Minimum Relative Humidity Research Articles

Analysis of the effect of meteorological factors on hemorrhagic fever with renal syndrome in Taizhou City, China, 2008–2020

BackgroundHemorrhagic fever with renal syndrome (HFRS) is endemic in Zhejiang Province, China, while few studies have concentrated on the influence of meteorological factors on HFRS incidence in the area. MethodsData on HFRS and meteorological factors from January 1, 2008 to December 31, 2020 in Taizhou City, Zhejiang Province were collected. Multivariate analysis was conducted to the relationship between meteorological factors including minimum temperatures, relative humidity, and cumulative rainfall with HFRS. ResultsThe HFRS incidence peaked in November and December and it was negatively correlated with average and highest average temperatures. Compared with median of meteorological factors, the relative risks (RR) of weekly average temperature at 12 ℃, weekly highest temperature at 18 ℃relative humidity at 40%, and cumulative rainfall at 240 mm were most significant and RRs were 1.41 (95% CI: 1.09–1.82), 1.32 (95% CI: 1.05–1.66), 2.18 (95% CI: 1.16–4.07), and 1.91 (95% CI: 1.16–2.73), respectively. Average temperature, precipitation, relative humidity had interactions on HFRS and the risk of HFRS occurrence increased with the decrease of average temperature and the increase of precipitation. ConclusionOur study results are indicative of the association of environmental factors with the HFRS incidence, probable recommendation could be use of environmental factors as early warning signals for initiating the control measure and response.

Climate factors and dengue fever in Burkina Faso from 2017 to 2019.

Dengue is now a major health concern in sub-Saharan Africa. Understanding the influence of local meteorological factors on the incidence of dengue is an important element for better prediction and control of this disease. This study aims to assess the impact of meteorological factors on dengue transmission in the central region of Burkina Faso. We analyzed the lagged effects of meteorological factors on the weekly incidence of dengue from 2017 to 2019 in the central region of Burkina Faso using a General Additive Model. The results show that maximum and minimum temperature, relative humidity, and wind speed have a significant non-linear effect on dengue cases in the region with 83% of case variance explained. The optimal temperature that increases dengue cases was 27°C to 32°C for the maximum temperature and 18°C to 20°C for the minimum temperature with a decrease beyond that. The maximum temperature shifted by six weeks had the best correlation with dengue incidence. The estimated number of dengue cases increases as the maximum relative humidity increases from 15 to 45% and then from 60 to 70%. In general, an increase in daily wind speed is estimated to decrease the number of daily dengue cases. The relationship between rainfall and dengue cases was not significant. This study provides local information about the effect of meteorological factors on dengue that should help improve predictive models of dengue cases in Burkina Faso and contribute to the control of this disease.

Meteorological and climatic variables predict the phenology of Ixodes ricinus nymph activity in France, accounting for habitat heterogeneity

Ixodes ricinus ticks (Acari: Ixodidae) are the most important vector for Lyme borreliosis in Europe. As climate change might affect their distributions and activities, this study aimed to determine the effects of environmental factors, i.e., meteorological, bioclimatic, and habitat characteristics on host-seeking (questing) activity of I. ricinus nymphs, an important stage in disease transmissions, across diverse climatic types in France over 8 years. Questing activity was observed using a repeated removal sampling with a cloth-dragging technique in 11 sampling sites from 7 tick observatories from 2014 to 2021 at approximately 1-month intervals, involving 631 sampling campaigns. Three phenological patterns were observed, potentially following a climatic gradient. The mixed-effects negative binomial regression revealed that observed nymph counts were driven by different interval-average meteorological variables, including 1-month moving average temperature, previous 3-to-6-month moving average temperature, and 6-month moving average minimum relative humidity. The interaction effects indicated that the phenology in colder climates peaked differently from that of warmer climates. Also, land cover characteristics that support the highest baseline abundance were moderate forest fragmentation with transition borders with agricultural areas. Finally, our model could potentially be used to predict seasonal human-tick exposure risks in France that could contribute to mitigating Lyme borreliosis risk.

