Assessments of Data-Driven Deep Learning Models on One-Month Predictions of Pan-Arctic Sea Ice Thickness

In recent years,deep learning methods have gradually been applied to prediction tasks related to Arctic sea ice concentration,but relatively little research has been conducted for larger spatial and temporal scales,mainly due to the limited time coverage of observations and reanalysis data.Meanwhile,deep learning predictions of sea ice thickness(SIT)have yet to receive ample attention.In this study,two data-driven deep learning(DL)models are built based on the ConvLSTM and fully convolutional U-net(FC-Unet)algorithms and trained using CMIP6 historical simulations for transfer learning and fine-tuned using reanalysis/observations.These models enable monthly predictions of Arctic SIT without considering the complex physical processes involved.Through comprehensive assessments of prediction skills by season and region,the results suggest that using a broader set of CMIP6 data for transfer learning,as well as incorporating multiple climate variables as predictors,contribute to better prediction results,although both DL models can effectively predict the spatiotemporal features of SIT anomalies.Regarding the predicted SIT anomalies of the FC-Unet model,the spatial correlations with reanalysis reach an average level of 89%over all months,while the temporal anomaly correlation coefficients are close to unity in most cases.The models also demonstrate robust performances in predicting SIT and SIE during extreme events.The effectiveness and reliability of the proposed deep transfer learning models in predicting Arctic SIT can facilitate more accurate pan-Arctic predictions,aiding climate change research and real-time business applications.

Parameterization, sensitivity, and uncertainty of 1-D thermodynamic thin-ice thickness retrieval

Retrieval of Thin-Ice Thickness(TIT)using thermodynamic modeling is sensitive to the parameterization of the independent variables(coded in the model)and the uncertainty of the measured input variables.This article examines the deviation of the classical model’s TIT output when using different parameterization schemes and the sensitivity of the output to the ice thickness.Moreover,it estimates the uncertainty of the output in response to the uncertainties of the input variables.The parameterized independent variables include atmospheric longwave emissivity,air density,specific heat of air,latent heat of ice,conductivity of ice,snow depth,and snow conductivity.Measured input parameters include air temperature,ice surface temperature,and wind speed.Among the independent variables,the results show that the highest deviation is caused by adjusting the parameterization of snow conductivity and depth,followed ice conductivity.The sensitivity of the output TIT to ice thickness is highest when using parameterization of ice conductivity,atmospheric emissivity,and snow conductivity and depth.The retrieved TIT obtained using each parameterization scheme is validated using in situ measurements and satellite-retrieved data.From in situ measurements,the uncertainties of the measured air temperature and surface temperature are found to be high.The resulting uncertainties of TIT are evaluated using perturbations of the input data selected based on the probability distribution of the measurement error.The results show that the overall uncertainty of TIT to air temperature,surface temperature,and wind speed uncertainty is around 0.09 m,0.049 m,and−0.005 m,respectively.

Towards a semi-empirical model of the sea ice thickness based on hyperspectral remote sensing in the Bohai Sea

Sea ice thickness is one of the most important input parameters for the prevention and mitigation of sea ice disasters and the prediction of local sea environments and climates. Estimating the sea ice thickness is currently the most important issue in the study of sea ice remote sensing. With the Bohai Sea as the study area, a semiempirical model of the sea ice thickness（SEMSIT） that can be used to estimate the thickness of first-year ice based on existing water depth estimation models and hyperspectral remote sensing data according to an optical radiative transfer process in sea ice is proposed. In the model, the absorption and scattering properties of sea ice in different bands（spectral dimension information） are utilized. An integrated attenuation coefficient at the pixel level is estimated using the height of the reflectance peak at 1 088 nm. In addition, the surface reflectance of sea ice at the pixel level is estimated using the 1 550–1 750 nm band reflectance. The model is used to estimate the sea ice thickness with Hyperion images. The first validation results suggest that the proposed model and parameterization scheme can effectively reduce the estimation error associated with the sea ice thickness that is caused by temporal and spatial heterogeneities in the integrated attenuation coefficient and sea ice surface. A practical semi-empirical model and parameterization scheme that may be feasible for the sea ice thickness estimation using hyperspectral remote sensing data are potentially provided.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

