| Help with this page: | RDA dataset description page video tour | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Citations: | This dataset has been cited 41 times. 2021
Albers, J. R., A. H. Butler, M. L. Breeden, A. O. Langford, and G. N. Kiladis, 2021: Subseasonal prediction of springtime Pacific–North American transport using upper-level wind forecasts. Weather Clim. Dynam., 2, 433-452, https://doi.org/10.5194/wcd-2-433-2021
Butler, A. H., and D. V. Domeisen, 2021: The wave geometry of final stratospheric warming events. Weather Clim. Dynam., 2, 453-474, https://doi.org/10.5194/wcd-2-453-2021
Ern, M., M. Diallo, P. Preusse, M. G. Mlynczak, M. J. Schwartz, Q. Wu, and M. Riese, 2021: The semiannual oscillation (SAO) in the tropical middle atmosphere and its gravity wave driving in reanalyses and satellite observations. Atmos. Chem. Phys., 21, 13763-13795, https://doi.org/10.5194/acp-21-13763-2021
Ha, P. M., R. Matsuda, Y. Kanaya, F. Taketani, and K. Sudo, 2021: Effects of heterogeneous reactions on tropospheric chemistry: A global simulation with the chemistry-climate model CHASER V4.0. Geosci. Model Dev., 14, 3813-3841, https://doi.org/10.5194/gmd-14-3813-2021
Jones, E., A. A. Wing, and R. Parfitt, 2021: A global perspective of tropical cyclone precipitation in reanalyses. J. Clim., 34, 8461-8480, https://doi.org/10.1175/JCLI-D-20-0892.1
Martineau, P., H. Nakamura, and Y. Kosaka, 2021: Influence of ENSO on North American subseasonal surface air temperature variability. Weather Clim. Dynam., 2, 395-412, https://doi.org/10.5194/wcd-2-395-2021
Millán, L. F., G. L. Manney, and Z. D. Lawrence, 2021: Reanalysis intercomparison of potential vorticity and potential-vorticity-based diagnostics. Atmos. Chem. Phys., 21, 5355-5376, https://doi.org/10.5194/acp-21-5355-2021
Ng, K. S., and G. C. Leckebusch, 2021: A new view on the risk of typhoon occurrence in the western North Pacific. Nat. Hazards Earth Sys. Sci., 21, 663-682, https://doi.org/10.5194/nhess-21-663-2021
Oehrlein, J., L. M. Polvani, L. Sun, and C. Deser, 2021: How Well Do We Know the Surface Impact of Sudden Stratospheric Warmings?. Geophys. Res. Lett., 48, https://doi.org/10.1029/2021GL095493
Quinting, J. F., and C. M. Grams, 2021: Toward a systematic evaluation of warm conveyor belts in numerical weather prediction and climate models. Part I: Predictor selection and logistic regression model. J. Atmos. Sci., 78, 1465-1485, https://doi.org/10.1175/JAS-D-20-0139.1
Richardson, D., A. S. Black, D. P. Monselesan, T. S. Moore II, J. S. Risbey, A. Schepen, D. T. Squire, and C. R. Tozer, 2021: Identifying periods of forecast model confidence for improved subseasonal prediction of precipitation. J. Hydrometeor., 22, 371-385, https://doi.org/10.1175/JHM-D-20-0054.1
Silverman, V., S. W. Lubis, N. Harnik, and K. Matthes, 2021: A synoptic view of the onset of the midlatitude QBO signal. J. Atmos. Sci., 78, 3759-3780, https://doi.org/10.1175/JAS-D-20-0387.1
Uma, K. N., S. S. Das, M. V. Ratnam, and K. V. Suneeth, 2021: Assessment of vertical air motion among reanalyses and qualitative comparison with very-high-frequency radar measurements over two tropical stations. Atmos. Chem. Phys., 21, 2083-2103, https://doi.org/10.5194/acp-21-2083-2021
Wang, G., T. Wang, and H. Xue, 2021: Validation and comparison of surface shortwave and longwave radiation products over the three poles. International Journal of Applied Earth Observation and Geoinformation, 104, 102538, https://doi.org/10.1016/j.jag.2021.102538
Yang, X., T. L. Delworth, F. Zeng, L. Zhang, W. F. Cooke, M. J. Harrison, A. Rosati, S. Underwood, G. P. Compo, and C. McColl, 2021: On the Development of GFDL's Decadal Prediction System: Initialization Approaches and Retrospective Forecast Assessment. J. Adv. Model. Earth Sys., 13, https://doi.org/10.1029/2021MS002529
2020
