Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

Scientific Advances and Weather Services of the China Meteorological Administration’s National Forecasting Systems during the Beijing 2022 Winter Olympics

Since the Beijing 2022 Winter Olympics was the first Winter Olympics in history held in continental winter monsoon climate conditions across complex terrain areas,there is a deficiency of relevant research,operational techniques,and experience.This made providing meteorological services for this event particularly challenging.The China Meteorological Administration(CMA)Earth System Modeling and Prediction Centre,achieved breakthroughs in research on short-and medium-term deterministic and ensemble numerical predictions.Several key technologies crucial for precise winter weather services during the Winter Olympics were developed.A comprehensive framework,known as the Operational System for High-Precision Weather Forecasting for the Winter Olympics,was established.Some of these advancements represent the highest level of capabilities currently available in China.The meteorological service provided to the Beijing 2022 Games also exceeded previous Winter Olympic Games in both variety and quality.This included achievements such as the“100-meter level,minute level”downscaled spatiotemporal resolution and forecasts spanning 1 to 15 days.Around 30 new technologies and over 60 kinds of products that align with the requirements of the Winter Olympics Organizing Committee were developed,and many of these techniques have since been integrated into the CMA’s operational national forecasting systems.These accomplishments were facilitated by a dedicated weather forecasting and research initiative,in conjunction with the preexisting real-time operational forecasting systems of the CMA.This program represents one of the five subprograms of the WMO’s high-impact weather forecasting demonstration project(SMART2022),and continues to play an important role in their Regional Association(RA)II Research Development Project(Hangzhou RDP).Therefore,the research accomplishments and meteorological service experiences from this program will be carried forward into forthcoming highimpact weather forecasting activities.This article provides an overview and assessment of this program and the operational national forecasting systems.

Optimal Assimilation of Microwave Upper-Level Sounding Data in CMA-GFS

Various approaches have been proposed to minimize the upper-level systematic biases in global numerical weather prediction(NWP)models by using satellite upper-air sounding channels as anchors.However,since the China Meteorological Administration Global Forecast System(CMA-GFS)has a model top near 0.1 hPa(60 km),the upper-level temperature bias may exceed 4 K near 1 hPa and further extend to 5 hPa.In this study,channels 12–14 of the Advanced Microwave Sounding Unit A(AMSU-A)onboard five satellites of NOAA and METOP,whose weighting function peaks range from 10 to 2 hPa are all used as anchor observations in CMA-GFS.It is shown that the new“Anchor”approach can effectively reduce the biases near the model top and their downward propagation in three-month assimilation cycles.The bias growth rate of simulated upper-level channel observations is reduced to±0.001 K d^(–1),compared to–0.03 K d^(–1)derived from the current dynamic correction scheme.The relatively stable bias significantly improves the upper-level analysis field and leads to better global medium-range forecasts up to 10 days with significant reductions in the temperature and geopotential forecast error above 10 hPa.

A Deep Learning Method for Bias Correction of ECMWF 24–240 h Forecasts

Correcting the forecast bias of numerical weather prediction models is important for severe weather warnings.The refined grid forecast requires direct correction on gridded forecast products,as opposed to correcting forecast data only at individual weather stations.In this study,a deep learning method called CU-net is proposed to correct the gridded forecasts of four weather variables from the European Centre for Medium-Range Weather Forecast Integrated Forecasting System global model(ECMWF-IFS): 2-m temperature,2-m relative humidity,10-m wind speed,and 10-m wind direction,with a forecast lead time of 24 h to 240 h in North China.First,the forecast correction problem is transformed into an image-toimage translation problem in deep learning under the CU-net architecture,which is based on convolutional neural networks.Second,the ECMWF-IFS forecasts and ECMWF reanalysis data(ERA5) from 2005 to 2018 are used as training,validation,and testing datasets.The predictors and labels(ground truth) of the model are created using the ECMWF-IFS and ERA5,respectively.Finally,the correction performance of CU-net is compared with a conventional method,anomaly numerical correction with observations(ANO).Results show that forecasts from CU-net have lower root mean square error,bias,mean absolute error,and higher correlation coefficient than those from ANO for all forecast lead times from 24 h to 240 h.CU-net improves upon the ECMWF-IFS forecast for all four weather variables in terms of the above evaluation metrics,whereas ANO improves upon ECMWF-IFS performance only for 2-m temperature and relative humidity.For the correction of the 10-m wind direction forecast,which is often difficult to achieve,CU-net also improves the correction performance.

