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Current Status and Future Challenges of Weather Radar Polarimetry: Bridging the Gap between Radar Meteorology/Hydrology/Engineering and Numerical Weather Prediction

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Advances in Atmospheric Sciences 2019年第6期36卷 571-588页

作者： Guifu ZHANG Vivek N.MAHALE Bryan J.PUTNAM Youcun QI Qing CAO ANDrew D.BYRD Petar BUKOVCIC Dusan S.ZRNIC Jidong GAO Ming XUE Youngsun JUNG Heather D.REEVES Pamela L.HEINSELMAN AlexANDer RYZHKOV Robert D.PALMER Pengfei ZHANG Mark WEBER Greg M.MCFARQUHAR Berrien MOORE III Yan ZHANG Jian ZHANG J.VIVEKANANDAN Yasser AL-RASHID Richard L.ICE Daniel S.BERKOWITZ Chong-chi TONG Caleb FULTON Richard J.DOVIAK University of Oklahoma noaa/national Weather Service Enterprise Electronics Corporation noaa/national severe storms laboratory national Center for Atmospheric Research Raytheon Company Radar Operations Center

After decades of research and development, the WSR-88 D(NEXRAD) network in the United States was upgraded with dual-polarization capability, providing polarimetric radar data(PRD) that have the potential to improve weather observations,quantification, forecasting, and warnings. The weather radar networks in China and other countries are also being upgraded with dual-polarization capability. Now, with radar polarimetry technology having matured, and PRD available both nationally and globally, it is important to understand the current status and future challenges and opportunities. The potential impact of PRD has been limited by their oftentimes subjective and empirical use. More importantly, the community has not begun to regularly derive from PRD the state parameters, such as water mixing ratios and number concentrations, used in numerical weather prediction(NWP) models.In this review, we summarize the current status of weather radar polarimetry, discuss the issues and limitations of PRD usage, and explore potential approaches to more efficiently use PRD for quantitative precipitation estimation and forecasting based on statistical retrieval with physical constraints where prior information is used and observation error is included. This approach aligns the observation-based retrievals favored by the radar meteorology community with the model-based analysis of the NWP community. We also examine the challenges and opportunities of polarimetric phased array radar research and development for future weather observation.

关键词： weather radar polarimetry radar meteorology numerical weather prediction data assimilation microphysics parameterization forward operator

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Synchronization of Radar Observations with Multi-Scale Storm Tracking

Synchronization of Radar Observations with Multi-Scale Storm...

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2011年海峡两岸气象科学技术研讨会

作者：杨洪平 Jian Zhang Carrie Langston Cooperative Institute for Mesoscale Meteorological Studies The University of Oklahomaand NOAA/OAR National Severe Storms Laboratory

＜正＞The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation.The reflectivity data are typically objectively analyzed onto 3-D Cartesian grid prior to being assimilated into the models.Existing multi-radar mosaic techniques usually perform spatial objective analysis of multiple radar data by assuming all the observations are valid at the same time.The assumption holds well for slow evolving precipitation systems.For fast evolving convective storms,this assumption may be violated and the mosaic of different radar volume scans observed at different time may result in inaccurate storm structure depictions.This study investigates synchronization techniques based on motion fields from a multi-scale storm tracking algorithm and their impact on the 3D radar mosaic analyses.

关键词： Synchronization Radar Multi-Scale Storm Tracking 3-D Mosaic

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Parameterized Forward Operators for Simulation and Assimilation of Polarimetric Radar Data with Numerical Weather Predictions

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Advances in Atmospheric Sciences 2021年第5期38卷 737-754页

作者： Guifu ZHANG Jidong GAO Muyun DU School of Meteorology University of OklahomaNormanOK 73072USA national severe storms laboratory/noaa NormanOK 73072USA Institute of Heavy Rain CMAWuhan 430205China

Many weather radar networks in the world have now provided polarimetric radar data(PRD)that have the potential to improve our understanding of cloud and precipitation microphysics,and numerical weather prediction(NWP).To realize this potential,an accurate and efficient set of polarimetric observation operators are needed to simulate and assimilate the PRD with an NWP model for an accurate analysis of the model state variables.For this purpose,a set of parameterized observation operators are developed to simulate and assimilate polarimetric radar data from NWP model-predicted hydrometeor mixing ratios and number concentrations of rain,snow,hail,and graupel.The polarimetric radar variables are calculated based on the T-matrix calculation of wave scattering and integrations of the scattering weighted by the particle size distribution.The calculated polarimetric variables are then fitted to simple functions of water content and volumeweighted mean diameter of the hydrometeor particle size distribution.The parameterized PRD operators are applied to an ideal case and a real case predicted by the Weather Research and Forecasting(WRF)model to have simulated PRD,which are compared with existing operators and real observations to show their validity and applicability.The new PRD operators use less than one percent of the computing time of the old operators to complete the same simulations,making it efficient in PRD simulation and assimilation usage.

