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Photovoltaic systems contribute substantially to electrical grid capacity, yet the intermittent nature of solar resources and uncertainties in output make grid balancing and reserve allocation challenging. Accurately forecasting solar production begins with reliably estimating clear-sky irradiance levels. While broadband atmospheric turbidity parameters can capture reductions in clear-sky conditions, the precision of these models hinges on data capturing current and future aerosol concentrations and water vapor levels. Obtaining such information requires observations that are both temporally and spatially proximate, given the high variability in turbidity. Unfortunately, these kinds of measurements are available in near real time only at a small—and shrinking—set of locations. This study introduces an innovative approach to infer time-dependent, site-specific turbidity levels using more widely accessible data from pyranometers or monitored PV system outputs. The technique leverages a long short-term memory (LSTM) recurrent neural network to isolate the turbidity-related component embedded within global irradiance (or PV power-derived) records, overcoming the natural suppression of this signal. Designed for near real-time implementation, the method supports solar forecasting needs. Evaluation focuses on two aspects: (1) its capacity to replicate turbidity values calculated historically from beam irradiance observations during clear-sky periods; and (2) its effectiveness when supplying turbidity estimates (via persistence forecasts based on daily averages) as inputs to clear-sky models.