scholarly journals Validação da reanálise do MERRA-2 com dados observados

2021 ◽  
Vol 4 ◽  
pp. 106-111
Author(s):  
Carla Claudino ◽  
Dirceu Luís Herdies ◽  
Mário Francisco Leal de Quadro ◽  
Pedro Cardoso De Sales Filho
Keyword(s):  
Polynomial Regression ◽  
Pearson Correlation ◽  
Local Environment ◽  
Reanalysis Data ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Climate Data ◽  
Temperature Records ◽  
Highly Correlated ◽  
Anderson Darling

Climate data such as temperature are key to understand a place’s dynamic. Since the classic period to study climate is thirty years, some areas face difficulties due to the lack of broad data record. Thus, one solution to this situation is the employment of reanalysis, like the Modern-Era Retrospective analysis for Research and Applications, Version 2, known as MERRA-2. Considering that the city of Itajaí, located in Santa Catarina, south of Brazil, has one automatic weather station with data since 2010, this study aimed to, statistically, evaluate MERRA-2’s data to fill the twenty-year gap in climate records for this location, regarding temperature, by analysing daily temperature records from 2010 to 2020, through basic statistic tests and other tests like Anderson-Darling normality test, paired t-test, Mann-Whiney, Pearson correlation, Spearman correlation, bias and polynomial regression analysis with Fitted Line Plot, all on Minitab® 18. The comparison was between observed data from A868-Itajaí automatic weather station and MERRA-2’s data. As a result, it was reached that none of the data is normally distributed, all the means and medians are different from each other, however, the observed data and the MERRA-2’s data are highly correlated. That said, this research provides substantial information to reassure the use of reanalysis data to fill the gaps due to the lack of observed temperature data in order to have a broad study when trying to comprehend a local environment.

scholarly journals History and Data Records of the Automatic Weather Station on Denali Pass (5715 m), 1990–2007

2020 ◽  
Vol 59 (12) ◽  
pp. 2113-2127
Author(s):  
Lea Hartl ◽  
Martin Stuefer ◽  
Tohru Saito ◽  
Yoshitomi Okura
Keyword(s):  
Wind Speed ◽  
Historical Record ◽  
Reanalysis Data ◽  
Meteorological Conditions ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Wind Speeds ◽  
Air Temperatures ◽  
History Of ◽  
Summit Region

AbstractWe present the data records and station history of an automatic weather station (AWS) on Denali Pass (5715 m MSL), Alaska. The station was installed by a team of climbers from the Japanese Alpine Club after a fatal accident involving Japanese climbers in 1989 and was operational intermittently between 1990 and 2007, measuring primarily air temperature and wind speed. In later years, the AWS was operated by the International Arctic Research Center of the University of Alaska Fairbanks. Station history is reconstructed from available documentation as archived by the expedition teams. To extract and preserve data records, the original datalogger files were processed. We highlight numerous challenges and sources of uncertainty resulting from the location of the station and the circumstances of its operation. The data records exemplify the harsh meteorological conditions at the site: air temperatures down to approximately −60°C were recorded, and wind speeds reached values in excess of 60 m s−1. Measured temperatures correlate strongly with reanalysis data at the 500-hPa level. An approximation of critical wind speed thresholds and a reanalysis-based reconstruction of the meteorological conditions during the 1989 accident confirm that the climbers faced extremely hazardous wind speeds and very low temperatures. The data from the Denali Pass AWS represent a unique historical record that can, we hope, serve as a basis for further monitoring efforts in the summit region of Denali.

scholarly journals Analysis of Rainfall and Temperature Changes and Variability over Glen Farm, South Africa

2021 ◽  
pp. 1-19
Author(s):  
G. Zuma-Netshiukhwi
Keyword(s):  
Agricultural Productivity ◽  
Annual Rainfall ◽  
Physiological Data ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Productivity Analysis ◽  
Climate Data ◽  
Weather Extremes ◽  
Crop Suitability

In the agricultural domain, decision-making is greatly guided by agricultural meteorology, which is the science that applies knowledge of weather and climate to qualitative and quantitative improvement in agricultural efficiency. The study area is challenged with increasing multifaceted agricultural production risks and complex agricultural ecosystems, which require analysis and understanding of local rainfall and temperature patterns. Digital technologies, such as the automatic weather station, play a pivotal role to monitor the physical environment, successively. This study engaged on a thorough analysis and interpretation of long-term rainfall and temperature data. The results would enable farmers and other users to comprehend valuable knowledge for improved productivity. The objectives of this paper were to analyse long-term climate data for Glen automatic weather station. To determine decadal climate patterns and trends, determine seasonal shifts, climate variability and climate change and quantify the frequency of the occurrence of weather extremes and develop suitable adaptation strategies relating to agronomic, phenological and physiological data necessary for crop modelling, operational evaluation and statistical analysis. The applied methods entailed Microsoft Excel and INSTAT Plus statistical software, which used to detect the interactions of environmental factors and suitable agricultural productivity. Understanding of rainfall and temperature patterns is required for agricultural management decisions, on planting date selection, crop suitability, livestock adaptation, ecosystem conservation. Agro meteorological knowledge derived from meteorological parameters, temperature, rainfall, wind and weather extremes, and may enhance agricultural productivity. Analysis of long-term and decadal trends in the time series indorse a sequence of alternately increasing and decreasing in mean annual rainfall and air temperature in Glen Farm.

