Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR System for Short-Range Forecasting of Convective Storms

Abhilash, S.; Sahai, A. K.; Mohankumar, K.; George, John P.; Das, Someshwar

doi:10.1007/s00024-012-0462-z

Cited by 21 publications

(20 citation statements)

References 47 publications

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“…The resulting analysis constitutes the best compromise between the model representation and observations. We selected the three-dimensional variational assimilation (3DVAR) method available for the WRF system (Barker et al, 2004. The aim of variational data assimilation is to find the best least-square fit between a background field x b and observations y o with an iterative minimisation of a cost function J(x) (Ide et al, 1997) JðxÞ ¼ 1…”

Section: Model Setup and Data Assimilationmentioning

confidence: 99%

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Bauer¹,

Schwitalla²,

Wulfmeyer³

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

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A B S T R A C T Quantitative precipitation estimation and forecasting (QPE and QPF) are among the most challenging tasks in atmospheric sciences. In this work, QPE based on numerical modelling and data assimilation is investigated. Key components are the Weather Research and Forecasting (WRF) model in combination with its 3D variational assimilation scheme, applied on the convection-permitting scale with sophisticated model physics over central Europe. The system is operated in a 1-hour rapid update cycle and processes a large set of in situ observations, data from French radar systems, the European GPS network and satellite sensors. Additionally, a free forecast driven by the ECMWF operational analysis is included as a reference run representing current operational precipitation forecasting. The verification is done both qualitatively and quantitatively by comparisons of reflectivity, accumulated precipitation fields and derived verification scores for a complex synoptic situation that developed on 26 and 27 September 2012. The investigation shows that even the downscaling from ECMWF represents the synoptic situation reasonably well. However, significant improvements are seen in the results of the WRF QPE setup, especially when the French radar data are assimilated. The frontal structure is more defined and the timing of the frontal movement is improved compared with observations. Even mesoscale bandlike precipitation structures on the rear side of the cold front are reproduced, as seen by radar. The improvement in performance is also confirmed by a quantitative comparison of the 24-hourly accumulated precipitation over Germany. The mean correlation of the model simulations with observations improved from 0.2 in the downscaling experiment and 0.29 in the assimilation experiment without radar data to 0.56 in the WRF QPE experiment including the assimilation of French radar data.

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Section: Model Setup and Data Assimilationmentioning

confidence: 99%

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Bauer¹,

Schwitalla²,

Wulfmeyer³

et al. 2015

Tellus A: Dynamic Meteorology and Oceanography

View full text Add to dashboard Cite

show abstract

“…Unfortunately, both the air density and the water vapor content are quite variably, especially on rainy days (Abdalla and Cavaleri, 2002). Therefore, the spatial observation errors in the radial velocity retrievals are unavoidable and might be the main factor that leads to poorer performance of the NWP model than that achieved without data 20 assimilation (Abhilash et al, 2012). Due to the frequent adjustment of the atmospheric motions, decreasing the assimilation time interval may reduce the risk of over correction (Xiao et al, 2005).…”

Section: Discussionmentioning

confidence: 99%

“…However, only assimilating radar velocity in Domain 2 or assimilating both types of radar data in Domain 2 made the rainfall forecast unsatisfactory. The observation errors in the radial velocity may have been the main factor that led to the poorest performance (Abhilash et al, 2012). Another reason is that assimilating radial velocity can only change the dynamic field, which changes quickly for small-scale regions, and 6 h may have been too long for the 5 assimilation time interval (Lin et al, 2011).…”

Section: [Table 2]mentioning

confidence: 99%

“…Based on the operating principle of Doppler radar, reflectivity contains information on the number of falling drops per unit volume, which depends on the hydrometeor number and size, whereas radial velocity is related to vertical atmospheric motions (Maiello et al, 2014). This means that assimilating reflectivity impacts the thermodynamic and dynamic fields, 15 whereas radial velocity assimilation only influences the dynamic fields (Xiao and Sun, 2007;Abhilash et al, 2012). Li et al (2012) indicated that assimilating radial velocity every 30 min could improve the accuracy of rainfall (caused by hurricane) intensity prediction.…”

