Browse

You are looking at 61 - 70 of 2,799 items for :

  • Weather and Forecasting x
  • All content x
Clear All
Weiguo Wang, Bin Liu, Lin Zhu, Zhan Zhang, Avichal Mehra, and Vijay Tallapragada

Abstract

A new physically based horizontal mixing-length formulation is introduced and evaluated in the Hurricane Weather and Research Forecasting (HWRF) Model. Recent studies have shown that the structure and intensity of tropical cyclones (TCs) simulated by numerical models are sensitive to horizontal mixing length in the parameterization of horizontal diffusion. Currently, many numerical models including the operational HWRF Model formulate the horizontal mixing length as a fixed fraction of grid spacing or a constant value, which is not realistic. To improve the representation of the horizontal diffusion process, the new formulation relates the horizontal mixing length to local wind and its horizontal gradients. The resulting horizontal mixing length and diffusivity are much closer to those derived from field measurements. To understand the impact of different mixing-length formulations, we analyze the evolutions of an idealized TC simulated by the HWRF Model with the new formulation and with the current formulation (i.e., constant values) of horizontal mixing length. In two real-case tests, the HWRF Model with the new formulation produces the intensity and track forecasts of Hurricanes Harvey (2017) and Lane (2018) that are much closer to observations. Retrospective runs of hundreds of forecast cycles of multiple hurricanes show that the mean errors in intensity and track simulated by HWRF with the new formulation can be reduced approximately by 10%.

Restricted access
K. J. Tory and J. D. Kepert

Abstract

Pyrocumulonimbus (pyroCb) clouds are difficult to predict and can produce extreme and unexpected wildfire behavior that can be very hazardous to fire crews. Many forecasters modify conventional thunderstorm diagnostics to predict pyroCb potential, by adding temperature (Δθ) and moisture increments (Δq) to represent smoke plume thermodynamics near the expected plume condensation level. However, estimating these Δθ and Δq increments is a highly subjective process that requires expert knowledge of all factors that might influence future fire size and intensity. In this paper, instead of trying to anticipate these Δθ and Δq increments for a particular fire, the minimum firepower required to generate pyroCb for a given atmospheric environment is considered. This concept, termed the pyroCb firepower threshold (PFT) requires only atmospheric information, removing the need for subjective estimates of the fire contribution. A simple approach to calculating PFT is presented that incorporates only basic plume-rise physics, yielding an analytic solution that offers important insight into plume behavior and pyroCb formation. Minimum increments of Δθ and Δq required for deep, moist convection, plus a minimum cloud-base height (zfc), are diagnosed on a thermodynamic diagram. Briggs’s plume rise equations are used to convert Δθ, zfc, and a mean horizontal wind speed U to a measure of the PFT: the minimum heat flux entering the base of the plume. This PFT is proportional to the product of U, Δθ, and the square of zfc. Plume behavior insights provided by the Briggs’s equations are discussed, and a selection of PFT examples presented.

Open access
Kevin Birk, Eric Lenning, Kevin Donofrio, and Matthew T. Friedlein

Abstract

Using vertical temperature profiles obtained from upper-air observations or numerical weather prediction models, the Bourgouin technique calculates areas of positive melting energy and negative refreezing energy for determining precipitation type. Energies are proportional to the product of the mean temperature of a layer and its depth. Layers warmer than 0°C consist of positive energy; those colder than 0°C consist of negative energy. Sufficient melting or freezing energy in a layer can produce a phase change in a falling hydrometeor. The Bourgouin technique utilizes these energies to determine the likelihood of rain (RA) versus snow (SN) given a surface-based melting layer and ice pellets (PL) versus freezing rain (FZRA) or RA given an elevated melting layer. The Bourgouin approach was developed from a relatively small dataset but has been widely utilized by operational forecasters and in postprocessing of NWP output. Recent analysis with a larger dataset suggests ways to improve the original technique, especially when discriminating PL from FZRA or RA. This and several other issues are addressed by a modified version of the Bourgouin technique described in this article. Additional enhancements include use of the wet-bulb profile rather than temperature, a check for heterogeneous ice nucleation, and output that includes probabilities of four different weather types (RA, SN, FZRA, PL) rather than the single most likely type. Together these revisions result in improved performance and provide a more viable and valuable tool for precipitation-type forecasts. Several National Weather Service forecast offices have successfully utilized the revised tool in recent winters.