A study on remote sensing models of sea ice thickness by microwave radiometry

Extrapolating from the propagation theories of electromagnetic waves in a layered medium, a three-layer medium model is deduced in this paper by using microwave radiometric remote sensing technology which is suitable to first-year sea ice condition of the northern part of China seas. Comparison with in situ data indicates that for microwave wavelength of 10 cm, the coherent model gives a quite good fit result for the thickness of sea ice less than 20 cm, and the incoherent model also works well for thickness within 20 to 40 cm. Based on three theoretical models, the inversion soft ware from microwave remote sensing data for calculating the thickness of sea ice can be set up. The relative complex dielectrical constants of different types of sea ice in the Liaodong Gulf calculated by using these theoretical models and measurement data are given in this paper. The extent of their values is (0. 5-4. 0)-j(0. 07~0. 19).

Improvements in Long-Lead Prediction of Early-Summer Subtropical Frontal Rainfall Based on Arctic Sea Ice

Seasonal prediction of East Asia(EA) summer rainfall, especially with a longer-lead time, is in great demand, but still very challenging. The present study aims to make long-lead prediction of EA subtropical frontal rainfall(SFR) during early summer(May-June mean, MJ) by considering Arctic sea ice(ASI) variability as a new potential predictor. A MJ SFR index(SFRI), the leading principle component of the empirical orthogonal function(EOF) analysis applied to the MJ precipitation anomaly over EA, is defined as the predictand. Analysis of 38-year observations(1979-2016) revealed three physically consequential predictors. A stronger SFRI is preceded by dipolar ASI anomaly in the previous autumn, a sea level pressure(SLP) dipole in the Eurasian continent, and a sea surface temperature anomaly tripole pattern in the tropical Pacific in the previous winter. These precursors foreshadow an enhanced Okhotsk High, lower local SLP over EA, and a strengthened western Pacific subtropical high. These factors are controlling circulation features for a positive SFRI. A physical-empirical model was established to predict SFRI by combining the three predictors. Hindcasting was performed for the 1979-2016 period, which showed a hindcast prediction skill that was, unexpectedly, substantially higher than that of a four-dynamical models’ ensemble prediction for the 1979-2010 period(0.72 versus 0.47). Note that ASI variation is a new predictor compared with signals originating from the tropics to mid-latitudes. The long-lead hindcast skill was notably lower without the ASI signals included, implying the high practical value of ASI variation in terms of long-lead seasonal prediction of MJ EA rainfall.

A strip thickness prediction method of hot rolling based on D_S information reconstruction

To improve prediction accuracy of strip thickness in hot rolling, a kind of Dempster/Shafer(D_S) information reconstitution prediction method(DSIRPM) was presented. DSIRPM basically consisted of three steps to implement the prediction of strip thickness. Firstly, iba Analyzer was employed to analyze the periodicity of hot rolling and find three sensitive parameters to strip thickness, which were used to undertake polynomial curve fitting prediction based on least square respectively, and preliminary prediction results were obtained. Then, D_S evidence theory was used to reconstruct the prediction results under different parameters, in which basic probability assignment(BPA) was the key and the proposed contribution rate calculated using grey relational degree was regarded as BPA, which realizes BPA selection objectively. Finally, from this distribution, future strip thickness trend was inferred. Experimental results clearly show the improved prediction accuracy and stability compared with other prediction models, such as GM(1,1) and the weighted average prediction model.

