scholarly journals Recent Changes in Downward Longwave Radiation at King Sejong Station, Antarctica

2008 ◽  
Vol 21 (22) ◽  
pp. 5764-5776 ◽  
Author(s):  
Hi Ku Cho ◽  
Jhoon Kim ◽  
Yeonjin Jung ◽  
Yun Gon Lee ◽  
Bang Yong Lee
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Air Temperature ◽  
Multiple Linear Regression Model ◽  
Longwave Radiation ◽  
Multiple Regression Model ◽  
Specific Humidity ◽  
Annual Data ◽  
Mean Values ◽  
Downward Longwave Radiation

Abstract Effects of cloud, air temperature, and specific humidity on downward longwave irradiance and their long-term variabilities are examined by analyzing the measurements made at the King Sejong Station in the Antarctic Peninsula during the period of 1996–2006. It has been shown that the downward longwave irradiance (DLR) is significantly correlated with three variables: air temperature, specific humidity, and cloudiness. Based on the relationship of the three variables with DLR, a multiple linear regression model has been developed in order to evaluate the relative contribution of each of the variables to the variation of DLR. The three variables together explained 75% of all the variance in daily mean DLR. The respective contribution from specific humidity and cloudiness to the variation of DLR was 46% and 23%; thus most of the DLR variability can be explained by the variations in the two variables. The annual mean of longwave cloud forcing shows 52 W m−2 with no remarkable seasonal cycle. It is also noted that the overcast cloud effect gives an increase by 65 W m−2 with respect to clear-sky flux throughout the year. It is suggested that the multiple regression model can be used to estimate the radiative forcings of variables influencing the DLR variability. A highly significant decrease in DLR with an average of −1.52 W m−2 yr−1 (−0.54% yr−1) is found in an analysis from the time series of the deseasonalized monthly mean values. Accordingly, the atmospheric flux emissivity, air temperature, and specific humidity have also decreased in their time series, while the cloudiness has increased insignificantly. Consequently, it may be concluded that the recent decrease in DLR is mainly attributed to the net cooling effect due to the decrease in air temperature and specific humidity, which overwhelm the slight warming effect in cloudiness. Analysis of mean monthly trends for individual months shows that, as for the annual data, the variations in DLR are mostly associated with those of air temperature, specific humidity, and cloudiness.

scholarly journals A Comparative Study of Bulk Parameterization Schemes for Estimating Cloudy-Sky Surface Downward Longwave Radiation

2019 ◽  
Vol 11 (5) ◽  
pp. 528 ◽  
Author(s):  
Jie Cheng ◽  
Feng Yang ◽  
Yamin Guo
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Multiple Linear Regression ◽  
Linear Regression Model ◽  
Multiple Linear Regression Model ◽  
Longwave Radiation ◽  
Acceptable Accuracy ◽  
Parameterization Schemes ◽  
Bulk Formula ◽  
Downward Longwave Radiation

Parameterization schemes (bulk formulae) are widely used to estimate all-sky surface downward longwave radiation (SDLR) due to the simple, readily available inputs and acceptable accuracy from local to regional scales. Seven widely used bulk formulae are evaluated using the ground measurements collected from 44 globally distributed flux measurement sites of five networks. The Bayesian model averaging (BMA) method is introduced to integrate multiple bulk formulae to obtain an estimate of cloudy-sky SDLR for the first time. The second multiple linear regression model of Carmona et al. (2014) performs the best, with BIAS, RMSE, and R2 of zero, 20.13 W·m−2 and 0.87, respectively. The BMA method can achieve balanced results that are close to the accuracy of the second multiple linear regression model of Carmona et al. (2014) and better than the average accuracy of seven bulk formulae, with BIAS, RMSE, and R2 of −1.08 W·m−2, 21.99 W·m−2 and 0.87, respectively. In addition, the bulk formula of Crawford and Duchon (1999) is preferred if there is insufficient data to calibrate the bulk formulae because it does not need local calibration and has an acceptable accuracy, with BIAS, RMSE, and R2 of 0.96 W·m−2, 26.58 W·m−2 and 0.82, respectively. The effects of climate type, land cover type, and surface elevation are also investigated to fully assess the applicability of each bulk formula and BMA. In general, there is no cloudy-sky bulk parametrization scheme that can be successfully applied everywhere.

