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- Author or Editor: Teddy Holt x
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Abstract
This paper examines the sensitivity of short-term forecasts of the western North Pacific subtropical high (WNPSH) and rainfall to sea surface temperature (SST) uncertainty using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). A comparison of analyzed SSTs with satellite observations of SST indicates that SST analysis errors are particularly pronounced on horizontal scales from 100 to 200 km, similar to the mesoscale eddy scales in the Kuroshio region. Since significant oceanic variations occur on these scales, it is of interest to examine the effects of representing this small-scale uncertainty with random, scale-dependent perturbations. An SST ensemble perturbation generation technique is used here that enables temporal and spatial correlations to be controlled and produces initial SST fields comparable to satellite observations. The atmospheric model develops large uncertainty in the Korea and Japan area due to the fluctuation in the horizontal pressure gradient caused by the location of the WNPSH. This, in turn, increases the variance of the low-level jet (LLJ) over southeast China, resulting in large differences in the moist transport flux from the tropical ocean and subsequent rainfall. Validation using bin-mean statistics shows that the ensemble forecast with the perturbed SST better distinguishes large forecast error variance from small forecast error variance. The results suggest that using the SST perturbation as a proxy for the ocean ensemble in a coupled atmosphere and ocean ensemble system is feasible and computationally efficient.
Abstract
This paper examines the sensitivity of short-term forecasts of the western North Pacific subtropical high (WNPSH) and rainfall to sea surface temperature (SST) uncertainty using the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). A comparison of analyzed SSTs with satellite observations of SST indicates that SST analysis errors are particularly pronounced on horizontal scales from 100 to 200 km, similar to the mesoscale eddy scales in the Kuroshio region. Since significant oceanic variations occur on these scales, it is of interest to examine the effects of representing this small-scale uncertainty with random, scale-dependent perturbations. An SST ensemble perturbation generation technique is used here that enables temporal and spatial correlations to be controlled and produces initial SST fields comparable to satellite observations. The atmospheric model develops large uncertainty in the Korea and Japan area due to the fluctuation in the horizontal pressure gradient caused by the location of the WNPSH. This, in turn, increases the variance of the low-level jet (LLJ) over southeast China, resulting in large differences in the moist transport flux from the tropical ocean and subsequent rainfall. Validation using bin-mean statistics shows that the ensemble forecast with the perturbed SST better distinguishes large forecast error variance from small forecast error variance. The results suggest that using the SST perturbation as a proxy for the ocean ensemble in a coupled atmosphere and ocean ensemble system is feasible and computationally efficient.
Abstract
An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.
Abstract
An ensemble Kalman filter (EnKF) has been adopted and implemented at the Naval Research Laboratory (NRL) for mesoscale and storm-scale data assimilation to study the impact of ensemble assimilation of high-resolution observations, including those from Doppler radars, on storm prediction. The system has been improved during its implementation at NRL to further enhance its capability of assimilating various types of meteorological data. A parallel algorithm was also developed to increase the system’s computational efficiency on multiprocessor computers. The EnKF has been integrated into the NRL mesoscale data assimilation system and extensively tested to ensure that the system works appropriately with new observational data stream and forecast systems. An innovative procedure was developed to evaluate the impact of assimilated observations on ensemble analyses with no need to exclude any observations for independent validation (as required by the conventional evaluation based on data-denying experiments). The procedure was employed in this study to examine the impacts of ensemble size and localization on data assimilation and the results reveal a very interesting relationship between the ensemble size and the localization length scale. All the tests conducted in this study demonstrate the capabilities of the EnKF as a research tool for mesoscale and storm-scale data assimilation with potential operational applications.
Abstract
Two extreme heat events impacting the New York City (NYC), New York, metropolitan region during 7–10 June and 21–24 July 2011 are examined in detail using a combination of models and observations. The U.S. Navy's Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) produces real-time forecasts across the region on a 1-km resolution grid and employs an urban canopy parameterization to account for the influence of the city on the atmosphere. Forecasts from the National Weather Service's 12-km resolution North American Mesoscale (NAM) implementation of the Weather Research and Forecasting (WRF) model are also examined. The accuracy of the forecasts is evaluated using a land- and coastline-based observation network. Observed temperatures reached 39°C or more at central urban sites over several days and remained high overnight due to urban heat island (UHI) effects, with a typical nighttime urban–rural temperature difference of 4°–5°C. Examining model performance broadly over both heat events and 27 sites, COAMPS has temperature RMS errors averaging 1.9°C, while NAM has RMSEs of 2.5°C. COAMPS high-resolution wind and temperature predictions captured key features of the observations. For example, during the early summer June heat event, the Long Island south shore coastline experienced a more pronounced sea breeze than was observed for the July heat wave.
