J53.5 Implementation and Preliminary Assessment of PQPF Guidance at NWS Miami–South Florida and Detroit/Pontiac, Michigan

Thursday, 11 January 2018: 2:30 PM
Room 18A (ACC) (Austin, Texas)
Ian R. Lee, NOAA, White Lake, MI; and P. Santos, K. Scharfenberg, B. Veenhuis Jr., J. A. Nelson Jr., J. R. Wiedenfeld, J. A. Thomas, and J. Maloney
Manuscript (2.7 MB)

Handout (2.4 MB)

Beginning in late 2016, the National Weather Service (NWS) Weather Prediction Center (WPC) began planning the Probabilistic Quantitative Precipitation Forecast (PQPF) experiment in conjunction with five NWS Weather Forecast Offices including Miami, FL, Taunton, MA, Milwaukee, WI, Wichita, KS, and Melbourne, FL. The goal of the PQPF experiment is to better communicate forecast rainfall uncertainty using probabilistic, rather than deterministic guidance. Probabilistic guidance derived from a suite of ensemble model members and a local office deterministic 72-hr storm total QPF fixed upon the latest 0000 UTC or 1200 UTC model cycle are used to create the PQPF guidance. The PQPF guidance is available in gridded format via the Graphical Forecast Editor (GFE) as probability of exceedance grids for 1.00, 2.00, 4.00, 8.00, and 16.00 inches, or as percentile grids corresponding to the 5th, 10th, 25th, 50th, 75th, 90th, and 95th percentiles.

To create the PQPF guidance, WPC combines the deterministic WPC QPF guidance with the QPF distribution from a multi-model ensemble. PQPF percentile grids and a grid which describes the distribution spread are sent to WFOs who may then adjust the probability distribution based on the local WFO QPF forecast. The final 10th and 90th percentile grids are used to represent the best case scenario and reasonable worst case scenario respectively, with the local office storm total QPF grid representing the most likely scenario. Graphics of these three grids are available internally to each participating office for evaluation along with verification statistics generated within the GFE.

This work presents a detailed description of the project, the methodology, and a preliminary assessment and verification of PQPF guidance at some of the participating pilot offices. Performance trends are also assessed in relation to biases observed towards differing precipitable water regimes and convectively-driven environments. An application utilizing PQPF guidance in a decision support services framework is also presented. Finally, improvements to the methodology using statistical calibration techniques to improve the distribution functions, expansion of participating offices, and availability of guidance for public feedback will be explored as part of the PQPF experiment in the future.

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