scholarly journals Medicane Zorbas: Origin and impact of an uncertain potential vorticity streamer

2019 ◽  
Author(s):  
Raphael Portmann ◽  
Juan Jesús González-Alemán ◽  
Michael Sprenger ◽  
Heini Wernli
Keyword(s):  
Potential Vorticity ◽  
Large Scale ◽  
Prediction Models ◽  
Initial Conditions ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Medium Range Weather Forecast ◽  
The Mediterranean ◽  
Upper Level ◽  
Jet Streak

Abstract. Mediterranean tropical-like cyclones (Medicanes) can have high societal impact and their accurate forecast remains a challenge for numerical weather prediction models. They are often triggered by upper-level potential vorticity (PV) anomalies, such as PV streamers and cut-offs. But knowledge is incomplete about their detailed formation processes and factors limiting their predictability. This study exploits a European Centre for Medium-Range Weather Forecast (ECMWF) operational ensemble forecast with an uncertain PV streamer over the Mediterranean, which, three days after initialisation, resulted in an uncertain development of Medicane Zorbas in September 2018. Using an ad-hoc clustering of the ensemble members according to the PV streamer position, it is demonstrated that uncertainty in the initial conditions near an upper-level jet streak over the Gulf of Saint Lawrence is the dominant source of the subsequent uncertainty in the position of the PV streamer over the Mediterranean. The initial condition uncertainty strongly amplifies baroclinically after 18 h in a region of strong quasi-geostrophic forcing for ascent in the left exit of a jet streak over the North Atlantic. The further amplification and downstream propagation of the tropopause-level PV uncertainty leads to a large spread in the position of the PV streamer over the Mediterranean after three days, directly limiting the predictability of the position, thermal structure and evolution of Zorbas. Two low-level airstreams possibly play a key role in linking the uncertainties of the large-scale upper-level flow with meso-scale uncertainties in the cyclone structure. Overall, this study is an illustrative example that uncertainties in large-scale initial conditions can determine the practical predictability limits of a high-impact weather event.

scholarly journals Sensitivity of Cloud-Resolving Simulations of Warm-Season Convection to Cloud Microphysics Parameterizations

2007 ◽  
Vol 135 (8) ◽  
pp. 2854-2868 ◽  
Author(s):  
Changhai Liu ◽  
Mitchell W. Moncrieff
Keyword(s):  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warm Season ◽  
Cloud Microphysics ◽  
Mixed Phase ◽  
Fine Grid ◽  
Upper Level ◽  
Heating Profiles ◽  
Microphysical Parameterization

Abstract This paper investigates the effects of cloud microphysics parameterizations on simulations of warm-season precipitation at convection-permitting grid spacing. The objective is to assess the sensitivity of summertime convection predictions to the bulk microphysics parameterizations (BMPs) at fine-grid spacings applicable to the next generation of operational numerical weather prediction models. Four microphysical parameterization schemes are compared: simple ice (Dudhia), four-class mixed phase (Reisner et al.), Goddard five-class mixed phase (Tao and Simpson), and five-class mixed phase with graupel (Reisner et al.). The experimentation involves a 7-day episode (3–9 July 2003) of U.S. midsummer convection under moderate large-scale forcing. Overall, the precipitation coherency manifested as eastward-moving organized convection in the lee of the Rockies is insensitive to the choice of the microphysics schemes, and the latent heating profiles are also largely comparable among the BMPs. The upper-level condensate and cloudiness, upper-level radiative cooling/heating, and rainfall spectrum are the most sensitive, whereas the domain-mean rainfall rate and areal coverage display moderate sensitivity. Overall, the three mixed-phase schemes outperform the simple ice scheme, but a general conclusion about the degree of sophistication in the microphysics treatment and the performance is not achievable.

scholarly journals Convective Snowbands Downstream of the Rocky Mountains in an Environment with Conditional, Dry Symmetric, and Inertial Instabilities

2010 ◽  
Vol 138 (12) ◽  
pp. 4416-4438 ◽  
Author(s):  
Russ S. Schumacher ◽  
David M. Schultz ◽  
John A. Knox
Keyword(s):  
Potential Vorticity ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Convective Available Potential Energy ◽  
Negative Potential ◽  
Moist Convection ◽  
Major Band ◽  
Near Surface ◽  
Individual Bands

