JRA-55CHS: An Atmospheric Reanalysis Produced with High-Resolution SST

Masunaga, Ryusuke; Nakamura, Hisashi; Kamahori, Hirotaka; Onogi, Kazutoshi; Okajima, Satoru

doi:10.2151/sola.2018-002

Cited by 20 publications

(19 citation statements)

References 35 publications

(31 reference statements)

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“…Satellite-derived estimates of convective precipitation are largely accurate in the low latitudes (Ebert et al, 2007;Chen et al, 2013), but the TMPA product is less accurate over the ocean due to the absence of local observations used for gauge adjustments (Sapiano and Arkin, 2009) and south of 40 • S due to limited local crosssensor calibration (Huffman et al, 2006). TRMM often underestimates precipitation in high-latitude regions with significant topography due to difficulties of satellite retrievals over snow-covered surfaces and/or due to the high elevations (Barros et al, 2006;Matthews et al, 2013). TRMM is also known to underestimate light rainfall and drizzle over subtropical and high-latitude oceans (Berg et al, 2010).…”

Section: Precipitationmentioning

confidence: 99%

BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

et al. 2019

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Abstract. The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand, and Southeast Asia. The production of the reanalysis with approximately 12 km horizontal resolution – BARRA-R – is well underway with completion expected in 2019. This paper describes the numerical weather forecast model, the data assimilation methods, the forcing and observational data used to produce BARRA-R, and analyses results from the 2003–2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed, surface pressure, and precipitation. Comparing against the global reanalyses ERA-Interim and MERRA-2, BARRA-R scores lower root mean square errors when evaluated against (point-scale) 2 m temperature, 10 m wind speed, and surface pressure observations. It also shows reduced biases in daily 2 m temperature maximum and minimum at 5 km resolution and a higher frequency of very heavy precipitation days at 5 and 25 km resolution when compared to gridded satellite and gauge analyses. Some issues with BARRA-R are also identified: biases in 10 m wind, lower precipitation than observed over the tropical oceans, and higher precipitation over regions with higher elevations in south Asia and New Zealand. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale (<2 km) models.

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Section: Precipitationmentioning

confidence: 99%

BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

et al. 2019

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“…J-OFURO3 is the latest version of this project and contains significant improvements over J-OFURO [6] and J-OFURO2 [7], including accuracy improvements and a new validation scheme [5]. Various parameters in J-OFURO3 have been validated with satisfactory results, such as the sea-surface LHF and SHF [5]; hence, J-OFURO3 is popular and is widely used in various applications [8,9]. J-OFURO3 provides daily net shortwave radiation (R ns ) and net longwave radiation (R nl ) data, and these values can be directly combined to obtain daily sea-surface R n values.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of the J-OFURO3 Sea Surface Net Radiation and Inconsistency Correction

Chen

Jiang

et al. 2021

Remote Sensing

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A new satellite-based product containing daily sea surface net radiation () values at a spatial resolution of 0.25° from 1988 to 2013, named the Japanese Ocean Flux Data Sets with Use of Remote Sensing Observations, version 3 (J-OFURO3), was recently generated and released. In this letter, the performance of the J-OFURO3 sea-surface product was fully evaluated by using observations from 55 global moored buoy sites. The overall accuracy was satisfactory, with root-mean-square difference (RMSD) of 24.05 and 10.76 Wm−2 at daily and monthly scales, respectively. However, an inconsistency issue was found in the long-term variations in the J-OFURO3 sea-surface values in approximately 2000; this inconsistency may be due to the replacement of the input dataset. To address this issue, a simple but effective inconsistency correction method was developed and conducted in this study. The analysis results demonstrated that the variations in the corrected J-OFURO3 sea-surface data were more reasonable and that its daily validation accuracy was significantly improved by decreasing the bias from 4.67 to 0.27 Wm−2 before the year 2000. Thereby, it is suggested that the inconsistency correction method should be applied before using the J-OFURO3 sea-surface data. However, the data users still should be cautious about another discontinuity issues caused by the quality of the input dataset itself.

