A Tutorial on Lateral Boundary Conditions as a Basic and Potentially Serious Limitation to Regional Numerical Weather Prediction

Warner, Thomas T.; Peterson, Ralph A.; Treadon, Russell E.

doi:10.1175/1520-0477(1997)078<2599:atolbc>2.0.co;2

Cited by 401 publications

(290 citation statements)

References 35 publications

(29 reference statements)

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“…One shortcoming of this approach is the transmission of GCM biases through the RCM lateral and lower boundaries, which may have a severe impact on the interior climate (e.g. Warner et al 1997;Done et al 2013).…”

Section: Introductionmentioning

confidence: 99%

Bias corrections of global models for regional climate simulations of high-impact weather

et al. 2013

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All global circulation models (GCMs) suffer from some form of bias, which when used as boundary conditions for regional climate models may impact the simulations, perhaps severely. Here we present a bias correction method that corrects the mean error in the GCM, but retains the sixhourly weather, longer-period climate-variability and climate change from the GCM. We utilize six different bias correction experiments; each correcting different bias components. The impact of the full bias correction and the individual components are examined in relation to tropical cyclones, precipitation and temperature. We show that correcting of all boundary data provides the greatest improvement.

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Section: Introductionmentioning

confidence: 99%

Bias corrections of global models for regional climate simulations of high-impact weather

et al. 2013

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“…The use of nested LAMs or RCMs as a climate downscaling technique, indeed, involves a number of issues, one of which is related to the lateral boundary conditions (LBCs) (Giorgi and Mearns 1999;Denis et al 2002). This drawback of RCMs is related to the fact that one is obliged to impose imperfect LBCs, inducing various errors at the boundaries (e.g., Warner et al 1997;Termonia et al 2009). Despite this, past and current applications with RCMs have shown that the one-way nesting strategy is a workable solution (Giorgi and Mearns 1999).…”

Section: Introductionmentioning

confidence: 99%

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

et al. 2013

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Daily summer precipitation over Belgium from the Aire Limitee Adaptation Dynamique Developpement International (ALADIN) model and a version of the model that has been updated with physical parameterizations, the so-called ALARO-0 model [ALADIN and AROME (Application de la Recherche a l'Operationnel a Meso-Echelle) combined model, first baseline version released in 1998], are compared with respect to station observations for the period 1961-90. The 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) is dynamically downscaled using both models on a horizontal resolution of 40 km, followed by a one-way nesting on high spatial resolutions of 10 and 4 km. This setup allows us to explore the relative importance of spatial resolution versus parameterization formulation on the model skill to correctly simulate extreme daily precipitation. Model performances are assessed through standard statistical errors and density, frequency, and quantile distributions as well as extreme value analysis, using the peak-over-threshold method and generalized Pareto distribution. The 40-km simulations of ALADIN and ALARO-0 show similar results, both reproducing the observations reasonably well. For the high-resolution simulations, ALARO-0 at both 10 and 4 km is in better agreement with the observations than ALADIN. The ALADIN model consistently produces too high precipitation rates. The findings demonstrate that the new parameterizations within the ALARO-0 model are responsible for a correct simulation of extreme summer precipitation at various horizontal resolutions. Moreover, this study shows that ALARO-0 is a good candidate model for regional climate modeling.

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“…There are many studies evaluating the value added by dynamical downscaling with the perfect boundary experiment (e.g., Castro et al 2005;Fu et al 2005;Xue et al 2007;Gao et al 2011). In addition to errors due to the quality of lateral boundary condition data (e.g., Gong and Wang 2000;Xue et al 2012), known sources of uncertainty generated through RCM experiments are horizontal resolution (e.g., Christensen et al 1998;Mo et al 2000;Wang et al 2003;Zhang et al 2003;Castro et al 2005;Xue et al 2007;Sato et al 2008), domain size and location (e.g., Treadon and Petersen 1993;Xue et al 2007;Gao et al 2011), lateral boundary settings (Denis et al 2003;Giorgi and Mearns 1999;Warner et al 1997), and physics schemes Liang et al 2004;Wang et al 2004). Meanwhile, the nudging setup (including nudging method and coefficient for time scale) has been introduced to overcome the problems caused by the above mentioned factors (e.g., Marbaix et al 2003;Gong and Wang 2000;Xu and Yang 2012).…”

Section: Introductionmentioning

confidence: 99%

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

Sato

Xue

2012

Clim Dyn

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Performance of a regional climate model (RCM), WRF, for downscaling East Asian summer season climate is investigated based on 11-summer integrations associated with different climate conditions with reanalysis data as the lateral boundary conditions. It is found that while the RCM is essentially unable to improve large-scale circulation patterns in the upper troposphere for most years, it is able to simulate better lower-level meridional moisture transport in the East Asian summer monsoon. For precipitation downscaling, the RCM produces more realistic magnitude of the interannual variation in most areas of East Asia than that in the reanalysis. Furthermore, the RCM significantly improves the spatial pattern of summer rainfall over dry inland areas and mountainous areas, such as Mongolia and the Tibetan Plateau. Meanwhile, it reduces the wet bias over southeast China. Over Mongolia, however, the performance of precipitation downscaling strongly depends on the year: the WRF is skillful for normal and wet years, but not for dry years, which suggests that land surface processes play an important role in downscaling ability. Over the dry area of North China, the WRF shows the worst performance. Additional sensitivity experiments testing land effects in downscaling suggest the initial soil moisture condition and representation of land surface processes with different schemes are sources of uncertainty for precipitation downscaling. Correction of initial soil moisture using the climatology dataset from GSWP-2 is a useful approach to robustly reducing wet bias in inland areas as well as to improve spatial distribution of precipitation. Despite the improvement on RCM downscaling, regional analyses reveal that accurate simulation of precipitation over East China, where the precipitation pattern is strongly influenced by the activity of the Meiyu/ Baiu rainfall band, is difficult. Since the location of the rainfall band is closely associated with both lower-level meridional moisture transport and upper-level circulation structures, it is necessary to have realistic upper-air circulation patterns in the RCM as well as lower-level moisture transport in order to improve the circulation-associated convective rainfall band in East Asia.

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A Tutorial on Lateral Boundary Conditions as a Basic and Potentially Serious Limitation to Regional Numerical Weather Prediction

Cited by 401 publications

References 35 publications

Bias corrections of global models for regional climate simulations of high-impact weather

Bias corrections of global models for regional climate simulations of high-impact weather

Multiscale Performance of the ALARO-0 Model for Simulating Extreme Summer Precipitation Climatology in Belgium

Validating a regional climate model’s downscaling ability for East Asian summer monsoonal interannual variability

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