Alvarez, M. S., C. S. Coelho, M. Osman, M. F. Firpo, and C. S. Vera, 2020: Assessment of ECMWF subseasonal temperature predictions for an anomalously cold week followed by an anomalously warm week in central and Southeastern South America during July 2017. Wea. Forecasting, 35, 1871-1889, https://doi.org/10.1175/WAF-D-19-0200.1
Daloz, A. S., M. Mateling, T. L'Ecuyer, M. Kulie, N. B. Wood, M. Durand, M. Wrzesien, C. W. Stjern, and A. P. Dimri, 2020: How much snow falls in the world's mountains? A first look at mountain snowfall estimates in A-train observations and reanalyses. Cryosphere, 14, 3195-3207, https://doi.org/10.5194/tc-14-3195-2020
Lachmy, O., and Y. Kaspi, 2020: The Role of Diabatic Heating in Ferrel Cell Dynamics. Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL090619
Shi, X., 2020: Enabling Smart Dynamical Downscaling of Extreme Precipitation Events With Machine Learning. Geophys. Res. Lett., 47, https://doi.org/10.1029/2020GL090309
Wright, J. S., X. Sun, P. Konopka, K. Krüger, B. Legras, A. M. Molod, S. Tegtmeier, G. J. Zhang, and X. Zhao, 2020: Differences in tropical high clouds among reanalyses: Origins and radiative impacts. Atmos. Chem. Phys., 20, 8989-9030, https://doi.org/10.5194/acp-20-8989-2020
2019
Agosta, C., C. Agosta, C. Amory, C. Kittel, A. Orsi, V. Favier, H. Gallée, H. Gallée, J. M. Lenaerts, M. R. Van Den Broeke, J. T. Lenaerts, J. M. Van Wessem, W. J. Van De Berg, and X. Fettweis, 2019: Estimation of the Antarctic surface mass balance using the regional climate model MAR (1979-2015) and identification of dominant processes. Cryosphere, 13, 281-296, https://doi.org/10.5194/tc-13-281-2019
Almonte, J. D., and R. E. Stewart, 2019: Precipitation transition regions over the southern Canadian Cordillera during January-April 2010 and under a pseudo-global-warming assumption. Hydrol. Earth Sys. Sci., 23, 3665-3682, https://doi.org/10.5194/hess-23-3665-2019
Hofstätter, M., M. Hofstätter, G. Blöschl, and G. Blöschl, 2019: Vb Cyclones Synchronized With the Arctic-/North Atlantic Oscillation. J. Geophys. Res. Atmos., 124, 3259-3278, https://doi.org/10.1029/2018JD029420
Li, Q., B. G. Reichl, B. Fox‐Kemper, A. J. Adcroft, S. E. Belcher, G. Danabasoglu, A. M. Grant, S. M. Griffies, R. Hallberg, T. Hara, R. R. Harcourt, T. Kukulka, W. G. Large, J. C. McWilliams, B. Pearson, P. P. Sullivan, L. Van Roekel, P. Wang, and Z. Zheng, 2019: Comparing Ocean Surface Boundary Vertical Mixing Schemes Including Langmuir Turbulence. J. Adv. Model. Earth Sys., 11, 3545-3592, https://doi.org/10.1029/2019MS001810
Roach, L. A., C. M. Bitz, C. Horvat, and S. M. Dean, 2019: Advances in Modeling Interactions Between Sea Ice and Ocean Surface Waves. J. Adv. Model. Earth Sys., 11, 4167-4181, https://doi.org/10.1029/2019MS001836
Xian, T., and C. R. Homeyer, 2019: Global tropopause altitudes in radiosondes and reanalyses. Atmos. Chem. Phys., 19, 5661-5678, https://doi.org/10.5194/acp-19-5661-2019
2018
Chabrillat, S., C. Vigouroux, Y. Christophe, A. Engel, Q. Errera, D. Minganti, B. M. Monge-Sanz, A. Segers, and E. Mahieu, 2018: Comparison of mean age of air in five reanalyses using the BASCOE transport model. Atmos. Chem. Phys., 18, 14715-14735, https://doi.org/10.5194/acp-18-14715-2018
Hofstätter, M., M. Hofstätter, A. Lexer, M. Homann, G. Blöschl, and G. Blöschl, 2018: Large-scale heavy precipitation over central Europe and the role of atmospheric cyclone track types. Int. J. Clmatol., 38, e497-e517, https://doi.org/10.1002/joc.5386
Kenigson, J. S., W. Han, B. Rajagopalan, Yanto, and M. Jasinski, 2018: Decadal shift of NAO-linked interannual sea level variability along the U.S. northeast coast. J. Clim., 31, 4981-4989, https://doi.org/10.1175/JCLI-D-17-0403.1
Kopte, R., P. Brandt, M. Claus, R. J. Greatbatch, and M. Dengler, 2018: Role of equatorial basin-mode resonance for the seasonal variability of the Angola Current at 11°S. J. Phys. Oceanogr., 48, 261-281, https://doi.org/10.1175/JPO-D-17-0111.1
Lambert, A., and M. L. Santee, 2018: Accuracy and precision of polar lower stratospheric temperatures from reanalyses evaluated from A-Train CALIOP and MLS, COSMIC GPS RO, and the equilibrium thermodynamics of supercooled ternary solutions and ice clouds. Atmos. Chem. Phys., 18, 1945-1975, https://doi.org/10.5194/acp-18-1945-2018
