Resolving co- and early post-seismic slip variations of the 2021 MW 7.4 Madoi earthquake in east Bayan Har block with a block-wide distributed deformation mode from satellite synthetic aperture radar data

On 21 May 2021(UTC),an MW 7.4 earthquake jolted the east Bayan Har block in the Tibetan Plateau.The earthquake received widespread attention as it is the largest event in the Tibetan Plateau and its surroundings since the 2008 Wenchuan earthquake,and especially in proximity to the seismic gaps on the east Kunlun fault.Here we use satellite interferometric synthetic aperture radar data and subpixel offset observations along the range directions to characterize the coseismic deformation of the earthquake.Range offset displacements depict clear surface ruptures with a total length of~170 km involving two possible activated fault segments in the earthquake.Coseismic modeling results indicate that the earthquake was dominated by left-lateral strike-slip motions of up to 7 m within the top 12 km of the crust.The well-resolved slip variations are characterized by five major slip patches along strike and 64%of shallow slip deficit,suggesting a young seismogenic structure.Spatial-temporal changes of the postseismic deformation are mapped from early 6-day and 24-day InSAR observations,and are well explained by time-dependent afterslip models.Analysis of Global Navigation Satellite System(GNSS)velocity profiles and strain rates suggests that the eastward extrusion of plateau is diffusely distributed across the east Bayan Har block,but exhibits significant lateral heterogeneities,as evidenced by magnetotelluric observations.The block-wide distributed deformation of the east Bayan Har block along with the significant co-and post-seismic stress loadings from the Madoi earthquake imply high seismic risks along regional faults,especially the Tuosuo Lake and Maqên-Maqu segments of the Kunlun fault that are known as seismic gaps.

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

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.

Assimilation of Doppler Radar Data with an Ensemble 3DEnVar Approach to Improve Convective Forecasting

An ensemble three-dimensional ensemble-variational(3DEnVar)data assimilation(E3DA)system was developed within the Weather Research and Forecasting model’s 3DVar framework to assimilate radar data to improve convective forecasting.In this system,ensemble perturbations are updated by an ensemble of 3DEnVar and the ensemble forecasts are used to generate the flow-dependent background error covariance.The performance of the E3DA system was first evaluated against one experiment without radar DA and one radar DA experiment with 3DVar,using a severe storm case over southeastern China on 5 June 2009.Results indicated that E3DA improved the quantitative forecast skills of reflectivity and precipitation,as well as their spatial distributions in terms of both intensity and coverage over 3DVar.The root-mean-square error of radial velocity from 3DVar was reduced by E3DA,with stronger low-level wind closer to observation.It was also found that E3DA improved the wind,temperature and water vapor mixing ratio,with the lowest errors at the surface and upper levels.3DVar showed moderate improvements in comparison with forecasts without radar DA.A diagnosis of the analysis revealed that E3DA increased vertical velocity,temperature,and humidity corresponding to the added reflectivity,while 3DVar failed to produce these adjustments,because of the lack of reasonable cross-variable correlations.The performance of E3DA was further verified using two convective cases over southern and southeastern China,and the reflectivity forecast skill was also improved over 3DVar.

Analysis of Doppler Radar Data about a Super Monomer Hailstorms in the Northeastern Qinghai-Tibet Plateau

[Objective] The Doppler radar data about a super monomer hailstorms in the northeastern Qinghai-Tibet Plateau in the Zhongchuan Airport in the Lanzhou City on September 6,2010 was studied.[Method] By dint of routine data and radar data,the low vortex shear line type and the super monomer hailstorm around the Zhongchuan Airport in the Lanzhou City on September 6,2010 were expounded.Basic product and secondary product of Doppler radar were used in this process to reflect the characteristics of strong convection weather.Some characteristics of this process shall be explored.[Result] A small gush of cold air from the cold vortex of 500 hPa in the middle and high layer provided impacts.The warm shear line provided water vapor and energy in the 700 hPa.There was strong convective weather in the upper air.Such 10 minutes of hailstorm was rarely seen in the drought land in the northwest.The characteristics of the strong convection were distinct and typical.The front showed no echo form.However,it can not be reflected in "strong wedge" in another form.In this process,characteristics of BWER and middle scale cyclone were distinct.And this was a typical hailstorm process caused by super monomer.[Conclusion] The study provided some helpful references for the forecast of strong convection weather in the Zhongchuan Airport in Lanzhou City.

Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case

This paper examines how assimilating surface observations can improve the analysis and forecast ability of a fourdimensional Variational Doppler Radar Analysis System(VDRAS).Observed surface temperature and winds are assimilated together with radar radial velocity and reflectivity into a convection-permitting model using the VDRAS four-dimensional variational(4DVAR) data assimilation system.A squall-line case observed during a field campaign is selected to investigate the performance of the technique.A single observation experiment shows that assimilating surface observations can influence the analyzed fields in both the horizontal and vertical directions.The surface-based cold pool,divergence and gust front of the squall line are all strengthened through the assimilation of the single surface observation.Three experiments—assimilating radar data only,assimilating radar data with surface data blended in a mesoscale background,and assimilating both radar and surface observations with a 4DVAR cost function—are conducted to examine the impact of the surface data assimilation.Independent surface and wind profiler observations are used for verification.The result shows that the analysis and forecast are improved when surface observations are assimilated in addition to radar observations.It is also shown that the additional surface data can help improve the analysis and forecast at low levels.Surface and low-level features of the squall line—including the surface warm inflow,cold pool,gust front,and low-level wind—are much closer to the observations after assimilating the surface data in VDRAS.

Impact of the Assimilation Frequency of Radar Data with the ARPS 3DVar and Cloud Analysis System on Forecasts of a Squall Line in Southern China

Assimilation configurations have significant impacts on analysis results and subsequent forecasts. A squall line system that occurred on 23 April 2007 over southern China was used to investigate the impacts of the data assimilation frequency of radar data on analyses and forecasts. A three-dimensional variational system was used to assimilate radial velocity data,and a cloud analysis system was used for reflectivity assimilation with a 2-h assimilation window covering the initial stage of the squall line. Two operators of radar reflectivity for cloud analyses corresponding to single-and double-moment schemes were used. In this study, we examined the sensitivity of assimilation frequency using 10-, 20-, 30-, and 60-min assimilation intervals. The results showed that analysis fields were not consistent with model dynamics and microphysics in general;thus, model states, including dynamic and microphysical variables, required approximately 20 min to reach a new balance after data assimilation in all experiments. Moreover, a 20-min data assimilation interval generally produced better forecasts for both single-and double-moment schemes in terms of equitable threat and bias scores. We conclude that a higher data assimilation frequency can produce a more intense cold pool and rear inflow jets but does not necessarily lead to a better forecast.

Percentile-based Neighborhood Precipitation Verification and Its Application to a Landfalling Tropical Storm Case with Radar Data Assimilation

The traditional threat score based on fixed thresholds for precipitation verification is sensitive to intensity forecast bias.In this study, the neighborhood precipitation threat score is modified by defining the thresholds in terms of the percentiles of overall precipitation instead of fixed threshold values. The impact of intensity forecast bias on the calculated threat score is reduced. The method is tested with the forecasts of a tropical storm that re-intensified after making landfall and caused heavy flooding. The forecasts are produced with and without radar data assimilation. The forecast with assimilation of both radial velocity and reflectivity produce precipitation patterns that better match observations but have large positive intensity bias.When using fixed thresholds, the neighborhood threat scores fail to yield high scores for forecasts that have good pattern match with observations, due to large intensity bias. In contrast, the percentile-based neighborhood method yields the highest score for the forecast with the best pattern match and the smallest position error. The percentile-based method also yields scores that are more consistent with object-based verifications, which are less sensitive to intensity bias, demonstrating the potential value of percentile-based verification.

