Narrow Results

This study introduces a mathematical model aimed at evaluating the potential influence of aerosol introduction into the atmosphere for inducing rainfall and managing atmospheric pollution. By expanding on the proposed model, we incorporate stochastic elements to encompass environmental white noises that impact the system’s dynamics. Both mathematical and numerical methods are employed to analyze the system’s behavior. In the context of the deterministic model, we examine the solutions’ positivity and boundedness, identify feasible equilibria, and scrutinize the stability characteristics both locally and globally. The analysis of the stochastic system encompasses discussions regarding the existence of a unique solution, its ultimate boundedness, and the conditions that prompt the establishment of a unique stationary distribution characterized by ergodic properties. Our simulations illustrate that augmenting cloud formation rates and externally introduced aerosols can amplify rainfall while mitigating atmospheric pollution levels. Minor intensities of white noise do not alter the system’s behavior, whereas significant intensities result in high-amplitude oscillations of the system’s variables. We explore the effects of white noise intensities using histograms and stationary distributions, highlighting long-term rainfall trends in a noisy environment.

This paper introduces a new technique in ecology to analyze spatial and temporal variability in environmental variables. By using simple statistics, we explore the relations between abiotic and biotic variables that influence animal distributions. However, spatial and temporal variability in rainfall, a key variable in ecological studies, can cause difficulties to any basic model including time evolution.

The study was of a landscape scale (three million square kilometers in eastern Australia), mainly over the period of 1998–2004. We simultaneously considered qualitative spatial (soil and habitat types) and quantitative temporal (rainfall) variables in a Geographical Information System environment. In addition to some techniques commonly used in ecology, we applied a new method, Functional Principal Component Analysis, which proved to be very suitable for this case, as it explained more than 97% of the total variance of the rainfall data, providing us with substitute variables that are easier to manage and are even able to explain rainfall patterns. The main variable came from a habitat classification that showed strong correlations with rainfall values and soil types.

The sterile insect technique (SIT) is a biological control technique that can be used either to eliminate or decay a wild mosquito population under a given threshold to reduce the nuisance or the epidemiological risk. In this work, we propose a model using a differential system that takes into account the variations of rainfall and temperature over time and study their impacts on sterile males’ releases strategies. Our model is as simple as possible to avoid complexity while being able to capture the temporal variations of an Aedes albopictus mosquito population in a domain treated by SIT, located in Réunion island. The main objective is to determine what period of the year is the most suitable to start a SIT control to minimize the duration of massive releases and the number of sterile males to release, either to reduce the mosquito nuisance, or to reduce the epidemiological risk. Since sterilization is not $100\%$ efficient, we also study the impact of different levels of residual fertility within the released sterile males population. Our study shows that rainfall plays a major role in the dynamics of the mosquito and the SIT control, that the best period to start a massive SIT treatment lasts from July to December, that residual fertility has to be as small as possible, at least for nuisance reduction. Indeed, when the main objective is to reduce the epidemiological risk, we show that residual fertility is not necessarily an issue. Increasing the size of the releases is not always interesting. We also highlight the importance of combining SIT with mechanical control, i.e., the removal of breeding sites, in particular when the initial mosquito population is large. Last but not least our study shows the usefulness of the modeling approach to derive various simulations to anticipate issues and demand in terms of sterile insects’ production.

Usage of a deterministic fractal-multifractal (FM) procedure to model high-resolution rainfall time series, as derived distributions of multifractal measures via fractal interpolating functions, is reported. Four rainfall storm events having distinct geometries, one gathered in Boston and three others observed in Iowa City, are analyzed. Results show that the FM approach captures the main characteristics of these events, as the fitted storms preserve the records' general trends, their autocorrelations and spectra, and their multifractal character.

Rainfall is a highly intermittent field over a wide range of time and space scales. We study a high resolution rainfall time series exhibiting large intensity fluctuations and localized events. We consider the return times of a given intensity, and show that the time series composed of these return times is itself also very intermittent, obeying to a hyperbolic probability density, entailing that the mean return time diverges. This is an unexpected property since mean return times are often introduced in meteorology, especially for the study of risk associated to extreme events. It suggests that the intermittency of first return times of extreme events should be taken into account when making statistical predictions.