Martineau, P., S. Son, M. Taguchi, and A. H. Butler, 2018: A comparison of the momentum budget in reanalysis datasets during sudden stratospheric warming events. Atmos. Chem. Phys., 18, 7169-7187, https://doi.org/10.5194/acp-18-7169-2018
Meng, X., and J. Cheng, 2018: Evaluating eight global reanalysis products for atmospheric correction of thermal infrared sensor-application to Landsat 8 TIRS10 data. Remote Sens., 10, https://doi.org/10.3390/rs10030474
Roach, L. A., C. Horvat, S. M. Dean, and C. M. Bitz, 2018: An Emergent Sea Ice Floe Size Distribution in a Global Coupled Ocean-Sea Ice Model. J. Geophys. Res. Oceans, 123, 4322-4337, https://doi.org/10.1029/2017JC013692
Roach, L. A., S. M. Dean, and J. A. Renwick, 2018: Consistent biases in Antarctic sea ice concentration simulated by climate models. Cryosphere, 12, 365-383, https://doi.org/10.5194/tc-12-365-2018
Schourup-Kristensen, V., C. Wekerle, D. A. Wolf-Gladrow, and C. Völker, 2018: Arctic Ocean biogeochemistry in the high resolution FESOM 1.4-REcoM2 model. Prog. Oceanogr., 168, 65-81, https://doi.org/10.1016/j.pocean.2018.09.006
Schulz, H., and B. Stevens, 2018: Observing the tropical atmosphere in moisture space. J. Atmos. Sci., 75, 3313-3330, https://doi.org/10.1175/JAS-D-17-0375.1
2017
Boothe, A. C., and C. R. Homeyer, 2017: Global large-scale stratosphere–troposphere exchange in modern reanalyses. Atmos. Chem. Phys., 17, 5537-5559, https://doi.org/10.5194/acp-17-5537-2017
Forsythe, N., H. J. Fowler, X. Li, S. Blenkinsop, and D. Pritchard, 2017: Karakoram temperature and glacial melt driven by regional atmospheric circulation variability. Nature Clim. Change, 7, 664-670, https://doi.org/10.1038/nclimate3361
Guillod, B. P., R. G. Jones, A. Bowery, K. Haustein, N. R. Massey, D. M. Mitchell, F. L. Otto, S. N. Sparrow, P. Uhe, D. H. Wallom, S. Wilson, and M. R. Allen, 2017: weather@home 2: validation of an improved global–regional climate modelling system. Geosci. Model Dev., 10, 1849-1872, https://doi.org/10.5194/gmd-10-1849-2017
Reeves Eyre, J. J., and X. Zeng, 2017: Evaluation of Greenland near surface air temperature datasets. The Cryosphere, 11, 1591-1605, https://doi.org/10.5194/tc-11-1591-2017
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Abstract: |
The Japan Meteorological Agency (JMA) conducted JRA-55, the second Japanese global atmospheric reanalysis project. It covers 55 years, extending back to 1958, coinciding with the establishment of the global radiosonde observing system. Compared to its predecessor, JRA-25, JRA-55 is based on a new data assimilation and prediction system (DA) that improves many deficiencies found in the first Japanese reanalysis. These improvements have come about by implementing higher spatial resolution (TL319L60), a new radiation scheme, four-dimensional variational data assimilation (4D-Var) with Variational Bias Correction (VarBC) for satellite radiances, and introduction of greenhouse gases with time varying concentrations. The entire JRA-55 production was completed in 2013, and thereafter will be continued on a real time basis. Specific early results of quality assessment of JRA-55 indicate that a large temperature bias in the lower stratosphere has been significantly reduced compared to JRA-25 through a combination of the new radiation scheme and application of VarBC (which also reduces unrealistic temperature variations). In addition, a dry land surface anomaly in the Amazon basin has been mitigated, and overall forecast scores are much improved over JRA-25. Most of the observational data employed in JRA-55 are those used in JRA-25. Additionally, newly reprocessed METEOSAT and GMS data were supplied by EUMETSAT and MSC/JMA respectively. Snow depth data over the United States, Russia and Mongolia were supplied by UCAR, RIHMI and IMH respectively. The Data Support Section (DSS) at NCAR has processed the 1.25 degree version of JRA-55 with the RDA (Research Data Archive) archiving and metadata system. The model resolution data has also been acquired, archived and processed as well, including transformation of the TL319L60 grid to a regular latitude-longitude Gaussian grid (320 latitudes by 640 longitudes, nominally 0.5625 degree). All RDA JRA-55 data is available for internet download, including complete subsetting and data format conversion services. |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Temporal Range: |
