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Jonathan Lin, Kerry Emanuel, and Jonathan L. Vigh

Abstract

This paper describes the development of a model framework for Forecasts of Hurricanes Using Large-Ensemble Outputs (FHLO). FHLO quantifies the forecast uncertainty of a tropical cyclone (TC) by generating probabilistic forecasts of track, intensity, and wind speed that incorporate the state-dependent uncertainty in the large-scale field. The main goal is to provide useful probabilistic forecasts of wind at fixed points in space, but these require large ensembles [O(1000)] to flesh out the tails of the distributions. FHLO accomplishes this by using a computationally inexpensive framework, which consists of three components: 1) a track model that generates synthetic tracks from the TC tracks of an ensemble numerical weather prediction (NWP) model, 2) an intensity model that predicts the intensity along each synthetic track, and 3) a TC wind field model that estimates the time-varying two-dimensional surface wind field. The intensity and wind field of a TC evolve as though the TC were embedded in a time-evolving environmental field, which is derived from the forecast fields of ensemble NWP models. Each component of the framework is evaluated using 1000-member ensembles and four years (2015–18) of TC forecasts in the Atlantic and eastern Pacific basins. We show that the synthetic track algorithm generates tracks that are statistically similar to those of the underlying global ensemble models. We show that FHLO produces competitive intensity forecasts, especially when considering probabilistic verification statistics. We also demonstrate the reliability and accuracy of the probabilistic wind forecasts. Limitations of the model framework are also discussed.

Open access
Kurt A. Hansen, Sharanya J. Majumdar, and Ben P. Kirtman

Abstract

The primary atmospheric oscillations and variables associated with subseasonal Atlantic tropical cyclone (TC) activity are identified, based on 37 years of reanalysis data. TC activity, represented by accumulated cyclone energy (ACE), is computed for combined phases of the Madden–Julian oscillation (MJO) and El Niño–Southern Oscillation (ENSO). The MJO influence on TC activity becomes greater when the ENSO state is cooler. There is also a shift in the favorable MJO phase for TC activity with ENSO state. For strong La Niñas, MJO phases 4 and 5 (enhanced convection over the Maritime Continent) are most likely to have above-average ACE. To investigate other potential factors that influence subseasonal TC activity, two novel methods are developed: ACE by year (ABY) and seasonal and climatology removed (SNCR). Both methods isolate subseasonal signals of environmental conditions in association with a variable of interest. Vorticity, sea surface temperature, relative humidity, and genesis potential all show little signal in association with subseasonal Atlantic TC activity. The most important identifier of enhanced TC activity is negative vertical wind shear anomalies in the main development region of the Atlantic basin, and positive shear anomalies in the subtropical Atlantic. The shear pattern associated with a favorable MJO for TCs is similar to but distinct from the shear pattern associated with enhanced subseasonal TC activity. These findings demonstrate a nonlinear MJO–ENSO interaction and a pattern of wind shear anomalies that is linked to subseasonal TC activity.

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Ding Chenchen, Fumin Ren, Yanan Liu, John L. McBride, and Tian Feng

Abstract

The intensity of the tropical cyclone has been introduced into the Dynamical-Statistical-Analog Ensemble Forecast (DSAEF) for Landfalling Typhoon (or tropical cyclone) Precipitation (DSAEF_LTP) model. Moreover, the accumulated precipitation prediction experiments have been conducted on 21 target tropical cyclones with daily precipitation ≥ 100 mm in South China from 2012 to 2016. The best forecasting scheme for the DSAEF_LTP model is identified, and the performance of the prediction is compared with three numerical weather prediction models (the European Centre for Medium-Range Weather Forecasts, the Global Forecast System, and T639). The forecasting ability of the DSAEF_LTP model for heavy rainfall (accumulated precipitation ≥ 250 and ≥100 mm) improves when the intensity of the tropical cyclone is introduced, giving some advantages over the three numerical weather prediction models. The selection of analog tropical cyclones with a maximum intensity (during precipitation over land) equaling to or higher than the initial intensity of the target tropical cyclone gives better forecasts. The prediction accuracy for accumulated precipitation is higher for tropical cyclones with higher intensity and higher observed precipitation, with in both cases positive linear correlations with the threat score.

