Impact of assimilating GOES imager clear‐sky radiance with a rapid refresh assimilation system for convection‐permitting forecast over Mexico

Yang, Chun; Liu, Zhiquan; Gao, Feng; Childs, Peter P.; Min, Jinzhong

doi:10.1002/2016jd026436

Cited by 36 publications

(15 citation statements)

References 49 publications

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“…We focus on assimilating AHI radiances at convective scales in this study as a follow on work of Yang et al (), who assimilated GOES imager radiances based on an operational hybrid 3‐D ensemble‐variational DA system using a 4 km configuration over a Mexico domain. In contrast with Yang et al (), this work is based on the operational 3DVar system of the Northeast China regional NWP center operated by the Liaoning Meteorological Bureau of China Meteorological Administration. Since there is much more land coverage in our domain, the radiances from three AHI water vapor channels (6.2, 6.9, and 7.3 μm) are chosen for assimilation because the other infrared channels are sensitive to land surface emission.…”

Section: Introductionmentioning

confidence: 99%

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Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for “7.19” Severe Storm Over North China

Wang

Liu

Yang³

et al. 2018

JGR Atmospheres

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Himawari‐8 is the first launched and operational new‐generation geostationary meteorological satellite. The Advanced Himawari Imager (AHI) on board Himawari‐8 provides continuous high‐resolution observations of severe weather phenomena in space and time. In this study, the capability to assimilate AHI radiances has been developed within the Weather Research and Forecasting (WRF) model's data assimilation system. As the first attempt to assimilate AHI using WRF data assimilation at convective scales, the added value of hourly AHI clear‐sky radiances from three water vapor channels on convection‐permitting (3 km) analyses and forecasts of the “7.19” severe rainstorm that occurred over north China during 18–21 July 2016 was investigated. Analyses were produced hourly, and 24 h forecasts were produced every 6 h. The results showed that improved wind and humidity fields were obtained in analyses and forecasts verified against conventional observations after assimilating AHI water vapor radiances when compared to the control experiment which assimilated only conventional observations. It was also found that the assimilation of AHI water vapor radiances had a clearly positive impact on the rainfall forecast for the first 6 h lead time, especially for heavy rainfall exceeding 100 mm when verified against the observed rainfall. Furthermore, the horizontal and vertical distribution of features in the moisture fields were improved after assimilating AHI water vapor radiances, eventually contributing to a better forecast of the severe rainstorm.

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Section: Introductionmentioning

confidence: 99%

“…The results showed that SEVIRI radiances had a positive impact on the very short range forecast. Yang et al () assimilated GOES imager radiances using the Weather Research and Forecasting (WRF) DA system (Barker et al, ) for convection‐permitting (4 km) forecasting over Mexico and obtained some significant positive results.…”

Section: Introductionmentioning

confidence: 99%

Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for “7.19” Severe Storm Over North China

Wang

Liu

Yang³

et al. 2018

JGR Atmospheres

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“…But for the hourly rapid update system, the 3-hourly global ensemble is not enough. In this situation, we can utilize the interpolation of the 3-hourly global ensemble to the hourly global ensemble as Yang et al (2017) [32] did, the time-expanded ensembles as Zhao et al 2015 [19] did, or a time-lagged ensemble as Wang et al 2017 [21] did. Furthermore, as the resolution of global ensemble in time and space will be increased in the future, the augmented global error covariance can be used for the rapid updated regional convective-permitting NWP.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

Efficient Regional Hybrid Ensemble-Variational Data Assimilation using the Global-Ensemble-Model-Augmented Error Covariance for Numerical Weather Prediction over Eastern China

