STUDY ON AIR TEMPERATURE REDUCTION BY SEA BREEZE BASED ON UPPER WEATHER DATA ANALYSIS

In November 2016, a 95 GHz cloud radar was permanently deployed in Reunion Island to investigate the vertical distribution of tropical clouds and monitor the temporal variability of cloudiness in the frame of the pan-European research infrastructure Aerosol, Clouds and Trace gases Research InfraStructure (ACTRIS). In the present study, reflectivity observations collected during the two first years of operation (2016–2018) of this vertically pointing cloud radar are relied upon to investigate the diurnal and seasonal cycle of cloudiness in the northern part of this island. During the wet season (December–March), cloudiness is particularly pronounced between 1–3 km above sea level (with a frequency of cloud occurrence of 45% between 12:00–19:00 LST) and 8–12 km (with a frequency of cloud occurrence of 15% between 14:00–19:00 LST). During the dry season (June–September), this bimodal vertical mode is no longer observed and the vertical cloud extension is essentially limited to a height of 3 km due to both the drop-in humidity resulting from the northward migration of the ITCZ and the capping effect of the trade winds inversion. The frequency of cloud occurrence is at its maximum between 13:00–18:00 LST, with a probability of 35% at 15 LST near an altitude of 2 km. The analysis of global navigation satellite system (GNSS)-derived weather data also shows that the diurnal cycle of low- (1–3 km) and mid-to-high level (5–10 km) clouds is strongly correlated with the diurnal evolution of tropospheric humidity, suggesting that additional moisture is advected towards the island by the sea breeze regime. The detailed analysis of cloudiness observations collected during the four seasons sampled in 2017 and 2018 also shows substantial differences between the two years, possibly associated with a strong positive Indian Ocean Southern Dipole (IOSD) event extending throughout the year 2017.

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Quantifying the Air Temperature Reduction with Greenery in UiTM Shah Alam: A Microscale Study

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/767/1/012004 ◽

2021 ◽

Vol 767 (1) ◽

pp. 012004

Author(s):

Muhammad Hamizan Hazeman ◽

Nurhanisah Hashim ◽

Pauziyah Mohammad Salim ◽

Illyani Ibrahim ◽

Siti Aekbal Salleh

Keyword(s):

Air Temperature ◽

Temperature Reduction

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Dewpoint Temperature Prediction Using Artificial Neural Networks

Journal of Applied Meteorology and Climatology ◽

10.1175/2007jamc1693.1 ◽

2008 ◽

Vol 47 (6) ◽

pp. 1757-1769 ◽

Cited By ~ 32

Author(s):

D. B. Shank ◽

G. Hoogenboom ◽

R. W. McClendon

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Air Temperature ◽

Heat Waves ◽

Weather Data ◽

Temperature Prediction ◽

Related Data ◽

Dewpoint Temperature ◽

Artificial Neural ◽

Selection Of

Abstract Dewpoint temperature, the temperature at which water vapor in the air will condense into liquid, can be useful in estimating frost, fog, snow, dew, evapotranspiration, and other meteorological variables. The goal of this study was to use artificial neural networks (ANNs) to predict dewpoint temperature from 1 to 12 h ahead using prior weather data as inputs. This study explores using three-layer backpropagation ANNs and weather data combined for three years from 20 locations in Georgia, United States, to develop general models for dewpoint temperature prediction anywhere within Georgia. Specific objectives included the selection of the important weather-related inputs, the setting of ANN parameters, and the selection of the duration of prior input data. An iterative search found that, in addition to dewpoint temperature, important weather-related ANN inputs included relative humidity, solar radiation, air temperature, wind speed, and vapor pressure. Experiments also showed that the best models included 60 nodes in the ANN hidden layer, a ±0.15 initial range for the ANN weights, a 0.35 ANN learning rate, and a duration of prior weather-related data used as inputs ranging from 6 to 30 h based on the lead time. The evaluation of the final models with weather data from 20 separate locations and for a different year showed that the 1-, 4-, 8-, and 12-h predictions had mean absolute errors (MAEs) of 0.550°, 1.234°, 1.799°, and 2.280°C, respectively. These final models predicted dewpoint temperature adequately using previously unseen weather data, including difficult freeze and heat stress extremes. These predictions are useful for decisions in agriculture because dewpoint temperature along with air temperature affects the intensity of freezes and heat waves, which can damage crops, equipment, and structures and can cause injury or death to animals and humans.

