The Climate change trains regularly some phenomena threatening directly the environment and the humanity. In this context, meteorology plays a more important role in the control of these phenomena. It is thus important to search resources allowing to contribute to the improvement of the numerical model for the predictions of weather and climate.

The objective of this work is to look for the weaknesses of the models in the simulation of exchanges between the surface and the atmosphere. These exchanges are quantified by sensible and latent heat fluxes. The preprocessing is done through the combined use of k-nearest neighbors algorithm (k-NN) and Autoregressive integrated moving average (ARIMA) model in order to estimate missing values. The processing is performed with the learning of the association rules and the knowledge extracted enables us to make some comparisons between observations and simulations by the numerical model. The postprocessing is made by logical and graphical reasoning that facilitates the visualization of links between the obtained rules.

This method is deployed on a database containing measured variables (sensible and latent heat flux, temperature and humidity of the air, wind speed and direction, rain, global radiation, etc.) at the experimental site of the Centre de Recherches Atmosphériques (CRA) which is one of the two sites composing the Pyrenean Plateforme for the Observation of the Atmosphere (P2OA) in France. The obtained and expressed results in the form of association rules have made it possible to highlight that the differences between model and observations from a surface flux point of view are often concomitant with an important difference on global radiation. The expected profits are relative to the generation of knowledge useful for the improvement in the quality of the prediction with a better analysis of the important concomitant factors during errors on a weather model.

气候变化定期训练一些直接威胁环境和人类的现象。在这种情况下，气象学在控制这些现象中起着更为重要的作用。因此，重要的是搜索可有助于改善天气预报和气候预测数值模型的资源。

这项工作的目的是在模拟地表与大气之间的交换中寻找模型的弱点。这些交换通过显热通量和潜热通量来量化。预处理是通过结合使用k最近邻算法（k-NN）和自回归综合移动平均值（ARIMA）模型来估计缺失值的。通过学习关联规则来执行处理，并且提取的知识使我们能够通过数值模型在观察和模拟之间进行一些比较。后处理是通过逻辑和图形推理进行的，这有助于可视化所获得规则之间的链接。

该方法被部署在数据库中，该数据库包含测量的变量（感热和潜热通量，空气的温度和湿度，风速和风向，降雨，全球辐射等），位于RecherchesAtmosphériques（CRA）实验中心它是构成法国比利牛斯山脉大气压观测（P2OA）的两个地点之一。以关联规则的形式获得和表示的结果使得有可能强调，从表面通量的观点来看，模型和观测值之间的差异通常伴随着整体辐射的重要差异。