scholarly journals Development of high spatial resolution weather data using daily meteorological observations over Indian region

MAUSAM ◽  
2021 ◽  
Vol 71 (4) ◽  
pp. 605-616
Author(s):  
NAGORI ROHIT ◽  
CHAUDHARI K. N.
Keyword(s):  
Spatial Resolution ◽  
Minimum Temperature ◽  
Spline Interpolation ◽  
Growth Dynamics ◽  
Weather Data ◽  
Maximum Temperature ◽  
Data Sets ◽  
Daily Weather Data ◽  
Quality Checks ◽  
Meteorological Observations

Finer spatial resolution interpolated weather data is essential to enable utilization of satellite-based images in studies related to crop growth dynamics, etc. Satellite data are available daily at 1 × 1 km or at the most within 5 × 5 km grid. To make the weather data timely available at the same spatial scale, the procedure has been developed to generate the spatially interpolated weather data product over India. Daily weather data (minimum & maximum temperature and rainfall) available at point scale on India Meteorology Department web site have been used in this study. A semi-automated user interactive Graphical User Interface (GUI) has been developed which quality checks the temperature data sets by filling the missing data sets as well as providing a platform to correct erroneous data which have been identified using statistical methods taking spatial as well as temporal incompatibility into account. Daily spatially interpolated product is generated in image form using thin plate spline interpolation technique that uses the quality checked weather data as well as elevation information from CARTODEM data in order to account for effect of      elevation on temperature. The validation was performed using “Jack-knife testing method” for three different seasons  i.e., monsoon, summer and winter. The mean absolute errors for decadal averaged products were found to vary within 1.2-1.5 °C for maximum temperature, 1.1-1.7 °C for minimum temperature, 1.0-7.0 mm for rainfall considering all seasons with higher error observed in monsoon for maximum temperature and rainfall and in winter for minimum temperature. It was found that errors were close to 1 °C for stations with elevation less than 550 m whereas in central portion of India, mean absolute errors were found to be less than 1 °C.

Relationship of weather and plant factors to alfalfa bloat in autumn

1991 ◽  
Vol 71 (3) ◽  
pp. 861-866 ◽  
Author(s):  
J. W. Hall ◽  
W. Majak
Keyword(s):  
Key Words ◽  
Total Nitrogen ◽  
Minimum Temperature ◽  
Dry Matter ◽  
Extreme Case ◽  
Weather Data ◽  
Maximum Temperature ◽  
Soluble Nitrogen ◽  
Acid Detergent Fiber ◽  
Relationship Of

The bloat status of cattle was recorded in the autumns of 6 yr when bloat occurred during the decade 1979–1988. Weather data were available for all 6 yr, plant dry matter, acid detergent fiber and plant chlorophyll for 3 yr and plant total nitrogen and soluble nitrogen for 4 yr. The percentages of dry matter and acid detergent fiber were lower and the concentrations of chlorophyll, total nitrogen and soluble nitrogen were higher on days when bloat occurred than when it did not. There was no difference in minimum temperature classified by bloat status on the same day, or in maximum temperature, hours of sunshine or precipitation classified by bloat status on the next day. Hours of sunshine and the temperature range were greater on days when bloat occurred. Bloat was observed after "killing frosts" of −2.2 °C in all years and in an extreme case after a daily minimum of −9.6 °C. Key words: Legume, bloat, alfalfa, cattle, climate

scholarly journals Frequency of compound hot-dry weather extremes has significantly increased in Australia since 1889

2021 ◽  
Author(s):  
Brian Collins
Keyword(s):  
Temporal Variations ◽  
Nonparametric Tests ◽  
Weather Data ◽  
Maximum Temperature ◽  
Daily Maximum Temperature ◽  
Daily Maximum ◽  
Grid Cells ◽  
Weather Extremes ◽  
Spatial Homogeneity ◽  
Daily Weather Data