Restricted access
Kelsey B. Thompson, Monte G. Bateman, and John R. Mecikalski

Abstract

A total of 13 ocean-based wind events from 2018, detected by buoys and Coastal-Marine Automated Network (C-MAN) stations, were analyzed using 1-min mesoscale sector Advanced Baseline Imager (ABI) cloud top brightness temperature (CTTB) data, as well as 1-min Geostationary Lightning Mapper (GLM) lightning data. The ABI and GLM instruments are located on the Geostationary Operational Environmental Satellite-16 (GOES-16) satellite. An oceanic wind event was defined as a buoy or C-MAN station-recorded peak wind gust of at least 14 m s−1, associated with a convective storm. The wind gust was required to exceed the wind speed by at least 4 m s−1 at the time of the event, but not exceed the corresponding wind speed by at least 4 m s−1 for more than 30 min. This study hypothesized that prior to a wind event, there should be unique signatures in ABI CTTB and GLM lightning datasets. The presumption was that the minimum CTTB and maximum flash rate should occur near the same time and prior to the event. The minimum CTTB occurred an average of 10.5 min and a median of 7 min prior to events, with a range from 29 min prior to 1 min after the event. Changes in CTTB were often subtle. A maximum flash rate occurred within 5 min of the minimum CTTB for 11 of the 12 events with lightning and did not exceed 11 flashes per minute for 9 of the 12 events with lightning. Operational weather forecasters might use CTTB and lightning trends to help identify storms capable of producing significant oceanic wind events.

Restricted access
Jason M. Cordeira and F. Martin Ralph

Abstract

The ability to provide accurate forecasts and improve situational awareness of atmospheric rivers (ARs) is key to impact-based decision support services and applications such as forecast-informed reservoir operations. The purpose of this study is to quantify the cool-season water year skill for 2017–20 of the NCEP Global Ensemble Forecast System forecasts of integrated water vapor transport along the U.S. West Coast commonly observed during landfalling ARs. This skill is summarized for ensemble probability-over-threshold forecasts of integrated water vapor transport magnitudes ≥ 250 kg m−1 s−1 (referred to as P 250). The P 250 forecasts near North-Coastal California at 38°N, 123°W were reliable and successful at lead times of ~8–9 days with an average success ratio > 0.5 for P 250 forecasts ≥ 50% at lead times of 8 days and Brier skill scores > 0.1 at a lead time of 8–9 days. Skill and accuracy also varied as a function of latitude and event characteristics. The highest (lowest) success ratios and probability of detection values for P 250 forecasts ≥ 50% occurred on average across Northern California and Oregon (Southern California), whereas the average probability of detection of more intense and longer duration landfalling ARs was 0.1–0.2 higher than weaker and shorter duration events at lead times of 3–9 days. The potential for these forecasts to enhance situational awareness may also be improved, depending on individual applications, by allowing for flexibility in the location and time of verification; the success ratios increased 10%–30% at lead times of 5–10 days allowing for flexibility of ±1.0° latitude and ±6 h in verification.

Restricted access
Craig S. Schwartz, Glen S. Romine, and David C. Dowell

Abstract

Using the Weather Research and Forecasting Model, 80-member ensemble Kalman filter (EnKF) analyses with 3-km horizontal grid spacing were produced over the entire conterminous United States (CONUS) for 4 weeks using 1-h continuous cycling. For comparison, similarly configured EnKF analyses with 15-km horizontal grid spacing were also produced. At 0000 UTC, 15- and 3-km EnKF analyses initialized 36-h, 3-km, 10-member ensemble forecasts that were verified with a focus on precipitation. Additionally, forecasts were initialized from operational Global Ensemble Forecast System (GEFS) initial conditions (ICs) and experimental “blended” ICs produced by combining large scales from GEFS ICs with small scales from EnKF analyses using a low-pass filter. The EnKFs had stable climates with generally small biases, and precipitation forecasts initialized from 3-km EnKF analyses were more skillful and reliable than those initialized from downscaled GEFS and 15-km EnKF ICs through 12–18 and 6–12 h, respectively. Conversely, after 18 h, GEFS-initialized precipitation forecasts were better than EnKF-initialized precipitation forecasts. Blended 3-km ICs reflected the respective strengths of both GEFS and high-resolution EnKF ICs and yielded the best performance considering all times: blended 3-km ICs led to short-term forecasts with similar or better skill and reliability than those initialized from unblended 3-km EnKF analyses and ~18–36-h forecasts possessing comparable quality as GEFS-initialized forecasts. This work likely represents the first time a convection-allowing EnKF has been continuously cycled over a region as large as the entire CONUS, and results suggest blending high-resolution EnKF analyses with low-resolution global fields can potentially unify short-term and next-day convection-allowing ensemble forecast systems under a common framework.

Restricted access
Chanh Kieu, Cole Evans, Yi Jin, James D. Doyle, Hao Jin, and Jonathan Moskaitis

