Weather Types Research Articles

Clustering Analysis of Autumn Weather Regimes in the Northeast United States

AbstractA k-means clustering method is applied to daily ERA5 500-hPa heights, sea level pressure, and 850-hPa winds, 1979–2008, to identify characteristic weather types (WTs) for September–November for the northeast United States. The resulting WTs are analyzed in terms of structure, frequency of occurrence, typical progressions, precipitation and temperature characteristics, and relation to teleconnections. The WTs are used to make a daily circulation-based distinction between early and late autumn and consider shifts in seasonality. Seven WTs are identified for the autumn season, representing a range of trough and ridge patterns. The largest average values of precipitation and greatest likelihood of extremes occur in the Midwestern Trough and Atlantic Ridge patterns. The greatest likelihood of extreme temperatures occurs in the Northeast Ridge. Some WTs are strongly associated with the phase of the North Atlantic Oscillation and Pacific–North America pattern, with frequency of occurrence for several WTs changing by more than a factor of 2. The two most common progressions between the WTs are one most frequent in September, Mid-Atlantic Trough to Northeast Ridge to Mid-Atlantic Trough, and one most frequent in mid-October–November, Midwestern Trough to Northeast Trough to Midwestern Trough. This seasonality allows for a daily WT-based distinction between early and late season. A preliminary trend analysis indicates an increase in early season WTs later in the season and a decrease in late season WTs earlier in the season; that is, a shift toward a longer period of warm season patterns and a shorter, delayed period of cold season patterns.

Cyclone wind hazard assessments for eastern coastal cities in India using a fast-predictive model

Tropical cyclone wind-induced hazards for the eastern coastal built environments of India can be severe. The extreme cyclone winds can cause damage to structures and infrastructures and losses of lives and economies. The current building code of India provides design wind speeds for the eastern coastal cyclone-prone regions. These design wind speeds were determined without considering extreme wind speeds associated with different weather types, such as thunderstorms or cyclones. The cyclone simulation technique has not been considered to update the design wind speeds for India's eastern coastal cyclone-prone regions. Several previous studies used the regional maximum sustained wind speeds to derive the design level cyclone-induced wind speeds. This approach differs from advanced cyclone simulation techniques and provides overly conservatism. The effect of using the regional maximum sustained wind speeds of cyclones in estimating the design wind speeds was investigated quantitatively in this study. A fast-predictive cyclone wind hazard model was proposed to assess the cyclone wind hazards. This model used simple wind field modeling and directly applied the historical best track datasets. The performance of this model was validated by comparing the predicted design level tropical cyclone wind speeds for selected coastal cities in the USA and China to those provided by the local building code or reported in previous studies using advanced tropical cyclone simulation models. This fast-predictive model was used to calculate the peak cyclone wind speeds and predict the 50-year return period cyclone wind speeds for twenty eastern coastal cities in India. The predicted 50-year return period cyclone wind speeds using the fast-predictive model were compared with those predicted using the regional maximum sustained wind speeds and current code design wind speeds.

Recent changes in the atmospheric circulation patterns during the dry-to-wet transition season in south tropical South America (1979-2020): Impacts on precipitation and fire season

AbstractWe analyze the characteristics of atmospheric variations over tropical South America using the pattern recognition framework of weather typing or atmospheric circulation patterns (CPs). During 1979-2020, nine CPs are defined in the region, using a k-means algorithm based on daily unfiltered 850 hPa winds over 0035°N-30°S, 90°W-30°W. CPs are primarily interpreted as stages of the annual cycle of the low-level circulation. We identified three “winter” CPs (CP7, CP8 and CP9), three “summer” CPs (CP3, CP4 and CP5) and three “transitional” CPs (CP1, CP2 and CP6). Significant long-term changes are detected during the dry-to-wet transition season (July-October) over south tropical South America (STSA). One of the wintertime patterns (CP9) increases from 20% in the 1980s to 35% in the last decade while the “transitional” CP2 decreases from 13% to 7%. CP9 is characterized by enhancement of the South American Low-Level Jet and increasing atmospheric subsidence over STSA. CP2 is characterized by southerly cold-air incursions and anomalous convective activity over STSA. The years characterized by high (low) frequency of CP9 (CP2) during the dry-to-wet transition season are associated with a delayed South American Monsoon onset and anomalous dry conditions over STSA. Consistently, a higher frequency of CP9 intensifies the fire season over STSA (1999-2020). Over the Brazilian states of Maranhão, Tocantins, Goiás and São Paulo, the seasonal frequency of CP9 explains around 35%-44% of the interannual variations of fire counts.