1957-12-31 21:00 +0000 to 2022-01-01 00:00 +0000 (Entire dataset)
1981-01-01 00:00 +0000 (JRA-55 1.25 Degree Constant Fields)
1981-01-01 00:00 +0000 (JRA-55 2.5 Degree Constant Fields)
1958-01-01 03:00 +0000 to 2022-01-01 00:00 +0000 (JRA-55 3-Hourly 1.25 Degree 2-Dimensional Average Diagnostic Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly 1.25 Degree 2-Dimensional Instantaneous Diagnostic Fields)
1958-01-01 03:00 +0000 to 2022-01-01 00:00 +0000 (JRA-55 3-Hourly 1.25 Degree Land Surface Average Diagnostic Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly 1.25 Degree Land Surface Forecast Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly 1.25 Degree Sea Ice Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly 1.25 Degree Total Column Forecast Fields)
1958-01-01 00:00 +0000 to 2022-01-01 00:00 +0000 (JRA-55 3-Hourly Model Resolution 2-Dimensional Average Diagnostic Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly Model Resolution 2-Dimensional Instantaneous Diagnostic Fields)
1957-12-31 21:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly Model Resolution 2-Dimensional Minimum-Maximum Diagnostic Fields)
1958-01-01 00:00 +0000 to 2022-01-01 00:00 +0000 (JRA-55 3-Hourly Model Resolution Land Surface Average Diagnostic Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly Model Resolution Land Surface Forecast Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly Model Resolution Sea Ice Fields)
1958-01-01 00:00 +0000 to 2021-12-31 21:00 +0000 (JRA-55 3-Hourly Model Resolution Total Column Forecast Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 1.25 Degree Isentropic Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 1.25 Degree Isobaric Analysis Fields)
1958-01-01 06:00 +0000 to 2022-01-01 00:00 +0000 (JRA-55 6-Hourly 1.25 Degree Isobaric Average Diagnostic Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 1.25 Degree Isobaric Forecast Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 1.25 Degree Land Surface Analysis Fields)
1958-01-01 18:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 1.25 Degree Snow Depth Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 1.25 Degree Surface Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 1.25 Degree Total Column Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 2.5 Degree Isobaric Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly 2.5 Degree Isobaric Forecast Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly Model Resolution Isentropic Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly Model Resolution Land Surface Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly Model Resolution Model Level Analysis Fields)
1958-01-01 00:00 +0000 to 2022-01-01 00:00 +0000 (JRA-55 6-Hourly Model Resolution Model Level Average Diagnostic Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly Model Resolution Model Level Forecast Fields)
1958-01-01 18:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly Model Resolution Snow Depth Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly Model Resolution Surface Analysis Fields)
1958-01-01 00:00 +0000 to 2021-12-31 18:00 +0000 (JRA-55 6-Hourly Model Resolution Total Column Analysis Fields)
1981-01-01 00:00 +0000 (JRA-55 Model Resolution Constant Fields)
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Updates: | Monthly | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Usage Restrictions: |
