A Statistical Sea-Breeze Prediction Algorithm for Charleston, South Carolina

Frysinger, James R.; Lindner, Bernhard Lee; Brueske, Stephen L.

doi:10.1175/1520-0434(2003)018<0614:asspaf>2.0.co;2

Cited by 12 publications

(14 citation statements)

References 12 publications

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“…() used the kernel approach but not Bayes' rule. The scatter plots in Figure a,b are essentially identical to Frysinger and Lindner, (, figure 3). In that case the aim was to find a proper threshold of the ratio U | U |/ c 2 , such that, for any value below this threshold, most of the non‐occurrences were excluded.…”

Section: Sea Breeze Identification and Predictionsupporting

confidence: 73%

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The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble

Cafaro

Frame

Methven

et al. 2019

Quart J Royal Meteoro Soc

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Dynamical downscaling of ensemble forecasts to convection‐permitting resolutions aims to improve forecast skill by explicitly resolving mesoscale dynamical features. The success of this approach is dependent on the ability of the model to spin up smaller features embedded in the larger‐scale flow and provide more local information than could be inferred from knowledge of the climatological response to the large‐scale flow alone. Here we test whether such additional information is obtained from the Met Office Global and Regional Ensemble Prediction Systems (MOGREPS) for the sea‐breeze phenomenon which is resolved properly only at convection‐permitting resolutions but is driven by large‐sale conditions that are well represented in the global driving model. The sea breeze is detected using a new automatic tracking algorithm suitable for use in convective‐scale forecast data. The skill of probabilistic forecasts of sea‐breeze occurrence from the high‐resolution ensemble is compared to that of a Bayesian forecast trained on paired high/low‐resolution ensemble members. This creates a statistical forecast of the high‐resolution ensemble member given knowledge of the global forecast ensemble alone. The aim of this paper is twofold: firstly to assess whether the information about sea‐breeze occurrence is encoded in a few large‐scale parameters and can be forecast by a statistical method, and secondly to estimate what information is gained by running the high‐resolution forecast beyond that which is contained in these large‐scale flow parameters. Comparison of the two forecasting methods using a variety of verification methods all lead to the same conclusion: although both the Bayesian forecast and convection‐permitting ensemble provide information about sea‐breeze occurrence, the convection‐permitting ensemble is significantly more able to discriminate between sea‐breeze events and non‐events for all lead times.

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Section: Sea Breeze Identification and Predictionsupporting

confidence: 73%

“…This has been done in past studies using mainly observational data (e.g. Frysinger and Lindner, 2003). In this study, in order to train the Bayesian model, we are using MOGREPS-UK data.…”

Section: Sea Breeze Prediction Using Mogreps-gmentioning

confidence: 99%

The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble

Cafaro

Frame

Methven

et al. 2019

Quart J Royal Meteoro Soc

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“…The five WXT collected temperature, wind speed, wind direction, pressure, relative humidity, and precipitation every 30 s, each coupled to a GPS unit (labeled PSB) with storage and transmission via cellular modem to the Suominet GPS data processing center (Ware et al 2000). A sea-breeze index (SBI) is calculated from the methods of Frysinger et al (2003) as SBI 5 (U 2 /T s 2 T a ) 5 (2gh/T s ), where U is the onshore synoptic wind speed measured at WXT-3 and T s 2 T a is the sea-to-air temperature gradient consistent with sensible heat flux. Instruments were placed along the coast near Mayaguez (Figure 1 and Table 1) and up to 5 km inland in the Añasco River valley to study the progression of the sea-breeze front.…”

Section: Observations and Processingmentioning

confidence: 99%

Topographic–Thermal Circulations and GPS-Measured Moisture Variability around Mayaguez, Puerto Rico

et al. 2015

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To investigate topographic-thermal circulations and the associated moisture variability over western Puerto Rico, field data were collected from 15 to 31 March 2011. Surface meteorological instruments and groundbased GPS receivers measured the circulation and precipitable water with high spatial and temporal resolution, and the Weather Research and Forecasting (WRF) Model was used to simulate the mesoscale flow at 1-km resolution. A westerly onshore flow of ;4 m s 21 over Mayaguez Bay was observed on many days, due to an interaction between thermally driven [38C (10 km) 21 ] seabreeze circulation and an island wake comprised of twin gyres. The thermally driven sea breeze occurred only when easterly synoptic winds favorably oriented the gyres with respect to the coast. Moisture associated with onshore flow was characterized by GPS measured precipitable water (PW). There is diurnal cycling of PW . 3 cm over the west coast during periods of onshore flow. The WRF Model tends to overestimate PW on the west side of the island, suggesting evapotranspiration as a process needing further attention. Fluctuations of PW affect local rainfall in times of convective instability.