Circulation weather types and hospital admissions for cardiovascular disease in Changchun, China

Epidemiological studies have reported significant associations between weather situations and health. Cardiovascular disease is a serious chronic non-communicable disease which causes mortality and morbidity, bringing large economic burden to patients' families. This study explored the relationship between cardiovascular disease (CVD) and weather conditions in Changchun, northeast China. The frequency distributions of 13 main circulation weather types (CWTs) were analyzed, and a comparison between air mass classification and hospital admissions was performed for various groups using an admission index (AI). The results indicated that women had a lower risk of CVD than men did. The risk of CVD for older people (aged ≥ 65years) was lower than that for young people (aged < 65years). Younger men had the highest risk. The risks of CVD were higher in all groups (i.e., men, women, older, and younger) under southwesterly (SW) and northerly (N) CWTs and were lowest under the anticyclone (A) CWT. The risk of CVD among men was higher than that for women under these CWTs. N type circulation is characterized by cold, dry weather and was most closely associated with an increased incidence of CVD. The most significant effect of N type circulation on AI was observed with a delay of 2days. SW type circulation is characterized by humid, hot weather and was the CWT that was second most closely associated with an increased incidence of CVD, with a peak in AI on the day that SW type circulation occurred. The results of this study could be provided to local health authorities as scientific guidelines for controlling and preventing CVD in Changchun, China.

All‐factor short‐term photovoltaic output power forecast

Accurately predicting photovoltaic output power is one of the most important basic tasks for the rapid development of the smart grid. The factors that influence photovoltaic output power are not fully considered in the current forecast model. To this end, an all-factor photovoltaic short-term output power prediction model based on improved ensemble empirical mode decomposition (EEMD) and the seeker optimization algorithm (SOA) -BP neural network model is proposed here. First, different similarity algorithms are used in the selection of similar days of different weather types. Second, the factors that affect the PV output power are divided into deterministic parameters and stochastic parameters. The stochastic parameters of similar daily samples are used to constitute a time series, which is decomposed into several stationary signals by EEMD, and then, these signals are trained separately by the SOA-BPNN model. Finally, the final forecast result can be synthesized with the deterministic parameters and the accumulation of the predicted data of individual signals. Compared with the results of different prediction models, the prediction accuracy of the proposed model is much higher.

Concurrent heatwaves and extreme Ozone (O3) episodes: combined atmospheric patterns and impact on human health

