Browse

You are looking at 121 - 130 of 2,799 items for :

  • Weather and Forecasting x
  • All content x
Clear All
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.

Restricted access
Michael Vellinga, Dan Copsey, Tim Graham, Sean Milton, and Tim Johns

Abstract

We evaluate the impact of adding two-way coupling between atmosphere and ocean to the Met Office deterministic global forecast model. As part of preoperational testing of this coupled NWP configuration we have three years of daily forecasts, run in parallel to the uncoupled operational forecasts. Skill in the middle and upper troposphere out to T + 168 h is generally increased compared to the uncoupled model. Improvements are strongest in the tropics and largely neutral in midlatitudes. We attribute the additional skill in the atmosphere to the ability of the coupled model to predict sea surface temperature (SST) variability in the (sub)tropics with greater skill than persisted SSTs as used in uncoupled forecasts. In the midlatitude, ocean skill for SST is currently marginally worse than persistence, possibly explaining why there is no additional skill for the atmosphere in midlatitudes. Sea ice is predicted more skillfully than persistence out to day 7 but the impact of this on skill in the atmosphere is difficult to verify. Two-way air–sea coupling benefits tropical cyclone forecasts by reducing median track and central pressure errors by around 5%, predominantly from T + 90 to T + 132 h. Benefits from coupling are largest for large cyclones, and for smaller storms coupling can be detrimental. In this study skill in forecasts of the Madden–Julian oscillation does not change with two-way air–sea coupling out to T + 168 h.

Restricted access
Steven M. Martinaitis, Benjamin Albright, Jonathan J. Gourley, Sarah Perfater, Tiffany Meyer, Zachary L. Flamig, Robert A. Clark, Humberto Vergara, and Mark Klein

Abstract

The flash flood event of 23 June 2016 devastated portions of West Virginia and west-central Virginia, resulting in 23 fatalities and 5 new record river crests. The flash flooding was part of a multiday event that was classified as a billion-dollar disaster. The 23 June 2016 event occurred during real-time operations by two Hydrometeorology Testbed (HMT) experiments. The Flash Flood and Intense Rainfall (FFaIR) experiment focused on the 6–24-h forecast through the utilization of experimental high-resolution deterministic and ensemble numerical weather prediction and hydrologic model guidance. The HMT Multi-Radar Multi-Sensor Hydro (HMT-Hydro) experiment concentrated on the 0–6-h time frame for the prediction and warning of flash floods primarily through the experimental Flooded Locations and Simulated Hydrographs product suite. This study describes the various model guidance, applications, and evaluations from both testbed experiments during the 23 June 2016 flash flood event. Various model outputs provided a significant precipitation signal that increased the confidence of FFaIR experiment participants to issue a high risk for flash flooding for the region between 1800 UTC 23 June and 0000 UTC 24 June. Experimental flash flood warnings issued during the HMT-Hydro experiment for this event improved the probability of detection and resulted in a 63.8% increase in lead time to 84.2 min. Isolated flash floods in Kentucky demonstrated the potential to reduce the warned area. Participants characterized how different model guidance and analysis products influenced the decision-making process and how the experimental products can help shape future national and local flash flood operations.

Restricted access
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.

Restricted access
Julián David Rojo Hernández, Óscar José Mesa, and Upmanu Lall

Abstract

El Niño–Southern Oscillation (ENSO) has global effects on the hydrological cycle, agriculture, ecosystems, health, and society. We present a novel nonhomogeneous hidden Markov model (NHMM) for studying the underlying dynamics of sea surface temperature anomalies (SSTA) over the region 15°N–15°S, 150°E–80°W from January 1856 to December 2019, using the monthly SSTA data from the Kaplan extended SST v2 product. This nonparametric machine learning scheme dynamically simulates and predicts the spatiotemporal evolution of ENSO patterns, including their asymmetry, long-term trends, persistence, and seasonal evolution. The model identifies five hidden states whose spatial SSTA patterns are similar to the so-called ENSO flavors in the literature. From the fitted NHMM, the model shows that there are systematic trends in the frequency and persistence of the regimes over the last 160 years that may be related to changes in the mean state of basin temperature and/or global warming. We evaluated the ability of NHMM to make out-of-sample probabilistic predictions of the spatial structure of temperature anomalies for the period 1995–2016 using a training period from January 1856 to December 1994. The results show that NHMMs can simulate the behavior of the Niño-3.4 and Niño-1.2 regions quite well. The NHMM results over this period are comparable or superior to the commonly available ENSO prediction models, with the additional advantage of directly providing insights as to the space patterns, seasonal, and longer-term trends of the SSTA in the equatorial Pacific region.

