Geomagnetic Kp Index and Earthquakes

In the ionosphere, the solar winds generate electrical currents. On the Earth surface, these currents cause magnetic field fluctuations. These fluctuations, penetrating the Earth interior, induce the electrical currents J, and, in the presence of the Earth magnetic field B, generate electromagnetic force, known as Lorentz force F = J × B. To study the relation of earthquakes and the Lorentz force, acting at the near onset times of strong earthquakes, we examine the Kp index, a logarithmic measure of the magnetic field deviation. The time varying Kp index gives us J, which in turn determines F. The variations of the Kp index were stacked by aligning their central times to the times of main earthquake shocks. This stacking method has been a popular and powerful tool in image processing, because it lifts up only the geomagnetic effect like carving a relief. The Lorentz force tilts the subtle force balance in the earth crust towards triggering the release of stress strain energy, initiating an earthquake in a similar way as a mountain climber’s step can trigger the avalanches. The internal dynamics, however, are highly statistical. Conventional statistical methods are used in combination with a newly devised method, which compares the time sequences of hypothetical random earthquakes to real ones. We find that the distinctive patterns of the Kp surges often strongly correlate to the onset of earthquake. This correlation depends on the seismic regions and the magnitudes of earthquakes. The stronger the earthquake is, more closely the Kp surge is associated. The statistical significance of nearly 100% is obtained for the Kp variations, synchronizing with more earthquakes in the Pacific Rim region. In parallel with the data analysis the historical studies are reviewed. The solar activities have been considered to influence the earthquake occurrences and the relation of the two has been studied extensively in the recent years as well as in the past century. A comprehensive list of publications is created with the brief introductions for each in the last chapter.

基于聚类分析法区域集合预报模式的初边值试验研究

选取2008年3月21—29日奥地利地区的降水天气过程,从欧洲中期天气预报中心全球集合数值预报模式生成的50个全球集合预报成员出发,通过聚类方法选取有代表性的16个成员,为中欧有限区域集合预报系统区域集合预报系统提供初边值扰动,进行初边条件不确定性数值试验,揭示初边条件不确定性对区域集合预报的影响。结果表明:经过聚类后选取的16个成员可以较好地代表50个全球集合预报成员大尺度背景的不确定性;集合平均结果在一定程度上修正了奥地利中部地区降水量级偏大和北部的降水空报情况;通过聚类后的集合系统得到的预报结果与控制预报相比表明,某些集合成员对降水预报有更好的指示意义;通过连续试验的对比,两个经聚类后得到的集合系统的降水预报评分均比业务预报的评分高,且集合离散度也较大;而成对和不成对的集合预报系统的预报结果基本相似。说明直接从全球大尺度集合预报模式成员中"降尺度"构造区域集合预报模式成员是可行有效的。

Statistically Extrapolated Nowcasting of Summertime Precipitation over the Eastern Alps

This paper presents a new multiple linear regression（MLR） approach to updating the hourly, extrapolated precipitation forecasts generated by the INCA（Integrated Nowcasting through Comprehensive Analysis） system for the Eastern Alps.The generalized form of the model approximates the updated precipitation forecast as a linear response to combinations of predictors selected through a backward elimination algorithm from a pool of predictors. The predictors comprise the raw output of the extrapolated precipitation forecast, the latest radar observations, the convective analysis, and the precipitation analysis. For every MLR model, bias and distribution correction procedures are designed to further correct the systematic regression errors. Applications of the MLR models to a verification dataset containing two months of qualified samples,and to one-month gridded data, are performed and evaluated. Generally, MLR yields slight, but definite, improvements in the intensity accuracy of forecasts during the late evening to morning period, and significantly improves the forecasts for large thresholds. The structure-amplitude-location scores, used to evaluate the performance of the MLR approach,based on its simulation of morphological features, indicate that MLR typically reduces the overestimation of amplitudes and generates similar horizontal structures in precipitation patterns and slightly degraded location forecasts, when compared with the extrapolated nowcasting.

