Why does this graph for sunlight intensity on land has a steeper slope during sunrise as compared to sunset?

Why does this graph for sunlight intensity on land has a steeper slope during sunrise as compared to sunset?

I got this image while checking weather data for a city in North India using Mathematica's Wolfram Alpha query

I noticed one feature in the graph which i could not explain . Why does the encircled part 'A' which denotes sunrise has slightly higher slope as compared to encircled part 'B' which denotes sunset !

I also checked the graph for summer month and the pattern was exactly opposite

Summer Month

Am i interpreting the data wrong or is it so that sun achieves its highest intensity during the day faster in winter months than in summers ? What could be the reason behind this ?

Too long for a comment, alas: notes:

Solar radiation measurements in the morning and evening are sensitive to obstruction (e.g., trees) on the east and west horizons. A delay of sunrise or early arrival of sunset at a particular station may be explained by examining panoramic site photos available at

Also dig around a bit starting at*m and you should find more data.

During the night the wind has died and the pollution from agriculture and man made chemicals have settled leaving air quality clear and the sunlight at sunrise is less obstructed and more intense.At sunset all of the above are still lingering in the air making sunlight less intense due to obstruction caused by the pollution.As for the winter being more intense, Precipitation is higher in winter months and the water molecules filter out a large percent of pollution.

Land-Atmosphere Interactions Exacerbated the Drought and Heatwave Over Northern Europe During Summer 2018

The 2018 drought and heatwave over northern Europe were exceptional, with unprecedented forest fires in Sweden, searing heat in Germany and water restrictions in England. Monthly, daily, and hourly data from ERA5, verified with in situ soil water content and surface flux measurements, are examined to investigate the subseasonal-to-seasonal progression of the event and the diurnal evolution of tropospheric profiles over Britain to quantify the anomalous land surface contribution to heat and drought. Data suggest the region entered an unprecedented condition of becoming a “hot spot” for land-atmosphere coupling, which exacerbated the heatwave across much of northern Europe. Land-atmosphere feedbacks were prompted by unusually low soil water over wide areas, which generated moisture limitations on surface latent heat fluxes, suppressing cloud formation, increasing surface net radiation, and driving temperatures higher during several multiweek episodes of extreme heat. We find consistent evidence in field data and reanalysis of a threshold of soil water content at most locations, below which surface fluxes and daily maximum temperatures become hypersensitive to declining soil water. Similar recent heatwaves over various parts of Europe in 2003, 2010, and 2019, combined with dire climate change projections, suggest such events could be on the increase. Land-atmosphere feedbacks may play an increasingly important role in exacerbating extremes, but could also contribute to their predictability on subseasonal time scales.


[1] Enterococci are the U.S. Environmental Protection Agency recommended fecal indicator bacteria for assessing recreational marine water quality. Traditional methods of enterococci analyses are time consuming, resulting in delays in issuing beach closures. Models can potentially circumvent these delays by forecasting times when beaches should be closed. The objective of this study is to develop an innovative coupled microbe-hydrodynamic-morphological model. The unique feature of this model is its capability of simulating the release of microbes attached to coastal beach sands as a result of combined wave and tidal forcing. A nearshore process model (XBeach) was coupled with a microbe transport-decay equation. This equation included source functions that accounted for microbial release from mobilized sand, groundwater flow, entrainment through pore water diffusion, rainfall-runoff loading, and a fate function that accounted for solar inactivation effects. The model successfully simulated observed spatial and temporal patterns of enterococci in the beach water, including the reproduction of diel and tidal fluctuations and the rapid decrease of enterococci levels from the waterline to offshore. Primary processes for enterococci loading to the water column included wave-induced sediment resuspension and tidal washing for the entrainment of enterococci from the pore water in the intertidal zone. Diffusion was the major mechanism to transport enterococci from the intertidal zone to offshore. Sunlight inactivation was a key process to reduce enterococci levels during the day and to produce the diurnal cycles. Rainfall runoff was found to be an intermittent source of enterococci to beach water, whereas groundwater exchange was of secondary importance. Sensitivity analyses suggested that the processes and coefficients related to enterococci loading have quasi-linear characteristics, whereas model results of enterococci levels were sensitive to both diffusion and sunlight inactivation coefficients, showing high nonlinearity and spatial and temporal dependence.

Performance of Dual-Axis Solar Tracker versus Static Solar System by Segmented Clearness Index in Malaysia

The performance of Dual-Axis Solar Tracker (DAST) and Static Solar System (SSS) with respect to clearness index in Malaysia is presented. An attempt to investigate the correlation between clearness index with energy gain and efficiency of DAST over SSS is being done experimentally. A good correlation could not be found out from the daily clearness index. It is due to the more profound advantage of DAST in the morning and evening compared to midday as it is able to follow the sun’s position. Hence, the daily clearness index is divided into three segments which are morning, midday, and evening to interpret the energy gain and efficiency better. A clearer correlation with low standard deviation can be observed on the segmented clearness index analysis. The energy gain and efficiency of seven cities in Malaysia is being estimated with the segmented clearness index and compared to the result generated from anisotropic radiation model. A similar trend is obtained and it has shown that the segmented clearness index could be utilized as a graphical method for estimation of energy gain and efficiency of DAST over SSS.

