Editing IPCC:AR6/WGI/Chapter-10 (section)

==== 10.2.2.4 Gridding ====

<div id="h3-11-siblings" class="h3-siblings"></div>

Derived gridded datasets require merging data from different sources of observations and/or reanalysis data on a regular grid ( [[#10.2.1.2|Section 10.2.1.2]] ; e.g., [[#Xie--1997|Xie and Arkin, 1997]] ). However, in situ observations are distributed irregularly, especially over sparsely populated areas. This leads to an interpolation challenge. Gridded products of climate variables, including temperature and precipitation, are strongly affected ( ''high confidence'' ) by the interpolation method over complex orography and data scarce regions ( [[#Hofstra--2008|Hofstra et al., 2008]] ; [[#Herrera--2016|Herrera et al., 2016]] ).

There are two main approaches to produce gridded datasets: (i) based on in situ observations only, and (ii) combining in situ observations with remote-sensing data and/or reanalysis data. The first approach has been widely employed in regions with high station density using interpolation techniques, such as inverse-distance weighting, optimal interpolation, and kriging ( [[#Chen--2008|Chen et al., 2008]] ; [[#Haylock--2008|Haylock et al., 2008]] ; [[#Frei--2014|Frei, 2014]] ; [[#Isotta--2014|Isotta et al., 2014]] ; Masson and [[#Frei--2014|Frei, 2014]] ; [[#Hiebl--2016|Hiebl and Frei, 2016]] ; [[#Nguyen-Xuan--2016|Nguyen-Xuan et al., 2016]] ). The second approach has been mainly applied in data-sparse regions with low station density, using simple bias adjustment, quantile mapping, and kriging techniques with in situ observations, remote-sensing and reanalysis data ( [[#Cheema--2012|Cheema and Bastiaanssen, 2012]] ; [[#Erdin--2012|Erdin et al., 2012]] ; Dinku et al., 2014; [[#Abera--2016|Abera et al., 2016]] ; [[#Krähenmann--2018|Krähenmann et al., 2018]] ).

Gridding of station data is affected by uncertainties stemming from measurement errors, inhomogeneities, the distribution of the underlying stations and the interpolation error, with station density being the dominant factor ( [[#Herrera--2019|Herrera et al., 2019]] ). Uncertainty due to interpolation is typically small for temperature but substantial for precipitation and its derivatives, such as drought indices ( [[#Chubb--2015|Chubb et al., 2015]] ; [[#Hellwig--2018|Hellwig et al., 2018]] ). The largest uncertainties typically occur in sparsely sampled mountain areas ( [[#10.2.2.5|Section 10.2.2.5]] ). Interpolation generally give rise to smoothing effects, such as low variability of the derived dataset with respect to the in situ observations ( [[#Chen--2019|Chen et al., 2019]] ). As a result, the effective resolution of gridded data is typically much lower than its nominal resolution. For instance, a 5 km gridded precipitation dataset for the European Alps has an effective resolution of about 10 to 25 km ( [[#Isotta--2014|Isotta et al., 2014]] ). In an example for precipitation in Spain, the effective resolution converged to the nominal resolution only when at least 6 to 7 stations were inside the gridcell ( [[#Herrera--2019|Herrera et al., 2019]] ). To account for the smoothing errors, new stochastic ensemble observation datasets have been introduced ( [[#Von%20Clarmann--2014|Von Clarmann, 2014]] ).

<div id="10.2.2.5" class="h3-container"></div>

<span id="observations-in-mountain-areas"></span>