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Luca Delle Monache, Stefano Alessandrini, Irina Djalalova, James Wilczak, Jason C. Knievel, and R. Kumar

Abstract

Air quality forecasts produced by the National Air Quality Forecasting Capability (NAQFC) help air quality forecasters across the United States in making informed decisions to protect public health from acute air pollution episodes. However, errors in air quality forecasts limit their value in the decision-making process. This study aims to enhance the accuracy of NAQFC air quality forecasts and reliably quantify their uncertainties using a statistical–dynamical method called the analog ensemble (AnEn), which has previously been found to efficiently generate probabilistic forecasts for other applications. AnEn estimates of the probability of the true state of a predictand are based on a current deterministic numerical prediction and an archive of prior analogous predictions paired with prior observations. The method avoids the complexity and real-time computational expense of model-based ensembles and is proposed here for the first time for air quality forecasting. AnEn is applied with forecasts from the Community Multiscale Air Quality (CMAQ) model. Relative to CMAQ raw forecasts, deterministic forecasts of surface ozone (O3) and particulate matter of aerodynamic diameter smaller than 2.5 μm (PM2.5) based on AnEn’s mean have lower systemic and random errors and are overall better correlated with observations; for example, when computed across all sites and lead times, AnEn’s root-mean-square error is lower than CMAQ’s by roughly 35% and 30% for O3 and PM2.5, respectively, and AnEn improves the correlation by 50% for O3 and PM2.5. Probabilistic forecasts from AnEn are statistically consistent, reliable, and sharp, and they quantify the uncertainty of the underlying prediction.

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Forest Cannon, Nina S. Oakley, Chad W. Hecht, Allison Michaelis, Jason M. Cordeira, Brian Kawzenuk, Reuben Demirdjian, Rachel Weihs, Meredith A. Fish, Anna M. Wilson, and F. Martin Ralph

Abstract

Short-duration, high-intensity rainfall in Southern California, often associated with narrow cold-frontal rainbands (NCFR), threaten life and property. While the mechanisms that drive NCFRs are relatively well understood, their regional characteristics, specific contribution to precipitation hazards, and their predictability in the western United States have received little research attention relative to their impact. This manuscript presents observations of NCFR physical processes made during the Atmospheric River Reconnaissance field campaign on 2 February 2019 and investigates the predictability of the observed NCFR across spatiotemporal scales and forecast lead time. Dropsonde data collected along transects of an atmospheric river (AR) and its attendant cyclone during rapid cyclogenesis, and radiosonde observations during landfall 24 h later, are used to demonstrate that a configuration of the Weather Research and Forecasting (WRF) Model skillfully reproduces the physical processes responsible for the development and maintenance of the impactful NCFR. Ensemble simulations provide quantitative uncertainty information on the representation of these features in numerical weather prediction and instill confidence in the utility of WRF as a forecast guidance tool for short- to medium-range prediction of mesoscale precipitation processes in landfalling ARs. This research incorporates novel data and methodologies to improve forecast guidance for NCFRs impacting Southern California. While this study focuses on a single event, the outlined approach to observing and predicting high-impact weather across a range of spatial and temporal scales will support regional water management and hazard mitigation, in general.

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John P. Cangialosi, Eric Blake, Mark DeMaria, Andrew Penny, Andrew Latto, Edward Rappaport, and Vijay Tallapragada

Abstract

It has been well documented that the National Hurricane Center (NHC) has made significant improvements in Atlantic basin tropical cyclone (TC) track forecasting during the past half century. In contrast, NHC’s TC intensity forecast errors changed little from the 1970s to the early 2000s. Recently, however, there has been a notable decrease in TC intensity forecast error and an increase in intensity forecast skill. This study documents these trends and discusses the advancements in TC intensity guidance that have led to the improvements in NHC’s intensity forecasts in the Atlantic basin. We conclude with a brief projection of future capabilities.

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Sim D. Aberson and J. Kaplan

Abstract

The relationship between the Madden–Julian oscillation (MJO) and tropical cyclone rapid intensification in the northern basins of the Western Hemisphere is examined. All rapid intensification events in the part of the Western Hemisphere north of the equator and the MJO phase and amplitude are compiled from 1974 to 2015. Rapid intensification events and the MJO tend to move in tandem with each other from west to east across the hemisphere, though rapid intensification appears most likely during a neutral MJO phase. The addition of this information to an operational statistical rapid intensification forecasting scheme does not significantly improve forecasts.

