Wyniki wyszukiwania - BazTech

Ograniczanie wyników

Znaleziono wyników: 3

Liczba wyników na stronie

Wyniki wyszukiwania

Wyszukiwano:
w słowach kluczowych: meteorological variables

Sortuj według:

Ogranicz wyniki do:

Trend Analysis of Aerosol Concentrations over Last Two Decades from MODIS Retrievals over Hyderabad District of India

Nandan A.K., Mathew Aneesh, Shekar Padala Raja

Geomatics and Environmental Engineering

2024

Vol. 18, no. 1

83--116

Air pollution is one of the grave concerns of the modern era, claiming millions of lives and adversely impacting the economy. Aerosols have been observed to play a significant role in negatively influencing climatological variables and human health in given areas. The current study aimed to study the trend of aerosols and particulates on daily, monthly, seasonal, and annual levels using a 20-year (2002–2021) daily mean aerosol optical depth (AOD) product released by moderate resolution imaging spectrometer (MODIS) sensors for the Hyderabad district in India. The results of the daily mean analysis revealed a rising trend in the number of days with severe AOD (>1), whereas examinations of the seasonal and monthly mean data from 2017 through 2022 showed that peak AOD values alternated between the summer, autumn, and winter seasons over the years. Trend analysis using Mann–Kendall, modified Mann–Kendall, and innovative trend analysis (ITA) tests revealed that AOD increased significantly from 2002 through 2021 (p < 0.05; Z > 0). Furthermore, correlation analysis was performed to check for correlations between AOD levels and certain meteorological factors for the Charminar and Secunderabad regions; it was noticed that temperature had a weak positive correlation with AOD (p < 0.05; r = 0.283 [Secunderabad] – p < 0.05; r = 0.301 [Charminar]), whereas relative humidity developed a very weak negative correlation with AOD (p < 0.05; r = −0.079 [Secunderabad] – p < 0.05; r = −0.109 [Charminar]).

Assessment of evidence based climate variability in Bhagirathi sub basin of India: a geostatistical analysis

Rehman Sufa, Sajjad Haroon, Masroor Md, Rahaman Md Hibjur, Roshani, Ahmed Raihan, Sahana Mehebub

Acta Geophysica

2022

Vol. 70, no 1

445--463

Climate variability analysis is essential for predicting the behavior of various extreme weather events and making communities resilient. Notwithstanding the profound concerns, climate variability assessment faces numerous challenges due to inadequate and sometimes unavailability of data at spatiotemporal scales. This study makes an attempt to analyse climate variability in the Bhagirathi Sub-basin of India. Six meteorological variables were analysed from fourteen weather stations located in the Sub-basin during 1968–2017. Modified Mann–Kendall test was employed to ascertain the trends in meteorological variables. One-way ANOVA was used to assess the relationship between and within the variables. A total of 432 households were selected for reaffirming climate variability and impact on landscape. Significant trends were detected in highest maximum, mean maximum (Mmax) and mean minimum (Mmin) temperatures, relative humidity (Rh), rainfall and vapour pressure (Vp) at annual and seasonal scales. Stations located in eastern and deltaic Sub-basins registered varying trends in these meteorological variables due to anthropogenic activities-induced land use changes. ANOVA revealed a robust relation among rainfall, Vp, Mmin and Mmax. Perceptions of the sampled households revealed that climate variability has considerably affected food intensity, vegetation, soil, water resources and agricultural pattern. We find modified Mann– Kendall method effective in analysing climate variability in the Sub-basin. Thus, this method can be utilized for effective analysis of climate variability at spatial scales in geographical regions.

Idea zastosowania zautomatyzowanych metod doboru zmiennych objaśniających dla celów optymalizacji kosztów zakupu danych wejściowych do prognozowania dobowego profilu obciążenia KSE

Czapaj Rafał, Szablicki Mateusz, Rzepka Piotr

Przegląd Elektrotechniczny

2019

R. 95, nr 8

29--32

Artykuł prezentuje możliwość skorzystania z metod statystycznych automatyzujących dobór zmiennych objaśniających na przykładzie dobowego obciążenia Krajowego Systemu Elektroenergetycznego. Automatyzacja pozwala na optymalizację kosztów zakupu prognoz wejściowych dzięki minimalizacji ich liczby, a uzyskane wyniki pozwalają dodatkowo na zmniejszenie nakładów pracy związanych z wyborem parametrów wejściowych (zmiennych objaśniających) na potrzeby późniejszego opracowywania prognoz dobowego obciążenia KSE.

The paper presents the possibility of using statistical methods to automate the selection of explanatory variables to balance the daily load of the National Power System (NPS). With automation, the cost of input forecast purchase may be optimized by minimizing their number, and the results also allow for a reduction in the effort required to select input parameters (explanatory variables) for later forecasting of NPS daily loads.