1. Introduction

Solar energy has gained tremendous attention in recent years due to various reasons such as the fluctuating of the price of crude oil, awareness of public on environment issues, supporting policies and subsidies taken by local government to boost renewable energy sectors, and price reduction of photovoltaic (PV) panels. Many large-scale solar farms were commissioned in USA, Europe, and China as the global PV price is dropping rapidly in recent years which agrees with the Swanson’s law [1]. However, the PV generated electricity is not competitively enough compared to fossil fuel (oil, gas, and coal) especially in urban areas. Hence, more intensive research and development on PV cell material science are required in order to overleap the conversion efficiency hurdle and reduce the manufacturing cost. Meanwhile, there are several approaches available for increasing the performance of PV system other than exploring on new material for PV cell. For instance, Maximum Power Point Tracking (MPPT) which able to draw maximum power by tracking and operating on the maximum power point of the PV arrays [2], solar tracking that able to maximize the power captured from sun by following the sun path [3], and so forth. Above all, solar tracking poses great advantage to enhance the PV system efficiency as compared to a static solar system [4]. Dual-Axis Solar Tracker (DAST) is a type of solar tracker with two rotational axes which enable it to align the PV panels and point directly towards the solar disk at all times [3, 5]. Solar irradiance (W/m 2 ) is a measure of amount of sunlight fall on a surface. It is the most crucial factor in determining the performance of a PV panel. The solar energy captured by PV panels is directly proportional with the solar irradiance received by PV panels. With PV panels facing the sun at all times, it ensures that maximum solar energy being converted into electrical energy during the course of the day. Hence, substantial gain can be obtained by using DAST compared to Static Solar System (SSS).

From the literature, various tracking methods have been proposed and validated around the world in previous works and each of them has its pros and cons in terms of efficiency, complexity, and cost. Figure 1 shows the minimum, maximum, and average efficiency of some solar tracking works being reported experimentally and by simulation in other countries [6–18]. Apparently, the difference in solar tracking efficiency that varies greatly among the countries reported due to different geographic location, local landscape, and climate [19]. Moreover, the efficiency of solar tracking in the same region during different seasons also differs significantly. The efficiency normally top in the summer, with a marginal performance in winter and the spring and fall have average efficiency.

Malaysia as a country which lies at 1° to 7° north of equator has an equatorial climate and long hours of sunshine throughout the year. There are enormous potential for solar energy to be successful at this land. However, the potential for the DAST is rarely reported and investigated in this region. Hence, a quantitative advantage of DAST over SSS in this country still remains unknown although the consistently long sun hours suggested a promising outcome. Thus, it would be one of the endeavors for this study to carry out an investigation on some of the cities in Malaysia regarding their performance enhancement for installation of DAST over SSS. The advantage on the financial perspective would also be analyzed and a comparison can be made on the suitability for DAST installation on seven cities in Peninsular Malaysia.

While having better efficiency over SSS, the additional costs for the DAST could not be overlooked. The tracking mechanism requires extra mechanical structure and motors to rotate the PV panels according to the sun’s position. Operational and maintenance cost of the DAST will also be higher than SSS. Hence, estimation of the efficiency/energy gain of DAST over SSS is essential, and need to be part of the site evaluation criteria. As a rule of thumb, the gain from the DAST over SSS would have to surpass the additional costs whereby the profitability and sustainability of the DAST especially in large scale solar power plant are guaranteed. However, as shown in Figure 1, the reported efficiency improvement can vary from as low as 10% to as high as 75%. The large variation of DAST efficiency complicates the evaluation process for adopting DAST over SSS. Besides that, performance enhancement of DAST over SSS for an equator region as Malaysia is not being investigated so far.

So far, comparison of efficiency and energy gain of DAST over SSS has been done by physically installing both systems on the site of interest. This method is not only expensive but also time consuming, since data over a sufficiently long period of time is needed to facilitate a meaningful comparison between the two systems. Moreover, the results obtained are not directly applicable onto other sites. Hence, there is a need for a method to estimate the gain of DAST over SSS in a more cost- and time-effective manner such that the site evaluation can be made more straightforward.

Cruz-Peragón et al. quantify the extra solar gain of DAST over SSS with respect to latitude of the cities in Spain based on the Reindl anisotropic model and Liu & Jordan isotropic model [20, 21]. Based on his finding, the former method is more useful and better represent the climate of Spain territory as compared to isotropic model. Most of the cities in Spain territory are suitable for DAST while a few cities are not recommended due to various reasons including high latitude, high rainfall, and coastal region.

In this paper, an attempt is made to correlate the performance enhancement of DAST based on the clearness index of the sites in Malaysia. Subsequently, this correlation is used to estimate the performance boost of DAST at seven cities in Malaysia.

2. Literature Review

2.1. Clearness Index

Clearness index is chosen as the sole variable in this work as it is derived from solar irradiance, the most fundamental factor that influences the performance of a PV system. Clearness index represents the ratio of the average global solar irradiation

on a horizontal surface to the extraterrestrial solar irradiation

on the same surface and given by the following Equations for daily and hourly values, respectively [22]. For daily:

The data of global solar radiation over a day and over an hour, , are available from measurements of total solar radiation on a horizontal surface by using a pyranometer. The hourly clearness index function could be utilized to calculate clearness index for longer period by replacing the global solar radiation and extraterrestrial radiation for the desired period into (2). Meanwhile, the extraterrestrial solar radiations and can be defined as the solar radiations incident on a horizontal plane outside the atmosphere over a day and a designated period of time within a day, respectively. In other words, this is the sunlight reaching the ground of the earth without the presence of atmosphere. The amount of extraterrestrial radiation reaching the earth ground relies on the relative position of the earth to the sun on its elliptical orbit around the sun and the earth declination through this orbit. and can be calculated by using (3) as follows:

The parameters in the equations can be found in the nomenclature.

from the sun passes through and is attenuated by the atmosphere before reaching the surface on the earth as . Global solar irradiation consists of direct/beam radiation, diffuse radiation, and reflected radiation. Beam radiation is coming directly from the solar disk on a clear sky without being obstructed by the clouds while diffuse radiation is the radiation found in cloudy day with the direct solar ray being firstly scattered by molecules and particles on the clouds before reaching the solar panels. There is also a very meager amount of radiation reflected from the clouds and ground surface, namely, reflected radiation. The components of global solar radiation are illustrated in Figure 2.

