Value-added Impact of Geostationary Hyperspectral Infrared Sounders on Local Severe Storm Forecasts—via a Quick Regional OSSE

Li, Zhenglong; Li, Jun; Wang, Pei; Lim, Agnes H. N.; Li, Jinlong; Schmit, Timothy J.; Atlas, Robert; Boukabara, Sid-Ahmed; Hoffman, Ross N.

doi:10.1007/s00376-018-8036-3

Cited by 34 publications

(38 citation statements)

References 58 publications

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“…Moreover, in [8], three machine learning approaches for convective initiation detection have been proposed. In [9], hyperspectral infrared observations from geostationary orbit are used to implement a framework for the prediction of local severe storms. The better accuracy of hyperspectral infrared observations in deep convection identification is also demonstrated in [10], where the difference in brightness temperature is investigated to assess the identification of uncertainty for different instruments.…”

Section: Introductionmentioning

confidence: 99%

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

et al. 2018

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This study investigates the value of satellite-based observational algorithms in supporting numerical weather prediction (NWP) for improving the alert and monitoring of extreme rainfall events. To this aim, the analysis of the very intense precipitation that affected the city of Livorno on 9 and 10 September 2017 is performed by applying three remote sensing techniques based on satellite observations at infrared/visible and microwave frequencies and by using maps of accumulated rainfall from the weather research and forecasting (WRF) model. The satellite-based observational algorithms are the precipitation evolving technique (PET), the rain class evaluation from infrared and visible observations (RainCEIV) technique and the cloud classification mask coupling of statistical and physics methods (C-MACSP). Moreover, the rain rates estimated by the Italian Weather Radar Network are also considered to get a quantitative evaluation of RainCEIV and PET performance. The statistical assessment shows good skills for both the algorithms (for PET: bias = 1.03, POD = 0.76, FAR = 0.26; for RainCEIV: bias = 1.33, POD = 0.77, FAR = 0.41). In addition, a qualitative comparison among the three technique outputs, rain rate radar maps, and WRF accumulated rainfall maps is also carried out in order to highlight the advantages of the different techniques in providing real-time monitoring, as well as quantitative characterization of rainy areas, especially when rain rate measurements from Weather Radar Network and/or from rain gauges are not available.

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Section: Introductionmentioning

confidence: 99%

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

et al. 2018

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“…Launched on 11 December 2016, FY4A is the first satellite among the second generation of the Chinese geostationary weather satellite series; the remaining satellites of this series are planned to be launched from 2019 to 2025 and beyond (Yang et al, ). Since severe weather warning, nowcasting and short‐term forecasting require nearly continuous monitoring of the vertical temperature and moisture structure of the atmosphere (Schmit et al, ), and since GIIRS has an unprecedented advantage to observe the fast‐changing water vapor and temperature structures related to severe weather events (J. Li et al, , ), FY4A will provide vital information necessary for enhancing nowcasting and NWP services, for example, improve local severe storm short range forecast via data assimilation (Z. Li et al, ). Besides the GIIRS sensor, the two other main advanced instruments onboard the FY‐4A platform are the Advanced Geosynchronous Radiation Imager (AGRI) and Lightning Mapping Imager.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al, 2011Li et al, , 2012, FY4A will provide vital information necessary for enhancing nowcasting and NWP services, for example, improve local severe storm short range forecast via data assimilation (Z. Li et al, 2018). Besides the GIIRS sensor, the two other main advanced instruments onboard the FY-4A platform are the Advanced Geosynchronous Radiation Imager (AGRI) and Lightning Mapping Imager.…”

Section: Introductionmentioning

confidence: 99%

Enhancing the Fast Radiative Transfer Model for FengYun‐4 GIIRS by Using Local Training Profiles

Han

et al. 2018

JGR Atmospheres

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With the successful launch of FengYun‐4A (FY‐4A), the first satellite in a new Chinese geostationary weather satellite series (FY‐4 series), which carries a high spectral resolution infrared (IR) sounder called GIIRS (Geosynchronous Interferometric Infrared Sounder), and vertical atmospheric profiles can be obtained frequently at the regional scale. A fast radiative transfer model is a key component for quantitative applications of GIIRS radiance measurements, including deriving soundings in near real time for situation awareness and radiance assimilation in numerical weather prediction models. The weighted least squares method on enhancing the accuracy of RTTOV (Radiative Transfer for TOVS) for GIIRS is developed. Besides, currently, fast radiative transfer models for IR sensors are based on global training profiles, since GIIRS is targeted for regional observations; it is beneficial for local weather related applications using local training profiles, which better represent the characteristics of that weather regime. A local training profile data set has been developed for GIIRS using the RTTOV approach, comparisons with line‐by‐line radiative transfer model indicate that weighted least squares method provides better accuracy (smaller root‐mean‐square error) in the brightness temperature simulation for the middlewave band of GIIRS than the ordinary least squares method, and the local training profiles have further remarkable improvements on brightness temperature simulation over the global training profiles, especially for GIIRS longwave band. The methods can be applied to RTTOV development for other IR sensors onboard the geostationary satellites.

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“…Operational SRFs are used to generate radiances for each of the sensors from the simulated IASI/Metop‐A radiances. Gaussian distributed random numbers based on each sensor's in‐orbit noise equivalent delta temperature, or NEdT, are generated to simulate observation noise (Li et al, , ). Linear regression coefficients are generated for each of the 10 non‐zero LZA classes.…”

Section: Methodologiesmentioning

confidence: 99%

“…The SeeBor database (Seemann et al, 2008) (Li et al, 2018(Li et al, , 2019. Linear regression coefficients are generated for each of the 10 non-zero LZA classes.…”

Section: 1029/2019gl083639mentioning

confidence: 99%

Homogenized Water Vapor Absorption Band Radiances From International Geostationary Satellites

Gunshor

et al. 2019

Geophysical Research Letters

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In the past 20+ years, GEO Imagers with infrared 6.5‐μm bands have been observing the Earth's atmosphere, providing useful information of upper tropospheric moisture. Due to the instrumental differences and local viewing angles in GEO satellites, these observations are not consistent for generating climate data records (CDRs). In this study, a methodology has been developed to homogenize the 6.5‐μm radiances from the international GEO satellites, to generate a consistent CDR. Validations with Infrared Atmospheric Sounding Interferometer radiances from Metops for 2015–2017 for seven GEO Imager sensors show that the GEO radiances are homogenized well with small standard deviation and biases of the differences (smaller for newer sensors), temporally stable radiometric accuracy, and weak angle dependency (even weaker for sensors with two water vapor bands). The homogenized 20+ years of consistent 6.5‐μm radiance CDR can be used to evaluate reanalysis and climate models, especially the diurnal variation of the model simulation.

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Value-added Impact of Geostationary Hyperspectral Infrared Sounders on Local Severe Storm Forecasts—via a Quick Regional OSSE

Cited by 34 publications

References 58 publications

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

Analysis of Livorno Heavy Rainfall Event: Examples of Satellite-Based Observation Techniques in Support of Numerical Weather Prediction

Enhancing the Fast Radiative Transfer Model for FengYun‐4 GIIRS by Using Local Training Profiles

Homogenized Water Vapor Absorption Band Radiances From International Geostationary Satellites

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