Application of ATOVS Radiance-Bias Correction to Typhoon Track Prediction with Ensemble Kalman Filter Data Assimilation

In this paper, firstly, the bias between observed radiances from the Advanced TIROS-N Operational Vertical Sounder （ATOVS） and those simulated from a model first-guess are corrected. After bias correction, the observed minus calculated （O-B） radiances of most channels were reduced closer to zero, with peak values in each channel shifted towards zero, and the distribution of O-B closer to a Gaussian distribution than without bias correction. Secondly, ATOVS radiance data with and without bias correction are assimilated directly with an Ensemble Kalman Filter （EnKF） data assimilation system, which are then adopted as the initial fields in the forecast model T106L19 to simulate Typhoon Prapiroon （2006） during the period 2-4 August 2006. The prediction results show that the assimilation of ATOVS radiance data with bias correction has a significant and positive impact upon the prediction of the typhoon＇s track and intensity, although the results are not perfect.

Recent advances in precipitation-bias correction and application

Significant progresses have been made in recent years in precipitation data analyses at regional to global scales. This paper re-views and synthesizes recent advances in precipitation-bias corrections and applications in many countries and over the cold re-gions. The main objective of this review is to identify and examine gaps in regional and national precipitation-error analyses. This paper also discusses and recommends future research needs and directions. More effort and coordination are necessary in the determinations of precipitation biases on large regions across national borders. It is important to emphasize that bias cor-rections of precipitation measurements affect both water budget and energy balance calculations, particularly over the cold regions.

Improved EOF-based bias correction method for seasonal forecasts and its application in IAP AGCM4.1

An effective improvement on the empirical orthogonal function(EOF)–based bias correctionmethod for seasonal forecasts is proposed in this paper,by introducing a stepwise regression method into the process of EOF time series correction.Using 30-year(1981–2010)hindcast results from IAP AGCM4.1(the latest version of this model),the improved method is validated for the prediction of summer(June–July–August)rainfall anomalies in Southeast China.The results in terms of the pattern correction coefficient(PCC)of rainfall anomalies shows that the 30-year-averaged prediction skill improves from 0.01 to 0.06 with the original correction method,and to 0.29 using the improved method.The applicability in real-time prediction is also investigated,using 2016 summer rainfall prediction as a test case.With a PCC of 0.59,the authors find that the new correction method significantly improves the prediction skill;the PCC using the direct prediction of the model is?0.04,and using the old bias correction method it is 0.37.

Bias correction of sea surface temperature retrospective forecasts in the South China Sea

Offline bias correction of numerical marine forecast products is an effective post-processing means to improve forecast accuracy. Two offline bias correction methods for sea surface temperature(SST) forecasts have been developed in this study: a backpropagation neural network(BPNN) algorithm, and a hybrid algorithm of empirical orthogonal function(EOF) analysis and BPNN(named EOF-BPNN). The performances of these two methods are validated using bias correction experiments implemented in the South China Sea(SCS), in which the target dataset is a six-year(2003–2008) daily mean time series of SST retrospective forecasts for one-day in advance, obtained from a regional ocean forecast and analysis system called the China Ocean Reanalysis(CORA),and the reference time series is the gridded satellite-based SST. The bias-correction results show that the two methods have similar good skills;however, the EOF-BPNN method is more than five times faster than the BPNN method. Before applying the bias correction, the basin-wide climatological error of the daily mean CORA SST retrospective forecasts in the SCS is up to-3°C;now, it is minimized substantially, falling within the error range(±0.5°C) of the satellite SST data.