The clearness index is higher in a sunny day as the solar radiation is dominated by beam radiation and less energy loss through diffusion and reflection. A higher clearness index will lead to a higher energy generated and efficiency for DAST. Hence, the relationship of clearness index with energy gain and efficiency of DAST is quantified and investigated experimentally. With the knowledge of the correlation, it is able to offer an alternative guideline for evaluating the performance enhancement of DAST to a specific site.

2.2. Diffuse Radiation Model

Diffuse radiation model is useful for evaluating the global irradiation over tilted surface by using the global solar radiation over horizontal surface. Liu and Jordan isotropic model [23] is widely used as it is the simplest diffusion model for obtaining the global irradiation over tilted surface. It assumes a uniform distribution of the ground-reflected radiation and sky-diffuse radiation on the celestial hemisphere. However, its simplistic nature has lead to some weaknesses. It neglected the contribution of circumsolar diffuse radiation and horizontal brightening to the total diffused radiation. These two components contribute a remarkable portion in the clear days. Circumsolar diffused radiation resulted from forward scattering of beam radiation and was mainly concentrated in the part of the sky around the solar disk.

Meanwhile, the horizon brightening component is concentrated near the horizon and is most profound in clear skies [24]. These additional components are schematically shown in Figure 3. Isotropic model tends to underestimate the amount of diffuse radiation in clear skies, which leads it into poorer response in clear days. Thus, the whole estimated irradiation can fall below the actual value from 3% to 9% [23, 24].

In order to have better estimation on the diffuse radiation, anisotropic models has to be adopted as larger diffuse components such as circumsolar diffuse radiation and horizontal brightening are taken into account. By analyzing various methodologies of anisotropic models, HDKR anisotropic model (Hay, Davies, Klucher, Reindl model) [21] is found to be fairly suitable as some correction factors are added to account for the horizon brightening on clear days as well as cloudiness.

Anisotropic model considers that the radiation on the tilted surface is contributed by three components which are beam, anisotropic diffuse, and solar radiation diffusely reflected form the ground as in

Erbs et al. correlation [25] in (5a), (5b), and (5c) makes it possible to obtain the isotropic diffuse component of radiation , by using the clearness index

at each time while the beam radiation is then being found from the difference between global solar radiation over horizontal surface and as in (6). Consider

Geometric factor is the ratio of beam radiation on tilted surface to that on a horizontal surface at any time [22] that can be obtained from (7). Anisotropy index is a function of the transmittance of the atmosphere for beam radiation. A modulating factor is added into HDKR diffusion model by Klucher to account for cloudiness of the sky as follows:

Equation (10) is the angle of incidence of the solar beam radiation on a tilted surface which is one of the important expressions to construct the anisotropic diffuse radiation model. The solar radiation obtained from this model is compared to the experimental result of the proposed DAST prototype. The DAST prototype is using equatorial tracking system. Photoresistors are used as the sensors to track the position of the sun. Hence, angle of incidence of the solar beam which is one of the elements of mathematical approach is not adopted for the solar tracking purpose of the proposed DAST prototype.

is the angle of incidence of beam radiation on tilted surface and solar zenith angle. Consider

In addition, the albedo (dimensionless) is the composite ground reflectance which is required to estimate the reflected irradiance. It normally takes a value of 0.2 except for the case of snowy ground it would have a higher value. is the slope of the tilted surface and together with solar azimuth angle would make sure that the PV system face the sun’s position at all times. The location latitude , declination , and Julian day are some additional variables to be considered in this model:

