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- Author or Editor: Zhiquan Liu x
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Abstract
Two parallel experiments were designed to evaluate whether assimilating microwave radiances with a cyclic, limited-area ensemble adjustment Kalman filter (EAKF) could improve track, intensity, and precipitation forecasts of Typhoon Morakot (2009). The experiments were configured identically, except that one assimilated microwave radiances and the other did not. Both experiments produced EAKF analyses every 6 h between 1800 UTC 3 August and 1200 UTC 9 August 2009, and the mean analyses initialized 72-h Weather Research and Forecasting model forecasts. Examination of individual forecasts and average error statistics revealed that assimilating microwave radiances ultimately resulted in better intensity forecasts compared to when radiances were withheld. However, radiance assimilation did not substantially impact track forecasts, and the impact on precipitation forecasts was mixed. Overall, net positive results suggest that assimilating microwave radiances with a limited-area EAKF system is beneficial for tropical cyclone prediction, but additional studies are needed.
Abstract
Two parallel experiments were designed to evaluate whether assimilating microwave radiances with a cyclic, limited-area ensemble adjustment Kalman filter (EAKF) could improve track, intensity, and precipitation forecasts of Typhoon Morakot (2009). The experiments were configured identically, except that one assimilated microwave radiances and the other did not. Both experiments produced EAKF analyses every 6 h between 1800 UTC 3 August and 1200 UTC 9 August 2009, and the mean analyses initialized 72-h Weather Research and Forecasting model forecasts. Examination of individual forecasts and average error statistics revealed that assimilating microwave radiances ultimately resulted in better intensity forecasts compared to when radiances were withheld. However, radiance assimilation did not substantially impact track forecasts, and the impact on precipitation forecasts was mixed. Overall, net positive results suggest that assimilating microwave radiances with a limited-area EAKF system is beneficial for tropical cyclone prediction, but additional studies are needed.
Abstract
This study examines the impact of assimilating Microwave Humidity Sounder (MHS) radiances in a limited-area ensemble Kalman filter (EnKF) data assimilation system. Two experiments spanning 11 August–13 September 2008 were run over a domain featuring the Atlantic basin using a 6-h full cycling analysis and forecast system. Deterministic 72-h forecasts were initialized at 0000 and 1200 UTC for a comparison of forecast impact. The two experiments were configured identically with the exception of the inclusion of the MHS radiances (AMHS) in the second to isolate the impacts of the MHS radiance data. The results were verified against several sources, and statistical significance tests indicate the most notable differences are in the midlevel moisture fields. Both configurations were characterized by high moisture biases when compared to the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, also known as ERA-I) specific humidity fields, as well as precipitable water vapor from an observationally based product. However, the AMHS experiment has midlevel moisture fields closer to the ERA-I and observation datasets. When reducing the verification domain to focus on the subtropical and easterly wave regions of the North Atlantic Ocean, larger improvements in midlevel moisture at nearly all lead times is seen in the AMHS simulation. Finally, when considering tropical cyclone forecasts, the AMHS configuration shows improvement in intensity forecasts at several lead times as well as improvements at early to intermediate lead times for minimum sea level pressure forecasts.
Abstract
This study examines the impact of assimilating Microwave Humidity Sounder (MHS) radiances in a limited-area ensemble Kalman filter (EnKF) data assimilation system. Two experiments spanning 11 August–13 September 2008 were run over a domain featuring the Atlantic basin using a 6-h full cycling analysis and forecast system. Deterministic 72-h forecasts were initialized at 0000 and 1200 UTC for a comparison of forecast impact. The two experiments were configured identically with the exception of the inclusion of the MHS radiances (AMHS) in the second to isolate the impacts of the MHS radiance data. The results were verified against several sources, and statistical significance tests indicate the most notable differences are in the midlevel moisture fields. Both configurations were characterized by high moisture biases when compared to the European Centre for Medium-Range Weather Forecasts interim reanalysis (ERA-Interim, also known as ERA-I) specific humidity fields, as well as precipitable water vapor from an observationally based product. However, the AMHS experiment has midlevel moisture fields closer to the ERA-I and observation datasets. When reducing the verification domain to focus on the subtropical and easterly wave regions of the North Atlantic Ocean, larger improvements in midlevel moisture at nearly all lead times is seen in the AMHS simulation. Finally, when considering tropical cyclone forecasts, the AMHS configuration shows improvement in intensity forecasts at several lead times as well as improvements at early to intermediate lead times for minimum sea level pressure forecasts.
Abstract
To improve the wind speed forecasts at turbine locations and at hub height, this study develops the WRFDA system to assimilate the wind speed observations measured on the nacelle of turbines (hereafter referred as turbine wind speed observations) with both 3DVAR and 4DVAR algorithms. Results exhibit that the developed data assimilation (DA) system helps in greatly improving the analysis and the forecast of wind turbine speed. Among three experiments with no cycling DA, with 2-h cycling DA, and with 4-h cycling DA, the last experiment generates the best analysis, improving the averaged forecasts (from T + 9 to T + 24) of wind speed over all wind farms by 32.5% in the bias and 6.3% in the RMSE. After processing the turbine wind speed observations into superobs, even bigger improvements are revealed when validating against either the original turbine wind speed observations or the superobs. Taken the results validated against the superobs as an example, the bias and RMSE of the forecasts (from T + 9 to T + 24) averaged over all wind farms are reduced by 38.8% and 12.0%, respectively. Compared to the best-performed 3DVAR experiment (4-h cycling and superobs), the experiment following the same DA strategy but using 4DVAR algorithm exhibits further improvements, especially for the averaged bias in the forecasts of all wind farms, and the changing amount in the forecasts of the enhanced wind farms. Compared to the control experiment, the 4DVAR experiment reduces the bias and RMSE in the forecasts (from T + 9 to T + 24) by 54.6% (0.66 m s−1) and 12.7% (0.34 m s−1).
Abstract
To improve the wind speed forecasts at turbine locations and at hub height, this study develops the WRFDA system to assimilate the wind speed observations measured on the nacelle of turbines (hereafter referred as turbine wind speed observations) with both 3DVAR and 4DVAR algorithms. Results exhibit that the developed data assimilation (DA) system helps in greatly improving the analysis and the forecast of wind turbine speed. Among three experiments with no cycling DA, with 2-h cycling DA, and with 4-h cycling DA, the last experiment generates the best analysis, improving the averaged forecasts (from T + 9 to T + 24) of wind speed over all wind farms by 32.5% in the bias and 6.3% in the RMSE. After processing the turbine wind speed observations into superobs, even bigger improvements are revealed when validating against either the original turbine wind speed observations or the superobs. Taken the results validated against the superobs as an example, the bias and RMSE of the forecasts (from T + 9 to T + 24) averaged over all wind farms are reduced by 38.8% and 12.0%, respectively. Compared to the best-performed 3DVAR experiment (4-h cycling and superobs), the experiment following the same DA strategy but using 4DVAR algorithm exhibits further improvements, especially for the averaged bias in the forecasts of all wind farms, and the changing amount in the forecasts of the enhanced wind farms. Compared to the control experiment, the 4DVAR experiment reduces the bias and RMSE in the forecasts (from T + 9 to T + 24) by 54.6% (0.66 m s−1) and 12.7% (0.34 m s−1).
