Spatial analysis of near-surface air humidity. Which variable?

Air humidity can be characterized by several variables (e.g. vapor pressure, dew point, relative humidity). Grid datasets of near-surface humidity are usually available for one of these only, say the daily mean relative humidity. If a different variable is needed in an application, say the vapor pressure deficit, the user is expected to derive it using familiar conversion formuli and a temperature dataset. A largely unnoticed problem of this procedure is that the conversion between variables is valid strictly for instantaneous values, but not for daily means considered in a daily grid dataset, because of non-linearity. In this study, the errors made with such conversion are examined using 10-minute observations at Swiss weather stations, applying the "inappropriate" conversions and comparing the results to the true daily means. The study is a preparatory step in the development of a gridded humidity dataset for Switzerland and its results point to more/less favorable decisions on the target variable.

The results show that the conversion introduces an error with a systematic component (bias) that is quantitatively significant. For example, calculating a daily mean vapor pressure deficit from daily mean relative humidity and temperature introduces a bias of about 10%. The bias is particularly large in summer and at the floor of major valleys, because of the large diurnal cycle of humidity and temperature. Our study explains the origin of the bias in terms of detail in the conversion formuli. An interesting result of our analysis is that conversions starting from dew-point temperature are less prone to error. It appears that dew-point temperature difference constitutes a relatively better choice of target variable for humidity grid datasets, at least with regard to precision of variable conversion.