3. Experimental Setup

A Dual-Axis Solar Tracker (DAST) and a Static Solar System (SSS) with horizontal orientation are used in this experiment. The solar tracker has two axis of rotation which enable it to rotate along the eastwest and northsouth axis. The type of Dual-Axis Tracking System that is used in this research falls into the category of equatorial as categorized by Alexandru [26]. The DAST proposed here is able to track the sun on two independent axes. The hardware prototype of the DAST and its rotational axes are shown in Figure 4. The daily motion of the sun (from east at dawn to the west at dusk) is tracked by the DAST on first axis (axis “1”: daily motion, E: East, W: West). The rotational range of this axis is ±70° with the position of solar noon as the reference position (0°). On the other hand, the seasonal variation of the Sun position is tracked by the DAST via the second axis (axis “2”: elevation, N: North, S: South). This axis has ±30° rotational range for tracking the altitude angle of sun during the different seasons. The reference (“zero”) positions for the angular fields of the two rotational axes is better illustrated by referring to Figure 5. Figure 5(a) shows the DAST on a horizontal position at which both daily (eastwest) axis and elevation (northsouth) axis on their reference (“zero”) position. The normal of the PV panel is coincided with the zenith axis at this position and acts as the reference position of the rotational ranges. The individual reference positions and rotational range of both axes are shown in Figures 5(b) and 5(c), respectively. The sun-tracking mechanism is based on two pairs of Photoresistors as sensors to locate the position of the sun. Two direct-current (DC) linear actuators rotate the PV panel towards the sun’s position upon the signals from microcontroller. The tracking is done on a fifteen-minute-basis as the sun moves at a slow speed along the sun path. The control technique adopted in the proposed DAST is a closed-loop (with photosensor) approach. The DAST would track the sun’s position based on the light intensity received by the photo-sensor. As the movement of the sun is slow in nature, continuous tracking of the sun’s position is not necessary. Hence, DAST tracks the sun once in every fifteen minutes. In other words, the fifteen minutes is an interval between consecutive tracking. This main purpose of this approach is to reduce the power consumption for doing redundant tracking. A Sanyo 210 Watt Monocrystalline PV panel is used in the DAST and static system, respectively. The technical characteristics of the PV panel are shown in Table 5. In order to make sure the PV panels on both systems are operating on the Maximum Power Point (MPP), Constant Voltage (CV) Maximum Power Point Tracker (MPPT) with buck converter is connected to PV panels [27]. The open-circuit voltage

, short-circuit current , maximum-power voltage,

and maximum-power current are measured and logged with an interval of 1 minute. The maximum power can be obtained via the product and . The energy generated in a day or an hour could be obtained by integrating the maximum power along the period of time. The energy gain

(%) of DAST over SSS which will be widely used in this work can be calculated from energy generated by DAST

and energy generated by SSS and the energy consumption for performing the tracking


Indeed, the energy consumption is an essential part in calculating the energy gain ( ). If the energy consumed in tracking is substantial, it might not be worthwhile for adopting a solar tracker. The energy gained from the benefit of tracking could not justify the energy consumed in doing the tracking. The Global Solar Irradiance of both systems is measured by using a well-calibrated Li-Cor (LI 210SA) pyranometer, respectively. The daily data collection starts from 7 am until 7 pm corresponding to the typical day time in Kuala Lumpur, Malaysia, where the experimental setup is installed. The data are logged into EEPROMs and extracted out at the end of the day for analysis in Microsoft Excel and Matlab.

4. Result

4.1. Results of Daily Power Generation

The maximum power obtained by DAST in a low clearness index day and high clearness index day has substantial difference. Figures 6 and 7 show an example of electrical power generation along a day for an overcast day with of 0.34 and a sunny day with of 0.62. In a day with low , the sun ray is blocked by the clouds which leads to no beam radiation falling on the solar panels. This is clearly indicated on the morning session (7 am to 9 am) and evening session (3 pm to 7 pm) on Figure 8. The diffused radiation is dominant in these two periods and there is no advantage for DAST over the SSS. Kelly and Gibson [28, 29] have shown that diffuse radiation is isotropically distributed over the whole sky in an overcast day. Hence, a PV panel positioned horizontally will receive maximum amount of isotropically distributed sky radiation compared to tilted position. Conversely, in a day with high , the beam radiation is playing a dominant role. 90% of the global solar radiation is made up by beam radiation [20]. DAST follows the position of the solar disk and ensures that maximum amount of beam radiation strikes the PV panels throughout the day. Figure 8 shows the difference of instantaneous power of DAST and SSS along the sunny day and cloudy day. Tremendous gain is obtained during the morning and evening sessions, while the gain at Table 1 shows the efficiency ( ) and energy gained by the DAST over SSS in two days with different value of clearness index. The efficiency of DAST over SSS varies from 24.91% at an overcast day to 82.12% at a clear day. Likewise, the electrical energy generated also increases drastically from 108 Whr/m 2 at a cloudy day to 603 Whr/m 2 at a clear day. Apparently, the efficiency of DAST over SSS and additional electrical energy generated in a day are influenced by the clearness index.

), energy gained by the DAST over SSS, and energy consumption under different clearness index.

Figure 9 presented an example for a day with the measured instantaneous irradiance of DAST and horizontally-positioned SSS together with the modeled irradiance of DAST. The modeled irradiance of DAST is generated from the global irradiance of horizontally positioned SSS.

Measured and modeled (anisotropic diffuse model) instantaneous irradiance of DAST and measured irradiance of horizontally positioned SSS.

There are two noticeable findings that can be interpreted from it. The first finding is that the anisotropic has demonstrated an inspiring result for estimating the irradiance of the DAST as both measured and modeled irradiance have very similar value. The experimental and modeled DAST would track the sun on a similar path in order to have the similar result shown. Hence, HDKR anisotropic diffused model can be reliably used to infer the instantaneous irradiances of DAST. The second finding is that the irradiances captured by both DAST and SSS are not far off from each other during the midday. It means the incidence angle of sun ray falling on both does not differ much at this period compared to other periods of the day. It agrees with the result from Figure 8 that the advantage of DAST lies on morning and evening session.

For the proposed DAST, the energy consumed is marginal compared to the additional energy gain from tracking. Thus, the energy consumption ( ) is not included in the calculation of the energy gain. The voltage of the tracking system and the current drawn during the tracking process are shown in Figure 10.

