Differences between downscaling with spectral and grid nudging using WRF

Liu, P.; Tsimpidi, Alexandra P.; Hu, Yongtao; Stone, Brian; Russell, Armistead G.; Nenes, Athanasios

doi:10.5194/acp-12-3601-2012

Cited by 138 publications

(139 citation statements)

References 29 publications

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“…However, while doing so, you also damage the physical correlation between the produced climatic variables, bringing higher uncertainty to the final product (Radu et al, 2008;Liu et al, 2012;Bullock et al, 2014). Data assimilation has the similar effect as it imports external data source into the modeling process (Fujita et al, 2007).…”

Section: Model Initializationmentioning

confidence: 93%

The Polar WRF Downscaled Historical and Projected Twenty-First Century Climate for the Coast and Foothills of Arctic Alaska

et al. 2018

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Climate change is most pronounced in the northern high latitude region. Yet, climate observations are unable to fully capture regional-scale dynamics due to the sparse weather station coverage, which limits our ability to make reliable climate-based assessments. A set of simulated data products was therefore developed for the North Slope of Alaska through a dynamical downscaling approach. The polar-optimized Weather Research and Forecast (Polar WRF) model was forced by three sources: The ERA-interim reanalysis data (for 1979-2014), the Community Earth System Model 1.0 (CESM1.0) historical simulation (for 1950-2005), and the CESM1.0 projected (for 2006-2100) simulations in two Representative Concentration Pathways (RCP4.5 and RCP8.5) scenarios. Climatic variables were produced in a 10-km grid spacing and a 3-h interval. The ERA-interim forced WRF (ERA-WRF) proves the value of dynamical downscaling, which yields more realistic topographical-induced precipitation and air temperature, as well as corrects underestimations in observed precipitation. In summary, dry and cold biases to the north of the Brooks Range are presented in ERA-WRF, while CESM forced WRF (CESM-WRF) holds wet and warm biases in its historical period. A linear scaling method allowed for an adjustment of the biases, while keeping the majority of the variability and extreme values of modeled precipitation and air temperature. CESM-WRF under RCP 4.5 scenario projects smaller increase in precipitation and air temperature than observed in the historical CESM-WRF product, while the CESM-WRF under RCP 8.5 scenario shows larger changes. The fine spatial and temporal resolution, long temporal coverage, and multi-scenario projections jointly make the dataset appropriate to address a myriad of physical and biological changes occurring on the North Slope of Alaska.

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Section: Model Initializationmentioning

confidence: 93%

The Polar WRF Downscaled Historical and Projected Twenty-First Century Climate for the Coast and Foothills of Arctic Alaska

et al. 2018

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“…This does not dismiss the fact that spectral nudging might outperform grid nudging in many other applications (e.g. Liu et al, 2012), but it seems to have little effect in the mean annual wind.…”

Section: Sensitivity To Nudging and Spin-up Periodmentioning

confidence: 99%

Wind climate estimation using WRF model output: method and model sensitivities over the sea

et al. 2014

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High-quality tall mast and wind lidar measurements over the North and Baltic Seas are used to validate the wind climatology produced from winds simulated by the Weather, Research and Forecasting (WRF) model in analysis mode. Biases in annual mean wind speed between model and observations at heights around 100 m are smaller than 3.2% at offshore sites, except for those that are affected by the wake of a wind farm or the coastline. These biases are smaller than those obtained by using winds directly from the reanalysis. We study the sensitivity of the WRF-simulated wind climatology to various model setup parameters. The results of the year-long sensitivity simulations show that the long-term mean wind speed simulated by the WRF model offshore in the region studied is quite insensitive to the global reanalysis, the number of vertical levels, and the horizontal resolution of the sea surface temperature used as lower boundary conditions. Also, the strength and form (grid vs spectral) of the nudging is quite irrelevant for the mean wind speed at 100 m. Large sensitivity is found to the choice of boundary layer parametrization, and to the length of the period that is discarded as spin-up to produce a wind climatology. It is found that the spin-up period for the boundary layer winds is likely larger than 12 h over land and could affect the wind climatology for points offshore for quite a distance downstream from the coast.

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“…The WRF configuration used for this study consists of the Single Moment-5 class microphysics scheme (Hong et al, 2004), the RRTMG radiation scheme (Mlawer et al, 1997), the Monin-Obukhov surface layer scheme (Janjic, 2003), and the NOAA Land Surface Model scheme for land surface physics (Chen et al, 2001). Sea surface temperature update, surface grid nudging (Liu et al, 2012, Bowden et al, 2012, ocean physics and topographic wind options are activated. Also, 30 the feedback option is activated, meaning that the simulation of the nested domains can influence the parent domain.…”

Section: Model Setupmentioning

confidence: 99%

Simulation of fine organic aerosols in the western Mediterranean area during the ChArMEx 2013 summer campaign

Cholakian¹,

Beekmann²,

Colette³

et al. 2017

Preprint

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especially at several hundred meters height from the surface; however over the Gulf of Genoa near the surface, the anthropogenic origin is of similar importance. A general assessment of other species was performed to evaluate the robustness of the simulations for this particular domain before evaluating OA simulation schemes. It is also shown that the Cap Corse site presents important orographic complexity which makes comparison between model simulations and observations difficult. A method was designed to estimate an orographic representativeness error 5 for a list of species and yields an uncertainty of between 50-85% for primary pollutants, and around 2-10% for secondary species, for these species model to observations comparisons are only little impacted by orography.

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Differences between downscaling with spectral and grid nudging using WRF

Cited by 138 publications

References 29 publications

The Polar WRF Downscaled Historical and Projected Twenty-First Century Climate for the Coast and Foothills of Arctic Alaska

The Polar WRF Downscaled Historical and Projected Twenty-First Century Climate for the Coast and Foothills of Arctic Alaska

Wind climate estimation using WRF model output: method and model sensitivities over the sea

Simulation of fine organic aerosols in the western Mediterranean area during the ChArMEx 2013 summer campaign

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