Climatological Regions Research Articles

Predicting malaria outbreaks using earth observation measurements and spatiotemporal deep learning modelling: a South Asian case study from 2000 to 2017

BackgroundMalaria remains one the leading communicable causes of death. Approximately half of the world's population is considered at risk of infection, predominantly in African and South Asian countries. Although malaria is preventable, heterogeneity in sociodemographic and environmental risk factors over time and across diverse geographical and climatological regions make outbreak prediction challenging. Data-driven approaches accounting for spatiotemporal variability could offer potential for location-specific early warning tools for malaria. MethodsIn this case study, we developed and internally validated a data fusion approach to predict malaria incidence in Pakistan, India, and Bangladesh using geo-referenced environmental factors. For 2000–17, district-level malaria incidence rates for each country were obtained from the US Agency for International Development's Demographic and Health Survey datasets. Environmental factors included average annual temperature, rainfall, and normalised difference vegetation index, obtained from the Advancing Research on Nutrition and Agriculture (known as AReNA) project conducted by the International Food Policy Research Institute in 2020. Data on night-time light intensity was derived from two satellites of the National Oceanic and Atmospheric Administration Defense Meteorological Satellite Program—Operational Linescan System: Nighttime Lights Time Series Version 4, and VIIRS Nighttime Day/Night Band Composites version 1. A multi-dimensional spatiotemporal long short-term memory (M-LSTM) model was developed using data from 2000–16 and internally validated for the year 2017. The M-LSTM model consisted of four hidden layers, each with 100 LSTM units; a fully connected layer was used, followed by linear regression, to predict the malaria incidence rate for 2017 using spatiotemporal partitioning. Model performance was measured using accuracy and root mean squared error. Country-specific models were produced for Bangladesh, India, and Pakistan. Bivariate geospatial heatmaps were produced for a qualitative comparison of univariate environmental factors with malaria rates. FindingsMalaria incidence was predicted with 80·6% accuracy in districts across Pakistan, 76·7% in districts across India, and 99·1% in districts across Bangladesh. The root mean squared error was 7 × 10–4 for Pakistan, 4·86 × 10–6 for India, and 1·32 × 10–5 for Bangladesh. Bivariate maps showed an inverse relationship between night-time lights and malaria rates; whereas high malaria rates were found in areas with high temperature, rainfall, and vegetation. InterpretationMalaria outbreaks could be forecasted using remotely measured environmental factors. Modelling techniques that enable simultaneously forecasting ahead in time as well as across large geographical areas might potentially empower regional decision makers to manage outbreaks early. FundingNIHR Oxford Biomedical Research Centre Programme and The Higher Education Commission of Pakistan.

Triple dip La-Nina, unorthodox circulation and unusual spin in air quality of India

The recent La-Nina phase of the El Nino Southern Oscillation (ENSO) phenomenon unusually lasted for third consecutive year, has disturbed global weather and linked to Indian monsoon. However, our understanding on the linkages of such changes to regional air quality is poor. We hereby provide a mechanism that beyond just influencing the meteorology, the interactions between the ocean and the atmosphere during the retreating phase of the La-Niña produced secondary results that significantly influenced the normal distribution of air quality over India through disturbed large-scale wind patterns. The winter of 2022–23 that coincided with retreating phase of the unprecedented triple dip La-Niña, was marred by a mysterious trend in air quality in different climatological regions of India, not observed in recent decades. The unusually worst air quality over South-Western India, whereas relatively cleaner air over the highly polluted North India, where levels of most toxic pollutant (PM2.5) deviating up to about ±30 % from earlier years. The dominance of higher northerly wind in the transport level forces influx and relatively slower winds near the surface, trapping pollutants in peninsular India, thereby notably increasing PM2.5 concentration. In contrast, too feeble western disturbances, and unique wind patterns with the absence of rain and clouds and faster ventilation led to a significant improvement in air quality in the North. The observed findings are validated by the chemical-transport model when forced with the climatology of the previous year. The novelty of present research is that it provides an association of air quality with climate change. We demonstrate that the modulated large-scale wind patterns linked to climatic changes may have far-reaching consequences even at a local scale leading to unusual changes in the distribution of air pollutants, suggesting ever-stringent emission control actions.

