scholarly journals Long-term correlations and cross-correlations in wind speed and solar radiation temporal series from Fernando de Noronha Island, Brazil

2015 ◽  
Vol 424 ◽  
pp. 90-96 ◽  
Author(s):  
Priscilla Sales dos Anjos ◽  
Antonio Samuel Alves da Silva ◽  
Borko Stošić ◽  
Tatijana Stošić
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Temporal Series ◽  
Fernando De Noronha ◽  
Cross Correlations

scholarly journals Long-term weather, hydrometric, and water chemistry datasets in high-temporal resolution at the La Salle River watershed in Manitoba, Canada

2017 ◽  
Author(s):  
Marcos R. C. Cordeiro ◽  
Jason A. Vanrobaeys ◽  
Henry F. Wilson
Keyword(s):  
Wind Speed ◽  
Relative Humidity ◽  
Solar Radiation ◽  
Water Chemistry ◽  
Temporal Resolution ◽  
Time Step ◽  
Canadian Prairies ◽  
River Watershed ◽  
Stream Discharge

Abstract. Lack of long-term datasets in fine temporal resolution hinders environmental studies and modelling efforts; to address this issue in the La Salle River watershed, in Canada, long-term weather (1990–2013), hydrometric (1990–2013 except years with no or poor data), and water chemistry (2009–2013) datasets were developed. The weather variables consisted of temperature, relative humidity, wind speed, solar radiation, and precipitation in an hourly time-step, which is required for physically-based modelling. The only hydrometric variable included in the dataset was stream discharge in a daily time-step, which is the usual time-frame for summarizing the results of long-term studies. The water chemistry data consisted of total nitrogen (TN), total dissolved nitrogen (TDN), total phosphorus (TP) and total dissolved phosphorus (TDP). Samples were collected weekly during the open water season at the same site as they hydrometric gauging station (05OG008) starting in August 2009 until October 2012 with some gaps (i.e. Fall 2011, Spring 2012, September 2012). In 2013 the frequency of sampling was increased to daily or sub-daily during high stream discharge and weekly during low stream discharge. An overview of the data indicates that values and trends are within ranges reported in the literature for the region. Mean annual, winter, and summer temperatures were 3.5 °C–10.7 °C and 17.2 °C, respectively. Annual relative humidity averaged 73.1 % but tended to be higher and more homogenous in cold seasons. Wind speed was very similar over the different seasons with annual average of 4.3 m/s. Solar radiation followed the typical curve reported for western Canada, with peak daily average values around 250 W/m2 in July. The precipitation records were mostly comprised of dry hours and the characteristic precipitation pattern of the Canadian Prairies with high frequency of small precipitation events as observed, with 75.3 % of the hourly precipitation being equal or less than 2 mm/h. The hydrometric characteristics of the dataset were also typical of the Canadian Prairies; the average peak discharge over the entire period was larger in April (2.3 m3/s) due to large amounts of snowmelt runoff. The average concentrations of TN, TDN, TP and TDP of 1.54, 1.35, 0.56, and 0.49 mg/L, respectively, were in agreement with values found in previous studies at the same location. The datasets for weather (https://doi.org/10.23684/ODI-2017-00957), discharge (https://doi.org/10.23684/ODI-2017-00959) and water chemistry (https://doi.org/10.23684/ODI-2017-00958) are accessible through the Government of Canada's Open Data portal (http://open.canada.ca).

scholarly journals Analysis of Meteorological Factor Multivariate Models for Medium- and Long-Term Photovoltaic Solar Power Forecasting Using Long Short-Term Memory

2020 ◽  
Vol 11 (1) ◽  
pp. 316
Author(s):  
Namrye Son ◽  
Mina Jung
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Cloud Cover ◽  
Solar Power ◽  
Short Term Memory ◽  
Numerical Models ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Solar power generation is an increasingly popular renewable energy topic. Photovoltaic (PV) systems are installed on buildings to efficiently manage energy production and consumption. Because of its physical properties, electrical energy is produced and consumed simultaneously; therefore solar energy must be predicted accurately to maintain a stable power supply. To develop an efficient energy management system (EMS), 22 multivariate numerical models were constructed by combining solar radiation, sunlight, humidity, temperature, cloud cover, and wind speed. The performance of the models was compared by applying a modified version of the traditional long short-term memory (LSTM) approach. The experimental results showed that the six meteorological factors influence the solar power forecast regardless of the season. These are, from most to least important: solar radiation, sunlight, wind speed, temperature, cloud cover, and humidity. The models are rated for suitability to provide medium- and long-term solar power forecasts, and the modified LSTM demonstrates better performance than the traditional LSTM.

scholarly journals Detrended Multiple Cross-Correlation Coefficient applied to solar radiation, air temperature and relative humidity

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Andrea de Almeida Brito ◽  
Heráclio Alves de Araújo ◽  
Gilney Figueira Zebende
Keyword(s):  
Relative Humidity ◽  
Solar Radiation ◽  
Solar Energy ◽  
Correlation Coefficient ◽  
Air Temperature ◽  
Cross Correlation ◽  
Global Solar Radiation ◽  
Meteorological Variables ◽  
Hourly Data ◽  
Cross Correlations

