Surface Meteorological Data Research Articles

Neural network predictions of pollutant emissions from open burning of crop residues: Application to air quality forecasts in southern China

Open burning of crop residues is a strong seasonal source of air pollutants in many parts of China, but the large day-to-day variability of the associated emissions pose a great challenge for air quality forecasts. Here we developed back-propagation neural network (BPNN) ensembles to forecast the daily fire pixel counts in Southern China during the month of January. The BPNN ensembles were trained using daily assimilated surface meteorological data (including air temperature, relative humidity, pressure, and winds) and daily fire pixel observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) for the month of January during the years 2003–2012. We showed that the BPNN ensembles successfully forecasted the day-to-day variability and the interannual variability of fire pixel counts over Southern China in January of the years 2013–2015, with correlation coefficients of 0.6–0.8 against the MODIS observations. We used the forecasted daily fire pixel counts in January 2014 and January 2015 to scale the climatological January biomass burning emissions from the Fire Inventory from NCAR (FINN) and applied the resulting forecasted daily biomass burning emissions to drive the WRF-Chem regional air quality model. The use of BPNN-ensemble-forecasted daily biomass burning pollutant emissions led to significant improvements in the daily forecasts of PM2.5 concentrations in Southern China for January 2014, with the mean bias of the simulated surface PM2.5 concentrations reduced from −9.1% to −1.2%. We repeated the sensitivity simulations for January 2015 and also found a modest improvement when using the forecasted daily biomass burning pollutant emissions (mean bias of the simulated surface PM2.5 concentrations reduced from −5.8% to −2%). Our approach can be applied to other source regions of biomass burning emissions to improve the accuracy of daily air quality forecasts.

Estimation of latent heat flux using satellite land surface temperature and a variational data assimilation scheme over a eucalypt forest savanna in Northern Australia

In this study, the performance of the combined-source variational data assimilation scheme (CS-VDA) is assessed in detail using in situ heat fluxes (i.e. sensible heat (H) and latent heat (LE)) collected at a Eucalypt forest savanna of Northern Australia (Howard Springs). The CS VDA scheme estimates surface turbulent heat fluxes via assimilation of sequences of land surface temperature (LST) and meteorological data into a surface energy balance model and a dynamic model. The main objectives of this paper were to extend previous studies to a semi-arid ecosystem and to evaluate the potential of using global meteorological forcing data (GMD) to drive the CS VDA model (rather than in-situ meteorological observations). In order to study the new errors associated with the use of GMD, the effects on LE of the uncertainty in air temperature and wind speed (the two key meteorological factors that controls the total estimation error) was quantitatively characterized. Using hourly in-situ measurements as inputs, the daily-averaged LE RMSEdaily was 54 W/m2, which agrees with the errors previously reported in the literature. As expected, replacing local meteorological data with GMD reduces the performance of the LE estimation (GMA: RMSEdaily = 82 W/m2, GLDAS: RMSEdaily = 151 W/m2). However, LE RMSE values at 8-day temporal scale for GMA are RMSE8-days = 32 W/m2, similar to those reported in this area for other models (MODIS (MOD16A2) and Breathing Earth System Simulator (BESS)). The error propagation analysis indicate that the CS VDA model is very sensitive to uncertainties in wind speed measurements. Moreover, there are large discrepancies between in situ and GMD wind speed. These two factors combined can explain the degradation in LE estimations. In this context, our study is a first step towards the characterization of an operational daily LE estimation scheme using hourly LST observations.

Evidence of Long-Term Trend of Visibility in the Sahel and Coevolution with Meteorological Conditions and Vegetation Cover during the Recent Period

In this study, the long term trend of the observed visibility data used directly (without conversion into dust concentrations) over Sahel was investigated between 1957 and 2013. Then, to review the influence of atmospheric factors and land surface conditions on this trend, the coevolution between the visibility and the dust surface mass concentration from MERRA-2 (Modern-Era Retrospective analysis for Research and Applications) reanalysis, the in-situ surface meteorological data (rainfall, relative humidity, wind speed, and air temperature), as well as the Normalized Difference Vegetation Index (NDVI) were analyzed from 2000 to 2013. We showed that the horizontal visibility has significantly decreased since the 1970s. The coevolution between the visibility and the dust surface mass concentration revealed that visibility decreased significantly with increments in dust concentrations. Visibility increases with rainfall and relative humidity. It is greater in areas of high vegetation cover than in deforested areas. Visibility is weakly correlated with wind speed and air temperature but generally, wind leads to a decrease in visibility, while warm air temperature is associated with a clearer sky and hence, high visibility. The worst visibility in the dry season results from high dust concentrations due to warm and dry wind conditions and less vegetation cover. Rainfall, relative humidity and vegetation cover are the dominant factors contributing to the decrease of dust loading in the Sahel.