There is high confidence that climate change has increased the probability of concurrent temperature-precipitation extremes, changed their spatial-temporal variations, and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily weather data (131 years, 1889-2019) at 700 grid cells (1◦ × 1◦) across Australia was obtained to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot-dry (CHD) events based on an ‘And’ hazard scenario (hotter than a threshold ‘And’ drier than a threshold). CHD extremes were defined as years with joint return periods of larger than 25 years. Mann-Kendall nonparametric tests was used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. A general cooling-wetting trend was observed over 1889-1989. Significant increasing trends were detected over 1990-2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). Results showed a significant increase in the association between temperature and precipitation at various temporal scales. The frequency of CHD extremes was mostly stable over 1889-1989, but significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south-east and south-west. Spatial homogeneity (i.e. connectedness) and propagation of extreme events from one grid cell to its neighbouring cells was investigated across Australia. It can be concluded that this connectedness has not significantly changed since 1889.

scholarly journals A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

2020 ◽  
Vol 8 (7) ◽  
pp. 09-17
Author(s):  
Varsha M., Dr. Poornima B.
Keyword(s):  
Prediction Model ◽  
Minimum Temperature ◽  
Blast Disease ◽  
Weather Data ◽  
Maximum Temperature ◽  
Research Station ◽  
Disease Prediction ◽  
Features Selection ◽  
Ensemble Classifiers ◽  
Epidemic Disease

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifier based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

scholarly journals Simulating the Impact of Climate Change on Growth and Yield of Maize Using CERES-Maize Model under Temperate Kashmir

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Bilal Ahmad Lone ◽  
Shivam Tripathi ◽  
Asma Fayaz ◽  
Purshotam Singh ◽  
Sameera Qayoom ◽  
...  
Keyword(s):  
Climate Variability ◽  
Minimum Temperature ◽  
Maize Yield ◽  
Climate Impact ◽  
Growth And Yield ◽  
Weather Data ◽  
Maximum Temperature ◽  
The Impact ◽  
Global Food ◽  
Process Based Models

Climate variability has been and continues to be, the principal source of fluctuations in global food production in countries of the developing world and is of serious concern. Process-based models use simplified functions to express the interactions between crop growth and the major environmental factors that affect crops (i.e., climate, soils and management), and many have been used in climate impact assessments. Average of 10 years weather data from 1985 to 2010, maximum temperature shows an increasing trend ranges from 18.5 to 20.5°C.This means there is an increase of 2°C within a span of 25 years. Decreasing trend was observed with respect to precipitation was observed with the same data. The magnitude of decrease was from 925 mm to 650 mm of rainfall which is almost decrease of 275 mm of rainfall in 25 years. Future climate for 2011-2090 from A1B scenario extracted from PRECIS run shows that overall maximum and minimum temperature increase by 5.39°C (±1.76) and 5.08°C (±1.37) also precipitation will decrease by 3094.72 mm to 2578.53 (±422.12) The objective of this study was to investigate the effects of climate variability and change on maize growth and yield of Srinagar Kashmir. Two enhanced levels of temperature (maximum and minimum by 2 and 4°C) and CO2 enhanced by 100 ppm & 200 ppm were used in this study with total combinations of 9 with one normal condition.  Elevation of maximum and minimum temperature by 4°C anthesis  and maturity of maize was earlier 14 days with a deviation of 18%  and  26 days with a deviation  of 20% respectively. Increase in temperature by 2 to 4°C alone or in combination with enhanced levels of CO2 by 100 and 200 ppm the growth and yield of maize was drastically declined with an reduction of about 40% in grain yield. Alone enhancement of CO2  at both the levels fails show any significant impact on maize yield.

scholarly journals Investigation of the maximum and minimum temperatures in the semi-arid region of northeastern Brazil

2011 ◽  
Vol 5 (2) ◽  
pp. 82
Author(s):  
Eunice Maia Andrade ◽  
Meilla Marielle Araújo Rodrigues ◽  
Marcos Amauri Bezerra Mendonça ◽  
Luiz Carlos Guerreiro Chaves ◽  
Rebeca Mendes Feitoza
Keyword(s):  
Minimum Temperature ◽  
Northeastern Brazil ◽  
Maximum Temperature ◽  
Data Sets ◽  
Air Temperatures ◽  
Irregular Pattern ◽  
Temperature Records ◽  
Earth’S Climate ◽  
Semi Arid Region ◽  
Semi Arid