Applying a multistate survival model to explore the role of fine particles in promoting frailty in the Medicare cohort.

Fine particle pollution is a well-established risk to human health. Observational epidemiology generally treats events as though they are independent of one another and so do not examine the role air pollution may play in promoting the progression of disease. Multistate survival models account for the complex pathway of disease to death. We employ a multistate survival model to characterize the role of chronic exposure to PM2.5 in affecting the rate at which Medicare beneficiaries transition to first hospitalization for cardiovascular disease and then subsequently death. We use an open cohort of Medicare beneficiaries and PM2.5 concentrations estimated with photochemical model predictions, satellite-based observations, land-use data, and meteorological variables. The multistate model included three transitions: (1) entry to cardiovascular hospital admission; (2) entry to death; and (3) cardiovascular hospital admission to death. The transition intensity was modeled using a Cox proportional hazards model. For a 1 µg/m3 increase in annual mean PM2.5, we estimate a nationally pooled hazard ratio of 1.022 (95% confidence interval [CI] = 1.018, 1.025) for the transition from entry to first cardiovascular hospital admission; 1.054 (95% CI = 1.039, 1.068) for the transition from entry to death; 1.036 (95% CI = 1.027, 1.044) for the transition from first cardiovascular hospital admission to death. The hazard ratios exhibited some heterogeneity within each of nine climatological regions and for each of the three transitions. We find evidence for the role of PM in both promoting chronic illness and increasing the subsequent risk of death.

Divergent sensitivity of vegetation to aridity between drylands and humid regions

The land surface has been drying over recent decades, which is inconsistent with the greening of the Earth. The extent and spatial variation in the sensitivity of vegetation to aridity changes in drylands and humid regions remain unclear. In this study, satellite observation and reanalysis data were used to analyze the relationship between vegetation growth and atmospheric aridity changes in different climatological regions on a global scale. Our results showed that the leaf area index (LAI) increased at a rate of 0.032/decade from 1982 to 2014, while the aridity index (AI) increased slightly at a rate of 0.005/decade. Over the past three decades, the sensitivity of the LAI to AI has decreased in drylands and increased in humid regions. Thus, the LAI and AI were decoupled in drylands, whereas the effect of aridity on vegetation was enhanced in humid regions during the study period. The physical and physiological effects of increasing CO2 concentration are responsible for the divergent responses of vegetation sensitivity to aridity in drylands and humid regions. The results of the structural equation models showed that the effect of increasing CO2 concentration via LAI and temperature, with respect to decreasing AI, enhanced the negative relationship between LAI and AI in humid regions. The greenhouse effect of increasing CO2 concentration resulted in an increase in temperature and a reduction in aridity, whereas the fertilization effect of CO2 increased LAI, thus creating an inconsistent trend with LAI and AI in drylands.

Harmonic Analysis of the Relationship between GNSS Precipitable Water Vapor and Heavy Rainfall over the Northwest Equatorial Coast, Andes, and Amazon Regions

This study finds the relationship between increases in precipitable water vapor (PWV), and intense rainfall events in four different climatological regions of South America’s equatorial northwest: the coast, Andes valley, high mountains, and Amazon. First, the PWV was derived from tropospheric zenith delay measured by Global Navigation Satellite System (GNSS) instrumentation located near meteorological stations within the regions of interest using hourly data from the year 2014. A harmonic analysis approach through continuous wavelet cross-spectrum and coherence, as well as discrete wavelets, was used to determine a measure of the lags found between PWV and specific heavy rain events and then compared with satellite IR images and meteorological anomalies. The link between PWV peaks and rainfall was the most evident on the coast, and less discernible in the other stations possibly due to local dynamic factors. The results showed a lag of 11 h between the preceding PWV increase and an intense rainfall event. This was apparent in all of the stations, except in Amazon where it was 6 h, with the highest precision at the coast and with the largest dispersion in the high mountains. The interpretation of this lag for each region is also discussed.

