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.

Seasonal Prediction of Indian Summer Monsoon Using WRF: A Dynamical Downscaling Perspective

Seasonal forecasting of the Indian summer monsoon by dynamically downscaling the CFSv2 output using a high resolution WRF model over the hindcast period of 1982-2008 has been performed in this study. The April start ensemble mean of the CFSv2 has been used to provide the initial and lateral boundary conditions for driving the WRF. The WRF model is integrated from 1st May through 1st October for each monsoon season. The analysis suggests that the WRF exhibits potential skill in improving the rainfall skill as well as the seasonal pattern and minimizes the meteorological errors as compared to the parent CFSv2 model. The rainfall pattern is simulated quite closer to the observation (IMD) in the WRF model over CFSv2 especially over the significant rainfall regions of India such as the Western Ghats and the central India. Probability distributions of the rainfall show that the rainfall is improved with the WRF. However, the WRF simulates copious amounts of rainfall over the eastern coast of India. Surface and upper air meteorological parameters show that the WRF model improves the simulation of the lower level and upper-level winds, MSLP, CAPE and PBL height. The specific humidity profiles show substantial improvement along the vertical column of the atmosphere which can be directly related to the net precipitable water. The CFSv2 underestimates the specific humidity along the vertical which is corrected by the WRF model. Over the Bay of Bengal, the WRF model overestimates the CAPE and specific humidity which may be attributed to the copious amount of rainfall along the eastern coast of India. Residual heating profiles also show that the WRF improves the thermodynamics of the atmosphere over 700 hPa and 400 hPa levels which helps in improving the rainfall simulation. Improvement in the land surface fluxes is also witnessed in the WRF model.

A Hybrid Statistical-Dynamical Downscaling of Air Temperature over Scandinavia Using the WRF Model

An accurate simulation of air temperature at local scales is crucial for the vast majority of weather and climate applications.In this work,a hybrid statistical–dynamical downscaling method and a high-resolution dynamical-only downscaling method are applied to daily mean,minimum and maximum air temperatures to investigate the quality of localscale estimates produced by downscaling.These two downscaling approaches are evaluated using station observation data obtained from the Finnish Meteorological Institute over a near-coastal region of western Finland.The dynamical downscaling is performed with the Weather Research and Forecasting(WRF)model,and the statistical downscaling method implemented is the Cumulative Distribution Function-transform(CDF-t).The CDF-t is trained using 20 years of WRF-downscaled Climate Forecast System Reanalysis data over the region at a 3-km spatial resolution for the central month of each season.The performance of the two methods is assessed qualitatively,by inspection of quantile-quantile plots,and quantitatively,through the Cramer-von Mises,mean absolute error,and root-mean-square error diagnostics.The hybrid approach is found to provide significantly more skillful forecasts of the observed daily mean and maximum air temperatures than those of the dynamical-only downscaling(for all seasons).The hybrid method proves to be less computationally expensive,and also to give more skillful temperature forecasts(at least for the Finnish near-coastal region).

Predictor Selection for CNN-based Statistical Downscaling of Monthly Precipitation

Convolutional neural networks(CNNs) have been widely studied and found to obtain favorable results in statistical downscaling to derive high-resolution climate variables from large-scale coarse general circulation models(GCMs).However, there is a lack of research exploring the predictor selection for CNN modeling. This paper presents an effective and efficient greedy elimination algorithm to address this problem. The algorithm has three main steps: predictor importance attribution, predictor removal, and CNN retraining, which are performed sequentially and iteratively. The importance of individual predictors is measured by a gradient-based importance metric computed by a CNN backpropagation technique, which was initially proposed for CNN interpretation. The algorithm is tested on the CNN-based statistical downscaling of monthly precipitation with 20 candidate predictors and compared with a correlation analysisbased approach. Linear models are implemented as benchmarks. The experiments illustrate that the predictor selection solution can reduce the number of input predictors by more than half, improve the accuracy of both linear and CNN models,and outperform the correlation analysis method. Although the RMSE(root-mean-square error) is reduced by only 0.8%,only 9 out of 20 predictors are used to build the CNN, and the FLOPs(Floating Point Operations) decrease by 20.4%. The results imply that the algorithm can find subset predictors that correlate more to the monthly precipitation of the target area and seasons in a nonlinear way. It is worth mentioning that the algorithm is compatible with other CNN models with stacked variables as input and has the potential for nonlinear correlation predictor selection.