Bias-Corrected Short-Range Ensemble Forecasts for Near-Surface Variables during the Summer Season of 2010 in Northern China

A running mean bias （RMB） correction ap- proach was applied to the forecasts of near-surface variables in a seasonal short-range ensemble forecasting experiment with 57 consecutive cases during summer 2010 in the northern China region. To determine a proper training window length for calculating RMB, window lengths from 2 to 20 days were evaluated, and 16 days was taken as an optimal window length, since it receives most of the benefit from extending the window length. The raw and 16-day RMB corrected ensembles were then evaluated for their ensemble mean forecast skills. The results show that the raw ensemble has obvious bias in all near-surface variables. The RMB correction can remove the bias reasonably well, and generate an unbiased ensemble. The bias correction not only reduces the ensemble mean forecast error, but also results in a better spreaderror relationship. Moreover, two methods for computing calibrated probabilistic forecast （PF） were also evaluated through the 57 case dates： 1） using the relative frequency from the RMB-eorrected ensemble; 2） computing the forecasting probabilities based on a historical rank histogram. The first method outperforms the second one, as it can improve both the reliability and the resolution of the PFs, while the second method only has a small effect on the reliability, indicating the necessity and importance of removing the systematic errors from the ensemble.

The role of bias correction on subseasonal prediction of Arctic sea ice during summer 2018

Subseasonal Arctic sea ice prediction is highly needed for practical services including icebreakers and commercial ships,while limited by the capability of climate models.A bias correction methodology in this study was proposed and performed on raw products from two climate models,the First Institute Oceanography Earth System Model(FIOESM)and the National Centers for Environmental Prediction(NCEP)Climate Forecast System(CFS),to improve 60 days predictions for Arctic sea ice.Both models were initialized on July 1,August 1,and September 1 in 2018.A 60-day forecast was conducted as a part of the official sea ice service,especially for the ninth Chinese National Arctic Research Expedition(CHINARE)and the China Ocean Shipping(Group)Company(COSCO)Northeast Passage voyages during the summer of 2018.The results indicated that raw products from FIOESM underestimated sea ice concentration(SIC)overall,with a mean bias of SIC up to 30%.Bias correction resulted in a 27%improvement in the Root Mean Square Error(RMSE)of SIC and a 10%improvement in the Integrated Ice Edge Error(IIEE)of sea ice edge(SIE).For the CFS,the SIE overestimation in the marginal ice zone was the dominant features of raw products.Bias correction provided a 7%reduction in the RMSE of SIC and a 17%reduction in the IIEE of SIE.In terms of sea ice extent,FIOESM projected a reasonable minimum time and amount in mid-September;however,CFS failed to project both.Additional comparison with subseasonal to seasonal(S2S)models suggested that the bias correction methodology used in this study was more effective when predictions had larger biases.

TWO BIAS CORRECTION SCHEMES FOR ATOVS RADIANCE DATA

To better assimilate Advanced TIROS Operational Vertical Sounder(ATOVS) radiance data and provide more accurate initial fields for a numerical model,two bias correction schemes are employed to correct the ATOVS radiance data.The difference in the two schemes lies in the predictors use in air-mass bias correction.The predictors used in SCHEME 1 are all obtained from model first-guess,while those in SCHEME 2 are from model first-guess and radiance observations.The results from the two schemes show that after bias correction,the observation residual became smaller and closer to a Gaussian distribution.For both land and ocean data sets,the results obtained from SCHEME 1 are similar to those from SCHEME 2,which indicates that the predictors could be used in bias correction of ATOVS data.

Bias Correction of Channel Brightness Temperature of FY-4A Hyperspectral GIIRS Based on Machine Learning

Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferometric infrared sounder(GIIRS)of FengYun-4 A(FY-4 A)observation and simulated brightness temperature based on background field,the brightness temperature bias correction of GIIRS channel is carried out based on random forest(RF)and extreme gradient boosting(XGBoost)machine learning.Based on the case data of Typhoon"Haishen",the correction effect of machine learning method is compared with Harris and Kelly’s"off-line"method,and the importance of different predictors to the bias correction is further discussed.The experimental results show that the systematic bias is effectively corrected,and the following conclusions are obtained:the correction effect is improved by adding geographic information(longitude and latitude)into the predictors;under the given combination of predictors,the correction effect of XGBoost is the best,followed by random forest,and finally offline method,but the three methods can correct the bias effectively;compared with long wave data of FY-4 A/GIIRS,machine learning may be more feasible for medium wave data bias correction.