When accessing the JRA data, you agree to the following Terms and Conditions: a. Conditions of use
b. Disclaimer
c. Intellectual property
(The complete JMA and NCAR agreement for JRA-55 may be viewed via the Documentation tab.) |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Variables: |
Meridional thermal energy flux Meridional water vapor flux Precipitable water Zonal thermal energy flux Zonal water vapor flux Meridional thermal energy flux Meridional water vapor flux Precipitable water Zonal thermal energy flux Zonal water vapor flux Geopotential height Montgomery stream function Potential vorticity Pressure Specific humidity Square of Brunt Vaisala frequency u-component of wind v-component of wind Vertical velocity (pressure) Geopotential height Montgomery stream function Potential vorticity Pressure Specific humidity Square of Brunt Vaisala frequency u-component of wind v-component of wind Vertical velocity (pressure) Canopy temperature Ground temperature Soil temperature Soil wetness Water equivalent of accumulated snow depth Canopy temperature Ground temperature Soil temperature Soil wetness Water equivalent of accumulated snow depth Geopotential height Specific humidity Temperature u-component of wind v-component of wind Vertical velocity (pressure) Dewpoint depression (or deficit) Geopotential height Relative divergence Relative humidity Relative vorticity Specific humidity Stream function Temperature u-component of wind v-component of wind Velocity potential Vertical velocity (pressure) Dewpoint depression (or deficit) Geopotential height Relative divergence Relative humidity Relative vorticity Specific humidity Stream function Temperature u-component of wind v-component of wind Velocity potential Vertical velocity (pressure) Snow depth Snow depth Potential temperature Pressure Relative humidity Specific humidity Temperature u-component of wind v-component of wind Dewpoint depression (or deficit) Potential temperature Pressure Pressure reduced to MSL Relative humidity Specific humidity Temperature u-component of wind v-component of wind Cloud ice Cloud liquid water Meridional thermal energy flux Meridional water vapor flux Precipitable water Total ozone Zonal thermal energy flux Zonal water vapor flux Cloud ice Cloud liquid water Meridional thermal energy flux Meridional water vapor flux Precipitable water Total ozone Zonal thermal energy flux Zonal water vapor flux Canopy temperature Ground temperature Mass concentration of condensed water in soil Moisture storage on canopy Moisture storage on ground cover Snow depth Soil temperature Soil wetness Water equivalent of accumulated snow depth Canopy temperature Ground temperature Mass concentration of condensed water in soil Moisture storage on canopy Moisture storage on ground cover Snow depth Soil temperature Soil wetness Water equivalent of accumulated snow depth Cloud ice Cloud liquid water Cloud water Geopotential height Ozone mixing ratio Specific humidity Temperature Total cloud cover u-component of wind Upward mass flux at cloud base v-component of wind Vertical velocity (pressure) Cloud ice Cloud liquid water Cloud water Ozone mixing ratio Total cloud cover Cloud ice Cloud liquid water Cloud water Ozone mixing ratio Total cloud cover Clear sky downward longwave radiation flux Clear sky downward solar radiation flux Clear sky upward longwave radiation flux Clear sky upward solar radiation flux Convective precipitation Downward longwave radiation flux Downward solar radiation flux Evaporation Frequency of deep convection Frequency of shallow convection Frequency of stratocumulus parameterization Large scale precipitation Latent heat flux Meridional momentum flux by long gravity wave Meridional momentum flux by short gravity wave Momentum flux, u-component Momentum flux, v-component Pressure Sensible heat flux Snowfall rate water equivalent Total precipitation Upward longwave radiation flux Upward solar radiation flux Zonal momentum flux by long gravity wave Zonal momentum flux by short