Radar Data Assimilation for the Simulation of Mesoscale Convective Systems

A heavy rainfall case related to Mesoscale Convective Systems (MCSs) over the Korean Peninsula was selected to investigate the impact of radar data assimilation on a heavy rainfall forecast. The Weather Research and Forecasting (WRF) three-dimensional variational (3DVAR) data assimilation system with tuning of the length scale of the background error covariance and observation error parameters was used to assimilate radar radial velocity and reffectivity data. The radar data used in the assimilation experiments were preprocessed using quality-control procedures and interpolated/thinned into Cartesian coordinates by the SPRINT/CEDRIC packages. Sensitivity experiments were carried out in order to determine the optimal values of the assimilation window length and the update frequency used for the rapid update cycle and incremental analysis update experiments. The assimilation of radar data has a positive influence on the heavy rainfall forecast. Quantitative features of the heavy rainfall case, such as the maximum rainfall amount and Root Mean Squared Differences (RMSDs) of zonal/meridional wind components, were improved by tuning of the length scale and observation error parameters. Qualitative features of the case, such as the maximum rainfall position and time series of hourly rainfall, were enhanced by an incremental analysis update technique. The positive effects of the radar data assimilation and the tuning of the length scale and observation error parameters were clearly shown by the 3DVAR increment.

ANALYSIS OF THE EFFECT OF 3DVAR AND ENSRF DIRECT ASSIMILATION OF RADAR DATA ON THE FORECAST OF A HEAVY RAINFALL EVENT

The present study designs experiments on the direct assimilation of radial velocity and reflectivity data collected by an S-band Doppler weather radar(CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by the United States National Centers for Environmental Prediction using the Weather Research and Forecasting(WRF) model,the WRF model with a three-dimensional variational(3DVAR) data assimilation system and the WRF model with an ensemble square root filter(EnSRF) data assimilation system.In addition,the present study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze-Huaihe River Basin from July 4 to July 5,2003,through numerical simulation.The results show the following.(1) The assimilation of the radar radial velocity data can increase the perturbations in the low-altitude atmosphere over the heavy rainfall region,enhance the convective activities and reduce excessive simulated precipitation.(2) The 3DVAR assimilation method significantly adjusts the horizontal wind field.The assimilation of the reflectivity data improves the microphysical quantities and dynamic fields in the model.In addition,the assimilation of the radial velocity and reflectivity data can better adjust the wind fields and improve the intensity and location of the simulated radar echo bands.(3) The EnSRF assimilation method can assimilate more small-scale wind field information into the model.The assimilation of the reflectivity data alone can relatively accurately forecast the rainfall centers.In addition,the assimilation of the radial velocity and reflectivity data can improve the location of the simulated radar echo bands.(4) The use of the 3DVAR and EnSRF assimilation methods to assimilate the radar radial velocity and reflectivity data can improve the forecast of precipitation,rain-band areal coverage and the center location and intensity of precipitation.

APPLICATION OF NOISE REDUCTION METHOD BASED ON CURVELET THRESHOLDING NEURAL NETWORK FOR POLAR ICE RADAR DATA PROCESSING

Due to the demand of data processing for polar ice radar in our laboratory, a Curvelet Thresholding Neural Network (TNN) noise reduction method is proposed, and a new threshold function with infinite-order continuous derivative is constructed. The method is based on TNN model. In the learning process of TNN, the gradient descent method is adopted to solve the adaptive optimal thresholds of different scales and directions in Curvelet domain, and to achieve an optimal mean square error performance. In this paper, the specific implementation steps are presented, and the superiority of this method is verified by simulation. Finally, the proposed method is used to process the ice radar data obtained during the 28th Chinese National Antarctic Research Expedition in the region of Zhongshan Station, Antarctica. Experimental results show that the proposed method can reduce the noise effectively, while preserving the edge of the ice layers.

Using MC Algorithm to Implement 3D Image Reconstruction for Yunnan Weather Radar Data

3D image reconstruction for weather radar data can not only help the weatherman to improve the forecast efficiency and accuracy, but also help people to understand the weather conditions easily and quickly. Marching Cubes (MC) algorithm in the surface rendering has more excellent applicability in 3D reconstruction for the slice images;it may shorten the time to find and calculate the isosurface from raw volume data, reflect the shape structure more accurately. In this paper, we discuss a method to reconstruct the 3D weather cloud image by using the proposed Cube Weighting Interpolation (CWI) and MC algorithm. Firstly, we detail the steps of CWI, apply it to project the raw radar data into the cubes and obtain the equally spaced cloud slice images, then employ MC algorithm to draw the isosurface. Some experiments show that our method has a good effect and simple operation, which may provide an intuitive and effective reference for realizing the 3D surface reconstruction and meteorological image stereo visualization.

