Precipitation and Temperature Forecast Performance at the Weather Prediction Center

Abstract

The role of the human forecaster in improving upon the accuracy of numerical weather prediction is explored using multiyear verification of human-generated short-range precipitation forecasts and mediumrange maximum temperature forecasts from the Weather Prediction Center (WPC). Results show that human-generated forecasts improve over raw deterministic model guidance. Over the past two decades, WPC human forecasters achieved a 20%-40% improvement over the North American Mesoscale (NAM) model and the Global Forecast System (GFS) for the 1 in. (25.4 mm) (24 h) 21 threshold for day 1 precipitation forecasts, with a smaller, but statistically significant, 5%-15% improvement over the deterministic ECMWF model. Medium-range maximum temperature forecasts also exhibit statistically significant improvement over GFS model output statistics (MOS), and the improvement has been increasing over the past 5 yr. The quality added by humans for forecasts of high-impact events varies by element and forecast projection, with generally large improvements when the forecaster makes changes $88F (4.48C) to MOS temperatures. Human improvement over guidance for extreme rainfall events [3 in. (76.2 mm) (24 h) 21 ] is largest in the short-range forecast. However, human-generated forecasts failed to outperform the most skillful downscaled, biascorrected ensemble guidance for precipitation and maximum temperature available near the same time as the human-modified forecasts. Thus, as additional downscaled and bias-corrected sensible weather element guidance becomes operationally available, and with the support of near-real-time verification, forecaster training, and tools to guide forecaster interventions, a key test is whether forecasters can learn to make statistically significant improvements over the most skillful of this guidance. Such a test can inform to what degree, and just how quickly, the role of the forecaster changes.

Evaluation of Optimized WRF Precipitation Forecast over a Complex Topography Region during Flood Season

“…Numerical weather forecasts have been commonly used to provide QPF products in recent years [3][4][5]. However, the global numerical weather forecast models can not meet the requirement of high spatial and temporal resolution of a specific region with complicated topography, due to the limitation of computer resources.…”

Machine learning and artificial intelligence to aid climate change research and preparedness

“…Directly enhancing forecasts is difficult due to their high resolution and lead times. However, ML and AI methods can post-process forecast model output by accounting for missing model resolution and correcting the resulting biases (Novak et al 2014). Similar ML-based disaggregation, but of ESM projections, may provide bespoke climate services at a very fine spatial scale (Knusel et al 2019).…”

How emergency managers (mis?)interpret forecasts

“…Improvements in remotely-sensed data collection and application and the development of physically-based distributed hydrological models have significantly enhanced six-hour flash-flood forecasting, for instance (Hapuarachchi, Wang, and Pagano, 2011), and numerical bias-corrected, ensemble forecasts now notably outperform experienced human forecasters (Novak et al, 2014). Improvements in remotely-sensed data collection and application and the development of physically-based distributed hydrological models have significantly enhanced six-hour flash-flood forecasting, for instance (Hapuarachchi, Wang, and Pagano, 2011), and numerical bias-corrected, ensemble forecasts now notably outperform experienced human forecasters (Novak et al, 2014).…”

“…Certainly, advances in science and technology and the era of big data suggest greater potential for utilising short-and long-term forecast information to decrease flood-related losses. Improvements in remotely-sensed data collection and application and the development of physically-based distributed hydrological models have significantly enhanced six-hour flash-flood forecasting, for instance (Hapuarachchi, Wang, and Pagano, 2011), and numerical bias-corrected, ensemble forecasts now notably outperform experienced human forecasters (Novak et al, 2014). On the longer time horizon, the Climate Prediction Center, the International Research Institute for Climate Prediction, and other organisations have taken advantage of research conducted over the past few decades and developed a number of long-lead forecasts (seasonal to 12 months) that offer modest to moderate skill in some regions to predict the impacts on the ground (Barnston et al, 1994;Dutton, 2002;Livezey and Timofeyeva, 2008;O'Lenic et al, 2008).…”