Synoptic Climatology of Weather Parameters Associated With Tropical Cyclone Events In The Coastal Areas of Bay of Bengal

Tropical cyclones (TCs) are the most devastating weather phenomena that trigger massive loss of property and life in the coastal areas of the Bay of Bengal (BoB). Scientific understanding of TCs occurrence can aid policy-makers and residents in coastal areas to take necessary actions and appropriate planning in advance. In this study, we aimed to examine the possible linkage of weather parameters with deadly 22 TCs events in the BoB from 1975 to 2014 using principal component analysis, K-mean clustering and General circulation model (GCMs). Results showed that among 22 TCs, cluster 1 belongs to 12 TCs which occurred under the same atmospheric situation when the sea level pressure (SLP) was below 990hPa, and the temperature ranged from 300C to 390C. A deep negative anomaly of SLP and temperature was observed up to 500hPa levels. In contrast, a negative depression was found at 300hPa geopotential height (GPH) over the study area. Cluster 2 consisted of 9 TCs when SLP was below 1000hPa, and the average temperature was 33.50C. A strong negative anomaly was noticed when surface level up to 500 hPa GPH, but dramatically this depression was completely absent at 300hPa geopotential height over the BoB and entire coastal region. Cluster 3 contained only 1 TCs when the atmospheric circumstance was completely diverse, and the SLP was above 1000hPa. The results of the GCM model revealed that the SLP was lower, and the temperature was higher over BoB compared to the North Indian Ocean. We identified the larger depression of SLP and unpredictable temperature anomalies at the upper atmosphere that can trigger an enormous unpredictability throughout the atmospheric level, leading to severe TCs. The outcomes of this study can improve our understanding of weather variables in the upper atmospheric column for forecasting the TCs system more accurately in the future.

Seasonal variations in the synoptic climatology of air pollution in Birmingham, UK

A synoptic typing approach was undertaken to examine the seasonal relationship (winter versus summer) between air mass types and pollutant concentrations of O3, PM10, NOx, NO2 and CO in Birmingham, UK, from 2000 to 2015. Daily means of seven surface meteorological variables were entered into a P-mode principal component analysis. Three principal components explained 72.2% (72.9%) of the variance in winter (summer). Cluster analysis was used to group together days with similar PC scores and thus similar meteorological conditions. Six clusters provided the best air mass classification in both seasons. High pollutant concentrations were associated with anticyclonic types. In particular, tropical (polar) continental air mass type was most likely to produce extremely high concentrations in summer (winter). In winter, a sequence of Polar Continental (cool and humid) and Binary Mid-latitude Anticyclonic Maritime—Sub-Polar Cyclonic Maritime (cold and dry) induced severe pollution episodes in all pollutants. Whilst the mean duration of severe pollution episodes varied little between winter and summer (O3 was an exception, with severe episodes lasting 20% longer in summer), high pollutant extremes were more common in winter. This was due to more favourable meteorological conditions (e.g. temperature inversions) and increased anthropogenic emissions during the cold season.

Cut-Off Lows and Extreme Precipitation in Eastern Spain: Current and Future Climate

This study presents a seasonal synoptic climatology of cut-off lows (COLs) that produced extreme precipitation in the Valencia region of Spain during 1998–2018 and uses simulations with the Weather Research and Forecasting (WRF) model to study how extreme COL precipitation may change in a future warmer climate. COLs were shown to be the main producer of extreme precipitation in the Valencia region, especially during the transition seasons. The strongest raining COL events occurred during September–November. Six-day composites of thermodynamic and dynamic fields and precipitation show that COLs that produce extreme precipitation in this region remain stationary over Spain for 2–3 days and tend to produce precipitation over the Valencia region for at least two consecutive days. In the low levels these COLs are characterized by low pressure over the Mediterranean sea and winds with an easterly, onshore component thus fueling precipitation. Comparison of current and future climate ensembles of WRF simulations of 14 September–November extreme precipitation producing COL events suggest that in a warmer climate extreme COL precipitation may increase by as much as 88% in northeastern Spain and 61% in the adjoining Mediterranean Sea. These projected increases in extreme COL precipitation in the northeast of Spain present additional challenges to a region where COL flooding already has significant socio-economic impacts. Additionally, about half of the future climate COL event simulations showed increases in precipitation in the Valencian region of eastern Spain. These results provide important nuance to projections of a decreasing trend of total precipitation in the Iberian Peninsula as the climate warms.

Seasonal Variations in the Synoptic Climatology of Air Pollution in Birmingham, United Kingdom

A synoptic typing approach was undertaken to examine the seasonal relationship (winter versus summer) between air mass types and pollutant concentrations of O 3 , PM10, NO x , NO 2 and CO in Birmingham, United Kingdom from 2000 to 2015. Daily means of seven surface meteorological variables were entered into a P-mode principal component analysis. Three principal components explained 72.2% (72.9%) of the variance in winter (summer). Cluster analysis was used to group together days with similar PC scores and thus homogeneous meteorological conditions. Six clusters provided the best air mass classification in both seasons. High pollutant concentrations were associated with anticyclonic types. In particular, tropical (polar) continental air mass type was most likely to produce extremely high concentrations in summer (winter). In winter, a sequence of Polar Continental (cool and humid) and Binary Mid-latitude Anticyclonic Maritime – Sub-Polar Cyclonic Maritime (cold and dry) induced severe pollution episodes in all pollutants. Whilst the mean duration of severe pollution episodes varied little between winter and summer (O 3 was an exception, with severe episodes lasting 20% longer in summer), high pollutant extremes were more common in winter. This was due to more favourable meteorological conditions (e.g., temperature inversions) and increased anthropogenic emissions during the cold season.