scholarly journals Influence of water vapor and aerosols on downward longwave radiation in the high mountain region of Musala peak, Bulgaria

2021 ◽  
Vol 44 ◽  
pp. 59-72
Author(s):  
Peter Nojarov ◽  
Todor Arsov ◽  
Ivo Kalapov ◽  
Hristo Angelov
Keyword(s):  
Water Vapor ◽  
Air Temperature ◽  
Main Tool ◽  
Longwave Radiation ◽  
Mountain Region ◽  
Specific Humidity ◽  
High Mountain ◽  
Air Temperatures ◽  
Downward Longwave Radiation ◽  
Influence Of Water

This study reveals the effect of aerosols and water vapor on downward longwave radiation in the high mountain region of Musala peak, Bulgaria. The investigated period is 01/01/2017 (Jan. 1st 2017) – 30/09/2019 (Sep. 30th 2019). Statistical methods are the main tool for discovering the relationships between the different elements. The results indicate that air temperature is the leading factor for downward longwave radiation, specific humidity, and amount of aerosols in the air. That is why, in order to reveal the pure relationship between downward longwave radiation, specific humidity and amount of aerosols in the atmosphere, the air temperature was cleared from the data series. After this procedure, the results show that specific humidity has a significant influence on the downward longwave radiation flux, and an increase of 1% of the specific humidity results in an increase of about 12-15% in the values   of the downward longwave radiation. At air temperatures around 0ºC the influence of water vapor on the downward longwave flux is highest, which is due to the phase transitions of the water – a process during which release/absorption of radiation in the longwave spectrum occurs. The amount of aerosols in the atmosphere also has a significant effect on this type of radiation, and an increase of 1% of the amount of aerosols in the air at air temperatures above –5.5°C results in an increase of the downward longwave radiation of about 2-4%. The findings of this study show that coarser and opaque aerosol particles have a stronger effect on downward longwave radiation. In the area of Musala peak, as the air temperature rises, there is an increase in the amount of aerosols in the air, a decrease in their size, and a transition from transparent to opaque aerosols. The combination of these different tendencies causes the influence of aerosols on downward longwave radiation to be strongest in the middle temperature interval – air temperatures between –5.5°C and +5.5°C. Due to the increased total amount of aerosols and increased amount of opaque aerosols, their influence on downward longwave radiation is significant also at air temperatures above 5.5°C.

scholarly journals Deep learning and regression modelling of cloudless downward longwave radiation

2019 ◽  
Vol 8 (1) ◽  
Author(s):  
Nsikan I. Obot ◽  
Ibifubara Humphrey ◽  
Michael A. C. Chendo ◽  
Sunday O. Udo
Keyword(s):  
Neural Networks ◽  
Regression Model ◽  
Water Vapour ◽  
Air Temperature ◽  
Vapour Pressure ◽  
Regression Models ◽  
Longwave Radiation ◽  
Water Vapour Pressure ◽  
Analytical Relationship ◽  
Downward Longwave Radiation

