scholarly journals Improving Princeton Forcing Dataset over Iran Using the Delta-Ratio Method

Water ◽  
2020 ◽  
Vol 12 (3) ◽  
pp. 630
Author(s):  
Qinghuan Zhang ◽  
Qiuhong Tang ◽  
Xingcai Liu ◽  
Seyed-Mohammad Hosseini-Moghari ◽  
Pedram Attarod
Keyword(s):  
Wind Speed ◽  
Minimum Temperature ◽  
Meteorological Data ◽  
Maximum Temperature ◽  
Ratio Method ◽  
Climate Data ◽  
Climatic Zones ◽  
Humid Zone ◽  
Time Period ◽  
Skill Scores

In this study, we corrected the bias in the Princeton forcing dataset, i.e., precipitation, maximum and minimum temperatures, and wind speed, by adjusting its long-term mean monthly climatology to match observations for the period 1988–2012 using the delta-ratio method. To this end, we collected meteorological data from 97 stations covering the domain of Iran. We divided Iran into three climatic zones based on the De Martonne classification, i.e., Arid, Humid, and Per-Humid zones, and then applied the delta-ratio method for each climatic zone separately to adjust the bias. After adjustment, the new datasets were compared to the observations in 1958–1987. Results based on four skill scores, including the Nash Sutcliffe efficiency (NSE), percent bias (PBIAS), root-mean-square error (RMSE), and R2, indicate that the adjustment greatly improved the quality of the gridded dataset, specifically, precipitation, maximum temperature, and wind speed. For example, NSE for annual precipitation during the validation time period increased from −0.03 to 0.72, PBIAS reduced from 29.2% to 6.6%, RMSE decreased by 182.44 mm, and R2 increased from 0.06 to 0.75. Assessing the results in different climatic zones of Iran reveals that precipitation improved more significantly in the Per-Humid zone followed by the Humid zone, while maximum temperature improved better in the Arid areas. For wind speed, the values improved comparably in the three climate zones. However, the delta values for monthly minimum temperature calculated during the adjustment time period cannot be applied in the validation time period, due to the fact that the Princeton climate data cannot follow the behavior of minimum temperature during the validation phase. In short, we showed that a simple bias adjustment approach, along with minimum observed station data, can significantly improve the performance of global gridded datasets.

scholarly journals SEASONAL AND INTERANNUAL PATTERN OF METEOROLOGICAL VARIABLES IN CUIABÁ, MATO GROSSO STATE, BRAZIL

2015 ◽  
Vol 33 (3) ◽  
pp. 477 ◽  
Author(s):  
Nadja Gomes Machado ◽  
Marcelo Sacardi Biudes ◽  
Carlos Alexandre Santos Querino ◽  
Victor Hugo De Morais Danelichen ◽  
Maísa Caldas Souza Velasque
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Meteorological Data ◽  
Seasonal Pattern ◽  
Global Radiation ◽  
Meteorological Variables ◽  
Maximum Temperature ◽  
Wet Season ◽  
Agricultural Producers ◽  
Mato Grosso

