Browse

You are looking at 1 - 10 of 3,198 items for :

  • Weather and Forecasting x
  • Refine by Access: All Content x
Clear All
Christopher Rodell
,
Rosie Howard
,
Piyush Jain
,
Nadya Moisseeva
,
Timothy Chui
, and
Roland Stull

Abstract

Wildfire agencies use Fire Danger Rating Systems (FDRS) to deploy resources and issue public safety measures. The most widely used FDRS is the Canadian Fire Weather Index (FWI) System, which uses weather inputs to estimate the potential for wildfires to start and spread. Current FWI forecasts provide a daily numerical value, representing potential fire severity at an assumed midafternoon time for peak fire activity. This assumption, based on typical diurnal weather patterns, is not always valid. To address this, we developed an hourly FWI (HFWI) system using numerical weather prediction. We validate HFWI against the traditional daily FWI (DFWI) by comparing HFWI forecasts with observation-derived DFWI values from 917 surface fire weather stations in western North America. Results indicate strong correlations between forecasted HFWI and the observation-derived DFWI. A positive mean bias in the daily maximum values of HFWI compared to the traditional DFWI suggests that HFWI can better capture severe fire weather variations regardless of when they occur. We confirm this by comparing HFWI with hourly Fire Radiative Power (FRP) satellite observations for nine wildfire case studies in Canada and the United States. We demonstrate HFWI’s ability to forecast shifts in fire danger timing, especially during intensified fire activity in the late evening and early morning hours, while allowing for multiple periods of increased fire danger per day—a contrast to the conventional DFWI. This research highlights the HFWI system’s value in improving fire danger assessments and predictions, hopefully enhancing wildfire management, especially during atypical fire behavior.

Restricted access
Darío Redolat
and
Robert Monjo

Abstract

It is widely known from energy balances that global oceans play a fundamental role in atmospheric seasonal anomalies via coupling mechanisms. However, numerical weather prediction models still have limitations in long-term forecasting due to their nonlinear sensitivity to initial deep oceanic conditions. As the Mediterranean climate has highly unpredictable seasonal variability, we designed a complementary method by supposing that (1) delayed teleconnection patterns provide information about ocean–atmosphere coupling on subseasonal timescales through the lens of (2) partially predictable quasi-periodic oscillations, since (3) forecast signals can be extracted by smoothing noise in a continuous lead-time horizon. To validate these hypotheses, subseasonal predictability of temperature and precipitation was analyzed at 11 reference stations in the Mediterranean area in the 1993–2021 period. The novel method, presented here, consists of combining lag-correlated teleconnections (15 indices) with self-predictability techniques of residual quasi-oscillation based on Wavelet (cyclic) and ARIMA (linear) analyses. The prediction skill of this Teleconnection-Wavelet-ARIMA (TeWA) combination was cross-validated and compared to that of the SEAS5-ECMWF model (3 months ahead). Results show that the proposed TeWA approach improves the predictability of first-month temperature and precipitation anomalies by 50–70% compared with the forecast of SEAS5. On a moving-averaged daily scale, the optimum prediction window is 30 days for temperature and 16 days for precipitation. The predictable ranges are consistent with atmospheric bridges in teleconnection patterns (e.g., ULMO) and are reflected by spatial correlation with SST. Our results suggest that combinations of the TeWA approach and numerical models could boost new research lines in subseasonal-to-seasonal forecasting.

Restricted access
Dylan J. Dodson
and
William A. Gallus Jr

Abstract

Ten bow echo events were simulated using the Weather Research and Forecasting (WRF) model with 3-km and 1-km horizontal grid spacing with both the Morrison and Thompson microphysics schemes to determine the impact of refined grid spacing on this often poorly simulated mode of convection. Simulated and observed composite reflectivity were used to classify convective mode. Skill scores were computed to quantify model performance at predicting all modes, and a new bow echo score was created to evaluate specifically the accuracy of bow echo forecasts. The full morphology score for runs using the Thompson scheme was noticeably improved by refined grid spacing, while the skill of Morrison runs did not change appreciably. However, bow echo scores for runs using both schemes improved when grid spacing was refined, with Thompson runs improving most significantly. Additionally, near storm environments were analyzed to understand why the simulated bow echoes changed as grid spacing was changed. A relationship existed between bow echo production and cold pool strength, as well as with the magnitude of microphysical cooling rates. More numerous updrafts were present in 1-km runs, leading to longer intense lines of convection which were more likely to evolve into longer-lived bow echoes in more cases. Large scale features, such as a low-level jet orientation more perpendicular to the convective line and surface boundaries, often had to be present for bow echoes to occur in the 3-km runs.