Abstract Background Though downward longwave radiation (DLR) models curb the paucity of data, they are mostly location dependent. Therefore, there is a need to evaluate their relevance given the increasing use of machine learning techniques. In this study, cloudless DLR estimates from regression models and soft computing models of neural networks (NN), support vector regression (SVR) and adaptive neuro-fuzzy inference system (ANFIS) were compared. Clear days from September 1992 to August 1994 and July 1995 to March 1998 in Ilorin (8.50 °N, 4.55 °E), Nigeria were considered, while the predictors for the models were water vapour pressure, e and air temperature, T. Results A new regression model in relation to the Boltzmann constant, σ: $$ \left(1.014\left(\frac{1.0\times {10}^{30}\times e}{T^{13}}\right)+0.699\right)\sigma {T}^4 $$1.0141.0×1030×eT13+0.699σT4, was better than other regression models and applicable at another location. Between 1 and 8, the sixth degree was the best polynomial kernel function in SVR models’ estimations of cloudless DLR. Though the new regression model was comparable to expert systems, ANFIS was still the best model due to its consistent high correlations and lowest estimation errors. Conclusions Experience-based computational procedures that combine enough logics with neural networks respond effectively to other data. Furthermore, the analytical relationship between water vapour pressure and air temperature in DLR’s mechanism should be redefined accordingly, while the sixth polynomial should be used as the default setting in SVR systems.

scholarly journals Mechanism of Seasonal Arctic Sea Ice Evolution and Arctic Amplification

2016 ◽  
Author(s):  
Kwang-Yul Kim ◽  
Benjamin D. Hamlington ◽  
Hanna Na ◽  
Jinju Kim
Keyword(s):  
Heat Flux ◽  
Sea Ice ◽  
Air Temperature ◽  
Surface Air Temperature ◽  
Longwave Radiation ◽  
The Arctic ◽  
Turbulent Heat ◽  
Arctic Amplification ◽  
Ice Melting ◽  
Downward Longwave Radiation

Abstract. Sea ice melting is proposed as a primary reason for the Artic amplification, although physical mechanism of the Arctic amplification and its connection with sea ice melting is still in debate. In the present study, monthly ERA-interim reanalysis data are analyzed via cyclostationary empirical orthogonal function analysis to understand the seasonal mechanism of sea ice melting in the Arctic Ocean and the Arctic amplification. While sea ice melting is widespread over much of the perimeter of the Arctic Ocean in summer, sea ice remains to be thin in winter only in the Barents-Kara Seas. Excessive turbulent heat flux through the sea surface exposed to air due to sea ice melting warms the atmospheric column. Warmer air increases the downward longwave radiation and subsequently surface air temperature, which facilitates sea surface remains to be ice free. A 1 % reduction in sea ice concentration in winter leads to ~ 0.76 W m−2 increase in upward heat flux, ~ 0.07 K increase in 850 hPa air temperature, ~ 0.97 W m−2 increase in downward longwave radiation, and ~ 0.26 K increase in surface air temperature. This positive feedback mechanism is not clearly observed in the Laptev, East Siberian, Chukchi, and Beaufort Seas, since sea ice refreezes in late fall (November) before excessive turbulent heat flux is available for warming the atmospheric column in winter. A detailed seasonal heat budget is presented in order to understand specific differences between the Barents-Kara Seas and Laptev, East Siberian, Chukchi, and Beaufort Seas.

scholarly journals Decrease in Serum Urate Level Is Associated With Loss of Visceral Fat in Male Gout Patients

2021 ◽  
Vol 12 ◽  
Author(s):  
Zijing Ran ◽  
Xiaomei Xue ◽  
Lin Han ◽  
Robert Terkeltaub ◽  
Tony R. Merriman ◽  
...  
Keyword(s):  
Regression Model ◽  
Visceral Fat ◽  
Multiple Linear Regression Model ◽  
Serum Urate ◽  
Multiple Regression Model ◽  
Final Visit ◽  
Visceral Fat Area ◽  
Serum Urate Level ◽  
Lowering Therapy ◽  
The Relationship