The DAST tracking is done within fifteen seconds duration for consecutive fifteen minutes. The tracking time is short as the sun movement does not vary much in fifteen minutes. The DAST tracks the sun from 7 am to 7 pm which is equivalent to twelve hours. The approximate energy consumption used for tracking in a day can be calculated as follows:

The energy consumption ( ) is compared to the difference between the energy generated between DAST and Static Solar System (SSS) in a low and high clearness index day, respectively. Apparently, the energy consumption is relatively small compared to the energy difference of DAST and SSS for the DAST proposed in both high clearness index (sunny) and low clearness index (cloudy) day as shown in Table 1. Hence, the energy consumption ( ) can be safely left out from the calculation of the energy gained by the DAST over SSS without significant impact on the final result. However, the impact of energy consumption ( ) should not be overlooked in a larger system as its value would loom large and become significant to the difference of energy between DAST and SSS.

4.2. Results of Energy Obtained by DAST and SSS

Figure 11 presented the daily energy captured by DAST and SSS for a month in a bar chart. There are various conditions such as sunny days, cloudy days, and rainy days during this month which is the typical climate of Malaysia. The weather in a month could act as a miniature of the weather in a year.

Hence, the underlying factor that determines the amount of advantage of DAST over SSS has to be found out. As a result, it leads to the investigation of the energy gain of both DAST and SSS with respect to clearness index. The clearness index is imperative for the performance evaluation of a PV system. It indicates the clarity of a day and the potential amount of sunlight for converting the solar energy into electrical energy by the PV system. The availability of abundant direct sunlight in a day has a profound impact on the energy generated by a PV system.

Generally, a direct proportional relationship could be hypothesized for both DAST and SSS with respect to clearness index. Based on the performance of both clear and overcast days reported on previous section, it could be deduced that DAST responds to clearness index on a more sensitive manner compared to a SSS. This is due to its ability to track the position of the sun and captured maximum sunlight from sunrise to sunset.

Figure 12 shows the energy captured by the DAST and SSS plotted against clearness index for a month. The trend lines for both systems are generated by using basic fitting in Matlab. The standard deviation of DAST and SSS is 0.0418 kWhr/m 2 and 0.0175 kWhr/m 2 , respectively. The slope for both systems with respect to clearness index is apparently distinct from each other. Both trends of SSS and DAST have shown a linear growth with respect to clearness index. However, DAST has a steeper slope compared to SSS. On the other hand, at the days with low clearness index (below 0.22), presumably rainy days, DAST would have lower energy gain than SSS as shown in the extrapolation from the graph. It is due to the fact that the available solar radiation in those days is dominantly diffuse radiation. Direct radiation would be absent or at a relatively minimal level. Therefore, DAST has no advantage over SSS at this range of clearness index.

On the other side of the clearness index, the energy captured by DAST increases in a steeper slope compared to SSS as the clearness index climbs from medium (0.4) to high (0.6) level. Thus, it fortifies the inference that greater amount of energy will be captured by DAST compared to SSS as the clearness index increase. In other words, the application of DAST ensures a performance boost of a PV system and the outcome is exceptionally well as the clearness index increases. In addition, the trends of both DAST and SSS with respect to clearness index show that a clearness index offers a good estimation for energy generation on different weather.

4.3. Results of Daily Clearness Index

An endeavor to discover the relationship between clearness index and tracking advantage of DAST is made by plotting both efficiency and additional electrical energy gained over the clearness index for one month duration. There are different kinds of weather within the period which includes sunny, overcast, partial cloudy, and rainy days. Figures 13 and 14 show the graphs of efficiency ( ) and Energy Gain ( ) versus daily Clearness Index ( ) by one month data of May 2013. A linear trend line is plotted via basic fitting for both graphs, respectively. Generally, these two graphs exhibit a weak trend of and proportional to . The standard deviation of the efficiency and energy gain is 7.1757% and 0.0355 kWhr/m 2 .

The advantage of DAST over SSS is more remarkable at sunny day with high (between 0.5 and 0.6) compared to the overcast and rainy day with low (between 0.2 and 0.4). A clear and quantitative conclusion could not be drawn on the advantage of DAST based on the daily clearness index. It appears that there are some days that the efficiency and energy gain is scattered far off from the linear trend lines. Hence, a look on the energy gain versus time on Figure 8 would give a more detailed insight on how the slope of the DAST tracking advantage changes in a day. It has been found that the effectiveness or energy gain of DAST is particularly well in the morning and evening as compared to during the midday. This explains the weak trend above as the daily clearness index lump the slopes of the DAST tracking advantage on morning, midday, and evening without considering that they have different value of slope.