BACKGROUND AND AIM: In the context of the global environmental change, more recurrent and/or intense heat waves and extreme ozone episodes are likely to occur during the next decades. The main purpose of this research is to assess how extreme temperature may trigger the appearance of high ozone levels and how the concurrence of these two extremes could be linked to the synoptic general circulation and impacts human health and wellbeing. METHODS: We have used surface maximum temperature and ozone observations during extended summers (April–September) in two cities from Morocco: Casablanca and Marrakech, between 2010 and 2019. A percentile-thresholding method was applied to identify heatwaves and ozone episodes and a statistical approach was used to assess the connection between the observed parameters and climate indexes (North Atlantic Oscillation (NAO), Mediterranean Oscillation (MO), and Saharan Oscillation (SaOI)). The weather types behind the concurrence of extremes were identified and Heat and Air Quality Indexes were assessed to determine their combined health risks. RESULTS:Our findings show that the concurrence of heatwaves and ozone episodes depends both on the specific city and the large-scale atmospheric circulation. The likely synoptic pattern behind the concurrence of these events occurs when the country is under the combined influence of an anticyclonic area in the north and the Saharan trough extending the depression centered in the south. This pattern generates a warm southern flow and may foster photochemical pollution and then impact human health. CONCLUSIONS:Our study is an attempt to assess extreme temperature and ozone episodes combined features inducing large-scale atmospheric circulation and health effects. It explores the hypothesis that particular weather patterns increase the vulnerability of individuals especially those sensitive to air pollution. This work is a first step to the establishment of an alert system and will help to provide recommendations for coping with concurrent heatwaves and air pollution episodes. KEYWORDS: Ozone, Temperature extremes, North Atlantic Oscillation, Mediterranean Oscillation, Saharan Oscillation

A comparison of different approaches for the definition of seasons in the Mediterranean region

AbstractΑ new method, based on the definition of weather types (WTs), for the determination of seasons in the Mediterranean region is adopted. This method combines principal component analysis and k‐means Cluster Analysis on daily grid point meteorological data for the 70‐year period 1949–2018 obtained from the NCEP/NCAR database. At first, the method is applied to the classification of WTs and subsequently to the definition of seasons based on the mean intra‐annual variations of the WTs' frequency. The resulting seasons are compared to a previous method of the seasons' definition, based on the long‐term mean intra‐annual variations of climatological parameters. Furthermore, a synthesis of the two methods is proposed. The analysis is applied not only to the 70‐year period 1949–2018, but also to the five overlapping 30‐year sub‐periods 1949–1978, 1959–1988, 1969–1998, 1979–2008, 1989–2018 in order to investigate possible long‐term changes of the seasons' characteristics. Four seasons are identified for all periods and for both methodological approaches. These seasons broadly correspond to the four conventional ones, but they differ in their onset and cessation dates as well as their duration. For the composite method, it is found that winter lasts about 4 months, summer is a little longer than 3 months and spring and autumn last around 2.5 months. The most remarkable findings regarding the seasons' characteristics are: (a) the start/end dates and duration of winter are almost identical in all three approaches, while summers' characteristics differ; (b) shortening of winter and spring in recent periods, associated with the delayed start and expedited end dates, respectively; and (c) the prolongation of autumn and summer, due to the delay of its end date, and the earlier onset and delayed end dates, respectively.

Hygroscopic properties of particulate matter and effects of their interactions with weather on visibility

The hygroscopic property of particulate matter (PM) influencing light scattering and absorption is vital for determining visibility and accurate sensing of PM using a low-cost sensor. In this study, we examined the hygroscopic properties of coarse PM (CPM) and fine PM (FPM; PM2.5) and the effects of their interactions with weather factors on visibility. A censored regression model was built to investigate the relationships between CPM and PM2.5 concentrations and weather observations. Based on the observed and modeled visibility, we computed the optical hygroscopic growth factor, fleft( {RH} right), and the hygroscopic mass growth, GM_{VIS}, which were applied to PM2.5 field measurement using a low-cost PM sensor in two different regions. The results revealed that the CPM and PM2.5 concentrations negatively affect visibility according to the weather type, with substantial modulation of the interaction between the relative humidity (RH) and PM2.5. The modeled fleft( {RH} right) agreed well with the observed fleft( {RH} right) in the RH range of the haze and mist. Finally, the RH-adjusted PM2.5 concentrations based on the visibility-derived hygroscopic mass growth showed the accuracy of the low-cost PM sensor improved. These findings demonstrate that in addition to visibility prediction, relationships between PMs and meteorological variables influence light scattering PM sensing.