Statistical Downscaling Retrieval of Land Surface Temperature in an Area with Complex Landforms in the Eastern Qinling Mountains of China Based on Sentinel-2/3 Satellite Data

The study of land surface temperature(LST)is of great significance for ecosystem monitoring and ecological environmental protection in the Qinling Mountains of China.In view of the contradicting spatial and temporal resolutions in extracting LST from satellite remote sensing(RS)data,the areas with complex landforms of the Eastern Qinling Mountains were selected as the research targets to establish the correlation between the normalized difference vegetation index(NDVI)and LST.Detailed information on the surface features and temporal changes in the land surface was provided by Sentinel-2 and Sentinel-3,respectively.Based on the statistically downscaling method,the spatial scale could be decreased from 1000 m to 10 m,and LST with a Sentinel-3 temporal resolution and a 10 m spatial resolution could be retrieved.Comparing the 1 km resolution Sentinel-3 LST with the downscaling results,the 10 m LST downscaling data could accurately reflect the spatial distribution of the thermal characteristics of the original LST image.Moreover,the surface temperature data with a 10 m high spatial resolution had clear texture and obvious geomorphic features that could depict the detailed information of the ground features.The results showed that the average error was 5 K on April 16,2019 and 2.6 K on July 15,2019.The smaller error values indicated the higher vegetation coverage of summer downscaling result with the highest level on July 15.

Evaluating the effects of topographical factors on the precipitation simulated by kilometer-scale versus quarter-degree dynamical downscaling models in eastern China

与传统的1/4度(≈25-30 km)动力降尺度模拟相比,公里尺度模拟的降水空间分布与观测结果更为接近.为了研究这一差异原因,本研究以华东地区为例,探究了地形因子在观测和模拟的降水中的作用.为了更好地体现地形因子对降水分布非均匀性的影响,以及不同地形因子作用的尺度差异,本研究采用多尺度地理加权回归模型,对五个主要地形因子与公里尺度和1/4度分辨率模拟的降水的关系进行了评估.基于观测数据的研究结果显示地形起伏度,地形高程和离海岸线距离对华东地区降水分布的非均匀性都有重要影响,其中地形起伏度在研究区大部分站点降水分布中起主导作用;公里尺度模拟结果基本反映了地形起伏度的主导作用;而1/4度模拟结果表现出降水对地形高程的过度依赖.本研究揭示了公里尺度地形分布对中国东部降水的非均匀分布的关键作用,研究结果可以为改进降水模拟提供新的思路.

Customized Optimization for Vehicle Acoustic Statistical Energy Analysis

Statistical Energy Analysis(SEA) is one of the conventional tools for predicting vehicle high-frequency acoustic responses.This study proposes a new method that can provide customized optimization solutions to meet NVH targets based on the specific needs of different project teams during the initial project stages.This approach innovatively integrates dynamic optimization,Radial Basis Function(RBF),and Fuzzy Design Variables Genetic Algorithm(FDVGA) into the optimization process of Statistical Energy Analysis(SEA),and also takes vehicle sheet metal into account in the optimization of sound packages.In the implementation process,a correlation model is established through Python scripts to link material density with acoustic parameters,weight,and cost.By combining Optimus and VaOne software,an optimization design workflow is constructed and the optimization design process is successfully executed.Under various constraints related to acoustic performance,weight and cost,a globally optimal design is achieved.This technology has been effectively applied in the field of Battery Electric Vehicle(BEV).

Statistical Downscaling for Multi-Model Ensemble Prediction of Summer Monsoon Rainfall in the Asia-Pacific Region Using Geopotential Height Field

The 21-yr ensemble predictions of model precipitation and circulation in the East Asian and western North Pacific （Asia-Pacific） summer monsoon region （0°-50°N, 100° 150°E） were evaluated in nine different AGCM, used in the Asia-Pacific Economic Cooperation Climate Center （APCC） multi-model ensemble seasonal prediction system. The analysis indicates that the precipitation anomaly patterns of model ensemble predictions are substantially different from the observed counterparts in this region, but the summer monsoon circulations are reasonably predicted. For example, all models can well produce the interannual variability of the western North Pacific monsoon index （WNPMI） defined by 850 hPa winds, but they failed to predict the relationship between WNPMI and precipitation anomalies. The interannual variability of the 500 hPa geopotential height （GPH） can be well predicted by the models in contrast to precipitation anomalies. On the basis of such model performances and the relationship between the interannual variations of 500 hPa GPH and precipitation anomalies, we developed a statistical scheme used to downscale the summer monsoon precipitation anomaly on the basis of EOF and singular value decomposition （SVD）. In this scheme, the three leading EOF modes of 500 hPa GPH anomaly fields predicted by the models are firstly corrected by the linear regression between the principal components in each model and observation, respectively. Then, the corrected model GPH is chosen as the predictor to downscale the precipitation anomaly field, which is assembled by the forecasted expansion coefficients of model 500 hPa GPH and the three leading SVD modes of observed precipitation anomaly corresponding to the prediction of model 500 hPa GPH during a 19-year training period. The cross-validated forecasts suggest that this downscaling scheme may have a potential to improve the forecast skill of the precipitation anomaly in the South China Sea, western North Pacific and the East Asia Pacific regions, where the anomaly correlation coefficient （ACC） has been improved by 0.14, corresponding to the reduced RMSE of 10.4% in the conventional multi-model ensemble （MME） forecast.

