  Precipitation data collected at sub-hourly resolution represents specific
challenges for missing data recovery by being largely stochastic in nature and
highly unbalanced in the duration of rain vs non-rain. Here we present a
two-step analysis utilising current machine learning techniques for imputing
precipitation data sampled at 30-minute intervals by devolving the task into
(a) the classification of rain or non-rain samples, and (b) regressing the
absolute values of predicted rain samples. Investigating 37 weather stations in
the UK, this machine learning process produces more accurate predictions for
recovering precipitation data than an established surface fitting technique
utilising neighbouring rain gauges. Increasing available features for the
training of machine learning algorithms increases performance with the
integration of weather data at the target site with externally sourced rain
gauges providing the highest performance. This method informs machine learning
models by utilising information in concurrently collected environmental data to
make accurate predictions of missing rain data. Capturing complex non-linear
relationships from weakly correlated variables is critical for data recovery at
sub-hourly resolutions. Such pipelines for data recovery can be developed and
deployed for highly automated and near instantaneous imputation of missing
values in ongoing datasets at high temporal resolutions.