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Hannah R. Young and Nicholas P. Klingaman

Abstract

Skillful seasonal forecasts can provide useful information for decision-makers, particularly in regions heavily dependent on agriculture, such as East Africa. We analyze prediction skill for seasonal East African rainfall and temperature one to four months ahead from two seasonal forecasting systems: the U.S. National Centers for Environmental Prediction (NCEP) Coupled Forecast System Model, version 2 (CFSv2), and the Met Office (UKMO) Global Seasonal Forecast System, version 5 (GloSea5). We focus on skill for low or high temperature and rainfall, below the 25th or above the 75th percentile, respectively, as these events can have damaging effects in this region. We find skill one month ahead for both low and high rainfall from CFSv2 for December–February in Tanzania, and from GloSea5 for September–November in Kenya. Both models have higher skill for temperature than for rainfall across Ethiopia, Kenya, and Tanzania, with skill two months ahead in some cases. Performance for rainfall and temperature change in the two models during certain El Niño–Southern Oscillation (ENSO) and Indian Ocean dipole (IOD) phases, the impacts of which vary by country, season, and sometimes by model. While most changes in performance are within the range of uncertainty due to the relatively small sample size in each phase, they are significant in some cases. For example, La Niña lowers performance for Kenya September–November rainfall in CFSv2 but does not affect skill in GloSea5.

Open access
Kyle M. Nardi, Cory F. Baggett, Elizabeth A. Barnes, Eric D. Maloney, Daniel S. Harnos, and Laura M. Ciasto

Abstract

Although useful at short and medium ranges, current dynamical models provide little additional skill for precipitation forecasts beyond week 2 (14 days). However, recent studies have demonstrated that downstream forcing by the Madden–Julian oscillation (MJO) and quasi-biennial oscillation (QBO) influences subseasonal variability, and predictability, of sensible weather across North America. Building on prior studies evaluating the influence of the MJO and QBO on the subseasonal prediction of North American weather, we apply an empirical model that uses the MJO and QBO as predictors to forecast anomalous (i.e., categorical above- or below-normal) pentadal precipitation at weeks 3–6 (15–42 days). A novel aspect of our study is the application and evaluation of the model for subseasonal prediction of precipitation across the entire contiguous United States and Alaska during all seasons. In almost all regions and seasons, the model provides “skillful forecasts of opportunity” for 20%–50% of all forecasts valid weeks 3–6. We also find that this model skill is correlated with historical responses of precipitation, and related synoptic quantities, to the MJO and QBO. Finally, we show that the inclusion of the QBO as a predictor increases the frequency of skillful forecasts of opportunity over most of the contiguous United States and Alaska during all seasons. These findings will provide guidance to forecasters regarding the utility of the MJO and QBO for subseasonal precipitation outlooks.

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Brandon McClung and Clifford F. Mass

Abstract

Strong, dry downslope winds over Northern and central California have played a critical role in regional wildfires. These events, sometimes called Diablo or North winds, are more frequent over the Bay Area and nearby coastal terrain than along the western slopes of the Sierra Nevada, where the highest frequency occurs over the midslopes of the barrier. For the Bay Area, there is a frequency minimum during midsummer, a maximum in October, and a declining trend from November to June. The Sierra Nevada locations have their minimum frequency from February to August, and a maximum from October to January. There is little trend in event frequency during the past two decades over either region. For the Bay Area sites, there is a maximum frequency during the early morning hours and a large decline midday, while the Sierra Nevada locations have a maximum frequency approximately three hours earlier. Before the onset of these downslope wind events, there is substantial amplification of upper-level ridging over the eastern Pacific, with sea level pressure increasing first over the Pacific Northwest and then over the Intermountain West. The coincident development of a coastal sea level pressure trough leads to a large pressure gradient over the Sierra Nevada and Northern California. Diablo–North wind events are associated with below-normal temperatures east of the Sierra Nevada, with rapid warming of the air as it subsides into coastal California. The large horizontal variability in the frequency and magnitude of these events suggests the importance of exposure, elevation, and mountain-wave-related downslope acceleration.

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