Letteratura scientifica selezionata sul tema "Cyclogenesis indices"

The influence of the Madden–Julian oscillation (MJO) over tropical Africa and Atlantic is explored during the Northern Hemisphere summer months. The MJO is assessed by using real-time multivariate MJO (RMM) indices. These indices divide the active convective signal of the MJO into 8 phases. Convection associated with the MJO is enhanced over tropical Africa during RMM phases 8, 1, and 2. Convection becomes suppressed over tropical Africa during the subsequent RMM phases (phases 3–7). African convective signals are associated with westward-propagating equatorial Rossby waves. The MJO modulates African easterly wave (AEW) activity. AEW activity is locally enhanced during RMM phases 1–3 and suppressed during RMM phases 6–8. Enhanced AEW activity occurs during periods of enhanced convection over tropical Africa, consistent with stronger or more frequent triggering of AEWs as well as more growth associated with latent heat release. Enhanced AEW activity occurs during the low-level westerly wind phase of the MJO, which increases the cyclonic shear on the equatorward side of the AEJ, increasing its instability. Atlantic tropical cyclogenesis frequency varies coherently with the MJO. RMM phases 1–3 show the greatest frequency of tropical cyclogenesis events whereas phases 7 and 8 show the least. RMM phase 2 is also the most likely phase to be associated with a train of three or more tropical cyclones over the tropical Atlantic. This observed evolution of tropical cyclogenesis frequency varies coherently with variations in AEW activity and the large-scale environment.

Abstract Variable selection for short-term forecasting (up to 72 h) of tropical cyclone (TC) genesis has been investigated. IBTrACS data (1979–2014) are used to identify the genesis time and position of over 2500 TCs between 30°N and 30°S. Tracks are extended using a tropical cloud cluster (TCC) dataset, which is also used to identify over 28 000 nondeveloping TCCs. Subsequently, corresponding local environment states at various atmospheric pressure levels are retrieved from ERA-Interim data. An initial selection of potentially favorable variables for TC genesis is made based on mutual information, which forms the set of nodes for graphical model structure learning using the Peter–Clark (PC) algorithm. Structure learning identifies the variables with the strongest influence on TC genesis, while taking into account the interrelationship with other variables. Variables are ranked based on the maximum observed p value in all (conditional) independence tests of the variable with the TC genesis node. The results indicate that potential vorticity (600 hPa), relative vorticity (925 hPa), and (vector) vertical wind shear (200–700 hPa) are the highest ranked variables for forecasting up to 72 h. These are followed by the basin and zonal wind speed (200 hPa), and for very short lead-time divergence (925 hPa), air temperature (300 hPa), and average vertical velocity. Predictive modeling with logistic regression confirms the superior performance of the top-ranked variables. The presented variable ranking (methodology) can be used as a building block for the creation of genesis indices or predictive models in the future.

Abstract A real-time statistical model based on the work of Leroy and Wheeler is developed via multiple logistic regression to predict weekly tropical cyclone activity over the Atlantic and east Pacific basins. The predictors used in the model include a climatology of tropical cyclone genesis for each ocean basin, an El Niño–Southern Oscillation (ENSO) index, and two indices representing the propagating Madden–Julian oscillation (MJO). The Atlantic model also includes a predictor representing the variability of sea surface temperature (SST) in the Main Development Region (MDR). These predictors are suggested as useful for the prediction of tropical cyclogenesis based on previous work in the literature and are further confirmed in this study using basic statistics. Univariate logistic regression models are generated for each predictor in each region to ensure the choice of prediction scheme. Using all predictors, cross-validated hindcasts are developed out to a seven-week forecast lead. A formal stepwise predictor selection procedure is implemented to select the predictors used in each region at each forecast lead. Brier skill scores and reliability diagrams are used to assess the skill and dependability of the models. Results show an increase in model skill over the time-varying climatology at predicting tropical cyclogenesis by the inclusion of the MJO out to a three-week forecast lead for the east Pacific and a two-week forecast lead for the Atlantic. The importance of ENSO and MDR SST for Atlantic genesis prediction is highlighted, and the uncertain effects of ENSO on east Pacific tropical cyclogenesis are revisited.