ObjectiveTo clarify the relationship between serum urate (SU) decrease and visceral fat area (VFA) reduction in patients with gout.MethodsWe retrospectively analyzed 237 male gout patients who had two sets of body composition and metabolic measurements within 6 months. Subjects included had all been treated with urate-lowering therapy (ULT) (febuxostat 20–80 mg/day or benzbromarone 25–50 mg/day, validated by the medical record). All patients were from the specialty gout clinic of The Affiliated Hospital of Qingdao University. The multiple linear regression model evaluated the relationship between change in SU [ΔSU, (baseline SU) – (final visit SU)] and change in VFA [ΔVFA, (baseline VFA) – (final visit VFA)].ResultsULT resulted in a mean (standard deviation) decrease in SU level (464.22 ± 110.21 μmol/L at baseline, 360.93 ± 91.66 μmol/L at the final visit, p <0.001) accompanied by a decrease in median (interquartile range) VFA [97.30 (81.15–118.55) at baseline, 90.90 (75.85–110.05) at the final visit, p < 0.001]. By multiple regression model, ΔSU was identified to be a significant determinant variable of decrease in VFA (beta, 0.302; p = 0.001).ConclusionsThe decrease in SU level is positively associated with reduced VFA. This finding provides a rationale for clinical trials to affirm whether ULT promotes loss of visceral fat in patients with gout.

FORECASTING CONSUMER PRICE INDEX (CPI) USING TIME SERIES MODELS AND MULTI REGRESSION MODELS (ALBANIA CASE STUDY)

2018 ◽  
Author(s):  
Eralda Gjika Dhamo ◽  
Llukan Puka ◽  
Oriana Zaçaj
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Consumer Price Index ◽  
Multiple Regression Model ◽  
Time Series Models ◽  
Financial Indicators ◽  
Consumer Prices ◽  
Approach Time

In this work we analyse the CPI index as the official index to measure inflation in Albania, Harmo-nized Indices of Consumer Prices (HICPs) as the bases for comparative measurement of inflation in European countries and other financial indicators that may affect CPI. This study is an attempt to model CPI based on combination of multiple regression model with time series forecasting models. In the first approach, time series models were used directly on the CPI time series index to obtain the forecast. In the second approach, the time series models (SARIMA, ETS) were used to model and simulate forecast for each subcomponent with significant correlation to CPI and then use the multiple regression model to obtain CPI forecast. The projection of this indicator is important for understand-ing the country's economic and social development. This study helps researchers in the field of time series modeling, economic analysis and investments.

scholarly journals Long-Term Electricity Demand Prediction via Socioeconomic Factors—A Machine Learning Approach with Florida as a Case Study

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 3996 ◽  
Author(s):  
Marwen Elkamel ◽  
Lily Schleider ◽  
Eduardo L. Pasiliao ◽  
Ali Diabat ◽  
Qipeng P. Zheng
Keyword(s):  
Time Series ◽  
Regression Model ◽  
Multiple Regression ◽  
Energy Demand ◽  
Mean Squared Error ◽  
Multiple Regression Model ◽  
Electricity Demand ◽  
Degree Days ◽  
Population Number ◽  
Demand Prediction

Predicting future energy demand will allow for better planning and operation of electricity providers. Suppliers will have an idea of what they need to prepare for, thereby preventing over and under-production. This can save money and make the energy industry more efficient. We applied a multiple regression model and three Convolutional Neural Networks (CNNs) in order to predict Florida’s future electricity use. The multiple regression model was a time series model that included all the variables and employed a regression equation. The univariant CNN only accounts for the energy consumption variable. The multichannel network takes into account all the time series variables. The multihead network created a CNN model for each of the variables and then combined them through concatenation. For all of the models, the dataset was split up into training and testing data so the predictions could be compared to the actual values in order to avoid overfitting and to provide an unbiased estimate of model accuracy. Historical data from January 2010 to December 2017 were used. The results for the multiple regression model concluded that the variables month, Cooling Degree Days, Heating Degree Days and GDP were significant in predicting future electricity demand. Other multiple regression models were formulated that utilized other variables that were correlated to the variables in the best-selected model. These variables included: number of visitors to the state, population, number of consumers and number of households. For the CNNs, the univariant predictions had more diverse and higher Root Mean Squared Error (RMSE) values compared to the multichannel and multihead network. The multichannel network performed the best out of the three CNNs. In summary, the multichannel model was found to be the best at predicting future electricity demand out of all the models considered, including the regression model based on the datasets employed.

