scholarly journals Some characteristics of fog over Guwahati airport

MAUSAM ◽  
2021 ◽  
Vol 59 (2) ◽  
pp. 159-166
Author(s):  
SURESH RAM ◽  
M. MOHAPATRA
Keyword(s):  
Coefficient Of Variation ◽  
Meteorological Parameters ◽  
Surface Wind ◽  
Dew Point ◽  
Statistical Characteristics ◽  
Occurrence Time ◽  
Probability Of Occurrence ◽  
Intraseasonal Variations ◽  
Duration Time

The statistical characteristics like frequencies of occurrence, time of onset, duration, time of dispersal and intensity of fog over Guwahati airport are found out and analysed using 10 years data during 1994-95 to 2003-04 for the months of November to February. Also the interannual and intraseasonal variations of occurrence of fog are analysed by calculating the coefficient of variation of monthly frequency of fog and by calculating the significant periodicities in the daily probability of occurrence of fog respectively. The meteorological parameters at 1200 UTC leading to fog in the following night or morning over Guwahati airport are analysed to find out the precursors for occurrence of fog. Statistical characteristics are given in tables and their significance discussed. It is observed that monitoring of Dew Point Depression (DPD) and surface wind can help prediction of occurrence of fog and its intensity over Guwahati airport.

scholarly journals Identification allergen Cladosporium spores concentration in the ambient of Ahvaz wastewater treatment plant and their relationship with meteorological variables, Ahvaz, Iran

2020 ◽  
Author(s):  
Nastaran Talepour ◽  
Mohammad Sadegh Hassanvand ◽  
Effat Abbasi-Montazeri ◽  
Seyed Mahmoud Latifi ◽  
Neamat Jaafarzadeh Haghighi Fard
Keyword(s):  
Wastewater Treatment ◽  
Wind Speed ◽  
Meteorological Parameters ◽  
Treatment Plant ◽  
Allergic Diseases ◽  
Airborne Fungi ◽  
Air Pressure ◽  
Dew Point ◽  
Uv Index ◽  
Ultraviolet Index

Introduction: Airborne Cladosporium spores in different regions of the world are known as the main cause of allergic diseases. This study aimed to identify the Cladosporium species airborne fungi in Ahvaz wastewater treat- ment plant area and its adjacent places and check the effect of some meteoro- logical parameters on their emissions. Materials and methods: Cladosporium spores were cultured on Sabouraud`s dextrose agar (SDA) medium in both cold and warm seasons. The passive sampling method was performed and after incubation, colonies were counted as CFU/Plate/h. Then, according to the macroscopic and microscopic charac- teristics of the genus, the fungal was studied. The meteorological parameters including temperature, humidity, air pressure, dew point, wind speed, and ultraviolet index were measured. Results: At least, 3358 colonies were counted. 1433 colonies were related  to the Cladosporium species. The amount of Cladosporium in indoor air was 46% of the total Cladosporium. The average of meteorological parameters includes temperature, humidity, air pressure, dew point, wind speed and UV index during the study were 27.8 °C, 32.9%, 548.7 °Kpa, 3.6°, 9.1 km / h and 3.9 respectively. 42.6% of the total number of colonies was related to the Cladosporium species. Cladospiromes had a direct correlation with the dew point, temperature, humidity, air pressure, wind speed, and ultraviolet index (Pvalue<0.05). Primary sludge dewatering has the greatest role in the Cladospo- rium spores emission. Conclusion: Considering the importance of Cladosporium spores in the ap- pearance of allergic diseases, and given that wastewater treatment workers spend most of their time outside, observing health and preventive measures is necessary in this regard.

scholarly journals Estimating Daily Dew Point Temperature Using Machine Learning Algorithms

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 582 ◽  
Author(s):  
Sultan Noman Qasem ◽  
Saeed Samadianfard ◽  
Hamed Sadri Nahand ◽  
Amir Mosavi ◽  
Shahaboddin Shamshirband ◽  
...  
Keyword(s):  
Meteorological Parameters ◽  
Gene Expression Programming ◽  
Machine Learning Algorithms ◽  
Dew Point ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
M5 Model Tree ◽  
Point Temperature ◽  
Dew Point Temperature ◽  
Input Parameters