The Characteristics of Raindrop Size Distributions in Different Climatological Regions in South Korea

To understand the microphysical characteristics of rainfall in four different climatological regions (called BOS, BUS, CPO, and JIN) in South Korea, DSDs and their variables, including the mass-weighted mean diameter (Dm) and normalized number concentration (logNw), were examined. To examine the characteristics of DSDs at four sites with different climatology and topography, data measured from Parsivel disdrometer and wind direction from Automatic Weather System (AWS) during rainy seasons from June to August for three years (2018 to 2020) were analyzed. The DSDs variables were calculated using Gamma distribution model. In the coastal area, larger raindrops with a lower number concentration occurred, whereas smaller raindrops with a higher number concentration dominated in the middle land and mountain region. The mountain area of CPO and middle land area of JIN had a larger contribution to the rain rate than that of the coastal area of BOS and JIN in the range of the smallest diameter. The contribution of the drop size to the total number concentration at the CPO and JIN sites was larger (smaller) than that at BOS and BUS in the smallest (larger) diameter. The average shape and slope parameter of gamma model were higher values at the mountain area than at other sites for both rain types, Z-R relation and polarimetric variables were also shown different values at the four studied sites. The intercept coefficient of Z-R relation showed higher values in the mountain area and middle land area than the coastal area. The slope values of Z-R relation were the smallest in the mountain area. The polarimetric variables of ZH and ZDR were shown highest (lowest) value at the coastal region of BOS (mountain area of CPO) site for both rain types. The Dm-rose, which shows the Dm distributions with the wind direction, was used in this study. In the coastal area (mountain and middle land area), the dominant wind was east–southeast (east) direction. The ratio of the smaller diameter to the middle size at BOS was much smaller than that at CPO. In the analysis of the hourly distribution of the Dm and logNw, there were two and four peaks of Dm at BUS and BOS, respectively. There was one peak of the Dm at the CPO and JIN sites. The time variation of the Dm was much higher than that of the logNw.

Extreme value innovative trend analysis methodology

The main concern of the article is to determine the trend analyses of upper and lower extreme values separately, apart from the classical central trend. The innovative trend analysis (ITA) modification is adapted for extreme value trend analysis using one standard deviation level from the arithmetic mean value. Extreme value trends emerge along completely different monotonous lines. The application of the proposed methodology is presented for the annual total rainfall time series of seven meteorological stations from each geographical and climatological regions of Turkey. In the future, it is recommended to use only the classical central trend extreme value trend analyses for refined forecasts.

Validating Hourly Satellite Based and Reanalysis Based Global Horizontal Irradiance Datasets over South Africa

This study validates the hourly satellite based and reanalysis based global horizontal irradiance (GHI) for sites in South Africa. Hourly GHI satellite based namely: SOLCAST, Copernicus Atmosphere Monitoring Service (CAMS), and Satellite Application Facility on Climate Monitoring (CMSAF SARAH) and two reanalysis based, namely, fifth generation European Center for Medium-Range Weather Forecasts atmospheric reanalysis (ERA5) and Modern-Era Retrospective Analysis for Research and Applications (MERRA2) were assessed by comparing in situ measured data from 13 South African Weather Service radiometric stations, located in the country’s six macro climatological regions, for the period 2013–2019. The in situ data were first quality controlled using the Baseline Surface Radiation Network methodology. Data visualization and statistical metrics relative mean bias error (rMBE), relative root mean square error (rRMSE), relative mean absolute error (rMAE), and the coefficient of determination (R2) were used to evaluate the performance of the datasets. There was very good correlation against in situ GHI for the satellite based GHI, all with R2 above 0.95. The R2 correlations for the reanalysis based GHI were less than 0.95 (0.931 for ERA5 and 0.888 for MERRA2). The satellite and reanalysis based GHI showed a positive rMBE (SOLCAST 0.81%, CAMS 2.14%, CMSAF 2.13%, ERA5 1.7%, and MERRA2 11%), suggesting consistent overestimation over the country. SOLCAST satellite based GHI showed the best rRMSE (14%) and rMAE (9%) combinations. MERRA2 reanalysis based GHI showed the weakest rRMSE (37%) and rMAE (22%) combinations. SOLCAST satellite based GHI showed the best overall performance. When considering only the freely available datasets, CAMS and CMSAF performed better with the same overall rMBE (2%), however, CAMS showed slightly better rRMSE (16%), rMAE (10%), and R2 (0.98) combinations than CMSAF rRMSE (17%), rMAE (11%), and R2 (0.97). CAMS and CMSAF are viable freely available data sources for South African locations.

