Daily HUME: Daily Homogenization, Uncertainty Measures and Extremes

全球变化不仅影响长期平均温度，而且还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，例如，热浪和寒潮是气象事件造成的相当大一部分发病率和死亡率的原因，每日数据集对于研究这种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同质观测数据。然而，观测记录中包含许多非气候变化，例如由于迁移、新的天气屏或仪器而导致的均匀性，这些变化不仅影响平均值，而且影响整个频率分布。为了提高全球每日温度记录的质量和可靠性，我们建议开发一种用于校正频率分布的每日温度数据自动均匀化方法。我们建议将均质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。通过不直接同质化每日数据，而是同质化每月指数（可能是每月的时刻），每月同质化方法的全部功能和理解可以转移到每日数据的同质化。此外，在优化框架中，可以客观和直接地确定最佳时间校正尺度，即校正是否最好每年（所有12个月得到相同的校正）、每半年、每季度或每月应用。这三个方面都是新的：同时均匀化整个网络，客观选择的自由度的调整和时间平均规模的校正模型。这种新的方法将被应用到均匀化的国际表面温度倡议的温度数据集。这个大数据集需要自动均质化方法。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。为了能够生成具有真实不均匀性的验证数据集，我们需要更好地理解日常数据中不均匀性的本质。因此，我们打算收集和研究并行测量（在一个位置的两个设置），这使我们能够研究频率分布的变化，如果一个设置被另一个取代。最后，我们将研究和量化由于均匀化后数据集中残留的持续错误而导致的不确定性，并利用这一点来提高均匀化算法的准确性。气候学家在使用均质化资料时，也必须了解不确定性。

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.