Simulation of Quasi-Linear Mesoscale Convective Systems in Northern China：Lightning Activities and Storm Structure

Two intense quasi-linear mesoscale convective systems（QLMCSs） in northern China were simulated using the WRF（Weather Research and Forecasting） model and the 3D-Var（three-dimensional variational） analysis system of the ARPS（Advanced Regional Prediction System） model.A new method in which the lightning density is calculated using both the precipitation and non-precipitation ice mass was developed to reveal the relationship between the lightning activities and QLMCS structures.Results indicate that,compared with calculating the results using two previous methods,the lightning density calculated using the new method presented in this study is in better accordance with observations.Based on the calculated lightning densities using the new method,it was found that most lightning activity was initiated on the right side and at the front of the QLMCSs,where the surface wind field converged intensely.The CAPE was much stronger ahead of the southeastward progressing QLMCS than to the back it,and their lightning events mainly occurred in regions with a large gradient of CAPE.Comparisons between lightning and non-lightning regions indicated that lightning regions featured more intense ascending motion than non-lightning regions;the vertical ranges of maximum reflectivity between lightning and non-lightning regions were very different;and the ice mixing ratio featured no significant differences between the lightning and non-lightning regions.

Modelling on seasonal lake ice evolution in central Asian arid climate zone: a case study

The seasonal cycle of ice thickness and temperature in Lake Wuliangsuhai,a typical shallow lake in the central Asian arid climate zone,was simulated using the HIGHTSI model and the MERRA-2 data as the meteorological forcing.The average ice growth rate was 0.64 cm·d^(−1) and −1.65 cm·d^(−1) for the growth and melting stage of the ice cover,respectively.The ice thickness agreed well with the field observations conducted in winter 2017,with a correlation coefficient of 0.97.The ice temperature field also agreed with observations in both daily variations and the vertical profile,and a better agreement in the daily amplitude and profile shape of ice temperature could be achieved if field data on physical properties of snow cover andmelting ice were available.This study proved the feasibility of both the HIGHTSI model and the MERRA-2 data for modeling the ice cover evolution in Lake Wuliangsuhai,providing a basis for a deep insight into the difference of lake ice evolution between central Asian arid climate zone and polar/sub-polar regions.

Using a skillful statistical model to predict September sea ice covering Arctic shipping routes

The rapid decrease in Arctic sea ice cover and thickness not only has a linkage with extreme weather in the midlatitudes but also brings more opportunities for Arctic shipping routes and polar resource exploration,both of which motivate us to further understand causes of sea-ice variations and to obtain more accurate estimates of seaice cover in the future.Here,a novel data-driven method,the causal effect networks algorithm,is applied to identify the direct precursors of September sea-ice extent covering the Northern Sea Route and Transpolar Sea Route at different lead times so that statistical models can be constructed for sea-ice prediction.The whole study area was also divided into two parts:the northern region covered by multiyear ice and the southern region covered by seasonal ice.The forecast models of September sea-ice extent in the whole study area(TSIE)and southern region(SSIE)at lead times of 1–4 months can explain over 65%and 79%of the variances,respectively,but the forecast skill of sea-ice extent in the northern region(NSIE)is limited at a lead time of 1 month.At lead times of 1–4 months,local sea-ice concentration and sea-ice thickness have a larger influence on September TSIE and SSIE than other teleconnection factors.When the lead time is more than 4 months,the surface meridional wind anomaly from northern Europe in the preceding autumn or early winter is dominant for September TSIE variations but is comparable to thermodynamic factors for NSIE and SSIE.We suggest that this study provides a complementary approach for predicting regional sea ice and is helpful in evaluating and improving climate models.

Deformation,structure and potential hazard of a landslide based on InSAR in Banbar county,Xizang(Tibet)