The Performance Assessment of Six Global Horizontal Irradiance Clear Sky Models in Six Climatological Regions in South Africa

This study assesses the performance of six global horizontal irradiance (GHI) clear sky models, namely: Bird, Simple Solis, McClear, Ineichen–Perez, Haurwitz and Berger–Duffie. The assessment is performed by comparing 1-min model outputs to corresponding clear sky reference 1-min Baseline Surface Radiation Network quality controlled GHI data from 13 South African Weather Services radiometric stations. The data used in the study range from 2013 to 2019. The 13 reference stations are across the six macro climatological regions of South Africa. The aim of the study is to identify the overall best performing clear sky model for estimating minute GHI in South Africa. Clear sky days are detected using ERA5 reanalysis hourly data and the application of an additional 1-min automated detection algorithm. Metadata for the models’ inputs were sourced from station measurements, satellite platform observations, reanalysis and some were modelled. Statistical metrics relative Mean Bias Error (rMBE), relative Root Mean Square Error (rRMSE) and the coefficient of determination (R2) are used to categorize model performance. The results show that each of the models performed differently across the 13 stations and in different climatic regions. The Bird model was overall the best in all regions, with an rMBE of 1.87%, rRMSE of 4.11% and R2 of 0.998. The Bird model can therefore be used with quantitative confidence as a basis for solar energy applications when all the required model inputs are available.

A Novel Framework for Selecting Informative Meteorological Stations Using Monte Carlo Feature Selection (MCFS) Algorithm

Spatial distribution of meteorological stations has a significant role in hydrological research. The meteorological data play a significant role in drought monitoring; in this regard, accurate and suitable provision of meteorological stations is becoming crucial to improve and strengthen the skill of drought prediction. In this perspective, the choice of meteorological stations in a specific region has substantial importance for accurate estimation and continuous monitoring of drought hazards at the regional level. However, installation and data mining on a large number of meteorological stations require high cost and resources. Therefore, it is necessary to rank and find dependencies among existing meteorological stations in a particular region for further climatological analysis and reanalysis of databases. In this paper, the Monte Carlo feature selection and interdependency discovery (MCFS-ID) algorithm-based framework is proposed to identify the important meteorological station in a particular region. We applied the proposed framework on 12 meteorological stations situated in varying climatological regions of Punjab (Pakistan). We employed the drought index SPTI on 1-, 3-, 6-, 9-, 12-, 24-, and 48-month time-scale data to find the interdependencies among meteorological stations at various locations. We found that Sialkot has significance regional importance for studying SPTI-3, SPTI-6, and SPTI-48 indices. This regional importance is based on scores of relative importance (RI); for example, the RI values for SPTI-3, SPTI-6, and SPTI-48 indices are 0.1570, 0.1080, and 0.0270, respectively. Furthermore, the Jhelum station has more relative importance (RI = 0.1410 and 0.1030) for SPTI-1 and SPTI-9 indices, while varying concentration behaviour is observed in the remaining time scales.