Temperature records all over the world provide evidence that the earth’s climate is changing. To investigate changes in the extreme temperatures of semi-arid regions, we analyzed 33 years (1975-2008) of monthly maximum and minimum air temperatures for three weather stations located in Quixeramobim, Crateús and Barbalha Cities, Ceará, Brazil. The data sets were provided by INMET (Instituto Nacional de Meteorologia), Brazil. Dataset of each station was shared in decades to better understand the temperature tendency as well as to identify the warmest one. The two most recent decades were the warmest at all three stations investigated, and the highest temperature values were observed for Barbalha station. The highest increases of maximum temperature occurred during the dry season (May/Dec), and the warmest month was October, during which temperature increases of up to 1.63 °C were observed in the 1980s. The minimum temperature increased substantially during the rainy season (Jan/Apr) and during the coldest months (Jun/Jul). The highest increase of minimum temperature (3.08°C) was observed in July at the Barbalha station. The Quixeramobim station showed no significant increases in minimum temperature. The results indicate that temperature increases occur in an irregular pattern, suggesting that various regional agents affect changes in temperature.

scholarly journals A Comparative Study of Ensemble Classifiers for Paddy Blast Disease Prediction Model

2020 ◽  
Vol 8 (9) ◽  
pp. 05-13
Author(s):  
Varsha M., Dr. Poornima B.
Keyword(s):  
Prediction Model ◽  
Minimum Temperature ◽  
Blast Disease ◽  
Weather Data ◽  
Maximum Temperature ◽  
Research Station ◽  
Disease Prediction ◽  
Features Selection ◽  
Ensemble Classifiers ◽  
Epidemic Disease

Paddy blast has become most epidemic disease in many rice growing countries. Various statistical methods have been used for the prediction of paddy blast but previously used methods failed in predicting diseases with good accuracy. However the need to develop new model that considers both weather factors and non weather  data called blast disease data that influences paddy disease to grow. Given this point we developed ensemble classifer based paddy disease prediction model taking weather data from January 2013 to December 2019 from Agricultural and Horticulture Research Station Kathalgere Davangere District. For the predictive model we collected 7 kinds of weather data and 7 kinds of disease related data that includes Minimum Temperature, Maximum Temperature, Temperautre Difference,Relative Humidity, Stages of Paddy Cultivation, Varities of seeds, Season of cropping and so on. It is observed and analyzed that Minimum Temperature, Humidity and Rainfall has huge correlation with occurrence of disease. Since some of the variables are non numeric to convert them to numeric data one hot encoding approach is followed and to improve efficiency of ensemble classifiers  4 different filter based features selection methods are used such as Pearson’s correlation, Mutual information, ANNOVA F Value, Chi Square. Three different ensemble classifiers are used as predictive models and classifiers are compared it is observed that Bagging ensemble technique has achieved  accuracy of 98% compared to Adaboost of 97% and Voting classifier of 88%. Other classification metrics are used evaluate different classifiers like precision, recall, F1 Score, ROC and precision recall score. Our proposed ensemble classifers for paddy blast disease prediction has achieved high precision and high recall but when the solutions of model are closely looked bagging classifier is better compared to other ensemble classifers that are proposed in predicting paddy blast disease.

