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利用陕西省94个国家气象站1961—2018年逐日气象资料,根据干旱灾害气候背景和社会经济环境,结合灾害风险评估相关理论方法,选取致灾因子危险性、孕灾环境脆弱性、承灾体暴露度、防灾减灾能力4个方面指标,建立干旱灾害风险评估指数,基于GIS平台,对陕西省不同季节进行干旱灾害风险区划。结果表明:(1)陕西各区域干旱致灾因子危险性季节差异明显,陕北北部除夏季外各季节干旱危险性较高,关中地区易发生伏旱。陕南的汉中各季节干旱危险性均较大,安康东部和商洛各季节干旱危险性则较小。(2)春季、夏季和秋季,陕南的汉中平原及安康的汉江河谷地带,关中的西安和渭南地区,陕北北部榆林地区为干旱孕灾环境高脆弱性区或较高区;冬季陕南大部、秦岭地区的高脆弱性区较其他三季范围有所减小;海拔较高的秦岭山地,关中平原和陕北北部各季节皆为低脆弱性或较低脆弱性地区。(3)承灾体暴露度的高风险区主要分布于关中地区。(4)全省抵御干旱风险能力最高地区为陕北黄河沿线、关中各地的城镇地区。(5)干旱灾害综合风险的高风险区主要在陕南巴山地区、秦岭南北两侧、陕北南部,陕南汉江平原、关中平原及陕北延安、榆林等地为干旱较低、低风险区。 相似文献
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利用陕西气象站点逐小时降水实况、精细化格点预报、数字高程、土地利用、灾情等资料,应用水动力模型FloodArea对暴雨洪涝进行淹没模拟,在淹没水深和范围的基础上叠置承灾体属性,引入承灾体的灾损曲线,建立暴雨洪涝灾害风险预评估模型,并从数量占比和灾情占比两个角度,以县为单元进行验证,利用格点降水量预报对陕西6次大范围暴雨过程灾害风险进行预评估以及效果检验。结果表明:暴雨洪涝气象风险预估结果与实际受灾地区分布基本吻合,正确预报率73.2%,模拟结果可信度高,对于降水区域集中暴雨的风险预评估性能较分散性暴雨较高,漏报率相对低,但是空报率较高;建立的暴雨洪涝灾害风险预评估及效果检验流程,提高了气象服务的针对性,可以用于洪涝风险预评估的实际业务中,对暴雨洪涝风险管理提供技术支撑。 相似文献
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In south China, warm-sector rainstorms are significantly different from the traditional frontal rainstorms due to complex mechanism, which brings great challenges to their forecast. In this study, based on ensemble forecasting, the high-resolution mesoscale numerical forecast model WRF was used to investigate the effect of initial errors on a warmsector rainstorm and a frontal rainstorm under the same circulation in south China, respectively. We analyzed the sensitivity of forecast errors to the... 相似文献
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准确反演气溶胶光学厚度（AOD）在气溶胶气候效应和环境效应研究中至关重要，仪器定标是目前AOD反演过程中最大的不确定性来源。Langley法作为应用最广泛的光度计定标方法，其对天气条件和大气洁净度要求苛刻，这在大气污染较重的地区难以实现，并可能导致AOD反演误差偏大。为了降低由不恰当的Langley定标所引起的AOD反演误差，本文提出一个利用辐射传输模式结合地面太阳直接辐射观测数据检验Langley定标结果合理性的方法，并利用西安2013年6月至2015年12月多滤波旋转遮光带辐射计（MFRSR）和直接辐射表（NIP）观测资料，探讨了方法的可行性。结果表明，该方法能够将Langley定标结果限定在较小的波动范围内，有效降低由定标不确定性引起的AOD反演误差，有助于提高单站点AOD观测精度。 相似文献
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Shang Chen Wenzhe Feng Liang He Wei Xiao Hao Feng Qiang Yu Jiandong Liu Jianqiang He 《水文研究》2024,38(2):e15091
Accurately estimated reference evapotranspiration (ET0) is essential to regional water management. The FAO recommends coupling the Penman–Monteith (P-M) model with the Ångström–Prescott (A-P) formula as the standard method for ET0 estimation with missing Rs measurements. However, its application is usually restricted by the two fundamental coefficients (a and b) of the A-P formula. This paper proposes a new method for estimating ET0 with missing Rs by combining machine learning with physical-based P-M models (PM-ET0). The benchmark values of the A-P coefficients were first determined at the daily, monthly, and yearly scales, and further evaluated in Rs and ET0 estimates at 80 national Rs measuring stations. Then, three empirical models and four machine-learning methods were evaluated in estimating the A-P coefficients. Machine learning methods were also used to estimate ET0 (ML-ET0) to compare with the PM-ET0. Finally, the optimal estimation method was used to estimate the A-P coefficients for the 839 regular weather stations for ET0 estimation without Rs measurement for China. The results demonstrated a descending trend for coefficient a from northwest to southeast China, with larger values in cold seasons. However, coefficient b showed the opposite distribution as the coefficient a. The FAO has recommended a larger a but a smaller b for southeast China, which produced the region's largest Rs and ET0 estimation errors. Additionally, the A-P coefficients calibrated at the daily scale obtained the best estimation accuracy for both Rs and ET0, and slightly outperformed the monthly and yearly coefficients without significant difference in most cases. The machine learning methods outperformed the empirical methods for estimating the A-P coefficients, especially for the sites with extreme values. Further, ML-ET0 outperformed the PM-ET0 with yearly A-P coefficients but underperformed those with daily and monthly ones. This study indicates an exciting potential for combining machine learning with physical models for estimating ET0. However, we found that using the A-P coefficients with finer time scales is unnecessary to deal with the missing Rs measurements. 相似文献
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一次飞机冷云增雨作业效果检验 总被引：1，自引：0，他引：1
最近60多年,全球范围内广泛开展了人工增雨作业,但人工增雨效果检验一直是个难题。传统上,利用雨量计和目标/对比区统计数据评估人工增雨效果,结果大多不确定。对一次人工增雨作业而言,从科学上给出令人信服的效果检验更是没有好的解决方案。2017年3月19日,陕西省实施业务飞机冷云增雨作业播撒含有750 g碘化银（AgI）的催化剂,播撒线长125 km。作业后卫星、雷达观测到一条与播云线对应的清晰的云迹线,地面雨滴谱仪观测到相应的雨强、雨滴数浓度、雨滴直径增大,表明播云使云体产生了增雨响应。针对这次增雨过程,从连片雷达回波中分离增雨作用造成的回波增强带（增雨影响回波）和确定了自然降水回波强度,建立增雨影响回波强度（Z）与地面雨强（I）的拟合关系（Z-I关系）,定量研究人工增雨的时、空演变。结果表明:（1）增雨影响时间约4 h,增雨影响回波区域（增雨影响区）面积为5448 km2。该区累计降雨总量和增雨总量分别为1.518×106 m3和8.04×105 m3,增雨影响区内增雨率达53%。（2）总降雨量、增雨量、自然降雨量随时间先增后减,总降雨量与增雨量的峰值同步,两者峰值都早于自然降雨峰值;催化后146 min （04时47分,世界时,下同）,每6 min增雨量达到最大,为4.9×104 m3;催化后174 min （05时15分）,增雨雷达回波面积达到最大（1711 km2）,面积峰值滞后增雨量峰值出现。（3）增雨影响区位于播撒线下游,呈条带状;区域内总降雨量空间分布为中间大边缘小,与增雨量空间分布一致。（4）此次增雨作业改变了降雨时、空分布,促进降雨形成,增加了地面降雨量。 相似文献
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