Browse

You are looking at 131 - 140 of 2,812 items for :

  • Weather and Forecasting x
  • Refine by Access: All Content x
Clear All
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
Benjamin A. Schenkel, Roger Edwards, and Michael Coniglio

Abstract

The cyclone-relative location and variability in the number of tornadoes among tropical cyclones (TCs) are not completely understood. A key understudied factor that may improve our understanding is ambient (i.e., synoptic-scale) deep-tropospheric (i.e., 850–200-hPa) vertical wind shear (VWS), which impacts both the symmetry and strength of deep convection in TCs. This study conducts a climatological analysis of VWS impacts upon tornadoes in TCs from 1995 to 2018, using observed TC and tornado data together with radiosondes. TC tornadoes were classified by objectively defined VWS categories, derived from reanalyses, to quantify the sensitivity of tornado frequency, location, and their environments to VWS. The analysis shows that stronger VWS is associated with enhanced rates of tornado production—especially more damaging ones. Tornadoes also become localized to the downshear half of the TC as VWS strengthens, with tornado location in strongly sheared TCs transitioning from the downshear-left quadrant in the TC inner core to the downshear-right quadrant in the TC outer region. Analysis of radiosondes shows that the downshear-right quadrant in strongly sheared TCs is most frequently associated with sufficiently strong near-surface speed shear and veering aloft, and lower-tropospheric thermodynamic instability for tornadoes. These supportive kinematic environments may be due to the constructive superposition of the ambient and TC winds, and the VWS-induced downshear enhancement of the TC circulation among other factors. Together, this work provides a basis for improving forecasts of TC tornado frequency and location.

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
Daniel J. Halperin, Andrew B. Penny, and Robert E. Hart

Abstract

Operational forecasting of tropical cyclone (TC) genesis has improved in recent years but still can be a challenge. Output from global numerical models continues to serve as a primary source of forecast guidance. Bulk verification statistics (e.g., critical success index) of TC genesis forecasts indicate that, overall, global models are increasingly able to predict TC genesis. However, as global model configurations are updated, TC genesis verification statistics will change. This study compares operational and retrospective forecasts from three configurations of NCEP’s Global Forecast System (GFS) to quantify the impact of model upgrades on TC genesis forecasts. First, bulk verification statistics from a homogeneous sample of model initialization cycles during the period 2013–14 are compared. Then, composites of select output fields are analyzed in an attempt to identify any key differences between hit and false alarm events. Bulk statistics indicate that TC genesis forecast performance decreased with the implementation of the 2015 version of the GFS, but then modestly recovered with the 2016 version of the model. In addition, the composite analysis suggests that false alarm forecasts in the 2015 version of the GFS may have been the result of inaccurately forecasting the location and/or strength of upper-level troughs poleward of the TC. There is also evidence of convective feedbacks occurring, such as ridging above the low-level circulation and upper-level convective outflow that were too strong, in this same set of false alarm forecasts. Overall, analyzing retrospective forecasts can assist forecasters in determining the strengths and weaknesses associated with a new configuration of a global model with respect to TC genesis.

Restricted access
Shawn L. Handler, Heather D. Reeves, and Amy McGovern

ABSTRACT

In this study, a machine learning algorithm for generating a gridded CONUS-wide probabilistic road temperature forecast is presented. A random forest is used to tie a combination of HRRR model surface variables and information about the geographic location and time of day per year to observed road temperatures. This approach differs from its predecessors in that road temperature is not deterministic (i.e., provides a forecast of a specific road temperature), but rather it is probabilistic, providing a 0%–100% probability that the road temperature is subfreezing. This approach can account for the varying controls on road temperature that are not easily known or able to be accounted for in physical models, such as amount of traffic, road composition, and differential shading by surrounding buildings and terrain. The algorithm is trained using road temperature observations from one winter season (October 2016–March 2017) and calibrated/evaluated using observations from the following winter season (October 2017–March 2018). Case-study analyses show the algorithm performs well for various scenarios and captures the temporal and spatial evolution of the probability of subfreezing roads reliably. Statistical evaluation for the predicted probabilities shows good skill as the mean area under the receiver operating characteristics curve is 0.96 and the Brier skill score is 0.66 for a 2-h forecast and only degrades slightly as lead time is increased. Additionally, the algorithm produces well-calibrated probabilities, and consistent discrimination between clearly above-freezing and subfreezing environments.

