 The acknowledgment that theory is crucial for guaranteeing the evidential reliability of measurement draws attention to the “problem of observational grounding”, which is an inverse challenge to the traditional threat of theory-ladenness (Tal 2016b). The challenge is to specify what role observation plays in measurement, and particularly what sort of connection with observation is necessary and/or sufficient to allow measurement to play an evidential role in the sciences. This problem is especially clear when one attempts to account for the increasing use of computational methods for performing tasks that were traditionally accomplished by measuring instruments. As Margaret Morrison (2009) and Wendy Parker (2017) argue, there are cases where reliable quantitative information is gathered about a target system with the aid of a computer simulation, but in a manner that satisfies some of the central desiderata for measurement such as being empirically grounded and backward-looking (see also Lusk 2016). Such information does not rely on signals transmitted from the particular object of interest to the instrument, but on the use of theoretical and statistical models to process empirical data about related objects. For example, data assimilation methods are customarily used to estimate past atmospheric temperatures in regions where thermometer readings are not available. Some methods do this by fitting a computational model of the atmosphere’s behavior to a combination of available data from nearby regions and a model-based forecast of conditions at the time of observation (Parker 2017). These estimations are then used in various ways, including as data for evaluating forward-looking climate models. Regardless of whether one calls these estimations “measurements”, they challenge the idea that producing reliable quantitative evidence about the state of an object requires observing that object, however loosely one understands the term  “observation”.[23]