The time series data of the monthly rainfall records (for the time period 1871–2002) in All India and different regions of India are analyzed. It is found that the distributions of the rainfall intensity exhibit perfect power law behavior. The scaling analysis revealed two distinct scaling regions in the rainfall time series.

Natural data sets, such as precipitation records, often contain geometries that are too complex to model in their totality with classical stochastic methods. In the past years, we have developed a promising deterministic geometric procedure, the fractal-multifractal (FM) method, capable of generating patterns as projections that share textures and other fine details of individual data sets, in addition to the usual statistics of interest. In this paper, we formulate an extension of the FM method around the concept of "closing the loop" by linking ends of two fractal interpolating functions and then test it on four geometrically distinct rainfall data sets to show that this generalization can provide excellent results.

Difference in rainfall between wet and dry seasons is increasing worldwide.

Rare carbon molecule detected in dying star gives glimpse of stellar evolution.

Whole genome sequencing of wild rice reveals the mechanisms underlying Oryza genome evolution.

BGI and TGAC join efforts to tackle global challenges in food security, energy and health.

A regeneration system for tartary buckwheat invented by CIB.

A new approach for the reduction of carbon dioxide to methane and acetic acid.

Launch of the Chinese-German Center for Bio-Inspired Materials at the Mainz University Medical Center.

Science: The early bird loses an ovary.

Disruptions of functional brain connectomes in individuals at risk for Alzheimer's disease.

A breakthrough in carbohydrate-based vaccine: One vaccine targets three unique glycan epitopes on cancer cells and cancer stem cells.

BSD Medical signs exclusive agreement for distribution of BSD's cancer treatment hyperthermia system in Taiwan.

Catalent announces major China expansion with two new facilities.

The monthly rainfall time series, spanning more than a century, recorded in several sites in the middle Argentina were analyzed. The power spetral density (PSD) method reveals the presence of annual and semi-annual cyclic fluctuations. The detrended fluctuation analysis (DFA) performed on the residual times series (after removing the periodicities) shows a scaling behavior, characterized by DFA scaling exponents ranging between 0.54 and 0.58. These findings could contribute to a better understanding of rainfall dynamics.

Polynomial chaos expansion (PCE) is widely adopted in geotechnical engineering as a surrogate model for probabilistic analysis. However, the traditional low-order PCE may be unfeasible for unsaturated transient-state models due to the high nonlinearity. In this study, a temporal-spatial surrogate model of adaptive sparse polynomial chaos expansions (AS-PCE) is established based on hyperbolic truncation with stepwise regression as surrogate models to improve computational efficiency. The uncertainty of pore water pressure of an unsaturated slope under transient-state rainfall infiltration considering hydraulic spatial variability is studied. The saturated coefficient of permeability $k_s$ is chosen to be spatial variability to account for the soil hydraulic uncertainty. The effects of location and time and the performances of AS-PCE are investigated. As rainfall goes on, the range of the pore pressure head becomes larger and the spatial variability of $k_s$ has little influence in the unsaturated zone with high matric suction. The pore pressure head under the water table suffers more uncertainty than it in the unsaturated zone. The $R^2$ in the high matric suction zone has a trend of rising first and then falling. Except for the high matric suction zone, the $R^2$ rise over time and they are almost 1 at the end of the time. It can be concluded that the AS-PCE performs better for low matric suction and positive pore pressure head and the fitting effect gradually increases as the rainfall progresses. The quartiles and at least up to second statistical moments can be characterized by the AS-PCE for transient infiltration in unsaturated soil slopes under rainfall.

As the source of replenishment, rainfall has an extensive impact because its variability shapes biologically efficient pulses of soil moisture recharge across layers from rainfall events. In this paper, a mathematical model is proposed to explore the importance of transpiration from agricultural crops and aerosols on the pattern of rainfall. For the system without seeding, the simulation results show destabilizing roles of parameters related to formation of cloud drops due to transpiration of agricultural crops, formation of raindrops due to cloud drops and growth of agricultural crops due to rain. The model without seeding is extended to its stochastic counterpart to encapsulate the uncertainty observed in some important parameters. We observe the variability in the system’s variables and found their distributions at certain fixed times, which explore the importance of stochasticity in the system. Our findings show that transpiration through agricultural crops plays an important role in cloud formation, and thus, affects the effectiveness of different rainfall events. Moreover, the combined actions of transpiration and seeding are much more beneficial in producing rain. Finally, we see the behavior of system by considering seasonal variations of some rate parameters.