Restricted access
Karl Schneider
,
Kelly Lombardo
,
Matthew R. Kumjian
, and
Kevin Bowley

Abstract

Convective snow (CS) presents a significant hazard to motorists and is one of the leading causes of weather-related fatalities on Pennsylvania roadways. Thus, understanding environmental factors promoting CS formation and organization is critical for providing relevant and accurate information to those impacted. Prior research has been limited, mainly focusing on frontal CS bands often called “snow squalls;” thus, these studies do not account for the diversity of CS organizational modes that is frequently observed, highlighting a need for a robust climatology of broader CS events. To identify such events, a novel, radar-based CS detection algorithm was developed and applied to WSR-88D radar data from 10 cold seasons in central Pennsylvania, during which 159 cases were identified. Distinct convective organization modes were identified: linear (frontal) snow squalls, single cells, multicells, and streamer bands. Each algorithm-flagged radar scan containing CS was manually classified as one of these modes. Interestingly, the moststudied frontal mode only occurred < 5% of the time, whereas multicellular modes dominated CS occurrence. Using the times associated with each CS mode, synoptic and local environmental information from model analyses were investigated. Key characteristics of CS environments compared to null cases include a 500-hPa trough in the vicinity, lower-tropospheric conditional instability, and sufficient moisture. Environments favorable for the different CS modes featured statistically significant differences in the 500-hPa trough axis position, surface-based CAPE, and the unstable layer depth, among others. These results provide insights into forecasting CS mode, explicitly presented in a forecasting decision tree.

Restricted access
Andrew Hazelton
,
Xiaomin Chen
,
Ghassan J. Alaka Jr.
,
George R. Alvey III
,
Sundararaman Gopalakrishnan
, and
Frank Marks

Abstract

Understanding how model physics impact tropical cyclone (TC) structure, motion, and evolution is critical for the development of TC forecast models. This study examines the impacts of microphysics and planetary boundary layer (PBL) physics on forecasts using the Hurricane Analysis and Forecast System (HAFS), which is newly operational in 2023. The “HAFS-B” version is specifically evaluated, and three sensitivity tests (for over 400 cases in 15 Atlantic TCs) are compared with retrospective HAFS-B runs. Sensitivity tests are generated by 1) changing the microphysics in HAFS-B from Thompson to GFDL, 2) turning off the TC-specific PBL modifications that have been implemented in operational HAFS-B, and 3) combining the PBL and microphysics modifications. The forecasts are compared through standard verification metrics, and also examination of composite structure. Verification results show that Thompson microphysics slightly degrades the days 3–4 forecast track in HAFS-B, but improves forecasts of long-term intensity. The TC-specific PBL changes lead to a reduction in a negative intensity bias and improvement in RI skill, but cause some degradation in prediction of 34-kt (1 kt ≈ 0.51 m s−1) wind radii. Composites illustrate slightly deeper vortices in runs with the Thompson microphysics, and stronger PBL inflow with the TC-specific PBL modifications. These combined results demonstrate the critical role of model physics in regulating TC structure and intensity, and point to the need to continue to develop improvements to HAFS physics. The study also shows that the combination of both PBL and microphysics modifications (which are both included in one of the two versions of HAFS in the first operational implementation) leads to the best overall results.

Significance Statement

A new hurricane model, the Hurricane Analysis and Forecast System (HAFS), is being introduced for operational prediction during the 2023 hurricane season. One of the most important parts of any forecast model are the “physics parameterizations,” or approximations of physical processes that govern things like turbulence, cloud formation, etc. In this study, we tested these approximations in one configuration of HAFS, HAFS-B. Specifically, we looked at two different versions of the microphysics (modeling the growth of water and ice in clouds) and boundary layer physics (the approximations for turbulence in the lowest level of the atmosphere). We found that both of these sets of model physics had important effects on the forecasts from HAFS. The microphysics had notable impacts on the track forecasts, and also changed the vertical depth of the model hurricanes. The boundary layer physics, including some of our changes based on observed hurricanes and turbulence-resolving models, helped the model better predict rapid intensification (periods where the wind speed increases quickly). Work is ongoing to improve the model physics for better forecasts of rapid intensification and overall storm structure, including storm size. The study also shows the combination of both PBL and microphysics modifications overall leads to the best results and thus was used as one of the two first operational implementations of HAFS.

Restricted access
Timothy B. Higgins
,
Aneesh C. Subramanian
,
Will E. Chapman
,
David A. Lavers
, and
Andrew C. Winters

Abstract

Accurate forecasts of weather conditions have the potential to mitigate the social and economic damages they cause. To make informed decisions based on forecasts, it is important to determine the extent to which they could be skillful. This study focuses on subseasonal forecasts out to a lead time of four weeks. We examine the differences between the potential predictability, which is computed under the assumption of a “perfect model”, of integrated vapor transport (IVT) and precipitation under extreme conditions in subseasonal forecasts across the northeast Pacific. Our results demonstrate significant forecast skill of extreme IVT and precipitation events (exceeding the 90 th percentile) into week 4 for specific areas, particularly when anomalously wet conditions are observed in the true model state. This forecast skill during weeks 3 and 4 is closely associated with a zonal extension of the North Pacific Jet. These findings of the source of skillful subseasonal forecasts over the US West Coast could have implications for water management in these regions susceptible to drought and flooding extremes. Additionally, they may offer valuable insights for governments and industries on the US West Coast seeking to make informed decisions based on extended weather prediction.