4.4. Results of Segmented Clearness Index

It is necessary to divide the clearness index into three segments period which are morning (0700–1100), midday (1101–1500), and evening (1501–1900) for a better visualization of the influence of clearness index. Nevertheless, the splitting of the day into three segments is not without a tradeoff. Since the clearness index is split into three periods, the efficiency of DAST over SSS for a single day could not be established as the portion contributed by efficiency for three periods and could not be summed into a total amount as energy gain. Thus, the total efficiency of a single day could not be obtained although the total energy gain of DAST can be summed from the three segments. The tradeoff is justified as a more accurate energy gain of DAST over SSS resulted from the segmented analysis. An accurate energy gain is extremely handy for estimating the additional profit generated by DAST as the Feed-In Tariff (FID) is based on the energy generated (kWhr) instead of efficiency. Figure 15 shows the energy gain of DAST over SSS versus clearness index on three segments period. The three trend lines are generated with polyfit and polyval functions in Matlab by fitting the experimental data points in least square sense. The trend lines on morning, midday, and evening for energy gain versus clearness index are fitted in second-order polynomial based on the scattered data points. Apparently, the trend of energy gain with respect to clearness index is clearer as the day is being segmented into three periods. Again, the sharp slopes of the efficiency and energy gain of DAST over SSS during the morning and evening period are different compared to the smaller slope in midday. The slope of the energy gain is lower at midday because SSS is positioned on a horizontal orientation. During the midday, sun light strikes on the PV panel of SSS with a smaller incidence angle compared to morning and evening. Most of the solar energy is captured by SSS within this period. Thus, the energy gained by DAST over SSS increases in a smaller slope as clearness index increases. During morning and evening, DAST has the advantage of facing the solar disk while SSS stay on its static horizontal position. As a result, the energy gained by DAST is a lot greater as the day is clearer and more solar energy is available. However, the advantage diminishes as the clearness index drops. As clearness index drops below approximately 0.15, the energy captured by DAST is less than SSS as there is only diffuse radiation available. A horizontally positioned SSS inclined position. Anyway, the energy captured on this level of clearness index is marginal. The standard deviation for the trend lines and data points are 0.0113 kWhr/m 2 , 0.0133 kWhr/m 2 , and 0.0109 kWhr/m 2 for morning, midday, and evening, respectively. It seems that the data points are distributed in a smaller range in the three segments clearness index compared to the daily clearness index shown in previous section. Hence, the energy gain of a DAST over SSS can be estimated in a better accuracy by using segmented clearness index graph given the clearness index of a site.

4.5. Comparison of Energy Gain of Seven Cities by Using Segmented Clearness Index and Anisotropic Model

The energy gain versus segmented clearness index graph is used to estimate the energy gain of other cities in Peninsular Malaysia based on the segmented clearness index of the cities, respectively. The performance improvement of DAST over SSS in seven cities of Peninsular Malaysia including Bayan Lepas, Ipoh, Kuantan, Muadzam Shah, Langkawi, Senai, and Subang are being estimated by the segmented clearness index graph method as shown in Figure 16. The hourly data of global irradiation over horizontal surface in 2009 for the seven cities is obtained from Malaysia Meteorological Department. These data were measured by using pyranometer in the weather stations at the seven cities, respectively. Segmented clearness index can be generated from these data and plotted on the segmented clearness index graphs to compute the energy gain of DAST over SSS. Moreover, these data are also put into HDKR anisotropic model to develop the global irradiation over the DAST [21]. Electrical energy generated by both DAST and SSS can be converted from the global irradiation, respectively, by using the average effectiveness of the PV panel on DAST and SSS, 0.15. The energy generated for both systems in a year are being averaged into a mean daily energy generated and divided into three segments. Subsequently, average energy gain of DAST over SSS on the three periods can be obtained by using the mean energy generated for both systems in three segments.

Energy gain of DAST over SSS for 7 cities developed by anisotropic model on 3 segments clearness index.

The average efficiency and energy gain of DAST over SSS in three segments of a day by using anisotropic model are plotted on the segmented clearness index graphs. It is observed that there are some similarities on the response of efficiency and energy gain of DAST over SSS with respect to the segmented clearness index in both anisotropic model and segmented clearness index curve from experimental. The slopes of both anisotropic model and experimental generally agree with each other albeit some discrepancies appearing due to some reasons. The value generated from anisotropic model tends to be slightly higher due to the horizon brightening component that may be estimated on a higher level than the actual level [22]. Besides that, an accurate estimation of the irradiation for anisotropic model cannot be obtained as there are a few days in a year that the weather stations in the seven cities were undergoing maintenance and data are not available during the short stint. Nonetheless, it has shown that the segmented experimental generated curve that can be used to estimate the energy gain of DAST over SSS with a low margin of error. The energy gain of DAST over SSS based on both anisotropic model and segmented experimental generated curve are tabulated on Tables 2, 3, and 4 for morning, midday, and evening. It has been shown that, in terms of energy gain of DAST over SSS, Langkawi has the greatest potential for installing DAST while Senai has the least advantage among the seven cities. Langkawi is an island and is close to the coastal area, the clearness index is higher, and clearer days are available throughout the year. Senai is on the southern part of the Peninsular Malaysia and in an inland site which leads to higher precipitation and relatively regular rainfall. Hence, it has lower clearness index on three segments of the day and lower energy gain of using DAST.


The recession of the current ice fields near the summit of Kilimanjaro has been shown to be controlled largely by climate. Despite detailed research into summit climate, including mass and energy balance modelling, understanding Kilimanjaro as a whole has been limited by lack of observations on the mountain slopes. Analysis of hourly air temperatures, relative humidities and vapour pressures from 22 weather stations installed between September 2012 and 2015 across the mountain from south-west to north-east are presented for the first time. Moisture is shown to move upslope on both sides of the mountain during the afternoon. The north-east slope is less humid and warmer on average than the south-west slope. Temperature differences between slopes reach 4–5 °C during the morning in the rainforest zone (2000–2500 m) and on the crater wall (5000–5550 m). Slope differences are broadly similar in size to local contrasts within the south-west slope caused by the rainforest (at 1890 m) and ice fields (at 5800 m). Although both slopes show similar moisture regimes, there are contrasts in moisture content particularly in the zone just above the current rainforest limit (3000–3200 m). This decoupling extends up to 5000 m in the afternoon because the upslope transport of moisture is both weaker and delayed on the NE slope. At night the upper slopes are highly correlated implying that free-air moisture is the dominant source. Very moist events at crater level tend to be associated with widespread moistening across the whole mountain. These results can be used both to argue for and against the role of deforestation being an important influence on summit climate and therefore ice field recession.

