The impact of initial and boundary conditions on severe weather event simulations using a high-resolution WRF model. Case study of the derecho event in Poland on 11 August 2017

<p>Nowadays, the use of 4D-VAR assimilation technique has been investigated in several scientific papers with the aim of improving the localization and timing of precipitation in complex orography regions. The results show the positive impact in rainfall forecast but, the need to resolve the tangent linear and adjoint model makes the 4D-VAR computationally too expensive. Hence, it is used in operationally only in large forecast centres. To the aim of exploring a more reasonable method, a comparison between a cycling 3D-VAR, that needs less computational resources, and 4D-VAR techniques is performed for a severe weather event occurred in Central Italy. A cut-off low (992 hPa), located in western side of Sicily region, was associated with a strong south-easterly flow over Central Adriatic region, which supplied a large amount of warm and moist air. This mesoscale configuration, coupled with the Apennines mountain range that further increased the air column instability, produced heavy rainfall in Abruzzo region (Central Italy).</p><p>The numerical simulations are carried out using the Weather Research and Forecasting (WRF) model. In-situ surface and upper-air observations are assimilated in combination with radar reflectivity and radial velocity data over a high-resolution domain. Several experiments have been performed in order to evaluate the impact of 4D-VAR and cycling 3D-VAR in the precipitation forecast. In addition, a statistical analysis has been carried out to objectively compare the simulations. Two different verification approaches are used: Receiver Operating Characteristic (ROC) curve and Fraction Skill Score (FSS). Both statistical scores are calculated for different threshold values in the study area and in the sub-regions where the maximum rainfall occurred.</p>

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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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Characterizing severe weather potential in synoptically weakly forced thunderstorm environments

10.5194/nhess-2017-291 ◽

2017 ◽

Author(s):

Paul W. Miller ◽

Thomas L. Mote

Keyword(s):

United States ◽

High Resolution ◽

Numerical Model ◽

Model Error ◽

Severe Weather ◽

Forecasting Model ◽

Weather Event ◽

Southeast United States ◽

Accurate Representation ◽

Severe Weather Event

Abstract. Weakly forced thunderstorms (WFTs), short-lived convection forming in synoptically quiescent regimes, are a contemporary forecasting challenge. The convective environments that support severe WFTs are often similar to those that yield only nonsevere WFTs, and additionally, only a small proportion individual WFTs will ultimately produce severe weather. The purpose of this study is to better characterize the relative severe weather potential in these settings as a function of the convective environment. Thirty near-storm convective parameters for > 200 000 WFTs in the Southeast United States are calculated from a high-resolution numerical forecasting model, the Rapid Refresh (RAP). For each parameter, the relative likelihood of WFT days with at least one severe weather event is assessed along a moving threshold. Parameters (and the values of them) that reliably separate severe-weather-supporting from nonsevere WFT days are highlighted. Only two convective parameters, vertical totals (VT) and total totals (TT), appreciably differentiate severe-wind-supporting and severe-hail-supporting days from nonsevere WFT days. When VTs exceeded values between 24.6–25.1 °C or TTs between 46.5–47.3 °C, severe-wind days were roughly 5 × more likely. Meanwhile, severe-hail days became roughly 10 × more likely when VTs exceeded 24.4–26.0 °C or TTs exceeded 46.3–49.2 °C. The stronger performance of VT and TT is partly attributed to the more accurate representation of these parameters in the numerical model. Under-reporting of severe weather and model error are posited to exacerbate the forecasting challenge by obscuring the subtle convective environmental differences enhancing storm severity.

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Characterizing severe weather potential in synoptically weakly forced thunderstorm environments

Natural Hazards and Earth System Science ◽

10.5194/nhess-18-1261-2018 ◽

2018 ◽

Vol 18 (4) ◽

pp. 1261-1277 ◽

Cited By ~ 7

Author(s):

Paul W. Miller ◽

Thomas L. Mote

Keyword(s):

