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Yu Zhang
,
Limin Wu
,
Michael Scheuerer
,
John Schaake
, and
Cezar Kongoli

Abstract

This article compares the skill of medium-range probabilistic quantitative precipitation forecasts (PQPFs) generated via two postprocessing mechanisms: 1) the mixed-type meta-Gaussian distribution (MMGD) model and 2) the censored shifted Gamma distribution (CSGD) model. MMGD derives the PQPF by conditioning on the mean of raw ensemble forecasts. CSGD, on the other hand, is a regression-based mechanism that estimates PQPF from a prescribed distribution by adjusting the climatological distribution according to the mean, spread, and probability of precipitation (POP) of raw ensemble forecasts. Each mechanism is applied to the reforecast of the Global Ensemble Forecast System (GEFS) to yield a postprocessed PQPF over lead times between 24 and 72 h. The outcome of an evaluation experiment over the mid-Atlantic region of the United States indicates that the CSGD approach broadly outperforms the MMGD in terms of both the ensemble mean and the reliability of distribution, although the performance gap tends to be narrow, and at times mixed, at higher precipitation thresholds (>5 mm). Analysis of a rare storm event demonstrates the superior reliability and sharpness of the CSGD PQPF and underscores the issue of overforecasting by the MMGD PQPF. This work suggests that the CSGD’s incorporation of ensemble spread and POP does help enhance its skill, particularly for light forecast amounts, but CSGD’s model structure and its use of optimization in parameter estimation likely play a more determining role in its outperformance.

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Michael Scheuerer
,
Scott Gregory
,
Thomas M. Hamill
, and
Phillip E. Shafer

Abstract

A Bayesian classification method for probabilistic forecasts of precipitation type is presented. The method considers the vertical wet-bulb temperature profiles associated with each precipitation type, transforms them into their principal components, and models each of these principal components by a skew normal distribution. A variance inflation technique is used to de-emphasize the impact of principal components corresponding to smaller eigenvalues, and Bayes’s theorem finally yields probability forecasts for each precipitation type based on predicted wet-bulb temperature profiles. This approach is demonstrated with reforecast data from the Global Ensemble Forecast System (GEFS) and observations at 551 METAR sites, using either the full ensemble or the control run only. In both cases, reliable probability forecasts for precipitation type being either rain, snow, ice pellets, freezing rain, or freezing drizzle are obtained. Compared to the model output statistics (MOS) approach presently used by the National Weather Service, the skill of the proposed method is comparable for rain and snow and significantly better for the freezing precipitation types.

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Thomas M. Hamill
,
Michael Scheuerer
, and
Gary T. Bates

Abstract

Analog postprocessing methods have previously been applied using precipitation reforecasts and analyses to improve probabilistic forecast skill and reliability. A modification to a previously documented analog procedure is described here that produces highly skillful, statistically reliable precipitation forecast guidance at ° grid spacing. These experimental probabilistic forecast products are available via the web in near–real time.

The main changes to the previously documented analog algorithm were as follows: (i) use of a shorter duration (2002–13), but smaller grid spacing, higher-quality time series of precipitation analyses for training and forecast verification (i.e., the Climatology-Calibrated Precipitation Analysis); (ii) increased training sample size using data from 19 supplemental locations, chosen for their similar precipitation analysis climatologies and terrain characteristics; (iii) selection of analog dates for a particular grid point based on the similarity of forecast characteristics at that grid point rather than similarity in a neighborhood around that grid point; (iv) using an analog rather than a rank-analog approach; (v) varying the number of analogs used to estimate probabilities from a smaller number (50) for shorter-lead forecasts to a larger number (200) for longer-lead events; and (vi) spatial Savitzky–Golay smoothing of the probability fields. Special procedures were also applied near coasts and country boundaries to deal with data unavailability outside of the United States while smoothing.

The resulting forecasts are much more skillful and reliable than raw ensemble guidance across a range of event thresholds. The forecasts are not nearly as sharp, however. The use of the supplemental locations is shown to especially improve the skill of short-term forecasts during the winter.

