检索结果-南通市图书馆

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

在线全文

学校读者我要写书评

暂无评论

Advances in Atmospheric Sciences 2024年第5期41卷 974-988页

作者： Zhongfeng XU Ying HAN Meng-Zhuo ZHANG Chi-Yung TAM Zong-Liang YANG Ahmed M.EL KENAWY Congbin FU Institute of Atmospheric Physics Chinese Academy of SciencesBeijing 100029China School of Atmospheric Sciences Nanjing UniversityNanjing 210093China Earth System Science Programme The Chinese University of Hong KongHong KongChina Department of Geological Sciences Jackson School of GeosciencesThe University of Texas at AustinAustinTexasUSA Instituto Pirenaico de Ecología Consejo Superior de Investigaciones Científicas(CSIC)Campus de Aula Dei1005Zaragoza 50059Spain Department of Geography Mansoura UniversityMansoura 35516Egypt

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.

关键词： bias correction multi-model ensemble mean dynamical downscaling interannual variability day-to-day variability validation

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

在线全文

学校读者我要写书评

暂无评论

Acta Oceanologica Sinica 2022年第2期41卷 41-50页

作者： Guijun Han Jianfeng Zhou Qi Shao Wei Li Chaoliang Li Xiaobo Wu Lige Cao Haowen Wu Yundong Li Gongfu Zhou School of Marine Science and Technology Tianjin UniversityTianjin 300072China Tianjin Key Laboratory for Oceanic Meteorology Tianjin 300074China

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.

关键词： sea surface temperature retrospective forecasts bias correction backpropagation neural network empirical orthogonal function analysis South China Sea

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

在线全文

学校读者我要写书评

暂无评论

Journal of Tropical Meteorology 2022年第2期28卷 154-168页

作者：高玉芳吴雨晴陈耀登喻伟顾天威武雅珍 School of Applied Meteorology Nanjing University of Information Science and TechnologyNanjing 210044 China School of Atmospheric Sciences Nanjing University of Information Science and TechnologyNanjing 210044 China Weather Tech Services LLCLongmontCO 80503 USA LEIHUA Electronic and Technology Research Institute Aviation Industry Corporation of ChinaWuxiJiangsu 214000 China

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.

关键词： streamflow forecast bias correction meteo-hydrological forecasting model WRF WRF-Hydro

bias correction and Ensemble Projections of Temperature Changes over Ten Subregions in CORDEX East Asia

在线全文

学校读者我要写书评

暂无评论

Advances in Atmospheric Sciences 2020年第11期37卷 1191-1210页

作者： Chenwei SHEN Qingyun DUAN Chiyuan MIAO Chang XING Xuewei FAN Yi WU Jingya HAN State Key Laboratory of Earth Surface Processes and Resource Ecology Faculty of Geographical ScienceBeijing Normal UniversityBeijing 100875China

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%.

关键词： CORDEX-EA bias correction BMA temperature projection

bias correction of Channel Brightness Temperature of FY-4A Hyperspectral GIIRS Based on Machine Learning

在线全文

学校读者我要写书评

暂无评论

Meteorological and Environmental Research 2022年第1期13卷 26-30页

作者： Gen WANG Jiao CHEN Yue WANG Anhui Meteorological Observatory Anhui Key Lab of Atmospheric Science and Satellite Remote SensingHefei 230031China Center of Central Asia Atmospheric Science Research Urumqi 830002China Anhui Institute of Meteorological Hefei 230031China

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.

关键词： FY-4A Hyperspectral GIIRS bias correction Random forest Extreme gradient boosting

System bias correction of short-term hub-height wind forecasts using the Kalman filter

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Protection and Control of Modern Power Systems 2021年第1期6卷 473-481页

作者： Jingjing Xu Ziniu Xiao Zhaohui Lin Ming Li International Center for Climate and Environment Science(ICCES) Institute of Atmospheric PhysicsChinese Academy of ScienceBeijingChina State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics(LASG) Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina Data61 Commonwealth Scientific and Industrial Research Organization(CSIRO)PerthAustralia

Wind energy is a fluctuating source for power systems, which poses challenges to grid planning for the wind power industry. To improve the short-term wind forecasts at turbine height, the bias correction approach Kalman filter (KF) is applied to 72-h wind speed forecasts from the WRF model in Zhangbei wind farm for a period over two years. The KF approach shows a remarkable ability in improving the raw forecasts by decreasing the root-mean-square error by 16% from 3.58 to 3.01 m s−1, the mean absolute error by 14% from 2.71 to 2.34 m s−1, the bias from 0.22 to − 0.19 m s−1, and improving the correlation from 0.58 to 0.66. The KF significantly reduces random errors of the model, showing the capability to deal with the forecast errors associated with physical processes which cannot be accurately handled by the numerical model. In addition, the improvement of the bias correction is larger for wind speeds sensitive to wind power generation. So the KF approach is suitable for short-term wind power prediction.

关键词： Wind forecasts Wind energy Numerical model bias correction Kalman filter Atmospheric boundary layer

A Deep Learning Method for bias correction of ECMWF 24–240 h Forecasts

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Advances in Atmospheric Sciences 2021年第9期38卷 1444-1459页

作者： Lei HAN Mingxuan CHEN Kangkai CHEN Haonan CHEN Yanbiao ZHANG Bing LU Linye SONG Rui QIN College of Information Science and Engineering Ocean University of ChinaQingdao 266100China Institute of Urban Meteorology China Meteorological AdministrationBeijing 100089China Department of Electrical and Computer Engineering Colorado State UniversityFort CollinsCO 80523USA

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.

关键词： numerical weather prediction bias correction deep learning ECMWF

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

在线全文

学校读者我要写书评

暂无评论

Acta Oceanologica Sinica 2020年第9期39卷 50-59页

作者： Jiechen Zhao Qi Shu Chunhua Li Xingren Wu Zhenya Song Fangli Qiao Key Laboratory of Marine Hazards Forecasting National Marine Environmental Forecasting CenterMinistry of Natural ResourcesBeijing 100081China Laboratory for Regional Oceanography and Numerical Modeling Pilot National Laboratory for Marine Science and Technology(Qingdao)Qingdao 266237China First Institute of Oceanography Ministry of Natural ResourcesQingdao 266061China College of Oceanic and Atmospheric Sciences Ocean University of ChinaQingdao 266100China Key Laboratory for Marine Science and Numerical Modeling Ministry of Natural ResourcesQingdao 266061China IMSG at Environmental Modeling Center National Centers for Environmental PredictionNational Oceanic and Atmospheric AdministrationCollege ParkMD 20740USA

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.

关键词： bias correction Arctic sea ice subseasonal prediction operational service

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

在线全文

学校读者我要写书评

暂无评论

Chinese Geographical Science 2021年第3期31卷 474-490页

作者： ZHANG Lanhui HE Chansheng TIAN Wei ZHU Yi Key Laboratory of West China's Environmental System(Ministry of Education) College of Earth and Environmental SciencesLanzhou UniversityLanzhou 730000China Department of Geography Western Michigan UniversityKalamazoo Michigan 49008USA

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.

关键词： evaluation Weather Research and Forecasting(WRF) Tropical Rainfall Measuring Mission(TRMM) precipitation bias correction high mountainous areas