Photocatalytic paints for NOx removal: Influence of various weathering conditions

Three commercial exterior photocatalytic paints, from STO, Boysen and Graphenstone and one In-house paint, are tested using the NO ISO; FT-IR analysis shows that all paints contain an acrylate binder, but the commercial paints also contain siloxane and/or silicate binders. All exhibit little or no photocatalytic activity (for NO removal) without weathering. The Graphenstone and In-house paints are only activated by ‘standard’ weathering, i.e. a continuous cycle of UV irradiation (0.76 mW cm-2, 8 h at 50 °C) plus condensation (4 h at 60 °C). In contrast, the STO and Boysen paints are also activated by ‘condensation-only’ weathering by water droplets at 60 °C for ca. 56 h. This is the first report of the efficacy of the latter type of weathering. The activity vs weathering time profiles for the STO photocatalytic paint show that it is almost exclusively activated by condensation-only weathering, which is fast (60% maximum activity within 8 h) and produces a film that is active and durable. In contrast, the In-house paint is only activated by standard weathering, which is relatively slow (only 25% maximum activity after 24 h) and involves the photocatalytic oxidation of the acrylate binder; as a consequence the In-house paint film is not very durable. Irrespective of the method of activation, all the photocatalytic paints exhibit little, or no, NOx removal activity towards the end of the 5 h irradiation period of the NO ISO, due to the accumulation of surface HNO3, which promotes the conversion of NO to NO2.

Precambrian paleosols on the Great Unconformity of the East European Craton: An 800 million year record of Baltica’s climatic conditions

Meso- and Neoproterozoic paleosols, collected from different areas of the East European Craton: Belarus, Estonia, Lithuania, and Ukraine, offer a chance to examine continental weathering sequences from early to advanced stages of weathering on a variety of different parent materials such as gneisses, granites, gabbros and amphibolites. They were studied using quantitative XRD of the bulk rock, XRD and Mössbauer of the clay fractions, microscopic, geochemical, carbonate stable isotopes and carbonate U-Pb geochronology methods. These paleosol profiles are on average 10 m thick, reddish coloured, and many of them are characterized by a well-developed and well-defined alteration sequence with uppermost horizons approaching the lateritic stage, as indicated by the Chemical Index of Alteration values reaching 90. The dominant type of weathering leads towards kaolinite and a Fe-oxide/hydroxide mineral assemblage through a smectitic intermediate stage. In the paleosol profiles developed on mafic parent materials, dioctahedral smectite is the first weathering product at the base, it dominates in the middle-upper horizons and it later becomes unstable and alters into kaolinite; whereas in paleosols developed on felsic parent materials kaolinite forms already at the initial stage of weathering, as a result of Na-plagioclase dissolution. Kaolinite content in the uppermost horizons reaches 34 wt% in the best developed profile, and Fe-minerals (hematite and goethite) show a clear increasing trend towards the top, reaching 12 wt%. It is likely that uppermost kaolinite-dominated horizons, which are lacking in some profiles, have been eroded. Such paleosol composition and ferric composition of smectites indicate oxidative weathering and are interpreted to represent a warm and humid climate, which seems to have prevailed on the EEC over the Meso- and Neoproterozoic, except well-documented glacial periods. The δ13C signatures of the pedogenic carbonates document microbial processes in the paleosols, which is also indicated by the elevated U/Th values in their top layers. One paleosol was dated with in situ pedogenic calcite U-Pb geochronology at 655 ± 45 Ma (2σ), confirming the estimate based on its stratigraphic position. The Proterozoic paleosol profiles, and dioctahedral smectite in particular, remained unaltered for over 900 Ma until the Paleozoic, when they were affected by low-temperature (<110 °C) diagenesis, evidenced by the presence of K-Ar dated illite–smectite and aluminoceladonite. The maximum and the most common degree of the dioctahedral smectite illitization is 26% S; the %S zonation indicates that the illitizing fluids invaded paleosols from the overlying sediments.