Sea breeze front identification on the northeastern coast of Brazil and its implications for meteorological conditions in the Sergipe region

The common idea that the sea breeze is a phenomenon that is always beneficial to the thermal from point of view is questioned in this study, since the sea breeze front (SBF) development is related with warmer days in the Sergipe region, located in the northeastern coast of Brazil. For this reason, the first climatology of the SBF and its potential implications on meteorological conditions were presented by using remote sensing (GOES-13 satellite image) and surface meteorological data. Results showed daily performance of the SBF: onset at 12:00 UTC and cessation at 19:00 UTC corresponding to a duration of 7 h, mean strength of 3.6 ms−1, and maximum inland penetration of 94 km from coast. In addition, we also observed SBF passage is related with an average increase in solar radiation (84 W m−2), temperature (1 °C), and a decrease in relative humidity (10%) and dew point temperature (1 °C). We hypothesize that, in certain circumstances, the SBF development is not beneficial for human thermal comfort in the Sergipe region.

Developing Fuzzy Inference System of the Observed Albireo Star’s Trail using Surface Meteorological Parameter over ITERA Astronomical Observatory (IAO)

This study aims to develop Fuzzy Inference System (FIS) trail of star seeing over ITERA Astronomical Observatory (IAO) using surface meteorological parameter during site selection to develop observatory. The one-day surface meteorological observation such as Pressure (P), Temperature (T), and Humidity (H) and trail of Albireo (β Cyg) star seeing value was taken from Mt. Betung, Lampung, Indonesia. The surface meteorological data are analyzed to obtain Albireo (β Cyg) star seeing value in 3 to 4 August 2017. By using statistical method obtained the seeing value in daily variation of Albireo (β Cyg) star trail with surface meteorological as a predictor parameter. The standard error of estimate (S-Value) between predictor parameter (pressure and humidity) with Albireo star seeing are reached 0.0027543 in two days variation. Based on the result, the three Membership Functions (MFs) with Gaussian function was proposed in this study to develop FIS trail of star. The result shows that the configuration analysis from input parameter FIS with 15 role bases from surface meteorological parameter has successful to targeted with Albireo (β Cyg) star trail. The 15 role bases from FIS was used to estimate seeing value from meteorological parameter in near future.

Estimation of evapotranspiration for the blown-sand region in the Ordos basin based on the SEBAL model

ABSTRACTEvapotranspiration plays a crucial role in the surface hydrothermal balance. On a regional scale, especially in the blown-sand areas, an accurate estimate of evapotranspiration has important guiding significance for a rational allocation of water resources and drought monitoring. The objective of the study is to estimate the surface evapotranspiration for the blown-sand region in the Ordos Basin based on the Surface Energy Balance Algorithm for Land (SEBAL) model using six phases of Landsat 8 remote sensing images during 2015–2016 and the corresponding surface meteorological observational data, analyze the characteristics of temporal and spatial distributions of evapotranspiration in the study area. The results show that (1) the daily evapotranspiration presents a seasonal variability with the highest in summer, lower in autumn and spring, and the lowest in winter; (2) the evapotranspiration is distinctly characterized by its spatial variability, which is related to the terrain and geomorphology in this area as well as the effect of human activities; and (3) the daily evapotranspiration estimated by the SEBAL model was validated using the Food and Agriculture Organization (FAO) Penman-Monteith (P–M) model, which yielded an mean relative error (MRE) of 19.5%. In summary, the remote sensing data based SEBAL model can be applied to robustly estimate evapotranspiration for the blown-sand regions, and the study results can be served as an important reference for the research of water circulation, rational utilization of water resources, and protection of the ecological environment in the arid and semi-arid regions.