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Rochelle P. Worsnop
,
Michael Scheuerer
, and
Thomas M. Hamill

Abstract

Probabilistic fire-weather forecasts provide pertinent information to assess fire behavior and danger of current or potential fires. Operational fire-weather guidance is provided for lead times fewer than seven days, with most products only providing day 1–3 outlooks. Extended-range forecasts can aid in decisions regarding placement of in- and out-of-state resources, prescribed burns, and overall preparedness levels. We demonstrate how ensemble model output statistics and ensemble copula coupling (ECC) postprocessing methods can be used to provide locally calibrated and spatially coherent probabilistic forecasts of the hot–dry–windy index (and its components). The univariate postprocessing fits the truncated normal distribution to data transformed with a flexible selection of power exponents. Forecast scenarios are generated via the ECC-Q variation, which maintains their spatial and temporal coherence by reordering samples from the univariate distributions according to ranks of the raw ensemble. A total of 20 years of ECMWF reforecasts and ERA-Interim reanalysis data over the continental United States are used. Skill of the forecasts is quantified with the continuous ranked probability score using benchmarks of raw and climatological forecasts. Results show postprocessing is beneficial during all seasons over CONUS out to two weeks. Forecast skill relative to climatological forecasts depends on the atmospheric variable, season, location, and lead time, where winter (summer) generally provides the most (least) skill at the longest lead times. Additional improvements of forecast skill can be achieved by aggregating forecast days. Illustrations of these postprocessed forecasts are explored for a past fire event.

Free access
Daniel Hodyss
,
Elizabeth Satterfield
,
Justin McLay
,
Thomas M. Hamill
, and
Michael Scheuerer

Abstract

Ensemble postprocessing is frequently applied to correct biases and deficiencies in the spread of ensemble forecasts. Methods involving weighted, regression-corrected forecasts address the typical biases and underdispersion of ensembles through a regression correction of ensemble members followed by the generation of a probability density function (PDF) from the weighted sum of kernels fit around each corrected member. The weighting step accounts for the situation where the ensemble is constructed from different model forecasts or generated in some way that creates ensemble members that do not represent equally likely states. In the present work, it is shown that an overweighting of climatology in weighted, regression-corrected forecasts can occur when one first performs a regression-based correction before weighting each member. This overweighting of climatology results in an increase in the mean-squared error of the mean of the predicted PDF. The overweighting of climatology is illustrated in a simulation study and a real-data study, where the reference is generated through a direct application of Bayes’s rule. The real-data example is a comparison of a particular method referred to as Bayesian model averaging (BMA) and a direct application of Bayes’s rule for ocean wave heights using U.S. Navy and National Weather Service global deterministic forecasts. This direct application of Bayes’s rule is shown to not overweight climatology and may be a low-cost replacement for the generally more expensive weighted, regression-correction methods.

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Matthew B. Switanek
,
Thomas M. Hamill
,
Lindsey N. Long
, and
Michael Scheuerer

Abstract

Tropical cyclones are extreme events with enormous and devastating consequences to life, property, and our economies. As a result, large-scale efforts have been devoted to improving tropical cyclone forecasts with lead times ranging from a few days to months. More recently, subseasonal forecasts (e.g., 2–6-week lead time) of tropical cyclones have received greater attention. Here, we study whether bias-corrected, subseasonal tropical cyclone reforecasts of the GEFS and ECMWF models are skillful in the Atlantic basin. We focus on the peak hurricane season, July–November, using the reforecast years 2000–19. Model reforecasts of accumulated cyclone energy (ACE) are produced, and validated, for lead times of 1–2 and 3–4 weeks. Week-1–2 forecasts are substantially more skillful than a 31-day moving-window climatology, while week-3–4 forecasts still exhibit positive skill throughout much of the hurricane season. Furthermore, the skill of the combination of the two models is found to be an improvement with respect to either individual model. In addition to the GEFS and ECMWF model reforecasts, we develop a statistical modeling framework that solely relies on daily sea surface temperatures. The reforecasts of ACE from this statistical model are capable of producing better skill than the GEFS or ECMWF model, individually, and it can be leveraged to further enhance the model combination reforecast skill for the 3–4-week lead time.

Free access
Michael Scheuerer
,
Matthew B. Switanek
,
Rochelle P. Worsnop
, and
Thomas M. Hamill

Abstract

Forecast skill of numerical weather prediction (NWP) models for precipitation accumulations over California is rather limited at subseasonal time scales, and the low signal-to-noise ratio makes it challenging to extract information that provides reliable probabilistic forecasts. A statistical postprocessing framework is proposed that uses an artificial neural network (ANN) to establish relationships between NWP ensemble forecast and gridded observed 7-day precipitation accumulations, and to model the increase or decrease of the probabilities for different precipitation categories relative to their climatological frequencies. Adding predictors with geographic information and location-specific normalization of forecast information permits the use of a single ANN for the entire forecast domain and thus reduces the risk of overfitting. In addition, a convolutional neural network (CNN) framework is proposed that extends the basic ANN and takes images of large-scale predictors as inputs that inform local increase or decrease of precipitation probabilities relative to climatology. Both methods are demonstrated with ECMWF ensemble reforecasts over California for lead times up to 4 weeks. They compare favorably with a state-of-the-art postprocessing technique developed for medium-range ensemble precipitation forecasts, and their forecast skill relative to climatology is positive everywhere within the domain. The magnitude of skill, however, is low for week-3 and week-4, and suggests that additional sources of predictability need to be explored.