Restricted access
Molly B. Smith, Ryan D. Torn, Kristen L. Corbosiero, and Philip Pegion

Abstract

Tropical cyclones (TCs) moving into the midlatitudes can produce extreme precipitation, as was the case with Hurricane Irene (2011). Despite the high-impact nature of these events, relatively few studies have explored the sensitivity of TC precipitation forecasts to model initial conditions. Here, the physical processes that modulate precipitation forecasts over the Northeast United States during Irene are investigated using an 80-member 0.5° Global Forecasting System (GFS) ensemble. The members that forecast the highest total precipitation over the Catskill Mountains in New York (i.e., wet members) are compared with the members that predicted the least precipitation (i.e., dry members). Results indicate that the amount of rainfall is tied to storm track, with the wetter members forecast to move farther west than the dry members. This variability in storm track appears to be associated with variability in analyzed upper-tropospheric potential vorticity (PV), such that the wetter members feature greater cyclonic PV southwest of Irene when Irene is off the Carolina coast. By contrast, the wetter members of a 3-km Weather Research and Forecasting (WRF) Model ensemble, initialized from the same GFS ensemble forecasts, show little sensitivity to track. Instead, the wetter members are characterized by stronger lower-tropospheric winds perpendicular to the eastern face of the Catskills, allowing maximum upslope forcing and horizontal moisture flux convergence during the period of heaviest rainfall. The drier members, on the other hand, have the greatest quasigeostrophic forcing for ascent, implying that the members’ differences in mesoscale topographic forcing are the dominant influence on rainfall rate.

Free 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
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
Taylor A. Gowan and John D. Horel

Abstract

Large wildfire outbreaks in Alaska are common from June to August. The Canadian Forest Fire Danger Rating System (CFFDRS) is used operationally by Alaskan fire managers to produce statewide fire weather outlooks and forecast guidance near active wildfires. The CFFDRS estimates of fire potential and behavior rely heavily on meteorological observations (precipitation, temperature, wind speed, and relative humidity) from the relatively small number of in situ stations across Alaska with precipitation being the most critical parameter. To improve the spatial coverage of precipitation estimates across Alaska for fire weather applications, a multisatellite precipitation algorithm was evaluated during six fire seasons (1 June–31 August 2014–19). Near-real-time daily precipitation estimates from the Integrated Multisatellite Retrievals for the Global Precipitation Mission (IMERG) algorithm were verified using 322 in situ stations across four Alaskan regions. For each region, empirical cumulative distributions of daily precipitation were obtained from station observations during each summer, and compared to corresponding distributions of interpolated values from IMERG grid points (0.1° × 0.1° grid). The cumulative distributions obtained from IMERG exhibited wet biases relative to the observed distributions for all regions, precipitation amount ranges, and summers. A bias correction approach using regional quantile mapping was developed to mitigate for the IMERG wet bias. The bias-adjusted IMERG daily precipitation estimates were then evaluated and found to produce improved gridded IMERG precipitation estimates. This approach may help to improve situational awareness of wildfire potential across Alaska and be appropriate for other high-latitude regions where there are sufficient in situ precipitation observations to help correct the IMERG precipitation estimates.

Restricted access
Kyo-Sun Sunny Lim, Eun-Chul Chang, Ruiyu Sun, Kwonil Kim, Francisco J. Tapiador, and GyuWon Lee

Abstract

This study evaluates the performance of several cloud microphysics parameterizations in simulating surface precipitation for two snowstorm cases during the International Collaborative Experiment held at the PyeongChang 2018 Olympics and Winter Paralympic Games (ICE-POP 2018) field campaign. We compared four different schemes in the Weather Research and Forecasting (WRF) Model, namely the double-moment 6-class (WDM6), the WRF single-moment 6-class (WSM6), and Thompson and Morrison parameterizations. Both WSM6 and WDM6 overestimated the precipitation amount for the shallow precipitation system because of the substantial amount of cloud ice, mostly generated by the deposition process. The simulated precipitation amount and distribution for the deep precipitation system showed no noticeable differences in the different cloud microphysics parameterizations. However, the simulated hydrometeor type at the surface using WSM6 and WDM6 showed good agreement with observations for all cases. The accuracy of the mean mass-weighted terminal velocity of cloud ice VI¯ applied in WSM6 and WDM6 is ±20%. The number concentration of cloud ice and the ice microphysics processes are newly retrieved with 1.2 times increased VI¯. For the shallow snowstorm, the precipitation amount was reduced by approximately 8% because of the inefficient deposition and its effects on the subsequent ice microphysical processes, such as the accretion of cloud ice by snow and the conversion from cloud ice to snow.

Restricted access