An IVAP-Based Dealiasing Method for Radar Velocity Data Quality Control

Liang, Xudong; Xie, Yanxin; Yin, Jinfang; Luo, Yi; Yao, Dan; Li, Feng

doi:10.1175/jtech-d-18-0216.1

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Besides, radar retrieval horizontal wind, which is retrieved by an improved version of the Integrating Velocity-Azimuth Process (IVAP) method (Liang et al, 2019), will be ingested by performing upper-level observation nudging. Most importantly, from 2019 onward, we will shift to using the ERA5 products as initial and boundary conditions for the WRF model.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

East Asia Reanalysis System (EARS)

Jinfang

Liang

Xie

et al. 2023

Preprint

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Abstract. Reanalysis data plays a vital role in weather and climate study, as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre of Medium-Range Weather Forecasts (ECMWF) global reanalysis EAR-Interim data at 6-h intervals; and hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in a three-dimensional variational data assimilation (3D-VAR) mode at analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12-km horizontal resolution, 74 sigma levels, and 3-hour intervals. Finally, an evaluation of EARS has been performed with the respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of the EAR-Interim, the regional reanalysis data of the EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of China Meteorological Administration (CMA) at http://data.cma.cn.

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Section: Conclusion and Future Outlookmentioning

confidence: 99%

East Asia Reanalysis System (EARS)

Jinfang

Liang

Xie

et al. 2023

Preprint

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show abstract

“…In the future, we will further inspect the regional high-resolution data against the observations from the Second Tibetan Plateau Scientific Expedition and Research (STEP) program, in particular using it in high-resolution studies over the East Asian monsoon region. In addition, radar retrieval horizontal wind, which is retrieved by an improved version of the integrating velocity-azimuth process (IVAP) method (Liang et al, 2019), will be ingested by performing upper-level observation nudging. Most importantly, from 2019 onward, we will shift to using the ERA5 products as initial and boundary conditions for the WRF model.…”

Section: Conclusion and Future Outlookmentioning

confidence: 99%

East Asia Reanalysis System (EARS)

Jinfang

Liang

Xie

et al. 2023

Earth Syst. Sci. Data

Self Cite

View full text Add to dashboard Cite

Abstract. Reanalysis data play a vital role in weather and climate study as well as meteorological resource development and application. In this work, the East Asia Reanalysis System (EARS) was developed using the Weather Research and Forecasting (WRF) model and the Gridpoint Statistical Interpolations (GSI) data assimilation system. The regional reanalysis system is forced by the European Centre for Medium-Range Weather Forecasts (ECMWF) global reanalysis ERA-Interim data at 6 h intervals. Hourly surface observations are assimilated by the Four-Dimension Data Assimilation (FDDA) scheme during the WRF model integration; upper observations are assimilated in three-dimensional variational data assimilation (3D-VAR) mode at the analysis moment. It should be highlighted that many of the assimilated observations have not been used in other reanalysis systems. The reanalysis runs from 1980 to 2018, producing a regional reanalysis dataset covering East Asia and surrounding areas at 12 km horizontal resolution, 74 sigma levels, and 3 h intervals. Finally, an evaluation of EARS has been performed with respect to the root mean square error (RMSE), based on the 10-year (2008–2017) observational data. Compared to the global reanalysis data of ERA-Interim, the regional reanalysis data of EARS are closer to the observations in terms of RMSE in both surface and upper-level fields. The present study provides evidence for substantial improvements seen in EARS compared to the ERA-Interim reanalysis fields over East Asia. The study also demonstrates the potential use of the EARS data for applications over East Asia and proposes further plans to provide the latest reanalysis in real-time operation mode. Simple data and updated information are available on Zenodo at https://doi.org/10.5281/zenodo.7404918 (Yin et al., 2022), and the full datasets are publicly accessible on the Data-as-a-Service platform of the China Meteorological Administration (CMA) at http://data.cma.cn (last access: 19 May 2023).

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Velocity estimation of thunderstorm movement and dealiasing of single Doppler radar during convective events

Samos,

Flocas,

Louka

et al. 2024

Acta Geophys.

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The use of meteorological radars in monitoring current weather conditions is crucial regarding the observation of the evolution and dissipation of thunderstorms. Thus, Doppler velocities being measured in each radar scan and velocity vectors derived from Numerical Weather Prediction (NWP) models—that are usually not as highly resolving as radar scans—are combined, as a monitoring utility during the evolution of a convective weather event. The objective of this study is to develop a new method that allows the implementation of a thunderstorm movement velocity estimation technique combining block matching and optical flow techniques. This new method constitutes a nowcasting (NWC) application that enables the use of a single Doppler radar without the need of using NWPs. The method relies on the estimation of the thunderstorm movement vector velocities (Doppler velocity) for each constant altitude plan position indicator (CAPPI) and through correction for aliasing errors to obtain 3D vector velocity fields for convective systems. The performance of the method is evaluated for selected case studies of convective thunderstorms under different synoptic scale conditions over Greece, a geographical area with challenges in forecasting due to its sharp relief and the need for optimization of the use of radar products.

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An IVAP-Based Dealiasing Method for Radar Velocity Data Quality Control

Cited by 6 publications

References 24 publications

East Asia Reanalysis System (EARS)

East Asia Reanalysis System (EARS)

East Asia Reanalysis System (EARS)

Velocity estimation of thunderstorm movement and dealiasing of single Doppler radar during convective events

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