Section: Introductionmentioning

confidence: 99%

“…Pan et al (2012) found that the WRF model can capture rainfall distributions in time and space better by assimilating both Doppler radar reflectivity and radial velocity. Abhilash et al (2012) went further and 20 compared the assimilation effects of radar reflectivity and radial velocity. Results showed that the assimilation of both radar reflectivity and radial velocity significantly improved most meteorological elements, including wind, temperature, moisture and rainfall.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Doppler radar and GTS Data Assimilation for NWP Rainfall Prediction of an Extreme Summer Storm in Northern China: from the Hydrological Perspective

Liu¹,

Tian²,

Yan³

et al. 2018

Preprint

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Abstract. Data assimilation is an effective tool in improving high-resolution rainfall of the numerical weather prediction 10 (NWP) systems which always fails in providing satisfactory rainfall products for hydrological use. The aim of this study is to explore the potential effects of assimilating different sources of observations from the Doppler weather radar and the Global Telecommunication System (GTS) in improving the mesoscale NWP rainfall products. A 24 h summer storm occurring over the Beijing-Tianjin-Hebei region of northern China on 21 July 2012 is selected in this study. The Weather Research and Forecasting (WRF) model is used to obtain 3 km rainfall forecasts, and the observations are assimilated using the three-15 dimensional variational (3D-Var) data assimilation method. Eleven data assimilation modes are designed for assimilating different combinations of observations in the two nested domains of the WRF model. Results show that the assimilation can largely improve the WRF rainfall products especially the accumulative process of rainfall, which is of great importance for hydrologic applications through the rainfall-runoff transformation process. Both radar reflectivity and GTS data are good choices for assimilation in improving the rainfall products, whereas special attentions should be paid for assimilating radial 20 velocity where unsatisfactory results are always found. Simultaneously assimilating GTS and radar data always perform better than assimilating radar data alone. The inclusion of GTS data in the nested domains when radar reflectivity and radial velocity are assimilated in the innermost domain show the best results among all the 11 assimilation modes. The assimilation efficiency of the GTS data is higher than both radar reflectivity and radial velocity considering the number of data assimilated and its effect. It is also found that the assimilation of more observations cannot guarantee further improvement of 25 the rainfall products, whereas the effective information contained in the assimilated data is of more importance than the data quantity. Potential improvements of data assimilation in improving the NWP rainfall products are discussed and suggestions are also made.Hydrol. Earth Syst. Sci. Discuss., https://doi

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Assimilation of Doppler radar radial wind data in the GRAPES mesoscale model with observation error covariances tuning

Yin

Han

Gao

et al. 2021

Quart J Royal Meteoro Soc

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The accurate estimation of observation error statistics for high-density remote-sensing data is a fundamental step toward optimally utilizing such data, including radar data, for operational numerical weather prediction. Based on the high-resolution (3 × 3 km) GRAPES model together with the three-dimensional variational assimilation system (GRAPES_3DVAR) developed by the China Meteorological Administration, a set of assimilation processes for radar radial wind data is designed. First, a scheme is proposed to guarantee the quality of radar radial wind data by correcting the velocity ambiguity for the data. The observation error covariances of the radar data in the GRAPES_3DVAR assimilation system are re-estimated by using a diagnostic method based on analysis residuals, showing that the re-estimated radial wind observation error covariances increase with altitude. An optimal regularization parameter is selected by the L-curve method to constrain the radar observations that were assimilated using inaccurate observation error covariances. Furthermore, to verify the assimilation effects of the refined radial velocity observation error covariances and the constrained observations by the regularization parameter on the analysis and forecasting, a numerical study is carried out on typhoon Yunque, which occurred on 3 August 2018. The results show that the modified Doppler radar wind data assimilation using the re-estimated radial velocity observation error covariances enhances the wind analysis field and the water vapour transport, and accordingly obtains better short-term precipitation estimates. The radar wind data assimilation results of using the constrained observations also indicate an enhanced wind analysis field and water vapour transport, which leads to better short-term precipitation estimates.

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Assimilation of Doppler Weather Radar Radial Velocity and Reflectivity Observations in WRF-3DVAR System for Short-Range Forecasting of Convective Storms

Cited by 21 publications

References 47 publications

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Quantitative precipitation estimation based on high-resolution numerical weather prediction and data assimilation with WRF – a performance test

Evaluation of Doppler radar and GTS Data Assimilation for NWP Rainfall Prediction of an Extreme Summer Storm in Northern China: from the Hydrological Perspective

Assimilation of Doppler radar radial wind data in the GRAPES mesoscale model with observation error covariances tuning

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