The Effect of Weather Pattern on the Second Wave of Coronavirus: A cross study between cold and tropical climates of France, Italy, Colombia, and Brazil

This study aims to explore and understand the common belief that COVID infection rate is highly dependent on either the outside temperature and/or the humidity. Thirty-six regions/states from two humid-tropical countries, namely Brazil and Colombia and two countries with temperate climate, France and Italy, are studied over the period of October to December. Daily outside temperature, relative humidity and hospitalization/cases are analyzed using Spearman’s correlation. The eighteen cold regions of France and Italy has seen an average drop in temperature from 10°C to 6°C and 17°C to 7°C, respectively, and France recorded an addition of 2.3 million cases, while Italy recorded an addition of 1.8 million cases. Outside temperature did not fluctuate much in tropical countries, but Brazil and Colombia added 4.17 million and 1.1 million cases, respectively. Köppen–Geiger classification showed the differences in weather pattern between the four countries, and the analysis showed that there is very weak correlation between either outside weather and/or relative humidity alone to the COVID-19 pandemic.

Extreme Atlantic hurricane seasons made more likely by ocean warming

Tropical cyclones are among the most damaging and fatal extreme weather events. An increase in Atlantic tropical cyclone activity has been observed, but attribution to global warming remains challenging due to large inter-annual variability and modelling challenges. Here we show that the increase in Atlantic tropical cyclone activity since the 1980s can be robustly ascribed to changes in atmospheric circulation as well as sea surface temperature (SST) increase. Using a novel weather pattern based statistical model, we find that the forced warming trend in Atlantic SSTs over the 1982–2018 period increased the probability of extremely active tropical cyclone seasons by 14 %. Seasonal atmospheric circulation remains the dominant factor explaining both inter-annual variability and the observed increase. Our weather pattern-based statistical decomposition helps to understand the role of atmospheric variability for the Atlantic tropical cyclone activity and provides a new perspective on the role of ocean warming.

Cloud regime analysis over Central Europe based on 14 years of satellite data

<p><span>The improvement of our understanding of the spatiotemporal variability of cloud properties and their governing processes is of high importance, given the crucial role of clouds in the climate system. The availability of long-term and high-quality satellite observations together with mature remote sensing techniques has made feasible the creation of multi-decadal climate data records for this purpose.</span></p><p><span>Various cloud classification techniques have been developed and applied in the past, each with distinct advantages and disadvantages, allowing studying clouds from different perspectives. One of these techniques is the creation of cloud regimes which provides information on the prevalence of simultaneously occurring cloud types over a region. This study uses the k-means clustering method, applied to 2-dimensional histograms of cloud top pressure and optical thickness, in order to derive and analyze cloud regimes over Europe during the last decade. Europe is selected for this work because it is an appropriate region for studying cloud regimes since the prevailing atmospheric circulation patterns and its diverse geomorphology, result in a mixture of diverse cloud types. In order to achieve that, the CLoud property dAtAset using Spinning Enhanced Visible and Infrared (SEVIRI) edition 2.1 (CLAAS-2.1) data record, which is produced by the EUMETSAT Satellite Application Facility on Climate Monitoring (CM SAF), is used as basis for the derivation of the cloud regimes. In particular, pixel-level Cloud Optical Thickness (COT) and Cloud Top Pressure (CTP) products of CLAAS-2.1, from 2004 to 2017, are used in order to compute 2D histograms on a 1°×1° spatial resolution. Then the k-means clustering algorithm is applied, treating each 2D COT-CTP histogram of each grid point and time step as an individual data point. Various sensitivity studies on the subsampling of the data and the selection of the cloud regimes were carried out, in order to test the robustness of the method and of the results.</span></p><p><span>In contrast to the previous studies and taking advantage of the geostationary orbit of Meteosat Second Generation (MSG), on which SEVIRI is aboard, a better sampling of the diurnal cycle of clouds is thus included in the derivation process of cloud regimes. Furthermore, the annual cycle of the produced cloud regimes is examined. In addition, for each regime, the time step with its highest spatial frequency of occurrence is selected for a visual comparison with the corresponding RGB image. Finally, a comparison of the cloud regimes against the synoptic large scale weather pattern classification is investigated. The weather pattern classification consists of 29 typical defined patterns of the daily synoptic circulation and it is produced by the German Weather Service (DWD).</span></p>

NUMERICAL SIMULATION OF SHIP NAVIGATION IN ROUGH SEAS BASED ON ECMWF DATA

Recently, several changes have been observed in the Earth’s environment. This is also applicable to the ocean environment. The concept of weather routing has been applied for ship navigation for a long time. Many service providers offer weather routing service with the availability of high-quality satellite data. Unfortunately, not much information is available in the public domain as to how much the recent change in the weather pattern has affected ship navigation. The purpose of this paper is to fill this information gap. We investigate the influence of recent changes in the ocean environment on ship navigation. Weather data from ECMWF, namely ERA-Interim, is used for this purpose. The ECMWF data for the last 27 years is analysed. We compute the statistical characteristics of this data for the first 10 years, last 10 years, and 27 years. The statistical characteristics of the data are determined based on “summer” and “winter” zones as defined by international maritime regulations. Six different worldwide commercial ship routes are selected covering all the ocean regions. Navigation on great ellipse with waypoint is considered. MMG type ship manoeuvring model for 3 different ship types (DTMB 5415, PCC, VLCC) is used. The added resistance due to wave, wind and the effort of keeping the ship on the desired course using autopilot in the rough ocean environment is included in the MMG model. The fuel consumption and the duration of each one of the voyage are computed. Based on the analysis and simulation results it is shown that: (i) The mean wave height, wave period, and wind speed has increased in some ocean zones and decreased in other ocean zones. If any change has occurred, it is uniform for both seasons (summer and winter). (ii) In which ocean regions there is a perceptible change in fuel consumption, average ship speed and voyage time due to the changes in the weather pattern. (iii) The changing weather pattern in different ocean zones affects each ship type differently.