Impacts and variability on the urban climate of Recife – Pernambuco, Brazil

The objective of this work was to identify monthly and annual oscillations and trends in meteorological variables: maximum and minimum air temperatures, precipitation, and relative humidity, number of rainy days, total sunshine, wind intensity and cloud cover. We used the theoretical probability distribution: Weibull, Log-normal and Logistics to adjust the values ​​of the variables mentioned above. The Kolmogorov-Smirnov test was used to verify the adjustment of the theoretical functions. The study of the spatial temporal behavior of rainfall and monthly, maximum and minimum temperatures, relative humidity, insolation, cloud cover and wind used was acquired from the National Institute of Meteorology, the data period comprising the series from 1962 to 2015. For The determination of the theoretical distributions of probability adjusted to the annual extremes of precipitation, maximum and minimum temperature of the air, total isolation and relative humidity, cloud cover and wind intensity, were used the maximum likelihood methods to estimate the parameters of the distributions. The Kolmogorov-Smirnov test was used to compare the fit and select the best theoretical distributions. The adjustments were also evaluated in graphs. Deforestation of native vegetation for the construction of districts, favelas and buildings above six floors, as well as high burnings, has contributed to the high rates of desertification, silting up rivers, streams, streams, wells, ponds, ponds and lagoons. Water table, causing extreme fluctuations in the contribution of meteorological elements and well-being in urban centers. The Weibull and Logistics distributions were the best fit for precipitation, insolation, relative humidity, minimum temperature and cloud coverage.

VERIFICATION OF DISTRICT-LEVEL WEATHER FORECAST OF KOLKATA AND ITS SUBURBS DURING MONSOON ‘2019 & 2020 FOR COMPARATIVE STUDY OF THE PERFORMANCE OF MODEL BETWEEN PRE COVID NON-LOCKDOWN AND COVID LOCKDOWN PERIOD

India Meteorological Department has started issuing district-level weather forecasts for up to 5 days on an operational basis from 1st June 2008. The weather parameters related to agro, namely rainfall, maximum and minimum temperature, wind speed, and direction, relative humidity, and cloudiness were chosen for outputs from the model. The rainfall forecast is generated based on multi-model ensemble techniques ( MME ) and ECMWF forecasts ( presently IMDGFS) are used for forecasting other parameters. These forecast generated for the districts of West Bengal by the model is further moderated by State Agro Met. Centre, Kolkata, and forwarded to six Agro Met. Field Units ( created by six agro-climatic zones in West Bengal ) and seven District Agro Met. Unit ( DAMU ) for preparation of weather-based District as well as Block level Agromet advisory bulletin which benefits the farmers in their crop production. Thus forecast verification of the model as well as moderated value for the monsoon season of 2019 and 2020 has been done to make a comparative study of the model performance concerning Kolkata and its suburbs based on Probability of Detection, False alarm, Heidke Skill score, Missing rate, Critical Success Index, True Skill Score, Hanssen, and Kuipers Index, etc. The monsoon rainfall of 2019 and 2020 was chosen to study the performance of the model concerning the pre-covid non-lockdown and covid lockdown period so that the effect of pollutants on the performance of the model can be analyzed. The verification results show that the model forecast, as well as a moderated forecast of this region, has to be more refined by taking inputs of other parameters and index that has been computed by different recent research works on this region because this region is under the influence of tropical climate. Moreover, the comparative study between monsoon 2019 and monsoon 2020 reveals that there have been changes in the performance of the model.

Regression Modeling Strategies to Predict and Manage Potato Leaf Roll Virus Disease Incidence and Its Vector

The potato leaf roll virus (PLRV) disease is a serious threat to successful potato production and is mainly controlled by integrated disease management; however, the use of chemicals is excessive and non-judicious, and it could be rationalized using a predictive model based on meteorological variables. The goal of the present investigation was to develop a disease predictive model based on environmental responses viz. minimum and maximum temperature, rainfall and relative humidity. The relationship between epidemiological variables and PLRV disease incidence was determined by correlation analysis, and a stepwise multiple regression was used to develop a model. For this purpose, five years (2010–2015) of data regarding disease incidence and epidemiological variables collected from the Plant Virology Section Ayub Agriculture Research Institute (AARI) Faisalabad were used. The model exhibited 94% variability in disease development. The predictions of the model were evaluated based on two statistical indices, residual (%) and root mean square error (RMSE), which were ≤±20, indicating that the model was able to predict disease development. The model was validated by a two-year (2015–2017) data set of epidemiological variables and disease incidence collected in Faisalabad, Pakistan. The homogeneity of the regression equations of the two models, five years (Y = −47.61 − 0.572x1 + 0.218x2 + 3.78x3 + 1.073x4) and two years (Y = −28.93 − 0.148x1 + 0.510x2 + 0.83x3 + 0.569x4), demonstrated that they validated each other. Scatter plots indicated that minimum temperature (5–18.5 °C), maximum temperature (19.1–34.4 °C), rainfall (3–5 mm) and relative humidity (35–85%) contributed significantly to disease development. The foliar application of salicylic acid alone and in combination with other treatments significantly reduced the PLRV disease incidence and its vector population over control. The salicylic acid together with acetamiprid proved the most effective treatment against PLRV disease incidence and its vector M. persicae.

