Weather and Forecasting

Henderson, John M.; Hamill, Thomas M.; Nehrkorn, Thomas

2025 Weather and Forecasting

doi: 10.1175/waf-d-24-0008.1

AbstractEnsemble forecast systems are an integral part of the National Weather Service’s (NWS) program for producing skillful, reliable deterministic and probabilistic forecasts, but statistical postprocessing is often required to address known deficiencies. Here we test improvements to the ensemble postprocessing procedure currently implemented at the NWS Meteorological Development Laboratory (MDL) that generate a deterministic quantitative precipitation forecast from multi-model ensemble means. The existing approach uses recent forecast and analysis data to estimate cumulative distributions of precipitation, and then applies a quantile mapping between the mean forecast and analysis. In the current work, we use a revised version of this approach that, in addition, utilizes ensemble mean inputs improved by a coalescence procedure based on the Feature Alignment Technique (FAT). This is a variational technique for determining the displacements needed to adjust precipitation features of individual ensemble members towards their positions in the raw ensemble mean. The performance of the combined coalescence-quantile mapping algorithm was evaluated over a two-year period of forecasts from version 12 of the NCEP Global Ensemble Forecast System (GEFSv12) after extensive testing of configurable parameters to ensure physical reasonableness of the precipitation fields. The coalescence procedure by itself was shown to correct some of the raw ensemble mean deficiencies related to position errors in the individual ensemble forecasts, especially as lead times increase. The combined approach resulted in improved forecasts as measured by fractional bias and equitable threat scores. Implementation of the techniques described here for operational guidance is being considered by NOAA and is applicable to other forecast models.

Murphy, Patrick; Mass, Clifford

2025 Weather and Forecasting

doi: 10.1175/waf-d-24-0025.1

AbstractCalifornia is vulnerable to large, rapidly growing wildfires that can threaten human lives and damage physical infrastructure. This study analyzes the daily growth of all satellite-observed California fires from 2003 through 2020 and relates that growth to local meteorological and environmental conditions. Fire growth is defined using both absolute and relative metrics (absolute growth—the daily burned area, relative growth—the daily percent increase in burned area). Near-surface environmental conditions are evaluated before, during, and after periods of fire growth for individual fires of varying sizes and spread rates across events with both high and low relative growth. Regional spatial patterns and distributions of near-surface dryness and winds during large absolute-growth events are examined and compared to climatological conditions. Overall, large absolute-growth events generally occur when dead fuels are driest, trailing short periods of increased atmospheric dryness. For large absolute-growth events, high relative growth is usually associated with strong winds while low relative-growth is generally associated with quiescent to climatological winds. Winds during high relative-growth events are greatest in autumn, surpassing the 95th percentile on average. For these autumnal events, atmospheric dryness is less than during other seasons, but atmospheric and fuel dryness are still sufficient for fire. Irrespective of season, winds during particularly rapid-growth events (high absolute and relative growth) are frequently downslope and influenced by local terrain regardless of the climatological conditions.

Kastman, Joshua S.; Tobin, Dana M.; Nash, Andy; Nelson, James A.; Cobb, Dan K.; Harnos, Kirstin J.

2025 Weather and Forecasting

doi: 10.1175/waf-d-23-0230.1

AbstractThe Winter Storm Severity Index (WSSI) is a tool designed to assist National Weather Service (NWS) operational forecasters, core partners and stakeholders, media, and the public in maintaining situational awareness of anticipated winter-weather-related impacts. The NWS Weather Prediction Center (WPC) produces the WSSI several times per day by combining the current official NWS gridded forecasts from the National Digital Forecast Database (NDFD) with non-meteorological datasets (e.g., climatology, land use, population) to create a graphical depiction of anticipated impacts to society due to winter weather. Impact severity output from the WSSI is designed to communicate spatial and temporal impact information in an easily consumable graphic. These impact scales range from Minor to Extreme, with a Winter Weather Area shown for areas where the winter-weather conditions are not expected to impact daily life. This product emerged in response to user needs for information about societal impacts that can occur from winter storm hazards. Specific hazards include snow amount, snow load, ice accumulation, flash freeze, blowing snow, and ground blizzard. The maximum value from any of these components contributes to a final overall impact component. Information about the WSSI, including a detailed overview of the product, recent social-science-backed improvements, components, and use in an operational setting are presented. Preliminary verification efforts are also detailed.

Johnson, Marcus; Snook, Nathan; Park, Jun; Xue, Ming; Brewster, Keith A.; Supinie, Timothy; Hu, Xiao-Ming

2025 Weather and Forecasting

doi: 10.1175/waf-d-24-0034.1

AbstractAs part of the 2022 NOAA Hazardous Weather Testbed Spring Forecasting Experiment, the Center for Analysis and Prediction of Storms produced FV3-LAM real-time ensemble forecasts to study its use in convection-allowing ensemble forecasts for the severe weather forecasting problem and to inform the optimization of the upcoming operational Rapid Refresh Forecast System. We evaluate deterministic and ensemble forecasts in terms of surrogate severe weather reports (SSRs) and surrogate severe probability forecasts (SSPFs) created from simulated 0–3- and 2–5-km updraft helicity (UH), and 10-m wind speed. Forecasts are verified against observed storm reports (OSRs) and observed severe probabilistic fields (OSPFs) derived from tornado and hail, wind, and all types of local storm reports, and 0600 UTC Day 1 convective outlooks issued by the Storm Prediction Center (SPC) for three cases.UH ensemble SSPFs have better reliability and discrimination when verified with OSRs from all storm reports. Spatial smoothing generally increases reliability while smaller smoothing lengths optimize discrimination ability. Case studies demonstrate that UH SSPFs are consistent with SPC Day 1 convective outlooks, indicating that these forecasts are qualitatively similar to operational guidance. The ensemble mean of SSRs is generally more skillful than individual members when based on UH, but not 10-m wind. In fact, SSRs and SSPFs based on 10-m wind display little skill predicting severe wind events; we therefore conclude that UH seems to better inform severe hazard risk, even compared to prognosed surface wind speed. Model resolution dictates its ability to prognose rotating updrafts and severe wind.