Evaluation of a Regional Reanalysis and ERA-Interim over East Asia Using In Situ Observations during 2013–14

Yang, Eun Gyeong; Kim, Hyun Mee

doi:10.1175/jamc-d-16-0227.1

Cited by 14 publications

(11 citation statements)

References 76 publications

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“…We show here that the cold biases in the SAT derived from the ECMWF reanalysis data set can mainly be attributed to the differences in elevation between the observation stations and the reanalysis models over Sichuan. Our results strongly agree with those of previous studies (Pepin and Losleben, ; Bordi et al ., ; You et al ., ; Yang and Kim, ), suggesting that the accuracy of reanalysis data should be taken into account in climate research and modelling over areas with specific and complex topography. The latest reanalysis data set, ERA‐5, has recently been released by the ECMWF (Hersbach and Dee, ; Albergel et al ., ), which encourages us to extend our evaluation.…”

Section: Discussionmentioning

confidence: 99%

Assessment of ECMWF reanalysis data in complex terrain: Can the CERA‐20C and ERA‐Interim data sets replicate the variation in surface air temperatures over Sichuan, China?

Luo

Yang³

et al. 2019

Intl Journal of Climatology

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Surface air temperatures (SATs) derived from the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA‐Interim and CERA‐20C reanalysis data sets are compared with data from 43 observation stations in Sichuan for 1979–2010. The results show (a) the temperatures from the ERA‐Interim and CERA‐20C data sets are strongly correlated with those from the observation stations, although significant cold biases are seen on both annual and seasonal timescales. (b) The biases in SATs are predominately influenced by the differences between the actual topography and the topography used in the reanalysis models. Larger differences in temperature are observed in the plateau and mountainous regions of Sichuan. We confirmed larger SAT biases at high altitudes by categorizing the elevation into four bands, each with a spacing of 1,000 m. (c) We reduced the biases resulting from elevation by using an elevation correction method with internal lapse rates derived from different reanalysis pressure levels. The annual mean bias was reduced from −2.86 to −0.75°C for the ERA‐Interim data set and from −5.27 to −2.21°C for the CERA‐20C data set. After calibration, the correlation coefficients between the difference in SAT (observed minus reanalysis data) and the difference in elevation (station elevation minus model elevation) decreased from −0.97 and −0.91 to −0.29 and −0.30 for the ERA‐Interim and CERA‐20C data sets, respectively. These significant differences should not be ignored in the application of reanalysis data sets to climate research. The evaluation and calibration of reanalysis data sets are essential before making assessments of regional climate change, especially over regions with complex topography.

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Section: Discussionmentioning

confidence: 99%

Assessment of ECMWF reanalysis data in complex terrain: Can the CERA‐20C and ERA‐Interim data sets replicate the variation in surface air temperatures over Sichuan, China?

Luo

Yang³

et al. 2019

Intl Journal of Climatology

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“…To consider the time correlation, a method by Wilks (2006), Kim and Kim (2014), and Yang and Kim ( 2017) is adopted where the sample variance in the paired t test is inflated by (1 1 r 1 )/(1 2 r 1 ) where r 1 is the lag-1 autocorrelation coefficient of the sample. Detailed mathematical formula of the t test with inflated sample variance can be found in Yang and Kim (2017). A significance level of 95% is adopted.…”