Restricted access
Namyoung Kang

Abstract

This study provides a statistical review on the forecast errors of tropical storm tracks and suggests a Bayesian procedure for updating the uncertainty about the error. The forecast track errors are assumed to form an axisymmetric bivariate normal distribution on a two-dimensional surface. The parameters are a mean vector and a covariance matrix, which imply the accuracy and precision of the operational forecast. A Bayesian method improves quantifying the varying parameters in the bivariate normal distribution. A normal-inverse-Wishart distribution is employed to determine the posterior distribution (i.e., the weights on the parameters). Based on the posterior distribution, the predictive probability density of track forecast errors is obtained as the marginal distribution. Here, “storm approach” is defined for any location within a specified radius of a tropical storm. Consequently, the storm approach probability for each location is derived through partial integration of the marginal distribution within the forecast storm radius. The storm approach probability is considered a realistic and effective representation of storm warning for communicating the threat to local residents since the location-specific interpretation is available on a par with the official track forecast.

Restricted access
Ryan A. Sobash, Glen S. Romine, and Craig S. Schwartz

Abstract

A feed-forward neural network (NN) was trained to produce gridded probabilistic convective hazard predictions over the contiguous United States. Input fields to the NN included 174 predictors, derived from 38 variables output by 497 convection-allowing model forecasts, with observed severe storm reports used for training and verification. These NN probability forecasts (NNPFs) were compared to surrogate-severe probability forecasts (SSPFs), generated by smoothing a field of surrogate reports derived with updraft helicity (UH). NNPFs and SSPFs were produced each forecast hour on an 80-km grid, with forecasts valid for the occurrence of any severe weather report within 40 or 120 km, and 2 h, of each 80-km grid box. NNPFs were superior to SSPFs, producing statistically significant improvements in forecast reliability and resolution. Additionally, NNPFs retained more large magnitude probabilities (>50%) compared to SSPFs since NNPFs did not use spatial smoothing, improving forecast sharpness. NNPFs were most skillful relative to SSPFs when predicting hazards on larger scales (e.g., 120 vs 40 km) and in situations where using UH was detrimental to forecast skill. These included model spinup, nocturnal periods, and regions and environments where supercells were less common, such as the western and eastern United States and high-shear, low-CAPE regimes. NNPFs trained with fewer predictors were more skillful than SSPFs, but not as skillful as the full-predictor NNPFs, with predictor importance being a function of forecast lead time. Placing NNPF skill in the context of existing baselines is a first step toward integrating machine learning–based forecasts into the operational forecasting process.

Restricted 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.

Restricted access
Dylan Steinkruger, Paul Markowski, and George Young

Abstract

The utility of employing artificial intelligence (AI) to issue tornado warnings is explored using an ensemble of 128 idealized simulations. Over 700 tornadoes develop within the ensemble of simulations, varying in duration, length, and associated storm mode. Machine-learning models are trained to forecast the temporal and spatial probabilities of tornado formation for a specific lead time. The machine-learning probabilities are used to produce tornado warning decisions for each grid point and lead time. An optimization function is defined, such that warning thresholds are modified to optimize the performance of the AI system on a specified metric (e.g., increased lead time, minimized false alarms, etc.). Using genetic algorithms, multiple AI systems are developed with different optimization functions. The different AI systems yield unique warning output depending on the desired attributes of the optimization function. The effects of the different optimization functions on warning performance are explored. Overall, performance is encouraging and suggests that automated tornado warning guidance is worth exploring with real-time data.

Restricted access
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.

Restricted access