Value of Real-Time Vegetation Fraction to Forecasts of Severe Convection in High-Resolution Models

Abstract Near-real-time values of vegetation fraction are incorporated into a 2-km nested version of the Advanced Research Weather Research and Forecasting (ARW) model and compared to forecasts from a control run that uses climatological values of vegetation fraction for eight severe weather events during 2004. It is hypothesized that an improved partitioning of surface sensible and latent heat fluxes occurs when incorporating near-real-time values of the vegetation fraction into models, which may result in improved forecasts of the low-level environmental conditions that support convection and perhaps even lead to improved explicit convective forecasts. Five of the severe weather events occur in association with weak synoptic-scale forcing, while three of the events occur in association with moderate or strong synoptic-scale forcing. Results show that using the near-real-time values of the vegetation fraction alters the values and structure of low-level temperature and dewpoint temperature fields compared to the forecasts using climatological vegetation fractions. The environmental forecasts that result from using the real-time vegetation fraction are more thermodynamically supportive of convection, including stronger and deeper frontogenetic circulations, and statistically significant improvements of most unstable CAPE forecasts compared to the control run. However, despite the improved environmental forecasts, the explicit convective forecasts using real-time vegetation fractions show little to no improvement over the control forecasts. The convective forecasts are generally poor under weak synoptic-scale forcing and generally good under strong synoptic-scale forcing. These results suggest that operational forecasters can best use high-resolution forecasts to help diagnose environmental conditions within an ingredients-based forecasting approach.

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Examining Terrain Effects on an Upstate New York Tornado Event Utilizing a High-Resolution Model Simulation

Weather and Forecasting ◽

10.1175/waf-d-21-0018.1 ◽

2021 ◽

Author(s):

Luke J. LeBel ◽

Brian H. Tang ◽

Ross A. Lazear

Keyword(s):

New York ◽

High Resolution ◽

Wind Shear ◽

Model Simulation ◽

Wrf Model ◽

Severe Weather ◽

Streamwise Vorticity ◽

Low Level ◽

Severe Convection ◽

The Impact

AbstractThe complex terrain at the intersection of the Mohawk and Hudson valleys of New York has an impact on the development and evolution of severe convection in the region. Specifically, previous research has concluded that terrain-channeled flow in the Mohawk and Hudson valleys likely contributes to increased low-level wind shear and instability in the valleys during severe weather events such as the historic 31 May 1998 event that produced a strong (F3) tornado in Mechanicville, New York.The goal of this study is to further examine the impact of terrain channeling on severe convection by analyzing a high-resolution WRF model simulation of the 31 May 1998 event. Results from the simulation suggest that terrain-channeled flow resulted in the localized formation of an enhanced low-level moisture gradient, resembling a dryline, at the intersection of the Mohawk and Hudson valleys. East of this boundary, the environment was characterized by stronger low-level wind shear and greater low-level moisture and instability, increasing tornadogenesis potential. A simulated supercell intensified after crossing the boundary, as the larger instability and streamwise vorticity of the low-level inflow was ingested into the supercell updraft. These results suggest that terrain can have a key role in producing mesoscale inhomogeneities that impact the evolution of severe convection. Recognition of these terrain-induced boundaries may help in anticipating where the risk of severe weather may be locally enhanced.

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Impact of Assimilating Dropsonde Observations from MPEX on Ensemble Forecasts of Severe Weather Events

Monthly Weather Review ◽

10.1175/mwr-d-15-0407.1 ◽

2016 ◽

Vol 144 (10) ◽

pp. 3799-3823 ◽

Cited By ~ 15

Author(s):

Glen S. Romine ◽

Craig S. Schwartz ◽

Ryan D. Torn ◽

Morris L. Weisman

Keyword(s):

