Comparative energetics of ERA-40, JRA-25 and NCEP-R2 reanalysis, in the wave number domain

Marques, Carlos; Corte‐Real, João

doi:10.1016/j.dynatmoce.2010.03.003

Cited by 29 publications

(23 citation statements)

References 27 publications

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“…Furthermore, in the latitude-altitude cross-section, the difference between the two reanalysis datasets in the southern hemisphere was larger than in the northern hemisphere, indicating that the main differences originated from the southern hemisphere. These results are similar to the difference between NCEP R2 and other reanalysis datasets (ERA-40 and JRA-25) reported previously (Marques et al 2010). We calculated the conversion rates of mean available potential energy to mean kinetic energy [C(P M , K M )] and eddy available potential energy to eddy kinetic energy [C(P E , K E )] using two formulations (the so-called 'vÁgrad z' and 'xÁa' formulations) for the two reanalysis datasets.…”

Section: Discussionsupporting

confidence: 80%

“…Because formulations of the energy cycle involve climate statistics, which are the deviations of time, zonal means, and variance and covariance of basic variables, it is very useful to either diagnose climate models (Sheng and Hayashi 1990;Boer and Lambert 2008;Hernández-Deckers and von Storch 2010;Marques et al 2011) or analyze the characteristics of various reanalysis datasets (Ulbrich and Speth 1991;Hu et al 2004;Li et al 2007;Marques et al 2009;Marques et al 2010). Recently, various reanalysis datasets such as the NCEP R2, ERA-40 from the European Center for Medium-range Weather Forecasts (ECMWF), and JRA-25 from the Japan Meteorological Agency (JMA) and the Central Research Institute of Electric Power Industry (CRIEPI), have been released to satisfy the demand for atmosphere datasets; many researchers have since used such reanalysis datasets to investigate the atmosphere energy cycle.…”

Section: Introductionmentioning

confidence: 99%

“…Li et al (2007) determined that the conversion rate C(P M , K M ) had changed in that its sign differed from that reported in a previous study (Oort 1983), which suggests that near-surface processes play an important role in the magnitude and sign of C(P M , K M ). Marques et al (2010) compared three reanalysis datasets (NCEP R2, ERA-40, and JRA-25) using global energy analysis for all seasons. They found that the Lorenz energy cycles of the three datasets were similar, but that appreciable differences appeared mainly in the southern hemisphere and that the magnitudes of the energy and conversion terms tended to follow the hierarchy of ERA-40 [ JRA-25 [ NCEP-R2.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Examination of the global lorenz energy cycle using MERRA and NCEP-reanalysis 2

Kim

2012

Clim Dyn

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In this study, the global Lorenz atmospheric energy cycle is evaluated using the Modern Era Retrospective analysis for Research and Applications (MERRA) and the National Center for Environmental Prediction and the Department of Energy (NCEP R2) reanalysis datasets over a 30-year period (1979-2008) for the annual, JJA, and DJF means. The energy cycle calculated from the two reanalysis datasets is largely consistent, but the energy cycle determined using the MERRA dataset is more active than that determined from the NCEP R2 dataset. For instance, with regard to the annual mean, the general discrepancy between the energy components in the global integral is about 5 %, whereas the discrepancy between the conversion components is about 16 %, with the exception of C(P M , K M ), which has a different sign in the global integrals. The latitude-altitude cross-section indicates that the difference in the energy cycle of the two reanalysis datasets is larger in the southern hemisphere than in the northern hemisphere. The conversion rates of mean available potential energy to mean kinetic energy [C(P M , K M )] and eddy available potential energy to eddy kinetic energy [C(P E , K E )] are also calculated using two formulations (socalled 'vÁgrad z' and 'xÁa') for the two reanalysis datasets. The differences in the conversion rate between the two reanalysis datasets for the global integral are not appreciable for the two formulations.

