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Wesley Ebisuzaki

Abstract

The vertical tilts of planetary waves as functions of zonal wavenumber and frequency were examined by two methods. First, the vertical tilts were computed by a cross-spectral analysis of the geopotential heights at different pressures. This generally used technique was not as sensitive as a cross-spectral analysis of height and temperature at a single level. The two methods yield similar vertical tilts; however, the latter method had a smaller error that allowed us to find statistically significant tilts in the troposphere that the former method did not find.

In the midlatitude troposphere, the eastward-moving waves had a westward tilt with height, as expected. However, the westward-moving waves with frequencies higher than 0.2 day−1 showed statistically significant eastward vertical tilts. For a free Rossby wave, this implies that the Eliassen-Palm flux is downward along with its energy propagation. A downward energy propagation suggests an upper-level source of these waves.

It was proposed that the eastward-tilting waves were forced by the nonlinear interaction of stationary waves and baroclinically unstable cyclone-scale waves. The predicted vertical tilt and phase speed were consistent with the observations. In addition, simulations of a general circulation model were analyzed. In the control run, eastward-tilting waves disappeared when the sources of stationary waves were removed. This is consistent with our theory.

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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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Glenn K. Rutledge, Jordan Alpert, and Wesley Ebisuzaki

An online archive of real-time and historical weather and climate model output and observational data is now available from the National Oceanic and Atmospheric Administration (NOAA). This archive, known as the NOAA National Operational Model Archive and Distribution System (NOMADS), was jointly initiated in 2001 by the National Climatic Data Center (NCDC), the National Centers for Environmental Prediction (NCEP), and the Geophysical Fluid Dynamics Laboratory (GFDL). At present, NOMADS provides access to real-time and historical 1) numerical weather prediction (NWP) model input and output, 2) GFDL's Coupled Global Climate Models (CGCM) output, 3) global and regional reanalysis from NCEP and the National Center for Atmospheric Research (NCAR), and 4) limited surface, upper-air, and satellite observational datasets from NCDC and NOAA's National Ocean Data Center (NODC) and Earth System Research Laboratory [formerly the Forecast System Laboratory (FSL)]. NOMADS is but one of many similar data services across the United States and abroad that are embracing and leveraging various pilot efforts toward distributed data access using agreed-to data transport and data format conventions. This allows for the interoperable access of various subsets of data across the Internet. These underlying community-driven agreements and the Open Source Project for a Network Data Access Protocol (OPeNDAP) data transport protocol form the core of NOMADS services as well as have the capability for “format neutral” access to data in many different formats and locations.

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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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Kingtse C. Mo, Muthuvel Chelliah, Marco L. Carrera, R. Wayne Higgins, and Wesley Ebisuzaki

Abstract

The large-scale atmospheric hydrologic cycle over the United States and Mexico derived from the 23-yr NCEP regional reanalysis (RR) was evaluated by comparing the RR products with satellite estimates, independent sounding data, and the operational Eta Model three-dimensional variational data assimilation (3DVAR) system (EDAS).

In general, the winter atmospheric transport and precipitation are realistic. The climatology and interannual variability of the Pacific, subtropical jet streams, and low-tropospheric moisture transport are well captured. During the summer season, the basic features and the evolution of the North American monsoon (NAM) revealed by the RR compare favorably with observations. The RR also captures the out-of-phase relationship of precipitation as well as the moisture flux convergence between the central United States and the Southwest. The RR is able to capture the zonal easterly Caribbean low-level jet (CALLJ) and the meridional southerly Great Plains low-level jet (GPLLJ). Together, they transport copious moisture from the Caribbean to the Gulf of Mexico and from the Gulf of Mexico to the Great Plains, respectively. The RR systematically overestimates the meridional southerly Gulf of California low-level jet (GCLLJ). A comparison with observations suggests that the meridional winds from the RR are too strong, with the largest differences centered over the northern Gulf of California. The strongest winds over the Gulf in the RR extend above 700 hPa, while the operational EDAS and station soundings indicate that the GCLLJ is confined to the boundary layer.

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Cheng-Hsuan Lu, Masao Kanamitsu, John O. Roads, Wesley Ebisuzaki, Kenneth E. Mitchell, and Dag Lohmann

Abstract

This study compares soil moisture analyses from the National Centers for Environmental Prediction–National Center for Atmospheric Research (NCEP–NCAR) global reanalysis (R-1) and the later NCEP– Department of Energy (DOE) Atmospheric Model Intercomparison Project (AMIP) global reanalysis (R-2). The R-1 soil moisture is strongly controlled by nudging it to a prescribed climatology, whereas the R-2 soil moisture is adjusted according to differences between model-generated and observed precipitation. While mean soil moisture fields from R-1 and R-2 show many geographic similarities, there are some major differences. This study uses in situ observations from the Global Soil Moisture Data Bank to evaluate the two global reanalysis products. In general, R-2 does a better job of simulating interannual variations, the mean seasonal cycle, and the persistence of soil moisture, when compared to observations. However, the R-2 reanalysis does not necessarily represent observed soil moisture characteristics well in all aspects. Sometimes R-1 provides a better soil moisture analysis on monthly time scales, which is likely a consequence of the deficiencies in the R-2 surface water balance.

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Masao Kanamitsu, Wesley Ebisuzaki, Jack Woollen, Shi-Keng Yang, J. J. Hnilo, M. Fiorino, and G. L. Potter

The NCEP–DOE Atmospheric Model Intercomparison Project (AMIP-II) reanalysis is a follow-on project to the “50-year” (1948–present) NCEP–NCAR Reanalysis Project. NCEP–DOE AMIP-II re-analysis covers the “20-year” satellite period of 1979 to the present and uses an updated forecast model, updated data assimilation system, improved diagnostic outputs, and fixes for the known processing problems of the NCEP–NCAR reanalysis. Only minor differences are found in the primary analysis variables such as free atmospheric geopotential height and winds in the Northern Hemisphere extratropics, while significant improvements upon NCEP–NCAR reanalysis are made in land surface parameters and land–ocean fluxes. This analysis can be used as a supplement to the NCEP–NCAR reanalysis especially where the original analysis has problems. The differences between the two analyses also provide a measure of uncertainty in current analyses.

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