Using National Blend of Models for Probabilistic Hydrometeorological Applications

The National Blend of Models (NBM) is the culmination of an effort to develop a nationally consistent set of foundational gridded guidance products based on well-calibrated National Weather Service (NWS) and non-NWS model information. These guidance products are made available to the National Centers for Environmental Prediction centers and NWS Weather Forecast Offices for use in their forecast process. As the NWS continues to shift emphasis from production of forecast products to impact-based decision support services for core partners, the deterministic and probabilistic output from the NBM will become increasingly important as a starting point to the forecast process. We believe the Probabilistic Quantitiave Precipitation Forecasts (PQPF) from this statistically calibrated multi-model ensemble (171 short range and 115 long-range inputs) could serve as a foundational dataset for the National Water Model and other hydrological applications to produce probabilistic river forecasts.

Jeff has more than 32 years of experience in the field of operational weather forecasting, most of it with the NWS. After graduating from San Jose State in 1988, he worked for Accu-Weather, Inc. from 1988-1991. He joined the NWS as an Intern in 1991 at Lake Charles, LA, and then moved to Dodge City, KS from 1993-1995 as a journey forecaster. Mr. Craven was the first Science and Operations Officer at Elko, NV from 1995-1996, and then joined the Storm Prediction Center (SPC) as a Mesoscale/Outlook Forecaster from 1996-2002. While at SPC, he received an M.S. in Meteorology from the University of Oklahoma in 2001. Jeff was the Science and Operations Officer at Jackson, MS from 2002-2006, and then at Milwaukee, WI from 2006-2015. Mr. Craven briefly was the Scientific Services Divsion Chief of NWS Central Region in Kansas City for 2015-2017, and then moved to his present position of Chief of Statistical Modeling Division at Meteorological Development Laboratory from 2017-present.