scholarly journals Trends in surface radiation and cloud radiative effect at four Swiss sites for the 1996–2015 period

2019 ◽  
Vol 19 (20) ◽  
pp. 13227-13241 ◽  
Author(s):  
Stephan Nyeki ◽  
Stefan Wacker ◽  
Christine Aebi ◽  
Julian Gröbner ◽  
Giovanni Martucci ◽  
...  
Keyword(s):  
Longwave Radiation ◽  
Ground Temperature ◽  
Surface Radiation ◽  
Specific Humidity ◽  
Shortwave Radiation ◽  
Radiative Effect ◽  
Cloud Properties ◽  
Cloud Radiative Effect ◽  
Downward Longwave Radiation ◽  
Integrated Water Vapour

Abstract. The trends of meteorological parameters and surface downward shortwave radiation (DSR) and downward longwave radiation (DLR) were analysed at four stations (between 370 and 3580 m a.s.l.) in Switzerland for the 1996–2015 period. Ground temperature, specific humidity, and atmospheric integrated water vapour (IWV) trends were positive during all-sky and cloud-free conditions. All-sky DSR and DLR trends were in the ranges of 0.6–4.3 W m−2 decade−1 and 0.9–4.3 W m−2 decade−1, respectively, while corresponding cloud-free trends were −2.9–3.3 W m−2 decade−1 and 2.9–5.4 W m−2 decade−1. Most trends were significant at the 90 % and 95 % confidence levels. The cloud radiative effect (CRE) was determined using radiative-transfer calculations for cloud-free DSR and an empirical scheme for cloud-free DLR. The CRE decreased in magnitude by 0.9–3.1 W m−2 decade−1 (only one trend significant at 90 % confidence level), which implies a change in macrophysical and/or microphysical cloud properties. Between 10 % and 70 % of the increase in DLR is explained by factors other than ground temperature and IWV. A more detailed, long-term quantification of cloud changes is crucial and will be possible in the future, as cloud cameras have been measuring reliably at two of the four stations since 2013.

scholarly journals Pengaruh PAD, Dana Perimbangan dan PMA terhadap Tingkat Kemajuan Daerah Kabupaten dan Kota di Provinsi Sulavesi Selatan

2018 ◽  
Author(s):  
Muhammad Syukri
Keyword(s):  
Regression Model ◽  
Quantitative Data ◽  
Applied Research ◽  
Descriptive Analysis ◽  
Multiple Linear Regression Model ◽  
Multiple Regression Model ◽  
Simultaneous Test ◽  
South Sulawesi ◽  
Direct Benefits ◽  
Research Procedure

The purpose of this study is to determine the effect of locally-generated revenue (PAD), balancing funds and Foreign Direct Investment (FDI) to Progress Regions Level districts and cities in South Sulawesi Province either simultaneously or partially. Type of research used is applied research with quantitative data. The data used obtained from the Statistics Central Institution (BPS), which covers 24 districts and cities of South Sulawesi Province in 2016 in the form of thousands of rupiah. The research procedure is (1) Descriptive Analysis. (2) Establish multiple regression model. (3) Partial test and simultaneous test, and (4) determine the coefficient determination. Based on the simultaneous test of multiple linear regression model that PAD (X1), Balance Funds (X2) and FDI (X3) have significant effect to regional progress level (Y). And partial model testing, only PAD (X1) and fund balancing (X2) which significantly influence to regional progress level (Y). Meanwhile, FDI (X3) has no significant effect on regional progress level (Y). Therefore, the cultivation of FDI should be targeted and can provide direct benefits for the community in improving their welfare that will impact on improving regional progress. The suggestions needed in the development of further research that is required addition of other variables that can affect the level of regional progress.

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