In the current study, the ability of three data-driven methods of Gene Expression Programming (GEP), M5 model tree (M5), and Support Vector Regression (SVR) were investigated in order to model and estimate the dew point temperature (DPT) at Tabriz station, Iran. For this purpose, meteorological parameters of daily average temperature (T), relative humidity (RH), actual vapor pressure (Vp), wind speed (W), and sunshine hours (S) were obtained from the meteorological organization of East Azerbaijan province, Iran for the period 1998 to 2016. Following this, the methods mentioned above were examined by defining 15 different input combinations of meteorological parameters. Additionally, root mean square error (RMSE) and the coefficient of determination (R2) were implemented to analyze the accuracy of the proposed methods. The results showed that the GEP-10 method, using three input parameters of T, RH, and S, with RMSE of 0.96°, the SVR-5, using two input parameters of T and RH, with RMSE of 0.44, and M5-15, using five input parameters of T, RH, Vp, W, and S with RMSE of 0.37 present better performance in the estimation of the DPT. As a conclusion, the M5-15 is recommended as the most precise model in the estimation of DPT in comparison with other considered models. As a conclusion, the obtained results proved the high capability of proposed M5 models in DPT estimation.

The Use of Large Scale Parameters in Small Scale Diffusion Studies

1957 ◽  
Vol 38 (1.1) ◽  
pp. 6-12 ◽  
Author(s):  
William G. Tank
Keyword(s):  
Large Scale ◽  
Meteorological Parameters ◽  
Surface Wind ◽  
Small Scale ◽  
Relative Importance ◽  
Wind Vector ◽  
Scale Parameters ◽  
Diffusion Studies ◽  
The Mean ◽  
Low Levels

A method is set forth whereby gaseous diffusion in the low levels of the atmosphere can be calculated by Roberts' diffusion equation (modified to consider instantaneous volume sources) using only large scale synoptic parameters that are readily obtainable from the surface analysis and pibal reports. The three pertinent meteorological parameters utilized are: (1) the mean surface wind, (2) the angle between the surface wind vector and the surface isobars, and (3) the height of the gradient level. Theoretical and observed dosage values are compared by means of dosage isopleth diagrams. Results show that the method yields quite satisfactory results, with regard to both dosage magnitude and distribution. The assumptions necessary for the application of the method and its limitations are mentioned and their relative importance discussed.

scholarly journals Prediction on the Seasonal Behavior of Hydrogen Sulfide Using a Neural Network Model

2011 ◽  
Vol 11 ◽  
pp. 992-1004 ◽  
Author(s):  
Byungwhan Kim ◽  
Joogong Lee ◽  
Jungyoung Jang ◽  
Dongil Han ◽  
Ki-Hyun Kim
Keyword(s):  
Neural Networks ◽  
Hydrogen Sulfide ◽  
Wind Speed ◽  
Meteorological Parameters ◽  
Dew Point ◽  
Industrial Complex ◽  
Seasonal Behavior ◽  
Generalized Regression Neural Networks ◽  
Main Effect ◽  
Optimized Model

Models to predict seasonal hydrogen sulfide (H2S) concentrations were constructed using neural networks. To this end, two types of generalized regression neural networks and radial basis function networks are considered and optimized. The input data for H2S were collected from August 2005 to Fall 2006 from a huge industrial complex located in Ansan City, Korea. Three types of seasonal groupings were prepared and one optimized model is built for each dataset. These optimized models were then used for the analysis of the sensitivity and main effect of the parameters. H2S was noted to be very sensitive to rainfall during the spring and summer. In the autumn, its sensitivity showed a strong dependency on wind speed and pressure. Pressure was identified as the most influential parameter during the spring and summer. In the autumn, relative humidity overwhelmingly affected H2S. It was noted that H2S maintained an inverse relationship with a number of parameters (e.g., radiation, wind speed, or dew-point temperature). In contrast, it exhibited a declining trend with a decrease in pressure. An increase in radiation was likely to decrease during spring and summer, but the opposite trend was predicted for the autumn. The overall results of this study thus suggest that the behavior of H2S can be accounted for by a diverse combination of meteorological parameters across seasons.