Using Statistical Downscaling to Quantify the GCM-Related Uncertainty in Regional Climate Change Scenarios: A Case Study of Swedish Precipitation

There are a number of sources of uncertainty in regional climate change scenarios. When statistical downscaling is used to obtain regional climate change scenarios, the uncertainty may originate from the uncertainties in the global climate models used, the skill of the statistical model, and the forcing scenarios applied to the global climate model. The uncertainty associated with global climate models can be evaluated by examining the differences in the predictors and in the downscaled climate change scenarios based on a set of different global climate models. When standardized global climate model simulations such as the second phase of the Coupled Model Intercomparison Project （CMIP2） are used, the difference in the downscaled variables mainly reflects differences in the climate models and the natural variability in the simulated climates. It is proposed that the spread of the estimates can be taken as a measure of the uncertainty associated with global climate models. The proposed method is applied to the estimation of global-climate-model-related uncertainty in regional precipitation change scenarios in Sweden. Results from statistical downscaling based on 17 global climate models show that there is an overall increase in annual precipitation all over Sweden although a considerable spread of the changes in the precipitation exists. The general increase can be attributed to the increased large-scale precipitation and the enhanced westerly wind. The estimated uncertainty is nearly independent of region. However, there is a seasonal dependence. The estimates for winter show the highest level of confidence, while the estimates for summer show the least.

Statistical Downscaling of Summer Temperature Extremes in Northern China

Two approaches of statistical downscaling were applied to indices of temperature extremes based on percentiles of daily maximum and minimum temperature observations at Beijing station in summer during 1960-2008. One was to downscale daily maximum and minimum temperatures by using EOF analysis and stepwise linear regression at first, then to calculate the indices of extremes; the other was to directly downseale the percentile-based indices by using seasonal large-scale temperature and geo-potential height records. The cross-validation results showed that the latter approach has a better performance than the former. Then, the latter approach was applied to 48 meteorological stations in northern China. The cross- validation results for all 48 stations showed close correlation between the percentile-based indices and the seasonal large-scale variables. Finally, future scenarios of indices of temperature extremes in northern China were projected by applying the statistical downsealing to Hadley Centre Coupled Model Version 3 （HadCM3） simulations under the Representative Concentration Pathways 4.5 （RCP 4.5） scenario of the Fifth Coupled Model Inter-comparison Project （CMIP5）. The results showed that the 90th percentile of daily maximum temperatures will increase by about 1.5℃, and the 10th of daily minimum temperatures will increase by about 2℃ during the period 2011- 35 relative to 1980-99.

A Quick Report on a Dynamical Downscaling Simulation over China Using the Nested Model

This paper describes a dynamical downscaling simulation over China using the nested model system,which consists of the modified Weather Research and Forecasting Model(WRF)nested with the NCAR Community Atmosphere Model(CAM).Results show that dynamical downscaling is of great value in improving the model simulation of regional climatic characteristics.WRF simulates regional detailed temperature features better than CAM.With the spatial correlation coefficient between the observation and the simulation increasing from 0.54 for CAM to 0.79 for WRF,the improvement in precipitation simulation is more perceptible with WRF.Furthermore,the WRF simulation corrects the spatial bias of the precipitation in the CAM simulation.