Spatially Explicit Modeling of Long-Term Drought Impacts on Crop Production in Austria

Droughts have serious and widespread impacts on crop production with substantial economic losses. The frequency and severity of drought events may increase in the future due to climate change. We have developed three meteorological drought scenarios for Austria in the period 2008-2040. The scenarios are defined based on a dry day index which is combined with bootstrapping from an observed daily weather dataset of the period 1975-2007. The severity of long-term drought scenarios is characterized by lower annual and seasonal precipitation amounts as well as more significant temperature increases compared to the observations. The long-term impacts of the drought scenarios on Austrian crop production have been analyzed with the biophysical process model EPIC (Environmental Policy Integrated Climate). Our simulation outputs show that—for areas with historical mean annual precipitation sums below 850 mm— already slight increases in dryness result in significantly lower crop yields i.e. depending on the drought severity, between 0.6% and 0.9% decreases in mean annual dry matter crop yields per 1.0% decrease in mean annual precipitation sums. The EPIC results of more severe droughts show that spring and summer precipitation may become a limiting factor in crop production even in regions with historical abundant precipitation.

Integrated Assessment of Natural Hazards, Including Climate Change’s Influences, for Cultural Heritage Sites: The Case of the Historic Centre of Rethymno in Greece

Within the framework of disaster risk management, this article proposes an interdisciplinary method for the analysis of multiple natural hazards, including climate change’s influences, in the context of cultural heritage. A taxonomy of natural hazards applicable to cultural heritage was developed based on the existing theoretical and conceptual frameworks. Sudden-onset hazards, such as earthquakes and floods, and slow-onset hazards, such as wetting–drying cycles and biological contamination, were incorporated into the hazard assessment procedure. Future alteration of conditions due to climate change, such as change in heat waves’ duration, was also taken into account. The proposed hazard assessment framework was applied to the case of the Historic Centre of Rethymno, a city on the northern coast of the island of Crete in Greece,to identify, analyze, and prioritize the hazards that have the potential to cause damage to the center’s historic structures. The assessment procedure includes climate model projections, GIS spatial modeling and mapping, and finally a hazard analysis matrix to enable the sharing of a better understanding of multiple hazards with the stakeholders.The results can facilitate decision making by providing the vulnerability and risk analysis with the nature and spatial distribution of the significant hazards within the study area and its setting.

Elevation Influence on Rainfall and a Parameterization Algorithm in the Beijing Area

Using a high-density automatic weather stations(AWS)dataset of hourly rainfall observations,the present study investigates the relationship between rainfall and elevation in the Beijing area,and further proposes a rainfall amount dependent parameterized algorithm considering the elevation effect on rainfall on hourly timescale.The parameterization equation is defined as a segmented nonlinear model,which calculates the mountain rainfall as a function of valley rainfall amount.Results show that there exists an evident enhancement of rainfall amount by elevation effect in the Beijing area.In particular,larger rainfall amount is generally found in higher mountains,especially for slight rain and moderate rain.Furthermore,six representative station pairs located in valleys and on mountains respectively are selected to estimate the values of optimal parameters in the parameterization equation.The parameterization algorithm of elevation dependence can produce a reduction in the root-mean-square error and obtain a much closer mountain rainfall total to the observations compared with those using no elevation dependence.Furthermore,the spatial distribution of rainfall is more realistic and accurate in mountainous terrain when elevation dependence is considered.This study helps to understand the variability of rainfall with complex terrain in the Beijing area,and gives a possible way to parameterize rainfall–elevation relationship on hourly timescale.

Assessing the inter-annual variability of separation distances around odour sources to protect the residents from odour annoyance

In recent years, there has been a growing concern about potential impacts on public health and wellbeing due to exposure to environmental odour. Separation distances between odour-emitting sources and residential areas can be calculated using dispersion models, as a means of protecting the neighbourhood from odour annoyance. This study investigates the suitability of using one single year of meteorological input data to calculate reliable direction-dependent separation distances. Accordingly, we assessed and quantified the inter-annual variability of separation distances at two sites with different meteorological conditions, one in Brazil and the other in Austria. A 5-year dataset of hourly meteorological observations was used for each site. Two odour impact criteria set in current regulations were selected to explore their effect on the separation distances. The coefficient of variation was used as a statistical measure to characterise the amount of annual variation. Overall,for all scenarios, the separation distances had a low degree of inter-annual variability(mean coefficient of variation values from 8% to 21%). Reasonable agreements from year to year were therefore observed at the two sites under investigation, showing that one year of meteorological data is a good compromise to achieve reliable accuracy. This finding can provide a more cost-effective solution to calculate separation distances in the vicinity of odour sources.