Ozone seasonal evolution and photochemical production regime in the polluted troposphere in eastern China derived from high-resolution Fourier transform spectrometry (FTS) observations

Abstract. The seasonal evolution of O3 and its photochemical production regime in a polluted region of eastern China between 2014 and 2017 has been investigated using observations. We used tropospheric ozone (O3), carbon monoxide (CO), and formaldehyde (HCHO, a marker of VOCs (volatile organic compounds)) partial columns derived from high-resolution Fourier transform spectrometry (FTS); tropospheric nitrogen dioxide (NO2, a marker of NOx (nitrogen oxides)) partial column deduced from the Ozone Monitoring Instrument (OMI); surface meteorological data; and a back trajectory cluster analysis technique. A broad O3 maximum during both spring and summer (MAM/JJA) is observed; the day-to-day variations in MAM/JJA are generally larger than those in autumn and winter (SON/DJF). Tropospheric O3 columns in June are 1.55×1018 molecules cm−2 (56 DU (Dobson units)), and in December they are 1.05×1018 molecules cm−2 (39 DU). Tropospheric O3 columns in June were ∼50 % higher than those in December. Compared with the SON/DJF season, the observed tropospheric O3 levels in MAM/JJA are more influenced by the transport of air masses from densely populated and industrialized areas, and the high O3 level and variability in MAM/JJA is determined by the photochemical O3 production. The tropospheric-column HCHO∕NO2 ratio is used as a proxy to investigate the photochemical O3 production rate (PO3). The results show that the PO3 is mainly nitrogen oxide (NOx) limited in MAM/JJA, while it is mainly VOC or mixed VOC–NOx limited in SON/DJF. Statistics show that NOx-limited, mixed VOC–NOx-limited, and VOC-limited PO3 accounts for 60.1 %, 28.7 %, and 11 % of days, respectively. Considering most of PO3 is NOx limited or mixed VOC–NOx limited, reductions in NOx would reduce O3 pollution in eastern China.

Springtime Beryllium-7, meteorology, and ozone variability in the southeastern United States

Ground level 24 h average observations of 7Be activity, combined with continuous ozone (O3), carbon monoxide (CO) and minimum relative humidity (minRH) measurements, were conducted at two non-urban sites in the Deep South of the United States. One site is inland at Yorkville, Georgia, YRK (33°55′27″N; 84°59′44″W); the other is near the coast of the Gulf of Mexico outside Pensacola, Florida, OLF (30°25′N:87°13′W). The objective of the measurements was to explore the potential for estimating a contribution to surface O3 from stratospheric and upper tropospheric air for during the springtime months from April 2 to June 13, 2015 using 7Be as a tracer. The observed 24 h average 7Be activities were 1.88 ± 0.66 mBq/m3 at YRK and 1.99 ± 0.68 mBq/m3 at OLF. Corresponding means of 24 h average O3 mixing ratios were 38.3 ± 10.1 ppbv for YRK and 28.7 ± 8.9 ppbv at OLF. A weak statistically significant O37Be regression based estimate of O3 from the stratosphere and upper troposphere was constrained to a conservative upper bound of 14 ± 4 (1σ) ppbv. From daily inspection of meteorological features, a lower bound of 3–7 ppbv was estimated. A multiple regression of O3 versus 7Be and daily minimum relative humidity (minRH) improved the statistical fit and indicated stratosphere-upper troposphere contributions of 8.6 ± 4.0 ppbv for YRK and 6.6 ± 3.3 ppbv for OLF. Daily variability of 7Be and O3 was explored with respect to meteorological variability. 7BeO3 relationships in this experiment were examined using (a) ozonesonde and vertical dew point temperature profiles, inferring very dry air as an upper atmosphere tracer descending into the boundary layer, (b) backward air trajectories, identifying 7Be spatial and temporal variation, and (c) precipitation, indicating 7Be scavenging and lowered O3 with suppression of photochemistry. The average 7Be measurements at the ground during the study are useful for helping estimate the long-term O3 contributions from the upper atmosphere. However, the day-to-day 7Be observations had limited value for determining the contributions of upper air O3 to daily O3 mixing ratios. Conventional surface and upper-air meteorological data could be used effectively as an alternative.

