Experiments with the Improved Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation over South China

In recent work, three physical factors of the Dynamical-Statistical-Analog Ensemble Forecast Model for Landfalling Typhoon Precipitation (DSAEF_LTP model) have been introduced, namely, tropical cyclone (TC) track, TC landfall season, and TC intensity. In the present study, we set out to test the forecasting performance of the improved model with new similarity regions and ensemble forecast schemes added. Four experiments associated with the prediction of accumulated precipitation were conducted based on 47 landfalling TCs that occurred over South China during 2004-2018. The first experiment was designed as the DSAEF_LTP model with TC track, TC landfall season, and intensity (DSAEF_LTP-1). The other three experiments were based on the first experiment, but with new ensemble forecast schemes added (DSAEF_LTP-2), new similarity regions added (DSAEF_LTP-3), and both added (DSAEF_LTP- 4), respectively. Results showed that, after new similarity regions added into the model (DSAEF_LTP-3), the forecasting performance of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥250 mm and ≥100 mm) improved, and the sum of the threat score (TS250 + TS100) increased by 4.44%. Although the forecasting performance of DSAEF_LTP-2 was the same as that of DSAEF_LTP-1, the forecasting performance was significantly improved and better than that of DSAEF_LTP-3 when the new ensemble schemes and similarity regions were added simultaneously (DSAEF_LTP-4), with the TS increasing by 25.36%. Moreover, the forecasting performance of the four experiments was compared with four operational numerical weather prediction models, and the comparison indicated that the DSAEF_LTP model showed advantages in predicting heavy rainfall. Finally, some issues associated with the experimental results and future improvements of the DSAEF_LTP model were discussed.     

Experiments of DSAEF_LTP Model with Two Improved Parameters for Accumulated Precipitation of Landfalling Tropical Cyclones over Southeast China

The Dynamical-Statistical-Analog Ensemble Forecast model for landfalling tropical cyclones (TCs) precipitation (DSAEF_LTP) utilises an operational numerical weather prediction (NWP) model for the forecast track, while the precipitation forecast is obtained by finding analog cyclones, and making a precipitation forecast from an ensemble of the analogs. This study addresses TCs that occurred from 2004 to 2019 in Southeast China with 47 TCs as training samples and 18 TCs for independent forecast experiments. Experiments use four model versions. The control experiment DSAEF_LTP_1 includes three factors including TC track, landfall season, and TC intensity to determine analogs. Versions DSAEF_LTP_2, DSAEF_LTP_3, and DSAEF_LTP_4 respectively integrate improved similarity region, improved ensemble method, and improvements in both parameters. Results show that the DSAEF_LTP model with new values of similarity region and ensemble method (DSAEF_LTP_4) performs best in the simulation experiment, while the DSAEF_LTP model with new values only of ensemble method (DSAEF_LTP_3) performs best in the forecast experiment. The reason for the difference between simulation (training sample) and forecast (independent sample) may be that the proportion of TC with typical tracks (southeast to northwest movement or landfall over Southeast China) has changed significantly between samples. Forecast performance is compared with that of three global dynamical models (ECMWF, GRAPES, and GFS) and a regional dynamical model (SMS-WARMS). The DSAEF_LTP model performs better than the dynamical models and tends to produce more false alarms in accumulated forecast precipitation above 250 mm and 100 mm. Compared with TCs without heavy precipitation or typical tracks, TCs with these characteristics are better forecasted by the DSAEF_LTP model.     

土地利用调整在配合小城镇建设方面的研究

目前,土地利用总体规划正在小城镇建中起到关键的作用,本文就当前我国小城镇规划中土地利用方面存在的主要问题进行具体的分析研究并提出合理的解决措施.     