Evolution of potential evapotranspiration and its sensitivity to climate change based on the Thornthwaite, Hargreaves, and Penman–Monteith equation in environmental sensitive areas of China

Global warming intensifies the water cycle and regional water condition differences by changing the surface environment. The ecological system in environmental sensitive areas of China (ESAC) is stressed by water resources, and the ecological effect of dry–wet evolution is even more profound. However, the applicability of different potential evapotranspiration (ET0) assessment methods and the response mechanism of ET0 to climate change remain unknown. In this study, we evaluated the reliability of ETTH, ETHarg, and ETPM derived from Thornthwaite (TH), Hargreaves (Harg), and Penman–Monteith (PM) formulas using the monthly average air temperature (Tavg), maximum air temperature (Tmax), minimum air temperature, sunshine duration (SSD), wind speed (WIN), and relative humidity (RHU) from 1961 to 2018. Further, non-parametric trend analysis, correlation analysis, multiple regression analysis, wavelet analysis, and seasonal and trend decomposition using Loess were used to analyze the spatial–temporal evolution of ET0 and the moisture index (MI) and its relationship with climate change in ESAC from 1961 to 2018. The results are as follows: (1) The P-M formula has excellent applicability in the research area. Under the background of average temperature rising at 0.035 °C yr−1, ETPM decreases with a rate of −0.59 mm yr−1, which indicates the “pan evaporation paradox” phenomenon. Using 1993 as the node, ETPM changed from a significant decrease to an increase. (2) WIN, SSD, Tavg, and Tmax significantly promoted changes in ETPM, and RHU and precipitation (Pr) were mainly reduced. The determinant factor of ETPM change was Tavg from 1961 to 2018. (3) MI change was mainly influenced by Pr, SSD, and ETPM. Pr promoted the change, whereas SSD and ETPM reduced it. The linear combination of ETPM and SSD could explain the change in MI. Our findings verified the applicability of PM in areas with severe interannual precipitation fluctuations, revealing the evolution of dry–wet pattern changes and their driving mechanisms in ESAC, which can deepen the cognition of the law of long-term land surface water cycle and effectively guide the formulation and implementation of regional drought and flood disaster response measures.

Is visitors’ expenditure at destination influenced by weather conditions?

ABSTRACT Weather has been shown to affect consumption patterns by altering people’s moods. This paper examines the impact of atmospheric conditions on destination expenditure considering cruise passengers’ onshore expenditure as the case study. We exploit quasi-random variation in a set of hourly real-time weather indicators in a port of call, through the Tourism Climate Index (TCI) and the Physiologically Equivalent Temperature (PET), to draw inference about their effect on destination expenditure. Therefore, we capture the specific atmospheric conditions encountered by tourists, alleviating the usual aggregation bias in related studies. In particular, information about mean and maximum air temperature, wind speed, rainfall, sunshine duration and mean and minimum relative humidity is considered. We estimate a heteroskedastic Tobit model with an inverse hyperbolic sine transformation of the dependent variable that deals with problems of non-normality and extreme values. Controlling for several sociodemographic characteristics and cruise size, we find consistent evidence that pleasant weather (either using TCI or PET indexes) increases onshore expenditure. Our findings have important implications for destination management.

Epidemiology of Late Blight of Potato, Its Progress and Apparent Rate of Infection

Late blight of potato caused by Phytophthora infestans is one of the most destructive diseases of potato world over. The devastation of the disease is very much related with climatic factors like temperature, relative humidity, fog or dew deposition and also sunshine hours. An experiment was conducted in Indo-Gangetic plains of West Bengal to know the effect of weather parameters on initiation and severity of the disease as well as progress and pattern of development of disease which will ultimately help to develop the effective spray schedule to manage the disease. From the experiment it is observed that 7 days prior to first appearance of late blight disease of potato the average maximum and minimum temperature varied between 24-26˚C and 7-8˚C respectively; average maximum and minimum relative humidity varied between 90-93% and 41-44% respectively; average sunshine hours per day varied between 6-8 hours. Correlation study of late blight disease severity with different weather parameters were also carried out. Maximum and minimum temperature as well as maximum and minimum relative humidity found to be positively correlated with late blight disease build up. While sunshine hour found to be negatively correlated with the disease build up. But only minimum temperature was significant at 0.01 level in all the planting dates and maximum temperature was significant at 0.01 level in two dates of planting. Apparent rate of infection of the disease and area under disease progress curve were calculated for different planting dates of potato. From the results it is evident that late blight of potato progressed almost in an exponential fashion starting from its first appearance. As the crop approached towards maturity and weather became unfavourable for the pathogen the rate of progress declined. This may be due to non-availability of healthy tissue.