Abstract

This study examines the dependence of tropical cyclone (TC) intensity forecast errors on track forecast errors in the Coupled Ocean–Atmosphere Mesoscale Prediction System for Tropical Cyclones (COAMPS-TC) model. Using real-time forecasts and retrospective experiments during 2015–18, verification of TC intensity errors conditioned on different 5-day track error thresholds shows that reducing the 5-day track errors by 50%–70% can help reduce the absolute intensity errors by 18%–20% in the 2018 version of the COAMPS-TC model. Such impacts of track errors on the TC intensity errors are most persistent at 4–5-day lead times in all three major ocean basins, indicating a significant control of global models on the forecast skill of the COAMPS-TC model. It is of interest to find, however, that lowering the 5-day track errors below 80 n mi (1 n mi = 1.852 km) does not reduce TC absolute intensity errors further. Instead, the 4–5-day intensity errors appear to be saturated at around 10–12 kt (1 kt ≈ 0.51 m s−1) for cases with small track errors, thus suggesting the existence of some inherent intensity errors in regional models. Additional idealized simulations under a perfect model scenario reveal that the COAMPS-TC model possesses an intrinsic intensity variation at the TC mature stage in the range of 4–5 kt, regardless of the large-scale environment. Such intrinsic intensity variability in the COAMPS-TC model highlights the importance of potential chaotic TC dynamics, rather than model deficiencies, in determining TC intensity errors at 4–5-day lead times. These results suggest a fundamental limit in the improvement of TC intensity forecasts by numerical models that one should consider in future model development and evaluation.

Restricted access
Guiting Song, Robert Huva, Yu Xing, and Xiaohui Zhong

Abstract

For most locations on Earth the ability of a numerical weather prediction (NWP) model to accurately simulate surface irradiance relies heavily on the NWP model being able to resolve cloud coverage and thickness. At horizontal resolutions at or below a few kilometers NWP models begin to explicitly resolve convection and the clouds that arise from convective processes. However, even at high resolutions, biases may remain in the model and result in under- or overprediction of surface irradiance. In this study we explore the correction of such systematic biases using a moisture adjustment method in tandem with the Weather Research and Forecasting (WRF) Model for a location in Xinjiang, China. After extensive optimization of the configuration of the WRF Model we show that systematic biases still exist—in particular for wintertime in Xinjiang. We then demonstrate the moisture adjustment method with cloudy days for January 2019. Adjusting the relative humidity by 12% through the vertical led to a root-mean-square error (RMSE) improvement of 57.8% and a 90.5% reduction in bias for surface irradiance.

Restricted access
Samantha Ferrett, Thomas H. A. Frame, John Methven, Christopher E. Holloway, Stuart Webster, Thorwald H.M. Stein, and Carlo Cafaro

Abstract

Forecasting rainfall in the tropics is a major challenge for numerical weather prediction. Convection-permitting (CP) models are intended to enable forecasts of high-impact weather events. Development and operation of these models in the tropics has only just been realised. This study describes and evaluates a suite of recently developed Met Office Unified Model CP ensemble forecasts over three domains in Southeast Asia, covering Malaysia, Indonesia and the Philippines.

Fractions Skill Score is used to assess the spatial scale-dependence of skill in forecasts of precipitation during October 2018 - March 2019. CP forecasts are skilful for 3-hour precipitation accumulations at spatial scales greater than 200 km in all domains during the first day of forecasts. Skill decreases with lead time but varies depending on time of day over Malaysia and Indonesia, due to the importance of the diurnal cycle in driving rainfall in those regions. Skill is largest during daytime when precipitation is over land and is constrained by orography. Comparison of CP ensembles using 2.2, 4.5 and 8.8 km grid spacing and an 8.8km ensemble with parameterised convection reveals that varying resolution has much less effect on ensemble skill and spread than the representation of convection. The parameterised ensemble is less skilful than CP ensembles over Malaysia and Indonesia and more skilful over the Philippines; however, the parameterised ensemble has large drops in skill and spread related to deficiencies in its diurnal cycle representation. All ensembles are under-spread indicating that future model development should focus on this issue.

Restricted access
Cameron J. Nixon and John T. Allen

Abstract

The paths of tornadoes have long been a subject of fascination since the meticulously drawn damage tracks by Dr. Tetsuya Theodore “Ted” Fujita. Though uncommon, some tornadoes have been noted to take sudden left turns from their previous path. This has the potential to present an extreme challenge to warning lead time, and the spread of timely, accurate information to broadcasters and emergency managers. While a few hypotheses exist as to why tornadoes deviate, none have been tested for their potential use in operational forecasting and nowcasting. As a result, such deviations go largely unanticipated by forecasters. A sample of 102 leftward deviant tornadic low-level mesocyclones was tracked via WSR-88D and assessed for their potential predictability. A simple hodograph technique is presented that shows promising skill in predicting the motion of deviant tornadoes, which, upon “occlusion,” detach from the parent storm’s updraft centroid and advect leftward or rearward by the low-level wind. This metric, a vector average of the parent storm motion and the mean wind in the lowest half-kilometer, proves effective at anticipating deviant tornado motion with a median error of less than 6 kt (1 kt ≈ 0.51 m s−1). With over 25% of analyzed low-level mesocyclones deviating completely out of the tornado warning polygon issued by their respective National Weather Service Weather Forecast Office, the adoption of this new technique could improve warning performance. Furthermore, with over 35% of tornadoes becoming “deviant” almost immediately upon formation, the ability to anticipate such events may inspire a new paradigm for tornado warnings that, when covering unpredictable behavior, are proactive instead of reactive.

Restricted access