Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model

Lim, Agnes H. N.; Nebuda, Sharon; Jung, James A.; Daniels, Jaime; Bailey, Andrew; Bresky, Wayne; Bi, Li; Mehra, Avichal

doi:10.3390/rs14133068

Cited by 3 publications

(2 citation statements)

References 22 publications

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“…The HWRF model has specific physical parameterization schemes and uses an advanced vortex initialization. However, numerous studies have focused on assimilating atmospheric motion vectors [20][21][22][23][24], infrared observations [25] or MW observations [26] into the HWRF model. All these methods showed some positive impacts on the resulting typhoon predictions.…”

Section: Introductionmentioning

confidence: 99%

Impacts of the All-Sky Assimilation of FY-3C and FY-3D MWHS-2 Radiances on Analyses and Forecasts of Typhoon Hagupit

Chen

Xian

et al. 2023

Remote Sensing

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With the Microwave Humidity Sounder-2 (MWHS-2)/Fengyun (FY)-3D in operation, this is the first study to evaluate the impact of a joint assimilation of MWHS-2 radiances under all-sky conditions from both the FY-3C and FY-3D satellites on typhoon forecasting within regional areas. In this study, Typhoon Hagupit in 2020 was chosen to investigate the impacts of assimilating MWHS-2 radiances; the forecasting performances of the joint assimilation method were slightly better than the experiments assimilating MWHS-2 observations from FY-3C or FY-3D only, and the results of the latter two experiments were comparable, especially in terms of the landfall location of Hagupit. With additional assimilated cloud- and precipitation-affected MWHS-2 observations, improved typhoon track and intensity forecasts as well as forecasts of the precipitation caused by Hagupit were achieved due to the improved analyses of relative humidity, temperature and wind fields around Hagupit compared to the clear-sky assimilation experiments. In addition, the channel-selection scheme evidently affected the forecasting performance; that is, the radiances from the MWHS-2 118 GHz and 183 GHz channels provided opposite results in terms of the Hagupit track, and this finding needs further investigation in the future.

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Section: Introductionmentioning

confidence: 99%

Impacts of the All-Sky Assimilation of FY-3C and FY-3D MWHS-2 Radiances on Analyses and Forecasts of Typhoon Hagupit

Chen

Xian

et al. 2023

Remote Sensing

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“…Zheng J et al [18] showed that the AIRS-retrieved temperature profile played a major role in improving the track forecasts of Hurricane Ike and Hurricane Irene. Lim A H N et al [19] proved that GOES-16 and -17 atmospheric motion vectors are beneficial for improving tropical cyclone forecasting, including track error, intensity error, minimum central pressure error, and storm size. Lee Y et al [20] showed that latent heating from GOES-16 has positive impacts in terms of improving the forecast.…”

Section: Introductionmentioning

confidence: 99%

Application of GOES-16 Atmospheric Temperature-Profile Data Assimilation in a Hurricane Forecast

Qian,

Bao,

Liu

et al. 2023

Atmosphere

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This paper selects the case of the Atlantic hurricane “Michael” in 2018 to evaluate the accuracy of the GOES-16 atmospheric temperature profile during the hurricane and its effect on forecasting. Based on the weather research and forecasting (WRF) model, the assimilation of GOES-16 atmospheric temperature-profile products was achieved by using three-dimensional variational (3DVar) and the ensemble transform Kalman filter/three-dimensional variational (ETKF/3DVAR) hybrid system (Hybrid) systems. And the impact of geostationary satellite GOES-16 atmospheric temperature-profile data assimilation on a hurricane forecast is evaluated. The results show that, during the hurricane, the root mean square errors of the GOES-16 atmospheric temperature profile are all within 2 k at the height of 200–1000 hPa, and the quality of the data is generally good. Assimilating the GOES-16 atmospheric temperature-profile data can indeed effectively improve the analysis increment and improve the prediction results. The assimilation increment obtained by the hybrid system has obvious “flow-dependent” characteristics, which can reasonably improve the initial field of the model. Its temperature increment has an obvious spiral structure, which is in line with the characteristics of the hurricane, and the adjustment of the wind field and geopotential height field is also more beneficial to the development of the hurricane. It has a positive impact on the forecast of track, intensity, and precipitation, and the hybrid system is improved more obviously. In addition, from the RMSE of the analysis field and the forecast field relative to the observation data of different elements, the hybrid system is superior to the 3DVar system.

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Potential Improvement of GK2A Clear-Sky Atmospheric Motion Vectors Using the Convolutional Neural Network Model

Choi,

Song

et al. 2024

Asia-Pac J Atmos Sci

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In this study, we propose a new approach to improve the accuracy of the horizontal atmospheric motion vector (AMV) in cloud-free skies and its forecasting. We adapted the optical flow of the convolutional neural network (CNN) framework model using two 10-min interval infrared images at water vapor channels (centered at 6.3, 7.0, and 7.3 $$\mu m$$ μ m ) from the Korean geostationary satellite GEO-KOMPSAT-2A (GK2A). Since all pixels had seamless AMVs calculated by CNN (CNN AMVs), we could also predict AMVs using the linear regression method. The tracking performance of the CNN-based algorithm was validated using AMVs retrieved from GK2A (GK2A AMVs) by estimating the difference between those values and the ECMWF (European Centre for Medium-Range Weather Forecasts) Reanalysis v5 (ERA5) wind data over Korea in 2022. CNN AMVs showed similar or better root-mean-square vector differences (RMSVDs) than GK2A AMVs (12.33–12.86 vs. 15.89–19.96 m/s). The RMSVDs of the forecasted AMVs were 2.74, 2.95, 3.41, and 4.79 m/s at lead times of 10, 20, 30, and 60 min, respectively. Consequently, our method showed higher accuracy for tracking motion in the production of AMVs and succeeded in forecasting AMVs. We expect that such potential improvements in computational accuracy for operational GK2A AMVs will contribute to increased accuracy when forecasting meteorological phenomena related to wind.

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Optimizing the Assimilation of the GOES-16/-17 Atmospheric Motion Vectors in the Hurricane Weather Forecasting (HWRF) Model

Cited by 3 publications

References 22 publications

Impacts of the All-Sky Assimilation of FY-3C and FY-3D MWHS-2 Radiances on Analyses and Forecasts of Typhoon Hagupit

Impacts of the All-Sky Assimilation of FY-3C and FY-3D MWHS-2 Radiances on Analyses and Forecasts of Typhoon Hagupit

Application of GOES-16 Atmospheric Temperature-Profile Data Assimilation in a Hurricane Forecast

Potential Improvement of GK2A Clear-Sky Atmospheric Motion Vectors Using the Convolutional Neural Network Model

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