Abstract
Two extreme heat events impacting the New York City (NYC), New York, metropolitan region during 7–10 June and 21–24 July 2011 are examined in detail using a combination of models and observations. The U.S. Navy's Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) produces real-time forecasts across the region on a 1-km resolution grid and employs an urban canopy parameterization to account for the influence of the city on the atmosphere. Forecasts from the National Weather Service's 12-km resolution North American Mesoscale (NAM) implementation of the Weather Research and Forecasting (WRF) model are also examined. The accuracy of the forecasts is evaluated using a land- and coastline-based observation network. Observed temperatures reached 39°C or more at central urban sites over several days and remained high overnight due to urban heat island (UHI) effects, with a typical nighttime urban–rural temperature difference of 4°–5°C. Examining model performance broadly over both heat events and 27 sites, COAMPS has temperature RMS errors averaging 1.9°C, while NAM has RMSEs of 2.5°C. COAMPS high-resolution wind and temperature predictions captured key features of the observations. For example, during the early summer June heat event, the Long Island south shore coastline experienced a more pronounced sea breeze than was observed for the July heat wave.
Abstract
Dust storms are a significant weather phenomenon in the Iraq region in winter and spring. Real-time dust forecasting using the U.S. Navy’s Coupled Ocean–Atmospheric Mesoscale Prediction System (COAMPS) with an in-line dust aerosol model was conducted for Operation Iraqi Freedom (OIF) in March and April 2003. Daily forecasts of dust mass concentration, visibility, and optical depth were produced out to 72 h on nested grids of 9-, 27-, and 81-km resolution in two-way nest interaction. In this paper, the model is described, as are examples of its application during OIF. The model performance is evaluated using ground weather reports, visibility observations, and enhanced satellite retrievals. The comparison of the model forecasts with observations for the severe dust storms of OIF shows that COAMPS predicted the arrival and retreat of the major dust events within 2 h. In most cases, COAMPS predicted the intensity (reduction in visibility) of storms with an error of less than 1 km. The forecasts of the spatial distribution of dust fronts and dust plumes were consistent with those seen in the satellite images and the corresponding cold front observations. A statistical analysis of dust-related visibility for the OIF period reveals that COAMPS generates higher bias, rms, and relative errors at the stations having high frequencies of dust storms and near the source areas. The calculation of forecast accuracy shows that COAMPS achieved a probability of dust detection of 50%–90% and a threat score of 0.3–0.55 at the stations with frequent dust storms. Overall, the model predicted more than 85% of the observed dust and nondust weather events at the stations used in the verification for the OIF period. Comparisons of the forecast rates and statistical errors for the forecasts of different lengths (12–72 h) for both dust and dynamics fields during the strong dust storm of 26 March revealed little dependence of model accuracy on forecast length, implying that the successive COAMPS forecasts were consistent for the severest OIF dust event.
Abstract
Dust storms are a significant weather phenomenon in the Iraq region in winter and spring. Real-time dust forecasting using the U.S. Navy’s Coupled Ocean–Atmospheric Mesoscale Prediction System (COAMPS) with an in-line dust aerosol model was conducted for Operation Iraqi Freedom (OIF) in March and April 2003. Daily forecasts of dust mass concentration, visibility, and optical depth were produced out to 72 h on nested grids of 9-, 27-, and 81-km resolution in two-way nest interaction. In this paper, the model is described, as are examples of its application during OIF. The model performance is evaluated using ground weather reports, visibility observations, and enhanced satellite retrievals. The comparison of the model forecasts with observations for the severe dust storms of OIF shows that COAMPS predicted the arrival and retreat of the major dust events within 2 h. In most cases, COAMPS predicted the intensity (reduction in visibility) of storms with an error of less than 1 km. The forecasts of the spatial distribution of dust fronts and dust plumes were consistent with those seen in the satellite images and the corresponding cold front observations. A statistical analysis of dust-related visibility for the OIF period reveals that COAMPS generates higher bias, rms, and relative errors at the stations having high frequencies of dust storms and near the source areas. The calculation of forecast accuracy shows that COAMPS achieved a probability of dust detection of 50%–90% and a threat score of 0.3–0.55 at the stations with frequent dust storms. Overall, the model predicted more than 85% of the observed dust and nondust weather events at the stations used in the verification for the OIF period. Comparisons of the forecast rates and statistical errors for the forecasts of different lengths (12–72 h) for both dust and dynamics fields during the strong dust storm of 26 March revealed little dependence of model accuracy on forecast length, implying that the successive COAMPS forecasts were consistent for the severest OIF dust event.