United States ◽

Initial Conditions ◽

Positive Impact ◽

The United States ◽

Early Morning ◽

Severe Weather ◽

Synoptic Scale ◽

Ensemble Forecasts ◽

Forecast Performance ◽

Weather Events

Over the central Great Plains, mid- to upper-tropospheric weather disturbances often modulate severe storm development. These disturbances frequently pass over the Intermountain West region of the United States during the early morning hours preceding severe weather events. This region has fewer in situ observations of the atmospheric state compared with most other areas of the United States, contributing toward greater uncertainty in forecast initial conditions. Assimilation of supplemental observations is hypothesized to reduce initial condition uncertainty and improve forecasts of high-impact weather. During the spring of 2013, the Mesoscale Predictability Experiment (MPEX) leveraged ensemble-based targeting methods to key in on regions where enhanced observations might reduce mesoscale forecast uncertainty. Observations were obtained with dropsondes released from the NSF/NCAR Gulfstream-V aircraft during the early morning hours preceding 15 severe weather events over areas upstream from anticipated convection. Retrospective data-denial experiments are conducted to evaluate the value of dropsonde observations in improving convection-permitting ensemble forecasts. Results show considerable variation in forecast performance from assimilating dropsonde observations, with a modest but statistically significant improvement, akin to prior targeted observation studies that focused on synoptic-scale prediction. The change in forecast skill with dropsonde information was not sensitive to the skill of the control forecast. Events with large positive impact sampled both the disturbance and adjacent flow, akin to results from past synoptic-scale targeting studies, suggesting that sampling both the disturbance and adjacent flow is necessary regardless of the horizontal scale of the feature of interest.

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Towards the operational use of tweets data in high impact weather scenarios: data mining and analytics in Basque Country.

10.5194/ems2021-245 ◽

2021 ◽

Author(s):

Santiago Gaztelumendi

Keyword(s):

Data Mining ◽

Social Media ◽

Real Time ◽

Basque Country ◽

Local Level ◽

High Impact ◽

Severe Weather ◽

Weather Events ◽

The World ◽

High Impact Weather

Although social media industry is now a very congested Marketplace, Twitter continues to maintain its status as a popular social media platform. There are 330 million monthly active users and 145 million daily active users on Twitter sending more than 6,000 tweets every second in the world. In Spain case 85% population are social media users, with around 5 million tweeter profiles for a population around 47 million. In the autonomous community of Basque country (2.17 million inhabitants) around 20% of citizens use Twitter.Twitter is a social tool that enables users to post messages (tweets) of up to 280 characters supporting a wide variety of social communication practices including photo and video attach. The Basque Meteorology Agency @Euskalmet with more than 115,3 K followers is one of the most popular accounts in Basque Country. Twitter is not only an opportunity to instantaneous spread messages to people without intermediaries, but also as a potential platform for valuable data acquisition using tweeter API capabilities. In this contribution, we present a study of different aspects related to the operational use of Twitter data in the context of high impact weather scenarios at local level.The most important activity in Euskalmet are actions in severe weather events. Before the event, mainly centered in forecast and communication, during the event in nowcast, surveillance and impact monitoring and after the event in post-event analysis. During all these complex processes real time tweets posted by local users offer a huge amount of data that conveniently processed could be useful for different purposes. For operational staff, working at office during severe weather episodes, is critical to understand the local effects that an adverse phenomenon is causing and the correct perception of the extent of impact and social alarm. For this purposes, among others, different information associated with posted tweets can be extracted and exploited conveniently. In this work, we present some results that demonstrate how different data mining and advances analytics techniques can be used in order to include social media data information for different tasks and particularly during high impact weather events.In this paper we summarize our experience during a proof of concept project for automatic real time tweeter analysis and the development of an operational tool for tweeter API data exploitation in the Basque Country. We present the main challenges and problems that we have had to face, including how to deal with the lack of geolocation information, since in the case of the Basque country, as in other parts of the world, tweets containing geotags are the exception, not the rule.