ABSTRACT. Cuiab´a is located on the border of the Pantanal and Cerrado, in Mato Grosso State, which is recognized as one of the biggest agricultural producers of Brazil. The use of natural resources in a sustainable manner requires knowledge of the regional meteorological variables. Thus, the objective of this study was to characterize the seasonal and interannual pattern of meteorological variables in Cuiab´a. The meteorological data from 1961 to 2011 were provided by the Instituto Nacional de Meteorologia (INMET – National Institute of Meteorology). The results have shown interannual and seasonal variations of precipitation, solar radiation, air temperature and relative humidity, and wind speed and direction, establishing two main distinct seasons (rainy and dry). On average, 89% of the rainfall occurred in the wet season. The annual average values of daily global radiation, mean, minimum and maximum temperature and relative humidity were 15.6 MJ m–2 y–1, 27.9◦C, 23.0◦C, 30.0◦C and 71.6%, respectively. Themaximum temperature and the wind speed had no seasonal pattern. The wind speed average decreased in the NWdirectionand increased in the S direction.Keywords: meteorological variables, climatology, ENSO. RESUMO. Cuiabá está localizado na fronteira do Pantanal com o Cerrado, no Mato Grosso, que é reconhecido como um dos maiores produtores agrícolas do Brasil. A utilização dos recursos naturais de forma sustentável requer o conhecimento das variáveis meteorológicas em escala regional. Assim, o objetivo deste estudo foi caracterizar o padrão sazonal e interanual das variáveis meteorológicas em Cuiabá. Os dados meteorológicos de 1961 a 2011 foram fornecidos pelo Instituto Nacional de Meteorologia (INMET). Os resultados mostraram variações interanuais e sazonais de precipitação, radiação solar, temperatura e umidade relativa do ar e velocidade e direção do vento, estabelecendo duas principais estações distintas (chuvosa e seca). Em média, 89% da precipitação ocorreu na estação chuvosa. Os valores médios anuais de radiação diária global, temperatura do ar média, mínima e máxima e umidade relativa do ar foram 15,6 MJ m–2 y–1, 27,9◦C, 23,0◦C, 30,0◦C e 71,6%, respectivamente. A temperatura máxima e a velocidade do vento não tiveram padrão sazonal. A velocidade média do vento diminuiu na direção NW e aumentou na direção S.Palavras-chave: variáveis meteorológicas, climatologia, ENOS.

scholarly journals A Hybrid Data-Driven Machine Learning Technique for Evapotranspiration Modeling in Various Climates

Atmosphere ◽  
2019 ◽  
Vol 10 (6) ◽  
pp. 311 ◽  
Author(s):  
Mohammad Valipour ◽  
Mohammad Ali Gholami Sefidkouhi ◽  
Mahmoud Raeini-Sarjaz ◽  
Sandra M. Guzman
Keyword(s):  
Wind Speed ◽  
Arid Regions ◽  
Meteorological Data ◽  
Gene Expression Programming ◽  
Coupled Model ◽  
Poor Performance ◽  
Maximum Temperature ◽  
Learning Technique ◽  
Hybrid Data ◽  
Model Versus

In the current research, gene expression programming (GEP) was applied to model reference evapotranspiration (ETo) in 18 regions of Iran with limited meteorological data. Initially, a genetic algorithm (GA) was employed to detect the most important variables for estimating ETo among mean temperature (Tmean), maximum temperature (Tmax), minimum temperature (Tmin), relative humidity (RH), sunshine (n), and wind speed (WS). The results indicated that a coupled model containing the Tmean and WS can predict ETo accurately (RMSE = 0.3263 mm day−1) for arid, semiarid, and Mediterranean climates. Therefore, this model was adjusted using the GEP for all 18 synoptic stations. Under very humid climates, it is recommended to use a temperature-based GEP model versus wind speed-based GEP model. The optimal and lowest performance of the GEP belonged to Shahrekord (SK), RMSE = 0.0650 mm day−1, and Kerman (KE), RMSE = 0.4177 mm day−1, respectively. This research shows that the GEP is a robust tool to model ETo in semiarid and Mediterranean climates (R2 > 0.80). However, GEP is recommended to be used cautiously under very humid climates and some of arid regions (R2 < 0.50) due to its poor performance under such extreme conditions.

scholarly journals Future climate uncertainty and spatial variability over Tamilnadu State, India

2015 ◽  
Vol 17 (1) ◽  
pp. 175-185
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Minimum Temperature ◽  
Climate Models ◽  
Regional Climate ◽  
Future Climate ◽  
Maximum Temperature ◽  
Projection Data ◽  
Climate Uncertainty