Meteorological Factors Affecting Winter Particulate Air Pollution in Ulaanbaatar from 2008 to 2016

Ulaanbaatar, the capital of Mongolia, is subject to high levels of atmospheric pollution during winter, which severely threatens the health of the population. By analyzing surface meteorological data, ground-based LIDAR data, and radiosonde data collected from 2008 to 2016, we studied seasonal variations in particulate matter (PM) concentration, visibility, relative humidity, temperature inversion layer thickness, and temperature inversion intensity. PM concentrations started to exceed the 24-h average standard (50 μg/m3) in mid-October and peaked from December to January. Visibility showed a significant negative correlation with PM concentration. Relative humidity was within the range of 60–80% when there were high PM concentrations. Both temperature inversion layer thickness and intensity reached maxima in January and showed similar seasonal variations with respect to PM concentration. The monthly average temperature inversion intensity showed a strong positive correlation with the monthly average PM2.5 concentration. Furthermore, the temperature inversion layer thickness exceeded 500 m in midwinter and overlaid the weak mixed layer during daytime. Radiative cooling enhanced by the basin-like terrain led to a stable urban atmosphere, which strengthened particulate air pollution.

Projecting the impacts of atmospheric conditions under climate change on air quality over the Pearl River Delta region

Anthropogenic climate change has been increasingly confirmed by weather observation and scientific literature in recent decades. Atmospheric stability, which has strong effects on vertical mixing of air pollutants and thus air quality, may be affected under climate change. This study aims to statistically assess the impacts of climate change alone on the future air quality in the Pearl River Delta region in the near future (2030–2039) and the far future (2090–2099) under two Representative Concentration Pathways (RCP) scenarios, i.e., RCP 4.5 and RCP 8.5, based on the future surface and upper level meteorological data projected by one regional climate model (RCM): WRF, and by four general circulation models (GCMs): CanESM2, MIROC, MRI-CGCM3 and MPI-ESM-LR. The arithmetic means of projections reveal an increase in the levels of air pollutants [ozone (O3), respirable suspended particulates (RSP) and sulphur dioxide (SO2)] in various seasons, even though a decrease is projected to occur in June-July-August. These changes in projected mean concentration are more significant in the far future, and under the RCP8.5 scenario. Among difference meteorological variables, surface temperature is most associated with the projected change in the three pollutants, with a range from 56.9% to 65.2% in all seasons and for all pollutants, relative to all contributions in RCP8.5 for example. Other notable associations include positive effects of vertical temperature gradient and the temperature–dew point difference on pollutant concentration. We found an increase in frequency of high pollution levels in December–January–February and March–April–May, as the occurrence proportion of pollutant concentration greater than the recent 95th percentile is 9.5%–9.6% and 6.4%–9.2%, respectively. We conclude that climate change alone is projected to have significant effect on air quality in the Pearl River Delta region in future, implying the necessity of more stringent air pollutant emission control policies to mitigate air pollution in the future.

Variability of winter haze over the Beijing-Tianjin-Hebei region tied to wind speed in the lower troposphere and particulate sources

This study analyzes the variability of winter haze days and visibility in the Beijing-Tianjin-Hebei (BTH) region, in relation to the regional average wind speed changes in the lower troposphere and emissions for 1961–2014. Winter mean surface meteorological data, NCEP/NCAR atmospheric reanalysis data, and fossil fuel emission data are used. The results reveal a significant increase in the haze days of +0.8 days decade−1 (p < .01), and a subsequent decline in visibility of −1.56 km decade−1 (p < .01). Most interestingly, an accelerated increase in the number of haze days was observed for the last 11-year period (+8.3 days decade−1, p < .01) of the study period (2004–2014). Regression analysis indicates that the increase of haze occurrence and decrease in visibility are partly due to the significant (p < .01) declining trend of the mean wind speed in the lower troposphere (−0.19 m s−1 decade−1 at 10 m, (−0.23 m s−1 decade−1 at 925 hPa), and − 0.21 m s−1 decade−1 at 850 hPa), and partly due to the declining (dust storm frequency as a proxy, −0.41 days dec−1) surrounding particulate sources and increasing fossil fuel emissions (total carbon emission as a proxy, +48,206.8 thousand metric tons dec−1). Specifically, wind speed changes in the lower troposphere explain 41.3% of the interannual varation of the winter haze days and 71.2% of the visibility variance. These are extended to 51.7% and 81.6% respectively when combined with information about the natural (dust storm frequency) and anthropogenic (fossil fuel emissions) particulate sources. Therefore, the analyses show that wind speed changes in the lower troposphere, together with the varied natural and anthropogenic sources of particulates, play a key role in modulating winter haze and visibility conditions in the BTH area.