Population Dynamics of Thrips Infesting Bt Cotton in Relation to Weather Factors

Population dynamics of thrips, Thrips tabaci Lindemann and their correlation with weather factors were investigated during 2017 to 2019 at ICAR-Central Institute for Cotton Research, Tamil Nadu. The incidence of thrips was recorded from the 39th SMW (Standard Meteorological Week) and peaked at 43rd SMW with the range of 0.7 to 14.7 thrips leaf-1 in 1st season (Kharif 2017-18). At the second season (Summer 2018), thrips incidence occurred at the 10th SMW and thrips population peaked in the 14th SMW and varied between 1.5 and 15.8 thrips leaf-1. Thrips incidence started during the 39th SMW during the third season (Kharif 2018-19). The thrips population peaked in the 42nd SMW, ranging from 1.4 to 11.2 thrips leaf-1. After reaching its peak, thrips population was constantly decreased. Correlation matrix revealed that thrips population correlated positively with minimum temperature, maximum temperature and relative humidity although there was a negative association with rainfall. These findings will be useful for evolving appropriate management practices for thrips.

Seasonal Incidence of Whitefly Bemisia tabaci (Genn.) on Mungbean

This study on the seasonal incidence of whitefly Bemisia tabaci (Gennadius) on mungbean was done during kharif, 2014. Weekly observations on incidence were made on five randomly selected tagged plants. The results revealed that the first incidence was observed during 22nd standard meteorological week (SMW)- 66.67 whitefly/plant. This reached at its peak (89.67 whitefly/plant) during the 34th SMW. Correlation coefficients between incidence and weather factors revealed that maximum temperature had a positive relationship (r = 0.51) while the minimum temperature and relative humidity- RH (morning) revealed a negative one (r = -0.03 and r = -0.52); and RH (evening) and rainfall showed a highly significant but negative correlation.

The impact of meteorological parameters on PM10 and visibility during the Middle Eastern dust storms.

Air pollution is one of the most pressing issues in populated Middle Eastern cities, in particular for the city of Ahvaz, Iran, imposing deleterious effects on the environment, public health, economy, culture, and other sectors. In this study, we investigate the relationship between meteorological parameters, PM10, AOD, air mass source origin, and visibility during severe desert dust storms (Average3h PM10 > 3200µgm-3) between 2009 and 2012. Six of seven such events occurred between February and March. Interestingly, for the seven cases there was always an alarming PM10 mass concentration peak (137-553µgm-3) between 12:00-18:00 (local time) that was 18-24h before the dominant peak of the storm (3279-4899µgm-3). The maximum wind speed over the multi-day periods examined for the dust storms is usually observed 6h before the alarming PM10 peak. The minimum relative humidity, dew point temperature and air pressure occurred ± 3h around the time of the alarming PM10 peak. Wind speed was the meteorological parameter that was consistently higher around the time of the first peak as compared to the second peak, with the reverse being true for sea level pressure. Based on four years of daily data in Ahvaz, PM10 was positively correlated with wind speed and air temperature and inversely correlated with sea level pressure and RH. An empirically-derived equation with R2 = 0.95 is reported to estimate the maximum PM10 concentration for severe desert dust events in the study region based on meteorological parameters. Finally, AOD is shown to correlate strongly (R2 = 0.86) with PM10 during periods with severe desert dust storms in the region.

Meteorological Influences on Spatiotemporal Variation of PM2.5 Concentrations in Atmospheric Pollution Transmission Channel Cities of the Beijing-Tianjin-Hebei Region, China.