Restricted access
Jingyi Wen
,
Zhiyong Meng
,
Lanqiang Bai
, and
Ruilin Zhou

Abstract

This study documents the features of tornadoes, their parent storms, and the environments of the only two documented tornado outbreak events in China. The two events were associated with Tropical Cyclone (TC) Yagi on 12 August 2018 with 11 tornadoes and with an extratropical cyclone (EC) on 11 July 2021 (EC 711) with 13 tornadoes. Most tornadoes in TC Yagi were spawned from discrete minisupercells, while a majority of tornadoes in EC 711 were produced from supercells imbedded in QLCSs or cloud clusters. In both events, the high-tornado-density area was better collocated with the K index rather than MLCAPE, and with entraining rather than non-entraining parameters possibly due to their sensitivity to midlevel moisture. EC 711 had a larger displacement between maximum entraining CAPE and vertical wind shear than TC Yagi, with the maximum entraining CAPE better collocated with the high-tornado-density area than vertical wind shear. Relative to TC Yagi, EC 711 had stronger entraining CAPE, 0–1-km storm relative helicity, 0–6-km vertical wind shear, and composite parameters such as an entraining significant tornado parameter, which caused its generally stronger tornado vortex signatures (TVSs) and mesocyclones with a larger diameter and longer life span. No significant differences were found in the composite parameter of these two events from U.S. statistics. Although obvious dry air intrusions were observed in both events, no apparent impact was observed on the potential of tornado outbreak in EC 711. In TC Yagi, however, the dry air intrusion may have helped tornado outbreak due to cloudiness erosion and thus the increase in surface temperature and low-level lapse rate.

Restricted access
Katherine E. McKeown
,
Casey E. Davenport
,
Matthew D. Eastin
,
Sarah M. Purpura
, and
Roger R. Riggin IV

Abstract

The evolution of supercell thunderstorms traversing complex terrain is not well understood and remains a short-term forecast challenge across the Appalachian Mountains of the eastern United States. Although case studies have been conducted, there has been no large multicase observational analysis focusing on the central and southern Appalachians. To address this gap, we analyzed 62 isolated warm-season supercells that occurred in this region. Each supercell was categorized as either crossing (∼40%) or noncrossing (∼60%) based on their maintenance of supercellular structure while traversing prominent terrain. The structural evolution of each storm was analyzed via operationally relevant parameters extracted from WSR-88D radar data. The most significant differences in radar-observed structure among storm categories were associated with the mesocyclone; crossing storms exhibited stronger, wider, and deeper mesocyclones, along with more prominent and persistent hook echoes. Crossing storms also moved faster. Among the supercells that crossed the most prominent peaks and ridges, significant increases in base reflectivity, vertically integrated liquid, echo tops, and mesocyclone intensity/depth were observed, in conjunction with more frequent large hail and tornado reports, as the storms ascended windward slopes. Then, as the supercells descended leeward slopes, significant increases in mesocyclone depth and tornado frequency were observed. Such results reinforce the notion that supercell evolution can be modulated substantially by passage through and over complex terrain.

Significance Statement

Understanding of thunderstorm evolution and severe weather production in regions of complex terrain remains limited, particularly for storms with rotating updrafts known as supercell thunderstorms. This study provides a systematic analysis of numerous warm season supercell storms that moved through the central and southern Appalachian Mountains. We focus on operationally relevant radar characteristics and differences among storms that maintain supercellular structure as they traverse the terrain (crossing) versus those that do not (noncrossing). Our results identify radar characteristics useful in distinguishing between crossing and noncrossing storms, along with typical supercell evolution and severe weather production as storms cross the more prominent peaks and ridges of the central and southern Appalachian Mountains.

Restricted access
Free access
Scott D. Rudlosky
,
Joseph Patton
,
Eric Palagonia
,
John Y. N. Cho
, and
James M. Kurdzo

Abstract

Quantifying the costs of radar outages allows value to be attributed to the alternate datasets that help mitigate outages. When radars are offline, forecasters rely more heavily on nearby radars, surface reports, numerical weather prediction models, and satellite observations. Monetized radar benefit models allow value to be attributed to individual radars for mitigating the threat to life from tornadoes, flash floods, and severe winds. Eighteen radars exceed $20 million in annual benefits for mitigating the threat to life from these convective hazards. The Jackson, MS radar (KJAN) provides the most value ($41.4 million), with the vast majority related to tornado risk mitigation ($29.4 million). During 2020-2023, the average radar is offline for 2.57% of minutes or 9.27 days per year, and experiences an average of 58.9 outages per year lasting 4.32 hours on average. Radar outage cost estimates vary by location and convective hazard. Outage cost estimates concentrate at the top, with 8, 2, 4, and 5 radars exceeding $1 million in outage costs during 2020, 2021, 2022, and 2023, respectively. The KJAN radar experiences outage frequencies of 4.92% and 5.50% during 2020 and 2023, resulting in outage cost estimates > $2 million both years. Combining outage cost estimates for all radars suggests that approximately $29.1 million in annual radar outage costs may be attributable as value to alternative datasets for helping to mitigate radar outage impacts.

Restricted access