3. Observational Results

3.1. Ionospheric Irregularities on Two Days in October 2008

3.1.1. Irregularities on 10 October 2008

[14] On 10 October 2008, the C/NOFS satellite orbit took it between 65°W and 85°W during two consecutive passes at 0208–0212 UT and 0351–0356 UT. C/NOFS passed very close to the magnetic equator at an altitude of ∼400 km during these periods. Solid and dashed lines in Figure 2 (top) show apex altitude of the first and second passes of the C/NOFS satellite which are mapped onto the magnetic equator. The apex altitudes of the two tracks were between 400 km and 450 km. The vertical line represents the longitude of Jicamarca. The star mark shows the 350 km IPP of Ancon for the U6 satellite radio wave measured at Ancon and mapped onto the magnetic equator. A dot (C1) and square (C2) on the solid and dashed lines, respectively, represent the tracks when the C/NOFS satellite was closest to the IPP of the U6 satellite. The apex altitudes of the satellite tracks were higher than the IPP by less than 100 km at C1 and C2. This geometry allows us to diagnose plasma bubbles using multiple observations. Plasma densities at 1 Hz sampling measured by the PLP for the first and second passes are shown in Figure 2 (middle) and Figure 2 (bottom), respectively. Frequent density depletions were observed during both passes with the undisturbed background density between 1.0 × 10 5 cm −3 and 2.0 × 10 5 cm −3 . The depletions on the first pass tended to be deeper and the ambient density was higher than in the second pass.

[15] The TEC observations were conducted by the LISN network. The line in Figure 2 (top) represents the apex altitude of IPPs between a ground-based GPS receiver and the PRN 15 GPS satellite at Jicamarca (J) and Bogota (B) from 0100 UT to 0500 UT. Dots and squares on the lines correspond to times C1 and C2, respectively. The vertical TEC data obtained during this time are shown in Figure 3 for both Jicamarca and Bogota. After 0206 UT, the Jicamarca TEC gradually drops from approximately 10 to 8 TECU and fluctuates between these values while in Bogota the drop is very steep and amounts to 8 TECU. It is apparent that the deep bubble at 0210:30 UT seen on C/NOFS is responsible for this dramatic TEC drop at Bogota.

[16] Figure 4 (top) shows S4 index of 250 MHz scintillation measurements. They were made at an elevation angle of 55°. The IPP mapped onto the magnetic equator is shown with the star mark in Figure 2 (top). The S4 index is defined as the ratio of the standard deviation of signal intensity fluctuations normalized with the mean signal intensity. Two vertical lines represent C1 and C2 in Figure 4. Weak scintillation of S4 < 0.5 started after 0100 UT. Figure 4 (bottom) shows eastward drift velocity derived from the spaced-receivers as described in section 2. Data gaps, such as those between 0200 and 0400 UT, are because of low S4 causing poor correlation between the spaced-receivers. Even though plasma bubbles were present, the zonal drifts were steady and fairly low, only about 50 m s −1 .

[17] On this night, the 50 MHz radar detected coherent scatter echoes at Jicamarca. Figure 5 shows the Range-Time-Intensity (RTI) map of the backscatter echo. The scale for UT and LT are shown on the top and bottom axes. Strong echoes of more than 40dB appeared at altitudes higher than 400 km after 0209 UT. The plume continued to rise to around 800 km at about 0345 UT. C/NOFS flew over Jicamarca at 0209 UT and 0353 UT (see Figure 2). It is quite fortuitous that the backscatter echoes expanded dramatically in altitude at about the same time as the C/NOFS over-flight reaching 600 km and eventually to almost 800 km. That a radar plume is co-located with a very disturbed low density region of plasma is consistent with earlier measurements [e.g., Tsunoda et al., 1982 ]. In general, when a radar plume appears like that in Figure 5, strong VHF scintillations (on the order of S4 > 0.6) are often observed [ Basu et al., 1996 ]. However, the S4 index was not dramatically large at Ancon despite the fine (3 m) structure of backscatter at Jicamarca because of the low background density. Investigation on the background density is performed in section 5.

[18] It is possible to clarify small scale structures (down to 15 m scale) using high resolution 512 Hz PLP data. Power Spectral Density (PSD) of the irregularities gives us information about their generation mechanism. Figure 6 (top) shows PLP high resolution data for three minutes from 0208 UT. There were many small scale structures inside some density depletions. Two samples with four second window were used for spectral studies, 0209:40–0209:44 UT and 0209:44–0209:48 UT. To obtain the PSD, the first step is to determine the quadratically detrended plasma density. The detrend line is represented by N 0(t). Using the instantaneous value of N (t) a time series of is obtained, the RMS value of which over 4 s is defined as the irregularity amplitude dN/N0. A Blackman-Harris window is applied to the time series and the FFT technique is used to obtain the power spectrum. Figure 6 (bottom) show PSD calculated from the two four-second samples. The irregularity amplitude dN/N0, is displayed on the top right of each plot. The PSDs are plotted as a function of both the wavelength λ and the wave number k, whose scales are given on the top and the bottom axes. To obtain λ, we recall that the PLP was sampling at a frequency of 512 Hz. Combining this with the satellite velocity V of 7.55 km s −1 , the Nyquist scale length is obtained to be approximately 30 m. Since the satellite was flying nearly perpendicular to the magnetic field, if we assume that the plasma structures larger than 30 m are stationary in the frame of the plasma, λ can be regarded as the horizontal scale in a direction perpendicular to the magnetic field. The wave number k was calculated from the relationship k = ω/V, where ω is the angular frequency.