Central-Pacific surface meteorology from the 2016 El Niño Rapid Response (ENRR) field campaign

Abstract. During the early months of the 2015/2016 El Niño event, scientists led by the Earth System Research Laboratory's Physical Sciences Division conducted the National Oceanic and Atmospheric Administration's (NOAA's) El Niño Rapid Response (ENRR) field campaign. One component of ENRR involved in situ observations collected over the near-equatorial eastern–central Pacific Ocean. From 25 January to 28 March 2016, standard surface meteorology observations, including rainfall, were collected at Kiritimati Island (2.0° N, 157.4° E) in support of twice-daily radiosonde launches. From 16 February to 16 March 2016, continuous measurements of surface meteorology, sea surface temperature, and downwelling shortwave radiation were made by NOAA Ship Ronald H. Brown. These were largely done in support of the four to eight radiosondes launched each day as the ship travelled from Hawaii to TAO buoy locations along longitudes 140 and 125° W and then back to port in San Diego, California. The rapid nature of these remote field deployments led to some specific challenges in addition to those common to many surface data collection efforts. This paper documents the two deployments as well as the steps taken to evaluate and process the data. The results are two multi-week surface meteorology data products and one accompanying set of surface fluxes, all collected in the core of the eastern–central Pacific's extremely warm waters. These data sets, plus metadata, are archived at the NOAA's National Centers for Environmental Information (NCEI) and are free for public access: surface meteorology from Kiritimati Island (https://doi.org/10.7289/V51Z42H4); surface meteorology and some surface fluxes from NOAA Ship Ronald H. Brown (https://doi.org/10.7289/V5SF2T80; https://doi.org/10.7289/V58050VP).

Research on precision management of farming season based on big data

In order to strengthen the scientific management of saline-alkali land and the accurate management of agricultural production, a scientific data governance platform for saline-alkali land was developed. Based on the data accumulation of big data platform, the relationship between wheat growth and meteorology was taken as the research object. Thirteen variables including atmospheric pressure, temperature, light, and precipitation were extracted from surface meteorological data for correlation analysis, and temperature, precipitation, and sunshine were selected as the characteristic variables; through discretization processing, we have finally determined the three indicators that can be categorized: accumulative temperature, sunshine hours, and temperature. Finally, the three indicators are combined with months to build a model of farming season and weather based on Apriori. The results show that when judging the farming season with the month as the index, the accuracy of the model is between 78.81 and 100%. When the temperature or accumulated temperature is taken as the index to judge the winter wheat farming time, the accuracy of the model is above 90%. This shows that accurate analysis of farming season can be achieved through big data correlation analysis, which provides technical support for the timely adoption of agricultural production.

The spatiotemporal inhomogeneity of pollutant concentrations and its dependence on regional weather conditions in a coastal city of China.

Hourly data for sulfur dioxide (SO2), nitrogen oxides (NOx), and inhalable particulate matter (PM10) over a 33-month period from a network of air quality monitoring stations across Qingdao, a major coastal city in eastern China, along with surface and upper-air meteorological data, are used to characterize the spatiotemporal variability of these pollutants in the region and the role of meteorological conditions play in pollution episodes. Large differences in the concentrations of all three pollutants are found between densely populated or industrial areas and suburban commercial or residential or coastal tourist areas, but the differences are relatively small between older and newer parts of the residential-commercial areas and between old and newly developed industrial areas. Wavelet analyses revealed a strong seasonal cycle for all three pollutants, introseasonal variability with a periodicity depending on pollutant and location, and diurnal and a semi-diurnal variability with season-dependent amplitude and phase. Low wind speed is found to be the leading factor for pollution buildup in the region. These results may prove useful for urban planning and development and implementation of effective air pollution control strategies for other coastal regions with economic development similar to Qingdao.