This paper presents an empirical analysis of the role of different climate change adaptation strategies in supporting food productivity in Ethiopia. The analysis relies on unique primary survey data on 1000 farms producing cereal crops in the Nile Basin, Ethiopia. Based on monthly collected meteorological station data, the Thin Plate Spline method of spatial interpolation is used to impute the household specific rainfall and temperature values of each household. The rainfall data is disaggregated at season level (Meher and Belg). Econometric results show that the implementation of adaptation strategies supports farm productivity. Changing crops is found to be the most successful strategy, followed by the implementation of soil conservation and tree planting. We complement the analysis with some evidence on the determinants of adaptation. We find that extension services (both formal and farmer-to-farmer) and information on future climate changes affect positively and significantly the probability of adaptation through changing crops and tree planting. This finding highlights the crucial role played by information dissemination in improving farmers' decision-making.

Many studies of climate change adaptation have relied on farmers’ perceptions of climate change to explain why farmers are adopting new farming methods, and to advise adaptation policy framework that justifies Climate Smart Agriculture (CSA) especially in Africa. These studies have rarely verified whether farmers’ perceptions are consistent with observed changes in meteorological conditions to establish sufficient premise. This study compares farmers’ perceptions of changes in precipitation and temperature in a rainfed agriculture region of Ghana against objective measurements made in nearby weather stations in the region. The study finds that farmers correctly perceived the increase in temperature over time but incorrectly perceived a reduction in precipitation, while objective data showed high fluctuations with no clear trend. It is possible that farmers mistakenly assumed reduction in soil moisture meant to support crop growth requirements was caused by less rainfall when in fact it was caused by higher temperature.

The North American Multi-Model Ensemble (NMME) has grown into a fully developed scientific database for seasonal and sub-seasonal climate forecasts, progressing prediction from global to regional scales. The NMME has continuously developed, with new models replacing old ones; it is hypothesized that this development will generate more accurate forecasts over time. However, to date, this hypothesis has not been verified in Central Africa (CA). This study investigates the hypothesis that the skill of NMME models will increase as the forecasting system advances, focusing on rainfall in CA. The study is conducted for the four configuration (phases) of NMME models, from the oldest to the most recent. The analyses are performed with Short Lead (SL) time and Long Lead (LL) time hindcasts very coherent with the perspectives of the CA. The results show from configuration 1 (phase 1) to configurations 4 (phase 4), the NMME models reasonably replicate the spatial structures in the seasonal rainfall climatology of the observations with a remarkable bias at LL. The mean absolute error and root mean square difference reveal small but incremental improvements in the prediction skills of NMME models from phase 1 to phase 4. The Pearson coefficient (r) increased in SL by about 1%, i.e., from 0.94 to 0.95 during June–August (JJA) season and about 4% during the September–November (SON), i.e., from $r=0.90$ in phase 1 to $r=0.94$ in phase 4, about 3% from phase 1 to phase 4 during the March–May (MAM). The categorical scores show that the Probability of Detection (POD) and False Alarm (FAR) increased very slightly from phase 1 to phase 4, but is it noted that the different combinations of the NMME forecasting system present difficulties in predicting rainy and dry events. It should be added that by introducing newer models into a multi-model ensemble as they are developed, and by eliminating older models, small skill gains are observed in the NMME forecasting system in CA.

Climate change poses mounting risks to agricultural development and rural livelihoods in Nigeria. This study investigates the impacts of climate change on agricultural sector employment in Nigeria. Agriculture provides income and sustenance for much of Nigeria’s rural population. However, smallholder rain-fed farming predominates, with minimal resilience to climate shifts. Historical data reveal rising temperatures and declining, erratic rainfall across Nigeria’s agro-ecological zones since the 1970s. Crop modeling predicts further climate changes will reduce yields of key staple crops. This threatens the viability of smallholder agriculture and risks widespread job losses. The study adopts a nonlinear autoregressive distributed lag (NARDL) modeling approach to evaluate climate change effects on agricultural sector employment in Nigeria from 1990 to 2020. Findings reveal reduced rainfall initially raises employment, as farming requires more labor in dry conditions. However, protracted droughts significantly reduce agricultural jobs. Increased temperatures consistently lower farm employment through reduced yields and incomes. Based on these findings, the study recommends that adaptive strategies are urgently needed to build resilience, promote climate-smart agriculture, and safeguard rural livelihoods.