关键词： forward operators polarimetric radar data data assimilation numerical weather prediction

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Assimilation of GOES-R Geostationary Lightning Mapper Flash Extent Density Data in GSI 3DVar, EnKF, and Hybrid En3DVar for the Analysis and Short-Term Forecast of a Supercell Storm Case

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Advances in Atmospheric Sciences 2024年第2期41卷 263-277页

作者： Rong KONG Ming XUE Edward R.MANSELL Chengsi LIU Alexandre O.FIERRO Center for Analysis and Prediction of storms Norman Oklahoma 73072U.S.A University of Oklahoma Norman Oklahoma 73072U.S.A noaa/national severe storms laboratory NormanOklahoma 73072U.S A.4Zentralanstalt für Meteorologie und Geodynamik Department of Forecasting Models-ZAMGVienna 1190Austria

Capabilities to assimilate Geostationary Operational Environmental Satellite “R-series ”(GOES-R) Geostationary Lightning Mapper(GLM) flash extent density(FED) data within the operational Gridpoint Statistical Interpolation ensemble Kalman filter(GSI-EnKF) framework were previously developed and tested with a mesoscale convective system(MCS) case. In this study, such capabilities are further developed to assimilate GOES GLM FED data within the GSI ensemble-variational(EnVar) hybrid data assimilation(DA) framework. The results of assimilating the GLM FED data using 3DVar, and pure En3DVar(PEn3DVar, using 100% ensemble covariance and no static covariance) are compared with those of EnKF/DfEnKF for a supercell storm case. The focus of this study is to validate the correctness and evaluate the performance of the new implementation rather than comparing the performance of FED DA among different DA schemes. Only the results of 3DVar and pEn3DVar are examined and compared with EnKF/DfEnKF. Assimilation of a single FED observation shows that the magnitude and horizontal extent of the analysis increments from PEn3DVar are generally larger than from EnKF, which is mainly caused by using different localization strategies in EnFK/DfEnKF and PEn3DVar as well as the integration limits of the graupel mass in the observation operator. Overall, the forecast performance of PEn3DVar is comparable to EnKF/DfEnKF, suggesting correct implementation.

关键词： GOES-R lightning data assimilation EnKF EnVar

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Synchronization of Radar Observations with Multi-Scale Storm Tracking

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Advances in Atmospheric Sciences 2009年第1期26卷 78-86页

作者：杨洪平 Jian ZHANG Carrie LANGSTON Wuhan Institute of Heavy Rain China Meteorological Administration Wuhan 430074 Cooperative Institute for Mesoscale Meteorological Studies The University of Oklahoma and NOAA/OAR National Severe Storms Laboratory Norman O.K. U.S.A

The 3-D radar reflectivity data has become increasingly important for use in data assimilation towards convective scale numerical weather prediction as well as next generation precipitation estimation. Typically, reflectivity data from multiple radars are objectively analyzed and mosaiced onto a regional 3-D Cartesian grid prior to being assimilated into the models. One multi-radar observations is the synchronization of all of the scientific issues associated with the mosaic of the observations. Since radar data is usually rapidly updated （-every 5-10 min）, it is common in current multi-radar mosaic techniques to combine multiple radar＇ observations within a time window by assunfing that the storms are steady within the window. The assumption holds well for slow evolving precipitation systems, but for fast evolving convective storms, this assumption may be violated and the mosaic of radar observations at different times may result in inaccurate storm structure depictions. This study investigates the impact of synchronization on storm structures in multiple radar data analyses using a multi-scale storm tracking algorithm.

关键词： synchronization radar multi-scale storm tracking

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Hybrid Methods for Computing the Streamfunction and Velocity Potential for Complex Flow Fields over Mesoscale Domains

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Advances in Atmospheric Sciences 2022年第9期39卷 1417-1431页

作者： Jie CAO Qin XU Haishan CHEN Shuping MA Key laboratory of Meteorological Disaster(KLME) Ministry of Education&Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters(CIC-FEMD)Nanjing University of Information Science&TechnologyNanjing 210044China Cooperative Institute for Mesoscale Meteorological Studies(CIMMS) University of OklahomaNorman 73072OklahomaUSA NOAA/OAR/National Severe Storms Laboratory Norman 73069OklahomaUSA Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