scholarly journals Experimental and simulated wheat data from across a temperature gradient along the River Nile in Egypt

2020 ◽  
Vol 6 ◽  
pp. 19-20
Author(s):  
Ahmed Mohammed Saad Kheir ◽  
Aly Abdelaal ◽  
Gerrit Hoogenboom ◽  
Senthold Asseng
Keyword(s):  
Temperature Gradient ◽  
Field Experiments ◽  
Plant Density ◽  
Surface Wind ◽  
Kernel Weight ◽  
Dew Point ◽  
Weather Data ◽  
River Nile ◽  
Planting Dates ◽  
Daily Weather Data

The dataset includes detailed field experiments from four locations across a temperature gradient along the River Nile. The data covering four contrasting environments from North (low temperature) to South (high temperature), includes Sakha (North delta, lower Egypt), Menofya (Middle delta), Benisuef (Middle Egypt) and Aswan (upper Egypt). Measurements included plant density, aboveground biomass, anthesis and maturity dates, grain yield, grains m-2, kernel weight and N content in grains as well as daily weather data (solar radiation, maximum and minimum temperature, precipitation, surface wind, relative humidity, dew point and vapor pressure) and soil characteristics and crop management. Wheat was sown under full irrigation and fertilization with two planting dates. Simulations include three DSSAT-Wheat models (CERES, NWHEAT and CROPSIM).

scholarly journals Some aspects of squall over Indira Gandhi International airport, New Delhi

MAUSAM ◽  
2021 ◽  
Vol 63 (4) ◽  
pp. 623-638
Author(s):  
SURESH RAM ◽  
M. MOHAPATRA
Keyword(s):  
Meteorological Parameters ◽  
Environmental Changes ◽  
Lower Troposphere ◽  
India Meteorological Department ◽  
Dew Point ◽  
New Delhi ◽  
Dry Air ◽  
Intensity Duration Frequency ◽  
Level Inversion ◽  
Period Of Occurrence

A study is undertaken to analyse the characteristics of squall over Delhi and to find out the potential precursors for its prediction. For this purpose, the squall data of Indira Gandhi International (IGI) airport along with the surface and upper air meteorological parameters recorded by India Meteorological Department have been considered for all individual months over the period of 2001-2010. Apart from the characteristics like period of occurrence, intensity, duration, frequency and nature of squall, the environmental changes due to squall and thermodynamic features and indices leading to squall have been analysed. Higher than normal warming of lower troposphere upto 700 hPa level in March, April & June and at 925 hPa in May accompanied with cold dry air advection leading to lower than normal dew point in middle and upper levels (500-300 hPa in March, May and June, 400-300 hPa in April) are favourable for occurrence of the squall over Delhi. The lower level inversion in March and April only also helps in the occurrence of squall. In monsoon months of July- September, cold and dry air advection in middle and upper tropospheric levels (8- 15° C below normal dew point at 400-300 hPa in July, about 15° C below normal dew point at 300-200 hPa in August and 17- 24° C below normal dew point at 500-300 hPa in September) favours occurrence of squall over Delhi. Unlike pre-monsoon months lower level moisture does not play any role for the occurrence of squall over Delhi in monsoon months. Significantly higher than normal SWEAT index in March to September at 0000 UTC can be used as predictor of squall over Delhi on that day. Total totals index is the next suitable precursor for all the months except June.

scholarly journals Meteorological observations in tall masts for the mapping of atmospheric flow in Norwegian fjords

2020 ◽  
Vol 12 (4) ◽  
pp. 3621-3640
Author(s):  
Birgitte Rugaard Furevik ◽  
Hálfdán Ágústsson ◽  
Anette Lauen Borg ◽  
Zakari Midjiyawa ◽  
Finn Nyhammer ◽  
...  
Keyword(s):  
Atmospheric Pressure ◽  
Complex Terrain ◽  
Meteorological Parameters ◽  
Temporal Frequency ◽  
Three Dimensional ◽  
Dew Point ◽  
The Arctic ◽  
Atmospheric Flow ◽  
Climatological Data ◽  
Meteorological Observations

