Evaluation of ERA‐Interim reanalysis precipitation products using England and Wales observations

Leeuw, Johannes de; Methven, John; Blackburn, Michael

doi:10.1002/qj.2395

Cited by 69 publications

(48 citation statements)

References 38 publications

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“…This comparison used as observations the CRU TS3.1 from the Climate Research Unit at the University of East Anglia (Mitchell and Jones, ), which consists of monthly statistical interpolations of ground stations in a 5 decimal degree grid. Similarly, ERA‐Interim over‐predicted daily precipitation in the UK by 20% compared with observations (England and Wales Precipitation (EWP); De Leeuw et al ., ). Further, in China, fits of ERA‐Interim data to observations of 755 meteorological sites were poor with average R 2 values for 10 catchments ranging from 0.28 to 0.55 (Fu et al ., ).…”

Section: Discussionmentioning

confidence: 97%

Accuracy of grid precipitation data for Brazil: application in river discharge modelling of the Tocantins catchment

Monteiro

Strauch

Srinivasan

et al. 2015

Hydrological Processes

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Abstract:Here, we compared grid precipitation data -Climate Forecast System Reanalysis (CFSR) and WATCH Forcing Data methodology applied to ERA-Interim (WFDEI) data -with Brazilian Weather Bureau (INMET) and Brazilian Water Agency (ANA) rain gauge data (n = 2027) for the period 1980-2010 in order to evaluate which grid data set better represents precipitation, and is thus more suitable for hydrological modelling of Brazilian water resources. We found that WFDEI outperformed CFSR according to three statistical indicators. We then applied and interpolated a simple bias correction to further improve WFDEI data before we used these data to model river discharge of the Tocantins catchment with the Soil and Water Assessment Tool (SWAT). Calibration (validation in parentheses; weighted averages of all gauges) had satisfactory statistical metrics: p-factor = 0.52 (0.47); r-factor = 0.84 (0.99); R 2 = 0.78 (0.71); bR 2 = 0.68 (0.47); NS = 0.70 (0.66); Pbias = À4.5 (4.0). Finally, the calibrated SWAT model was used to assess the spatial distribution of the catchment's water resources. Annual green water flow (evapotranspiration) increased from the south-east (640-840 mm yr À1 ) to north-west (1140-1440 mm) of the Tocantins catchment, while green water storage (soil water content) increased from south (330-1070 mm) to north (2180-3290 mm). Blue water (water yield) had a less clear pattern, with lower values in the south and the central borders of the catchment (20-560 mm) and higher values along the central axis and the north (920-1460 mm). Our analysis suggested that WFDEI was an accurate representation of Brazilian precipitation. For large catchments, we therefore recommend the use of WFDEI instead of sparse and often missing rain gauge data in modelling Brazilian water resources.

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Section: Discussionmentioning

confidence: 97%

Accuracy of grid precipitation data for Brazil: application in river discharge modelling of the Tocantins catchment

Monteiro

Strauch

Srinivasan

et al. 2015

Hydrological Processes

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“…Fluxes which are computed from the forecasts as accumulations suffer from 'spin-up' from the initial state. Previous work analysing precipitation (e.g., Kobold and Sušelj 2005;Simmons et al 2010;Kållberg 2011;Hawcroft et al 2012;de Leeuw et al 2014;Hawcroft et al 2015) has shown that lead times between 12-24 h are less affected by this problem. Therefore data at 3-hourly intervals extracted from the period 12-24 h in each successive forecast are combined to create each season of data (see Hawcroft et al 2015, Figure 11).…”

Section: Erai Reanalysismentioning

confidence: 96%

Using satellite and reanalysis data to evaluate the representation of latent heating in extratropical cyclones in a climate model

et al. 2016

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exhibit biases in cloud structure, with more clouds at lower altitudes than those observed in ISCCP in the warm conveyor belt region. As a result, latent heat release in ERAI is concentrated at lower altitudes. CloudSat indicates that much precipitation may be produced at too low an altitude in both HiGEM and ERAI, particularly ERAI, and neither capture observed variability in precipitation intensity. The potential vorticity structure in composite extratropical cyclones in HiGEM and ERAI is also compared. A more pronounced tropopause ridge evolves in HiGEM on the leading edge of the composite as compared to ERAI. One future area of research to be addressed is what impact these biases in the representation of latent heating have on climate projections produced by HiGEM. The biases found in ERAI indicate caution is required when using reanalyses to study cloud features and precipitation processes in extratropical cyclones or using reanalysis to evaluate climate models' ability to represent their structure.

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“…Uncertainty in the reanalysis data (e.g., Boilley and Wald, ; Nkiaka et al, ) may affect the results. However, quite good performance of the ERA‐Interim dataset has been found in previous studies (e.g., de Leeuw et al, ; Bumke, ; Nkiaka et al, ). For example, Nkiaka et al .…”

Section: Model Description and Methodologymentioning

confidence: 99%

Impacts of global warming on the surface water balance components in Iran as simulated by RegCM4

Moslemzadeh

Irannejad

Alizadeh

2019

Intl Journal of Climatology

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The impacts of future global warming on the climate of semi‐arid to arid regions of Iran have remained mostly unexplored. Using Regional Climate Model version 4.4 (RegCM4) under the RCP4.5 and RCP8.5 scenarios, changes in surface water balance components in Iran are investigated for the future period 2070 to 2099 relative to the base period 1970 to 1999. The initial and boundary conditions for RegCM4 were derived from the CSIRO‐Mk3.6 general circulation model (GCM). Model results during the base period are compared against the European Centre for Medium‐Range Weather Forecasts (ECMWF) reanalysis Interim (ERA‐Interim) and the Climatic Research Unit (CRU) datasets. Analyses of the ERA‐Interim and CRU datasets and model simulations during the base period indicated that due to the large latitudinal extent of Iran and its complex topography, different regions of the country experience a diverse range of climate, with monthly precipitation ranging from 0.1 mm day−1 over arid regions of southeastern Iran to more than 2.5 mm day−1 over wet coastal regions of northern Iran. In the future period, near‐surface temperatures over all regions of Iran will increase under the two scenarios, with more temperature rises under the RCP8.5 scenario. Under the two scenarios, precipitation over most regions of Iran will increase in winter and decrease in summer. As winter will be warmer in the future under the two scenarios, and a warmer air has a higher water vapour storage capacity, precipitation will increase in the future winter climate. On the other hand, the decrease of precipitation in summer is due to an increase of the altitude of the lifting condensation level under the warmer climate. It is shown that increases in the surface temperature and precipitation in the future winter are associated with an increase of evaporatranspiration in nearly all regions of Iran.

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Evaluation of ERA‐Interim reanalysis precipitation products using England and Wales observations

Cited by 69 publications

References 38 publications

Accuracy of grid precipitation data for Brazil: application in river discharge modelling of the Tocantins catchment

Accuracy of grid precipitation data for Brazil: application in river discharge modelling of the Tocantins catchment

Using satellite and reanalysis data to evaluate the representation of latent heating in extratropical cyclones in a climate model

Impacts of global warming on the surface water balance components in Iran as simulated by RegCM4

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