Assimilating Doppler radar radial velocity and reflectivity observations in the weather research and forecasting model by a proper orthogonal‐decomposition‐based ensemble, three‐dimensional variational assimilation method

Pan, Xiaoduo; Tian, Xiangjun; Li, Xiaoqiong; Xie, Zhenghui; Shao, Aimei; Lu, Chin‐Song

doi:10.1029/2012jd017684

Cited by 25 publications

(23 citation statements)

References 56 publications

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“…The assimilation of radar velocity cannot directly influence the physical process of rainfall, although it can affect the water vapor transport via the atmospheric motions. However, if the assimilated radar velocity cannot improve the atmospheric circulation predictions, the water vapor field may not be improved, and thus the rainfall forecasts may also be 15 found unimproved (Pan et al, 2012;Dong and Xue, 2013). In reality, the accuracy of the radial velocity data depends on the atmospheric refractive index, which is affected by the air density and the water vapor content (Montmerle and Faccani, 2010;Maiello et al, 2014).…”

Section: Discussionmentioning

confidence: 99%

“…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. Sun et al (2012) found that the pattern and location of forecasted rainfall were noticeably improved with radar reflectivity assimilation.…”

Section: Introductionmentioning

confidence: 99%

“…Sun et al (2012) found that the pattern and location of forecasted rainfall were noticeably improved with radar reflectivity assimilation. 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.…”

Section: Introductionmentioning

confidence: 99%

“…10 Doppler radar can provide two types of observations for assimilation, i.e., radar reflectivity and radial velocity. Both have been found to have positive impacts on NWP rainfall simulations and forecasts, especially for heavy rainfall events (Li et al, 2012). 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).…”

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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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

View full text Add to dashboard Cite

show abstract

“…Single observation tests must be performed before the real data experiments. In the current single observation test, num_pseudo was set to 1 for each control variable at the grid point (9,30,30), which is the location relative to the total (40,60,60). The first dimension is the vertical model layer (9/40), the second dimension is latitude (30/60), and the last dimension is longitude (30/60).…”

Section: Single Observation Testmentioning

confidence: 99%

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

Pan

Cheng

et al. 2017

Remote Sensing

Self Cite

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Individually, ground-based, in situ observations, remote sensing, and regional climate modeling cannot provide the high-quality precipitation data required for hydrological prediction, especially over complex terrains. Data assimilation techniques can be used to bridge the gap between observations and models by assimilating ground observations and remote sensing products into models to improve precipitation simulation and forecasting. However, only a small portion of satellite-retrieved precipitation products assimilation research has been implemented over complex terrains in an arid region. Here, we used the weather research and forecasting (WRF) model to assimilate two satellite precipitation products (The Tropical Rainfall Measuring Mission: TRMM 3B42 and Fengyun-2D: FY-2D) using the 4D-Var data assimilation method for a typical inland river basin in northwest China's arid region, the Heihe River Basin, where terrains are very complex. The results show that the assimilation of remote sensing precipitation products can improve the initial WRF fields of humidity and temperature, thereby improving precipitation forecasting and decreasing the spin-up time. Hence, assimilating TRMM and FY-2D remote sensing precipitation products using WRF 4D-Var can be viewed as a positive step toward improving the accuracy and lead time of numerical weather prediction models, particularly over regions with complex terrains.

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The influence of erroneous background, beam-blocking and microphysical non-linearity on the application of a four-dimensional variational Doppler radar data assimilation system for quantitative precipitation forecasts

et al. 2014

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A series of observation system simulation experiments (OSSEs) and a real case study are conducted to investigate the application of the Doppler radar data assimilation technique for numerical model quantitative precipitation forecasts (QPFs). A four-dimensional variational Doppler radar analysis system (VDRAS) is adopted for all experiments. The first set of OSSEs demonstrates that when the background field contains the imperfect information predicted from a mesoscale model, the incorrect convective-scale perturbations in the background can result in spurious scattered precipitation. However, a smoothing procedure can be used to remove the fine structures from the primitive model output in order to avoid this over-prediction. Results from the second set of OSSEs indicate that the lack of low-elevation data owing to radar scan and/or beam blockage could significantly alter the retrieved low-level thermal and dynamical structures when a different number of data assimilation cycles is applied. These impacts could lower the rainfall forecast capability of the model. The third set of OSSEs shows that, when the rainwater is assimilated over a long assimilation window, the non-linearity embedded in the microphysical process could lead the minimization algorithm in a wrong direction, causing a further degradation of the rainfall prediction. However, using multiple short assimilation cycles produces better minimization and forecast results than those obtained with a single long cycle. A real case experiment based on data collected during Intensive Operation Period (IOP) #8 of the 2008 Southwest Monsoon Experiment (SoWMEX) is conducted to provide a verification of the conclusions obtained from OSSEs under a realistic framework.

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Assimilating Doppler radar radial velocity and reflectivity observations in the weather research and forecasting model by a proper orthogonal‐decomposition‐based ensemble, three‐dimensional variational assimilation method

Cited by 25 publications

References 56 publications

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

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

Effects of 4D-Var Data Assimilation Using Remote Sensing Precipitation Products in a WRF Model over the Complex Terrain of an Arid Region River Basin

The influence of erroneous background, beam-blocking and microphysical non-linearity on the application of a four-dimensional variational Doppler radar data assimilation system for quantitative precipitation forecasts

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