Evaluation of Precipitation Datasets from TRMM Satellite and Down-scaled Reanalysis Products with Bias-correction in Middle Qilian Mountain,China

Accurate estimates of precipitation are fundamental for hydrometeorological and ecohydrological studies,but are more difficult in high mountainous areas because of the high elevation and complex terrain.This study compares and evaluates two kinds of precipitation datasets,the reanalysis product downscaled by the Weather Research and Forecasting(WRF)output,and the satellite product,the Tropical Rainfall Measuring Mission(TRMM)Multisatellite Precipitation Analysis(TMPA)product,as well as their bias-corrected datasets in the Middle Qilian Mountain in Northwest China.Results show that the WRF output with finer resolution perfonns well in both estimating precipitation and hydrological simulation,while the TMPA product is unreliable in high mountainous areas.Moreover,bias-corrected WRF output also performs better than bias-corrected TMPA product.Combined with the previous studies,atmospheric reanalysis datasets are more suitable than the satellite products in high mountainous areas.Climate is more important than altitude for the\falseAlarms'events of the TRMM product.Designed to focus on the tropical areas,the TMPA product mistakes certain meteorological situations for precipitation in subhumid and semiarid areas,thus causing significant"falseAlarms"events and leading to significant overestimations and unreliable performance.Simple linear bias correction method,only removing systematical errors,can significantly improves the accuracy of both the WRF output and the TMPA product in arid high mountainous areas with data scarcity.Evaluated by hydrological simulations,the bias-corrected WRF output is more reliable than the gauge dataset.Thus,data merging of the WRF output and gauge observations would provide more reliable precipitation estimations in arid high mountainous areas.

Bias Correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

Regional climate models(RCMs)participating in the Coordinated Regional Downscaling Experiment(CORDEX)have been widely used for providing detailed climate change information for specific regions under different emissions scenarios.This study assesses the effects of three common bias correction methods and two multi-model averaging methods in calibrating historical(1980−2005)temperature simulations over East Asia.Future(2006−49)temperature trends under the Representative Concentration Pathway(RCP)4.5 and 8.5 scenarios are projected based on the optimal bias correction and ensemble averaging method.Results show the following:(1)The driving global climate model and RCMs can capture the spatial pattern of annual average temperature but with cold biases over most regions,especially in the Tibetan Plateau region.(2)All bias correction methods can significantly reduce the simulation biases.The quantile mapping method outperforms other bias correction methods in all RCMs,with a maximum relative decrease in root-mean-square error for five RCMs reaching 59.8%(HadGEM3-RA),63.2%(MM5),51.3%(RegCM),80.7%(YSU-RCM)and 62.0%(WRF).(3)The Bayesian model averaging(BMA)method outperforms the simple multi-model averaging(SMA)method in narrowing the uncertainty of bias-corrected results.For the spatial correlation coefficient,the improvement rate of the BMA method ranges from 2%to 31%over the 10 subregions,when compared with individual RCMs.(4)For temperature projections,the warming is significant,ranging from 1.2°C to 3.5°C across the whole domain under the RCP8.5 scenario.(5)The quantile mapping method reduces the uncertainty over all subregions by between 66%and 94%.

Bias Correction in Wind Direction Forecasting Using the Circular-Circular Regression Method

Wind direction forecasting plays an important role in wind power prediction and air pollution management. Weather quantities such as temperature, precipitation, and wind speed are linear variables in which traditional model output statistics and bias correction methods are applied. However, wind direction is an angular variable; therefore, such traditional methods are ineffective for its evaluation. This paper proposes an effective bias correction technique for wind direction forecasting of turbine height from numerical weather prediction models, which is based on a circular-circular regression approach. The technique is applied to a 24-h forecast of 65-m wind directions observed at Yangmeishan wind farm, Yunnan Province, China, which consistently yields improvements in forecast performance parameters such as smaller absolute mean error and stronger similarity in wind rose diagram pattern.