Maize–lablab intercropping is promising in supporting the sustainable intensification of smallholder cropping systems under high climate risk in southern Africa

2019 ◽  
Vol 56 (1) ◽  
pp. 104-117 ◽  
Author(s):  
Edith Rapholo ◽  
Jude J. O. Odhiambo ◽  
William C. D. Nelson ◽  
Reimund P. Rötter ◽  
Kingsley Ayisi ◽  
...  
Keyword(s):  
Soil Water ◽  
Southern Africa ◽  
Minimum Temperature ◽  
Cropping Systems ◽  
Sustainable Intensification ◽  
Climate Risk ◽  
Weather Data ◽  
Maximum Temperature ◽  
Cropping Season ◽  
Maize Yields

AbstractIdentifying options for the sustainable intensification of cropping systems in southern Africa under prevailing high climate risk is needed. With this in mind, we tested an intercropping system that combined the staple crop maize with lablab, a local but underutilised legume. Grain and biomass productivity was determined for four variants (i) sole maize (sole-maize), (ii) sole lablab (sole-lablab), (iii) maize/lablab with both crops sown simultaneously (intercropped-SP) and (iv) maize/lablab with lablab sown 28 days after the maize crop (intercropped-DP). Soil water and weather data were monitored and evaluated. The trial was conducted for two seasons (2015/2016 and 2016/2017) at two sites in the Limpopo Province, South Africa: Univen (847 mm rainfall, 29.2 °C maximum and 18.9 °C minimum temperature average for the cropping season over the years 2008–2017) and Syferkuil (491 mm rainfall, with 27.0 °C maximum and 14.8 °C minimum temperature). Analysis revealed three key results: The treatment with intercropped-SP had significantly lower maize yields (2320 kg ha−1) compared with maize in intercropped-DP (2865 kg ha−1) or sole-maize (2623 kg ha−1). As expected, maize yields in the El Niño affected in season 2015/2016 were on average 1688 kg ha−1 lower than in 2016/2017. Maize yields were significantly lower (957 kg ha−1) at Univen, the warmer site with higher rainfall, than at Syferkuil. In 2015/2016, maximum temperature at Univen exceeded 40 °C around anthesis. Furthermore, soil water was close to the estimated permanent wilting point (PWP) for most of the cropping season, which indicates possible water limitations. In Syferkuil, the soil water was maintained well above PWP. Lablab yields were low, around 500 ha−1, but stable as they were not affected by treatment across season and site. Overall, the study demonstrated that intercropped-DP appears to use available soil water more efficiently than sole maize. Intercropped-DP could therefore be considered as an option for sustainable intensification under high climate risk and resource-limited conditions for smallholders in southern Africa.

scholarly journals Investigating effect of socio-economic and climatic variables in urban water consumption prediction via Gaussian process regression approach

2017 ◽  
Vol 18 (1) ◽  
pp. 84-93 ◽  
Author(s):  
Kiyoumars Roushangar ◽  
Farhad Alizadeh
Keyword(s):  
Gaussian Process ◽  
Water Consumption ◽  
Gaussian Process Regression ◽  
Climatic Variables ◽  
Weather Data ◽  
Maximum Temperature ◽  
Urban Water ◽  
Input Structure ◽  
Daily Weather Data ◽  
Consumption Prediction

Abstract Due to the importance of forecasting urban water demand (WD), the present study investigated the capability of Gaussian process regression (GPR) using different features as input data. WD could be represented as a function of various variables such as climatic, socioeconomic, institutional and management factors to understand it better. Therefore, in the present study, GPR was used to predict daily WD by taking advantage of several socio-economic and climatic variables for Hamadan city, located in the west of Iran. The selected variables were comprised of daily weather data such as rainfall (R), maximum temperature (Tmax), mean temperature (Tmean), min temperature (Tmin) and relative humidity, Socioeconomic variables such as average monthly water bill (MWB), population (P), number of households (NH), gross national product, and inflation rate (I). The results indicated that GPR could predict the daily WD accurately in terms of statistical evaluations criteria. Sensitivity analysis among various climatic and socio-economic data showed the best input structure in water consumption prediction via GPR. Accordingly, the results approved the substantial impression of WD with three day-lag, I, MWB and Tmax as the input dataset.

scholarly journals Future climate change scenario in hot semi-arid climate of Saurashtra, Gujarat by using statistical downscaling by LARS-WG model

