  Very large datasets are often encountered in climatology, either from a
multiplicity of observations over time and space or outputs from deterministic
models (sometimes in petabytes= 1 million gigabytes). Loading a large data
vector and sorting it, is impossible sometimes due to memory limitations or
computing power. We show that a proposed algorithm to approximating the median,
"the median of the median" performs poorly. Instead we develop an algorithm to
approximate quantiles of very large datasets which works by partitioning the
data or use existing partitions (possibly of non-equal size). We show the
deterministic precision of this algorithm and how it can be adjusted to get
customized precisions.
