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Wesley Ebisuzaki

Abstract

When analyzing pairs of time series, one often needs to know whether a correlation is statistically significant. If the data are Gaussian distributed and not serially correlated, one can use the results of classical statistics to estimate the significance. While some techniques can handle non-Gaussian distributions, few methods are available for data with nonzero autocorrelation (i.e., serially correlated). In this paper, a nonparametric method is suggested to estimate the statistical significance of a computed correlation coefficient when serial correlation is a concern. This method compares favorably with conventional methods.

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Wesley Ebisuzaki

Abstract

A 14-yr simulation of a GCM forced by observed SST and sea ice is compared with observations as well as a GCM simulation that used climatological surface conditions. The low frequency (periods > 2 months) behavior in both simulations and observations is examined, and it is found that the anomalous boundary conditions were the cause of much of the low-frequency variability in the simulations. Without the anomalous boundary conditions, the low-frequency spectra was often flat, suggesting that the internal variability was producing a white noise-like spectra. The anomalous boundary conditions were found to be very important in determining the low-frequency behavior of the model. If the future values of the SST and sea ice were known, then the predictability for certain variables could be quite high for low-frequency signals (periods > 3 months). Specific zones showed predictability for low-frequency signals in excess of 70% explained variance. These zones were often related to ENSO, as the Southern Oscillation is the strongest intradecadal phenomenon that is forced by the anomalous boundary conditions. This study gives a lower bound on the variance explained by the anomalous surface forcings.

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Dirceu L. Herdies, Vernon E. Kousky, and Wesley Ebisuzaki

Abstract

A data assimilation study was performed to assess the impact of observations from the South American Low-Level Jet Experiment (SALLJEX) on analyses in the region east of the Andes Mountains from western Brazil to central Argentina. The Climate Data Assimilation Systems (CDAS)-1 and -2 and the Global Data Assimilation System (GDAS) were run with and without the additional SALLJEX rawinsondes and pilot balloon observations. The experiments for each data assimilation system revealed similar features, with a stronger low-level flow east of the Andes when SALLJEX data were included. GDAS had the strongest low-level jet (LLJ) when compared with observations. In the experiments that used additional rawinsonde and pilot balloon data, the LLJ was displaced westward in comparison to the analyses run without the SALLJEX data. The vertical structure of the meridional wind in the analyses was much closer to observed rawinsonde profiles in the experiments that included SALLJEX data than in the control experiments, and the results show that, although there are more pilot balloon observations than rawinsonde observations in the SALLJEX dataset, most of the improvements in the analyses can be obtained by only including rawinsonde observations. This was especially true for GDAS. The results of this study can serve as a benchmark for similar data impact studies using higher-resolution data assimilation systems.

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Masao Kanamitsu, Cheng-Hsuan Lu, Jae Schemm, and Wesley Ebisuzaki

Abstract

Using the NCEP–DOE reanalysis (R-2) soil wetness and the NCEP Seasonal Forecast System, seasonal predictability of the soil moisture and near-surface temperature, and the role of land surface initial conditions are examined. Two sets of forecasts were made, one starting from climatological soil moisture as initial condition and the other from R-2 soil moisture analysis. Each set consisted of 10-member ensemble runs of 7-month duration. Initial conditions were taken from the first 5 days of April, 12 h apart, for the 1979–96 period.

The predictive skill of soil moisture was found to be high over arid/semiarid regions. The model prediction surpassed the persisted anomaly forecast, and the soil moisture initial condition was essential for skillful predictions over these areas. Over temperate zones with more precipitation, and over tropical monsoon regions, the predictive skill of the soil moisture declined steeply in the first 3–4 months. This is due to the difficulties in predicting precipitation accurately. In contrast, the situation was very different over tropical South America where tropical SST forcing controlled the precipitation and where the model simulated the precipitation well. The forecast starting from climatological soil moisture approached the forecast skill of initial soil moisture in 3–4 months; after that the effect of initial soil moisture information tended to disappear.

The near-surface temperature anomaly forecast was closely related to the soil moisture anomaly forecast, but the skill was lower. The verification of temperature made against the U.S. 344 climate division data indicated that the improvement in the forecast skill was not an artifact of the R-2 soil moisture analysis.

It was suggested that the equatorial Pacific SST anomaly had an impact on the soil moisture anomaly over the continental United States during the first month of integration, and then it contributed positively toward the prediction of near-surface temperature during the following months.

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Kingtse C. Mo, Eric Rogers, Wesley Ebisuzaki, R. Wayne Higgins, J. Woollen, and M. L. Carrera

Abstract

During the 2004 North American Monsoon Experiment (NAME) field campaign, an extensive set of enhanced atmospheric soundings was gathered over the southwest United States and Mexico. Most of these soundings were assimilated into the NCEP operational global and regional data assimilation systems in real time. This presents a unique opportunity to carry out a series of data assimilation experiments to examine their influence on the NCEP analyses and short-range forecasts. To quantify these impacts, several data-withholding experiments were carried out using the global Climate Data Assimilation System (CDAS), the Regional Climate Data Assimilation System (RCDAS), and the three-dimensional variational data assimilation (3DVAR) Eta Model Data Assimilation System (EDAS) for the NAME 2004 enhanced observation period (EOP).

The impacts of soundings vary between the assimilation systems examined in this study. Overall, the influence of the enhanced soundings is concentrated over the core monsoon area. While differences at upper levels are small, the differences at lower levels are more substantial. The coarse-resolution CDAS does not properly resolve the Gulf of California (GoC), so the assimilation system is not able to exploit the additional soundings to improve characteristics of the Gulf of California low-level jet (GCLLJ) and the associated moisture transport in the GoC region. In contrast, the GCLLJ produced by RCDAS is conspicuously stronger than the observations, though the problem is somewhat alleviated with additional special NAME soundings. For EDAS, soundings improve the intensity and position of the Great Plains low-level jet (GPLLJ). The soundings in general improve the analyses over the areas where the assimilation system has the largest uncertainties and errors. However, the differences in regional analyses owing to the soundings are smaller than the differences between the two regional data assimilation systems.

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