<div> <p>The present study analyses future climate uncertainty for the 21st century over Tamilnadu state for six weather parameters: solar radiation, maximum temperature, minimum temperature, relative humidity, wind speed and rainfall. The climate projection data was dynamically downscaled using high resolution regional climate models, PRECIS and RegCM4 at 0.22&deg;x0.22&deg; resolution. PRECIS RCM was driven by HadCM3Q ensembles (HQ0, HQ1, HQ3, HQ16) lateral boundary conditions (LBCs) and RegCM4 driven by ECHAM5 LBCs for 130 years (1971-2100). The deviations in weather variables between 2091-2100 decade and the base years (1971-2000) were calculated for all grids of Tamilnadu for ascertaining the uncertainty. These deviations indicated that all model members projected no appreciable difference in relative humidity, wind speed and solar radiation. The temperature (maximum and minimum) however showed a definite increasing trend with 1.8 to 4.0&deg;C and 2.0 to 4.8&deg;C, respectively. The model members for rainfall exhibited a high uncertainty as they projected high negative and positive deviations (-379 to 854 mm). The spatial representation of maximum and minimum temperature indicated a definite rhythm of increment from coastal area to inland. However, variability in projected rainfall was noticed.</p> </div> <p>&nbsp;</p>

scholarly journals Long term monthly and inter-seasonal weather variability analysis for the lower Shivalik foothills of Punjab

MAUSAM ◽  
2022 ◽  
Vol 73 (1) ◽  
pp. 173-180
Author(s):  
NAVNEET KAUR ◽  
M.J. SINGH ◽  
SUKHJEET KAUR
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Linear Regression Method ◽  
Post Monsoon ◽  
Sunshine Hours ◽  
Annual Minimum Temperature ◽  
Weather Parameters

This paper aims to study the long-term trends in different weather parameters, i.e., temperature, rainfall, rainy days, sunshine hours, evaporation, relative humidity and temperature over Lower Shivalik foothills of Punjab. The daily weather data of about 35 years from agrometeorological observatory of Regional Research Station Ballowal Saunkhri representing Lower Shivalik foothills had been used for trend analysis for kharif (May - October), rabi (November - April), winter (January - February), pre-monsoon (March - May), monsoon (June - September) and post monsoon (October - December) season. The linear regression method has been used to estimate the magnitude of change per year and its coefficient of determination, whose statistical significance was checked by the F test. The annual maximum temperature, morning and evening relative humidity has increased whereas rainfall, evaporation sunshine hours and wind speed has decreased significantly at this region. No significant change in annual minimum temperature and diurnal range has been observed. Monthly maximum temperature revealed significant increase except January, June and December, whereas, monthly minimum temperature increased significantly for February, March and October and decreased for June. Among different seasons, maximum temperature increased significantly for all seasons except winter season, whereas, minimum temperature increased significantly for kharif and post monsoon season only. The evaporation, sunshine hours and wind speed have also decreased and relative humidity decreased significantly at this region. Significant reduction in kharif, monsoon and post monsoon rainfall has been observed at Lower Shivalik foothills. As the region lacks assured irrigation facilities so decreasing rainfall and change in the other weather parameters will have profound effects on the agriculture in this region so there is need to develop climate resilient agricultural technologies.

scholarly journals Seasonal variation of some atmospheric parameters in fresh water swamp and Sudan Savanna areas of Nigeria

2021 ◽  
Vol 40 (4) ◽  
pp. 740-750
Author(s):  
F.O. Aweda ◽  
J.O. Agbolade ◽  
J.A. Oyewole ◽  
M. Sanni
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Minimum Temperature ◽  
Negative Relationship ◽  
Maximum Temperature ◽  
Atmospheric Parameters ◽  
Freshwater Swamp ◽  
Significant Negative Relationship ◽  
Sudan Savanna

