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observations. They used a model spatial resolution of 1/12° × 1/12° in order to capture the rapidly varying wave field generated by Hurricane Bonnie, which is much finer than is typically implemented in operational forecasting. For example, the fine-resolution wave model grid for the Gulf of Maine Ocean Observing System (GoMOOS; information online at www.gomoos.org ) is 0.2° × 0.2°. In this study, three modern widely used third-generation spectral wave models are evaluated: (a) the Simulating Waves
observations. They used a model spatial resolution of 1/12° × 1/12° in order to capture the rapidly varying wave field generated by Hurricane Bonnie, which is much finer than is typically implemented in operational forecasting. For example, the fine-resolution wave model grid for the Gulf of Maine Ocean Observing System (GoMOOS; information online at www.gomoos.org ) is 0.2° × 0.2°. In this study, three modern widely used third-generation spectral wave models are evaluated: (a) the Simulating Waves
. This work aims to extend the MPAS model to the application of subseasonal forecasting by evaluating its performance. The motivation for the utility of the MPAS model is due to some of its advantages, e.g., flexible resolution settings, multiple physics parameterization schemes, open-source. This study assessed the forecast skill for week-2 wintertime SAT over the Northern Hemisphere by the MPAS and compared the forecast skill of the MPAS with that by operational forecast systems that participate in
. This work aims to extend the MPAS model to the application of subseasonal forecasting by evaluating its performance. The motivation for the utility of the MPAS model is due to some of its advantages, e.g., flexible resolution settings, multiple physics parameterization schemes, open-source. This study assessed the forecast skill for week-2 wintertime SAT over the Northern Hemisphere by the MPAS and compared the forecast skill of the MPAS with that by operational forecast systems that participate in
accompanied by as much as a 35°C temperature increase over 36 h within the Fort Greely mesonet available via MesoWest. The rapid warming and onset of the downslope windstorm allows for an evaluation of the model’s performance when weather conditions are rapidly changing. Downslope windstorms have been studied extensively at various locales across Alaska ( Murray 1956 ; Colman and Dierking 1992 ; Overland and Bond 1993 ; Hopkins 1994 ; Nance and Colman 2000 ) and in the continental United States
accompanied by as much as a 35°C temperature increase over 36 h within the Fort Greely mesonet available via MesoWest. The rapid warming and onset of the downslope windstorm allows for an evaluation of the model’s performance when weather conditions are rapidly changing. Downslope windstorms have been studied extensively at various locales across Alaska ( Murray 1956 ; Colman and Dierking 1992 ; Overland and Bond 1993 ; Hopkins 1994 ; Nance and Colman 2000 ) and in the continental United States
performance of the WRF than other models, however, may be an artifact of climatologically low wind speed in interior Alaska in general and in June 2005 in particular; the June average wind speed for Fairbanks is 3.3 m s −1 . 5. Fire indices evaluation WRF data and observation derived fire indices do not differ significantly. For all fire indices, the spatial standard deviation increases with time for the predictions and observations because the level of fire danger develops differently at the various
performance of the WRF than other models, however, may be an artifact of climatologically low wind speed in interior Alaska in general and in June 2005 in particular; the June average wind speed for Fairbanks is 3.3 m s −1 . 5. Fire indices evaluation WRF data and observation derived fire indices do not differ significantly. For all fire indices, the spatial standard deviation increases with time for the predictions and observations because the level of fire danger develops differently at the various
1. Introduction The track, intensity, and size of tropical cyclones (TCs) have been used as evaluation parameters in assessing TC forecasts or the performance of TC numerical forecast models since the first attempts were made at forecasting TCs in the Atlantic region in the 1870s ( Sheets 1990 ). For instance, Neumann and Pelissier (1981) analyzed Atlantic tropical cyclone forecast errors in track and intensity, separately. Liu and Xie (2012) used errors in track, intensity, and size to
1. Introduction The track, intensity, and size of tropical cyclones (TCs) have been used as evaluation parameters in assessing TC forecasts or the performance of TC numerical forecast models since the first attempts were made at forecasting TCs in the Atlantic region in the 1870s ( Sheets 1990 ). For instance, Neumann and Pelissier (1981) analyzed Atlantic tropical cyclone forecast errors in track and intensity, separately. Liu and Xie (2012) used errors in track, intensity, and size to
