Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radarwind data: Case study of a squall line

Li, Xin; Zeng, Mingjian; Wang, Yuan; Wang, Wenlan; Wu, Haiying; Haixia, Mei

doi:10.1007/s00376-016-5255-3

Cited by 27 publications

(11 citation statements)

References 24 publications

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“…The forecasts are launched twice a day from 1 September to 20 September 2008 to provide a large number of prediction samples for the estimation of background error covariance. According to the differences between samples, estimate the covariance matrix of background error [18], as follows:…”

Section: Wrfda 3dvar Data Assimilation Systemmentioning

confidence: 99%

“…Zhao et al [17] (2008) updated the hydrometers in the NWP model directly by applying general analytic relationship when assimilating the radar reflectivity in two storm cases with promising results. As far as the authors know, there is very few published work with radar reflectivity within the framework of WRFDA with UV control variables (Li et al, (2016) [18], Sun et al, (2016) [13]). The control variables related to hydrometeors have become one of the key technologies to effectively assimilate hydrometeor-related observations, such as radar reflectivity.…”

Section: Introductionmentioning

confidence: 99%

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Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

et al. 2021

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The impact of assimilating radar radial velocity and reflectivity on the analyses and forecast of Hurricane IKE is investigated within the framework of the WRF (Weather Research and Forecasting) model and its three-dimensional variational (3DVar) data assimilation system, including the hydrometeor control variables. Hurricane IKE in the year 2008 was chosen as the study case. It was found that assimilating radar data is able to effectively improve the small-scale information of the hurricane vortex area in the model background. Radar data assimilation experiments yield significant cyclonic wind increments in the inner-core area of the hurricane, enhancing the intensity of the hurricane in the model background. On the other hand, by extending the traditional control variables to include the hydrometeor control variables, the assimilation of radar reflectivity can effectively adjust the water vapor and hydrometeors of the background, further improving the track and intensity forecast of the hurricane. The precipitation forecast skill is also enhanced to some extent with the radar data assimilation, especially with the extended hydrometeor control variables.

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Section: Wrfda 3dvar Data Assimilation Systemmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

et al. 2021

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“…Another natural choice is to use velocity components (U,V) as control variables for wind analyses (CV option 7; CV7), which includes U component, V component, full temperature (T), full surface pressure (Ps), and pseudo-relative humidity (RHs). The effects of the two momentum control variable options-stream function velocity potential (ψ,χ) and horizontal wind components (U,V)-in radar Vr data assimilation have been discussed in previous studies [13]. To assimilate the hydrometers of radar reflectivity data, the total water qt (sum of water vapor, cloud water, and cloud rain) is used as the moisture CV instead of the RH s (Xiao et al, 2007) [14].…”

Section: B Modeling In Wrfda-3dvarmentioning

confidence: 99%

Effect of Adding Hydrometeor Mixing Ratios Control Variables on Assimilating Radar Observations for the Analysis and Forecast of a Typhoon

Shen

Min

2019

Atmosphere

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The variational data assimilation (DA) method seeks the optimal analyses by minimizing a cost function with respect to control variables (CVs). CVs are extended in this study to include hydrometeor mixing ratios related variables besides the widely used sets of CVs (momentum fields, surface pressure, temperature, and pseudo-relative humidity). The impacts of the extra CVs are investigated in terms of hydrometeor mixing ratios to the assimilation of radar radial velocity (Vr) and reflectivity (RF) for the analysis and prediction of Typhoon Chanthu (2010). It is found that the background error statistics of the extended CVs from the National Meteorological Center (NMC) method is reliable. The track forecast is improved significantly by including hydrometeor mixing ratios as CVs to assimilate radar Vr and RF. The DA experiments using the hydrometer CVs show much improved intensity analysis and forecast. It also improves the precipitation forecast skills to some extent. The positive impact is significant using a direct RF assimilation scheme, when Vr and RF data are applied together. It suggests that when we applying an indirect RF assimilation scheme, the fitting of more hydrometers in the cost function will tend to cause a slight degradation for other variables such as the wind and temperature.

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“…The system assimilates observations of the surface and upper air, multiple radar data, the Cross-track Infrared Sounder, and the Advanced Himawari Imager radiance as well as plenty of other sources (Li et al, 2016;Li X et al, 2019). The model physical schemes utilize the WRF Single-Moment 6-class Microphysics (Hong, 2006a), the Monin-Obukhov surface layer (Monin and Obukhov, 1954), the Noah land surface (Chen and Dudhia, 2001), the Yonsei University planetary boundary layer (Hong S. Y. et al, 2006), the rapid radiative transfer model longwave radiation (Mlawer et al, 1997), the Dudhia shortwave radiation (Dudhia, 1989), and the Kain-Fritsch cumulus parameterization (Kain and Fritsch, 1992;Kain, 2004).…”

Section: Model Descriptionmentioning

confidence: 99%

Forecasts of the Warm-Sector Heavy Rainfall With a Warm Shear Pattern Over Coastal Areas of the Yangtze–Huaihe River in a Regional Business Forecast Model

et al. 2022

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Based on the observational hourly precipitation data and the ERA5 reanalysis datasets, the short-term forecasts of the warm-sector heavy rainfall with warm-type shear line (WRWS) events over the coastal areas of the Yangtze–Huaihe River (YHR) are investigated in the regional business model Precision Weather Analysis and Forecasting System (PWAFS). Evaluations and diagnoses are carried out via objective estimations and composition analyses for the rainy season of 2017. Results show that the short-term forecasts of PWAFS are characterized by considerable skills for WRWS events in the coastal areas of YHR in view of the object-based diagnostic evaluation, which, however, tend to generate the rain belts with northeast shift phases and weaker intensities. Meantime, the threat score results for WRWS-associated processes show that the model forecasting skill declines sharply as the precipitation intensity increases. Moreover, composition differences of the synoptic-scale thermodynamic characteristics between observations and forecast results are diagnosed to reveal the possible mechanisms of the short-term forecast biases toward WRWS. The zonal westerlies are overestimated in the model, while the southerlies are underestimated in the lower troposphere over coastal areas of YHR, leading to the northeastward shifted shear line and the absent moisture channel associated with the East China Sea at the boundary layer. Attributed to these atmospheric circulation biases, the accumulated warm and moist energy is weaker at the boundary layer, and hence, the short-term forecasts of the rain-belt location for WRWS over the YHR coastal areas have northeast shifting phases with weaker intensities of precipitation in forecasts of the regional business model PWAFS.

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Evaluation of two momentum control variable schemes and their impact on the variational assimilation of radarwind data: Case study of a squall line

Cited by 27 publications

References 24 publications

Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

Applications of Radar Data Assimilation with Hydrometeor Control Variables within the WRFDA on the Prediction of Landfalling Hurricane IKE (2008)

Effect of Adding Hydrometeor Mixing Ratios Control Variables on Assimilating Radar Observations for the Analysis and Forecast of a Typhoon

Forecasts of the Warm-Sector Heavy Rainfall With a Warm Shear Pattern Over Coastal Areas of the Yangtze–Huaihe River in a Regional Business Forecast Model

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