Abstract. The aim of this paper is to analyse indices of extreme precipitation in Krivošije, Montenegro, the wettest Mediterranean region, from the period 1951–2007 and their relationships with atmospheric circulation using "SynopVis Grosswetterlagen" (SVG) series. Data from two stations were analysed, namely Crkvice (42°34'N and 18°39'E) and Herceg Novi (42°27'N and 18°31'E). Four indices of precipitation extremes (SDII, R75p, R95p, R95pTOT) were assessed including number of dry days. The results suggest that the number of days with precipitation decreased. To analyse the relationship between extreme precipitation events and circulation types we have used an efficiency coefficient (Ec). Regarding relation to atmospheric circulation, westerly, southwesterly and northwesterly circulation types with anticyclonic features over Central Europe are more frequent for dry days (days with R<1.0 mm) and northerly, easterly and southerly types for wet and very wet days (R75p and R95p indices). The types with cyclonic condition over Central Europe show a large proportion of wet and very wet days. Also, activity of Genoa cyclogenesis and orographic influence over a small area are the main reasons for the high precipitation amounts recorded in the Krivošije region (Crkvice).

An important environmental control of both tropical cyclone intensity and genesis is vertical wind shear. One hypothesized pathway by which vertical shear affects tropical cyclones is midlevel ventilation—or the flux of low-entropy air into the center of the tropical cyclone. Based on a theoretical framework, a ventilation index is introduced that is equal to the environmental vertical wind shear multiplied by the nondimensional midlevel entropy deficit divided by the potential intensity. The ventilation index has a strong influence on tropical cyclone climatology. Tropical cyclogenesis preferentially occurs when and where the ventilation index is anomalously low. Both the ventilation index and the tropical cyclone's normalized intensity, or the intensity divided by the potential intensity, constrain the distribution of tropical cyclone intensification. The most rapidly intensifying storms are characterized by low ventilation indices and intermediate normalized intensities, while the most rapidly weakening storms are characterized by high ventilation indices and high normalized intensities. Since the ventilation index can be derived from large-scale fields, it can serve as a simple and useful metric for operational forecasts of tropical cyclones and diagnosis of model errors.

AbstractThe evolution of tropical cyclone activity under climate change remains a crucial scientific issue. Physical theory of cyclogenesis is limited, observational datasets suffer from heterogeneities in space and time, and state-of-the-art climate models used for future projections are still too coarse (~100 km of resolution) to simulate realistic systems. Two approaches can nevertheless be considered: 1) perform dedicated high-resolution (typically <50 km) experiments in which tropical cyclones can be tracked and 2) assess cyclone activity from existing low-resolution multimodel climate projections using large-scale indices as proxies. Here we explore these two approaches with a particular focus on the southern Indian Ocean. We first compute high-resolution experiments using the rotated-stretched configuration of our climate model (CNRM-CM6-1), which is able to simulate realistic tropical cyclones. In a 2-K warmer world, the model projects a 20% decrease in the frequency of tropical cyclones, together with an increase in their maximum lifetime intensity, a slight poleward shift of their trajectories, and a substantial delay (about 1 month) in the cyclone season onset. Large-scale indices applied to these high-resolution experiments fail to capture the overall decrease in cyclone frequency, but are able to partially represent projected changes in the spatiotemporal distribution of cyclone activity. Last, we apply large-scale indices to multimodel CMIP5 projections and find that the seasonal redistribution of cyclone activity is consistent across models.

Abstract For years, various indices of seasonal West African precipitation have served as useful predictors of the overall tropical cyclone activity in the Atlantic Ocean. Since the mid-1990s, the correlation unexpectedly deteriorated. In the present study, statistical techniques are developed to describe the nonstationary nature of the correlations between annual measures of Atlantic tropical cyclone activity and three selected West African precipitation indices (namely, western Sahelian precipitation in June–September, central Sahelian precipitation in June–September, and Guinean coastal precipitation in the preceding year’s August–November period). The correlations between these parameters are found to vary over the period from 1921 to 2007 on a range of time scales. Additionally, considerable year-to-year variability in the strength of these correlations is documented by selecting subsamples of years with respect to various meteorological factors. Broadly, in years when the environment in the main development region is generally favorable for enhanced tropical cyclogenesis (e.g., when sea surface temperatures are high, when there is relatively little wind shear through the depth of the troposphere, or when the relative vorticity in the midtroposphere is anomalously high), the correlations between indices of West African monsoon precipitation and Atlantic tropical cyclone activity are considerably weaker than in years when the overall conditions in the region are less conducive. Other more remote climate parameters, such as the phase of the Southern Oscillation, are less effective at modulating the nature of these interactions.

