The impact of chemical lateral boundary conditions on CMAQ predictions of tropospheric ozone over the continental United States

Abstract There is a growing demand for regional-scale climate predictions and assessments. Quantifying the impacts of uncertainty in initial conditions and lateral boundary forcing data on regional model simulations can potentially add value to the usefulness of regional climate modeling. Results from a regional model depend on the realism of the driving data from either global model outputs or global analyses; therefore, any biases in the driving data will be carried through to the regional model. This study used four popular global analyses and achieved 16 driving datasets by using different interpolation procedures. The spread of the 16 datasets represents a possible range of driving data based on analyses to the regional model. This spread is smaller than typically associated with global climate model realizations of the Arctic climate. Three groups of 16 realizations were conducted using the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) in an Arctic domain, varying both initial and lateral boundary conditions, varying lateral boundary forcing only, and varying initial conditions only. The response of monthly mean atmospheric states to the variations in initial and lateral driving data was investigated. Uncertainty in the regional model is induced by the interaction between biases from different sources. Because of the nonlinearity of the problem, contributions from initial and lateral boundary conditions are not additive. For monthly mean atmospheric states, biases in lateral boundary conditions generally contribute more to the overall uncertainty than biases in the initial conditions. The impact of initial condition variations decreases with the simulation length while the impact of variations in lateral boundary forcing shows no clear trend. This suggests that the representativeness of the lateral boundary forcing plays a critical role in long-term regional climate modeling. The extent of impact of the driving data uncertainties on regional climate modeling is variable dependent. For some sensitive variables (e.g., precipitation, boundary layer height), even the interior of the model may be significantly affected.

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Cloud-Resolving Ensemble Simulations of Mediterranean Heavy Precipitating Events: Uncertainty on Initial Conditions and Lateral Boundary Conditions

Monthly Weather Review ◽

10.1175/2010mwr3487.1 ◽

2011 ◽

Vol 139 (2) ◽

pp. 403-423 ◽

Cited By ~ 82

Author(s):

Benoît Vié ◽

Olivier Nuissier ◽

Véronique Ducrocq

Keyword(s):

Boundary Conditions ◽

Case Studies ◽

Short Range ◽

Initial Conditions ◽

Ensemble Prediction ◽

Lateral Boundary ◽

Synoptic Scale ◽

Lateral Boundary Conditions ◽

Convective Scale ◽

The Impact

Abstract This study assesses the impact of uncertainty on convective-scale initial conditions (ICs) and the uncertainty on lateral boundary conditions (LBCs) in cloud-resolving simulations with the Application of Research to Operations at Mesoscale (AROME) model. Special attention is paid to Mediterranean heavy precipitating events (HPEs). The goal is achieved by comparing high-resolution ensembles generated by different methods. First, an ensemble data assimilation technique has been used for assimilation of perturbed observations to generate different convective-scale ICs. Second, three ensembles used LBCs prescribed by the members of a global short-range ensemble prediction system (EPS). All ensembles obtained were then evaluated over 31- and/or 18-day periods, and on 2 specific case studies of HPEs. The ensembles are underdispersive, but both the probabilistic evaluation of their overall performance and the two case studies confirm that they can provide useful probabilistic information for the HPEs considered. The uncertainty on convective-scale ICs is shown to have an impact at short range (under 12 h), and it is strongly dependent on the synoptic-scale context. Specifically, given a marked circulation near the area of interest, the imposed LBCs rapidly overwhelm the initial differences, greatly reducing the spread of the ensemble. The uncertainty on LBCs shows an impact at longer range, as the spread in the coupling global ensemble increases, but it also depends on the synoptic-scale conditions and their predictability.

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Soil Field Model Interoperability: Challenges and Impact on Screen Temperature Forecast Skill during the Nordic Winter

Journal of Hydrometeorology ◽

10.1175/jhm-d-11-095.1 ◽

2012 ◽

Vol 13 (4) ◽

pp. 1215-1232 ◽

Cited By ~ 3

Author(s):

Jørn Kristiansen ◽

Dag Bjørge ◽

John M. Edwards ◽

Gabriel G. Rooney

Keyword(s):