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Low-Level Jets over the Mid-Atlantic States: Warm-Season Climatology and a Case Study

Journal of Applied Meteorology and Climatology ◽

10.1175/jam2313.1 ◽

2006 ◽

Vol 45 (1) ◽

pp. 194-209 ◽

Cited By ~ 56

Author(s):

Da-Lin Zhang ◽

Shunli Zhang ◽

Scott J. Weaver

Keyword(s):

Real Time ◽

Great Plains ◽

Thermal Gradient ◽

Warm Season ◽

Heat Fluxes ◽

Time Model ◽

Low Level ◽

Mountainous Regions ◽

Low Level Jets

Abstract Although considerable research has been conducted to study the characteristics of the low-level jets (LLJs) over the Great Plains states, little is known about the development of LLJs over the Mid-Atlantic states. In this study, the Mid-Atlantic LLJ and its associated characteristics during the warm seasons of 2001 and 2002 are documented with both the wind profiler data and the daily real-time model forecast products. A case study with three model sensitivity simulations is performed to gain insight into the three-dimensional structures and evolution of an LLJ and the mechanisms by which it developed. It is found that the Mid-Atlantic LLJ, ranging from 8 to 23 m s−1, appeared at an average altitude of 670 m and on 15–25 days of each month. About 90% of the 160 observed LLJ events occurred between 0000 and 0600 LST, and about 60% had southerly to westerly directions. Statistically, the real-time forecasts capture most of the LLJ events with nearly the right timing, intensity, and altitude, although individual forecasts may not correspond to those observed. For a selected southwesterly LLJ case, both the observations and the control simulation exhibit a pronounced diurnal cycle of horizontal winds in the lowest 1.5 km. The simulation shows that the Appalachian Mountains tend to produce a sloping mixed layer with northeasterly thermal winds during the daytime and reversed thermal winds after midnight. With additional thermal contrast effects associated with the Chesapeake Bay and the Atlantic Ocean, the daytime low-level winds vary significantly from the east coast to the mountainous regions. The LLJ after midnight tends to be peaked preferentially around 77.5°W near the middle portion of the sloping terrain, and it decreases eastward as a result of the opposite thermal gradient across the coastline from the mountain-generated thermal gradient. Although the Mid-Atlantic LLJ is much weaker and less extensive than that over the Great Plains states, it has a width of 300–400 km (to its half-peak value) and a length scale of more than 1500 km, following closely the orientation of the Appalachians. Sensitivity simulations show that eliminating the surface heat fluxes produces the most significant impact on the development of the LLJ, then topography and the land–sea contrast, with its area-averaged intensity reduced from 12 m s−1 to about 6, 9, and 10 m s−1, respectively.

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A Real-Time Convection-Allowing Ensemble Prediction System Initialized by Mesoscale Ensemble Kalman Filter Analyses

Weather and Forecasting ◽

10.1175/waf-d-15-0013.1 ◽

2015 ◽

Vol 30 (5) ◽

pp. 1158-1181 ◽

Cited By ~ 41

Author(s):

Craig S. Schwartz ◽

Glen S. Romine ◽

Morris L. Weisman ◽

Ryan A. Sobash ◽

Kathryn R. Fossell ◽

...

Keyword(s):