Assimilation of High Frequency Radar Data into a Shelf Sea Circulation Model

High Frequency(HF) radar current data is assimilated into a shelf sea circulation model based on optimal interpolation(OI) method. The purpose of this work is to develop a real-time computationally highly efficient assimilation method to improve the forecast of shelf current. Since the true state of the ocean is not known, the specification of background error covariance is arduous. Usually, it is assumed or calculated from an ensemble of model states and is kept in constant. In our method, the spatial covariances of model forecast errors are derived from differences between the adjacent model forecast fields, which serve as the forecast tendencies. The assumption behind this is that forecast errors can resemble forecast tendencies, since variances are large when fields change quickly and small when fields change slowly. The implementation of HF radar data assimilation is found to yield good information for analyses. After assimilation, the root-mean-square error of model decreases significantly. Besides, three assimilation runs with variational observation density are implemented. The comparison of them indicates that the pattern described by observations is much more important than the amount of observations. It is more useful to expand the scope of observations than to increase the spatial interval. From our tests, the spatial interval of observation can be 5 times bigger than that of model grid.

IMPROVEMENT OF NUMERICAL SIMULATION OF TYPHOON RANANIM (0414) BY USING DOPPLER RADAR DATA

Typhoon Rananim (0414) has been simulated by using the non-hydrostatic Advanced Regional Prediction System (ARPS) from Center of Analysis and Prediction of Storms (CAPS). The prediction of Rananim has generally been improved with ARPS using the new generation CINRAD Doppler radar data. Numerical experiments with or without using the radar data have shown that model initial fields with the assimilated radar radial velocity data in ARPS can change the wind field at the middle and high levels of the troposphere; fine characteristics of the tropical cyclone (TC) are introduced into the initial wind, the x component of wind speed south of the TC is increased and so is the y component west of it. They lead to improved forecasting of TC tracks for the time after landfall. The field of water vapor mixing ratio, temperature, cloud water mixing ratio and rainwater mixing ratio have also been improved by using radar reflectivity data. The model’s initial response to the introduction of hydrometeors has been increased. It is shown that horizontal model resolution has a significant impact on intensity forecasts, by greatly improving the forecasting of TC rainfall, and heavy rainstorm of the TC specially, as well as its distribution and variation with time.

A Comparison of the Radar Ray Path Equations and Approximations for Use in Radar Data Assimilation

雷达光线路径方程被用来决定每雷达大小的物理地点。这些方程为为诊断，显示器和数字天气预言(NWP ) 印射雷达数据到计算格子是必要的。他们也被用来为雷达数据的吸收决定前面的操作员进预报模型。在这篇论文，跟踪方法的一条逐步的光线被开发。在与强烈冷锋有关的不同地点的光线路径方程上的大气的折射率的影响对从新跟踪方法导出的光线路径被检验。雷达光线路径不在这种情况中对强壮的逆温引起的折射率的突然地垂直的坡度和大潮湿坡度很敏感，这被显示出。在纸，使用简化直光线路径方程引起的错误是也检验了。如果，将在雷达大小的物理地点有重要错误，这被发现地球“ s 弯曲没被考虑，特别在更低的举起角度。为横梁高度计算的方程的一种减少的形式用 Taylorseries 扩大被导出。它是更有效的计算联盟者并且也避免需要使用两倍精确变量在原来的形式减轻二个大术语之间的小差别。这种减少的形式的精确性被发现为为使用建模足够。

METHODS OF RADAR DATA COMPRESSION AND TARGET IDENTIFICATION BASED ON BIORTHOGONAL FDWT

In this paper, by using the biorthogonal quadrature filters, the biorthogonal mul-tiresolution analysis of finite dimension space equipped with inner product and the fast discrete wavelet transform (FDWT) are constructed. The dual transform method is proposed and the radar data storage is reduced by it. The method of choosing the wavelet coefficients, and the methods of correlation and nearest neighbor classification in wavelet domain based on the compressed data, are presented. The experimental results of the classification, using the high resolution range returns from six kinds of aircrafts, show that the methods of transform, compression and recognition are efficient.