gravity wave Clear sky downward longwave radiation flux Clear sky downward solar radiation flux Clear sky upward longwave radiation flux Clear sky upward solar radiation flux Convective precipitation Downward longwave radiation flux Downward solar radiation flux Evaporation Large scale precipitation Latent heat flux Meridional momentum flux by long gravity wave Meridional momentum flux by short gravity wave Momentum flux, u-component Momentum flux, v-component Pressure Sensible heat flux Snowfall rate water equivalent Total precipitation Upward longwave radiation flux Upward solar radiation flux Zonal momentum flux by long gravity wave Zonal momentum flux by short gravity wave Adiabatic heating rate Adiabatic meridional acceleration Adiabatic moistening rate Adiabatic zonal acceleration Cloud workfunction Convective heating rate Convective meridional acceleration Convective moistening rate Convective zonal acceleration Gravity wave meridional acceleration Gravity wave zonal acceleration Large scale condensation heating rate Large scale moistening rate Longwave radiative heating rate Solar radiative heating rate Upward mass flux Upward mass flux at cloud base Vertical diffusion heating rate Vertical diffusion meridional acceleration Vertical diffusion moistening rate Vertical diffusion zonal acceleration Adiabatic heating rate Adiabatic meridional acceleration Adiabatic moistening rate Adiabatic zonal acceleration Cloud workfunction Convective heating rate Convective meridional acceleration Convective moistening rate Convective zonal acceleration Gravity wave meridional acceleration Gravity wave zonal acceleration Large scale condensation heating rate Large scale moistening rate Longwave radiative heating rate Solar radiative heating rate Upward mass flux Upward mass flux at cloud base Vertical diffusion heating rate Vertical diffusion meridional acceleration Vertical diffusion moistening rate Vertical diffusion zonal acceleration Evapotranspiration Ground heat flux Interception loss Water run-off Evapotranspiration Ground heat flux Interception loss Water run-off Brightness temperature High cloud cover Low cloud cover Medium cloud cover Pressure Pressure reduced to MSL Relative humidity Specific humidity Surface roughness Temperature Total cloud cover u-component of wind v-component of wind Brightness temperature Dewpoint depression (or deficit) High cloud cover Low cloud cover Medium cloud cover Pressure Pressure reduced to MSL Relative humidity Specific humidity Surface roughness Temperature Total cloud cover u-component of wind v-component of wind Ice cover Ice cover Maximum temperature Maximum wind speed Minimum temperature Geopotential Land cover Type of vegetation Geopotential Land cover Type of vegetation Geopotential Land cover Type of vegetation |
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Contributors: |
JP/JMA Japan Meteorological Agency, Japan
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| NCAR Climate Data Guide: | Dataset Assessment | Expert Guidance | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related Resources: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Publications: |
Kobayashi, S., Y. Ota, Y. Harada, A. Ebita, M. Moriya, H. Onoda, K. Onogi, H. Kamahori, C. Kobayashi, H. Endo, K. Miyaoka, and K. Takahashi, 2015: The JRA-55 Reanalysis: General Specifications and Basic Characteristics J. Met. Soc. Jap., 93(1), 5-48 (DOI: 10.2151/jmsj.2015-001).
Chen, G., T. Iwasaki, H. Qin, and W. Sha, 2014: Evaluation of the Warm-Season Diurnal Variability over East Asia in Recent Reanalyses JRA-55, ERA-Interim, NCEP CFSR, and NASA MERRA J. Climate, 27(14), 5517-5537 (DOI: 10.1175/JCLI-D-14-00005.1).
Onogi, K., J. Tsutsui, H. Koide, M. Sakamoto, S. Kobayashi, H. Hatsushika, T. Matsumoto, N. Yamazaki, H. Kamahori, K. Takahashi, S. Kadokura, K. Wada, K. Kato, R. Oyama, T. Ose, N. Mannoji, and R. Taira, 2007: The JRA-25 Reanalysis J. Met. Soc. Jap., 85(3), 369-432 (DOI: 10.2151/jmsj.85.369).