Kalman Filter ◽

Real Time ◽

Ensemble Kalman Filter ◽

Wrf Model ◽

Severe Weather ◽

Ensemble Prediction ◽

Limited Area ◽

Ensemble Forecasts ◽

Ensemble Prediction System ◽

Weather Events

Abstract In May and June 2013, the National Center for Atmospheric Research produced real-time 48-h convection-allowing ensemble forecasts at 3-km horizontal grid spacing using the Weather Research and Forecasting (WRF) Model in support of the Mesoscale Predictability Experiment field program. The ensemble forecasts were initialized twice daily at 0000 and 1200 UTC from analysis members of a continuously cycling, limited-area, mesoscale (15 km) ensemble Kalman filter (EnKF) data assimilation system and evaluated with a focus on precipitation and severe weather guidance. Deterministic WRF Model forecasts initialized from GFS analyses were also examined. Subjectively, the ensemble forecasts often produced areas of intense convection over regions where severe weather was observed. Objective statistics confirmed these subjective impressions and indicated that the ensemble was skillful at predicting precipitation and severe weather events. Forecasts initialized at 1200 UTC were more skillful regarding precipitation and severe weather placement than forecasts initialized 12 h earlier at 0000 UTC, and the ensemble forecasts were typically more skillful than GFS-initialized forecasts. At times, 0000 UTC GFS-initialized forecasts had temporal distributions of domain-average rainfall closer to observations than EnKF-initialized forecasts. However, particularly when GFS analyses initialized WRF Model forecasts, 1200 UTC forecasts produced more rainfall during the first diurnal maximum than 0000 UTC forecasts. This behavior was mostly attributed to WRF Model initialization of clouds and moist physical processes. The success of these real-time ensemble forecasts demonstrates the feasibility of using limited-area continuously cycling EnKFs as a method to initialize convection-allowing ensemble forecasts, and future real-time high-resolution ensemble development leveraging EnKFs seems justified.

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A Real-Time Weather-Adaptive 3DVAR Analysis System for Severe Weather Detections and Warnings

Weather and Forecasting ◽

10.1175/waf-d-12-00093.1 ◽

2013 ◽

Vol 28 (3) ◽

pp. 727-745 ◽

Cited By ~ 39

Author(s):

Jidong Gao ◽

Travis M. Smith ◽

David J. Stensrud ◽

Chenghao Fu ◽

Kristin Calhoun ◽

...

Keyword(s):

Real Time ◽

Three Dimensional ◽

Development Stage ◽

Horizontal Resolution ◽

Severe Weather ◽

Radar Observations ◽

Weather Events ◽

Weather Situation ◽

Analysis System ◽

Hazardous Weather

Abstract A real-time, weather-adaptive three-dimensional variational data assimilation (3DVAR) system has been adapted for the NOAA Warn-on-Forecast (WoF) project to incorporate all available radar observations within a moveable analysis domain. The key features of the system include 1) incorporating radar observations from multiple Weather Surveillance Radars-1988 Doppler (WSR-88Ds) with NCEP forecast products as a background state, 2) the ability to automatically detect and analyze severe local hazardous weather events at 1-km horizontal resolution every 5 min in real time based on the current weather situation, and 3) the identification of strong circulation patterns embedded in thunderstorms. Although still in the early development stage, the system performed very well within the NOAA's Hazardous Weather Testbed (HWT) Experimental Warning Program during preliminary testing in spring 2010 when many severe weather events were successfully detected and analyzed. This study represents a first step in the assessment of this type of 3DVAR analysis for use in severe weather warnings. The eventual goal of this real-time 3DVAR system is to help meteorologists better track severe weather events and eventually provide better warning information to the public, ultimately saving lives and reducing property damage.

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Development of the High-resolution Operational System for Numerical Prediction of Weather and Severe Weather Events for the Moscow Region

Russian Meteorology and Hydrology ◽

10.3103/s1068373920070018 ◽

2020 ◽

Vol 45 (7) ◽

pp. 455-465 ◽

Cited By ~ 1

Author(s):

G. S. Rivin ◽

I. A. Rozinkina ◽

R. M. Vil’fand ◽

D. B. Kiktev ◽

K. O. Tudrii ◽

...

Keyword(s):

High Resolution ◽

Severe Weather ◽

Numerical Prediction ◽

Moscow Region ◽

Operational System ◽

Weather Events

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Forward and Reverse Shear Environments during Polar Low Genesis over the Northeast Atlantic

Monthly Weather Review ◽

10.1175/mwr-d-15-0314.1 ◽

2016 ◽

Vol 144 (4) ◽

pp. 1341-1354 ◽

Cited By ~ 16

Author(s):

Annick Terpstra ◽

Clio Michel ◽

Thomas Spengler

Keyword(s):