Three types of previously used numerical methods are revisited for computing the streamfunctionψand velocity potentialχfrom the horizontal velocity v in limited domains.The first type,called the SOR-based method,uses a classical successive over-relaxation(SOR)scheme to computeψ(orχ)first with an arbitrary boundary condition(BC)and thenχ(orψ)with the BC derived from v.The second type,called the spectral method,uses spectral formulations to construct the inner part of(ψ,χ)-the inversion of(vorticity,divergence)with a homogeneous BC,and then the remaining harmonic part of(ψ,χ)with BCs from v.The third type,called the integral method,uses integral formulas to compute the internally induced(ψ,χ)-the inversion of domain-internal(vorticity,divergence)using the free-space Greenꞌs function without BCs and then the remaining harmonicψ(orχ)with BCs from v minus the internally-induced part.Although these methods have previously been successfully applied to flows in large-scale and synoptic-scale domains,their accuracy is compromised when applied to complex flows over mesoscale domains,as shown in this paper.To resolve this problem,two hybrid approaches,the integral-SOR method and the integral-spectral method,are developed by combining the first step of the integral method with the second step adopted from the SOR-based and spectral methods,respectively.Upon testing these methods on real-case complex flows,the integral-SOR method is significantly more accurate than the integral-spectral method,noting that the latter is still generally more accurate than the three previously-used methods.The integral-SOR method is recommended for future applications and diagnostic studies of complex flows.

关键词： streamfunction and velocity potential complex flow fields successive over-relaxation method mesoscale study

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Utilization of Specific Attenuation for Rainfall Estimation in Southern China

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Journal of Tropical Meteorology 2021年第1期27卷 48-61页

作者：张阿思黄朝盈陈生 ZHANG Peng-fei 胡东明肖柳斯 School of Atmospheric Sciences/Key laboratory of Tropical Atmosphere-Ocean System Sun Yat-sen UniversityZhuhaiGuangdong 519000 China Southern laboratory of Ocean Science and Engineering ZhuhaiGuangdong 519000 China Cooperative Institute for Mesoscale Meteorological Studies University of Oklahomaand NOAA/OAR/National Severe Storms LaboratoryOKC 73072 USA Guangzhou Meteorological Observatory Guangzhou 511430 China

This study uses rain gauge observations to assess the performance of different radar estimators R(ZH),R(KDP)and R(A)in estimating precipitation based on the observations of an S-band polarimetric radar over southern China during a typical convective storm and an extremely severe typhoon,i.e.,Typhoon Manghkut.These radar estimators were derived from observations of a local autonomous particle size and velocity(Parsivel)unit(APU)disdrometer.A key parameter,alpha(α),which is the ratio of specific attenuation A to specific differential phase KDP with three fixed values(α=0.015 dB deg^(-1),α=0.0185 dB deg^(-1)andα=0.03 dB deg^(-1))was examined to test the sensitivity of the R(A)rain retrievals.The results show that:(1)All radar estimators can capture the spatio-temporal patterns of two precipitation events,R(A)withα=0.0185 dB deg^(-1)is well correlated with gauge measurement via higher Pearson’s correlation coefficient(CC)of 0.87,lower relative bias(RB)of 16%,and lower root mean square error(RMSE)of 17.09 mm in the convective storm while it underestimates the typhoon event with RB of 35%;(2)R(A)withα=0.03 dB deg^(-1)shows the best statistical scores with the highest CC(0.92),lowest RB(7%)and RMSE(25.74 mm)corresponding to Typhoon Manghkut;(3)R(A)estimates are more efficient in mitigating the impact of partial beam blockage.The results indicate thatαis remarkably influenced by the variation of drop size distribution.Thus,more work is needed to establish an automated and optimizedαfor the R(A)relation during different rainfall events over different regions.

关键词： QPE S-band dual polarization weather radar southern China

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Iterative Methods for Solving the Nonlinear Balance Equation with Optimal Truncation

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Advances in Atmospheric Sciences 2021年第5期38卷 755-770页

作者： Qin XU Jie CAO noaa/national severe storms laboratory NormanOklahoma 73069USA Cooperative Institute for Mesoscale Meteorological Studies University of OklahomaNormanOklahoma 73072USA Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China

