An Evaluation of MÉRA, a High-Resolution Mesoscale Regional Reanalysis

Whelan, Eoin; Gleeson, Emily; Hanley, J. Richard

doi:10.1175/jamc-d-17-0354.1

Cited by 21 publications

(20 citation statements)

References 54 publications

(65 reference statements)

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“…This Met Éireann reanalysis, abbreviated MÉRA, spans the years 1981 to 2015. An analysis of surface parameters including screen-level and soil temperatures, 10-m wind speeds, mean sea-level pressure and precipitation is presented in [11,12]. This paper extends the analysis of MÉRA to include an evaluation of GHI with particular emphasis on its use in the validation of clouds.…”

Section: The Danish Networkmentioning

confidence: 99%

Using Shortwave Radiation to Evaluate the HARMONIE-AROME Weather Model

Nielsen

Gleeson

2018

Atmosphere

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Evaluation of global shortwave irradiance forecasts from the HARMONIE-AROME weather prediction model is presented in this paper. We give examples of how such an evaluation can be used when testing a weather model or reanalysis product. We specifically use the non-dimensional clear sky and variability indices. We have tested seven months of HARMONIE-AROME 40h1.1 output against Danish global irradiance stations and 35 years of the Irish Met Éireann reanalysis (MÉRA) simulations. MÉRA, which is run with HARMONIE-AROME 38h1.2, is shown to have a significantly lower bias than the previously available global horizontal irradiance (GHI) reanalysis data from the ERA-Interim dataset. The Danish HARMONIE-AROME 40h1.1 has a negative bias during the summer months that is not seen in the Irish HARMONIE-AROME 38h1.2. For both model runs, we find a negative bias in the shortwave irradiance forecasts on days with thick clouds. This suggest that the model has too much cloud water in thick clouds.

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Section: The Danish Networkmentioning

confidence: 99%

Using Shortwave Radiation to Evaluate the HARMONIE-AROME Weather Model

Nielsen

Gleeson

2018

Atmosphere

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“…ERA5 also has a greater number of vertical levels, with the first vertical level closest to the observations at 10 m. MÉRA does not assimilate satellite data, which could be important for SW or cloud properties. The skill of regional reanalyses depends on the quality of the driving reanalysis; previous studies found that MÉRA performs better than its driving reanalysis ERA-interim [19], and studies have also found that ERA5 performs better than ERA-interim [5,12,27]. Therefore, the proposed development of a new MÉRA model with ERA5 as the driving global reanalysis should lead to an improved regional scale reanalysis for Ireland.…”

Section: Discussionmentioning

confidence: 99%

“…It should be noted that all three reanalyses use conventional observations in the form of surface land observations (including the temperature and wind variables) from synoptic weather stations as a source of input to the model's data assimilation process [3,19,21,22]. Therefore, the validations performed on these datasets are not truly an independent comparison.…”

Section: Methodsmentioning

confidence: 99%

“…This can often be attributed to mismatches in model elevation or topography [17,18]. Although MÉRA has a tendency to overpredict at lower wind speeds and underpredict at higher wind speeds, the standard deviation of wind speeds has been shown to match well with observations, which can be attributed to increased resolution and improved model physics [3,19].…”

Section: Introductionmentioning

confidence: 99%

“…Temperature fields have been shown to generally be in good agreement with station observations. MÉRA outperforms ERA-Interim for 2 m temperature, often as a result of increased model spatial resolution [19]. Daily minimum temperatures in MERRA-2 tend to be too high, whereas daily maximum temperatures tend to be too low, and this is largely due to corresponding errors in radiation in the model [20].…”

Section: Introductionmentioning

confidence: 99%

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Which Reanalysis Dataset Should We Use for Renewable Energy Analysis in Ireland?

et al. 2021

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Attention should always be given to which reanalysis dataset to use when preparing analysis for a project. The accuracies of three reanalysis datasets, two global (ERA5 and MERRA-2) and one high-resolution regional reanalysis (MÉRA), are assessed by comparison with observations at seven weather observing stations around Ireland. Skill scores are calculated for the weather variables at these stations that are most relevant to the renewable energy sector: 10 m wind for wind power; surface shortwave radiation (SW) and 2 m temperature for photovoltaic power generation. The choice of which reanalysis dataset to use is important when future planning depends on this data. The newer ERA5 generally outperforms the other two reanalyses. However, this is not always true, and the best performing reanalysis dataset often depends on the variable of interest and location. As errors are significant for these reanalysis datasets, consideration should also be given to datasets specifically tailored to renewable energy resource modelling.

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Evaluation of the precipitation of the East Asia regional reanalysis system mainly over mainland China

Wang

Liang

Yin

et al. 2022

Intl Journal of Climatology

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In this article, precipitation in a new regional reanalysis dataset, namely, the East Asia regional reanalysis system (EARS‐CMA) with a 12‐km resolution based on the Weather Research and Forecast (WRF) model, ERA‐Interim, ERA5, TRMM 3B42V7, Climate Prediction Center Morphing Technique (CMORPH) and CN05.1, is compared in 2008–2017. The results show that EARS‐CMA can capture the spatial features and temporal variation in precipitation over East Asia well. Focusing on mainland China, CMORPH performs worse in regard to winter precipitation and dryer areas than TRMM against CN05.1. Although ERA5 behaves better with integrated properties compared with ERA‐Interim and EARS‐CMA over East Asia, EARS‐CMA produces more reliable annual, summer and winter mean precipitation pattern than ERA5 over the subregions dominated by large scales in mainland China, including the Tibetan Plateau and northwestern China, similar to its driving fields, ERA‐Interim. In addition, the improvement from ERA‐Interim to EARS‐CMA can be obtained in reasonably reproducing precipitation seasonality in South China, Indo‐China and southern Japan. Thus, the compatibility of EARS is outstanding in maintaining the advantages of its corresponding driving fields, ERA‐Interim, as well as developing finer small scales. Although slightly behind ERA5 in the subregions affected by tropical systems and monsoons, EARS‐CMA is competitive in acting as reference data for daily, seasonal, annual and interannual precipitation estimations over mainland China.

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An Evaluation of MÉRA, a High-Resolution Mesoscale Regional Reanalysis

Cited by 21 publications

References 54 publications

Using Shortwave Radiation to Evaluate the HARMONIE-AROME Weather Model

Using Shortwave Radiation to Evaluate the HARMONIE-AROME Weather Model

Which Reanalysis Dataset Should We Use for Renewable Energy Analysis in Ireland?

Evaluation of the precipitation of the East Asia regional reanalysis system mainly over mainland China

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