Impact of Moisture Information From Advanced Himawari Imager Measurements on Heavy Precipitation Forecasts in a Regional NWP Model

Wang, Pei; Li, Jun; Lu, Bing; Schmit, Timothy J.; Lu, Jiazhen; Lee, Yong‐Keun; Li, Jinlong; Liu, Zhiquan

doi:10.1029/2017jd028012

Cited by 24 publications

(27 citation statements)

References 48 publications

(67 reference statements)

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“…In a recent study, Wang et al () showed a positive impact from AHI radiance assimilation in predicting heavy precipitation from 19 to 20 July 2016 in Beijing. In another study, Wang, Li, et al () compared AHI radiance assimilation and LPW assimilation; they demonstrated that assimilation of the three derived LPWs yielded improved precipitation prediction.…”

Section: Three Lpw Retrieved From Himawari‐8 Ahi Radiance Measurementsmentioning

confidence: 99%

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Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

Feng

et al. 2019

JGR Atmospheres

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Understanding moisture information ahead of tropical cyclone (TC) convection is very important for predicting TC track, intensity, and precipitation. The advanced Himawari imager onboard the Japanese Himawari‐8/‐9 satellite can provide high spatial and temporal resolution moisture information. Three‐layered precipitable water (LPW) with its three water vapor absorption infrared bands can be assimilated to generate better understanding and prediction of TC evolution. The impacts of LPW assimilation in the Weather Research and Forecasting model with nine combinations of physical parameterization schemes, including three cumulus parameterization (CP) and three microphysics parameterization (MP) schemes on TC prediction, have been comprehensively analyzed using Typhoon Hato as a case study. The results indicate that LPW assimilation reduces the average track error and speed up TC movement by better adjustment of the atmospheric circulation fields via changing the vertical structure of moisture and thermal profile. The track forecasts retain sensitivity to CP schemes after LPW assimilation. Also, LPW assimilation improves TC intensity prediction because the latent heat release process is accurately adjusted. It has been revealed that LPW assimilation can weaken the intensity sensitivity to MP schemes more than to CP schemes. Skill scores were used to evaluate precipitation forecasts after Hato's landfall. The results indicate that heavy precipitation forecasts are more sensitive to the choice of MP schemes. After LPW assimilation, the equitable threat scores among different results become similar and all forecast skills are increased. In addition, group statistic results with different initial time show the same conclusions.

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Section: Three Lpw Retrieved From Himawari‐8 Ahi Radiance Measurementsmentioning

confidence: 99%

“…The assimilation of precipitable water developments from assimilating total precipitable water (Rakesh et al, ) to the three‐layered precipitable water (LPW) retrieved from AHI. The results with LPW assimilation are comparable or similar to radiance assimilation (Wang et al, ).…”

Section: Introductionmentioning

confidence: 99%

Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

Feng

et al. 2019

JGR Atmospheres

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“…Combined WLS + threshold method and the local training profiles is used for RTTOV GIIRS coefficients in this study, the approach can also be applied to RTTOV development for other geostationary sensors such as AGRI onboard FY‐4 series, Advanced Baseline Imager onboard the GOES‐R series, Advanced Himawari Imager onboard Himawari‐8/‐9, and InfraRed Sounder onboard the future Meteosat Third Generation series, for local weather applications such as assimilating high solution water vapor information from advanced geostationary imagers for improving heavy precipitation forecast (Wang et al, ). It should be noted that although local training profiles show superiority on global training profiles for GIIRS and could benefit regional weather related applications, the global coefficients are still useful for intercalibration between different geostationary IR sensors for consistent climate data record.…”

Section: Summary and Future Workmentioning

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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“…Another important application of the ABI LPW product is to improve LSS forecasts through assimilating high temporal and spatial resolution moisture information into regional and storm‐scale NWP models. Wang et al () found the improvement from assimilation of LPW in storm‐scale NWP model on heavy precipitation forecasts over those from the assimilation of conventional data. Comparisons between IR band radiance assimilation and LPW assimilation show overall similar or comparable impact on precipitation forecasts, but LPW assimilation provides better impact over land than that of radiance assimilation.…”

Section: Applicationsmentioning

confidence: 99%

“…LPWs are also very useful for improving NWP model forecasts. Wang et al () found that assimilation of LPWs from AHI provides improvement on heavy precipitation forecasts of local severe storms (LSS) over those from the assimilation of conventional data, and AHI IR band radiance assimilation and LPW assimilation show overall similar or comparable impact on precipitation forecasts. Recently, Lu et al () found that LPW assimilation reduces the average track error and speeds up tropical cyclone (TC) movement by better adjustment of the atmospheric circulation fields via changing the vertical structure of moisture and temperature profiles.…”

Section: Introductionmentioning

confidence: 99%

Legacy Atmospheric Profiles and Derived Products From GOES‐16: Validation and Applications

Schmit

Lee

et al. 2019

Earth and Space Science

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The Geostationary Operational Environmental Satellite‐R (GOES‐R) series started a new era for the U.S. geostationary satellite observing system. The Advanced Baseline Imager (ABI) onboard the GOES‐R series has fine temporal (30 s to 10 min) and spatial resolutions (0.5–2 km), and 16 spectral bands. However, due to the lack of an infrared sounder, the ABI is used to continue the legacy atmospheric profile (LAP) products that the previous GOES Sounder has, including the legacy atmospheric moisture profile, legacy atmospheric temperature profile, total precipitable water, layered precipitable water, and derived atmospheric stability indices. The ABI LAP retrieval algorithms have been developed under the GOES‐R series Algorithm Working Group (AWG) program funded by the GOES‐R Program Office. The LAP products from GOES‐16 have been operational and validated with a series of reference data sets including radiosonde observations, the Global Positioning System from SuomiNet, the Advanced Microwave Scanning Radiometer 2 total precipitable water measurements, as well as global operational analysis from National Oceanic and Atmospheric Administration and European Centre for Medium‐Range Weather Forecasts models, for almost a year (from 2017 to 2018) to assure the data quality for applications. In addition, the LAP products have been successfully demonstrated at the Hazardous Weather Testbed experiments in the summer of 2017 and the spring of 2018. Both validation results and Hazardous Weather Testbed demonstrations indicate that the GOES‐R series LAP products meet the product requirements and provide added value over NWP short‐range forecasts, especially for middle‐upper tropospheric moisture, in situation awareness and nowcasting.

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Impact of Moisture Information From Advanced Himawari Imager Measurements on Heavy Precipitation Forecasts in a Regional NWP Model

Cited by 24 publications

References 48 publications

Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

Impact of Assimilating Himawari‐8‐Derived Layered Precipitable Water With Varying Cumulus and Microphysics Parameterization Schemes on the Simulation of Typhoon Hato

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

Legacy Atmospheric Profiles and Derived Products From GOES‐16: Validation and Applications

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