An Experiment of a Statistical Downscaling Forecast Model for Summer Precipitation over China

A combination of the optimal subset regression (OSR) approach,the coupled general circulation model of the National Climate Center (NCC-CGCM) and precipitation observations from 160 stations over China is used to construct a statistical downscaling forecast model for precipitation in summer.Retroactive forecasts are performed to assess the skill of statistical downscaling during the period from 2003 to 2009.The results show a poor simulation for summer precipitation by the NCCCGCM for China,and the average spatial anomaly correlation coefficient (ACC) is 0.01 in the forecast period.The forecast skill can be improved by OSR statistical downscaling,and the OSR forecast performs better than the NCC-CGCM in most years except 2003.The spatial ACC is more than 0.2 in the years 2008 and 2009,which proves to be relatively skillful.Moreover,the statistical downscaling forecast performs relatively well for the main rain belt of the summer precipitation in some years,including 2005,2006,2008,and 2009.However,the forecast skill of statistical downscaling is restricted to some extent by the relatively low skill of the NCCCGCM.

Comparison of a Very-fine-resolution GCM with RCM Dynamical Downscaling in Simulating Climate in China

Regional climate simulation can generally be improved by using an RCM nested within a coarser-resolution GCM.However, whether or not it can also be improved by the direct use of a state-of-the-art GCM with very fine resolution, close to that of an RCM, and, if so, which is the better approach, are open questions. These questions are important for understanding and using these two kinds of simulation approaches, but have not yet been investigated. Accordingly, the present reported work compared simulation results over China from a very-fine-resolution GCM（VFRGCM） and from RCM dynamical downscaling. The results showed that：（1） The VFRGCM reproduces the climatologies and trends of both air temperature and precipitation, as well as inter-monthly variations of air temperature in terms of spatial pattern and amount, closer to observations than the coarse-resolution version of the GCM. This is not the case, however, for the inter-monthly variations of precipitation.（2） The VFRGCM captures the climatology, trend, and inter-monthly variation of air temperature, as well as the trend in precipitation, more reasonably than the RCM dynamical downscaling method.（3） The RCM dynamical downscaling method performs better than the VFRGCM in terms of the climatology and inter-monthly variation of precipitation. Overall,the results suggest that VFRGCMs possess great potential with regard to their application in climate simulation in the future,and the RCM dynamical downscaling method is still dominant in terms of regional precipitation simulation.

Statistical Downscaling Prediction of Summer Precipitation in Southeastern China

A statistical downscaling approach based on multiple-linear-regression(MLR) for the prediction of summer precipitation anomaly in southeastern China was established,which was based on the outputs of seven operational dynamical models of Development of a European Multi-model Ensemble System for Seasonal to Interannual Prediction(DEMETER) and observed data.It was found that the anomaly correlation coefficients(ACCs) spatial pattern of June-July-August(JJA) precipitation over southeastern China between the seven models and the observation were increased significantly;especially in the central and the northeastern areas,the ACCs were all larger than 0.42(above 95% level) and 0.53(above 99% level).Meanwhile,the root-mean-square errors(RMSE) were reduced in each model along with the multi-model ensemble(MME) for some of the stations in the northeastern area;additionally,the value of RMSE difference between before and after downscaling at some stations were larger than 1 mm d-1.Regionally averaged JJA rainfall anomaly temporal series of the downscaling scheme can capture the main characteristics of observation,while the correlation coefficients(CCs) between the temporal variations of the observation and downscaling results varied from 0.52 to 0.69 with corresponding variations from-0.27 to 0.22 for CCs between the observation and outputs of the models.

A New Statistical Downscaling Scheme for Predicting Winter Precipitation in China

An effective statistical downscaling scheme was developed on the basis of singular value decomposition to predict boreal winter(December-January-February)precipitation over China.The variable geopotential height at 500 hPa(GH5)over East Asia,which was obtained from National Centers for Environmental Prediction’s Coupled Forecast System(NCEP CFS),was used as one predictor for the scheme.The preceding sea ice concentration(SIC)signal obtained from observed data over high latitudes of the Northern Hemisphere was chosen as an additional predictor.This downscaling scheme showed significantly improvement in predictability over the original CFS general circulation model(GCM)output in cross validation.The multi-year average spatial anomaly correlation coefficient increased from–0.03 to 0.31,and the downscaling temporal root-mean-square-error(RMSE)decreased significantly over that of the original CFS GCM for most China stations.Furthermore,large precipitation anomaly centers were reproduced with greater accuracy in the downscaling scheme than those in the original CFS GCM,and the anomaly correlation coefficient between the observation and downscaling results reached～0.6 in the winter of 2008.

