sensing monitoring of forest fires. This method can satisfy both radiation normalization and observation angle normalization, and reduce the discrepancies in forest fire monitoring between multi-source sensors. The study was based on Himawari-8 data; the longitude, latitude, solar zenith angle, solar azimuth angle, emissivity, slope, aspect, elevation, and brightness temperature values were collected as modeling parameters. The mixed-effects brightness temperature inversion normalization (MEMN) model based on FY-4A and Himawari-8 satellite sensors is fitted by multiple stepwise regression and mixed-effects modeling methods. The results show that, when the model is tested by Himawari-8 data, the coefficient of determination (R2) reaches 0.8418, and when it is tested by FY-4A data, R2 reaches 0.8045. At the same time, through comparison and analysis, the accuracy of the MEMN method is higher than that of the random forest normalization method (RF) (R2=0.7318), the pseudo-invariant feature method (PIF) (R2=0.7264), and the automatic control scatter regression method (ASCR) (R2=0.6841). The MEMN model can not only reduce the discrepancies in forest fire monitoring owing to different satellite sensors between FY-4A and Himawari-8, but also improve the accuracy and timeliness of forest fire monitoring.

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“…A large number of LST retrieval algorithms have been proposed. Several typical algorithms, such as the split-window (SW) algorithm [6]- [9], single-channel algorithm [10], [11], and temperature and emissivity separation (TES) algorithm [12]- [14]. SW algorithm could estimate LST based on differential water vapor absorption in two adjacent TIR bands, which do not need to eliminate the influence of atmosphere using some information about real-time atmospheric profiles.…”

Section: Introductionmentioning

confidence: 99%