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Section: Discussionsupporting

confidence: 80%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Examination of the global lorenz energy cycle using MERRA and NCEP-reanalysis 2

Kim

2012

Clim Dyn

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“…One important reason for the application is that the JRA-25 precipitation influenced by multiple physical processes, together with atmospheric circulation, were better simulated when compared with some other reanalysis datasets, including the NCEP R-2 applied as the LBC in this study. Moreover, since its model characteristics are the same as the seasonal forecast model, the JRA-25 is an appropriate reference to validate the seasonal climate simulations (Tosiyuki 2008;Marquesa et al 2010;Yokoi 2015).…”

Section: Experimental Designmentioning

confidence: 99%

Validating the dynamic downscaling ability of WRF for East Asian summer climate

Gao

Hou

Xue

et al. 2015

Theor Appl Climatol

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To better understand the regional climate model (RCM) performance for East Asian summer climate and the influencing factors, this study evaluated the dynamic downscaling ability of the Weather Research Forecast (WRF) RCM. According to the comprehensive comparison studies on different physical processes and experimental settings, the optimal combination of WRF model setups can be obtained for East Asian precipitation and temperature simulations. Furthermore, based on the optimal combination, when compared with climate observations, WRF shows high ability to downscale NCEP DOE Reanalysis-2, which provided initial and lateral boundary conditions for the WRF, especially for the precipitation simulation due to the better simulated lowlevel water vapor flux. However, the strengthened Western North Pacific Subtropical High (WPSH) from WRF simulation results in the positive anomaly for summer rainfall.

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“…Reanalysis data provide an alternative that can present meteorological data that are homogeneous in both time and space (Marques et al, 2010). However, the reliability of reanalysis data sets is often questioned (Kistler et al, 2001;Wu and Xie, 2003;Kinter et al, 2004;Huang et al, 2010) because reanalysis data is inevitably affected by numerical models, assimilation systems and meteorological observation systems (Bengston et al, 2004).…”

Section: Introductionmentioning

confidence: 99%

Evaluation of NCEP‐2 and CFSR reanalysis seasonal temperature data in China using detrended fluctuation analysis

Zhao

Jiang

2017

Intl Journal of Climatology

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ABSTRACT:In the present study, the performance of the National Centers for Environmental Prediction/Department of Energy (NCEP/DOE) Atmospheric Model Intercomparison Project (AMIP-II) reanalysis (NCEP-2) and NCEP Climate Forecast System Reanalysis (CFSR) seasonal temperature data for China was quantitatively evaluated by using detrended fluctuation analysis. The results indicate that the quality of the NCEP-2 temperature data is the highest in autumn, while the quality of CFSR data set is the highest in summer. Both the quality of NCEP-2 and that of CFSR temperature data is the lowest in winter. The quality is very low in eastern Tibetan Plateau and most of Xinjiang at a significance level of 0.05 for the NCEP-2 and CFSR reanalysis temperature data including three elements: the daily average temperature (T mean ), the daily maximum temperature (T max ) and the daily minimum temperature (T min ). The performance of the NCEP-2 T mean data is poorer than those of the NCEP-2 T max and T min data for all four seasons, while the performance of CFSR T mean is better than that of CFSR T max and T min for all four seasons. In addition, the NCEP-2 T max , T min and CFSR T mean , T max data perform well in most of central and eastern China for all four seasons. The quality of the CFSR T mean is better than that of the NCEP-2 T mean for all four seasons. However, the NCEP-2 T max and T min data have higher credibility than that of the CFSR T max and T min data for all four seasons. Therefore, it is important to consider the reliability of the NCEP-2 and CFSR daily temperature reanalysis data in different districts and seasons when drawing conclusions from the different reanalysis data.KEY WORDS detrended fluctuation analysis; scaling exponent; long-range correlation; quality of reanalysis data

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Comparative energetics of ERA-40, JRA-25 and NCEP-R2 reanalysis, in the wave number domain

Cited by 29 publications

References 27 publications

Examination of the global lorenz energy cycle using MERRA and NCEP-reanalysis 2

Examination of the global lorenz energy cycle using MERRA and NCEP-reanalysis 2

Validating the dynamic downscaling ability of WRF for East Asian summer climate

Evaluation of NCEP‐2 and CFSR reanalysis seasonal temperature data in China using detrended fluctuation analysis

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