The Tibetan Plateau is characterized by complex geological conditions and a relatively fragile ecological environment.In recent years,there has been continuous development and increased human activity in the Tibetan Plateau region,leading to a rising risk of landslides.The landslide in Banbar County,Xizang(Tibet),have been perturbed by ongoing disturbances from human engineering activities,making it susceptible to instability and displaying distinct features.In this study,small baseline subset synthetic aperture radar interferometry(SBAS-InSAR)technology is used to obtain the Line of Sight(LOS)deformation velocity field in the study area,and then the slope-orientation deformation field of the landslide is obtained according to the spatial geometric relationship between the satellite’s LOS direction and the landslide.Subsequently,the landslide thickness is inverted by applying the mass conservation criterion.The results show that the movement area of the landslide is about 6.57×10^(4)m^(2),and the landslide volume is about 1.45×10^(6)m^(3).The maximum estimated thickness and average thickness of the landslide are 39 m and 22 m,respectively.The thickness estimation results align with the findings from on-site investigation,indicating the applicability of this method to large-scale earth slides.The deformation rate of the landslide exhibits a notable correlation with temperature variations,with rainfall playing a supportive role in the deformation process and displaying a certain lag.Human activities exert the most substantial influence on the spatial heterogeneity of landslide deformation,leading to the direct impact of several prominent deformation areas due to human interventions.Simultaneously,utilizing the long short-term memory(LSTM)model to predict landslide displacement,and the forecast results demonstrate the effectiveness of the LSTM model in predicting landslides that are in a continuous development and movement phase.The landslide is still active,and based on the spatial heterogeneity of landslide deformation,new recommendations have been proposed for the future management of the landslide in order to mitigate potential hazards associated with landslide instability.

Oxide Thickness Effects on n-MOSFETs Under On-State Hot-Carrier Stress

Hot carrier induced (HCI) degradation of surface channel n MOSFETs with different oxide thicknesses is investigated under maximum substrate current condition.Results show that the key parameters m and n of Hu's lifetime prediction model have a close relationship with oxide thickness.Furthermore,a linear relationship is found between m and n .Based on this result,the lifetime prediction model can be expended to the device with thinner oxides.

The state and fate of lake ice thickness in the Northern Hemisphere

Lake ice thickness(LIT)is important for regional hydroclimate systems,lake ecosystems,and human activities on the ice,and is thought to be highly susceptible to global warming.However,the spatiotemporal variability in LIT is largely unknown due to the difficulty in deriving in situ measurements and the lack of an effective remote sensing platform.Despite intensive development and applications of lake ice models driven by general circulation model output,evaluation of the global LIT is mostly based on assumed“ideal”lakes in each grid cell of the climate forcing data.A method for calculating the actual global LIT is therefore urgently needed.Here we use satellite altimetry to retrieve ice thickness for 16 large lakes in the Northern Hemisphere(Lake Baikal,Great Slave Lake,and others)with an accuracy of~0.2 m for almost three decades.We then develop a 1-D lake ice model driven primarily by remotely sensed data and cross-validated with the altimetric LIT to provide a robust means of estimating LIT for lakes larger than 50 km^(2)across the Northern Hemisphere.Mean LIT(annual maximum ice thickness)for 1313 simulated lakes and reservoirs covering~840,000 km^(2)for 2003–2018 is 0.63±0.02 m,corresponding to~485 Gt of water.LIT changes are projected for 2071–2099 under RCPs 2.6,6.0,and 8.5,showing that the mean LIT could decrease by~0.35 m under the worst concentration pathway and the associated lower ice road availability could have a significant impact on socio-economic activities.

A novel ground surface subsidence prediction model for sub-critical mining in the geological condition of a thick alluvium layer

A substantial number of the coal mines in China are in the geological condition of thick alluvium layer. Under these circumstances, it does not make sense to predict ground surface subsidence and other deformations by using conventional prediction models. This paper presents a novel ground surface subsidence prediction model for sub-critical mining in the geological condition of thick alluvium layer. The geological composition and mechanical properties of thick alluvium is regarded as a random medium, as are the uniformly distributed loads on rock mass; however, the overburden of the rock mass in the bending zone is looked upon as a hard stratum controlling the ground surface subsidence. The different subsidence and displacement mechanisms for the rock mass and the thick alluvium layer are respectively considered and described in this model, which indicates satisfactory performances in a practical prediction case.

