Browse

You are looking at 101 - 110 of 2,787 items for :

  • Weather and Forecasting x
  • All content x
Clear All
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
Benjamin C. Trabing and Michael M. Bell

Abstract

The characteristics of official National Hurricane Center (NHC) intensity forecast errors are examined for the North Atlantic and east Pacific basins from 1989 to 2018. It is shown how rapid intensification (RI) and rapid weakening (RW) influence yearly NHC forecast errors for forecasts between 12 and 48 h in length. In addition to being the tail of the intensity change distribution, RI and RW are at the tails of the forecast error distribution. Yearly mean absolute forecast errors are positively correlated with the yearly number of RI/RW occurrences and explain roughly 20% of the variance in the Atlantic and 30% in the east Pacific. The higher occurrence of RI events in the east Pacific contributes to larger intensity forecast errors overall but also a better probability of detection and success ratio. Statistically significant improvements to 24-h RI forecast biases have been made in the east Pacific and to 24-h RW biases in the Atlantic. Over-ocean 24-h RW events cause larger mean errors in the east Pacific that have not improved with time. Environmental predictors from the Statistical Hurricane Intensity Prediction Scheme (SHIPS) are used to diagnose what conditions lead to the largest RI and RW forecast errors on average. The forecast error distributions widen for both RI and RW when tropical systems experience low vertical wind shear, warm sea surface temperature, and moderate low-level relative humidity. Consistent with existing literature, the forecast error distributions suggest that improvements to our observational capabilities, understanding, and prediction of inner-core processes is paramount to both RI and RW prediction.

Restricted access
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
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
Ty J. Buckingham and David M. Schultz

Abstract

Nine tornado outbreaks (days with three or more tornadoes) have occurred in the United Kingdom from quasi-linear convective systems (QLCSs) in the 16 years between 2004 and 2019. Of the nine outbreaks, eight can be classified into two synoptic categories: type 1 and type 2. Synoptic categories are derived from the location of the parent extratropical cyclone and the orientation of the surface front associated with the QLCS. Environmental differences between the categories are assessed using ERA5 reanalysis data. Type 1 events are characterized by a confluent 500-hPa trough from the west, meridional cold front, strong cross-frontal wind veer (about 90°), cross-frontal temperature decrease of 2°–4°C, prefrontal 2-m dewpoint temperatures of 12°–14°C, a prefrontal low-level jet, and prefrontal 0–1- and 0–3-km bulk shears of 15 and 25 m s−1, respectively. In contrast, type 2 events are characterized by a diffluent 500-hPa trough from the northwest, zonal front, weaker cross-frontal wind veer (≤45°), much smaller cross-frontal temperature decrease, lower prefrontal 2-m dewpoint temperatures of 6°–10°C, and weaker prefrontal 0–1- and 0–3-km bulk shears of 10 and 15 m s−1, respectively. Analysis of the Met Office radar reflectivity mosaics revealed that narrow cold-frontal rainbands developed in all type 1 events and subsequently displayed precipitation core-and-gap structures. Conversely, type 2 events did not develop narrow cold-frontal rainbands, although precipitation cores developed sporadically within the wide cold-frontal rainband. Type 1 events produced tornadoes 2–4 h after core-and-gap development, whereas type 2 events produced tornadoes within 1 h of forming cores and gaps. All events produced tornadoes during a relatively short time period (1–3 h).

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.

Restricted access
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.

Restricted access
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