Evaluation of an atmospheric model with surface and ABL meteorological data for energy applications in structured areas

This paper provides the performance evaluation of the meteorological component of The Air Pollution Model (TAPM), a nestable prognostic model, in predicting meteorological variables in urban areas, for both its surface layer and atmospheric boundary layer (ABL) turbulence parameterizations. The model was modified by incorporating four urban land surface types, replacing the existing single urban surface. Control runs were carried out over the wider area of Kozani, an urban area in NW Greece. The model was evaluated for both surface and ABL meteorological variables by using measurements of near-surface and vertical profiles of wind and temperature. The data were collected by using monitoring surface stations in selected sites as well as an acoustic sounder (SOnic Detection And Ranging (SODAR), up to 300 m above ground) and a radiometer profiler (up to 600 m above ground). The results showed the model demonstrated good performance in predicting the near-surface meteorology in the Kozani region for both a winter and a summer month. In the ABL, the comparison showed that the model’s forecasts generally performed well with respect to the thermal structure (temperature profiles and ABL height) but overestimated wind speed at the heights of comparison (mostly below 200 m) up to 3–4 ms−1.

Meteorological conditions during a severe, prolonged regional heavy air pollution episode in eastern China from December 2016 to January 2017

A severe, prolonged and harmful regional heavy air pollution episode occurred in eastern China from December 2016 to January 2017. In this paper, the pollutant characteristics and the meteorological formation mechanism of this pollution event, including climate anomalies, surface weather conditions, planetary boundary layer structure and large-scale circulation features, were analysed based on observational pollution data, surface meteorological data, sounding data and ERA-Interim reanalysis data. The results are as follows. (1) Five pollution stages were identified in eastern China. The two most severe episodes occurred from December 27, 2016 to January 4, 2017 and from January 8 to 12 2017. During these two pollution episodes, fine mode particles were major contributors, and hourly PM2.5 concentrations often exceeded 150 μg/m3, reaching a maximum of 333 μg/m3 at Fuyang station. Gaseous pollutants were transformed into secondary aerosols through heterogeneous reactions on the surface of PM2.5. (2) Compared with the same period over the years 2000–2016, 2017 presented meteorological field climate anomalies in conjunction with unfavourable surface conditions (weak winds, high relative humidity, fewer hours of sunshine, high cloud cover) and adverse atmospheric circulation (weak East Asian winter monsoon and an abnormal geopotential height of 500 hPa), which caused poorer visibility in 2017 than in the other analysed years. (3) During the development of heavy pollution event, unfavourable surface weather conditions, including poorer visibility, weaker pressure, higher relative humidity, lower wind speed with unfavourable wind direction and less precipitation suppressed the horizontal diffusion ability of air pollutants. Furthermore, the unfavourable structure of the atmospheric boundary layer was the key cause of the rapid PM2.5 increase. The deep, strong temperature inversion layer and weak vertical wind velocity could have suppressed vertical motion and enhanced the stability of the near-surface atmosphere, causing the air pollutants to accumulate at low levels and exacerbating the air pollution problem. Finally, a persistent stagnant weather system with a weak geopotential height field of 1000 hPa and warm air advection at 850 hPa was the main feature of atmospheric circulation associated with the heavy pollution.

Erratum and addendum to the paper ‘A new ZTD model based on permanent ground-based GNSS-ZTD data’, Survey Review, 2016, 48(351), 385–391

We correct some errors and give some more research for our paper published in Survey Review, 2016, 48(351), 385–391. This note is a correction owing to the use of the questionable surface meteorological measurements of the International GNSS Service (IGS) and also gives more sufficient studies to validate our method due to our inadequate study. Numerical weather model values of surface meteorological data were used to check the IGS surface meteorological measurements and long-time zenith tropospheric delay values at 76 radiosonde stations in Russia and near Russia were added as test data. Revised, updated and additional results were shown and analysed, which provide more evidence to show that the new proposed ISAAS model has a better accuracy than the Saastamoinen in Russia.