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“…Even though the re-processed and NRT data do not constitute a homogeneous timeseries, OSTIA is favoured over other SST reanalyses owing to its higher spatial resolution. 25 Masunaga et al (2015Masunaga et al ( , 2018 have shown steep SST gradients, unresolved by coarse SST reanalyses, can influence the organization of long-lived rain bands and enhancement or reduction of surface convergence, and this is particularly problematic for atmosphere-only reanalyses as thermal structure and motions in the marine atmospheric boundary later are not well constrained by data assimilation.…”

Section: Boundary Conditions 15mentioning

confidence: 99%

BARRA v1.0: The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

Su¹,

Eizenberg²,

Steinle³

et al. 2018

Preprint

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The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia (BARRA) is the first atmospheric regional reanalysis over a large region covering Australia, New Zealand and southeast Asia. The production of the reanalysis with approximately 12 km lateral resolution -BARRA-Ris well underway with completion expected in 2019. 15 This paper describes the numerical weather forecast model, the data assimilation methods, and the forcing and observational data used to produce BARRA-R, and analyses results from the 2007-2016 reanalysis. BARRA-R provides a realistic depiction of the meteorology at and near the surface over land as diagnosed by temperature, wind speed and precipitation. It shows closer agreement with point-scale observations and gridded analysis of observations, than leading global reanalyses. In particular, BARRA-R improves upon ERA-Interim global reanalysis in several areas at point-scale to 25 km resolution. BARRA-R shows 20 reduced negative biases in (point-scale) 10 m wind speed during strong wind periods, reduced biases in (5 km gridded) daily temperature maximum and minimum, and higher frequency of very heavy precipitation days at 5 km and 25 km resolution.Few issues with BARRA-R are also identified; some of which are common in reanalyses, such as biases in 10 m wind, and others that are more specific to BARRA such as grid point storms. Some of these issues could be improved through dynamical downscaling of BARRA-R fields using convective-scale (< 2 km) models. 25 IntroductionReanalyses are widely used for climate monitoring and studying climate change as they provide spatially complete records of the atmosphere for long periods that are a balance between physical consistency and observations. This is achieved by using data assimilation techniques that produce an observation-constrained model estimate of the atmosphere, by drawing short-term model states towards observations from multiple, disparate sources to form an atmospheric analysis. The use of a physically 30 realistic model enables the estimation of unobserved parameters from the limited and irregularly distributed collection of observed parameters.Geosci. Model Dev. Discuss., https://doi.Global-scale reanalyses using global atmospheric circulation models (GCMs) have advanced in quality and quantity during the past two decades (Dee et al., 2014;Hartmann et al., 2013). At present, the available global reanalyses established for the satellite era include the NCEP/NCAR reanalysis at 210 km horizontal resolution (Kalnay et al., 1996), the Japanese 55-year Reanalysis (JRA-55) at 60 km (Ebita et al., 2011), the Modern-Era Retrospective analysis for Research and Applications-2 (MERRA-2) at about 50 km (Gelaro et al., 2017) and the European Centre for Medium Range Weather Forecasts (ECMWF) 5 ReAnalysis Interim (ERA-Interim) at ~79 km (Dee et al., 2011). The latter will be replaced by the new ERA-5 ~31 km reanalysis (Hersbach and Dee, 2016). The global reanalyses have the advantages of providing globally consistent and homo...

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JRA-55CHS: An Atmospheric Reanalysis Produced with High-Resolution SST

Cited by 20 publications

References 35 publications

BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

BARRA v1.0: the Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

Evaluation of the J-OFURO3 Sea Surface Net Radiation and Inconsistency Correction

BARRA v1.0: The Bureau of Meteorology Atmospheric high-resolution Regional Reanalysis for Australia

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