Abstract Convective snowbands moved slowly over Wyoming and northern Colorado on 16–17 February 2007 and produced up to 71 mm (2.8 in.) of snow that was unpredicted by operational numerical weather prediction models and human forecasters. The northwest–southeast-oriented bands lasted for over 6 h, comprising both a single major band (more than 30 km wide) and multiple minor bands (about 10 km wide). The convective bands initiated within the ascending branch of a secondary circulation associated with both near-surface and elevated frontogenesis, but the bands remained nearly stationary while the near-surface frontogenesis moved quickly equatorward. The bands occurred downstream of complex terrain on the anticyclonic-shear side of a midlevel jet streak, where conditional, dry symmetric (negative potential vorticity), and inertial (negative absolute vorticity) instabilities were present. To determine the mechanisms responsible for the development and organization of these bands, simulations using a convection-permitting numerical model are conducted. In contrast to the operational models, these simulations are able to produce convective bands in the same area and at about the same time as that observed. The simulated bands occurred in an environment with a nearly well-mixed, baroclinic boundary layer, positive convective available potential energy, and widespread negative potential vorticity. Individual bands initiated on the low-momentum side of vorticity banners downstream of mountains, and in association with frontogenetical ascent along two baroclinic zones. In addition, ascent caused by both frontogenesis and banded moist convection produced additional narrow regions of negative vorticity by transporting low-momentum air upward and creating strong horizontal gradients in wind speed. This event is similar to other observed instances of banded convection in the western United States on the anticyclonic-shear side of strong mid- and upper-tropospheric jets in environments lacking large-scale saturation. In contrast, these events differ from previously published banded precipitation events in the comma head of extratropical cyclones and downstream of mountains where large-scale saturation is present.

scholarly journals Simulations of deep convection in the Mediterranean area using 3DVAR of conventional and non-conventional data

2005 ◽  
Vol 2 ◽  
pp. 65-71 ◽  
Author(s):  
R. Ferretti ◽  
C. Faccani ◽  
D. Cimini ◽  
F. S. Marzano ◽  
A. Memmo ◽  
...  
Keyword(s):  
Large Scale ◽  
Initial Conditions ◽  
Deep Convection ◽  
Weather Prediction ◽  
Rain Gauge ◽  
Precipitation Forecast ◽  
Variational Assimilation ◽  
First Case ◽  
Surface Data ◽  
The Mediterranean

Abstract. In autumn deep convection in the Mediterranean region is a common phenomenon. The local events characterized by deep convection are still a difficult task even for high resolution numerical weather prediction. Three flood cases, produced by convection either embedded in a large scale system or locally developed, occurring in Italy, are presented. All these case were not correctly forecasted: Sardinia (Cagliari, 13 November 1999); Calabria (Soverato, 7 September 2000) and Sicily (Catania, 16 September 2003). The first case occurred during the Mesoscale Alpine Programme (MAP) campaign, therefore a lot of data are available; for the second one only data from SSM/I and local rain-gauge are available; the third one occurred during the operational experimentation of the TOUGH project. The last one was not well predicted even using the operational assimilation of ground based GPS. To improve the forecast of these cases the assimilation of several data is tested. The variational assimilation performed using 3DVAR of GPS, SSM/I and surface and upper air data is applied to improve the Initial Conditions of the Sicily case. The Sardinia case is improved using either GPS and surface data, whereas for the Soverato case only ZTD is assimilated. The experiments are performed using the MM5 model from Pennsylvania State University/National Center for Atmospheric Research (PSU/NCAR); the model is initialized using the new Initial Conditions produced by the variational assimilation of conventional and non conventional data. The results show that the assimilation of the retrieved quantities does produces large improvement in the precipitation forecast. Large sensitivity to the assimilation of surface data and brightness temperature from SSM/I is found.