Sorptive Interactions of Fungicidal 2-(4'-Thiazolyl) Benzimidazole with Soils of Divergent Physicochemical Composition

Thiabendazole, 2-(4'-thiazolyl) Benzimidazole fungicide is rampantly used in Pakistan for controlling fungalgrowth in addition to combating various fungus driven diseases. Thiabendazole leaching and mobility patterns can beeasily predicted through investigation of Thiabendazole adsorption and desorption behavior in soils. Present work iscarried out by conducting a batch equilibration experiment for evaluation of Thiabendazole adsorption and desorption insoils from four diverse Pakistani climatological regions. Data revealed Thiabendazole had moderate to weak adsorption inselected soils with distribution co-efficient Kd(ads) ranging from 13.33 to 24.04 µg/ml in selected soils. The TBZ adsorptionin soils best fitted with Freundlich model (R2>0.87). The Freundlich adsorption coefficient (Kf(ads)) values ranged from4.51 to 8.90 µg/ml. Thiabendazole adsorption trends in the selected soils were positively influenced by the clay contentand soil organic matter while it was negatively influenced by soils’ pH. The Freundlich desorption coefficient (Kf(des))values spanned over a range of 1.03 to 6.43 µg/ml indicating decreased desorption from soils with creditable affinities forThiabendazole adsorption. The adsorptive interactions between Thiabendazole and selected soils were primarily physicalconfirmed through lower values of Gibbs free energy ∆G ≤ - 40kJ/mol. Thiabendazole desorption was highly hysterical inall soils with profound irreversibility. Thiabendazole possessed medium mobility patterns in selected soils. The loweradsorptive capability of Thiabendazole in selected soils points towards its lower application rates for combating long termenvironmentally perilous implications.

Sorptive Interactions of Fungicidal 2-(4'-Thiazolyl) Benzimidazole with Soils of Divergent Physicochemical Composition

Thiabendazole, 2-(4'-thiazolyl) Benzimidazole fungicide is rampantly used in Pakistan for controlling fungalgrowth in addition to combating various fungus driven diseases. Thiabendazole leaching and mobility patterns can beeasily predicted through investigation of Thiabendazole adsorption and desorption behavior in soils. Present work iscarried out by conducting a batch equilibration experiment for evaluation of Thiabendazole adsorption and desorption insoils from four diverse Pakistani climatological regions. Data revealed Thiabendazole had moderate to weak adsorption inselected soils with distribution co-efficient Kd(ads) ranging from 13.33 to 24.04 µg/ml in selected soils. The TBZ adsorptionin soils best fitted with Freundlich model (R2>0.87). The Freundlich adsorption coefficient (Kf(ads)) values ranged from4.51 to 8.90 µg/ml. Thiabendazole adsorption trends in the selected soils were positively influenced by the clay contentand soil organic matter while it was negatively influenced by soils’ pH. The Freundlich desorption coefficient (Kf(des))values spanned over a range of 1.03 to 6.43 µg/ml indicating decreased desorption from soils with creditable affinities forThiabendazole adsorption. The adsorptive interactions between Thiabendazole and selected soils were primarily physicalconfirmed through lower values of Gibbs free energy ∆G ≤ - 40kJ/mol. Thiabendazole desorption was highly hysterical inall soils with profound irreversibility. Thiabendazole possessed medium mobility patterns in selected soils. The loweradsorptive capability of Thiabendazole in selected soils points towards its lower application rates for combating long termenvironmentally perilous implications.

Growth Variations in the Geoduck Panopea globosa in Different Climatological Regions of Northwestern Mexico

Growth curves of the geoduck Panopea globosa were compared from four zones in northwestern Mexico, with three zones in the Gulf of California (Guaymas, Puerto Penasco, and San Felipe) and one zone on the Pacific Coast of the southern Baja California Peninsula (Bahia Magdalena). The curves were shaped using the von Bertalanffy growth model. The lengths-at-age data for each location were obtained from fishery-independent data at each location. The average asymptotic lengths of P. globosa were 114.4, 156.6, 161.5, and 168.4 mm in Guaymas, Puerto Penasco, San Felipe, and Bahia Magdalena, respectively. The Kimura's likelihood ratio test was used as the goodness-of-fit trial. In comparison with geoducks from the other locations, geoducks from Guaymas, located on the central Gulf of California, were the shortest, with a significant difference in length from those at the other three sites. Geoducks from the upper Gulf of California (Puerto Penasco and San Felipe) and Pacific Coast had no significant differences among them. Because the fishing management strategy for this species uses the minimum legal size (MLS), the MLS should vary regionally to promote sustainable use.