Abstract Tropical cyclones (TCs) generated over the Arabian Sea can cause significant damage to infrastructure, human lives, landfall, and property near inshore and maritime trade route areas. A key to successful prediction of TCs is a skillful prediction of potential cyclogenesis locations. This study focuses on evaluating three genesis potential indices (GPIs) derived from a global reanalysis (ERA5) and dynamically downscaling using a regional model (WRF) for two TC cases: Gonu in 2007 and Kyarr in 2019, selected by analyzing the accumulated cyclone energy trend from the International Best Track Archive for Climate Stewardship (IBTrACS) dataset over the period of 1981–2019. The two TCs belong to category 4 and above on the Saffir–Simpson scale. To test the sensitivity of downscaling to cumulus parameterizations, two WRF experiments were conducted using the Kain–Fritsch and New Tiedke cumulus schemes, respectively. The calculated genesis locations with help of the three GPIs were compared with IBTrACS. The results show that 1) all indices have reasonable skills in reproducing genesis locations, although their performances differ somewhat; 2) the dynamic downscaling with two WRF experiments added value to the study by comparing two numerical schemes for estimating genesis locations; and 3) WRF with the New Tiedke and Kain–Fritsch schemes showed good skill in reproducing the spatial distribution of the most relevant dynamical parameters. The pattern correlations are well correlated with environmental parameters of untransformed GPI and higher correlations with binary logarithmic transformed GPI. The applicability to other cyclones is also tested (e.g., TC Nilofar in 2014) with encouraging results. This study demonstrates the usefulness of GPIs for forecasting TC genesis in the region. Significance Statement The trend analysis of accumulated cyclone energy (ACE) over the Arabian Sea (AS) shows an increase over the period of 1981–2019 with the highest ACE values for 2019. The genesis potential indices (GPI) show strong ability for use as a forecasting tool for tropical cyclone genesis, and hence, are helpful for providing a reference for future studies. WRF experiments were able to reproduce the GPI with slight differences from the observations and ERA5. WRF schemes show good performance in reproducing key meteorological fields. The analysis of the GPI and WRF schemes shows the potential to be implemented for maritime forecasts of the tropical cyclones in the region. This study will be helpful scientifically and strategically with a significant impact on socioeconomic activities in the region.

Abstract The clustering in time (seriality) of extratropical cyclones is responsible for large cumulative insured losses in western Europe, though surprisingly little scientific attention has been given to this important property. This study investigates and quantifies the seriality of extratropical cyclones in the Northern Hemisphere using a point-process approach. A possible mechanism for serial clustering is the time-varying effect of the large-scale flow on individual cyclone tracks. Another mechanism is the generation by one “parent” cyclone of one or more “offspring” through secondary cyclogenesis. A long cyclone-track database was constructed for extended October–March winters from 1950 to 2003 using 6-h analyses of 850-mb relative vorticity derived from the NCEP–NCAR reanalysis. A dispersion statistic based on the variance-to-mean ratio of monthly cyclone counts was used as a measure of clustering. It reveals extensive regions of statistically significant clustering in the European exit region of the North Atlantic storm track and over the central North Pacific. Monthly cyclone counts were regressed on time-varying teleconnection indices with a log-linear Poisson model. Five independent teleconnection patterns were found to be significant factors over Europe: the North Atlantic Oscillation (NAO), the east Atlantic pattern, the Scandinavian pattern, the east Atlantic–western Russian pattern, and the polar–Eurasian pattern. The NAO alone is not sufficient for explaining the variability of cyclone counts in the North Atlantic region and western Europe. Rate dependence on time-varying teleconnection indices accounts for the variability in monthly cyclone counts, and a cluster process did not need to be invoked.

