Four-Dimensional Variational Data Assimilation for Limited-Area Models: Lateral Boundary Conditions, Solution Uniqueness, and Numerical Convergence

Abstract. A major linear mesoscale convective system caused severe weather over northern France, Belgium, the Netherlands and northwestern Germany on 3 January 2014. The storm was classified as a cold-season derecho with widespread wind gusts exceeding 25 m s−1. While such derechos occasionally develop along cold fronts of extratropical cyclones, this system formed in a postfrontal air mass along a baroclinic surface pressure trough and was favoured by a strong large-scale air ascent induced by an intense mid-level jet. The lower-tropospheric environment was characterised by weak latent instability and strong vertical wind shear. Given the poor operational forecast of the storm, we analyse the role of initial and lateral boundary conditions to the storm's development by performing convection-resolving limited-area simulations with operational analysis and reanalysis datasets. The storm is best represented in simulations with high temporally and spatially resolved initial and lateral boundary conditions derived from ERA5, which provide the most realistic development of the essential surface pressure trough. Moreover, simulations at convection-resolving resolution enable a better representation of the observed derecho intensity. This case study is testimony to the usefulness of ensembles of convection-resolving simulations in overcoming the current shortcomings of forecasting cold-season convective storms, particularly for cases not associated with a cold front.

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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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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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Formulation of the Lateral Boundary Conditions for the NCAR Limited-Area Model

Journal of Applied Meteorology ◽

10.1175/1520-0450(1974)013<0008:fotlbc>2.0.co;2 ◽

1974 ◽

Vol 13 (1) ◽

pp. 8-16 ◽

Cited By ~ 16

Author(s):

David L. Williamson ◽

Gerald L. Browning

Keyword(s):

Boundary Conditions ◽

Limited Area ◽

Lateral Boundary ◽

Limited Area Model ◽

Area Model ◽

Lateral Boundary Conditions

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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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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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