The study is based on secondary data. Data on maximum and minimumtemperature for Thiruvananthapuram district of Kerala was used for the analysis. Daily data corresponds to maximum and minimumtemperature were recorded and maintained by the Department of Agricultural Meteorology, College of Agriculture, Vellayani were collected for the period from 1985 to 2013. Monthly mean was worked our using the daily data. An attempt was made to develop the ARIMA model for monthly mean data for forecasting separately for maximum and minimumtemperature of Thiruvananthapuram district of Kerala. Descriptive statistics was done using mean, standard deviation (SD) and coefficient of variation (CV). ARIMA (Auto Regressive Integrated Moving Average) model or Box- Jenkins model, being called as (p, d, q) model, where p and q denotes the number of auto-regressive and moving average terms and d is the order of differencing, which can be expressed in the following form
The normal probability distribution, also known as Gaussian distribution, was discovered by a German mathematician Carl Friedrich Gauss in the year 1809. Some authors credit this discovery to a French mathematician Abraham De Moivre who published a paper in 1738 that showed the normal distribution as an approximation to the binomial distribution discovered by James Bernoulli. The normal probability distribution plays the key role not only in the development of most of the theories in statistics but also in the analysis of data associated to many real phenomena. There are innumerable phenomena where one can think of applying the theory of normal probability distribution to analyze the phenomena based on data collected from them. In the case of temperature at a location, it is a reality that temperature is a variable which changes continuously over time.. The temperature at a location corresponds, in a year, to one maximum value and one minimum value that ought to be constants if the pattern of this change is not influenced by some unnatural factor/factors. There is scope of applying the theory of normal probability distribution in estimating the annual maximum and minimumtemperature at a location. In this study a method of has been framed of for estimating the annual maximum and minimumtemperature at a location by the application of the area property of normal probability distribution. The method has been applied in estimating the annual maximum and minimum of temperature in the context of Assam.
Abstract:- In a developing country, like India where agriculture is the base for national economy, the weather conditions including MinimumTemperature, MaximumTemperature and Pressure play a leading role. Therefore having accurate weather forecasting information may allow farmers to make their better decisions on managing their farms. Similar statement can be made for industries where accurate weather condition prediction may help them in their proper development as these are the factors that affect them directly or indirectly. Soft computing using ANN is an effective approach to construct a computationally intelligent system that is able to process non-linear weather conditions within a specific domain, and make predictions. The proposed work focus on modeling of such an intelligent system that can forecast with minimum error rate in term of MSE with a better Architecture.
In this study, sunshine based Angstrom-Prescott model along with multiple linear regression model based on three meteorological parameter (relative sunshine hours, maximum and minimumtemperature, minimum relative humidity) were nthly average global solar radiation for the year 2011 of Jumla. The estimated solar radiation from these models were compared with the measured solar radiation which were found to be in close agreement with each error (MBE), mean percentage error (MPE), root mean square error (RMSE) and ) were performed to evaluate the model. The multiple linear regression model has a higher (0.95) and lower value of RMSE (0.3614) which indicate a better agreement between the measured and estimated global solar radiation. It is suggested that the multiple linear model can be employed for the estimation of monthly global solar radiation on horizontal surface for Jumla and other region with similar climatic condition where radiation data are
In the context of climate change, the frequency and intensity of extreme events such as floods and droughts will increase which could put tremendous challenges in water resources management in the coming days. While scientific knowledge on climate threats and changing climate patterns are essential, it is also important to consider the impacts in relation to how the threats are perceived and handled by local people. This paper intends to assess the trend and people’s perception on temperature and precipitation. Three focus groups’ discussion and a total number of 240 house- holds were interviewed during field visit. The collected information was scaled from the least pre- ferred-1 to the most preferred-5 based on their preferences. The trend of mean of annual average, maximum and minimumtemperature indicates that the temperature has increased significantly and precipitation intensity and magnitude are also in increasing trend in the monsoon and post- monsoon seasons which may raise the extreme flood events. These facts were verified with the people’s perception. This finding could be useful for formulation of effective flood management policy and plan in this river basin as well as very applicable for other similar areas.