The year in year out variation in atmospheric parameters, solar radiation, and meteorological variables such as ambient temperature, relative humidity RH, wind speed etc, are posies that can be and are used to describe the atmospheric conditions. Ten years of data obtained from the Nigerian Meteorological Agency (NiMet) was analysed. Results showed that solar radiation rises from January to get to its peak in April which is maintained till August before it begins to fall again with the Sudan savanna area (Maiduguri) having a value of (15.70 MJm-2month-1) and freshwater swamp area (Ikeja) having the value of (10.16 MJm-2month-1). The extraterrestrial radiations calculated for the two stations are 333.53 (MJm-2month-1) and 195.53 (MJm-2month-1) respectively. However, the relative humidity of Ikeja (84.54%) is higher as compared to that of Maiduguri (42.23%). The minimum temperature ranges observed for the two stations varies from (22 - 24)0C and (12 - 26)°C, while the maximum temperature was as high as 33°C and 40°C obtained in April for Ikeja and Maiduguri, respectively. Similarly, the average wind speed is higher for Ikeja (4.97m/s) than for Maiduguri (4.62m/s). The result of the statistical correlation reveals that, in Maiduguri, solar radiation was found to have a significant negative relationship with relative humidity (r = -.256, p<0.01) and a significant positive relationship with minimum and maximum temperature (p<0.05). This means that minimum and maximum temperatures increase as solar radiation increases (p<0.05). Relative humidity decreases as solar radiation increases. In Ikeja, solar radiation was found to have a significant negative relationship with relative humidity (r =-.350, p<0.01) and wind speed (r = -146, p<0.05) and significant positive relationship with minimum temperature (r =.410, p<0.05) and maximum temperature (r =.575, p<0.01). In conclusion, the variables like relative humidity, minimum temperature and wind speed are higher in the freshwater swamp area of Nigeria as compared to the Sudan savanna area, while the solar radiation, extraterrestrial radiation and maximum temperature are generally higher in the Sudan savanna area of Nigeria.

scholarly journals Forecasting Dengue Hotspots Associated With Variation in Meteorological Parameters Using Regression and Time Series Models

2021 ◽  
Vol 9 ◽  
Author(s):  
Seema Patil ◽  
Sharnil Pandya
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Random Forest ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Support Vector ◽  
Forecasting Model ◽  
Random Forest Regression ◽  
Mean Wind Speed ◽  
Best Fit

For forecasting the spread of dengue, monitoring climate change and its effects specific to the disease is necessary. Dengue is one of the most rapidly spreading vector-borne infectious diseases. This paper proposes a forecasting model for predicting dengue incidences considering climatic variability across nine cities of Maharashtra state of India over 10 years. The work involves the collection of five climatic factors such as mean minimum temperature, mean maximum temperature, relative humidity, rainfall, and mean wind speed for 10 years. Monthly incidences of dengue for the same locations are also collected. Different regression models such as random forest regression, decision trees regression, support vector regress, multiple linear regression, elastic net regression, and polynomial regression are used. Time-series forecasting models such as holt's forecasting, autoregressive, Moving average, ARIMA, SARIMA, and Facebook prophet are implemented and compared to forecast the dengue outbreak accurately. The research shows that humidity and mean maximum temperature are the major climate factors and exhibit strong positive and negative correlation, respectively, with dengue incidences for all locations of Maharashtra state. Mean minimum temperature and rainfall are moderately positively correlated with dengue incidences. Mean wind speed is a less significant factor and is weakly negatively correlated with dengue incidences. Root mean square error (RMSE), mean absolute error (MAE), and R square error (R2) evaluation metrics are used to compare the performance of the prediction model. Random Forest Regression is the best-fit regression model for five out of nine cities, while Support Vector Regression is for two cities. Facebook Prophet Model is the best fit time series forecasting model for six out of nine cities. Based on the prediction, Mumbai, Thane, Nashik, and Pune are the high-risk regions, especially in August, September, and October. The findings exhibit an effective early warning system that would predict the outbreak of other infectious diseases. It will help the relevant authorities to take accurate preventive measures.