A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data

In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) network. For depicting the effectiveness of our proposed schema, a regularization technique for feature selection so as to avoid overfitting is implemented. Several classification models covering three different categorization methods namely the Bayesian, decision trees, and meta/ensemble methods, have been investigated in a real dataset. The experimental analysis illustrates that the utilization of the regularization technique could offer a significant boost in forecasting performance.

Precipitation Characteristics of Warm Season Weather Types in the Southeastern United States of America

Daily weather types (WTs) over the Southeast United States have been analyzed using 850 hPa winds from reanalysis data from March to October of 1979–2019. Six WTs were obtained. WTs 1–3 represent mid-latitude synoptic systems propagating eastward. WT4 is a summer-type pattern predominantly occurring in June–August, with the center of the North Atlantic Subtropical High (NASH) along the Gulf coast in the southern United States. WT5 is most frequent from August to middle October, with the NASH pushed further north and southerly winds over the northern Great Plains. An anticyclone centered at the Carolina coast characterizes WT6, which occurs in all months but is slightly more frequent in the spring and fall, especially in October, corresponding to fair weather in the region. WTs 1, 2 and 3 can persist for only a few days. WTs 4, 5 and 6 can have long spells of persistence. Besides self-persistence, the most observed progression loop is WT1 to WT2, to WT3, and then back to WT1, corresponding to eastward-propagating waves. WTs 4 and 5 are likely to show persistence, with long periods of consecutive days. WT6 usually persists but can also transfer to WT3, i.e., a change from fair weather in the Southeast U.S. to rainy weather in the Mississippi River Valley. A diurnal cycle of precipitation is apparent for each WT, especially over coastal plains. The nocturnal precipitation in central U.S. is associated with WT3. WTs 1–3 are more frequent in El Niño years, corresponding to stronger westerly wave activities and above normal rainfall in the Southeast U.S. in the spring. The positive rainfall anomaly in the Mississippi and Ohio River valley in El Niño years is also associated with more frequent WT3.

A Modeling Study on the Downslope Wind of “Katevatos” in Greece and Implications for the Battle of Arachova in 1826

Downslope winds and lee gravity waves are common features of mountainous environments. A similar weather type at Mt. Parnassos in Arachova, Greece is known as “Katevatos” and has devastating results for the population and visitors at the local touristic resorts. In this study, we analyze three incidents of this atmospheric pattern at local scale resolution (1 × 1 km) with WRF model. This is the first study of this local weather hazard, and the following key factors are identified. (I) The main synoptic forcing is the propagation of an upper-level trough from central Europe towards the Balkans. (II) The associated generation of a surface low-pressure system over the Aegean Sea results in a northeast flow in the lower troposphere that is perpendicular to the main topographic ridge of Mt. Parnassos. (III) Generation of gravity waves and downward reflection of wave energy at the critical level between the upper level flow and the undercutting northeast current result in the formation of “Katevatos” downslope wind at the lee side of the mountain. This hurricane-scale wind is accompanied with horizontal transport of frozen rain and snow from the mountain tops towards the village of Arachova. This wind pattern appeared also during the battle of Arachova in November 1826 between the Greek and Ottoman forces resulting in enormous casualties due to the adverse weather conditions.

Statistical downscaling of daily precipitation over southeastern South America: Assessing the performance in extreme events