Can Artificial Climate Trends in Global Reanalysis be Reduced by Dynamical Downscaling:A Case Study of China

In this study, the ability of dynamical downscaling for reduction of artificial climate trends in global reanalysis is tested in China. Dynamical downscaling is performed using a 60-km horizontal resolution Regional Integrated Environmental Model System （RIEMS） forced by the NCEP-Department of Energy （DOE） reanalysis II （NCEP-2）. The results show that this regional climate model （RCM） can not only produce dynamically consis- tent fine scale fields of atmosphere and land surface in the regional domain, but it also has the ability to minimize artificial climate trends existing in the global reanalysis to a certain extent. As compared to the observed 2-meter temperature anomaly averaged across China, our model can simulate the observed inter-annual variation and variability as well as reduce artificial climate trends in the reanalysis by approximately 0.10℃ decade-1 from 1980 to 2007. The RIEMS can effectively reduce artificial trends in global reanalysis for areas in western China, especially for regions with high altitude mountains and deserts, as well as introduce some new spurious changes in other local regions. The model simulations overesti- mated observed winter trends for most areas in eastern China with the exception of the Tibetan Plateau, and it greatly overestimated observed summer trends in the Si- chuan Basin located in southwest China. This implies that the dynamical downscaling of RCM for long-term trends has certain seasonal and regional dependencies due to imperfect physical processes and parameterizations.

Seasonal Prediction of June Rainfall over South China:Model Assessment and Statistical Downscaling

The performances of various dynamical models from the Asia-Pacific Economic Cooperation（APEC） Climate Center（APCC） multi-model ensemble（MME） in predicting station-scale rainfall in South China（SC） in June were evaluated.It was found that the MME mean of model hindcasts can skillfully predict the June rainfall anomaly averaged over the SC domain.This could be related to the MME＇s ability in capturing the observed linkages between SC rainfall and atmospheric large-scale circulation anomalies in the Indo-Pacific region.Further assessment of station-scale June rainfall prediction based on direct model output（DMO） over 97 stations in SC revealed that the MME mean outperforms each individual model.However,poor prediction abilities in some in-land and southeastern SC stations are apparent in the MME mean and in a number of models.In order to improve the performance at those stations with poor DMO prediction skill,a station-based statistical downscaling scheme was constructed and applied to the individual and MME mean hindcast runs.For several models,this scheme can outperform DMO at more than 30 stations,because it can tap into the abilities of the models in capturing the anomalous Indo-Paciric circulation to which SC rainfall is considerably sensitive.Therefore,enhanced rainfall prediction abilities in these models should make them more useful for disaster preparedness and mitigation purposes.

A Statistical Parameter Analysis and SVM Based Fault Diagnosis Strategy for Dynamically Tuned Gyroscopes

Gyro's fault diagnosis plays a critical role in inertia navigation systems for higher reliability and precision. A new fault diagnosis strategy based on the statistical parameter analysis (SPA) and support vector machine (SVM) classification model was proposed for dynamically tuned gyroscopes (DTG). The SPA, a kind of time domain analysis approach, was introduced to compute a set of statistical parameters of vibration signal as the state features of DTG, with which the SVM model, a novel learning machine based on statistical learning theory (SLT), was applied and constructed to train and identify the working state of DTG. The experimental results verify that the proposed diagnostic strategy can simply and effectively extract the state features of DTG, and it outperforms the radial-basis function (RBF) neural network based diagnostic method and can more reliably and accurately diagnose the working state of DTG.

A Statistical-Dynamical Scheme for the Extraseasonal Prediction of Summer Rainfall for 160 Observation Stations across China

The purpose of this study was to design and test a statistical-dynamical scheme for the extraseasonal（one season in advance） prediction of summer rainfall at 160 observation stations across China.The scheme combined both valuable information from the preceding observations and dynamical information from synchronous numerical predictions of atmospheric circulation factors produced by an atmospheric general circulation model.First,the key preceding climatic signals and synchronous atmospheric circulation factors that were not only closely related to summer rainfall but also numerically predictable were identified as the potential predictors.Second,the extraseasonal prediction models of summer rainfall were constructed using a multivariate linear regression analysis for 15 subregions and then 160 stations across China.Cross-validation analyses performed for the period 1983-2008 revealed that the performance of the prediction models was not only high in terms of interannual variation,trend,and sign but also was stable during the whole period.Furthermore,the performance of the scheme was confirmed by the accuracy of the real-time prediction of summer rainfall during 2009 and 2010.

Temporal Statistical Downscaling of Precipitation and Temperature Forecasts Using a Stochastic Weather Generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.