scholarly journals Climate Change Projection In Kashmir Valley (J&K)

2017 ◽  
Vol 12 (1) ◽  
pp. 107-115 ◽  
Author(s):  
Saqib Parvaze ◽  
Latief Ahmad ◽  
Sabah Parvaze ◽  
Raihana Kanth
Keyword(s):  
21St Century ◽  
Minimum Temperature ◽  
Decision Support Tool ◽  
Annual Precipitation ◽  
Maximum Temperature ◽  
Climatic Data ◽  
Climate Data ◽  
Kashmir Valley ◽  
Support Tool ◽  
Period Increase

The decision support tool viz. SDSM (Statistical Downscaling Model) was used to downscale climate data of future years for Kashmir province of Jammu & Kashmir state. The 21st century projected data for the A1B scenario was adjusted by using observed climatic data recorded during the period 1985-2015 for the region. The data from the same period was taken as the baseline for the analysis. This data was thereon analyzed for monthly, seasonal, cropping season and annual periods to enumerate the variation of maximum temperature, minimum temperature and precipitation in Kashmir valley of Jammu & Kashmir state in the 21st century. The modelled data obtained exhibited no significant change in maximum and minimum temperature for the period 2021-2050 but for the same period increase in annual precipitation was exhibited. For the period2051-2100, decreasing trend of annual temperature was exhibited whereas for annual precipitation, an increasing trend was exhibited.

scholarly journals Relationship between Climate Variables and New Daily COVID-19 Cases in Dhaka, Bangladesh

2020 ◽  
Author(s):  
M. Mofijur ◽  
Islam Md Rizwanul Fattah ◽  
A. B. M. Saiful Islam ◽  
S.M. Ashrafur Rahman ◽  
Mohammad Asaduzzaman Chowdhury
Keyword(s):  
Air Quality ◽  
Wind Speed ◽  
Minimum Temperature ◽  
Maximum Temperature ◽  
Dhaka City ◽  
Climate Variables ◽  
Average Temperature ◽  
Climate Indicators ◽  
The World ◽  
The Relationship

The present study investigates the relationship between the transmission of COVID-19 infections and climate indicators in Dhaka City, Bangladesh, using coronavirus infections data available from the Institute of Epidemiology, Disease Control and Research (IEDCR), Bangladesh. The Spearman-ranked correlation test was carried out to study the association of seven climate indicators, including humidity, air quality, minimum temperature, precipitation, maximum temperature, mean temperature and wind speed with the COVID-19 outbreak in Dhaka City, Bangladesh. The study found that, among the seven indicators, only three indicators (air quality, minimum temperature and average temperature) have a significant relationship with new COVID-19 cases. The results of this paper will give health regulators and policymakers valuable information to lessen the COVID-19 infection in Dhaka and other countries around the world.

scholarly journals Comparative Study of Evapotranspiration Variation and its Relationship with other Climatic Parameters in Asaba and Uyo

2018 ◽  
Vol 3 (2) ◽  
Author(s):  
Okwunna M Umego ◽  
Temitayo A Ewemoje ◽  
Oluwaseun A Ilesanmi
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Reference Evapotranspiration ◽  
Meteorological Data ◽  
Small Variation ◽  
Climatic Variables ◽  
Maximum Temperature ◽  
Climatic Parameters ◽  
Sunshine Hours

This study was carried out to assess the variations of Reference Evapotranspiration (ETO also denoted with RET) calculated using FAO-56 Penman Monteith model of two locations Asaba and Uyo and evaluate its relationships with the variations of other climatic parameters. Meteorological data of forty one years (1975-2015) and thirty five years (1981-2015) period for Asaba and Uyo, respectively gotten from Nigeria Meteorological Agency, Abuja were used. It was observed that the variations of Evapotranspiration (ET) in both locations were in line with two seasons (rainy and dry) normally experienced in Nigeria having its highest value in March (4.8 mm/day) for Asaba and for Uyo in February (4.5 mm/day); and its lowest value in August (3.1 mm/day) for Asaba and in July (2.9 mm/day) for Uyo. ET variation when compared with other climatic variables in both locations was observed to have the same trend with maximum temperature, solar radiation and sunshine hours. It also has the same variation with minimum temperature though with slight deviation. It was observed that ET variation is inversely proportional to the variation relative humidity. Wind speed displayed relatively small variation in its trend over the study period and is not in line with the variations of ET.Keywords— Evapotranspiration, Climatic Variables, FAO Penman-Monteith Model, Variations