Predictability of large-scale atmospheric flow patterns connected to extreme precipitation events in the Mediterranean

2021 ◽  
Author(s):  
Nikolaos Mastrantonas ◽  
Linus Magnusson ◽  
Florian Pappenberger ◽  
Jörg Matschullat
Keyword(s):  
Extreme Precipitation ◽  
Mediterranean Region ◽  
Large Scale ◽  
Prediction Models ◽  
Weather Prediction ◽  
Flow Patterns ◽  
Atmospheric Flow ◽  
Extreme Precipitation Events ◽  
The Mediterranean ◽  
Precipitation Events

<p>The Mediterranean region frequently experiences extreme precipitation events with devastating consequences for the affected societies, economies, and environment. Being able to provide reliable and skillful predictions of such events is crucial for mitigating their adverse impacts and related risks. One important part of the risk mitigation chain is the sub-seasonal predictability of such extremes, with information provided at such timescales supporting a range of actions, as for example warn decision-makers, and preposition materials and equipment.</p><p>This work focuses on the predictability of large-scale atmospheric flow patterns connected to extreme precipitation events in the Mediterranean. Previous research has identified strong connections between localized extremes and large-scale patterns. This is promising to provide useful information at sub-seasonal timescales. For such lead times, the Numerical Weather Prediction models are more skillful in predicting large-scale patterns than localized extremes. Here, we analyze the usefulness of these connections at sub-seasonal timescales by using the ECMWF extended-range forecasts. We aim at quantifying related benefits for the different areas in the Mediterranean region and providing insights that are of interest to the operational community.</p><p>Initial results suggest that the ECMWF forecasts provide skillful information in the predictability of large-scale patterns up to about 15 days lead time.</p><p> </p><p><img src="https://contentmanager.copernicus.org/fileStorageProxy.php?f=gnp.3687c29b370068376801161/sdaolpUECMynit/12UGE&app=m&a=0&c=49e65b5908090e0787f0f7f4f8930219&ct=x&pn=gnp.elif&d=1" alt=""></p><p>Large-scale patterns over the Mediterranean based on anomalies of sea level pressure (color shades) and geopotential at 500 hPa (contours) (Figure adapted from Mastrantonas et al, 2020)</p>

scholarly journals Revisiting Sensitivity to Horizontal Grid Spacing in Convection-Allowing Models over the Central and Eastern United States

2019 ◽  
Vol 147 (12) ◽  
pp. 4411-4435 ◽  
Author(s):  
Craig S. Schwartz ◽  
Ryan A. Sobash
Keyword(s):  
United States ◽  
Large Scale ◽  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Precipitation Forecast ◽  
Eastern United States ◽  
Grid Spacing ◽  
Cool Season ◽  
Horizontal Grid

Abstract Hourly accumulated precipitation forecasts from deterministic convection-allowing numerical weather prediction models with 3- and 1-km horizontal grid spacing were evaluated over 497 forecasts between 2010 and 2017 over the central and eastern conterminous United States (CONUS). While precipitation biases varied geographically and seasonally, 1-km model climatologies of precipitation generally aligned better with those observed than 3-km climatologies. Additionally, during the cool season and spring, when large-scale forcing was strong and precipitation entities were large, 1-km forecasts were more skillful than 3-km forecasts, particularly over southern portions of the CONUS where instability was greatest. Conversely, during summertime, when synoptic-scale forcing was weak and precipitation entities were small, 3- and 1-km forecasts had similar skill. These collective results differ substantially from previous work finding 4-km forecasts had comparable springtime precipitation forecast skill as 1- or 2-km forecasts over the central–eastern CONUS. Additional analyses and experiments suggest the greater benefits of 1-km forecasts documented here could be related to higher-quality initial conditions than in prior studies. However, further research is needed to confirm this hypothesis.