Comparison of methodologies for cloud cover estimation in Brazil - A case study

Clouds are the major modulator of the shortwave and longwave radiation components of the Earth's energy balance and, as such, help to regulate the planet's temperature. In the energy sector, clouds are a source of instability in the generation of energy using solar technologies. This study aims at comparing three approaches to get cloud cover information in the Southeastern region of Brazil during the period of approximately three months. The first method, assumed as reference, uses all-sky camera pictures for the cloud cover estimation. The other two methodologies use downward longwave radiation with surface meteorological data and geostationary satellite data. Both methods presented good agreement with the camera for clear sky and overcast conditions, with probabilities of detection of 92.8% and 80.7% for the longwave method and 93.3% and 87.6% for the satellite method, respectively. The major problem occurs with the broken-clouds sky scenario, with probabilities of detection above 38%, where each method has its own specificity, such as, longwave emissivity of the atmosphere, spatial resolution and view geometry. The long-wave method has the minor R correlation with the camera (87%) when compared with the satellite method (93%) and requires a daily normalization, which make it not usable for instantaneous measurements. Regarding the satellite method, the most important issue is the spatial resolution, which has the major impact on the broken-clouds sky scenarios. The cloud masking works properly for large clouds with, at least, the size comparable to the satellite image pixel. Furthermore, the method using the all-sky camera also needs to be improved, because it presented some deficiencies, like very bright areas around the sun, sometimes identified as clouds, leading to cloud cover overestimation.

Precipitable water vapor characterization in the coastal regions of China based on ground-based GPS

Water vapor plays an important role in climate change; thus, studying the spatial distribution and temporal variation of precipitable water vapor (PWV) in the coastal regions of China would help researchers to understand the climate characteristics of those regions. In this paper, 6-year 1-h interval PWV were derived from 27 Global Positioning System stations observations of Chinese coastal GPS observation network, surface meteorological data and European Center for Medium-Range Weather Forecasts (ERA-Interim) reanalysis products. The present study provides the use of these data to investigate the spatial-temporal variability of water vapor throughout the coastal regions of China. Latitude is the main factor affecting the spatial distribution of GPS-derived PWV; that is, PWV decreased by about 1.5mm for each 1° increase of latitude. For regions at the same latitude, a region that is relatively close to the ocean will have a higher content of PWV. The PWV in the southeastern and southwestern coastal regions of China is significantly higher in summer; this may be influenced by the southeastern and southwestern water vapor inflow corridors. The PWV obviously varies monthly, reaching a minimum in January; however, the timing of the maximum varied but usually appeared in June, July or August and was affected by the monsoons. The PWV varies largely between summer and winter with a larger gradient of change in PWV with latitude in winter than in summer. The positive correlation coefficient between PWV and the surface temperature varied in different seasons; this is related to the changes of temperature and the horizontal motion of water vapor. Use of the Fast Fourier Transform method showed that the PWV time series data have multi-scale characteristics. The amplitude and phase of the PWV time series in annual, semiannual, four month and seasonal cycles were extracted through harmonic wave analysis. The amplitude of four month and seasonal cycles did not pass significance testing. The maximum amplitude of the annual PWV cycle is located in mid-latitudes with 22.09mm, which gradually decreases towards high latitude and equator area. The peak time of annual PWV cycle occurs in July, which does not agree with the timing of the maximum monthly average PWV. The semiannual cycle amplitudes is between 0.42 and 6.32mm, with no significant correlation between their distribution and latitude. The peak time of semiannual PWV cycles is during about January–March and July–September, and the peak time gradually becomes late from north to south.

Solar powered green campus: a simulation study

Large-scale deployment of the renewable energy system in India is required to achieve a target of 175 GW of green electricity by 2022. Higher educational institutes with a lot of free areas can play a vital role in reducing the conventional energy consumption and carbon footprint. The main aim of this paper is to locate and analyze the feasibility of developing 100% solar PV based academic campus at MANIT – Bhopal, India. Annual electricity consumption of MANIT, Bhopal is analyzed and accordingly 5 MW capacity solar PV based captive plant is proposed. Free open space area of the campus including rooftop area is inspected through geographical coordinates utilizing the NASA surface meteorology data and Google SketchUp. The performance of the proposed solar campus is analyzed using PVSyst and Solar Advisory Model (SAM) software. The proposed plant at MANIT campus can generate around 8000 MWh per annum of electricity to meet the 100% energy requirements of the campus with the annual reduction of 73 318.0 tonnes of carbon footprint. Development and promotion of such sustainable green concepts will be a significant step towards transforming the academic campus into an energy efficient and environmentally sustainable community.