Calibration of a water vapour Raman lidar using GRUAN-certified radiosondes and a new trajectory method

Abstract. Raman lidars have been designated as potential candidates for trend studies by the Network for the Detection of Atmospheric Composition Change (NDACC) and GCOS (Global Climate Observing System) Reference Upper Air Network (GRUAN); however, for such studies improved calibration techniques are needed as well as careful consideration of the calibration uncertainties. Trend determinations require frequent, accurate, and well-characterized measurements. However, water vapour Raman lidars produce a relative measurement and require calibration in order to transform the measurement into a mixing ratio, a conserved quantity when no sources or sinks for water vapour are present. Typically, the calibration is done using a reference instrument such as a radiosonde. We present an improved trajectory technique to calibrate water vapour Raman lidars based on the previous work of Whiteman et al. (2006), Leblanc and Mcdermid (2008), Adam et al. (2010), and Herold et al. (2011), who used radiosondes as an external calibration source and matched the lidar measurements to the corresponding radiosonde measurement. However, they did not consider the movement of the radiosonde relative to the air mass and fronts. Our trajectory method is a general technique which may be used for any lidar and only requires that the radiosonde report wind speed and direction. As calibrations can be affected by a lack of co-location with the reference instrument, we have attempted to improve their technique by tracking the air parcels measured by the radiosonde relative to the field of view of the lidar. This study uses GRUAN Vaisala RS92 radiosonde measurements and lidar measurements taken by the MeteoSwiss RAman Lidar for Meteorological Observation (RALMO), located in Payerne, Switzerland, from 2011 to 2016 to demonstrate this improved calibration technique. We compare this technique to the traditional radiosonde–lidar calibration technique which does not involve tracking the radiosonde and uses the same integration time for all altitudes. Both traditional and our trajectory methods produce similar profiles when the water vapour field is homogeneous over the 30 min calibration period. We show that the trajectory method reduces differences between the radiosonde and lidar by an average of 10 % when the water vapour field is not homogeneous over a 30 min calibration period. We also calculate a calibration uncertainty budget that can be performed on a nightly basis. The calibration uncertainty budget includes the uncertainties due to phototube paralysis, aerosol extinctions, the assumption of the Ångström exponent, and the radiosonde. The study showed that the radiosonde was the major source of uncertainty in the calibration at 4 % of the calibration value. This trajectory method showed small improvements for RALMO's calibration but would be more useful for stations in different climatological regions or when non-co-located radiosondes are the only available calibration source.

GIS Applications to Investigate the Linkage between Geomorphological Catchment Characteristics and Response Time: A Case Study in Four Climatological Regions, South Africa

In flood hydrology, geomorphological catchment characteristics serve as fundamental input to inform decisions related to design flood estimation and regionalization. Typically, site-specific geomorphological catchment characteristics are used for regionalization, while flood statistics are used to test the homogeneity of the identified regions. This paper presents the application and comparison of Geographical Information Systems (GIS) modelling tools for the estimation of catchment characteristics to provide an enhanced understanding of the linkage between geomorphological catchment characteristics and response time. It was evident that catchment response variability is not exclusively related to catchment area, but rather associated with the increasing spatial–temporal heterogeneity of other catchment characteristics as the catchment scale increases. In general, catchment and channel geomorphology overruled the impact that catchment variables might have on the response time and resulting runoff. Shorter response times and higher peak flows were evident in similar-sized catchments characterized by lower shape factors, circularity ratios, and shorter centroid distances and associated higher elongation ratios, drainage densities and steeper slopes. The GIS applications not only enabled the inclusion of a more diverse selection of catchment characteristics as opposed to when manual methods are used, but the high degree of association between the different GIS-based methods also confirmed their preferential use.

