Convective‐scale assimilation of radar data: progress and challenges

Sun, Juanzhen

doi:10.1256/qj.05.149

Cited by 137 publications

(109 citation statements)

References 108 publications

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“…Research on data assimilation methods for models with kilometer-scale resolution (so-called cloudresolving, or cloud-permitting models) is still in its infancy (Sun, 2005), and many methods are being explored (Sun and Crook, 1998;Caya et al, 2005;Kawabata et al, 2007). The most common method in operational use at the time of writing is latent heat nudging (LHN: Jones and Macpherson, 1997;Leuenberger, 2005;Leuenberger and Rossa, 2007;Montmerle et al, 2007;Stephan et al, 2008;Dixon et al, 2009), although a variety of more advanced techniques have been studied in research contexts.…”

Section: Radar Data Assimilation: One Avenue To Improving Srnwp Qpfmentioning

confidence: 99%

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

Rossa¹,

Liechti

Zappa

et al. 2011

Atmospheric Research

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Publication informationAtmospheric Research, 100 (2-3): 150-167Publisher Elsevier This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT A C C E P T E D M A N U S C R I P T ACCEPTED MANUSCRIPT AbstractQuantifying uncertainty in flood forecasting is a difficult task, given the multiple and strongly nonlinear model components involved in such a system. Much effort has been and is being invested in the quest of dealing with uncertain precipitation observations and forecasts and the propagation of such uncertainties through hydrological and hydraulic models predicting river discharges and risk for inundation. The COST 731 Action is one of these and constitutes a European initiative which deals with the quantification of forecast uncertainty in hydro-meteorological forecast systems.COST 731 addresses three major lines of development: (1) combining meteorological and hydrological models to form a forecast chain, (2) propagating uncertainty information through this chain and make it available to end users in a suitable form, (3)

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Section: Radar Data Assimilation: One Avenue To Improving Srnwp Qpfmentioning

confidence: 99%

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

Rossa¹,

Liechti

Zappa

et al. 2011

Atmospheric Research

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“…Since NWP models are sensitive to initial conditions, setting up accurate initial conditions is critical in short-term forecasting. Thus, additional quality control procedures are required for radar-data assimilation besides normal radar data quality control techniques (e.g., removing ground clutters, non-meteorological echoes, sun strobes) [10], while those high-quality data may not be available in an emergent situation [3]. Secondly, radar-only nowcasting requires significantly less computational resources than NWP models.…”

Section: Tropical Cyclone and Precipitation Nowcastingmentioning

confidence: 99%

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

Tang

Matyas

2018

Atmosphere

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Accurate observational data and reliable prediction models are both essential to improve the quality of precipitation forecasts. The spiraling trajectories of air parcels within a tropical cyclone (TC) coupled with the large sizes of these systems brings special challenges in making accurate short-term forecasts, or nowcasts. Doppler weather radars are ideal instruments to observe TCs when they move over land, and traditional nowcasts incorporate radar data. However, data from dozens of radars must be mosaicked together to observe the entire system. Traditional single-radar-based reflectivity tracking methods commonly employed in nowcasting are not suitable for TCs as they are not able to capture the circular motion of these systems. Thus, this paper focuses on improving short-term predictability of TC precipitation with Doppler weather radar observations based on: a multi-scale motion vector retrieval algorithm, an optimization technique and a semi-Lagrangian advection scheme. Motion fields of precipitation regions are obtained by a multi-level motion vector retrieval algorithm, then corrected and smoothed by the optimization technique using mass and smooth constraints. Predicted precipitation regions are then extrapolated using the semi-Lagrangian advection scheme. A case study of Hurricane Isabel (2003) shows that the combination of these methods may increase reliable rainfall prediction to about 5 h as the TC moves over land.

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“…To study the influences of variations in data assimilation cycle lengths on forecasts, and to implement shorter updating of model background states, HF radar data were temporally linearly interpolated onto intervals less than sixty minutes. The reasons for using temporally interpolated radar data are, firstly, Sun [45] suggested that it was desirable to have rapid update cycles to capture the temporal change and to obtain useful information from the model background states. The radar data were not regarded as a single snap-shot by Sun [45], but each point in the three-dimensional data volume was assimilated at the timestep closest to the measurement time in a sequential scan.…”

Section: Tests Of Data Assimilation Cycle Lengthsmentioning

confidence: 99%

“…The reasons for using temporally interpolated radar data are, firstly, Sun [45] suggested that it was desirable to have rapid update cycles to capture the temporal change and to obtain useful information from the model background states. The radar data were not regarded as a single snap-shot by Sun [45], but each point in the three-dimensional data volume was assimilated at the timestep closest to the measurement time in a sequential scan. Secondly, in operational data assimilation forecasting systems, measurements from different resources at different timesteps can be assimilated into models.…”

Section: Tests Of Data Assimilation Cycle Lengthsmentioning

confidence: 99%

Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data

Ren

Hartnett

2017

Remote Sensing

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Abstract:In order to improve the forecasting ability of numerical models, a sequential data assimilation scheme, nudging, was applied to blend remotely sensing high-frequency (HF) radar surface currents with results from a three-dimensional numerical, EFDC (Environmental Fluid Dynamics Code) model. For the first time, this research presents the most appropriate nudging parameters, which were determined from sensitivity experiments. To examine the influence of data assimilation cycle lengths on forecasts and to extend forecasting improvements, the duration of data assimilation cycles was studied through assimilating linearly interpolated temporal radar data. Data assimilation nudging parameters have not been previously analyzed. Assimilation of HF radar measurements at each model computational timestep outperformed those assimilation models using longer data assimilation cycle lengths; root-mean-square error (RMSE) values of both surface velocity components during a 12 h model forecasting period indicated that surface flow fields were significantly improved when implementing nudging assimilation at each model computational timestep. The Data Assimilation Skill Score (DASS) technique was used to quantitatively evaluate forecast improvements. The averaged values of DASS over the data assimilation domain were 26% and 33% for east-west and north-south velocity components, respectively, over the half-day forecasting period. Correlation of Averaged Kinetic Energy (AKE) was improved by more than 10% in the best data assimilation model. Time series of velocity components and surface flow fields were presented to illustrate the improvement resulting from data assimilation application over time.

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Convective‐scale assimilation of radar data: progress and challenges

Cited by 137 publications

References 108 publications

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

The COST 731 Action: A review on uncertainty propagation in advanced hydro-meteorological forecast systems

A Nowcasting Model for Tropical Cyclone Precipitation Regions Based on the TREC Motion Vector Retrieval with a Semi-Lagrangian Scheme for Doppler Weather Radar

Hindcasting and Forecasting of Surface Flow Fields through Assimilating High Frequency Remotely Sensing Radar Data

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