土地利用调整在配合小城镇建设方面的研究

目前,土地利用总体规划正在小城镇建中起到关键的作用,本文就当前我国小城镇规划中土地利用方面存在的主要问题进行具体的分析研究并提出合理的解决措施。     

构建中国自然灾害防灾减灾新体系

在全面建成小康社会的决胜阶段即“十三五”时期，我国进一步加大了防灾减灾科技投入，以防灾减灾业务需求为导向，围绕国家战略部署和防灾减灾业务链、创新链实施了一批重大科技项目，形成了成灾理论研究、关键技术研发、仪器装备研制、应用示范、专业队伍建设等综合防范体系，为“十四五”我国新时期自然灾害防治创新体系的构建打下了坚实的基础，并促进了中国特色防灾减灾事业的持续稳固发展。     

地形校正对U-Net深度神经网络分类器分类精度的影响

在国土资源监测、森林资源调查等多个领域中,基于遥感影像的分类技术受到了广泛应用。在利用传统分类器对地表覆盖分类提取中,地形效应是制约分类精度提升的一种因素,其影响可通过适当的校正模型减弱,且已证明地形校正能够对分类精度的提升起到积极作用。相比于传统分类器,基于深度学习理论的深度神经网络分类器具有深层特征学习和表达的优势,在图像分类领域兴起并逐渐用于土地覆盖分类且取得了不错的精度提升。本文初步探究了地形校正在利用深度神经网络分类器U-Net进行地表覆盖分类时对分类精度的影响情况。以Landsat 8 OLI 30 m影像为数据源,结合GDEM_V2 30 m地形数据,在GlobeLand 30和全国30 m森林分类结果的基础上,利用U-Net深度神经网络分类器实现了山区地表覆盖分类提取,并就不同训练样本获取方式及不同精细程度分类体系下地形校正前后的分类精度做了对比分析。分类结果表明：（1）规则网格裁切和坡向辅助裁切这两种训练样本获取方式下,地形校正后的分类精度较校正前不变或有极小幅度的降低,降低范围在0.9%—1.39%;（2）在对更精细的森林类型分类中,地形校正后的分类精度较校正前下降了1.66%。本文初步探究得到：在规则网格裁切和坡向辅助裁切这两种训练样本获取方式及不同精细程度的分类体系下,地形校正均未能提高U-Net深度神经网络分类器的分类精度。     

二氧化碳地质封存环境监测现状及建议

CO2地质封存是应对全球气候变化的新举措,许多国家都开展了相关的工程项目或研究。环境监测作为验证场址在当前以及较长一段时间后的合理性和安全性的重要手段,是工程实施的重要工作。对国内外CO2地质封存环境监测的相关工作以及项目所采用的监测技术进行总结,并利用监测选择工具MST得出中石化胜利油田CO2-EOR项目适用的监测技术,在此基础上提出该项目背景监测的对象、技术和频率等框架,为项目监测工作的开展提供参考依据,最后简略提出环境监测工作的方法和大纲。     

浅谈城乡规划建设与土地管理

土地资源作为经济发展的重要载体,如何处理好土地资源管理与城镇规划建设两者的关系,成为当前形势下土地管理部门面临的重要课题。面对日益严峻的用地形势,笔者认为要统筹看待土地管理工作和城乡规划建设,并从规范土地管理模式、优化土地资源配置方面,提出了有效利用土地资源、统筹做好城乡规划与土地管理的措施。     

Temporal and spatial differences of the industrial restructuring of Guangdong Province in the 1990S

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On the Improvement of the DSAEF＿LTP Model to Heavy Precipitation Simulation of Landfalling Tropical Cyclones over China in 2018

In this study, the Dynamical-Statistical-Analog Ensemble Forecast model (DSAEF_LTP model) for landfalling tropical cyclone (LTC) precipitation was employed to simulate the precipitation of 10 LTCs that occurred Pover China in 2018. With adding parameter‘similarity region scheme’(SRS) values and introducing TC intensity into the generalized initial value (GIV), four groups of precipitation simulation experiments were designed to verify the forecasting ability of the improved model for more TC samples. Results show that the simulation ability of the DSAEF_LTP model can be optimized regardless of whether adding SRS values only, or introducing TC intensity into GIV, while the experiment with both the two improvements shows a more prominent advantage in simulating the heavier precipitation of LTCs. Compared with four NWP models (i.e., ECMWF, GFS, GRAPES and SMS-WARMS), the overall forecasting performance of the DSAEF_LTP model achieves a better result in simulating precipitation at the thresholds over 250 mm and performs slightly better than NWP models at the thresholds over 100 mm.