Surface Temperature ◽

Sea Surface Temperature ◽

Surface Fluxes ◽

Heat Fluxes ◽

Sea Surface ◽

Synoptic Scale ◽

Low Level ◽

Wind Speed Maximum ◽

Polar Low ◽

Baroclinic Zone

Abstract The synoptic and subsynoptic environments associated with polar low genesis are examined. Ambient pre–polar low environments are classified as forward or reverse shear conditions based on the angle between the thermal and mean wind. Forward shear environments are associated with a synoptic-scale ridge over Scandinavia, featuring a zonally oriented baroclinic zone extending throughout the troposphere with a wind speed maximum at the tropopause. Similar to typical midlatitude cyclogenesis, concurrent wavelike development occurs both in the lower and upper troposphere along the baroclinic zone and the mean propagation direction is eastward, parallel to isolines of sea surface temperature. Reverse shear environments exhibit a distinctly different structure and are characterized by a trough over Scandinavia, associated with a synoptic-scale, occluded cyclone. The genesis area exhibits strong cold air advection on its right-hand side and polar low development occurs on the warm side of an intense low-level jet. The environment resembles the characteristics conducive to secondary development associated with frontal instability. Polar lows developing in this configuration propagate mainly southward, perpendicular to isolines of sea surface temperature. The two genesis environments exhibit similar temperature differences between the sea surface and atmosphere near the surface, yet the magnitude of the surface fluxes is approximately double during reverse shear conditions due to stronger low-level winds. The ratio between surface sensible and latent heat fluxes is close to unity for both shear environments.

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The new Italian regional reanalysis SPHERA: benefits of the convection-permitting resolution in detecting severe-weather events

10.5194/ems2021-134 ◽

2021 ◽

Author(s):

Antonio Giordani ◽

Ines Cerenzia ◽

Tiziana Paccagnella ◽

Silvana Di Sabatino

Keyword(s):

High Resolution ◽

Dynamical Downscaling ◽

Temporal Frequency ◽

Severe Weather ◽

Added Value ◽

Limited Area ◽

Reanalysis Dataset ◽

Weather Events ◽

Regional Reanalysis ◽

Global Reanalysis

In recent years the interest towards the development of limited-area atmospheric reanalysis datasets has been growing more and more. Regional reanalyses in fact, as a consequence of the restricted domain that they cover, provide a data distribution displaced on a much finer grid compared to a coarser global dataset. This permits to better resolve those patterns related to rapid and high-impact weather events, first and foremost convection. Furthermore, with a finer horizontal resolution, a consistent increase in the level of detail in the description of the orography is also gained, that is a crucial point to achieve especially in a very complex territory such as Italy. This study presents the first application of the novel regional reanalysis dataset developed at ARPAE-SIMC: the High rEsolution ReAnalysis over Italy (SPHERA). SPHERA is a high-resolution convection-permitting reanalysis over the Italian domain and the surrounding seas covering 25 years, from 1995 to 2020, at hourly temporal frequency. SPHERA is based on the non-hydrostatic limited-area model COSMO, and produced by a dynamical downscaling of the global reanalysis ERA5, developed at ECMWF. A nudging data assimilation scheme is applied in order to steer the model outcomes towards the surface and upper-air observations. All the available conventional observations have been used.The added value of SPHERA in representing severe-weather and convective events is evident from its preliminar validation, which was performed on the multidecadal period against various datasets of surface observations, joined with the comparison against the global reanalysis ERA5. In fact, a clear advantage of SPHERA on its driver ERA5 is found for the detection of events with moderate to intense daily and sub-daily rainfalls, which are characterized by a strong seasonal and geographical component, that is further investigated. We report also the preliminary sensitivity analysis on the dimension of the box used to operate the upscaling for the validation of SPHERA, a process necessary to reduce the errors caused by geographical mismatches between observed and simulated events localizations, which are particularly frequent in case of strongly-localized and rapid processes. Furthermore, in order to give a quantitative evaluation of the performance of the new reanalysis in particular conditions, the results of the simulations for specific case studies involving the occurrence of severe-precipitation events in recent years was performed, focusing on events having different dynamical genesis, but interrelated by the important damages they caused. From this analysis, for which also a comparison with other regional reanalyses is performed, the advantage of SPHERA in representing the most intense rainfall occurrences, in terms of location, intensity and timing, clearly emerges.

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