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

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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Estimating the Uncertainty in a Regional Climate Model Related to Initial and Lateral Boundary Conditions

Journal of Climate ◽

10.1175/jcli-3293.1 ◽

2005 ◽

Vol 18 (7) ◽

pp. 917-933 ◽

Cited By ~ 72

Author(s):

Wanli Wu ◽

Amanda H. Lynch ◽

Aaron Rivers

Keyword(s):

Boundary Conditions ◽

Climate Model ◽

Initial Conditions ◽

Climate Modeling ◽

Regional Climate ◽

Regional Climate Modeling ◽

Regional Model ◽

Lateral Boundary ◽

Lateral Boundary Conditions ◽

The Impact

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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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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Regional data assimilation of multi-spectral MOPITT observations of CO over North America

Atmospheric Chemistry and Physics ◽

10.5194/acp-15-6801-2015 ◽

2015 ◽

Vol 15 (12) ◽

pp. 6801-6814 ◽

Cited By ~ 18

Author(s):

Z. Jiang ◽

D. B. A. Jones ◽

J. Worden ◽

H. M. Worden ◽

D. K. Henze ◽

...

Keyword(s):

High Resolution ◽

Data Assimilation ◽

Boundary Conditions ◽

North American ◽

A Priori ◽

Sensitivity Analyses ◽

Small Scale ◽

Lateral Boundary ◽

Free Troposphere ◽

Lateral Boundary Conditions

Abstract. Chemical transport models (CTMs) driven with high-resolution meteorological fields can better resolve small-scale processes, such as frontal lifting or deep convection, and thus improve the simulation and emission estimates of tropospheric trace gases. In this work, we explore the use of the GEOS-Chem four-dimensional variational (4D-Var) data assimilation system with the nested high-resolution version of the model (0.5° × 0.67°) to quantify North American CO emissions during the period of June 2004–May 2005. With optimized lateral boundary conditions, regional inversion analyses can reduce the sensitivity of the CO source estimates to errors in long-range transport and in the distributions of the hydroxyl radical (OH), the main sink for CO. To further limit the potential impact of discrepancies in chemical aging of air in the free troposphere, associated with errors in OH, we use surface-level multispectral MOPITT (Measurement of Pollution in The Troposphere) CO retrievals, which have greater sensitivity to CO near the surface and reduced sensitivity in the free troposphere, compared to previous versions of the retrievals. We estimate that the annual total anthropogenic CO emission from the contiguous US 48 states was 97 Tg CO, a 14 % increase from the 85 Tg CO in the a priori. This increase is mainly due to enhanced emissions around the Great Lakes region and along the west coast, relative to the a priori. Sensitivity analyses using different OH fields and lateral boundary conditions suggest a possible error, associated with local North American OH distribution, in these emission estimates of 20 % during summer 2004, when the CO lifetime is short. This 20 % OH-related error is 50 % smaller than the OH-related error previously estimated for North American CO emissions using a global inversion analysis. We believe that reducing this OH-related error further will require integrating additional observations to provide a strong constraint on the CO distribution across the domain. Despite these limitations, our results show the potential advantages of combining high-resolution regional inversion analyses with global analyses to better quantify regional CO source estimates.

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

Environmental Fluid Mechanics ◽

10.1007/s10652-008-9092-5 ◽

2008 ◽

Vol 9 (1) ◽

pp. 43-58 ◽

Cited By ~ 55

Author(s):

Youhua Tang ◽

Pius Lee ◽

Marina Tsidulko ◽

Ho-Chun Huang ◽

Jeffery T. McQueen ◽

...

