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Michael J. Erickson, Brian A. Colle, and Joseph J. Charney

Abstract

The performance of a multimodel ensemble over the northeast United States is evaluated before and after applying bias correction and Bayesian model averaging (BMA). The 13-member Stony Brook University (SBU) ensemble at 0000 UTC is combined with the 21-member National Centers for Environmental Prediction (NCEP) Short-Range Ensemble Forecast (SREF) system at 2100 UTC. The ensemble is verified using 2-m temperature and 10-m wind speed for the 2007–09 warm seasons, and for subsets of days with high ozone and high fire threat. The impacts of training period, bias-correction method, and BMA are explored for these potentially hazardous weather events using the most recent consecutive (sequential training) and most recent similar days (conditional training). BMA sensitivity to the selection of ensemble members is explored. A running mean difference between forecasts and observations using the last 14 days is better at removing temperature bias than is a cumulative distribution function (CDF) or linear regression approach. Wind speed bias is better removed by adjusting the modeled CDF to the observation. High fire threat and ozone days exhibit a larger cool bias and a greater negative wind speed bias than the warm-season average. Conditional bias correction is generally better at removing temperature and wind speed biases than sequential training. Greater probabilistic skill is found for temperature using both conditional bias correction and BMA compared to sequential bias correction with or without BMA. Conditional and sequential BMA results are similar for 10-m wind speed, although BMA typically improves probabilistic skill regardless of training.

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Michael J. Erickson, Joseph J. Charney, and Brian A. Colle

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A fire weather index (FWI) is developed using wildfire occurrence data and Automated Surface Observing System weather observations within a subregion of the northeastern United States (NEUS) from 1999 to 2008. Average values of several meteorological variables, including near-surface temperature, relative humidity, dewpoint, wind speed, and cumulative daily precipitation, are compared on observed wildfire days with their climatological average (“climatology”) using a bootstrap resampling approach. Average daily minimum relative humidity is significantly lower than climatology on wildfire occurrence days, and average daily maximum temperature and average daily maximum wind speed are slightly higher on wildfire occurrence days. Using the potentially important weather variables (relative humidity, temperature, and wind speed) as inputs, different formulations of a binomial logistic regression model are tested to assess the potential of these atmospheric variables for diagnosing the probability of wildfire occurrence. The FWI is defined using probabilistic output from the preferred binomial logistic regression configuration. Relative humidity and temperature are the only significant predictors in the binomial logistic regression. The binomial logistic regression model is reliable and has more probabilistic skill than climatology using an independent verification dataset. Using the binomial logistic regression output probabilities, an FWI is developed ranging from 0 (minimum potential) to 3 (high potential) and is verified independently for two separate subdomains within the NEUS. The climatology of the FWI reproduces observed fire occurrence probabilities between 1999 and 2008 over a subdomain of the NEUS.

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Michael J. Erickson, Brian A. Colle, and Joseph J. Charney

Abstract

The Short-Range Ensemble Forecast (SREF) system is verified and bias corrected for fire weather days (FWDs) defined as having an elevated probability of wildfire occurrence using a statistical Fire Weather Index (FWI) over a subdomain of the northeastern United States (NEUS) between 2007 and 2014. The SREF is compared to the Rapid Update Cycle and Rapid Refresh analyses for temperature, relative humidity, specific humidity, and the FWI. An additive bias correction is employed using the most recent previous 14 days [sequential bias correction (SBC)] and the most recent previous 14 FWDs [conditional bias correction (CBC)]. Synoptic weather regimes on FWDs are established using cluster analysis (CA) on North American Regional Reanalysis sea level pressure, 850-hPa temperature, 500-hPa temperature, and 500-hPa geopotential height. SREF severely underpredicts FWI (by two indices at FWI = 3) on FWDs, which is partially corrected using SBC and largely corrected with CBC. FWI underprediction is associated with a cool (ensemble mean error of −1.8 K) and wet near-surface model bias (ensemble mean error of 0.46 g kg−1) that decreases to near zero above 800 hPa. Although CBC improves reliability and Brier skill scores on FWDs, ensemble FWI values exhibit underdispersion. CA reveals three synoptic weather regimes on FWDs, with the largest cool and wet biases associated with a departing surface low pressure system. These results suggest the potential benefit of an operational analog bias correction on FWDs. Furthermore, CA may help elucidate model error during certain synoptic weather regimes.

