Daily HUME: Daily Homogenization, Uncertainty Measures and Extremes

全球变化不仅影响长期平均温度，而且可能导致频率分布的进一步变化，特别是在它们的尾部。对整个频率分布的研究是重要的，因为，例如，由于气象事件造成的发病率和死亡率的很大一部分是由热波和寒波造成的。日常数据集对于研究此类极端天气和气候至关重要，因此也是具有巨大社会经济后果的政治决策的基础。可靠地评估这些变化需要高质量的均匀观测数据。然而，不幸的是，测量记录包含许多非气候变化，例如，由于迁移、新的天气屏幕或仪器造成的均匀性。这种变化不仅影响均值，而且影响整个频率分布。为了提高全球日温度记录的质量和可靠性，我们建议开发一种校正频率分布的日温度数据的自动均匀化方法。我们建议将均匀化描述为一个优化问题，并使用遗传算法解决它。通过这种方式，整个温度网络可以同时均匀化，从而提高灵敏度，同时避免设置错误（虚假）中断。通过不直接对每日数据进行均质化，而是对月度指数（可能是月度矩）进行均质化，可以将月度均质化方法的全部功能和理解延续到每日数据的均质化中。此外，在优化框架中，可以客观而直接地确定最佳时间校正尺度，即校正是否最好每年应用（所有十二个月都得到相同的校正），半年，季节性或每月。所有三个方面都是新的：整个网络的同时均质化，调整自由度的客观选择和校正模型的时间平均尺度。这种新方法将应用于国际表面温度倡议的温度数据集的均匀化。这个大数据集需要一种自动均匀化方法。为了验证该方法，我们将生成一个具有已知不均匀性的人工气候数据集。为了能够生成具有实际非同质性的验证数据集，我们需要更好地理解日常数据中非同质性的本质。因此，我们打算收集和研究平行测量（一个位置的两个设置），这使我们能够研究如果一个设置被另一个设置取代时频率分布的变化。最后，我们将研究和量化均匀化后数据集中遗留的持续误差所导致的不确定性，并利用这一点来提高均匀化算法的准确性。对于使用均匀化数据的气候学家来说，不确定性的知识也是必不可少的。

Global change not only affects the long-term mean temperature, but may also lead to further changes in the frequency distribution and especially in their tails. The study of the whole frequency distribution is important as, e.g., heat and cold waves are responsible for a considerable part of morbidity and mortality due to meteorological events.Daily datasets are essential for studying such extremes of weather and climate and therefore the basis for political decisions with enormous socio-economic consequences. Reliably assessing such changes requires homogeneous observational data of high quality. Unfortunately, however, the measurement record contains many non-climatic changes, e.g. homogeneities due to relocations, new weather screens or instruments.Such changes affect not only the means, but the whole frequency distribution.To increase the quality and reliability of global daily temperature records, we propose to develop an automatic homogenisation method for daily temperature data that corrects the frequency distribution. We propose to describe homogenisation as an optimisation problem and solve it using a genetic algorithm. In this way, entire temperature networks can be homogenised simultaneously leading to an increase in sensitivity, while avoiding setting false (spurious) breaks. By not homogenising the daily data directly, but by homogenising monthly indices (probably the monthly moments), the full power and understanding of monthly homogenization methods can be carried over to the homogenisation of daily data. Furthermore, in an optimisation framework, the optimal temporal correction scale can be determined objectively and straightforwardly, that is whether the corrections are best applied annually (all twelve months get the same correction), semi-annually, seasonally or monthly. All three aspects are new: the simultaneous homogenisation of an entire network, the objective selection of the degrees of freedom of the adjustments and of the temporal averaging scale of the correction model.This new method will be applied to homogenise the temperature datasets of the International Surface Temperature Initiative. This large dataset necessitates an automatic homogenisation method. To validate the method, we will generate an artificial climate dataset with known inhomogeneities. To be able to generate such a validation dataset with realistic inhomogeneities, we need to understand the nature of inhomogeneities in daily data much better. Therefore, we intend to collect and study parallel measurements (two set-ups at one location), which allow us to study the changes in the frequency distribution if one set-up is replaced by the other. Finally, we will study and quantify the uncertainties due to persistent errors remaining in the dataset after homogenisation and utilise this to improve the accuracy of the homogenisation algorithm. The knowledge of uncertainties is also indispensable for climatologists using the homogenised data.