A topological framework for real-time 3D weather radar data processing

Real-time 3D weather radar data processing makes it possible to efficiently simulate meteorological processes in digital Earth and support the assessment of meteorological disasters.The current real-time meteorological operation system can only deal with radar data within 2D space as a flat map and lacks supporting 3D characteristics.Thus,valuable 3D information imbedded in radar data cannot be completely presented to meteorological experts.Due to the large amount of data and high complexity of radar data 3D operation,regular methods are not competent for supporting real-time 3D radar data processing and representation.This study aims to perform radar data 3D operations with high efficiency and instant speed to provide real-time 3D support for the meteorological field.In this paper,a topological framework composed of basic inner topological objects is proposed along with the quadtree structure and LOD architecture,based on which 3D operations on radar data can be conducted in a split second and 3D information can be presented in real time.As the applications of the proposed topological framework,two widely used 3D algorithms in the meteorological field are also implemented in this paper.Finally,a case study verifies the applicability and validity of the proposed topological framework.

Classification of vegetative types in Changbai Mountain based on optical and microwave remote sensing data

Highly accurate vegetative type distribution information is of great significance for forestry resource monitoring and management.In order to improve the classification accuracy of forest types,Sentinel-1 and 2 data of Changbai Mountain protection development zone were selected,and combined with DEM to construct a multi-featured random forest type classification model incorporating fusing intensity,texture,spectral,vegetation index and topography information and using random forest Gini index(GI)for optimization.The overall accuracy of classification was 94.60%and the Kappa coefficient was 0.933.Comparing the classification results before and after feature optimization,it shows that feature optimization has a greater impact on the classification accuracy.Comparing the classification results of random forest,maximum likelihood method and CART decision tree under the same conditions,it shows that the random forest has a higher performance and can be applied to forestry research work such as forest resource survey and monitoring.

Evaluation of Two Momentum Control Variable Schemes and Their Impact on the Variational Assimilation of Radar Wind Data:Case Study of a Squall Line

Different choices of control variables in variational assimilation can bring about different influences on the analyzed atmospheric state.Based on the WRF model's three-dimensional variational assimilation system,this study compares the behavior of two momentum control variable options—streamfunction velocity potential(ψ–χ) and horizontal wind components(U–V)—in radar wind data assimilation for a squall line case that occurred in Jiangsu Province on 24 August 2014.The wind increment from the single observation test shows that the ψ–χ control variable scheme produces negative increments in the neighborhood around the observation point because streamfunction and velocity potential preserve integrals of velocity.On the contrary,the U–V control variable scheme objectively reflects the information of the observation itself.Furthermore,radial velocity data from 17 Doppler radars in eastern China are assimilated.As compared to the impact of conventional observation,the assimilation of radar radial velocity based on the U–V control variable scheme significantly improves the mesoscale dynamic field in the initial condition.The enhanced low-level jet stream,water vapor convergence and low-level wind shear result in better squall line forecasting.However,the ψ–χ control variable scheme generates a discontinuous wind field and unrealistic convergence/divergence in the analyzed field,which lead to a degraded precipitation forecast.

Burden Distribution Calculation of Bell-Less Top of Blast Furnace Based on Multi-Radar Data

The burden distribution real-time estimation problem of multi-loop charging based on the real multi-radar data is resolved.Firstly , an iterative algorithm is introduced to calculate the radial coordinate of the pile-top.Then , based on the multi-radar data , the burden profile is estimated by a cubic-curve equation at the end of the multi-loop charging.Furthermore , the burden profile before the next multi-loop charging is calculated based on multi-radar data by considering the impact of burden descent.On the basis of these burden profiles , a more accurate thickness ratio of ore to coke ( RO/C ) at the radial direction of blast furnace can be obtained.Finally , an example is given to calculate the burden profiles and RO/C by using the real multi-radar data sampled from Baosteel , which shows the effectiveness of the method introduced.

Study on the Variational Assimilation Technique for the Retrieval of Wind Fields from Doppler Radar Data

这篇论文从 Doppler 雷达数据为风地的检索介绍一种变化吸收技术。吸收信息包括了光线的速度(RV ) 和雷达回波的运动。在这种吸收技术，钥匙正在转变雷达回波的运动到测量变量的一架新雷达 -- “明显的速度(AV ) ”。因此，风的信息被增加，并且仅仅由 AV 恢复二维的风的不确定被把 RV 与 AV 相结合有效地克服。借助于 CMA GRAPES-3Dvar 和 CINRADdata，一些实验被执行。结果证明风领域的检索的方法在获得天气系统的构造是有用的。