| Deriving improved 6-h probabilistic QPFs (PQPFs) by blending two model-produced PQPFs : preliminary results - :6912 | National Weather Service (NWS)
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Deriving improved 6-h probabilistic QPFs (PQPFs) by blending two model-produced PQPFs : preliminary results
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    "Over the past several years, the Meteorological Development Laboratory (MDL) has been producing experimental 6-h probabilistic quantitative precipitation forecasts (PQPFs) with a 'high-resolution' MOS (model output statistics) approach (HR; Charba and Samplatsky 2011b, henceforth referenced as CS). Since 1 February 2010, the Hydrometeorological Prediction Center (HPC) of National Centers for Environmental Prediction has also been producing experimental 6-h PQPFs with a multi-model QPF distributions method, where the HPC deterministic QPF is the mode of the distribution (Novak et al. 2011). Subsequently, MDL has been conducting ongoing comparative verification of the HR and HPC PQPFs on the 4-km national Hydrologic Rainfall Analysis Project (HRAP) grid (CS), which is native to HR. A significant benefit of conducting the comparative verification on the HRAP grid is that the verifying 'Stage IV' precipitation data are also native to this grid. While this requires interpolating the HPC PQPFs from a 32-km grid, standard bi-quadratic interpolation well preserves the original grids. Note that prior to the verification the Stage IV precipitation data are subjected to supplemental quality control at MDL to remove sporadic residual errors (CS). Comparative Brier skill scores (BSS) for the contiguous United States (CONUS) have consistently shown that HR and HPC PQPFs have similar skill considering all precipitation thresholds in the day 1 to day 3 forecast range (12 - 30 h and 60 - 78 h forecast projections, respectively). This is shown in Fig. 1 for recent cool and warm season samples; close inspection of this figure reveals HR with slightly better skill for light precipitation thresholds, HR and HPC have about equal skill for moderate thresholds, and HPC has slightly better skill for heavy thresholds"--Introduction.

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