. However, these scores alone can be misleading, especially in high-resolution models. A finescale convective product may show skill as part of a decision process that is not captured by these standard statistics; these common metrics may even show zero skill when calculated. Additional metrics are then needed to provide insights into the evaluation process. Object-oriented methods, also referred to as feature-based approaches, can be used in a supplementary nature to common metrics in an evaluation
. However, these scores alone can be misleading, especially in high-resolution models. A finescale convective product may show skill as part of a decision process that is not captured by these standard statistics; these common metrics may even show zero skill when calculated. Additional metrics are then needed to provide insights into the evaluation process. Object-oriented methods, also referred to as feature-based approaches, can be used in a supplementary nature to common metrics in an evaluation
performance of the National Oceanic and Atmospheric Administration (NOAA) operational Smoke Forecast System from the forecaster point of view. Since the analysis focused on quantifying agreements between forecast and observed smoke plumes, a definition of concentration threshold was required to delineate the plumes. They evaluated model performance using various concentration thresholds, two different satellite products, and two evaluation metrics [figure of merit in space ( Boybeyi et al. 2001 ) and
performance of the National Oceanic and Atmospheric Administration (NOAA) operational Smoke Forecast System from the forecaster point of view. Since the analysis focused on quantifying agreements between forecast and observed smoke plumes, a definition of concentration threshold was required to delineate the plumes. They evaluated model performance using various concentration thresholds, two different satellite products, and two evaluation metrics [figure of merit in space ( Boybeyi et al. 2001 ) and
. 2005 ). The operational requirement for our application is that the wind fields are automatically generated from the observational data of the WegenerNet in near–real time and stored to the WegenerNet archives with a spatial resolution of 100 m × 100 m and a time resolution of 30 min. Furthermore, the model performance of these produced wind fields has to be evaluated for periods with representative weather conditions. Reporting this work, the paper is structured as follows. Section 2 provides a
. 2005 ). The operational requirement for our application is that the wind fields are automatically generated from the observational data of the WegenerNet in near–real time and stored to the WegenerNet archives with a spatial resolution of 100 m × 100 m and a time resolution of 30 min. Furthermore, the model performance of these produced wind fields has to be evaluated for periods with representative weather conditions. Reporting this work, the paper is structured as follows. Section 2 provides a
for the Northern Atlantic (NATL) basin. Nevertheless, the utility of numerical models is still limited for RI forecasts (i.e., having low detection rates and high false alarm rates) and more effort is needed to evaluate and analyze model forecasts to identify the defects of numerical models in RI forecasting and to make further improvements. The existing RI evaluation frameworks focus on the model performance at separate lead times rather than over the entirety of each forecast cycle (typically
for the Northern Atlantic (NATL) basin. Nevertheless, the utility of numerical models is still limited for RI forecasts (i.e., having low detection rates and high false alarm rates) and more effort is needed to evaluate and analyze model forecasts to identify the defects of numerical models in RI forecasting and to make further improvements. The existing RI evaluation frameworks focus on the model performance at separate lead times rather than over the entirety of each forecast cycle (typically
simulate near-surface exchange processes requires careful and thorough evaluation of the model output to identify and correct potential model biases. We focused our investigation on the southeast United States, where the only known evaluation of the HRRR’s performance is a recent study by Wagner et al. (2019) that used observations from the Atmospheric Emitted Radiance Interferometer (AERI; Knuteson et al. 2004 ; Turner and Blumberg 2019 ) installed on the Collaborative Lower Atmosphere Mobile
simulate near-surface exchange processes requires careful and thorough evaluation of the model output to identify and correct potential model biases. We focused our investigation on the southeast United States, where the only known evaluation of the HRRR’s performance is a recent study by Wagner et al. (2019) that used observations from the Atmospheric Emitted Radiance Interferometer (AERI; Knuteson et al. 2004 ; Turner and Blumberg 2019 ) installed on the Collaborative Lower Atmosphere Mobile