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Michael J. Erickson, Benjamin Albright, and James A. Nelson

Abstract

The Weather Prediction Center’s Excessive Rainfall Outlook (ERO) forecasts the probability of rainfall exceeding flash flood guidance within 40 km of a point. This study presents a comprehensive ERO verification between 2015 and 2019 using a combination of flooding observations and proxies. ERO spatial issuance frequency plots are developed to provide situational awareness for forecasters. Reliability of the ERO is assessed by computing fractional coverage of the verification within each probabilistic category. Probabilistic forecast skill is evaluated using the Brier skill score (BSS) and area under the relative operating characteristic (AUC). A “probabilistic observation” called practically perfect (PP) is developed and compared to the ERO as an additional measure of skill. The areal issuance frequency of the ERO varies spatially with the most abundant issuances spanning from the Gulf Coast to the Midwest and the Appalachians. ERO issuances occur most often in the summer and are associated with the Southwestern monsoon, mesoscale convective systems, and tropical cyclones. The ERO exhibits good reliability on average, although more recent trends suggest some ERO-defined probabilistic categories should be issued more frequently. AUC and BSS are useful bulk skill metrics, while verification against PP is useful in bulk and for shorter-term ERO evaluation. ERO forecasts are generally more skillful at shorter lead times in terms of AUC and BSS. There is no trend in ERO area size over 5 years, although ERO forecasts may be getting slightly more skillful in terms of critical success index when verified against the PP.

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Michael J. Erickson, Joshua S. Kastman, Benjamin Albright, Sarah Perfater, James A. Nelson, Russ S. Schumacher, and Gregory R. Herman

Abstract

The Flash Flood and Intense Rainfall (FFaIR) Experiment developed within the Hydrometeorology Testbed (HMT) of the Weather Prediction Center (WPC) is a pseudo-operational platform for participants from across the weather enterprise to test emerging flash flood forecasting tools and issue experimental forecast products. This study presents the objective verification portion of the 2017 edition of the experiment, which examines the performance from a variety of guidance tools (deterministic models, ensembles, and machine-learning techniques) and the participants’ forecasts, with occasional reference to the participants’ subjective ratings. The skill of the model guidance used in the FFaIR Experiment is evaluated using performance diagrams verified against the Stage IV analysis. The operational and FFaIR Experiment versions of the excessive rainfall outlook (ERO) are evaluated by assessing the frequency of issuances, probabilistic calibration, Brier skill score (BSS), and area under relative operating characteristic (AuROC). An ERO first-guess field called the Colorado State University Machine-Learning Probabilities method (CSU-MLP) is also evaluated in the FFaIR Experiment. Among convection-allowing models, the Met Office Unified Model generally performed optimally throughout the FFaIR Experiment when using performance diagrams (at the 0.5- and 1-in. thresholds; 1 in. = 25.4 mm), whereas the High-Resolution Rapid Refresh (HRRR), version 3, performed best subjectively. In terms of subjective and objective ensemble scores, the HRRR ensemble scored optimally. The CSU-MLP overpredicted lower risk categories and underpredicted higher risk categories, but it shows future promise as an ERO first-guess field. The EROs issued by the FFaIR Experiment forecasters had improved BSS and AuROC relative to the operational ERO, suggesting that the experimental guidance may have aided forecasters.

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Michael P. Meredith, Hugh J. Venables, Andrew Clarke, Hugh W. Ducklow, Matthew Erickson, Melanie J. Leng, Jan T. M. Lenaerts, and Michiel R. van den Broeke

Abstract

Climate change west of the Antarctic Peninsula is the most rapid of anywhere in the Southern Hemisphere, with associated changes in the rates and distributions of freshwater inputs to the ocean. Here, results from the first comprehensive survey of oxygen isotopes in seawater in this region are used to quantify spatial patterns of meteoric water (glacial discharge and precipitation) separately from sea ice melt. High levels of meteoric water are found close to the coast, due to orographic effects on precipitation and strong glacial discharge. Concentrations decrease offshore, driving significant southward geostrophic flows (up to ~30 cm s−1). These produce high meteoric water concentrations at the southern end of the sampling grid, where collapse of the Wilkins Ice Shelf may also have contributed. Sea ice melt concentrations are lower than meteoric water and patchier because of the mobile nature of the sea ice itself. Nonetheless, net sea ice production in the northern part of the sampling grid is inferred; combined with net sea ice melt in the south, this indicates an overall southward ice motion. The survey is contextualized temporally using a decade-long series of isotope data from a coastal Antarctic Peninsula site. This shows a temporal decline in meteoric water in the upper ocean, contrary to expectations based on increasing precipitation and accelerating deglaciation. This is driven by the increasing occurrence of deeper winter mixed layers and has potential implications for concentrations of trace metals supplied to the euphotic zone by glacial discharge. As the regional freshwater system evolves, the continuing isotope monitoring described here will elucidate the ongoing impacts on climate and the ecosystem.

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Arthur J. Miller, Michael A. Alexander, George J. Boer, Fei Chai, Ken Denman, David J. Erickson III, Robert Frouin, Albert J. Gabric, Edward A. Laws, Marlon R. Lewis, Zhengyu Liu, Ragu Murtugudde, Shoichiro Nakamoto, Douglas J. Neilson, Joel R. Norris, J. Carter Ohlmann, R. Ian Perry, Niklas Schneider, Karen M. Shell, and Axel Timmermann
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