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REGRESI LINEAR BIVARIAT SIMPEL DAN APLIKASINYA PADA DATA CUACA DI CILACAP

Jurnal Ilmiah Matematika dan Pendidikan Matematika ◽

10.20884/1.jmp.2014.6.1.2902 ◽

2014 ◽

Vol 6 (1) ◽

pp. 45 ◽

Cited By ~ 1

Author(s):

Saniyah Saniyah ◽

Budi Pratikno

Keyword(s):

Linear Regression ◽

Regression Model ◽

Air Temperature ◽

Model Test ◽

Predictor Variable ◽

Error Model ◽

Test Method ◽

Weather Data ◽

Mean Deviation ◽

Response Variables

This study discusses the simple bivariate linear regression on weather data in Cilacap district. This simple bivariate linear regression using the two response variables, rainfall () and humidity of an area (), and one predictor variable, the air temperature (). Regression model test method is a Wilk's Lamda test, the value of Wilk's Lamda = 0.881101 less than lambda table 0.903. The results show that the model and the both parameters are significant, with mean deviation error model is .

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Urban weather generation: the intercomparison of three emerging models

10.5194/egusphere-egu21-16001 ◽

2021 ◽

Author(s):

Csilla Gal

Keyword(s):

Land Use ◽

Wind Speed ◽

Air Temperature ◽

Numerical Models ◽

Weather Generator ◽

Weather Data ◽

Urban Air ◽

Area Of Interest ◽

Built Form ◽

Urban Weather

Cities modify the background climate through the surface-atmosphere interaction. This modification is function of urban design features, such as the configuration of buildings and the amount of vegetation. Compared to the undisturbed climate of the region, the climate of cities is characterized by higher temperature and lower wind speed. This modification is especially pronounce in dense urban areas. The climate modification of cities is not static, but varies in space and time. The spatial variations are governed by land use and built form differences, as well as by the presence or absence of green and blue infrastructures. Due to the spatial complexity of cities and the general lack of urban weather station networks in most places, the amount of available urban weather data is limited. As a consequence, planners, engineers and public health professionals can only approximate the climate impact of built environments in their respective fields.Over the past years, several numerical simulation models have emerged that are able to model the influence of built areas on the atmosphere at the local scale and thus, deliver urban weather data for an area of interest. The aim of this study is to assess the performance of three numerical models with an ability to predict site-specific urban air temperature. The evaluated models are the Urban Weather Generator (UWG), the Vertical City Weather Generator (VCWG) and the Surface Urban Energy and Water Balance Scheme (SUEWS). Although the models differ in their scopes, modeling approaches and applications, they all derive the urban weather data from rural observations considering the land use and built form characteristics of the site.The models are evaluated against air temperature measurements from the dense, 13th District of Budapest (Hungary). The field measurement utilized simple air temperature and relative humidity loggers placed in non-aspirated solar radiation screens at four shaded sites. The two week measurement period encompassed a five-day-long anticyclonic period with clear sky and low wind speed. Preliminary results indicate a good general agreement between modeled and observed values with root mean square error below or at 2&#186;C and index of agreement between 0.92-0.96. During the anticyclonic period most models slightly overestimate the daily maximum and underestimated the daily minimum urban air temperature.

