### Introduction

This diagnostic aims to detect suspicious values or outliers (due to e.g. data corruption, unrealistic simulations…) through the computation and display of histograms. All time series of the dataset variable available in the CDS are considered. A histogram is an approximate representation of the variable data distribution showing the different frequencies of appearance of all the observed values. Thus, outliers are usually identified in a histogram as data points clearly isolated from the main data distribution. The histograms are available for the global domain. Finally, the time evolution of the variable data distribution for the global domain is also shown at the bottom of this page. All plots are interactive allowing the user to zoom and move across the figure for better browsing.

In order to automatically identify possible outliers, two different boundaries are used: control limits and tolerance limits:

The control limits are based on specific statistical analyses and depend on the distribution of the variable considered. Any value located outside the control limits should be considered suspicious of being unrealistic and further manual inspection is recommended. A WARNING message is issued in this case. The methodology for estimating the control limits is described briefly at the bottom of this page.

The tolerance limits are completely independent of the distribution of the variable considered and are predefined acceptable boundaries based on physical considerations. When available, the boundaries based on the World Meteorological Organization (WMO) are used as tolerance limits [Guide to the Global Observing System - WMO No488, appendix VI-2]. Any value located outside the tolerance limits should be considered as an unrealistic data point, flagged with an ERROR message. Values very slightly outside the tolerance limits may appear because of data packing issues.