Synoptic Climatology of Lake-Effect Snow Events off the Western Great Lakes

As the mesoscale dynamics of lake-effect snow (LES) are becoming better understood, recent and ongoing research is beginning to focus on the large-scale environments conducive to LES. Synoptic-scale composites are constructed for Lake Michigan and Lake Superior LES events by employing an LES case repository for these regions within the U.S. North American Regional Reanalysis (NARR) data for each LES event were used to construct synoptic maps of dominant LES patterns for each lake. These maps were formulated using a previously implemented composite technique that blends principal component analysis with a k-means cluster analysis. A sample case from each resulting cluster was also selected and simulated using the Advanced Weather Research and Forecast model to obtain an example mesoscale depiction of the LES environment. The study revealed four synoptic setups for Lake Michigan and three for Lake Superior whose primary differences were discrepancies in a surface pressure dipole structure previously linked with Great Lakes LES. These subtle synoptic-scale differences suggested that while overall LES impacts were driven more by the mesoscale conditions for these lakes, synoptic-scale conditions still provided important insight into the character of LES forcing mechanisms, primarily the steering flow and air–lake thermodynamics.

Synoptic climatology of southern Indian Ocean and paleoclimate proxy interpretation

<p>Weather systems in the southern Indian Ocean drive synoptic-scale precipitation, temperature and wind variability in East Antarctica, sub-Antarctic islands and southern Australia.  Over seasonal to decadal timescales, the mean condition associated with combinations of these synoptic weather patterns (e.g., extratropical cyclones, fronts and regions of high pressure) is often referred to as variability in the westerly wind belt or the Southern Annular Mode (SAM). The westerly wind belt is generally considered to be zonally symmetric around Antarctica however, on a daily timescale this is not the case. To capture the daily variability of regional weather systems, we used synoptic typing (Self-Organising Maps) to group weather patterns based on similar features, which are often lost when using monthly or seasonal mean fields. We identified nine key regional weather types based on anomaly pattern and strength. These include four meridional nodes, three mixed nodes, one zonal node and one transitional node. The meridional nodes are favourable for transporting warm, moist air masses to the subantarctic and Antarctic region, and are associated with increased precipitation and temperature where the systems interact with the Antarctic coastline.  These nodes have limited association with the SAM, especially during austral spring.  In contrast, the zonal and mixed nodes were strongly correlated with the SAM however, the regional synoptic representation of SAM positive conditions is not zonally symmetric and is represented by three separate nodes.  These different types of SAM positive conditions mean that the commonly used hemispheric Marshall index often fails to capture the regional variability in surface weather conditions in the southern Indian Ocean. Our results show the importance of considering different synoptic set ups of SAM conditions, particularly SAM positive, and identify conditions that are potentially missed by SAM variability (e.g., extreme precipitation events). Our results are particularly important to consider when interpreting SAM or westerly wind belt reconstructions in the study region (from ice cores, tree rings, or lake sediments).  Here we present a case study using the synoptic typing results to enhance our understanding of the Law Dome (East Antarctica) ice core record, focussing on links to large scale modes of climate variability and Australian hydroclimate.  These results enhance the usefulness of ice core proxies in coastal East Antarctica and assist with determining where and how it is appropriate to use coastal East Antarctic ice core records for reconstructions of large scale modes of climate variability (e.g. SAM and ENSO) and remote hydroclimate conditions.</p>

The impact of atmospheric circulation on wind energy resource using self-organizing maps

<p>The sensitivity of wind to the Earth’s energy budget and the changes it causes in the climate system has a significant impact on the wind energy sector. The scope of this work is to examine the association of atmospheric circulation with the wind speed distribution characteristics on different timescales over Greece. Emphasis is given to the effect of specific regimes on the wind speed distributions at different locations. The work is based on using synoptic climatology as a tool for providing information regarding wind variability. This approach allows a more detailed description of the effect of changes in large-scale atmospheric circulation on wind energy potential. The atmospheric classification methodology, upon the selection of relevant atmospheric variables and domains, includes a Principal Components Analysis for dimension reduction purposes and subsequently, the classification is performed using an artificial neural network and in particular self-organizing maps. In the resulting feature map, the neighboring nodes are inter-connected and each one is associated with the composites of the selected large-scale variables. Upon the assignment and the characterization of each day in one of the resulting patterns, a daily catalog is constructed and frequency analysis is performed. In the context of estimating wind energy potential variability for each atmospheric pattern, the fit of multiple probability functions to the surface wind speed frequency distributions is performed. The most suitable function is selected based on a set of difference and correlation statistical measures, along with the use of goodness-of-fit statistical tests. The study employs the ERA5 reanalysis dataset with a 0.25° spatial resolution from 1979/01/01 up to 2019/12/31 and the wind field data are extracted at the 10m and the 100m levels. The approach could be valuable to the wind energy industry and can provide the required scientific understanding for the optimal siting of Wind Energy Conversion Systems considering the atmospheric circulation and the electricity interconnection infrastructure in the region. Considering the emerging issue of energy safety, accurate wind energy production estimates can contribute towards the establishment of wind as the primary energy source and in meeting the increasing energy demand.</p>