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Weather data analysis in energy simulation

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/609/7/072010 ◽

2019 ◽

Vol 609 ◽

pp. 072010

Author(s):

Yu Ying Wang ◽

Chun Yin Siu ◽

Zaiyi Liao

Keyword(s):

Data Analysis ◽

Energy Simulation ◽

Weather Data

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Random forest predictive model development with uncertainty analysis capability for the estimation of evapotranspiration in an arid oasis region

Hydrology Research ◽

10.2166/nh.2020.012 ◽

2020 ◽

Vol 51 (4) ◽

pp. 648-665

Author(s):

Min Wu ◽

Qi Feng ◽

Xiaohu Wen ◽

Ravinesh C. Deo ◽

Zhenliang Yin ◽

...

Keyword(s):

Random Forest ◽

Uncertainty Analysis ◽

Predictive Model ◽

Air Temperature ◽

Model Development ◽

Influential Factor ◽

Weather Data ◽

Data Sets ◽

Oasis Area ◽

Lower Uncertainty

Abstract The study evaluates the potential utility of the random forest (RF) predictive model used to simulate daily reference evapotranspiration (ET0) in two stations located in the arid oasis area of northwestern China. To construct an accurate RF-based predictive model, ET0 is estimated by an appropriate combination of model inputs comprising maximum air temperature (Tmax), minimum air temperature (Tmin), sunshine durations (Sun), wind speed (U2), and relative humidity (Rh). The output of RF models are tested by ET0 calculated using Penman–Monteith FAO 56 (PMF-56) equation. Results showed that the RF model was considered as a better way to predict ET0 for the arid oasis area with limited data. Besides, Rh was the most influential factor on the behavior of ET0, except for air temperature in the proposed arid area. Moreover, the uncertainty analysis with a Monte Carlo method was carried out to verify the reliability of the results, and it was concluded that RF model had a lower uncertainty and can be used successfully in simulating ET0. The proposed study shows RF as a sound modeling approach for the prediction of ET0 in the arid areas where reliable weather data sets are available, but relatively limited.

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Weather Data Analysis Using Hadoop: Applications and Challenges

IOP Conference Series Materials Science and Engineering ◽

10.1088/1757-899x/551/1/012044 ◽

2019 ◽

Vol 551 ◽

pp. 012044 ◽

Cited By ~ 1

Author(s):

Mohammed Adam Ibrahim Fakherldin ◽

Khalid Adam ◽

Noor Akma Abu Bakar ◽

Mazlina Abdul Majid

Keyword(s):

Data Analysis ◽

Weather Data

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The Influence of Forests on Atmospheric Heating during the Snowmelt Season

Journal of Applied Meteorology ◽

10.1175/1520-0450-34.2.511 ◽

1995 ◽

Vol 34 (2) ◽

pp. 511-519 ◽

Cited By ~ 8

Author(s):

Takeshi Yamazaki

Keyword(s):

Data Analysis ◽

Air Temperature ◽

Forest Canopy ◽

Sensible Heat Flux ◽

Ground Surface ◽

Circulation Model ◽

Balance Model ◽

Local Circulation ◽

Atmospheric Heating ◽

Forested Areas

Abstract Atmospheric heating during the snowmelt season has been studied by means of data analysis and numerical model experiments. As a result of the data analysis, it was shown that in some examples the daytime air temperature rose above 0°C, even if the ground surface was covered by snow. Moreover, it was found that the number of days when the daytime air temperature rose above 0°C was large when the duration of sunshine increased. However, the increase was not related to the wind speed. Therefore, the air temperature over snow cover increases during the daytime if the sunshine is strong even under calm conditions (weak advection). On the other hand, the following result was obtained with the use of a local circulation model combined with a canopy heat balance model. The atmosphere was heated over the plains if forested areas existed around the plains, even if the plains surfaces were covered by snow without forests. An upward sensible heat flux was supplied from the forest canopy, resulting in atmospheric heating. It was concluded that the existence of forests was one of the main causes of atmospheric heating during the snowmelt season.

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