United States ◽

High Resolution ◽

Southeastern United States ◽

Model Error ◽

Severe Weather ◽

Forecasting Model ◽

Weather Event ◽

Accurate Representation ◽

Relative Odds ◽

Severe Weather Event

Abstract. Weakly forced thunderstorms (WFTs), short-lived convection forming in synoptically quiescent regimes, are a contemporary forecasting challenge. The convective environments that support severe WFTs are often similar to those that yield only non-severe WFTs and, additionally, only a small proportion of individual WFTs will ultimately produce severe weather. The purpose of this study is to better characterize the relative severe weather potential in these settings as a function of the convective environment. Thirty-one near-storm convective parameters for > 200 000 WFTs in the Southeastern United States are calculated from a high-resolution numerical forecasting model, the Rapid Refresh (RAP). For each parameter, the relative odds of WFT days with at least one severe weather event is assessed along a moving threshold. Parameters (and the values of them) that reliably separate severe-weather-supporting from non-severe WFT days are highlighted. Only two convective parameters, vertical totals (VTs) and total totals (TTs), appreciably differentiate severe-wind-supporting and severe-hail-supporting days from non-severe WFT days. When VTs exceeded values between 24.6 and 25.1 ∘C or TTs between 46.5 and 47.3 ∘C, odds of severe-wind days were roughly 5× greater. Meanwhile, odds of severe-hail days became roughly 10× greater when VTs exceeded 24.4–26.0 ∘C or TTs exceeded 46.3–49.2 ∘C. The stronger performance of VT and TT is partly attributed to the more accurate representation of these parameters in the numerical model. Under-reporting of severe weather and model error are posited to exacerbate the forecasting challenge by obscuring the subtle convective environmental differences enhancing storm severity.

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Sensitivity of Precipitation Statistics to Resolution, Microphysics, and Convective Parameterization: A Case Study with the High-Resolution WRF Climate Model over Europe

Journal of Hydrometeorology ◽

10.1175/jhm-d-14-0221.1 ◽

2015 ◽

Vol 16 (4) ◽

pp. 1857-1872 ◽

Cited By ~ 39

Author(s):

Alexandre B. Pieri ◽

Jost von Hardenberg ◽

Antonio Parodi ◽

Antonello Provenzale

Keyword(s):

High Resolution ◽

Seasonal Cycle ◽

Climate Model ◽

Regional Climate ◽

Wrf Model ◽

Explicit Representation ◽

Weather Research And Forecasting ◽

Microphysics Scheme ◽

The Impact

Abstract We explore the impact of different resolutions, convective closures, and microphysical parameterizations on the representation of precipitation statistics (climatology, seasonal cycle, and intense events) in 20-yr-long simulations over Europe with the regional climate Weather Research and Forecasting (WRF) Model. The simulations are forced in the period 1979–98, using as boundary conditions the ERA-Interim fields over the European region. Special attention is paid to the representation of precipitation in the Alpine area. We consider spatial resolutions ranging from 0.11° to 0.037°, allowing for an explicit representation of convection at the highest resolution. Our results show that while there is a good overall agreement between observed and modeled precipitation patterns, the model outputs display a positive precipitation bias, particularly in winter. The choice of the microphysics scheme is shown to significantly affect the statistics of intense events. High resolution and explicitly resolved convection help to considerably reduce precipitation biases in summer and the reproduction of precipitation statistics.

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Splitting of the middle layer of LPW SAFNWC/MSG satellite product in order to improve the monitoring of pre-convective environments

Advances in Science and Research ◽

10.5194/asr-2-71-2008 ◽

2008 ◽

Vol 2 (1) ◽

pp. 71-75

Author(s):

G. Cuevas ◽

M. A. Martinez ◽

M. Velazquez ◽

J. Ruiz ◽

M. Manso

Keyword(s):

Neural Networks ◽

Precipitable Water ◽

Middle Layer ◽

Horizontal Resolution ◽

Severe Weather ◽

Convective System ◽

Weather Event ◽

Radar Images ◽

The Impact ◽

Severe Weather Event

Abstract. Seven of the infrared channels from the Spinning Enhanced Visible and Infrared Imagery (SEVIRI) instrument, on board the Meteosat Second Generation (MSG), are used to retrieve Layer Precipitable Water (LPW) and Stability Analysis Imagery (SAI) in the SAFNWC framework. Both products are retrieved using a statistical retrieval based on neural networks; they are routinely generated every fifteen minutes at a satellite horizontal resolution of 3 km in NADIR only in cloud-free areas. Many factors are involved in the development of severe weather and these parameters are only some of the indicators. However, due to the high resolution of these products, the use of them in conjunction with satellite and radar images can help to identify mesoscale features related to convection. The MSG moisture and parcel instability time trend fields are especially useful during the period previous to convection. Once the outbreak of convection occurs, the products calculated in the clear air pixels surrounding the convective system can give us hints to anticipate its evolution. SAFNWC LPW and SAI were analyzed for a severe weather event during August 2004. A thunderstorm over Teruel (Spain) produced intense precipitation and hail; a tornado developed while this thunderstorm was moving towards SE. The pre-convective parcel potential buoyancy and moisture SAFNWC products changed in a way that was consistent with the observed intense convective activity. In previous studies, the atmospheric moisture in medium levels, which has been proven to be relevant in some cases, was represented by only one level parameter (ML: middle layer LPW). However, it was observed that this layer is too thick to do an adequate analysis of moisture available for convection. Hence, an improvement on the LPW algorithm has been carried out by splitting the middle layer into two new sub-layers (approximately separated at 700 hPa) and training two new neural networks. The impact of monitoring moisture in the new sub-layers separately in this severe weather event has been tested, and the improvements achieved have been evaluated.

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