Abstract
Two seemingly unrelated postprocessing concepts retaining inter‐variable, spatial and temporal rank dependence structures from raw ensemble forecasts are compared and unified. The first concept combines ensemble model output statistics (EMOS), which fits and adjusts a parametric probability density function, with the ensemble copula coupling (ECC) reordering notion. The second concept is the member‐by‐member postprocessing (MBMP), based on a direct parametric adjustment of the raw ensemble members. It is shown that MBMP can be interpreted as a specific EMOS‐ECC variant. In an application to ensemble forecasts for temperature, EMOS‐ECC and MBMP succeed in outperforming the raw ensemble and conserving correlation structures.
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