AbstractDue to the importance of generating energy sustainably, with the Sun being a large solar power plant for the Earth, we study the cross-correlations between the main meteorological variables (global solar radiation, air temperature, and relative air humidity) from a global cross-correlation perspective to efficiently capture solar energy. This is done initially between pairs of these variables, with the Detrended Cross-Correlation Coefficient, ρDCCA, and subsequently with the recently developed Multiple Detrended Cross-Correlation Coefficient, $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2. We use the hourly data from three meteorological stations of the Brazilian Institute of Meteorology located in the state of Bahia (Brazil). Initially, with the original data, we set up a color map for each variable to show the time dynamics. After, ρDCCA was calculated, thus obtaining a positive value between the global solar radiation and air temperature, and a negative value between the global solar radiation and air relative humidity, for all time scales. Finally, for the first time, was applied $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}$$DMCx2 to analyze cross-correlations between three meteorological variables at the same time. On taking the global radiation as the dependent variable, and assuming that $${\boldsymbol{DM}}{{\boldsymbol{C}}}_{{\bf{x}}}^{{\bf{2}}}={\bf{1}}$$DMCx2=1 (which varies from 0 to 1) is the ideal value for the capture of solar energy, our analysis finds some patterns (differences) involving these meteorological stations with a high intensity of annual solar radiation.

scholarly journals Atmospheric influences on infrared-laser signals used for occultation measurements between Low Earth Orbit satellites

2011 ◽  
Vol 4 (10) ◽  
pp. 2273-2292 ◽  
Author(s):  
S. Schweitzer ◽  
G. Kirchengast ◽  
V. Proschek
Keyword(s):  
Wind Speed ◽  
Solar Radiation ◽  
Thermal Radiation ◽  
Rayleigh Scattering ◽  
Infrared Laser ◽  
Low Earth Orbit ◽  
Earth Orbit ◽  
Wind Speed Profile ◽  
Upper Troposphere ◽  
Trace Species

Abstract. LEO-LEO infrared-laser occultation (LIO) is a new occultation technique between Low Earth Orbit (LEO) satellites, which applies signals in the short wave infrared spectral range (SWIR) within 2 μm to 2.5 μm. It is part of the LEO-LEO microwave and infrared-laser occultation (LMIO) method that enables to retrieve thermodynamic profiles (pressure, temperature, humidity) and altitude levels from microwave signals and profiles of greenhouse gases and further variables such as line-of-sight wind speed from simultaneously measured LIO signals. Due to the novelty of the LMIO method, detailed knowledge of atmospheric influences on LIO signals and of their suitability for accurate trace species retrieval did not yet exist. Here we discuss these influences, assessing effects from refraction, trace species absorption, aerosol extinction and Rayleigh scattering in detail, and addressing clouds, turbulence, wind, scattered solar radiation and terrestrial thermal radiation as well. We show that the influence of refractive defocusing, foreign species absorption, aerosols and turbulence is observable, but can be rendered small to negligible by use of the differential transmission principle with a close frequency spacing of LIO absorption and reference signals within 0.5%. The influences of Rayleigh scattering and terrestrial thermal radiation are found negligible. Cloud-scattered solar radiation can be observable under bright-day conditions, but this influence can be made negligible by a close time spacing (within 5 ms) of interleaved laser-pulse and background signals. Cloud extinction loss generally blocks SWIR signals, except very thin or sub-visible cirrus clouds, which can be addressed by retrieving a cloud layering profile and exploiting it in the trace species retrieval. Wind can have a small influence on the trace species absorption, which can be made negligible by using a simultaneously retrieved or a moderately accurate background wind speed profile. We conclude that the set of SWIR channels proposed for implementing the LMIO method (Kirchengast and Schweitzer, 2011) provides adequate sensitivity to accurately retrieve eight trace species of key importance to climate and atmospheric chemistry (H2O, CO2, 13CO2, C18OO, CH4, N2O, O3, CO) in the upper troposphere/lower stratosphere region outside clouds under all atmospheric conditions. Two further species (HDO, H218O) can be retrieved in the upper troposphere.

scholarly journals Atmospheric Thermal and Dynamic Vertical Structures of Summer Hourly Precipitation in Jiulong of the Tibetan Plateau

Atmosphere ◽  
2021 ◽  
Vol 12 (4) ◽  
pp. 505
Author(s):  
Yonglan Tang ◽  
Guirong Xu ◽  
Rong Wan ◽  
Xiaofang Wang ◽  
Junchao Wang ◽  
...  
Keyword(s):  
Wind Speed ◽  
Tibetan Plateau ◽  
Precipitation Amount ◽  
Summer Precipitation ◽  
The Tibetan Plateau ◽  
Hourly Precipitation ◽  
Air Convection ◽  
Precipitation Variation ◽  
Long Term Observation

It is an important to study atmospheric thermal and dynamic vertical structures over the Tibetan Plateau (TP) and their impact on precipitation by using long-term observation at representative stations. This study exhibits the observational facts of summer precipitation variation on subdiurnal scale and its atmospheric thermal and dynamic vertical structures over the TP with hourly precipitation and intensive soundings in Jiulong during 2013–2020. It is found that precipitation amount and frequency are low in the daytime and high in the nighttime, and hourly precipitation greater than 1 mm mostly occurs at nighttime. Weak precipitation during the daytime may be caused by air advection, and strong precipitation at nighttime may be closely related with air convection. Both humidity and wind speed profiles show obvious fluctuation when precipitation occurs, and the greater the precipitation intensity, the larger the fluctuation. Moreover, the fluctuation of wind speed is small in the morning, large at noon and largest at night, presenting a similar diurnal cycle to that of convective activity over the TP, which is conductive to nighttime precipitation. Additionally, the inverse layer is accompanied by the inverse humidity layer, and wind speed presents multi-peaks distribution in its vertical structure. Both of these are closely related with the underlying surface and topography of Jiulong. More studies on physical mechanism and numerical simulation are necessary for better understanding the atmospheric phenomenon over the TP.

Sign in / Sign up

Export Citation Format

Share Document