Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager—A Case Study

Xiao-wei, Jiang; Li, Jun; Li, Zhenglong; Xue, Yan-Jie; Di, Di; Wang, Pei; Li, Jinlong

doi:10.3390/rs12040670

Cited by 7 publications

(2 citation statements)

References 40 publications

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“…Di et al [24] pointed out that moisture spatial variations within 12 to 16 km resolution are non-negligible, especially over land and in the pre-convection environment, and this information is an obvious indicator of several storm occurrences/development. Besides, evaluations on reanalysis datasets and regional model forecast products using GEO WV measurements from advanced imagers onboard the geostationary weather satellites both show non-negligible moisture bias at higher and lower troposphere [25,26]. Especially, the reanalysis dataset is not a near real-time product that has a period of lag time, and it is not available for operational applications.…”

Section: Introductionmentioning

confidence: 99%

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

Zhu

Zhou

et al. 2023

Remote Sensing

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The advanced imagers onboard the new generation of geostationary satellites could provide multilayer atmospheric moisture information with unprecedented high spatial and temporal resolutions, while the physical retrieval algorithm (One-Dimensional Variational, 1DVAR) is performed for operational atmospheric water vapor products with reduced resolutions, which is due to the limited computational efficiency of the physical retrieval algorithm. In this study, a typical cost-efficient machine learning (Random Forecast, RF) algorithm is adopted and compared with the physical retrieval algorithm for retrieving the atmospheric moisture from the measurements of Advance Himawari Imager (AHI) onboard the Himawari-8 satellite during the typhoon Maria (201808). It is found that the accuracy of the RF-based algorithm has much high computational efficiency and provides moisture retrievals with accuracy 35–45% better than that of 1DVAR, which means the retrieval process can be conducted at full spatial resolution for potential operational application. Both the Global Forecast System (GFS) forecasts and the AHI measurements are necessary information for moisture retrievals; they provide added value for each other.

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Section: Introductionmentioning

confidence: 99%

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

Zhu

Zhou

et al. 2023

Remote Sensing

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“…Water vapor absorption band radiance measurements from the advanced imagers onboard the new generation of geostationary weather satellites allow us to quantitatively evaluate the environmental moisture fields from NWP models. Paper [25] focuses on this issue, analyzing the behavior and uncertainty of the moisture fields of three different NWP models under different weather conditions for better understanding and enhancing storm prediction.…”

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confidence: 99%

Editorial for the Special Issue “Remote Sensing of Atmospheric Components and Water Vapor”

Cachorro

Antón

2020

Remote Sensing

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The observation/monitoring of atmospheric components and water vapor in the atmosphere is today open to very different remote sensing techniques, most of them based on the radiation-matter interaction covering the full electromagnetic spectrum. This SI collects some papers regarding the retrieval, calibration, validation, analysis of data and uncertainties, as well as comparative studies on atmospheric gases and water vapor by remote sensing techniques, where different types of sensors, instruments, and algorithms are used or developed.

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Applications of Geostationary Hyperspectral Infrared Sounder Observations: Progress, Challenges, and Future Perspectives

Menzel

Schmit

et al. 2022

Bulletin of the American Meteorological Society

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A hyperspectral infrared (IR) sounder from geostationary orbit provides nearly continuous measurements of atmospheric thermodynamic and dynamic information within a weather cube, specifically the atmospheric temperature, moisture, and wind information at different pressure levels that are critical for improving high impact weather (HIW) nowcasting and numerical weather prediction (NWP). Geostationary hyperspectral IR sounders (GeoHIS) have been onboard China’s Fengyun-4 series since 2016 and will be onboard Europe’s Meteosat Third Generation (MTG) series in the 2024 time frame; the U.S. and other countries are also planning to include GeoHIS instruments on their next generation of geostationary weather satellites. Although availability of on-orbit GeoHIS data are limited currently, studies have been conducted and progress has been made on developing the applications of high temporal resolution GeoHIS observations. These include but are not limited to deriving three-dimensional wind fields for nowcasting and NWP applications, trending atmospheric instability for warning in pre-convective environments, conducting impact studies with data from the experimental Geostationary Interferometric Infrared Sounder (GIIRS) onboard Fengyun-4A, preparing observing system simulation experiments (OSSEs), and monitoring diurnal variation of atmospheric composition. This paper provides an overview of the current applications of GeoHIS, discusses the data processing challenges, and provides perspectives on future development. The purpose is to provide direction on utilization of the current and assist preparation for the upcoming GeoHIS observations for nowcasting, NWP and other applications.

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Evaluation of Environmental Moisture from NWP Models with Measurements from Advanced Geostationary Satellite Imager—A Case Study

Cited by 7 publications

References 40 publications

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

Retrieval of Atmospheric Water Vapor Content in the Environment from AHI/H8 Using Both Physical and Random Forest Methods—A Case Study for Typhoon Maria (201808)

Editorial for the Special Issue “Remote Sensing of Atmospheric Components and Water Vapor”

Applications of Geostationary Hyperspectral Infrared Sounder Observations: Progress, Challenges, and Future Perspectives

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