The Application of a Meteo-hydrological Forecasting System with Rainfall Bias Correction in a Small and Medium-sized Catchment

Meteo-hydrological forecasting models are an effective way to generate high-resolution gridded rainfall data for water source research and flood forecast.The quality of rainfall data in terms of both intensity and distribution is very important for establishing a reliable meteo-hydrological forecasting model.To improve the accuracy of rainfall data,the successive correction method is introduced to correct the bias of rainfall,and a meteo-hydrological forecasting model based on WRF and WRF-Hydro is applied for streamflow forecast over the Zhanghe River catchment in China.The performance of WRF rainfall is compared with the China Meteorological Administration Multi-source Precipitation Analysis System(CMPAS),and the simulated streamflow from the model is further studied.It shows that the corrected WRF rainfall is more similar to the CMPAS in both temporal and spatial distribution than the original WRF rainfall.By contrast,the statistical metrics of the corrected WRF rainfall are better.When the corrected WRF rainfall is used to drive the WRF-Hydro model,the simulated streamflow of most events is significantly improved in both hydrographs and volume than that of using the original WRF rainfall.Among the studied events,the largest improvement of the NSE is from-0.68 to 0.67.It proves that correcting the bias of WRF rainfall with the successive correction method can greatly improve the performance of streamflow forecast.In general,the WRF/WRF-Hydro meteo-hydrological forecasting model based on the successive correction method has the potential to provide better streamflow forecast in the Zhanghe River catchment.

Rainfall Variability under Present and Future Climate Scenarios Using the Rossby Center Bias-Corrected Regional Climate Model

This study sought to determine the spatial and temporal variability of rainfall under past and future climate scenarios. The data used comprised station-based monthly gridded rainfall data sourced from the Climate Research Unit (CRU) and monthly model outputs from the Fourth Edition of the Rossby Centre (RCA4) Regional Climate Model (RCM), which has scaled-down nine GCMs for Africa. Although the 9 Global Climate Models (GCMs) downscaled by the RCA4 model was not very good at simulating rainfall in Kenya, the ensemble of the 9 models performed better and could be used for further studies. The ensemble of the models was thus bias-corrected using the scaling method to reduce the error;lower values of bias and Normalized Root Mean Square Error (NRMSE) were recorded when compared to the uncorrected models. The bias-corrected ensemble was used to study the spatial and temporal behaviour of rainfall under baseline (1971 to 2000) and future RCP 4.5 and 8.5 scenarios (2021 to 2050). An insignificant trend was noted under the baseline condition during the March-May (MAM) and October-December (OND) rainfall seasons. A positive significant trend at 5% level was noted under RCP 4.5 and 8.5 scenarios in some stations during both MAM and OND seasons. The increase in rainfall was attributed to global warming due to increased anthropogenic emissions of greenhouse gases. Results on the spatial variability of rainfall indicate the spatial extent of rainfall will increase under both RCP 4.5 and RCP 8.5 scenario when compared to the baseline;the increase is higher under the RCP 8.5 scenario. Overall rainfall was found to be highly variable in space and time, there is a need to invest in the early dissemination of weather forecasts to help farmers adequately prepare in case of unfavorable weather. Concerning the expected increase in rainfall in the future, policymakers need to consider the results of this study while preparing mitigation strategies against the effects of changing rainfall patterns.