scholarly journals CLIMATIC VARIABILITY AND LIVELIHOOD VULNERABILITY IN KADUNA STATE, NIGERIA

2017 ◽  
Vol 1 (1) ◽  
pp. 36
Author(s):  
Muhammad, N. ◽  
Manu, H.I. ◽  
Maina- Bukar, Y. ◽  
Abdullahi, Y.R.
Keyword(s):  
Local Government ◽  
International Development ◽  
Minimum Temperature ◽  
Climatic Variability ◽  
Vulnerability Index ◽  
The State ◽  
Maximum Temperature ◽  
Sample Population ◽  
Climatic Zones ◽  
Farming Households

Purpose: This paper focused on livelihood vulnerability induced by climatic variability amongst farming households in Kaduna state, Nigeria. Methodology: The research used a sample population of 400 using Taro Yamane formula which represents about 0.05% of the population of the three selected local government areas and it purposively targeted farming households heads (FHHH) in one of each of the three eco-climatic zones in the state. Kagarko, BirninGwari and Makarfi local government areas were based on their eco-climatic location and rurality to represent humid, sub-humid and dry sub humid zones of the state respectively. A multi stage sampling technique was further adopted in which farming districts and villages were selected for the administration of 400 structured questionnaires proportionately distributed proportionately to the three local government areas. The Department for International Development (DFID) sustainable livelihoods framework was adopted in the design of the structured questionnaires. Coefficient of Variation (CV %) was deployed to determine the variability of rainfall and temperature of the three eco-climatic zones of the past thirty six years (1981-2016) which was employed into the Micah Hahn’s Livelihood Vulnerability Index model.The results show that Kagarko (humid) had a CV% of 105.43 of rainfall, 9.06 CV% of maximum temperature and CV% of 17.63 in minimum temperature. BirninGwari (sub-humid) had a CV% of 119.64 in rainfall, CV% of 14.17 in maximum temperature and CV% of 15.92 in minimum temperature while Makarfi (dry sub-humid) had a CV% of 124.71 in rainfall, CV%  of 9.72 in maximum temperature and 16.29 CV% in minimum temperature. The livelihood vulnerability index (LVI) of Kagarko was calculated to be 0.35, Makarfi and BirninGwari were calculated to be 0.36 respectively and vulnerability spider diagrams were used to capture and compare results. On a vulnerability scale of 0-1, the three eco-climatic zones were found to be very vulnerable to climatic variability. The paper has proved the applicability of Co-efficient of Variation (CV %) into the LVI model which is a departure from previous users who have consistently deployed Mean Standard Deviation into the model. Results: This study will serve as a spring board to meet the Sustainable Development Goals (SDGs) targets on vulnerable communities in Kaduna state. It is discovered that farmers in Makarfi and BirninGwari, even though in different eco-climatic zones of sub-humid and dry sub humid zones respectively, share equal level of livelihood vulnerability index of 0.36 while Kagarko area which is in humid zone, is having 0.35. These indicated that all the areas are within the very vulnerable values on a vulnerability scale of 0-1. The vulnerability levels of the study area can be attributed to weak Natural, Financial and Physical capitals. Recommendations: The paper recommended Integrated Farmers’ Livelihoods Support Strategy (IFLISS) so as to build the resilience of farming households’ livelihood capitals and reduce vulnerability levels.

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