This study uses the meteorological data provided by 2 different sources which are the Department of Irrigation and Drainage (DID) and Malaysia Meteorological Department (MMD). In this study, 3 types of climate variable were selected which is total rainfall, temperature, and relative humidity. Both sources have their own pros and cons. For example, MMD very helpful to provide the data for all kind of variable, but we are facing some problem with the location where the data was collected. MMD only can provide one station to collect all these 3 data. The location of this station is Seremban City. While, it differs from DID which only provide rainfall data, but they can provide this data at a different location. In other words, this department provides many stations to collect the rain reading data per day. 4 stations were selected which very close with 4 regions that have been divided during pre-processing clinical data. During the early stage, all this data was given in daily format. Based on a few previous studies, it almost impossible to predict accurately in daily the occurrence of disease. Thus, a few things must be done with this data before we proceed with data analysis. Authors were decided to convert the data from daily to weekly. Thus, they sum up the value of rainfall every 7 days while for temperature and humidity, they take the mean value for every 7 days. Since all this data are weekly, temperature data can be processed again to create another 2 variable which a maximum and minimumtemperature for the given week. Thus, for the temperature variable, we successfully create 3 covariates as input for our model. As mentioned before 7% of raw meteorological data was missing. All this missing data may be erased as a solution to solve this problem, but it may also affect missing out on very important information which would interfere with the accuracy of this model. After doing a few analyses, authors have found rainfall in Seremban have a constant pattern for every year (from 2011 until 2017) or in other words, it was seasonally. Thus, it will help in handle missing data problem. Linear regression was applied to predict both of this variable. Authors also found temperature and rainfall have a strong correlation by doing the linear regression analysis. For example, if rainfall data is to be predicted, the temperature will become as a predictor (x-axis) while vice versa if temperature data need to be predicted as shown in Figure 2. This regression was considered as successful because the error percentage is below than 20% as shown in Figure 3.
Figure 9 shows the ACC comparison between the second- day RAMS forecast and the second day of a 48-h persistence forecast for mean temperature. The second day is chosen because it does not show the best or the worst performance, so provides a good representation of the whole forecast be- haviour. There is large day-to-day variability and the RAMS forecast is usually better (70% of the cases) than persistence. This behaviour is also shown by minimum (69%) and maxi- mum (75%) temperature second-day forecasts (not shown). As discussed in the previous section, the RAMS forecast was particularly poor for four consecutive days in September (days 100–103, from 14 to 17 September). The second-day RAMS forecast is useful in 90% of cases for mean tempera- ture, in 76% of cases for minimumtemperature, and in 92% of cases for maximumtemperature. So, despite the RMSE of maximumtemperature being the largest, the RAMS fore- cast better follows the day-to-day variability of this parame- ter. Similar considerations apply to other forecast days (not shown).
parasite (13 days for Plasmodium falciparum)  and the incubation period (seven days to four weeks), it is assumed that malaria incidence in a given month is asso- ciated with climatic conditions of the same month and those of the previous month. Most of those who become ill in a given month were bitten by mosquitoes in the pre- vious month. The data are available in different scale and units (malaria and humidity data are unit-free, rainfall is measured in centimetres (cm) and temperature in degrees centigrade (°c)). To avoid the effect of scale in our modelling, the data are first standardized. Throughout this study we adopt the following notation for the vari- ables:R n is the rainfall, T x is the maximumtemperature, T n is the minimumtemperature, H x is the maximum humidity, H n is the minimum humidity and R np , T xp , T np ,
This study investigates the statistical relationship between climatic variables and aspects of cot- ton production (G. barbadense), and the effects of climatic factors prevailing prior to flowering or subsequent to boll setting on flower and boll production and retention in cotton. The effects of specific climatic factors during both pre- and post-anthesis periods on boll production and reten- tion are mostly unknown. Thus, an understanding of these relationships may help physiologists to determine control mechanisms of production in cotton plants. Evaporation, sunshine duration, relative humidity, surface soil temperature at 1800 h, and maximum air temperature, are the im- portant climatic factors that significantly affect flower and boll production. The least important variables were found to be surface soil temperature at 0600 h and minimumtemperature. There was a negative correlation between flower and boll production and either evaporation or sun- shine duration, while that correlation with minimum relative humidity was positive. Higher min- imum relative humidity, short period of sunshine duration, and low temperatures enhanced flower and boll formation.