En raison de leurs impacts dévastateurs sur les populations et les infrastructures des pays concernés, l'évolution future de l'activité cyclonique tropicale dans le contexte du réchauffement climatique est une question de grande importance. Deux méthodes existent pour évaluer l'activité cyclonique tropicale en changement climatique dans les modèles de climat : l'utilisation d'algorithmes de détection (traqueurs) de cyclones ou l'utilisation d'indices de cyclogénèse qui traduisent des relations statistiques liant l'activité cyclonique observée à des variables atmosphériques de grande échelle. Ces deux méthodes tendent à fournir des projections opposées dans les simulations climatiques. Motivée par ce désaccord, cette thèse propose alors d'explorer ces deux approches dans le but d'apporter des améliorations à chacune d'elles. Dans un premier temps, le traqueur de cyclones tropicaux du CNRM est appliqué à la réanalyse ERA5 du Centre européen pour les prévisions météorologiques à moyen terme, et évalué à l'aide de la base de données d'observations des cyclones IBTrACS. Ses performances sont évaluées en termes de probabilité de détection et de taux de fausses alarmes (POD et FAR), après optimisation des paramètres de détection, et en appliquant un filtre des systèmes de moyennes latitudes adéquat. Plusieurs métriques d'évaluation de la similarité des trajectoires détectées dans ERA5 avec celles observées sont ensuite proposées puis comparées. Ces métriques novatrices sont complémentaires au POD et au FAR et montrent que l'optimisation des paramètres de détection s'accompagne d'une légère amélioration de la similarité des trajectoires. De nouveaux indices de cyclogénèse sont ensuite construits sur ERA5 par régression de Poisson entre des prédicteurs de grande échelle thermiques et dynamiques, et la base de données IBTrACS. Les régressions sont déclinées à différentes résolutions spatiales et temporelles ainsi qu'à l'échelle globale et pour les différents bassins océaniques. La résolution temporelle accrue permet la correction du biais équatorial présent dans les indices les plus communément utilisés. La variabilité interannuelle des indices apparaît cependant robuste aux modifications apportées aux coefficients de pondération des variables de grande échelle. Suite à ce constat, l'apport de l'ajout de prédicteurs dans les régressions est évalué sur ERA5 ainsi que dans le modèle ARPEGE ; d'une part en ajoutant explicitement un diagnostique du mode de variabilité El Niño (ENSO) dans l'indice, et d'autre part en remplaçant l'humidité relative à 600 hPa par le déficit de saturation d'humidité intégré sur la colonne (VPD). L'ajout du diagnostique ENSO permet alors d'améliorer la variabilité interannuelle de l'indice dans la plupart des bassins océaniques. Les corrélations avec les séries observées est rendue statistiquement significative au seuil de 95% dans tous les bassins à l'exception du Nord Atlantique. L'utilisation du VPD permet quant à elle d'annuler les tendances à la hausse dans la période historique observée dans les indices basés sur l'humidité relative. L'indice obtenu présente donc un meilleur accord avec les observations. Lorsqu'appliqué à des simulations climatiques ARPEGE à très haute résolution, sous le scénario RCP8.5, le VPD amplifie également la diminution de l'activité cyclonique
Given their devastating impact on the populations and infrastructures of the countries concerned the future evolution of tropical cyclone activity in the context of global warming is an issue of great importance. Two methods exist for assessing tropical cyclone activity under climate change in climate models: the use of cyclone detection algorithms (trackers) or the use of cyclogenesis indices, which translate statistical relationships linking observed cyclone activity to large-scale atmospheric variables. These two methods tend to provide opposite projections in climate simulations. Motivated by this disagreement, this thesis proposes to explore these two approaches, with the aim of making improvements to each. Firstly, the CNRM tropical cyclone tracker is applied to the ERA5 reanalysis of the European Centre for Medium-Range Weather Forecasts, and evaluated using the IBTrACS database of cyclone observations. Its performance is evaluated in terms of detection probability and false alarm rate (POD and FAR), after optimizing detection parameters and applying an appropriate mid-latitude system filter. Several metrics for assessing the similarity of the tracks detected in ERA5 with those observed are then proposed and compared. These innovative metrics are complementary to POD and FAR, and show that optimizing detection parameters is accompanied by a slight improvement in track similarity. New cyclogenesis indices are then constructed on ERA5 by Poisson regression between large-scale thermal and dynamic predictors, and the IBTrACS database. The regressions are run at different spatial and temporal resolutions, as well as on a global scale and for different ocean basins. The increased temporal resolution enables the equatorial bias present in the most commonly used indices to be corrected. However, the interannual variability of the indices appears to be robust to changes in the weighting coefficients of the large-scale variables. Following this observation, the contribution of adding predictors to the regressions is evaluated on ERA5 as well as in the ARPEGE model; on the one hand by explicitly adding a diagnostic of the El Niño (ENSO) variability mode to the index, and on the other hand by replacing the relative humidity at 600 hPa by the integrated moisture saturation deficit on the column (VPD). The addition of ENSO diagnostics improves the interannual variability of the index in most ocean basins. Correlations with observed series are made statistically significant at the 95% threshold in all basins except the North Atlantic. The use of the VPD cancels out the upward trends in the historical period observed in indices based on relative humidity. The resulting index is therefore in better agreement with observations. When applied to very high-resolution ARPEGE climate simulations, under the RCP8.5 scenario, the VPD also amplifies the decrease in cyclonic activity