scholarly journals On Modeling of COVID-19 for the Indian Subcontinent using Polynomial and Supervised Learning Regression

2020 ◽  
Author(s):  
Dishita Neve ◽  
Honey Patel ◽  
Harsh S. Dhiman
Keyword(s):  
Polynomial Regression ◽  
Indian Subcontinent ◽  
Human Life ◽  
Pearson Correlation ◽  
Series Representation ◽  
Bayesian Regression ◽  
Support Vector ◽  
The Novel ◽  
Root Cause ◽  
Highly Correlated

AbstractCOVID-19, a recently declared pandemic by WHO has taken the world by storm causing catastrophic damage to human life. The novel cornonavirus disease was first incepted in the Wuhan city of China on 31st December 2019. The symptoms include fever, cough, fatigue, shortness of breath or breathing difficulties, and loss of smell and taste. Since the devastating phenomenon is essentially a time-series representation, accurate modeling may benefit in identifying the root cause and accelerate the diagnosis. In the current analysis, COVID-19 modeling is done for the Indian subcontinent based on the data collected for the total cases confirmed, daily recovered, daily deaths, total recovered and total deaths. The data is treated with total confirmed cases as the target variable and rest as feature variables. It is observed that Support vector regressions yields accurate results followed by Polynomial regression. Random forest regression results in overfitting followed by poor Bayesian regression due to highly correlated feature variables. Further, in order to examine the effect of neighbouring countries, Pearson correlation matrix is computed to identify geographic cause and effect.

scholarly journals Uncertainty Evaluation of Wind Speed Sensor in Automatic Weather Station

2021 ◽  
Vol 768 (1) ◽  
pp. 012008
Author(s):  
Zhen Yang ◽  
Husheng Zhang ◽  
Qiang Wang ◽  
Cuicui Li ◽  
Wenlong Xu ◽  
...  
Keyword(s):  
Wind Speed ◽  
Uncertainty Evaluation ◽  
Weather Station ◽  
Automatic Weather Station ◽  
Speed Sensor

Corsock Automatic Weather Station

Weather ◽  
2003 ◽  
Vol 58 (8) ◽  
pp. 291-294
Author(s):  
G. A. J. Bowles
Keyword(s):  
Weather Station ◽  
Automatic Weather Station

Ongoing Experience with Ohio’s Automatic Weather Station Network

1993 ◽  
Vol 9 (5) ◽  
pp. 437-441 ◽  
Author(s):  
D. L. Elwell ◽  
J. C. Klink ◽  
J. R. Holman ◽  
M. J. Sciarini
Keyword(s):  
Weather Station ◽  
Automatic Weather Station ◽  
Station Network

scholarly journals Hematological and Genetic Predictors of Daytime Hemoglobin Saturation in Tanzanian Children with and without Sickle Cell Anemia

2013 ◽  
Vol 2013 ◽  
pp. 1-6 ◽  
Author(s):  
Sharon E. Cox ◽  
Julie Makani ◽  
Charles R. Newton ◽  
Andrew M. Prentice ◽  
Fenella J. Kirkham
Keyword(s):  
Sickle Cell ◽  
Sickle Cell Anemia ◽  
Clinical Chemistry ◽  
Pearson Correlation ◽  
Transferrin Saturation ◽  
Full Blood Count ◽  
Hemoglobin F ◽  
Hemoglobin Saturation ◽  
Highly Correlated ◽  
Glucose 6 Phosphate Dehydrogenase

Low hemoglobin oxygen saturation (SpO2) is common in Sickle Cell Anemia (SCA) and associated with complications including stroke, although determinants remain unknown. We investigated potential hematological, genetic, and nutritional predictors of daytime SpO2 in Tanzanian children with SCA and compared them with non-SCA controls. Steady-state resting pulse oximetry, full blood count, transferrin saturation, and clinical chemistry were measured. Median daytime SpO2 was 97% (IQ range 94–99%) in SCA (N = 458), lower () than non-SCA (median 99%, IQ range 98–100%; N = 394). Within SCA, associations with SpO2 were observed for hematological variables, transferrin saturation, body-mass-index z-score, hemoglobin F (HbF%), genotypes, and hemolytic markers; mean cell hemoglobin (MCH) explained most variability (, Adj ). In non-SCA only age correlated with SpO2. -thalassemia 3.7 deletion highly correlated with decreased MCH (Pearson correlation coefficient 0.60, ). In multivariable models, lower SpO2 correlated with higher MCH (-coefficient 0.32, ) or with decreased copies of -thalassemia 3.7 deletion (-coefficient 1.1, ), and independently in both models with lower HbF% (-coefficient 0.15, ) and Glucose-6-Phosphate Dehydrogenase genotype (-coefficient 1.12, ). This study provides evidence to support the hypothesis that effects on red cell rheology are important in determining SpO2 in children with SCA. Potential mechanisms and implications are discussed.

Sign in / Sign up

Export Citation Format

Share Document