Ensemble Precipitation Forecast Postprocessing with Ensemble Coalescence and Quantile Mapping

John M. Henderson Atmospheric and Environmental Research, Lexington, Massachusetts

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Thomas M. Hamill NOAA/Physical Sciences Laboratory, Boulder, Colorado

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Thomas Nehrkorn Atmospheric and Environmental Research, Lexington, Massachusetts

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Abstract

Ensemble 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 generates a deterministic quantitative precipitation forecast from multimodel 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 toward their positions in the raw ensemble mean. The performance of the combined coalescence–quantile mapping algorithm was evaluated over a 2-yr period of forecasts from version 12 of the NCEP Global Ensemble Forecast System (GEFSv12) after extensive testing of configurable parameters to ensure the 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 the National Oceanic and Atmospheric Administration (NOAA) and is applicable to other forecast models.

Significance Statement

Ensemble weather prediction systems provide information about the uncertainty in weather forecasts. However, the raw output from these models is voluminous and suffers from biases. Here, we combine two statistical approaches to correct for the biases and create a single deterministic forecast that is more manageable to use by stakeholders. The two techniques—coalescence and then quantile mapping—are applied in sequence and the resulting model forecast is compared against observations. Our approaches improve the statistical performance with precipitation fields that have more realistic extrema and a smaller extent of small precipitation amounts. The techniques are being evaluated for operational implementation by the National Weather Service.

Nehrkorn: Retired.

Hamill’s current affiliation: The Weather Company, Brookhaven, Georgia.

© 2025 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John M. Henderson, john.henderson2@mail.mcgill.ca

Abstract

Ensemble 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 generates a deterministic quantitative precipitation forecast from multimodel 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 toward their positions in the raw ensemble mean. The performance of the combined coalescence–quantile mapping algorithm was evaluated over a 2-yr period of forecasts from version 12 of the NCEP Global Ensemble Forecast System (GEFSv12) after extensive testing of configurable parameters to ensure the 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 the National Oceanic and Atmospheric Administration (NOAA) and is applicable to other forecast models.

Significance Statement

Ensemble weather prediction systems provide information about the uncertainty in weather forecasts. However, the raw output from these models is voluminous and suffers from biases. Here, we combine two statistical approaches to correct for the biases and create a single deterministic forecast that is more manageable to use by stakeholders. The two techniques—coalescence and then quantile mapping—are applied in sequence and the resulting model forecast is compared against observations. Our approaches improve the statistical performance with precipitation fields that have more realistic extrema and a smaller extent of small precipitation amounts. The techniques are being evaluated for operational implementation by the National Weather Service.

Nehrkorn: Retired.

Hamill’s current affiliation: The Weather Company, Brookhaven, Georgia.

© 2025 American Meteorological Society. This published article is licensed under the terms of the default AMS reuse license. For information regarding reuse of this content and general copyright information, consult the AMS Copyright Policy (www.ametsoc.org/PUBSReuseLicenses).

Corresponding author: John M. Henderson, john.henderson2@mail.mcgill.ca

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