NASA's alphasat propagation terminals: Milan, Italy, and Edinburgh, Scotland

SummarySince May of 2014, NASA's Glenn Research Center has operated measurement campaigns for the Alphasat Aldo Paraboni Propagation Experiment alongside the European community of propagation experimenters. Presently, three NASA stations have been deployed to distinct climatological regions across Europe. NASA's participation in the campaign began in 2014 through a collaborative effort with the Politecnico di Milano (POLIMI) to jointly operate a 20/40 GHz ground terminal at the POLIMI campus in Milan, Italy. Subsequently, a single‐channel 40 GHz terminal was deployed to Edinburgh, Scotland in March 2016 in collaboration with Heriot‐Watt University (HWU). A third terminal was deployed to NASA's Madrid Deep Space Communications Complex (MDSCC) in March 2017 with NASA'S Jet Propulsion Laboratory (JPL), also observing the 40 GHz beacon. In addition, a fourth station is planned for deployment to Andøya, Norway by early 2019 in collaboration with the Norwegian Defence Research Establishment (FFI). This paper will detail the design and results of the two most established terminals, Milan and Edinburgh, which together comprise 11 station years of propagation measurements.

Assessment of future changes in Southeast Asian precipitation using the NASA Earth Exchange Global Daily Downscaled Projections data set

Extreme precipitation and associated flooding cause severe damage to society and the environment. Future climate projections suggest an intensification of precipitation extremes in many regions. However, there is an increasing need for climate change impact assessment at higher spatial resolution, particularly for regions with complex geography such as Southeast Asia (SEA). In this study, we analysed the NASA Earth Exchange 0.25° resolution daily precipitation projections from an ensemble of 20 climate models under two emission scenarios RCP4.5 and RCP8.5. The variability in future precipitation projections is analysed and quantified for six geographical subregions, two climatological regions (wet and dry), and the low‐elevation coastal zones in SEA. Various aspects of precipitation structure are studied using indices that characterize precipitation amount, number of heavy precipitation days, extreme precipitation amount, and maximum daily precipitation at annual and seasonal scales. The results show substantial increases in mean and extreme precipitation in many parts of SEA by the end of the 21st century under both emission scenarios, thus increasing the region's vulnerability to precipitation‐driven hazards. The projected centennial increase in total annual precipitation relative to the baseline period of 1970–1999 when averaged over all land grid cells is about 15% under RCP8.5 scenario, with larger values (∼20%) over mainland SEA and Philippines and smaller values (∼6%) in Java island. The projected changes in extreme precipitation are stronger compared to the total annual precipitation under both emission scenarios. The New Guinea and Java regions show the largest and smallest increases in annual maximum daily precipitation, with ensemble mean values of 30 and 17%, respectively, under RCP8.5 scenario. The results also reveal large inter‐model spread in projected changes, particularly during boreal winter and summer months.

Evaluation of Himawari-8 surface downwelling solar radiation by ground-based measurements