Two types of existing iterative methods for solving the nonlinear balance equation(NBE)are revisited.In the first type,the NBE is rearranged into a linearized equation for a presumably small correction to the initial guess or the subsequent updated solution.In the second type,the NBE is rearranged into a quadratic form of the absolute vorticity with the positive root of this quadratic form used in the form of a Poisson equation to solve NBE iteratively.The two methods are rederived by expanding the solution asymptotically upon a small Rossby number,and a criterion for optimally truncating the asymptotic expansion is proposed to obtain the super-asymptotic approximation of the solution.For each rederived method,two iterative procedures are designed using the integral-form Poisson solver versus the over-relaxation scheme to solve the boundary value problem in each iteration.Upon testing with analytically formulated wavering jet flows on the synoptic,sub-synoptic and meso-αscales,the iterative procedure designed for the first method with the Poisson solver,named M1a,is found to be the most accurate and efficient.For the synoptic wavering jet flow in which the NBE is entirely elliptic,M1a is extremely accurate.For the sub-synoptic wavering jet flow in which the NBE is mostly elliptic,M1a is sufficiently accurate.For the meso-αwavering jet flow in which the NBE is partially hyperbolic so its boundary value problem becomes seriously ill-posed,M1a can effectively reduce the solution error for the cyclonically curved part of the wavering jet flow,but not for the anti-cyclonically curved part.

关键词： nonlinear balance iterative method optimal truncation

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EFFECTS OF SEA SURFACE TEMPERATURE AND ITS DIURNAL VARIATION ON DIURNAL VARIATION OF RAINFALL:A PARTITIONING ANALYSIS BASED ON SURFACE RAINFALL BUDGET

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Journal of Tropical Meteorology 2012年第1期18卷 89-97页

作者：崔晓鹏李小凡 laboratory of Cloud-Precipitation Physics and severe storms(LACS) Institute of Atmospheric PhysicsChinese Academy of Sciences noaa/NESDIS/Office of Research and Applications

The effects of sea surface temperature(SST) and its diurnal variation on diurnal variation of rainfall are examined in this study by analyzing a series of equilibrium cloud-resolving model experiments which are imposed with zero large-scale vertical velocity.The grid rainfall simulation data are categorized into eight rainfall types based on rainfall processes including water vapor convergence/divergence,local atmospheric drying/moistening,and hydrometeor loss/convergence or gain/divergence.The rainfall contributions of the rainfall types with water vapor convergence are insensitive to the increase in SST from 27°C to 29°C during the nighttime,whereas they are decreased during the daytime.The rainfall contributions of the rainfall types with water vapor convergence are decreased as the SST increases from 29°C to 31°C but the decreases are larger during the nighttime than during the daytime.The rainfall contributions of the rainfall types with water vapor convergence are decreased by the inclusion of diurnal variation of SST with diurnal difference of 1°C during the nighttime,but the decreases are significantly slowed down as the diurnal difference of SST increases from 1°C to 2°C.The rainfall contributions of the rainfall types with water vapor convergence are insensitive to the inclusion of diurnal variation of SST during the daytime.

关键词： sea surface temperature diurnal variation diurnal variation of rainfall surface rainfall budget

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Analyses and Forecasts of a Tornadic Supercell Outbreak Using a 3DVAR System Ensemble

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Advances in Atmospheric Sciences 2016年第5期33卷 544-558页

作者： Zhaorong ZHUANG Nusrat YUSSOUF Jidong GAO Cooperative Institute for Mesoscale Meteorological Studies University of OklahomaNormanOK 73072USA Center of Numerical Weather Prediction National Meteorological CenterChina Meteorological AdministrationBeijing 100081 noaa/national severe storms laboratory NormanOK 73072USA

As part of noaa＇s ＂Warn-On-Forecast＂ initiative, a convective-scale data assimilation and prediction system was developed using the WRF-ARW model and ARPS 3DVAR data assimilation technique. The system was then evaluated using retrospective short-range ensemble analyses and probabilistic forecasts of the tornadic supercell outbreak event that occurred on 24 May 2011 in Oklahoma, USA. A 36-member multi-physics ensemble system provided the initial and boundary conditions for a 3-km convective-scale ensemble system. Radial velocity and reflectivity observations from four WSR-88 Ds were assimilated into the ensemble using the ARPS 3DVAR technique. Five data assimilation and forecast experiments were conducted to evaluate the sensitivity of the system to data assimilation frequencies, in-cloud temperature adjustment schemes, and fixed- and mixed-microphysics ensembles. The results indicated that the experiment with 5-min assimilation frequency quickly built up the storm and produced a more accurate analysis compared with the 10-min assimilation frequency experiment. The predicted vertical vorticity from the moist-adiabatic in-cloud temperature adjustment scheme was larger in magnitude than that from the latent heat scheme. Cycled data assimilation yielded good forecasts, where the ensemble probability of high vertical vorticity matched reasonably well with the observed tornado damage path. Overall, the results of the study suggest that the 3DVAR analysis and forecast system can provide reasonable forecasts of tornadic supercell storms.

关键词： ensemble 3DVAR analysis radar data assimilation probabilistic forecast supercell storm

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