The Ensemble of Tropical Cyclone Track Forecasting Models in the North Atlantic Basin (1976–2000)

Aberson, Sim D.

doi:10.1175/1520-0477(2001)082<0000:teotct>2.3.co;2

Cited by 43 publications

(32 citation statements)

References 20 publications

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“…With the advent of high-performance computing, advanced highresolution mesoscale models are employed to simulate the above processes for better prediction of tropical cyclones (e.g. Chen et al, 1995;Liu et al, 1997;Kurihara et al, 1998;Aberson, 2001;Wang, 2001;Krishnamurti, 2005;Braun et al, 2006;Fierro et al, 2009;Smith and Thomsen, 2010;Nolan et al, 2009;Gentry and Lackmann, 2010;among others).…”

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confidence: 99%

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

et al. 2015

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Abstract. The tropical cyclone (TC) track and intensity predictions over Bay of Bengal (BOB) using the Advanced Research Weather Research and Forecasting (ARW) model are evaluated for a number of data assimilation experiments using various types of data. Eight cyclones that made landfall along the east coast of India during 2008-2013 were simulated. Numerical experiments included a control run (CTL) using the National Centers for Environmental Prediction (NCEP) 3-hourly 0.5 × 0.5 • resolution Global Forecasting System (GFS) analysis as the initial condition, and a series of cycling mode variational assimilation experiments with Weather Research and Forecasting (WRF) data assimilation (WRFDA) system using NCEP global PrepBUFR observations (VARPREP), Atmospheric Motion Vectors (VARAMV), Advanced Microwave Sounding Unit (AMSU) A and B radiances (VARRAD) and a combination of PrepBUFR and RAD (VARPREP+RAD). The impact of different observations is investigated in detail in a case of the strongest TC, Phailin, for intensity, track and structure parameters, and finally also on a larger set of cyclones. The results show that the assimilation of AMSU radiances and Atmospheric Motion Vectors (AMV) improved the intensity and track predictions to a certain extent and the use of operationally available NCEP PrepBUFR data which contains both conventional and satellite observations produced larger impacts leading to improvements in track and intensity forecasts. The forecast improvements are found to be associated with changes in pressure, wind, temperature and humidity distributions in the initial conditions after data assimilation. The assimilation of mass (radiance) and wind (AMV) data showed different impacts. While the motion vectors mainly influenced the track predictions, the radiance data merely influenced forecast intensity. Of various experiments, the VARPREP produced the largest impact with mean errors (India Meteorological Department (IMD) observations less the model values) of 78, 129, 166, 210 km in the vector track position, 10.3, 5.8, 4.8, 9.0 hPa deeper than IMD data in central sea level pressure (CSLP) and 10.8, 3.9, −0.2, 2.3 m s −1 stronger than IMD data in maximum surface winds (MSW) for 24, 48, 72, 96 h forecasts respectively. An improvement of about 3-36 % in track, 6-63 % in CSLP, 26-103 % in MSW and 11-223 % in the radius of maximum winds in 24-96 h lead time forecasts are found with VARPREP over CTL, suggesting the advantages of assimilation of operationally available PrepBUFR data for cyclone predictions. The better predictions with PrepBUFR could be due to quality-controlled observations in addition to containing different types of data (conventional, satellite) covering an effectively larger area. The performance degradation of VARPREP+RAD with the assimilation of all available observations over the domain after 72 h could be due to poor area coverage and bias in the radiance data.

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Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

et al. 2015

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“…The NOAA National Hurricane Center (NHC), a part of the National Centers for Environmental Prediction (NCEP), is responsible for forecasting TCs in the Atlantic and east Pacific basins, while NCEP's Environmental Modeling Center (EMC) develops the numerical model guidance for the forecasters. With support from NOAA's Hurricane Research Division (HRD) and others in the research community, continual progress has been made in improving forecasts of the TC track over the past 30 years (Franklin et al 2003a;Aberson 2001). Advancements in state-of-the-art global and regional modeling systems at EMC and other operational numerical weather prediction centers have led to improvements in track skill over the past three decades, including a significant acceleration in improvements over the past decade.…”

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The Intensity Forecasting Experiment: A NOAA Multiyear Field Program for Improving Tropical Cyclone Intensity Forecasts

Rogers

Aberson

Black

et al. 2006

Bulletin of the American Meteorological Society

180

115

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In 2005, NOAA's Hurricane Research Division (HRD), part of the Atlantic Oceanographic and Meteorological Laboratory, began a multiyear experiment called the Intensity Forecasting Experiment (IFEX). By emphasizing a partnership among NOAA's HRD, Environmental Modeling Center (EMC), National Hurricane Center (NHC), Aircraft Operations Center (AOC), and National Environmental Satellite Data Information Service (NESDIS), IFEX represents a new approach for conducting hurricane field program operations. IFEX is intended to improve the prediction of tropical cyclone (TC) intensity change by 1) collecting observations that span the TC life cycle in a variety of environments; 2) developing and refining measurement technologies that provide improved real-time monitoring of TC intensity, structure, and environment; and 3) improving the understanding of the physical processes important in intensity change for a TC at all stages of its life cycle. This paper presents a summary of the accomplishments of IFEX during the 2005 hurricane season. New and refined technologies for measuring such fields as surface and three-dimensional wind fields, and the use of unmanned aerial vehicles, were achieved in a variety of field experiments that spanned the life cycle of several tropical cyclones, from formation and early organization to peak intensity and subsequent landfall or extratropical transition. Partnerships with other experiments during 2005 also expanded the spatial and temporal coverage of the data collected in 2005. A brief discussion of the plans for IFEX in 2006 is also provided.

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“…Tropical cyclone track forecast research (Elsberry and Marks 1999) and operational (NWS 1999) goals are based on the continuous improvements in track forecasts during the past three decades (McAdie and Lawrence 2000;Aberson 2001). McAdie and Lawrence (2000) found that average annual forecast position errors between 1970 and 1998 was improved to 1.0, 1.7, and 1.9 % annually at 24-, 48-, and 72-h lead time, respectively.…”

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Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)

et al. 2014

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Forecasting, with precision, the location of landfall and the height of surge of cyclonic storms prevailing over any ocean basin is very important to cope with the associated disasters. The main objective of the present research is to develop models to forecast the exact location of landfall and the surge heights of tropical cyclones over North Indian Ocean. Artificial neural network (ANN) model is developed for forecasting the location (latitude-longitude) of landfall, and neuro-fuzzy coupled (NFC) model is developed for forecasting the surge heights. The sea surface temperature, vertical wind, minimum pressure at the centre of the cyclones, maximum wind speed and pressure drop at the centre are taken to form the input matrix of both the models. The result shows that position of landfall can be predicted with high accuracy with ANN model. The result further reveals that the prediction error with NFC model in forecasting the surge height is 2.448 % with 6-h lead time, whereas the error is observed to increase with the increase in lead time. However, for forecasting the surge height with 18-h lead time, the accuracy is observed to be 11.4 %. The skill of both the models is verified through validation with the observations of cyclones Jal (2010) and Thane (2011).

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The Ensemble of Tropical Cyclone Track Forecasting Models in the North Atlantic Basin (1976–2000)

Cited by 43 publications

References 20 publications

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

Impact of local data assimilation on tropical cyclone predictions over the Bay of Bengal using the ARW model

The Intensity Forecasting Experiment: A NOAA Multiyear Field Program for Improving Tropical Cyclone Intensity Forecasts

Predictability of landfall location and surge height of tropical cyclones over North Indian Ocean (NIO)

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