Keyword(s):

United States ◽

Boundary Conditions ◽

Tropospheric Ozone ◽

Lateral Boundary ◽

Lateral Boundary Conditions ◽

The Impact

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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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The Impact of Multisatellite Data on the Initialization and Simulation of Hurricane Lili’s (2002) Rapid Weakening Phase

Monthly Weather Review ◽

10.1175/mwr3287.1 ◽

2007 ◽

Vol 135 (2) ◽

pp. 526-548 ◽

Cited By ~ 55

Author(s):

Xiaoyan Zhang ◽

Qingnong Xiao ◽

Patrick J. Fitzpatrick

Keyword(s):

Data Assimilation ◽

Satellite Data ◽

Inner Core ◽

Surface Wind ◽

Storm Track ◽

Outer Core ◽

Intensity Change ◽

Track Forecast ◽

Surface Winds ◽

The Impact

Abstract Numerical experiments have been conducted to examine the impact of multisatellite data on the initialization and forecast of the rapid weakening of Hurricane Lili (in 2002) from 0000 UTC to landfall in Louisiana on 1300 UTC 3 October 2002. Fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) 4DVAR sensitivity runs were conducted separately with QuikSCAT surface winds, the Geostationary Operational Environmental Satellite-8 (GOES-8) cloud drift–water vapor winds, and Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) temperature–dewpoint sounding data to investigate their individual impact on storm track and intensity. The results were compared against a simulation initialized from a Global Forecast System background interpolated to the MM5 grid. Assimilating QuikSCAT surface wind data improves the analyzed outer-core surface winds, as well as the inner-core low-level temperature and moisture fields. Substantial adjustments of winds are noted on the periphery of the hurricane by assimilating GOES-8 satellite-derived upper-level winds. Both track forecasts initialized at 1200 UTC 2 October 2002 with four-dimensional variational data assimilation (4DVAR) of QuikSCAT and GOES-8 show improvement compared to those initialized with the model background. Assimilating Aqua MODIS sounding data improves the outer-core thermodynamic features. The Aqua MODIS data has a slight impact on the track forecast, but more importantly shows evidence of impacting the model intensity predicting by retarding the incorrect prediction of intensification. All three experiments also show that bogusing of an inner-core wind vortex is required to depict the storm’s initial intensity. To properly investigate Lili’s weakening, data assimilation experiments that incorporate bogusing vortex, QuikSCAT winds, GOES-8 winds, and Aqua MODIS sounding data were performed. The 4DVAR satellite-bogus data assimilation is conducted in two consecutive 6-h windows preceding Lili’s weakening. Comparisons of the results between the experiments with and without satellite data indicated that the satellite data, particularly the Aqua MODIS sounding information, makes an immediate impact on the hurricane intensity change beyond normal bogusing procedures. The track forecast with the satellite data is also more accurate than just using bogusing alone. This study suggests that dry air intrusion played an important role in Lili’s rapid weakening. It also demonstrates the potential benefit of using satellite data in a 4DVAR context—particularly high-resolution soundings—on unusual cases like Hurricane Lili.

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Impact of Lateral Boundary Conditions on Regional Analyses

Monthly Weather Review ◽

10.1175/mwr-d-16-0245.1 ◽

2017 ◽

Vol 145 (4) ◽

pp. 1361-1379 ◽

Cited By ~ 3

Author(s):

Kamel Chikhar ◽

Pierre Gauthier

Keyword(s):

Data Assimilation ◽

Boundary Conditions ◽

Large Scale ◽

Climate Model ◽

Systematic Errors ◽

Global Climate Models ◽

Lateral Boundary ◽

Time Frequency ◽

Lateral Boundary Conditions ◽

Regional Analyses

Abstract Regional and global climate models are usually validated by comparison to derived observations or reanalyses. Using a model in data assimilation results in a direct comparison to observations to produce its own analyses that may reveal systematic errors. In this study, regional analyses over North America are produced based on the fifth-generation Canadian Regional Climate Model (CRCM5) combined with the variational data assimilation system of the Meteorological Service of Canada (MSC). CRCM5 is driven at its boundaries by global analyses from ERA-Interim or produced with the global configuration of the CRCM5. Assimilation cycles for the months of January and July 2011 revealed systematic errors in winter through large values in the mean analysis increments. This bias is attributed to the coupling of the lateral boundary conditions of the regional model with the driving data particularly over the northern boundary where a rapidly changing large-scale circulation created significant cross-boundary flows. Increasing the time frequency of the lateral driving and applying a large-scale spectral nudging significantly improved the circulation through the lateral boundaries, which translated in a much better agreement with observations.

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