Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales
Techniques and challenges in the assimilation of atmospheric water observations for numerical weather prediction towards convective scales
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DOI：
10.1002/qj.3652
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发表时间：
2019-12
影响因子：
8.9
通讯作者：
R. Bannister;H. Chipilski;O. Martínez‐Alvarado
中科院分区：
文献类型：
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作者：
R. Bannister;H. Chipilski;O. Martínez‐Alvarado
虽然现代数值天气预报模式相当好地代表了潮湿大气过程的大尺度结构，但它们往往很难保持对流降雨等小尺度特征的准确预报。尽管高分辨率模型可以解决更多的流动，因此可以说是更准确的，潮湿的对流变得越来越非线性和动态不稳定。重要的是，模型的初始条件通常是次优的，这使得通过改进数据同化来提高预测准确性的空间。为了解决有关使用大气水相关观测的问题-特别是在对流尺度（也称为风暴尺度）-本文讨论了与水相关的量的观测和同化。特别强调的是背景误差统计变分和混合方法，需要特别注意水变量。讨论了大气水信息的对流尺度数据同化的挑战，这些挑战比在更大的尺度上更难解决。一些最重要的挑战包括更大程度的不均匀性和更低程度的流动平滑性，大量与水有关的观测结果（如雷达、微波和红外仪器），需要分析一系列水凝物，预报中位置误差的重要性日益增加，更复杂的远期模型，允许使用间接观测（例如受云和降水影响的观测），需要考虑大气水与动力场和质量场之间的流量相关多元“平衡”，以及大气水变量固有的非高斯性质。
While contemporary numerical weather prediction models represent the large‐scale structure of moist atmospheric processes reasonably well, they often struggle to maintain accurate forecasts of small‐scale features such as convective rainfall. Even though high‐resolution models resolve more of the flow, and are therefore arguably more accurate, moist convective flow becomes increasingly nonlinear and dynamically unstable. Importantly, the models' initial conditions are typically sub‐optimal, leaving scope to improve the accuracy of forecasts with improved data assimilation. To address issues regarding the use of atmospheric water‐related observations – especially at convective scales (also known as storm scales) – this article discusses the observation and assimilation of water‐related quantities. Special emphasis is placed on background error statistics for variational and hybrid methods which need special attention for water variables. The challenges of convective‐scale data assimilation of atmospheric water information are discussed, which are more difficult to tackle than at larger scales. Some of the most important challenges include the greater degree of inhomogeneity and lower degree of smoothness of the flow, the high volume of water‐related observations (e.g. from radar, microwave and infrared instruments), the need to analyse a range of hydrometeors, the increasing importance of position errors in forecasts, the greater sophistication of forward models to allow use of indirect observations (e.g. cloud‐ and precipitation‐affected observations), the need to account for the flow‐dependent multivariate “balance” between atmospheric water and both dynamical and mass fields, and the inherent non‐Gaussian nature of atmospheric water variables.