Soil Moisture ◽

High Resolution ◽

Boundary Conditions ◽

Limited Area ◽

Lateral Boundary ◽

Error Source ◽

Main Error ◽

Lateral Boundary Conditions ◽

The Impact ◽

Grid Length

Abstract The high-resolution (4-km grid length) Met Office (UKMO) Unified Model forecasts driven by the coarser-resolution (8-km grid length) High-Resolution Limited-Area Model (HIRLAM), UM4, often produce significantly colder screen-level (2 m) temperatures in winter over Norway than forecast with HIRLAM itself. To diagnose the main error source of this cold bias this study focuses on the forecast initial and lateral boundary conditions, particularly the initialization of soil moisture and temperature. The soil variables may be used differently by land surface schemes of varying complexity, representing a challenge to model interoperability. In a set of five experiments, daily UM4 forecasts are driven by alternating initial and lateral boundary conditions from two different parent models: HIRLAM and Met Office North Atlantic and Europe (NAE). The experiment period is November 2007. Points for scientific examination into the topics of model interoperability and sensitivity to soil initial conditions are identified. The soil moisture is the main error source and is therefore important also in winter, rather than being a challenge only in summer. The day-to-day variability in the forecast error is large with the larger errors on days with strong longwave heat loss at the surface (i.e., the forecast sensitivity to soil moisture content is significant but variable). The much drier soil in HIRLAM compared to NAE reduces the heat capacity of the soil layers and affects the heat flux from the surface soil layer to the surface. Normalizing the respective soil moisture fields reduces these differences. The impact of ground snow is quite limited.

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Review of comparison of chemical lateral boundary conditions for air quality print directions over the contiguous United States during intrusion events by Tang et al.

10.5194/acp-2020-587-rc1 ◽

2020 ◽

Author(s):

Anonymous

Keyword(s):

United States ◽

Air Quality ◽

Boundary Conditions ◽

Lateral Boundary ◽

Lateral Boundary Conditions

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Effect of boundary conditions on adjoint-based forecast sensitivity observation impact in a regional model

Journal of Atmospheric and Oceanic Technology ◽

10.1175/jtech-d-20-0040.1 ◽

2021 ◽

Author(s):

Hyun Mee Kim ◽

Dae-Hui Kim

Keyword(s):

Boundary Conditions ◽

Forecast Error ◽

Error Reduction ◽

Regional Model ◽

Buffer Size ◽

Lateral Boundary ◽

Modeling Framework ◽

Lateral Boundary Conditions ◽

Observation Impact ◽

The Impact

AbstractIn this study, the effect of boundary condition configurations in the regional Weather Research and Forecasting (WRF) model on the adjoint-based forecast sensitivity observation impact (FSOI) for 24 h forecast error reduction was evaluated. The FSOI has been used to diagnose the impact of observations on the forecast performance in several global and regional models. Different from the global model, in the regional model, the lateral boundaries affect forecasts and FSOI results. Several experiments with different lateral boundary conditions were conducted. The experimental period was from 1 to 14 June 2015. With or without data assimilation, the larger the buffer size in lateral boundary conditions, the smaller the forecast error. The nonlinear and linear forecast error reduction (i.e., observation impact) decreased as the buffer size increased, implying larger impact of lateral boundaries and smaller observation impact on the forecast error. In all experiments, in terms of observation types (variables), upper-air radiosonde observations (brightness temperature) exhibited the greatest observation impact. The ranking of observation impacts was consistent for observation types and variables among experiments with a constraint in the response function at the upper boundary. The fractions of beneficial observations were approximately 60%, and did not considerably vary depending on the boundary conditions specified when calculating the FSOI in the regional modeling framework.

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Comparing Impacts of Satellite Data Assimilation and Lateral Boundary Conditions on Regional Model Forecasting: A Case Study of Hurricane Sandy (2012)

Weather and Forecasting ◽

10.1175/waf-d-16-0077.1 ◽

2017 ◽

Vol 32 (2) ◽

pp. 595-608

Author(s):

Tong Zhu ◽

Sid Ahmed Boukabara ◽

Kevin Garrett

Keyword(s):

Data Assimilation ◽

Boundary Conditions ◽

Satellite Data ◽

Regional Model ◽

Hurricane Sandy ◽

Track Forecast ◽

Lateral Boundary ◽

Lateral Boundary Conditions ◽

Outer Domain ◽

The Impact

Abstract The impacts of both satellite data assimilation (DA) and lateral boundary conditions (LBCs) on the Hurricane Weather Research and Forecasting (HWRF) Model forecasts of Hurricane Sandy 2012 were assessed. To investigate the impact of satellite DA, experiments were run with and without satellite data assimilated, as well as with all satellite data but excluding Geostationary Operational Environmental Satellite (GOES) Sounder data. To gauge the LBC impact, these experiments were also run with a variety of outer domain (D-1) sizes. The inclusion of satellite DA resulted in analysis fields that better characterized the tropical storm structures including the warm core anomaly and wavenumber-1 asymmetry near the eyewall, and also served to reduce the forecast track errors for Hurricane Sandy. The specific impact of assimilating the GOES Sounder data showed positive impacts on forecasts of the storm minimum sea level pressure. Increasing the D-1 size resulted in increases in the day 4/5 forecast track errors when verified against the best track and the Global Forecast System (GFS) forecast, which dominated any benefits from assimilating an increased volume of satellite observations due to the larger domain. It was found that the LBCs with realistic environmental flow information could provide better constraints on smaller domain forecasts. This study demonstrated that satellite DA can improve the analysis of a hurricane asymmetry, especially in a shear environment, and then lead to a better track forecast, and also emphasized the importance of the LBCs and the challenges associated with the evaluation of satellite data impacts on regional model prediction.