基于早期股直肌超声纹理特征的首次有创机械通气患者30天死亡风险预测模型的构建与验证

目的基于早期股直肌超声纹理特征,构建首次有创机械通气患者30天死亡风险预测模型及验证。方法采用便利抽样法选择2022年1月至2023年12月入住河南省人民医院呼吸重症患者为研究对象,按8∶2随机分为训练集(n=304)和测试集(n=76)。两组均在插管后第1天、第3天、第5天和第7天使用床旁超声收集纹理特征参数。训练集依据第30天是否死亡为终点事件分为死亡组(n=80)和幸存组(n=224)。采用广义估计模型筛选患者30天死亡的影响因素并建立模型,绘制受试者工作特征(receiver operating characteristic,ROC)曲线下面积(area under receiver operating characteristic curve,AUC)评价模型的区分度,通过校准图和Hosmer-Lemeshow评价模型的校准度。结果年龄、氧合指数(P/F)、患肾脏疾病、使用类固醇激素、股直肌厚度变化率、均值和角二阶距是首次机械通气患者30天死亡的影响因素(P<0.05)。训练集和测试集的AUC分别为0.832(95%CI 0.767~0.898)和0.819(95%CI 0.722~0.916),提示模型的区分度良好。训练集和测试集的Hosmer-Lemeshow分别为5.969(P=0.651)和4.336(P=0.826),校准曲线与对角线重合度较高,说明预测模型的校准度较好。结论本研究基于早期股直肌超声纹理特征建立的预测模型可有效地预测首次机械通气患者30天死亡风险。

改性磁性纳米粒子稳定的稠油O/W型乳状液的流变性影响因素及管输压降预测模型

旨在探讨改性磁性纳米粒子质量分数、溶液酸碱度(pH)、油水比、NaCl浓度、搅拌速度以及乳化温度对稠油O/W型乳状液流变特性的影响规律,并结合Zeta电位、界面张力和油滴分布揭示了其作用机制。为了更深入且精确地分析以上因素对乳状液流动特性的影响,开展了六因素三水平的正交流变实验,采用幂律流体压降公式计算了各组条件下单位管长压降,并应用SPSS软件进行了方差分析和非线性回归分析,构建了一个适用于O/W型乳状液的管输压降预测模型。最后,使用Matlab软件对压降进行了最优解的求解。研究结果显示,油水比对乳状液流变特性影响最为显著,且改性磁性纳米粒子可以成功制备出油水比为8∶2的稠油O/W型乳状液。当改性磁性纳米粒子质量分数控制在0.07%,含油率维持在50.38%,NaCl浓度为0.12 mol/L,搅拌速度设置为664.10 r/min,且乳化温度保持在16.92℃时,该稠油O/W型乳状液的单位长度管输压降能达到最小值,即66.93Pa/m,该最优方案表明较低质量分数的改性磁性纳米粒子能在低温条件下实现稠油大幅度减阻输送。此外,通过实证研究,所构建的模型在严格的正交实验条件下展现出较好的管输压降预测能力,从而提供了一种可靠的方法来评估和优化稠油O/W型乳状液的输送性能。

基于地质概化的坚硬厚基岩覆岩裂隙场演变特征研究

精准掌握采动覆岩导水裂隙带演化特征是防止工作面突水灾害发生的重要基础,尤其对于西部矿井高强开采条件尤为重要。以榆树湾煤矿201西翼盘区为背景,以各砂岩层单轴抗压强度、RQD、砂岩层厚度作为系统聚类分析指标提出了厚基岩地质概化方法,系统研究了不同基岩层厚度对导水裂隙带发育高度、裂隙角度、频次、节理应变能演变规律的影响,确定了导水裂隙带高度与节理总应变能的量化关系。研究结果表明:基于地质概化方法模拟所得导水裂隙带发育高度随基岩厚度增大呈现对数正相关增大趋势,且增加速率逐渐减小,该规律通过与邻近矿井实测结果对比分析,验证了其准确性;随着基岩厚度的增大,导水裂隙带内垂直裂隙发育频次显著增大,水平裂隙反之,这表明基岩厚度的增大对导水裂隙带内水平裂纹的萌生与扩展具有一定抑制作用;导水裂隙带发育高度与覆岩能量释放程度密切相关,随节理最大应变能和总应变能的增加呈指数增加特点,且增加速率逐渐增大。