AbstractThe performance of multiple empirical statistical downscaling (ESD) methods was assessed for simulating daily precipitation during 1979–2017 over southeastern South America (SESA), a region where extremes are remarkable. Meteorological stations were used as reference and three gridded precipitation products were included to account for observational uncertainty. The set of ESD models involved different configurations of the analogue method (ANs), deterministic and stochastic versions of neural networks (NNs) and generalized linear models (GLMs) and circulation‐conditioned GLMs (GLM_WTs). The years with the largest number of extreme events (wet years) were calibrated separately. The spatio‐temporal variability of extremes was assessed by analysing their intensity, spatial extent, frequency and interannual variability. An overall good performance of the ESD models was found for several aspects of daily precipitation. ESD performance dispersion was usually contained in the observational spread. No particular model configuration was found to perform best in all aspects, indicating the advantage of considering a multi‐model ensemble. The ANs tended to follow the stations, satisfactorily simulating daily precipitation and its extremes. The deterministic GLMs strongly underestimated precipitation estimates and were not able to represent extreme frequencies and intensities, but this was alleviated by employing a stochastic version of the method. Furthermore, the use of weather types to condition the GLMs (GLM_WTs) considerably improved model performance, particularly for the annual cycle and the spatial structure of extreme precipitation. The NNs adequately reproduced the spatial behaviour and intra‐annual variability of extreme precipitation, although they underestimated its intensity in their deterministic version. An analysis of the regional time series of extremes showed consistency among datasets and evidenced the influence of the ENSO teleconnection on the wet years, which were commonly well‐simulated by the statistical models. ESD models presented good skills in simulating a wetter climate over SESA, which is of particular importance in a climate change scenario.

Precipitation and synoptic weather types on hazardous weather days in the Southeastern US

Precipitation and synoptic weather types on hazardous weather days in the Southeastern US

Forest connectivity percolation thresholds for fire spread under different weather conditions

How fuel influences fire spread at different spatial scales has been broadly studied but it is still under research. Although prior research has generally explored fuel effects at the stand scale, there is increasing recognition that forest structure and composition at the landscape scale can regulate fire size when weather conditions allow fire to have enough initial capacity to spread. Simulation models have usually been adopted to test forest landscape structure’s effect on fire activity, but we still lack empirical evidence of these effects on a broad scale and using multiple fires. In this study, we use the graph theory and the percolation theory to test the relationship between forest functional connectivity and fire size in a Mediterranean region (Catalonia, NE Spain, ~32000 km2) and under different weather conditions. Using a total sample of 554 fires that occurred between 1987 and 2019, we relate fire size with forest connectivity inside the fire perimeter, which we describe with different interpretations of forest-land cover maps and at different fire dispersal distances (10-, 100- and 2000- meters spotting capacity). Previously, fires were classified according to the weather and drought conditions in which they occurred (windy, arid or regular days, and dry or non-dry conditions) with the hypothesis that the capacity of fire to “read” the forest landscape differs according to weather variables. We found that a forest percolation threshold existed for most types of fires: forest connectivity had a positive influence on fire size until a threshold at which the landscape becomes continuous and other processes to control fire cease to have effect. This percolation threshold is about 0.40 for fires occurring under arid conditions. Under regular weather conditions, the threshold is non-existent and forest connectivity always exerts a positive effect on fire size. Notably, forest connectivity has no influence on fire size on windy- and dry-weather days. All these results are of paramount relevance for forest and landscape management aimed at reducing wildfire risk, since for fires driven by extreme weather conditions (more prone to happen in the future), landscape offers less opportunities to stop them. Researchers and managers need to identify the probability of experiencing each of the different weather types in a climate change context to boost the management of forests and landscapes in a cost-effective way.

A Weather-Type Classification and Its Application to Near-Surface Wind Climate Change Projections over the Adriatic Region

The main goal of this study is to present a recently developed classification method for weather types based on the vorticity and the location of the synoptic centers relative to the Adriatic region. The basis of the present objective classification, applied to the Adriatic region, is the subjective classification developed by Poje. Our algorithm considered daily mean sea-level pressure and 500 hPa geopotential height to define one out of 17 possible weather types. We applied the algorithm to identify which weather type was relevant in the generation of the two typical near-surface winds over the Adriatic region, namely Bora and Sirocco. Two high-resolution (0.11°) EURO-CORDEX regional climate models were used, SMHI-RCA4 and DHMZ-RegCM4, forced by several CMIP5 global climate models and analyzed for two 30-year periods: near-present day and mid-21st century climate conditions under the high-end Representative Concentration Pathway (RCP8.5) scenario. Bora and Sirocco days were extracted for each weather type and a distribution over the 30-year period was presented. Our results suggest that in the winter season, climate model projections indicate a reduction in the main cyclonic types relevant in the formation of Bora over the entire Adriatic region and an increase in the number of anticyclonic types relevant in Sirocco events. In contrast, for the summer season, an increase in the main anticyclonic Bora-related weather types is found in the ensemble over the northern Adriatic region.