Using the chronic obstructive pulmonary disease (COPD) medical records from January 1st to December 31st of 2013 and the Meteorological observa- tion data, the air pollution data in the same time periods, generalized additive models were used to quantitatively analyze the relationship between COPD hospitalizations and temperature with controlling the confounding effects of time trend, meteorological factors and air pollution index (AQI). Results showed: variable temperature in 24 h (BT), 3d lagged minimumtemperature (Tm3) and 5d lagged diurnal maximumtemperature and minimum tempera- ture range (Tc5) have different effects on COPD hospitalizations. When BT is between −4.4˚C and −0.7˚C, the relative risk (RR) increases to 1.0207 (95% CI: 1.0074 - 1.0342 ） with every 1˚C increase in BT; when Tm3 is between −3.6˚C and 3.2˚C, the relative risk (RR) increases to 1.0118 (95% CI: 1.0015 - 1.0222 ） with every 1˚C increase in Tm3, and when Tm3 is greater than 20.5˚C, the relative risk (RR) increases to 1.0069 (95% CI: 1.0005 - 1.0133) with every 1˚C increase in Tm3; when Tc5 is between 0.9˚C and 8.6˚C, if the Tc5 in- creases 1˚C, the relative risk (RR) increases to 1.0125 (95% CI: 1.0066 - 1.0185. There are different effects for weather in different seasons on COPD hospitalizations: in autumn and winter, it is mainly of little BT and heavy air pollution weather; in spring, the large Tc5 weather is a main feature, and in summer, it’s mainly of high temperature and low pressure weather. The re- sults help to provide some guidance on COPD forecasting services.
This study centers on applying the statistical downscaling technique to the daily minimum and maximum temperatures of Port Harcourt from the period 1985-2014. To select the period of calibration, the wilby and wigley assump- tion of 2014 was adopted. However, the Bruckner circle assumption was adopted in selecting the normal under review. Secondary data of minimum and maximum temperatures for Port Harcourt were collected from the archive of Nigerian meteorological agency (NIMET). The grid cell of the HadCM3 cor- responding to the Port Harcourt meteorological station was selected from the HadCM3 website to generate the largescale predictors. Data for temperature was there after normalized for the period of calibration. To analyze data, ANOVA and Paired t tests were used. Result showed that, the model was sig- nificant at p < 0.05 implying that the largescale predictors of the HadCM3 have performed significantly and that temperature pattern in the area is sig- nificantly dependent on them. The Duncan statistics showed that in the A2 scenario Maximumtemperature will rise with a mean difference of 3.1˚C from 1960-2080, while for B2 the increase will be 0.18˚C for same period. For minimumtemperature, the ANOVA also showed a difference of 0.21˚C and 0.11˚C for A2 and B2 respectively. The paired t test statistics showed that these variations in temperatures for both maximum and minimum at A2 and B2 scenarios are significant at p < 0.05. Reforestation, afforestation, carbon sequestration are strongly advocated.
Maximum and minimum temperatures time series of Congo-Brazzaville are analyzed for trend and discontinuities over the period 1932 to 2010. Temperatures series show an irregular increase. A total of 8 synoptic stations show positive trends in their annual mean maximumtemperature se- ries, and 7 of them are significant, with higher trends for urban stations. Annual mean minimumtemperature showed 6 stations having positive trends. This increase is in relation with observa- tions at regional scale. However, the differences are observed between large towns (Brazzaville and Pointe-Noire), and small or rural towns (Dolisie, Sibiti, Impfondo, Djambala). Trends in diur- nal temperature range (DTR) are large positive trends in maximumtemperature that are mainly observed in cities. The curve of DTR shows a decreasing trend which indicates the increasing of minimum temperatures. The effects of urbanization on temperature trends are investigated. Most stations regarded as urban stations are still useful for trend analysis; being situated on the sub- urban of the studied cities, they are therefore, not substantially influenced by the urban heat isl- and.