Understanding the spatiotemporal characteristics of PM2.5 concentrations and identifying their associated meteorological factors can provide useful insight for implementing air pollution interventions. In this study, we used daily air quality monitoring data for 28 air pollution transmission channel cities in the Beijing–Tianjin–Hebei region during 2014–2019 to quantify the relative contributions of meteorological factors on spatiotemporal variation in PM2.5 concentration by combining time series and spatial perspectives. The results show that annual mean PM2.5 concentration significantly decreased in 24 of the channel cities from 2014 to 2019, but they all still exceeded the Grade II Chinese Ambient Air Quality Standards (35 μg m−3) in 2019. PM2.5 concentrations exhibited clear spatial agglomeration in the most polluted season, and their spatial pattern changed slightly over time. Meteorological variables accounted for 31.96% of the temporal variation in PM2.5 concentration among the 28 cities during the study period, with minimum temperature and average relative humidity as the most critical factors. Spatially, atmospheric pressure and maximum temperature played a key role in the distribution of PM2.5 concentration in spring and summer, whereas the effect of sunshine hours increased greatly in autumn and winter. These findings highlight the importance of future clean air policy making, but also provide a theoretical support for precise forecasting and prevention of PM2.5 pollution.

The Effect of Rainfall, Temperature, and Relative Humidity on the Yield of Cassava, Yam, and Maize in the Ashanti Region of Ghana

This study examined the consequences of changes in minimum temperature, maximum temperature, relative humidity, and rainfall on the yields of maize, cassava, and yam per hectare of land in the Ashanti Region of Ghana. Correlation analysis of each climatic condition on the yield of each crop per hectare of land revealed that each of the climatic conditions was significant in predicting the crop yields. Separate multiple linear regression models were obtained for crop yield per hectare of land under all the climatic conditions. The regression models showed that an increase in maximum temperature reduces the yield of all the crops, whereas an increase in minimum temperature reduces only the yield of maize. Increases in relative humidity reduce the yield of maize alone, while increases in rainfall reduce the yield of only cassava. The significant multiple linear regression model for each crop yield indicated that 63.8% of the variations in the yield of maize per hectare of land, 74.3% of the variations in the yield of cassava per hectare of land, and 64.2% of the variations in the yield of yam per hectare of land are accounted for by minimum temperature, maximum temperature, relative humidity, and rainfall. We encourage the Government of Ghana, the Ministry of Food and Agriculture, and all stakeholders in the agriculture sector to increase their campaign on the consequences of climate change on the yield of these crops. They should educate farmers on the effects of overreliance on rainfed and traditional agricultural methods, introduce them to modern methods of agriculture, and provide them with varieties of these crops with higher-yielding capacities in higher temperatures.

Impact analysis of environmental and social factors on early-stage COVID-19 transmission in China by machine learning

As a highly contagious disease, COVID-19 caused a worldwide pandemic and it is still ongoing. However, the infection in China has been successfully controlled although its initial transmission was also nationwide and has caused a serious public health crisis. The analysis on the early-stage COVID-19 transmission in China is worth investigating for its guiding significance on prevention to other countries and regions.In this study, we conducted the experiments from the perspectives of COVID-19 occurrence and intensity. We eliminated unimportant factors from 113 variables and applied four machine learning-based classification and regression models to predict COVID-19 occurrence and intensity, respectively. The influence of each important factor was analysed when applicable.Our optimal model on COVID-19 occurrence prediction presented an accuracy of 91.91% and the best R2 of intensity prediction reached 0.778. Linear regression-based model was identified as unable to fit and predict the intensity, and thus only the variable influence on COVID-19 occurrence can be explained.We found that (1) CO VID-19 was more likely to occur in prosperous cities closer to the epicentre and located on higher altitudes, (2) and the occurrence was higher under extreme weather and high minimum relative humidity. (3) Most air pollutants increased the risk of COVID-19 occurrence except NO2 and O3, and there existed a lag effect of 6–7 days. (4) NPIs (non-pharmaceutical interventions) did not show apparent effect until two weeks after.

Simulation of dew point temperature in different time scales based on grasshopper algorithm optimized extreme gradient boosting