Shop Weatherly – A Weather based Smart E-Commerce System Using CNN

In this current era of modern online shopping, people want to spend as little time as possible when it comes to buying products, therefore they prefer online shopping. People go shopping when the weather gets changed. For travelers, there is no such E-commerce platform that can recommend clothes according to any city weather. Even when people want to gift clothes to someone living in another country there is no such platform that gives recommendation of clothes according to that city's weather. They usually face problems when they want to buy weather-based products from various E-commerce platforms where they see mixed clothes of all types of weather which is very time-consuming, they become so confused most of the time that they think about whether they should buy or not. In this paper, we proposed a novel idea by using Convolutional Neural Network Algorithm of deep learning for developing an e-commerce platform that is unique in a way that it recommends clothes according to the city weather which provides hassle-free environment eventually saves customer's time thereby increasing customer satisfaction.

Evaluation of Tourism-Climate Conditions in the Region of Kłodzko Land (Poland)

Kłodzko Land is one of the most important regions of Poland in terms of tourism and health issues. Numerous tourism attractions and health resorts make the region attractive for both tourist and bathers. The goal of this paper was to evaluate the impact of weather conditions on tourism-related conditions and their changes in the multiannual period. In the analysis, the indices of heat days, the UTCI (Universal Thermal Climate Index) and CTIS (Climate Tourism Information Scheme) tools were used. The research on heat days and the UTCI indicated a significant increase in the heat-stress frequency over the last decades. Simultaneously, the number of weather types related to cold stress has considerably decreased. Such trends were noticed in the entire region, in all the considered hypsometric zones. The rising tendency was also observed for strong and very strong heat stress (UTCI &gt; 32 °C), which negatively affects health problems. The analysis showed that the most extreme thermal and biothermal conditions, in terms of heat stress, occur under southern and eastern anticyclonic circulation. The CTIS analysis showed that favorable weather conditions for most of tourism activities are noticed in the warm half-year. The usefulness of weather conditions for tourism can vary depending on atmospheric circulation.

Microphysical features of typhoon and non-typhoon rainfall observed in Taiwan, an island in the northwestern Pacific

Abstract. Information about the raindrop size distribution (RSD) is vital for comprehending the precipitation microphysics, improving the rainfall estimation algorithms, and appraising the rainfall erosivity. Previous research has revealed that the RSD exhibits diversity with geographical location and weather type, which leads to the assessment of the region and weather-specific RSDs. Based on long-term (2004 to 2016) disdrometer measurements in northern Taiwan, this study attempts to demonstrate the RSD aspects of summer seasons that were bifurcated into two weather conditions, namely typhoon (TY) and non-typhoon (NTY) rainfall. The results show a higher concentration of small drops and a lower concentration of large-sized drops in TY compared to NTY rainfall, and this behavior persisted even after characterizing the RSDs into different rainfall rate classes. RSDs expressed in gamma parameters show higher mass-weighted mean diameter (Dm) and lower normalized intercept parameter (Nw) values in NTY than TY rainfall. Moreover, sorting these two weather conditions (TY and NTY rainfall) into stratiform and convective regimes revealed a larger Dm in NTY than in TY rainfall. The RSD empirical relations used in the valuation of rainfall rate (Z–R, Dm–R, and Nw–R) and rainfall kinetic energy (KE–R and KE–Dm) were enumerated for TY and NTY rainfall, and they exhibited profound diversity between these two weather conditions. Attributions of RSD variability between the TY and NTY rainfall to the thermodynamical and microphysical processes are elucidated with the aid of reanalysis, remote sensing, and ground-based data sets.