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W. Y. Chen

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W. Y. Chen

Abstract

Surface wind data obtained from the United States and Canadian International Field Year for the Great Lakes (IFYGL) buoy network are used to estimate the vorticity and divergence feeds over Like Ontario. Hourly values of these variables are computed for the period May–October 1972. Lakewide temperature,, relative humidity and wind speed for the same period of time are also analyzed for their spectral characteristics.

Distinct land-lake breeze circulations are observed on calm days in summer, when the flow is divergent and anticyclonic during the day and convergent and cyclonic at night. The six-month averaged daily values show similar but weaker diurnal variations. The changes from land-to-lake and lake-to-land breeze occur at about 0800 and 1800 local time, respectively. The six-month average of the divergent field is zero and that of the vorticity field is cyclonic with a value of 3.5×10−6 s−1.

Spectral characteristics are also investigated by analyzing the six-month time series. In addition to diurnal variations, the features of synoptic-scale motions are also obtained. Power spectral densities, coherence spectra, phase difference spectra and calculated time lags between selected variables are analyzed and presented.

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W. Y. Chen

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Wilbur Y. Chen

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The natural variability over the North Pacific, where the influence of tropical El Niño–Southern Oscillation (ENSO) events is substantial, is examined to determine whether there is a large change owing to a difference in the ENSO forcing anomaly. The hindcast ensemble runs of the Seasonal Forecast Model of the National Centers for Environmental Prediction are analyzed for this assessment. Four sets of 10-member ensemble hindcasts out to 7 months with T42 horizontal resolution and another four sets with T62 resolution are examined in detail. The results consistently indicate that the natural variability, on both seasonal and monthly time scales, is significantly smaller during El Niño boreal winters than during La Niña boreal winters. The implication is that the predictability on both seasonal and monthly time scales over the North Pacific is potentially higher during El Niño winters than during La Niña winters.

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W. Y. Chen

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W. Y. Chen

Abstract

Experiments on dynamical extended-range forecasting (DERF) conducted at the National Meteorological Center (NMC) are analysed for the relationships between skill of medium-extended-range forecasts over the Pacific-North America region and the fluctuations of the Pacific-North American (PNA) mode of low-frequency variability. To isolate the effects of the prominent El Niño-Southern Oscillation anomalies that prevailed during the DERF period, the performance of forecasts is evaluated separately for the North Pacific and the North Atlantic sectors. Distinct features are observed. Much better skill in both dynamical and persistence forecasts is found for the Pacific sector than the Atlantic sector.

The relationships between the polarity and amplitude of the PNA mode and the predictability of the prediction model are also investigated. The PNA circulation regime in the initial conditions as a predictor of forecast skill is contrasted in detail with the PNA mode in the forecasts. The statistical significance of the relationships is examined. The results indicate that the PNA mode in the forecasts is a better predictor for forecasting the forecast skill than the PNA mode in the initial conditions.

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W. Y. Chen

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The skill of a medium-range numerical forecast can fluctuate widely from day to day. Providing an a priori estimate of the skill of the forecast is therefore important. Existing approaches include Monte Carlo Forecasting and Lagged Average Forecasting, both of which employ the spread between members of an ensemble of forecasts as a predictor. Instead of working with an ensemble, a new approach to predicting forecast skill is proposed that employs the persistence of the model forecast (within the latest integration) as the predictor. The correlation between this simple predictor and the forecast skill is found to be significant for the entire medium range, both over limited regions (e.g., the Pacific North America sector) and over the Northern Hemisphere.

Both root-mean-square and pattern correlation skill scores are used to assess the performance of the forecast and the degree of persistence. The statistical significance of the results is estimated using a Monte Carlo technique. Discrimination of forecast skill is demonstrated using the recent Dynamical Extended Range Forecast experiments carried out by the National Meteorological Center, and is confirmed in tests with independent data.

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W. Y. Chen

Abstract

Six hundred cases of wintertime 1- to 10-day operational forecasts made by the National Meteorological Center's Medium-Range Forecasting System are examined for their variabilities in performance. In addition to hemispheric-wide assessment, the North Pacific/North America (PAC) and North Atlantic/Eurasia (ATL) sectors are also evaluated separately and compared. Tests of statistical significance of results are performed. During El Niño/Southern Oscillation (ENSO) winters, the forecast skill over the PAC sector is found significantly higher than over the ATL sector. For wintertime as a whole, the average skill over the PAC sector is also found significantly higher during ENSO winters than during non-ENSO winters. Therefore clear interannual variability in skill can be detected for the PAC sector. Within ENSO winters, the contribution to better performance in the PAC sector comes mainly when the large-scale circulation is PNA-like (where PNA stands for the dominant circulation mode of the PAC sector), consistent with the recent results of Palmer that the skill of forecast over the PAC sector is strongly correlated to the fluctuation of low-frequency PNA-mode. Other characteristics of skill are also investigated. In general, much larger variability in skill is found for the ATL sector than for the PAC sector. The ratio of the former to the latter can be as large as 2; for example, for the 1996/87 ENSO winter. The potential usefulness of a simple amplitude time series that represents the extent of PNA-like circulation of forecasts in prediction of forecast skill is also assessed.

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W. Y. Chen

Abstract

Experiments on dynamical extended range forecasting (DERF) conducted at the National Meteorological Center (NMC) are utilized to obtain estimates of the upper and lower bounds of dynamical predictability for the NMC operational medium-range forecasting systems (MRFS). Owing to the extended range of the integrations (up to 30 days) the upper bound can be estimated without resorting to an empirical model of error growth such as that used by Lorenz (1982). At the root-mean-square error level that equals the climatological standard deviation (about 100 m), the average limit of dynamical predictability is about 14 days, in good agreement with the recent results of Lorenz using the operational forecasting model of the European Centre for Medium Range Weather Forecasts (ECMWF). The current level of forecast skill is also evaluated, indicating loss of useful skill at about 6 days, in agreement with criterion of Saha and Van den Dool. This skill level is much below its potential, thereby leaving considerable room for improvement. The possible contributions to extending the forecast skill from model improvements, as well as initial data error reductions, are also assessed.

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W. Y. Chen

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Monthly sea-level pressure anomalies at Easter Island, Rapa, Tahiti and Darwin, for the period 1951–79, are analysed to reveal their temporal characteristics and the coherence and time lead/lag relationships among them. For interannual scale oscillations, the pressure variations at Rapa and Easter Island lead those at Tahiti and those of opposite phase at Darwin by 2–8 months, where the large lead times are associated with lower frequency oscillations. Of the four stations, Rapa has the smallest percentage of its variance in the range of periods containing the Southern Oscillation; Darwin has the largest. Spatial smoothing by combining stations enhances the percentage of variance in the longer periods. Combining Tahiti and Darwin gives a substantial increase; combining Rapa and Easter Island, much less. Normalization of the time series through division by the monthly standard deviations is also treated. The combination of Tahiti and Darwin is recommended as a Southern Oscillation Index for diagnostic studies; the combination of Rapa and Easter Island may be found useful for prognostic applications.

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