Abstract. Since 2014, 11 tall meteorological masts have been erected in coastal areas of mid-Norway in order to provide observational data for a detailed description of the wind conditions at several potential fjord crossing sites. The planned fjord crossings are part of the Norwegian Public Roads Administration (NPRA) Coastal Highway E39 project. The meteorological masts are 50–100 m high and located in complex terrain near the shoreline in Halsafjorden, Julsundet and Storfjorden in the Møre og Romsdal county of Norway. Observations of the three-dimensional wind vector are made at 2–4 levels of each mast with a temporal frequency of 10 Hz. The dataset is corroborated with observed profiles of temperature at two masts, as well as observations of precipitation, atmospheric pressure, relative humidity and dew point at one site. The first masts were erected in 2014, and the measurement campaign will continue until at least 2024. The current paper describes the observational setup, and observations of key atmospheric parameters are presented and put in context with observations and climatological data from a nearby reference weather station. The 10 min and 10 Hz wind data, as well as other meteorological parameters, are publicly available through the Arctic Data Centre (https://doi.org/10.21343/z9n1-qw63; Furevik et al., 2019).

A Study in Objective Forecasting

1949 ◽  
Vol 30 (1) ◽  
pp. 10-15 ◽  
Author(s):  
Irving I. Gringorten
Keyword(s):  
Meteorological Parameters ◽  
Weather Station ◽  
Forecasting Accuracy ◽  
Probability Of Occurrence ◽  
Objective System

Meteorological parameters can be divided into predictors and predictands, with the former being used to predict the latter. This report describes a project in which some 20 predictors were used to predict fog and stratus at Randolph Field, Texas, in competition with usual methods of the regular weather station there. The results yielded an overall increase in forecasting accuracy and skill by the objective system. But the most important feature of the objective system is that it enables one to state the probability of occurrence of each event.

scholarly journals Improving Snowfall Forecasting by Accounting for the Climatological Variability of Snow Density

2006 ◽  
Vol 21 (1) ◽  
pp. 94-103 ◽  
Author(s):  
Eric C. Ware ◽  
David M. Schultz ◽  
Harold E. Brooks ◽  
Paul J. Roebber ◽  
Sara L. Bruening
Keyword(s):  
Neural Network ◽  
Wind Speed ◽  
Extreme Values ◽  
Surface Wind ◽  
Statistical Characteristics ◽  
Minor Role ◽  
Probabilistic Prediction ◽  
Probabilistic Forecasting ◽  
Snow Density ◽  
Low Level

Abstract Accurately forecasting snowfall is a challenge. In particular, one poorly understood component of snowfall forecasting is determining the snow ratio. The snow ratio is the ratio of snowfall to liquid equivalent and is inversely proportional to the snow density. In a previous paper, an artificial neural network was developed to predict snow ratios probabilistically in three classes: heavy (1:1 &lt; ratio &lt; 9:1), average (9:1 ≤ ratio ≤ 15:1), and light (ratio &gt; 15:1). A Web-based application for the probabilistic prediction of snow ratio in these three classes based on operational forecast model soundings and the neural network is now available. The goal of this paper is to explore the statistical characteristics of the snow ratio to determine how temperature, liquid equivalent, and wind speed can be used to provide additional guidance (quantitative, wherever possible) for forecasting snowfall, especially for extreme values of snow ratio. Snow ratio tends to increase as the low-level (surface to roughly 850 mb) temperature decreases. For example, mean low-level temperatures greater than −2.7°C rarely (less than 5% of the time) produce snow ratios greater than 25:1, whereas mean low-level temperatures less than −10.1°C rarely produce snow ratios less than 10:1. Snow ratio tends to increase strongly as the liquid equivalent decreases, leading to a nomogram for probabilistic forecasting snowfall, given a forecasted value of liquid equivalent. For example, liquid equivalent amounts 2.8–4.1 mm (0.11–0.16 in.) rarely produce snow ratios less than 14:1, and liquid equivalent amounts greater than 11.2 mm (0.44 in.) rarely produce snow ratios greater than 26:1. The surface wind speed plays a minor role by decreasing snow ratio with increasing wind speed. Although previous research has shown simple relationships to determine the snow ratio are difficult to obtain, this note helps to clarify some situations where such relationships are possible.

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