2020

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An efficient regional hybrid ensemble-variational (EnVar) data assimilation method using the global-ensemble-model-augmented error covariance is proposed and preliminarily tested in this study. This method uses the global ensemble error covariance as the complementary low-resolution regional ensemble error covariance. The high-resolution dynamic ensemble mean is used as the first guess in hybrid EnVar and then re-centered to the updated high-resolution dynamic ensemble perturbations after minimization analysis. In this study, the proposed method is implemented into the Weather Research and Forecasting Model’s (WRF) data assimilation system coupled with the ensemble transform Kalman filter (ETKF) and preliminarily tested for numerical weather prediction during the Mei-Yu season over eastern China. It is found that the experiment containing fewer regional dynamic ensemble members but augmented with global ensemble error covariance obtains similar results to the experiment containing many more regional dynamic ensemble members. However, the former experiment only takes up one third of the latter experiment’s computational cost. The method proposed in this study also outperforms the 3DVar, hybrid EnVar using the pure global ensemble error covariance, as well as the hybrid EnVar using regional ETKF ensemble with a smaller size. The method proposed in this paper effectively combines the contributions of the ensemble error covariance from both the global and the regional models to produce better initial conditions for the regional WRF data assimilation system.

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“…Results show a positive impact for almost all upper-air variables; the main improvements are obtained for geopotential height and humidity [29][30][31]. The impact of assimilating Geostationary Operational Environmental Satellite (GOES) imager radiances on the analysis and forecast of a convective process over Mexico was assessed for the first time using a rapid refresh assimilation system with a convection permitting model setting [32]. Improved humidity and temperature analysis and significant standard deviation reductions were produced when the assimilation is activated [32].…”

Section: Introductionmentioning

confidence: 99%

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

et al. 2020

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Solar power generation is highly fluctuating due to its dependence on atmospheric conditions. The integration of this variable resource into the energy supply system requires reliable predictions of the expected power production as a basis for management and operation strategies. This is one of the goals of the Solar Cloud project, funded by the Italian Ministry of Economic Development (MISE)—to provide detailed forecasts of solar irradiance variables to operators and organizations operating in the solar energy industry. The Institute of Methodologies for Environmental Analysis of the National Research Council (IMAA-CNR), participating to the project, implemented an operational chain that provides forecasts of all the solar irradiance variables at high temporal and horizontal resolution using the numerical weather prediction Advanced Research Weather Research and Forecasting (WRF-ARW) Solar version 3.8.1 released by the National Center for Atmospheric Research (NCAR) in August 2016. With the aim of improving the forecast of solar irradiance, the three-dimensional (3D-Var) data assimilation was tested to assimilate radiances from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) aboard the Meteosat Second Generation (MSG) geostationary satellite into WRF Solar. To quantify the impact, the model output is compared against observational data. Hourly Global Horizontal Irradiance (GHI) is compared with ground-based observations from Regional Agency for the Protection of the Environment (ARPA) and with MSG Shortwave Solar Irradiance estimations, while WRF Solar cloud coverage is compared with Cloud Mask by MSG. A preliminary test has been performed in clear sky conditions to assess the capability of the model to reproduce the diurnal cycle of the solar irradiance. The statistical scores for clear sky conditions show a positive performance of the model with values comparable to the instrument uncertainty and a correlation of 0.995. For cloudy sky, the solar irradiance and the cloud cover are better simulated when the SEVIRI radiances are assimilated, especially in the short range of the simulation. For the cloud cover, the Mean Bias Error one hour after the assimilation time is reduced from 41.62 to 20.29 W/m2 when the assimilation is activated. Although only two case studies are considered here, the results indicate that the assimilation of SEVIRI radiance improves the performance of WRF Solar especially in the first 3 hour forecast.

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Impact of assimilating GOES imager clear‐sky radiance with a rapid refresh assimilation system for convection‐permitting forecast over Mexico

Cited by 36 publications

References 49 publications

Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for “7.19” Severe Storm Over North China

Added Value of Assimilating Himawari‐8 AHI Water Vapor Radiances on Analyses and Forecasts for “7.19” Severe Storm Over North China

Efficient Regional Hybrid Ensemble-Variational Data Assimilation using the Global-Ensemble-Model-Augmented Error Covariance for Numerical Weather Prediction over Eastern China

3D-VAR Data Assimilation of SEVIRI Radiances for the Prediction of Solar Irradiance in Italy Using WRF Solar Mesoscale Model—Preliminary Results

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