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The Effect of the Chemical Lateral Boundary Conditions on CMAQ Simulations of Tropospheric Ozone for East Asia

Journal of Korean Society for Atmospheric Environment ◽

10.5572/kosae.2012.28.5.581 ◽

2012 ◽

Vol 28 (5) ◽

pp. 581-594 ◽

Cited By ~ 6

Author(s):

Sung-Chul Hong ◽

Jae-Bum Lee ◽

Jin-Young Choi ◽

Kyung-Jung Moon ◽

Hyun-Ju Lee ◽

...

Keyword(s):

Boundary Conditions ◽

East Asia ◽

Tropospheric Ozone ◽

Lateral Boundary ◽

Lateral Boundary Conditions

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Simulations of Present and Future Climates in the Western United States with Four Nested Regional Climate Models

Journal of Climate ◽

10.1175/jcli3669.1 ◽

2006 ◽

Vol 19 (6) ◽

pp. 873-895 ◽

Cited By ~ 81

Author(s):

P. B. Duffy ◽

R. W. Arritt ◽

J. Coquard ◽

W. Gutowski ◽

J. Han ◽

...

Keyword(s):

United States ◽

Boundary Conditions ◽

Water Content ◽

Climate Models ◽

Regional Climate ◽

Regional Climate Models ◽

Western United States ◽

Lateral Boundary ◽

Present Climate ◽

Lateral Boundary Conditions

Abstract In this paper, the authors analyze simulations of present and future climates in the western United States performed with four regional climate models (RCMs) nested within two global ocean–atmosphere climate models. The primary goal here is to assess the range of regional climate responses to increased greenhouse gases in available RCM simulations. The four RCMs used different geographical domains, different increased greenhouse gas scenarios for future-climate simulations, and (in some cases) different lateral boundary conditions. For simulations of the present climate, RCM results are compared to observations and to results of the GCM that provided lateral boundary conditions to the RCM. For future-climate (increased greenhouse gas) simulations, RCM results are compared to each other and to results of the driving GCMs. When results are spatially averaged over the western United States, it is found that the results of each RCM closely follow those of the driving GCM in the same region in both present and future climates. This is true even though the study area is in some cases a small fraction of the RCM domain. Precipitation responses predicted by the RCMs in many regions are not statistically significant compared to interannual variability. Where the predicted precipitation responses are statistically significant, they are positive. The models agree that near-surface temperatures will increase, but do not agree on the spatial pattern of this increase. The four RCMs produce very different estimates of water content of snow in the present climate, and of the change in this water content in response to increased greenhouse gases.

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Impacts of Mountain–Plains Solenoid on Diurnal Variations of Rainfalls along the Mei-Yu Front over the East China Plains

Monthly Weather Review ◽

10.1175/mwr-d-11-00041.1 ◽

2012 ◽

Vol 140 (2) ◽

pp. 379-397 ◽

Cited By ~ 68

Author(s):

Jianhua Sun ◽

Fuqing Zhang

Keyword(s):

Boundary Conditions ◽

Diurnal Variations ◽

Central China ◽

East China ◽

Lateral Boundary ◽

Moist Convection ◽

Lateral Boundary Conditions ◽

Mesoscale Convective ◽

The Impact ◽

Convective Vortices

Convection-permitting numerical experiments using the Weather Research and Forecasting (WRF) model are performed to examine the impact of a thermally driven mountain–plains solenoid (MPS) on the diurnal variations of precipitation and mesoscale convective vortices along the mei-yu front over the east China plains during 1–10 July 2007. The focus of the analyses is a 10-day simulation that used the 10-day average of the global analysis at 0000 UTC as the initial condition and the 10-day averages every 6 h as lateral boundary conditions (with diurnal variations only). Despite differences in the rainfall intensity and location, this idealized experiment successfully simulated the observed diurnal variation and eastward propagation of rainfall and mesoscale convective vortices along the mei-yu front. It was found that the upward branch of the MPS, along with the attendant nocturnal low-level jet, is primarily responsible for the midnight-to-early-morning rainfall enhancement along the mei-yu front. The MPS is induced by differential heating between the high mountain ranges in central China and the low-lying plains in east China. Diabatic heating from moist convection initiated and/or enhanced by the solenoid circulation subsequently leads to the formation of a mesoscale convective vortex that further organizes and amplifies moist convection while propagating eastward along the mei-yu front. The downward branch of the MPS, on the other hand, leads to the suppression of precipitation over the plains during the daytime. The impacts of this regional MPS on the rainfall diurnal variations are further attested to by another idealized WRF simulation that uses fixed lateral boundary conditions.

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