Simple Adjoint Retrievals of Microburst Winds from Single-Doppler Radar Data

Xu, Qin; Qiu, Chongjian; Gu, Hengfei; Yu, Jin-Xiang

doi:10.1175/1520-0493(1995)123<1822:saromw>2.0.co;2

Cited by 43 publications

(25 citation statements)

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“…Techniques of using single-Doppler radar to recover the unobserved cross-beam wind components have been developed since the 1990s (e.g., Sun et al 1991;Shapiro et al 1995;Xu et al 1995;Liou and Luo 2001). On the other hand, when data from more than one radar are available, numerous methods of using multiple-radar (usually two) measurements to reconstruct a full three-dimensional wind field have also been developed since the late 1960s by adopting the traditional geometric approach (e.g., Armijo 1969;Ray et al 1975;Doviak et al 1976;Brandes 1977;Ray et al 1978), or more recently the variational approach (e.g., Scialom and Lemaitre 1990;Protat and Zawadzki 1999;Shapiro and Mewes 1999;Gao et al 1999Gao et al , 2004.…”

Section: A Multiple-doppler Wind Synthesis Methodsmentioning

confidence: 99%

“…Their study indicated that the general storm evolution could be well predicted within a period of 35 min. Zhao et al (2006) presented that significant improvements can be achieved in the three-dimensional wind forecasts by assimilating the flow field obtained from a three-and-a-half-dimensional variational data assimilation (3.5DVAR) system (Xu et al 2001), along with the thermodynamic variables retrieved by a scheme similar to G78 into the U.S. Navy's Coupled Ocean-Atmosphere Mesocale Prediction System (COAMPS).…”

Section: Introductionmentioning

confidence: 99%

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Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

Liou

Chiou

Chen

et al. 2014

Monthly Weather Review

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This research combines an advanced multiple-Doppler radar synthesis technique with the thermodynamic retrieval method, originally proposed by Gal-Chen, and a moisture/temperature adjustment scheme, and formulates a sequential procedure. The focus is on applying this procedure to improve the model quantitative precipitation nowcasting (QPN) skill in the convective scale up to 3 hours. A series of (observing system simulation experiment) OSSE-type tests and a real case study are conducted to investigate the performance of this algorithm under different conditions. It is shown that by using the retrieved three-dimensional wind, thermodynamic, and microphysical parameters to reinitialize a fine-resolution numerical model, its QPN skill can be significantly improved. Since the Gal-Chen method requires the horizontal average properties of the weather system at each altitude, utilization of in situ radiosonde(s) to obtain this additional information for the retrieval is tested. When sounding data are not available, it is demonstrated that using the model results to replace the role played by observing devices is also a feasible choice. The moisture field is obtained through a simple, but effective, adjusting scheme and is found to be beneficial to the rainfall forecast within the first hour after the reinitialization of the model.Since this algorithm retrieves the unobserved state variables instantaneously from the wind measurements and directly uses them to reinitialize the model, fewer radar data and a shorter model spinup time are needed to correct the rainfall forecasts, in comparison with other data assimilation techniques such as fourdimensional variational data assimilation (4DVAR) or ensemble Kalman filter (EnKF) methods.

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Section: A Multiple-doppler Wind Synthesis Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

Liou

Chiou

Chen

et al. 2014

Monthly Weather Review

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“…Examples of past research include the simple adjoint method (Qiu and Xu 1992;Xu et al 1995;Xu et al 2001a;Xu et al 2001b). The simple adjoint method retrieves the unobserved cross-beam winds by tracking, using a system involving the time integration of a two-dimensional advection equation and its adjoint.…”

Section: Nssl's Approachmentioning

confidence: 99%

Development of NEXRAD Wind Retrievals as Input to Atmospheric Dispersion Models

Fast¹,

Newsom²,

Allwine³

et al. 2007

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SummaryTwo techniques that derive spatially varying wind fields from operational radar measurements have been evaluated in this study. Based on results, we propose that one technique be implemented operationally in a phased approach. The other technique would be suitable for operational implementation only after additional modifications have been made and evaluated. These two techniques would require additional funding to implement, maintain operational versions of these codes, and perform additional research to improve their accuracy.Atmospheric dispersion models (ADMs) are used by emergency-response organizations to address the consequences of potential airborne releases of harmful materials. The wind fields in ADMs are based on meteorological measurements and forecasts made by atmospheric models. In the United States, routine meteorological measurements from surface stations, towers, rawinsondes, and radar wind profilers are available to drive ADMs. Large metropolitan areas usually have a network of surface stations; however, the wind-speed and direction measurements from these stations are usually not representative of conditions above the surface. There are currently relatively few measurements that can characterize the winds above the surface. One potentially rich, yet untapped, source of meteorological data for routine use in ADMs is from the Next Generation Weather Radar (NEXRAD) systems with 141 nearly identical WSR-88D Doppler radar units installed throughout the United States.The objective of this study is to determine the feasibility that routinely collected data from the Doppler radars can appropriately be used in ADMs for emergency response. We have evaluated the computational efficiency and accuracy of two variational mathematical techniques that derive the u-and v-components of the wind from radial velocities obtained from Doppler radars. A review of the scientific literature indicated that the techniques employ significantly different approaches in applying the variational techniques: 2-D Variational (2DVar), developed by the National Oceanic and Atmospheric Administration's National Severe Storms Laboratory and Variational Doppler Radar Analysis System (VDRAS), developed by the National Center for Atmospheric Research. We designed a series of numerical experiments in which both models employed the same horizontal domain and resolution encompassing Oklahoma City for a 2-week period during the summer of 2003 so that the computed wind retrievals could be fairly compared. Both models ran faster than real-time on one typical dual-processor computer, indicating that they could be used to generate wind retrievals in near real-time. The simpler approach of 2DVar allowed it to execute ~2.5 faster than VDRAS. The simpler approach of 2DVar allowed it to execute ~2.5 times faster than VDRAS.The accuracy of 2DVar and VDRAS is quantified by comparing the derived wind components with independent, meteorological measurements made by radar wind profilers. Both systems qualitatively reproduced the observed diurn...

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“…In this method, a simplified reflectivity advection equation is used to predict the reflectivity and the time-mean velocity that advects the reflectivity field in this equation is estimated by minimizing the difference between the radar observed and predicted reflectivity fields. The method was then refined and successfully tested with many real radar observations [7][8]. Built on the above success, a three-dimensional simple adjoint (3D-SA) method was developed by Xu et al [9].…”

Section: Introductionmentioning

confidence: 99%

Retrieving wind field in vertical cross-section from phased-array radar echo movement

Qiu

2010

The 2nd International Conference on Information Science and Engineering

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The previously developed 2-dimensional simple adjoint method is upgraded to retrieve wind field in vertical cross-section from phased array radar rapid scans. The upgraded method includes two steps. First, the horizontal wind field is retrieved on the conical surface at each elevation angle of radar scan. Second, the vertical velocity field is retrieved with the horizontal advection terms given from the first step. adjoint method; wind retrieval; phased array radar I.

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Simple Adjoint Retrievals of Microburst Winds from Single-Doppler Radar Data

Cited by 43 publications

References 0 publications

Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

Improving the Model Convective Storm Quantitative Precipitation Nowcasting by Assimilating State Variables Retrieved from Multiple-Doppler Radar Observations

Development of NEXRAD Wind Retrievals as Input to Atmospheric Dispersion Models

Retrieving wind field in vertical cross-section from phased-array radar echo movement

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