Abstract. Observations from the new Japanese geostationary satellite Himawari-8 permit quasi-real-time estimation of global shortwave radiation at an unprecedented temporal resolution. However, accurate comparisons with ground-truthing observations are essential to assess their uncertainty. In this study, we evaluated the Himawari-8 global radiation product AMATERASS using observations recorded at four SKYNET stations in Japan and, for certain analyses, from the surface network of the Japanese Meteorological Agency in 2016. We found that the spatiotemporal variability of the satellite estimates was smaller than that of the ground observations; variability decreased with increases in the time step and spatial domain. Cloud variability was the main source of uncertainty in the satellite radiation estimates, followed by direct effects caused by aerosols and bright albedo. Under all-sky conditions, good agreement was found between satellite and ground-based data, with a mean bias in the range of 20–30 W m−2 (i.e., AMATERASS overestimated ground observations) and a root mean square error (RMSE) of approximately 70–80 W m−2. However, results depended on the time step used in the validation exercise, on the spatial domain, and on the different climatological regions. In particular, the validation performed at 2.5 min showed largest deviations and RMSE values ranging from about 110 W m−2 for the mainland to a maximum of 150 W m−2 in the subtropical region. We also detected a limited overestimation in the number of clear-sky episodes, particularly at the pixel level. Overall, satellite-based estimates were higher under overcast conditions, whereas frequent episodes of cloud-induced enhanced surface radiation (i.e., measured radiation was greater than expected clear-sky radiation) tended to reduce this difference. Finally, the total mean bias was approximately 10–15 W m−2 under clear-sky conditions, mainly because of overall instantaneous direct aerosol forcing efficiency in the range of 120–150 W m−2 per unit of aerosol optical depth (AOD). A seasonal anticorrelation between AOD and global radiation differences was evident at all stations and was also observed within the diurnal cycle.

Markov Chain-Based Stochastic Modeling of Chloride Ion Transport in Concrete Bridges

Over the last decade, there has been a lot of interest in models for evaluating and predicting the condition of existing bridges in North America due to the large number of them in an advanced state of deterioration. The models have been used to develop optimal strategies to prolong the service life of bridges, allocate limited financial and technical resources and maintain the required level of reliability of the bridges. The main process of deterioration of concrete bridges is corrosion of the reinforcing steel due to chloride ions and models for this type of deterioration can be classified as physical or statistical. The physical models describe the diffusion of chloride ions in concrete and chemical reactions while the statistical models, such as Markov chains, are used to model the progression of states of deterioration of the concrete structures. The physical models are appropriate to analyze deterioration processes for various structures and conditions of exposure but are computationally too demanding for the portfolio analysis of a large number of structures. Markov chain models have been extensively used for the latter purpose but require an extensive historical database to correctly estimate transition probabilities between deterioration states. The objective of this paper is to propose a novel procedure for estimating transition probabilities for Markov chain models by utilizing targeted simulations from physical models. The transition probabilities can be derived from specific climatological regions and concrete properties. The proposed framework provides the required input for portfolio analyses for a large number of different types of structures exposed to different climatological conditions. The procedure is demonstrated with the TransChlor® software and for a group of structures located in Montreal, Canada.

The local response of El Niño events and changing disease distribution in Tanzania

Climate is a key determinant of a number of disease pathogen lifecycles and disease transmission processes, particularly within tropical climates such as that experienced in Tanzania. Over recent decades, climate‐related diseases such as malaria, chikungunya and bacterial meningitis have shown notable changes in their spatial distribution, with instances of both re‐emergence and expansion beyond previously known boundaries being recorded. The unpredicted change in disease distribution already experienced in Tanzania has placed a significant burden on health systems and available resources, and whilst a number of factors are involved, climate remains the least understood aspect within epidemiological changes. Here we examine how climate extremes – particularly El Niño events – influence key environmental and climatic elements which promote epidemiological expansion.This study investigates the baseline climatology in five of Tanzania’s varying climatological regions using the Met Office MIDAS dataset for the period 1985–1995. Its aim is to characterise the average climate and investigate the impacts of El Niño on the climatology of these regions, and to explore associated changes in disease distribution to allow identification, in the present, of changes which are anticipated to occur in the future to be put into context. The years 1997 and 2015 are used to examine the climate extremes imposed by El Niño events through statistical comparison methods. The results demonstrate that average climate conditions vary beyond previously documented observations, with each region of Tanzania responding differently to the onset of El Niño, thus potentially promoting a spatially variable disease response. These results are particularly marked for areas of greater climatic and environmental sensitivity within Tanzania. Once further understood, knowledge of this relationship could be applied to more local analysis and aid in predicting future outbreaks within Tanzania.