Free access
Matthew B. Switanek
,
Joseph J. Barsugli
,
Michael Scheuerer
, and
Thomas M. Hamill

Abstract

Monthly tropical sea surface temperature (SST) data are used as predictors to make statistical forecasts of cold season (November–March) precipitation and temperature for the contiguous United States. Through the use of the combined-lead sea surface temperature (CLSST) model, predictive information is discovered not just in recent SSTs but also from SSTs up to 18 months prior. We find that CLSST cold season forecast anomaly correlation skill is higher than that of the North American Multimodel Ensemble (NMME) and the SEAS5 model from the European Centre for Medium-Range Weather Forecasts (ECMWF) when averaged over the United States for both precipitation and 2-m air temperature. The precipitation forecast skill obtained by CLSST in parts of the Intermountain West is of particular interest because of its implications for water resources. In those regions, CLSST dramatically improves the skill over that of the dynamical model ensembles, which can be attributed to a robust statistical response of precipitation in this region to SST anomalies from the previous year in the tropical Pacific.

Free access
Rochelle P. Worsnop
,
Michael Scheuerer
,
Francesca Di Giuseppe
,
Christopher Barnard
,
Thomas M. Hamill
, and
Claudia Vitolo

Abstract

Wildfire guidance two weeks ahead is needed for strategic planning of fire mitigation and suppression. However, fire forecasts driven by meteorological forecasts from numerical weather prediction models inherently suffer from systematic biases. This study uses several statistical-postprocessing methods to correct these biases and increase the skill of ensemble fire forecasts over the contiguous United States 8–14 days ahead. We train and validate the postprocessing models on 20 years of European Centre for Medium-Range Weather Forecasts (ECMWF) reforecasts and ERA5 reanalysis data for 11 meteorological variables related to fire, such as surface temperature, wind speed, relative humidity, cloud cover, and precipitation. The calibrated variables are then input to the Global ECMWF Fire Forecast (GEFF) system to produce probabilistic forecasts of daily fire indicators, which characterize the relationships between fuels, weather, and topography. Skill scores show that the postprocessed forecasts overall have greater positive skill at days 8–14 relative to raw and climatological forecasts. It is shown that the postprocessed forecasts are more reliable at predicting above- and below-normal probabilities of various fire indicators than the raw forecasts and that the greatest skill for days 8–14 is achieved by aggregating forecast days together.

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Thomas M. Hamill
,
Eric Engle
,
David Myrick
,
Matthew Peroutka
,
Christina Finan
, and
Michael Scheuerer

Abstract

The U.S. National Blend of Models provides statistically postprocessed, high-resolution multimodel ensemble guidance, providing National Weather Service forecasters with a calibrated, downscaled starting point for producing digital forecasts.

Forecasts of 12-hourly probability of precipitation (POP12) over the contiguous United States are produced as follows: 1) Populate the forecast and analyze cumulative distribution functions (CDFs) to be used later in quantile mapping. Were every grid point processed without benefit of data from other points, 60 days of training data would likely be insufficient for estimating CDFs and adjusting the errors in the forecast. Accordingly, “supplemental” locations were identified for each grid point, and data from the supplemental locations were used to populate the forecast and analyzed CDFs used in the quantile mapping. 2) Load the real-time U.S. and Environment Canada (now known as Environment and Climate Change Canada) global deterministic and ensemble forecasts, interpolated to ⅛°. 3) Using CDFs from the past 60 days of data, apply a deterministic quantile mapping to the ensemble forecasts. 4) Dress the resulting ensemble with random noise. 5) Generate probabilities from the ensemble relative frequency. 6) Spatially smooth the forecast using a Savitzky–Golay smoother, applying more smoothing in flatter areas.

Forecasts of 6-hourly quantitative precipitation (QPF06) are more simply produced as follows: 1) Form a grand ensemble mean, again interpolated to ⅛°. 2) Quantile map the mean forecast using CDFs of the ensemble mean and analyzed distributions. 3) Spatially smooth the field, similar to POP12.

Results for spring 2016 are provided, demonstrating that the postprocessing improves POP12 reliability and skill, as well as the deterministic forecast bias, while maintaining sharpness and spatial detail.

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