Bias Correction Technique for Estimating Quantiles of Finite Populations under Simple Random Sampling without Replacement

In this paper, the problem of nonparametric estimation of finite population quantile function using multiplicative bias correction technique is considered. A robust estimator of the finite population quantile function based on multiplicative bias correction is derived with the aid of a super population model. Most studies have concentrated on kernel smoothers in the estimation of regression functions. This technique has also been applied to various methods of non-parametric estimation of the finite population quantile already under review. A major problem with the use of nonparametric kernel-based regression over a finite interval, such as the estimation of finite population quantities, is bias at boundary points. By correcting the boundary problems associated with previous model-based estimators, the multiplicative bias corrected estimator produced better results in estimating the finite population quantile function. Furthermore, the asymptotic behavior of the proposed estimators is presented. It is observed that the estimator is asymptotically unbiased and statistically consistent when certain conditions are satisfied. The simulation results show that the suggested estimator is quite well in terms of relative bias, mean squared error, and relative root mean error. As a result, the multiplicative bias corrected estimator is strongly suggested for survey sampling estimation of the finite population quantile function.

Hydrological response under CMIP6 climate projection in Astore River Basin,Pakistan

Climate change strongly influences the available water resources in a watershed due to direct linkage of atmospheric driving forces and changes in watershed hydrological processes.Understanding how these climatic changes affect watershed hydrology is essential for human society and environmental processes.Coupled Model Intercomparison Project phase 6(CMIP6)dataset of three GCM's(BCC-CSM2-MR,INM-CM5-0,and MPIESM1-2-HR)with resolution of 100 km has been analyzed to examine the projected changes in temperature and precipitation over the Astore catchment during 2020-2070.Bias correction method was used to reduce errors.In this study,statistical significance of trends was performed by using the Man-Kendall test.Sen's estimator determined the magnitude of the trend on both seasonal and annual scales at Rama Rattu and Astore stations.MPI-ESM1-2-HR showed better results with coefficient of determination(COD)ranging from 0.70-0.74 for precipitation and 0.90-0.92 for maximum and minimum temperature at Astore,Rama,and Rattu followed by INM-CM5-0 and BCC-CSM2-MR.University of British Columbia Watershed model was used to attain the future hydrological series and to analyze the hydrological response of Astore River Basin to climate change.Results revealed that by the end of the 2070s,average annual precipitation is projected to increase up to 26.55%under the SSP1-2.6,6.91%under SSP2-4.5,and decrease up to 21.62%under the SSP5-8.5.Precipitation also showed considerable variability during summer and winter.The projected temperature showed an increasing trend that may cause melting of glaciers.The projected increase in temperature ranges from-0.66℃ to 0.50℃,0.9℃ to 1.5℃ and 1.18℃ to 2℃ under the scenarios of SSP1-2.6,SSP2-4.5 and SSP5-8.5,respectively.Simulated streamflows presented a slight increase by all scenarios.Maximum streamflow was generated under SSP5-8.5 followed by SSP2-4.5 and SSP1-2.6.The snowmelt and groundwater contributions to streamflow have decreased whereas rainfall and glacier melt components have increased on the other hand.The projected streamflows(2020-2070)compared to the control period(1990-2014)showed a reduction of 3%-11%,2%-9%,and 1%-7%by SSP1-2.6,SSP2-4.5,and SSP5-8.5,respectively.The results revealed detailed insights into the performance of three GCMs,which can serve as a blueprint for regional policymaking and be expanded upon to establish adaption measures.

Fractional order distance regularized level set method with bias correction

The existing level set segmentation methods have drawbacks such as poor convergence,poor noise resistance,and long iteration times.In this paper,a fractional order distance regularized level set segmentation method with bias correction is proposed.This method firstly introduces fractional order distance regularized term to punish the deviation between the level set function(LSF)and the signed distance function.Secondly a series of covering template is constructed to calculate fractional derivative and its conjugate of image pixel.Thirdly introducing the offset correction term and fully using the local clustering property of image intensity,the local clustering criterion of image intensity is defined and integrated with the neighborhood center to obtain the global criterion of image segmentation.Finally,the fractional distance regularization,offset correction,and external energy constraints are combined,and the energy optimization segmentation method for noisy image is established by level set.Experimental results show that the proposed method can accurately segment the image,and effectively improve the efficiency and robustness of exiting state of the art level set related algorithms.