MAUSAM ◽  
2021 ◽  
Vol 68 (4) ◽  
pp. 589-596
Author(s):  
JAYANTA SARKAR ◽  
J. R. CHICHOLIKAR
Keyword(s):  
Climate Change ◽  
Statistical Downscaling ◽  
Minimum Temperature ◽  
Daily Rainfall ◽  
Arid Climate ◽  
Weather Data ◽  
Maximum Temperature ◽  
Future Climate Change ◽  
Semi Arid ◽  
Far Future

Climate change is considered to be the greatest challenge faced by mankind in the twenty first century which can lead to severe impacts on different major sectors of the world such as water resources, agriculture, energy and tourism and are likely to alter trends and timing of precipitation and other weather drivers. Analyses and prediction of change in critical climatic variables like rainfall and temperature are, therefore, extremely important. Keeping this in mind, this study aims to verify the skills of LARS-WG (Long Ashton Research - Weather Generator), a statistical downscaling model, in simulating weather data in hot semi-arid climate of Saurashtra and analyze the future changes of temperature (maximum and minimum) and precipitation downscaled by LARS-WG based on IPCC SRA2 scenario generated by seven GCMs' projections for the near (2011-2030), medium (2046-2065) and far (2080-2099) future periods. Rajkot (22.3° N, 70.78° E) observatory of IMD, representing hot semi-arid climate of Saurashtra, Gujarat state was chosen for this purpose. Daily rainfall, maximum and minimum temperature data for the period of 1969-2013 have been utilized.             LARS-WG is found to show reasonably good skill in downscaling daily rainfall and excellent skill in downscaling maximum and minimum temperature. The downscaled rainfall indicated no coherent change trends among various GCMs’ projections of rainfall during near, medium and far future periods. Contrary to rainfall projections, simulations from the seven GCMs have coherent results for both the maximum and minimum temperatures. Based on the ensemble mean of seven GCMs, projected rainfall at Rajkot in monsoon season (JJAS) showed an increase in near future, i.e., 2011-2030, medium future (2046-2065) and far future (2080-2099) periods to the tune of 2, 11 and 14% respectively compared to the baseline value. Model studies indicating tropospheric warming leading to enhancement of atmospheric moisture content could be the reason for this increasing trend. Further, at the study site summer (MAM) maximum temperature is projected to increase by 0.5, 1.7 and 3.3°C during 2011-2030, 2046-2065 and 2080-2099 respectively and winter (DJF) minimum temperature is projected to increase by 0.8, 2.2 and 4.5 °C during 2011-2030, 2046-2065 and 2080-2099 respectively.  

scholarly journals Modelling long memory in maximum and minimum temperature series in India

MAUSAM ◽  
2021 ◽  
Vol 68 (2) ◽  
pp. 317-326
Author(s):  
RANJIT KUMAR PAUL
Keyword(s):  
Time Series ◽  
Long Memory ◽  
Minimum Temperature ◽  
Fractional Integration ◽  
Predictive Ability ◽  
Temperature Series ◽  
Weather Data ◽  
Maximum Temperature ◽  
Percentage Error ◽  
Temperature Pattern

Time series analysis of weather data can be a very valuable tool to investigate its variability pattern and, maybe, even to predict short- and long-term changes in the time series. In this study, the long memory behaviour of monthly minimum and maximum temperature of India for the period 1901 to 2007 by means of fractional integration techniques has been investigated. The results show that the time series can be specified in terms of autoregressive fractionally integrated moving average (ARFIMA) process. Both the series were found to be integrated with orders of integration smaller than 0.5 ensuring the long memory stationarity. Wavelet methodology in frequency domain with Haar wavelet filter was applied in order to see the oscillation at different scale and at different time epochs of the series. Multiresolution analysis (MRA) was carried out to explore the local as well as global variations in both the temperature series over the years. The variability in minimum temperature is found to be more than maximum temperature. Though there is no clear significance trend in the temperature series in the long run, but there are pockets of change in the temperature pattern. The predictive ability of ARFIMA model was investigated in terms of relative mean absolute percentage error.

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