Section: Resultsmentioning

confidence: 99%

A Multiresolution Ensemble Hybrid 4DEnVar for Global Numerical Prediction

Kay

Wang

2020

Monthly Weather Review

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A multiresolution ensemble (MR-ENS) method is developed to resolve a wider range of scales of the background error covariance (BEC) in the hybrid four-dimensional ensemble–variational (4DEnVar) while saving computational costs. MR-ENS is implemented in the NCEP Global Forecast System (GFS) gridpoint statistical interpolation (GSI) hybrid 4DEnVar. MR-ENS generates analysis increment by incorporating high-resolution static BEC and flow-dependent ensemble BECs from both high and low resolutions. MR-ENS is compared with three 4DEnVar update approaches: 1) the single-resolution (SR)-Low approach where the analysis increments are generated from the ensemble BEC and the static BEC at the same low resolution; 2) the dual-resolution (DR) approach where the analysis increment is generated using the high-resolution static BEC and low-resolution ensemble BEC; and 3) the SR-High approach, which is the same as 1) except that all covariances are at high-resolution. Experiments show that MR-ENS improves global and tropical cyclone track forecasts compared to SR-Low and DR. Inclusion of the high-resolution ensemble leads to increased background ensemble spread, better fitting of the background to observations, increased effective ranks, more accurate ensemble error correlation, and increased power of analysis increment at small scales. The majority of the improvement of MR-ENS relative to SR-Low is due to the partial use of high-resolution background ensemble. Compared to SR-High, MR-ENS decreases the overall cost by about 40% and shows comparable global and tropical cyclone track forecast performances. Diagnostics show that particularly in the tropics, MR-ENS improves the analysis increment over a wide range of scales and increases the effective rank of the ensemble BEC to the degree comparable to SR-High.

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“…As part of this effort, regional reanalysis over East Asia were produced based on the Unified Model for the two-year period of 2013-14 and it was confirmed that regional reanalysis over East Asia is beneficial (Yang and Kim 2017;. However, because UM was no longer available for generating regional reanalysis over East Asia, another numerical weather prediction (NWP) model and its data assimilation (DA) method are required.…”

Section: Introductionmentioning

confidence: 99%

Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis

Yang

Kim

2021

Preprint

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Abstract. The East Asia Regional Reanalysis (EARR) system is developed based on the advanced hybrid gain data assimilation method (AdvHG) using Weather Research and Forecasting (WRF) model and conventional observations. Based on EARR, the high-resolution regional reanalysis and reforecast fields are produced with 12 km horizontal resolution over East Asia for 2010–2019. The newly proposed AdvHG is based on the hybrid gain approach, weighting two different analysis for an optimal analysis. The AdvHG is different from the hybrid gain in that 1) E3DVAR is used instead of EnKF, 2) 6 h forecast of ERA5 is used to be more consistent with WRF, and 3) the pre-existing, state-of-the-art reanalysis is used. Thus, the AdvHG can be regarded as an efficient approach to generate regional reanalysis dataset due to cost savings as well as the use of the state-of-the-art reanalysis. The upper air variables of EARR are verified with those of ERA5 for January and July 2017 and the two-year period of 2017–2018. For upper air variables, ERA5 outperforms EARR over two years, whereas EARR outperforms (shows comparable performance to) ERA-I and E3DVAR for January in 2017 (July in 2017). EARR better represents precipitation than ERA5 for January and July in 2017. Therefore, though the uncertainties of upper air variables of EARR need to be considered when analyzing them, the precipitation of EARR is more accurate than that of ERA5 for both two seasons. The EARR data presented here can be downloaded from https://doi.org/10.7910/DVN/7P8MZT for data on pressure levels and https://doi.org/10.7910/DVN/Q07VRC for precipitation.

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Evaluation of a Regional Reanalysis and ERA-Interim over East Asia Using In Situ Observations during 2013–14

Cited by 14 publications

References 76 publications

Assessment of ECMWF reanalysis data in complex terrain: Can the CERA‐20C and ERA‐Interim data sets replicate the variation in surface air temperatures over Sichuan, China?

Assessment of ECMWF reanalysis data in complex terrain: Can the CERA‐20C and ERA‐Interim data sets replicate the variation in surface air temperatures over Sichuan, China?

A Multiresolution Ensemble Hybrid 4DEnVar for Global Numerical Prediction

Development of East Asia Regional Reanalysis based on advanced hybrid gain data assimilation method and evaluation with E3DVAR, ERA-5, and ERA-Interim reanalysis

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