A study of the observations shows that the minimumtemperature of 13.25 o C occurs in January which corresponds to the Harmattan season while the maximumtemperature did occur in March which corresponds to a season of maximum heat in the region. High temperatures of more than 35 o C and low temperatures of less than 15 o C have both shown adverse effects on mortality and an estimated 7.7% of mortality was attributable to non- optimum temperature . From the analysis temperatures in December and January were low and was associated with high cases of pneumonia and during the months of high temperatures (March and April) cases of meningitis was high. The descriptive statistic of the monthly minimum observation of each year show that in 1954 for example, average minimal temperature was 22.08 o C, the minimal minimumtemperature was 18.5 the maximum minimal temperature was 39.8 o C while the sample standard deviation of the maximal temperature in 2014 was 2.17 o CThe time series plot (Figure 2) of both the average minimal temperature and the average maximal temperatures from January 1954 to December 2014 seems to follow a common long-run path prompting the question whether the long-run trend is the same in the two temperatures. The Unit Root tests performed suggest that there is no unit root in the data; which means the data are stationary.
Abstract Making deductions and expectations about cli- mate has been a challenge all through mankind’s history. Challenges with exact meteorological directions assist to foresee and handle problems well in time. Different strategies have been investigated using various machine learning techniques in reported forecasting systems. Cur- rent research investigates climate as a major challenge for machine information mining and deduction. Accordingly, this paper presents a hybrid neural model (MLP and RBF) to enhance the accuracy of weather forecasting. Proposed hybrid model ensure precise forecasting due to the spe- cialty of climate anticipating frameworks. The study con- centrates on the data representing Saudi Arabia weather forecasting. The main input features employed to train individual and hybrid neural networks that include average dew point, minimumtemperature, maximumtemperature, mean temperature, average relative moistness, precipita- tion, normal wind speed, high wind speed and average cloudiness. The output layer composed of two neurons to represent rainy and dry weathers. Moreover, trial and error approach is adopted to select an appropriate number of inputs to the hybrid neural network. Correlation coefficient, RMSE and scatter index are the standard yard sticks adopted for forecast accuracy measurement. On individual standing MLP forecasting results are better than RBF, however, the proposed simplified hybrid neural model comes out with better forecasting accuracy as compared to
Dengue is the world most serious arboviral diseases with regard to the num- ber of people infected. In 2012, WHO informed that Indonesia is the second largest with dengue cases among the endemic countries. The most prevalent province in Indonesia of dengue cases outside java island was North Sumatra where Medan city was recorded as the highest cases within the province. Ur- banization, demographic change and warming temperatures were related with the recent expansion of the primary vector of dengue; Aedes aegypti and Aedes albopictus . In this paper, we examined the relation between climate factors and dengue cases in the study area. The association of them was per- formed through Generalized Additive Models (GAM), considering the num- ber of dengue patients as response variable and climatic factor such as preci- pitation, minimumtemperature, maximumtemperature, average tempera- ture and relative humidity as predictor variables. In addition, using this mod- el vulnerability map was constructed. The result stated that climate variation influenced the number of dengue haemorrhagic fever (DHF) patients as 66.1% with precipitation variable was more important followed by maximumtemperature. Furthermore, the highest risk of dengue was located in the main city of Medan.
during the study period is given in Tables 19-25. It also includes the coefficient of determination (R 2 ) value which helps us to indicate the quality prediction of dependent variable. Analysis shows that in most cases the average air maximumtemperature, average minimumtemperature, average soil temperature, total solar radiation, total PET and rainfall significantly different in each month from the year 2007 to 2011. However, there are some exceptions and data analysis revealed that there were no significance difference in June for average maximumtemperature from 2007 to 2011 and June, August for average minimumtemperature. The mean average soil temperature in May, July, August, and September for year 2007 to 2011 are not significantly different. In case of total solar radiation as well, there are months of May, June, August and September which did not show any sig- nificant difference. The mean total PET in April, June, August and September for 2007 to 2011 were not found significantly different. Similar observation is made for the mean rainfall in July for year 2011, 2007, 2008 and 2010 which are not all significant to each other.