This paper studies the statistical characteristics of a unique long-term high-resolution precipitable water vapor (PWV) data set at Darwin, Australia, from 12 March 2002 to 28 February 2011. To understand the convective precipitation processes for climate model development, the U.S. Department of Energy’s Atmospheric Radiation Measurement (ARM) program made high-frequency radar observations of PWV at the Darwin ARM site and released the best estimates from the radar data retrievals for this time period. Based on the best estimates, we produced a PWV data set on a uniform 20-s time grid. The gridded data were sufficient to show the fractal behavior of precipitable water with Hausdorff dimension equal to 1.9. Fourier power spectral analysis revealed modulation instability due to two sideband frequencies near the diurnal cycle, which manifests as nonlinearity of an atmospheric system. The statistics of PWV extreme values and daily rainfall data show that Darwin’s PWV has El Nino Southern Oscillation (ENSO) signatures and has potential to be a predictor for weather forecasting. The right skewness of the PWV data was identified, which implies an important property of tropical atmosphere: ample capacity to hold water vapor. The statistical characteristics of this long-term high-resolution PWV data will facilitate the development and validation of climate models, particularly stochastic models.

Based on temperature and rainfall recorded at 34 meteorological stations in Bangladesh during 1989–2018, the trends of yearly average maximum and minimum temperatures have been found to be increasing at the rates of 0.025^∘C and 0.018^∘C per year. Analysis of seasonal average maximum temperature showed increasing trend for all seasons except the late autumn season. The increasing trend was particularly significant for summer, rainy and autumn seasons. Seasonal average minimum temperature data also showed increasing trends for all seasons. The trend of yearly average rainfall has been found to be decreasing at a rate of 0.014mm per year in the same period; especially, for most of the meteorological stations the rainfall demonstrates an increasing trend for rainy season and a decreasing trend in the winter season. It means that in Bangladesh dry periods became drier and wet periods became wetter.

As one of the poorest countries in the world, agriculture is Bangladesh’s economic pillar, leading to Bangladesh’s economy becoming vulnerable to global warming. The generation of high-resolution climate predictions in Bangladesh can help to reduce the huge damage and losses inflicted by disasters linked to climate. The statistical downscaling model (SDSM) is the most widely used software on robust climate downscaling and prediction analysis. In this study, by using the SDSM model, we established the statistical relationship between observed climate data in Bangladesh and the large-scale low-resolution NCEP data and used three statistical indicators to evaluate the prediction performance of the SDSM software. Our results show that the SDSM software is more suitable for forecasting humidity/temperature in Bangladesh than rainfall.

In this paper, I advance and empirically support the indigenous religious values hypothesis, which holds that religions espouse values indigenous to the countries in which they developed. To identify the indigenous values of a religion’s homeland, I rely on the negative relationship between individualism and rainfall variation. I find strong empirical support for the hypothesis that contemporary individualism depends on rainfall variation in the homelands of religions to which a country’s population adheres. Indeed, this relationship explains over a quarter of the international variation in individualism. This effect is robust to controls for the role of religion in institutional and technological transfers and the confluence of conversion and colonisation. In keeping with the explicitly religious nature of the mechanism proposed here, I also find that rainfall variation in religion homelands plays a greater role in explaining the values of countries with greater religious freedom and the values of individuals who are more religious or members of religious minorities.

Through warming of the oceans, human-induced climate change effects accumulate. The result is higher ocean heat content (OHC), sea levels and sea surface temperatures (SSTs). The resulting environment invigorates tropical cyclones to make them more intense, bigger and longer lasting, and greatly increases their flooding rains. Here, the focus is on Atlantic hurricanes in 2017, featuring Harvey, Irma and Maria, and the huge damage that occurred. Hurricanes keep tropical oceans cooler as a consequence of their strong winds that increase evaporation. Planning for such supercharged hurricanes (adaptation) by increasing resilience (e.g., better building codes, flood protection) and preparing for contingencies (such as evacuation routes, power cuts) is essential but not adequate in many areas, including Texas, Florida and Puerto Rico where Harvey, Irma and Maria took their toll.