Although this article is a comprehensive report of JRA-25, an outline of JMA's numerical prediction system as of 2004 is described. This article is referenced by current and pending JMA JRA-55 articles.
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| How to Cite This Dataset:
RIS BibTeX
|
|||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Total Volume: |
88.18 TB (Entire dataset) JRA-55 6-Hourly Model Resolution Total Column Analysis Fields: 191.54 GB JRA-55 6-Hourly 1.25 Degree Total Column Analysis Fields: 29.34 GB JRA-55 6-Hourly Model Resolution Isentropic Analysis Fields: 5.20 TB JRA-55 6-Hourly 1.25 Degree Isentropic Analysis Fields: 1.06 TB JRA-55 6-Hourly Model Resolution Land Surface Analysis Fields: 107.58 GB JRA-55 6-Hourly 1.25 Degree Land Surface Analysis Fields: 17.40 GB JRA-55 6-Hourly Model Resolution Model Level Analysis Fields: 10.34 TB JRA-55 6-Hourly 1.25 Degree Isobaric Analysis Fields: 2.43 TB JRA-55 6-Hourly 2.5 Degree Isobaric Analysis Fields: 614.57 GB JRA-55 6-Hourly Model Resolution Snow Depth Analysis Fields: 3.84 GB JRA-55 6-Hourly 1.25 Degree Snow Depth Analysis Fields: 621.47 MB JRA-55 6-Hourly Model Resolution Surface Analysis Fields: 268.15 GB JRA-55 6-Hourly 1.25 Degree Surface Analysis Fields: 52.80 GB JRA-55 3-Hourly Model Resolution Total Column Forecast Fields: 612.91 GB JRA-55 3-Hourly 1.25 Degree Total Column Forecast Fields: 93.88 GB JRA-55 3-Hourly Model Resolution Land Surface Forecast Fields: 399.59 GB JRA-55 3-Hourly 1.25 Degree Land Surface Forecast Fields: 64.63 GB JRA-55 6-Hourly Model Resolution Model Level Forecast Fields: 20.69 TB JRA-55 6-Hourly 1.25 Degree Isobaric Forecast Fields: 850.74 GB JRA-55 6-Hourly 2.5 Degree Isobaric Forecast Fields: 215.25 GB JRA-55 3-Hourly Model Resolution 2-Dimensional Average Diagnostic Fields: 2.22 TB JRA-55 3-Hourly 1.25 Degree 2-Dimensional Average Diagnostic Fields: 305.09 GB JRA-55 6-Hourly Model Resolution Model Level Average Diagnostic Fields: 36.20 TB JRA-55 6-Hourly 1.25 Degree Isobaric Average Diagnostic Fields: 4.56 TB JRA-55 3-Hourly Model Resolution Land Surface Average Diagnostic Fields: 153.69 GB JRA-55 3-Hourly 1.25 Degree Land Surface Average Diagnostic Fields: 24.86 GB JRA-55 3-Hourly Model Resolution 2-Dimensional Instantaneous Diagnostic Fields: 995.98 GB JRA-55 3-Hourly 1.25 Degree 2-Dimensional Instantaneous Diagnostic Fields: 164.28 GB JRA-55 3-Hourly Model Resolution Sea Ice Fields: 76.61 GB JRA-55 3-Hourly 1.25 Degree Sea Ice Fields: 8.74 GB JRA-55 3-Hourly Model Resolution 2-Dimensional Minimum-Maximum Diagnostic Fields: 229.84 GB JRA-55 1.25 Degree Constant Fields: 14.30 MB JRA-55 2.5 Degree Constant Fields: 3.61 MB JRA-55 Model Resolution Constant Fields: 70.06 MB
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Formats: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Related RDA Datasets: |
|
||||||||||||||||||||||||||||||||||||||||||||||||||||||
| More Details: | View more details for this dataset, including dataset citation, data contributors, and other detailed metadata | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| RDA Blog: | Read posts on the RDA blog that are related to this dataset | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data Access: | Click here or on the "Data Access" tab in the navigation bar near the top of the page | ||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Metadata Record: | |||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Data License: | This work is licensed under a Creative Commons Attribution 4.0 International License. |