基于水分光谱指数的烟草叶片等效水厚度估测

为及时、准确监测烟草叶片的水分状况,连续2年开展不同基因型烤烟品种的水分胁迫试验,测定烟叶的光谱反射率和叶片等效水厚度(EWT),系统分析350~2500 nm波段范围内任意2个波段组合而成的比值水分指数(SRWI)和归一化水分指数(NDWI),并构建烟草EWT的光谱指数预测模型。结果表明:(1)不同基因型烟草的叶片等效水厚度均随灌水量的减少而降低。(2)不同水分处理的烟叶光谱反射率在可见光波段和近红外波段均发生了规律性变化。(3)筛选出的烟草叶片等效水厚度的光谱敏感波段主要集中在可见光区域的500~600 nm、近红外区域的700~900和1000~1250 nm、短波红外区域的1900~2000 nm,最佳水分光谱指数分别为NDWI(R1920,R1930)、SRWI(R1930,R1920),核心波段为1920、1930 nm。(4)利用水分光谱指数构建的烟草叶片等效水厚度的线性和非线性预测模型,以极限学习机模型(ELM)的精准度和稳定性最佳(P-R2=0.853**,T-R2=0.855**,RMSE=0.004)。表明可利用水分光谱指数NDWI(R1920,R1930)、SRWI(R1930,R1920)结合机器学习模型实现烟草叶片含水量的精准监测。

Asphalt pavement water film thickness detection and prediction model:A review

Over the course of storm or rainfall event,water thickness builds up on road surface resulting in a loss of contact between vehicle tires and road surface and puts drivers into immediate danger especially at high speeds.Therefore this is a considerably dangerous condition of the road and the realistic measurements and prediction model of water film thickness(WFT)on pavement surface is crucial for determining the road friction coefficient and evaluating the impact of rainfall on traffic safety.A review of the principle as well as critical evaluation of current detection methods of pavement WFT were compared for consistency and accuracy in this paper.The method selection guidelines are given for different road surface water film thickness detection requirements.This paper also introduces the latest development of WFT detection and prediction models for asphalt pavement,and gives the calculation elements and conditions of different WFT prediction models from different modeling ideas,which provides a basis for the selection and optimization of WFT models for future researchers.This article also suggests a few insights as further research directions on this topic.(1)The research can consider the influencing factors of WFT to conduct research on the delineation standard of pavement WFT.(2)In order to meet the future traffic safety dynamic early warning needs,road factors of different material types,disease conditions and linear conditions should be studied,as well as a comprehensive and accurate real-time water film thickness detection and evaluation method considering meteorological factors of rainfall timing,scale and intensity.(3)The prediction model of WFT should be further studied by the analytical method to clarify the influence of the pavement WFT on the driving safety.

基于LSTM的粮堆“热皮”厚度预测模型构建

本文根据粮仓密集阵列式测温系统所提取的温度数据,构建了一种基于长短期记忆网络(Long Short-Term Memory,LSTM)的粮堆“热皮”厚度预测模型。结果表明,该模型可以较好地预测粮堆“热皮”厚度。东侧列间、西侧列间和层间3种基于LSTM的“热皮”厚度预测模型拟合效果均较好,3种模型R2均高于0.889,预测误差较低。其中层间“热皮”预测模型的预测效果最好,训练集和验证集R2分别为0.936和0.921,平均绝对预测误差分别为0.058 m和0.049 m。表明基于LSTM的粮堆“热皮”厚度预测模型的预测性能具有一定优势,能够实现对储粮过程中粮仓“热皮”厚度的准确预测,为粮堆“热皮”的定量分析提供了新的思路,有助于仓储管理人员实现辅助决策,为保障粮食安全储藏提供理论借鉴。