Kermanshah province is located in the middle of western part of Iran with an area of 25008 km 2 within 45º 24' and maximum 48º 07' of east longitude and 33º 40' and maximum 35º 18' of north latitude  the fig .1 shows Location of Kermanshah province in Iran. Kermanshah has a climate which is heavily influenced by the proximity of the Zagros mountains, classified as a hot dry summer Mediterranean climate . The city's altitude and exposed location relative to westerly winds makes precipitation a little bit high (more than twice that of Tehran) , but at the same time produces huge diurnal temperature swings especially in the virtually rainless summers, which remain extremely hot during the day . Kermanshah experiences rather cold winters and there are usually rainfalls in fall and spring. Snow cover is seen for at least a couple of weeks in winter. This province meets Kurdestan province from north, Lorestan and Ilam provinces from south and Hamadan province from east and has 330 km of common border with Iraq . All data were calculated using observations from Kermanshah airport regional station was coincides with those obtained by using in situ automatic weather station . For this purpose, the amounts of incoming solar radiation on a daily basis were estimated considering local in Station No. 40766 (altitude, latitude and longitude) and climatic (Humidity, temperature, pressure, length of day, sunshine hours, solar angle, sky albedo, absorption by aerosols, ground albedo, air mass, absorption by ozone, and Rayleigh distribution) characteristics .
For further studies concerning the use of IRT in automatic udder health monitoring, it is of particular interest to evaluate the resistance of IRT measurements towards falsifications by contaminations in the ROI’s. Results of precision analysis in this study indicate that evaluation parameter ‘Max-Min’ has the poorest precision in all methods, followed by evaluation parameter ‘Min’. This is most probably due to contaminations, since ‘Min’ represents the pixel with the lowest temperature in the ROI. Evaluation parameters ‘Avg’ and ‘Max’ both show good precision: ‘Avg’ takes the temperatures of all pixels in the ROI into account, thereby reducing the impact of outliers. ‘Max’ stands for the pixel with the highest temperature in the ROI, which most likely originates from the udder surface if no external heat sources are nearby. It is thus conceivable that evaluation parameters ‘Avg’ and ‘Max’ are robust to a certain level of contamination and usable for the evaluation of thermograms taken without extensive preparations. However, this assumption has to be validated by further research. GLAS (2008) also concludes that minimum values of USST are prone to falsifications and should not be used for evaluation. Cows were tethered with a collar which reduced their mobility, thereby measurements were simplified and could consistently be recorded from the same angle with the same distance. The automatic image recognition software was able to correctly detect the hindquarters in all thermograms. Installation of an IRT- camera in stables where cows can move freely can involve further challenges: The Active Shape Approach algorithm would not only have to be trained for naturally occurring variations in the shape of the udder, but also in recognizing the udder from different angles and from different distances.
An immediate result is that the parameter values estimated using the complete corn data were found to closely match the values estimated using only the ten percent sample of corn data. The difference in each estimated value was less than 1% for most parameters, with the largest difference being 5% for the maximum volume per irrigation (ARMX) parameter. Optimization of the three variables that are associated with irrigation system management (IRI, ARMN, and ARMX, Ta- ble 1) resulted in similar values across the four crops (Ta- bles 4 to 7). The minimum application interval (IRI) for all crops was approximately 10.3 d (Tables 4 to 7) and is longer than typically experienced under current production and en- vironmental constraints. Given sufficient data, EPIC applies reasonable total amounts of water on a countywide basis (e.g. corn in Table 8), but appears to do so in fewer, larger, appli- cations. The average well capacity for all wells 45 ha. Us- ing these values, it is possible to apply an irrigation depth of 37 mm every 5.4 d. A common practice is for constant irri- gation during critical growth stages so as to maximize yields. Additional model runs indicate that EPIC outputs are not par-