Tropical Cyclone Prediction on Subseasonal Time-Scales (original) (raw)
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Monthly Weather Review, 2010
Recent work suggests that there may exist skill in forecasting tropical cyclones (TC) using dynamically based ensemble products, such as those obtained from the ECMWF Monthly Forecast System (ECMFS). The ECMFS features an ensemble of 51 coupled ocean–atmosphere simulations integrated to 32 days once per week. Predicted levels of TC activity in the North Atlantic Ocean with these monthly ensemble forecasts is compared with the observed variability during the months of June–October during 2008 and 2009. Results indicate that the forecast system can capture large-scale regions that have a higher or lower risk of TC activity and that it has skill above climatology for the Gulf of Mexico and the “Main Development Region” on intraseasonal time scales. Regional forecast skill is traced to the model’s ability to capture the large-scale evolution of deep-layer vertical shear, the frequency of easterly waves, and the variance in 850-hPa relative vorticity. The predictability of TC activity, a...
Weather and Forecasting, 2015
Operational global medium-range ensemble forecasts of tropical cyclone (TC) activity (genesis plus the subsequent track) are systematically evaluated to understand the skill of the state-of-the-art ensembles in forecasting TC activity as well as the relative benefits of a multicenter grand ensemble with respect to a single-model ensemble. The global ECMWF, JMA, NCEP, and UKMO ensembles are evaluated from 2010 to 2013 in seven TC basins around the world. The verification metric is the Brier skill score (BSS), which is calculated within a 3-day time window over a forecast length of 2 weeks to examine the skill from short- to medium-range time scales (0–14 days). These operational global medium-range ensembles are capable of providing guidance on TC activity forecasts that extends into week 2. Multicenter grand ensembles (MCGEs) tend to have better forecast skill (larger BSSs) than does the best single-model ensemble, which is the ECMWF ensemble in most verification time windows and mo...
On the Seasonal Forecasting of Regional Tropical Cyclone Activity
Journal of Climate, 2014
Tropical cyclones (TCs) are a hazard to life and property and a prominent element of the global climate system; therefore, understanding and predicting TC location, intensity, and frequency is of both societal and scientific significance. Methodologies exist to predict basinwide, seasonally aggregated TC activity months, seasons, and even years in advance. It is shown that a newly developed high-resolution global climate model can produce skillful forecasts of seasonal TC activity on spatial scales finer than basinwide, from months and seasons in advance of the TC season. The climate model used here is targeted at predicting regional climate and the statistics of weather extremes on seasonal to decadal time scales, and comprises high-resolution (50 km × 50 km) atmosphere and land components as well as more moderate-resolution (~100 km) sea ice and ocean components. The simulation of TC climatology and interannual variations in this climate model is substantially improved by correcti...
Journal of Climate, 2016
Seasonal forecast skill of the basinwide and regional tropical cyclone (TC) activity in an experimental coupled prediction system based on the ECMWF System 4 is assessed. As part of a collaboration between the Center for Ocean–Land–Atmosphere Studies (COLA) and the ECMWF called Project Minerva, the system is integrated at the atmospheric horizontal spectral resolutions of T319, T639, and T1279. Seven-month hindcasts starting from 1 May for the years 1980–2011 are produced at all three resolutions with at least 15 ensemble members. The Minerva system demonstrates statistically significant skill for retrospective forecasts of TC frequency and accumulated cyclone energy (ACE) in the North Atlantic (NA), eastern North Pacific (EP), and western North Pacific. While the highest scores overall are achieved in the North Pacific, the skill in the NA appears to be limited by an overly strong influence of the tropical Pacific variability. Higher model resolution improves skill scores for the A...
Identifying Subseasonal Variability Relevant to Atlantic Tropical Cyclone Activity
Weather and Forecasting, 2020
The primary atmospheric oscillations and variables associated with subseasonal Atlantic tropical cyclone (TC) activity are identified, based on 37 years of reanalysis data. TC activity, represented by accumulated cyclone energy (ACE), is computed for combined phases of the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO). The MJO influence on TC activity becomes greater when the ENSO state is cooler. There is also a shift in the favorable MJO phase for TC activity with ENSO state. For strong La Niñas, MJO phases 4 and 5 (enhanced convection over the Maritime Continent) are most likely to have above-average ACE. To investigate other potential factors that influence subseasonal TC activity, two novel methods are developed: ACE by year (ABY) and seasonal and climatology removed (SNCR). Both methods isolate subseasonal signals of environmental conditions in association with a variable of interest. Vorticity, sea surface temperature, relative humidity, and genesis ...
Weather and Forecasting, 2014
The practical predictability of tropical cyclone (TC) intensity in terms of mean absolute forecast error with respect to different conditions at forecast initialization was explored through convection-permitting hindcasts of all Atlantic storms during the 2008–12 hurricane seasons using the Weather Research and Forecasting (WRF) Model. Averaged over a total of 2190 simulations, the day 1–5 performance of these WRF hindcasts was comparable to two operational regional-scale hurricane prediction models used by the National Hurricane Center (NHC) but was slightly inferior to the NHC official forecasts. It was found that the prediction accuracy of TC intensity, both at the initialization time and the targeted forecast hours, was strongly correlated with the TC intensity. On average, for both the WRF hindcasts and the NHC official forecasts, stronger intensities and larger intensity variations led to larger forecast errors. A number of synoptic-scale environmental parameters, such as vert...
Assessing the Skill of Operational Atlantic Seasonal Tropical Cyclone Forecasts
Weather and Forecasting, 2003
Since 1984, W. Gray of Colorado State University and a team of researchers have been issuing seasonal tropical cyclone forecasts for the North Atlantic Ocean. Prior to this, little work had been done in the area of long-term tropical cyclone forecasting because researchers saw minimal potential skill in any prediction models and no obvious benefits to be gained. However, seasonal forecasts have been attracting more attention as economic and insured losses from hurricane-related catastrophes rose sharply during recent decades. Initially, the forecasts issued by Gray consisted of output from simple statistical prediction models. Over time, the models became increasingly more complex and sophisticated, with new versions being introduced in 1992, 1993, 1994, 1996, and 1997. In addition, based on a combination of experience with the statistical models and other qualitative considerations such as examinations of analog years, the statistical forecasts were modified to create adjusted seasonal forecasts. This analysis assessed the skill demonstrated, if any, of both the statistical and adjusted forecasts over the benchmarks of climatology and persistence and examined whether the adjusted forecasts were more accurate than the statistical forecasts. The analysis indicates that, over the past 18 yr, both the statistical and adjusted forecasts demonstrated some skill over climatology and persistence. There is also evidence to suggest that the adjusted forecast was more skillful than the statistical model forecast.
Experimental Dynamical Seasonal Forecasts of Tropical Cyclone Activity at IRI
Weather and Forecasting, 2009
The International Research Institute for Climate and Society (IRI) has been issuing experimental seasonal tropical cyclone activity forecasts for several ocean basins since early 2003. In this paper the method used to obtain these forecasts is described and the forecast performance is evaluated. The forecasts are based on tropical cyclone-like features detected and tracked in a low-resolution climate model, namely ECHAM4.5. The simulation skill of the model using historical observed sea surface temperatures (SSTs) over several decades, as well as with SST anomalies persisted from the previous month's observations, is discussed. These simulation skills are compared with skills of purely statistically based hindcasts using as predictors recently observed SSTs. For the recent 6-yr period during which real-time forecasts have been made, the skill of the raw model output is compared with that of the subjectively modified probabilistic forecasts actually issued.