Dew point temperature (Tdew) plays an important role in hydrology, meteorology, and other related research. This study evaluated the ability of a new machine learning model (hybrid extreme gradient boosting with grasshopper optimization algorithm (GOA-XGBoost)) to estimate Tdew and compared it with two other tree-based models (XGBoost and random forest (RF)). We collected meteorological data namely actual vapor pressure (ea), maximum air temperature (Tmax), minimum air temperature (Tmin), maximum relative humidity (RHmax), minimum relative humidity (RHmin), atmospheric pressure (Pa), 2 m high wind speed (Ud), during 2016–2019 on daily and hourly time scales from the Sijiqinglin station in China to train, test, and validate each model. The results showed that the GOA-XGBoost model performed best, and the RF model had severe over-fitting problems during the validation phase at daily time scale. The models showed the best accuracy and stability when the input was ea (on average R2 = 1.000, RMSE = 0.296℃, MBE = 0.001℃, MAE = 0.167℃, and KGE = 0.991). The models had more significant errors when the inputs were Tmax, Tmin (on average R2 = 0.721, RMSE = 6.756℃, MBE = -0.101℃, MAE = 5.071℃, and KGE = 0.771). The estimation loss exhibited by the models were similar for the hourly and daily scale patterns. T and RH were the most basic meteorological factors and adding extraneous factors would affect the estimation accuracy of the model. The variability of meteorological data varied less on an hourly scale than on a daily scale. Therefore, the accuracy of the models was higher, but the data set and the volume of operations became larger. This led to a possible reduction in model stability, but the hourly scales are better suited for assessing the effects of simulations in extreme situations. Taking accuracy and stability into account, the GOA-XGBoost model was the best model and the most practical input for both time scales was ea. Therefore, in subsequent studies, the GOA-XGBoost model can be combined with the input ea to estimate Tdew accurately.

Seasonal incidence of mustard aphid, Lipaphis erysimi kaltenbach on Rabi mustard crop

The maximum population of Lipaphis erysimi were recorded in 8th standard week (80.00) of February on mustard crop when average atmospheric temperature was 21.4°C, relative humidity 66% and rainfall 6.0 mm. The population was slowly decline and reached zero in13 standard week of month of March when average maximum temperature 26.90 °C, average maximum relative humidity 46.5%, average maximum rainfall 3.0 mm. The aphid population showed a nonsignificant negative co-relation with maximum temperature while minimum temperature relative humidity rainfall showed a non-significant positive correlation.

Prediction of Rainfall based on Statistical and Computational Approach

A comparative study is done in this paper in the prediction of rainfall at ground level multiple linear regression and, feature selection and k-means clustering method. Based on the past observations of the last three days atmospheric parameters like minimum and maximum Temperature, minimum and maximum relative humidity, minimum and maximum air pressure, minimum and maximum vapour pressure and minimum and maximum radiation the model is developed. In this paper it is observed that considering the seasonality effect better results can be achieved. It has also been observed that the selection of appropriate features can also improve the performance of the prediction.

MULTIVARIATE MODELS FOR PREDICTING GLOBAL SOLAR RADIATION IN JOS, NIGERIA

This study developed two to six multivariate regression equations that reliably predict global radiation in Jos (Latitude 9.87 °𝑁 and Longitude 8.75 °𝐸). Thirty-one years (1980 – 2010) observed monthly mean daily global solar radiation, sunshine hours, maximum and minimum temperatures, cloud cover, rainfall, relative humidity and wind speed data were used in this study with the clearness index as the response variable and other variables as predictors. The seven validation indices employed are the coefficient of determination (R2), Mean Bias Error (MBE), Root Mean Square Error (RMSE), Mean Percentage Error (MPE), t – test, Nash – Sutcliffe Equation (NSE) and Index of Agreement (IA) to determine the reliability, suitability and applicability of the developed models. The results in this study revealed that all the developed multivariate models were found reliable for global solar radiation estimation in Jos depending on the obtainable meteorological data measured in the location. The correlation between the measured and predicted (developed) global solar radiation shows a perfect correlation as depicted from the figures.

The future climatic variabilities in the Mano River union, its implications on socio-economic development

Knowledge of climatic variability of any country or region is essential to socioeconomic development. This is particularly important as most sectors of national development can be altered if the climatic conditions are unsuitable. A hypothetical case lies in the fact that agriculture largely depends on apposite climate for fruitful production. The Mano River Region consists of some of the world's poorest and least developed nations (Guinea, Sierra Leone, Liberia, Cote D'Ivoire). The primary means of sustenance in this region is rain-fed agriculture. Knowledge of current and future climatic conditions in the region will be of significant benefit to the economy of the region. This work investigates the current and future state of climate in the region using six climatic parameters (Maximum and minimum temperature, Precipitation , Relative Humidity, Wind Speed and Solar Radiation) spanning 1975-2018. To model the im-pact/relationship, Kalman Filter was used. These variables were grouped into state transition and control variables. Transfer functions which depict relationships between every two variables at a time with one being input and the other considered as output were used to determine state transition and control variable matrices. Control variables (population and land use) were introduced to control the dynamism of the model in MATLAB environment. Results show that there is a drastic variation in climate in the region within the period of the data. This work establishes that there are rapid variabilities in these parameters which can be attributed to increase in population and loss of vegetation.