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“…Other studies have used satellite imagery to detect the line of SB convective clouds along the coast (e.g., Planchon, Damato, Dubreuil and Gouery, 2006), a method that detects only those SBs that produce clouds. Another more comprehensive method of SB detection is to analyze wind, temperature and moisture shifts using station data (Koch and Ray, 1997;Hughes, 2011;Frysinger, Lindner and Brueske, 2003). This method would likely result in a higher frequency of SB events because SB thin lines are typically only visible along strong SB fronts.…”

Section: Sb Detectionmentioning

confidence: 99%

Influence of the North Atlantic Subtropical High on wet and dry sea-breeze events in North Carolina, United States

Luchetti

Ferreira

Rickenbach

et al. 2017

Ingeo

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The sea-breeze (SB) is an important source of summertime precipitation in North Carolina (NC, southeast United States). However, not all SB events produce precipitation. A climatology of wet and dry SB events in NC is used to investigate the conditions that are conducive to precipitation associated with the sea breeze.Radar imagery was used to detect 88 SB events that occurred along the NC coast between MaySeptember of 2009-2012. The majority (85%) of SB events occurred during offshore flow (53%) or during flow that was parallel to the coast (22%). SB events were separated into dry (53%) and wet (47%) events and differences in the dynamic and thermodynamic parameters of the environment in which they formed were analyzed. Significant differences in dynamic and thermodynamic conditions were found. SB dry events occurred under stronger winds (6.00 ± 2.36 ms -1 ) than SB wet events (4.02 ± 2.16 ms -1 ). Moreover, during SB wet events larger values of convective available potential energy and lower values of convective inhibition were present, conditions that favor precipitation. Overall, the SB wet events accounted for 20-30% of the May-September precipitation along the NC coastal region. The position of the North Atlantic Subtropical High (NASH) controls both moisture availability and winds along the NC coast, thus providing a synoptic-scale control mechanism for SB precipitation. In particular, it was shown that when the NASH western ridge is located along the southeast coast of the United States, it causes a moist southwesterly flow along the NC coast that may favor the occurrence of SB wet events.Keywords: Sea breeze; precipitation; North Carolina; North Atlantic Subtropical High; wind direction; wind speed. Luchetti, N.T., Nieto Ferreira, R. Rickenbach, T.M., Nissenbaum, M.R. and McAuliffe J.D.Investigaciones Geográficas, nº 68, pp. 09 -25. ResumenLa brisa marina (BM) es una importante fuente de precipitación de verano en Carolina del Norte (NC en su sigla en inglés), sudeste de Estados Unidos. Sin embargo, no todos los eventos de BM producen precipitación. En este trabajo se utiliza una climatología de eventos de BM lluviosos y secos en NC para investigar las condiciones que conducen a la precipitación.Se utilizaron imágenes de radar para detectar 88 eventos de BM ocurridos a lo largo de la costa NC entre mayo y septiembre de 2009 a 2012. La mayoría (85%) de los eventos de BM ocurrieron durante períodos de viento hacia el mar (53%) o viento paralelo a la costa (22%). Los eventos BM se separaron en eventos secos (53%) y lluviosos (47%) y se analizaron las diferencias en los parámetros dinámicos y termodinámicos del entorno en el que se formaron. Se encontraron diferencias significativas en las condiciones dinámicas y termodinámicas. Eventos de BM secos ocurrieron bajo vientos más fuertes (6,00 ± 2,36 ms-1) que los eventos de BM lluviosos (4,02 ± 2,16 ms-1). Las BM lluviosas ocurrieron bajo valores de energía potencial convectiva disponible más altos y valores del parámetro de inhibición convectiva más ...

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A Statistical Sea-Breeze Prediction Algorithm for Charleston, South Carolina

Cited by 12 publications

References 12 publications

The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble

The added value of convection‐permitting ensemble forecasts of sea breeze compared to a Bayesian forecast driven by the global ensemble

Topographic–Thermal Circulations and GPS-Measured Moisture Variability around Mayaguez, Puerto Rico

Influence of the North Atlantic Subtropical High on wet and dry sea-breeze events in North Carolina, United States

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