Spurious Grid-Scale Precipitation in the North American Regional Reanalysis

2007 ◽  
Vol 135 (6) ◽  
pp. 2168-2184 ◽  
Author(s):  
Gregory L. West ◽  
W. James Steenburgh ◽  
William Y. Y. Cheng
Keyword(s):  
North American ◽  
Prediction Models ◽  
Weather Prediction ◽  
Regional Climate ◽  
Potential Temperature ◽  
Climate Data ◽  
The North ◽  
North American Regional Reanalysis ◽  
Upper Level ◽  
Regional Reanalysis

Abstract Spurious grid-scale precipitation (SGSP) occurs in many mesoscale numerical weather prediction models when the simulated atmosphere becomes convectively unstable and the convective parameterization fails to relieve the instability. Case studies presented in this paper illustrate that SGSP events are also found in the North American Regional Reanalysis (NARR) and are accompanied by excessive maxima in grid-scale precipitation, vertical velocity, moisture variables (e.g., relative humidity and precipitable water), mid- and upper-level equivalent potential temperature, and mid- and upper-level absolute vorticity. SGSP events in environments favorable for high-based convection can also feature low-level cold pools and sea level pressure maxima. Prior to 2003, retrospectively generated NARR analyses feature an average of approximately 370 SGSP events annually. Beginning in 2003, however, NARR analyses are generated in near–real time by the Regional Climate Data Assimilation System (R-CDAS), which is identical to the retrospective NARR analysis system except for the input precipitation and ice cover datasets. Analyses produced by the R-CDAS feature a substantially larger number of SGSP events with more than 4000 occurring in the original 2003 analyses. An oceanic precipitation data processing error, which resulted in a reprocessing of NARR analyses from 2003 to 2005, only partially explains this increase since the reprocessed analyses still produce approximately 2000 SGSP events annually. These results suggest that many NARR SGSP events are not produced by shortcomings in the underlying Eta Model, but by the specification of anomalous latent heating when there is a strong mismatch between modeled and assimilated precipitation. NARR users should ensure that they are using the reprocessed NARR analyses from 2003 to 2005 and consider the possible influence of SGSP on their findings, particularly after the transition to the R-CDAS.

scholarly journals Dynamical and Physical Processes Leading to Tropical Cyclone Intensification under Upper-Level Trough Forcing

2013 ◽  
Vol 70 (8) ◽  
pp. 2547-2565 ◽  
Author(s):  
Marie-Dominique Leroux ◽  
Matthieu Plu ◽  
David Barbary ◽  
Frank Roux ◽  
Philippe Arbogast
Keyword(s):  
Angular Momentum ◽  
Tropical Cyclone ◽  
Potential Vorticity ◽  
Weather Prediction ◽  
Three Dimensional ◽  
Vertical Wind Shear ◽  
Thick Layer ◽  
Limited Area ◽  
Southwest Indian Ocean ◽  
Upper Level

Abstract The rapid intensification of Tropical Cyclone (TC) Dora (2007, southwest Indian Ocean) under upper-level trough forcing is investigated. TC–trough interaction is simulated using a limited-area operational numerical weather prediction model. The interaction between the storm and the trough involves a coupled evolution of vertical wind shear and binary vortex interaction in the horizontal and vertical dimensions. The three-dimensional potential vorticity structure associated with the trough undergoes strong deformation as it approaches the storm. Potential vorticity (PV) is advected toward the tropical cyclone core over a thick layer from 200 to 500 hPa while the TC upper-level flow turns cyclonic from the continuous import of angular momentum. It is found that vortex intensification first occurs inside the eyewall and results from PV superposition in the thick aforementioned layer. The main pathway to further storm intensification is associated with secondary eyewall formation triggered by external forcing. Eddy angular momentum convergence and eddy PV fluxes are responsible for spinning up an outer eyewall over the entire troposphere, while spindown is observed within the primary eyewall. The 8-km-resolution model is able to reproduce the main features of the eyewall replacement cycle observed for TC Dora. The outer eyewall intensifies further through mean vertical advection under dynamically forced upward motion. The processes are illustrated and quantified using various diagnostics.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

scholarly journals Model error in weather forecasting

2001 ◽  
Vol 8 (6) ◽  
pp. 357-371 ◽  
Author(s):  
D. Orrell ◽  
L. Smith ◽  
J. Barkmeijer ◽  
T. N. Palmer
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Error ◽  
Local Model ◽  
State Dependent ◽  
Rapid Divergence ◽  
Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

scholarly journals Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

2019 ◽  
Vol 147 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Louis Wicker ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Systems ◽  
State Variables ◽  
Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

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