[19] Both PSDs in Figure 6 (bottom) follow power law for k < 180 rad km −1 . There is a break in the slope around k = 90 rad km −1 (λ = 70m). The slopes for 1 < k < 90 and 90 < k < 180 are represented with blue and red lines, respectively, in Figure 6. The number in parentheses is the uncertainty factor of the slope. Slopes for the lower frequency were −1.84 ± 0.07 and −1.58 ± 0.07, while those for the higher frequency were −4.64 ± 0.35 and −3.90 ± 0.31. The slopes for the higher frequency were larger than those for the lower frequency. The differences in the slopes indicate that the irregularities were formed by different processes. The break in the spectrum and steeper slopes for the higher frequency were also reported by Rodrigues et al. [2009] . As they pointed out, some increase of PSD for k > 180 rad km −1 could be caused by noise in the PLP instrument.

[20] Such two slope spectra were also seen for other time windows for which dN/N0 were more than several percent. Figure 7 shows time series of dN/N0 and slopes of PSD. The dN/N0 and slopes of PSD were derived for each four-second window, overlapped by two seconds. Those values are derived by the same procedure as those shown in Figure 6. The dotted, dashed, and solid lines show dN/N0, slope 1 and slope 2. Slope 1 and slope 2 stand for slopes of 1 < k < 90 and 90 < k < 180, respectively. There were several packets of large dN/N0 (>10%) during the three minutes. Those durations were from 20 s to one minute, depending on the threshold of dN/N0. This periodicity can be easily seen in Figure 6. The spatial scales corresponding to them are from 150 km to 450 km, which are consistent with the zonal scale of plasma bubbles reported by Fukao et al. [2006] . When dN/N0 was below a few percent, both slope 1 and slope 2 became small. The anti-correlation between dN/N0 and the slopes agrees with previous studies [e.g., Kelley et al., 1982 ]. These authors found a clear difference between slope 1 and slope 2 for small dN/N0 slope 1 was between −1.5 and −2 while slope 2 was steeper than −4. Similar double slopes were found for the second pass (figure not shown). The double slopes and the break frequencies were different from those reported during high solar activity period [ Basu et al., 1983 ]. This will be discussed in section 5.

3.1.2. Irregularities on 5 October 2008

[21] On 5 October 2008, C/NOFS orbited close to the magnetic equator between 65°W and 85°W during 0218–0223 UT and 0402–0406 UT, which was a similar observational geometry to 10 October 2008. Figure 8 shows the apex altitude of the two tracks in the same format as in Figure 2. The apex altitude of the two tracks was 400–450 km and 450–500 km, respectively. Plasma density measured by the PLP for the first and second passes are shown in Figure 8 (middle) and Figure 8 (bottom), respectively. Electron density along those tracks was between 1 × 10 5 cm −3 and 3 × 10 5 cm −3 . Only small fluctuations were seen along the two tracks.

[22] The TEC data obtained during this time are shown in Figure 9 for both Jicamarca and Bogota. The contrast between the TEC observed on this day and on October 10 is fairly spectacular. The TEC at Bogota is much lower earlier in the evening and it falls monotonically up to local midnight indicating the weak nature of the EIA. The TEC at Jicamarca, on the other hand is larger on Oct 5 and shows fluctuations that grow up to local midnight. In fact the large increase in TEC just prior to local midnight indicates that plasma was transported toward the equator. In the next section, we will be able to show the equatorward transport with a latitudinal array of GPS receivers.

[23] Strong 250 MHz scintillation was observed on this day in spite of the small in situ fluctuations at 400 km altitude. Figure 10 (top) shows the S4 index in the same format as that of Figure 3. Several packets of large S4 index (>0.6) were seen after 0130 UT. The significant scintillation continued till 0330 UT. Between 0400 UT to 0500 UT, S4 index was fairly large and was accompanied by larger eastward speed of the irregularities. The speed increased in magnitude up to 150 m s −1 just after 0400 UT.

3.2. Background Electron Density/TEC on 5 and 10 October 2008

[24] We have presented a great contrast between two days of data which were shown in the previous section. On 10 October, low scintillations were observed at Ancon with severe electron density disturbance in C/NOFS perigee measurements. On the other hand, on 5 October strong scintillations were observed without electron density disturbance in C/NOFS data. To understand the contradictory results of these two days, meridional distribution of electron density and TEC were investigated. The meridional distribution of electron density can be a key to the understanding of irregularity generation and associated scintillation activity.

[25] The Digisonde at Jicamarca provides foF2 near the magnetic equator. Zonal distance between Jicamarca and Ancon is about 300 km, but because of the large field of view of the measurement, we assume there is no significant difference in the electron density between them. Red star marks in Figure 11 (top) show foF2 against local time. The foF2 values at 1900 LT on 9 October (0000 UT on 10 October) and 2000 LT (0100 UT on 10 October) were 5.9 MHz and 4.7 MHz, corresponding to electron density n = 4.3 × 10 5 and 2.7 × 10 5 cm −3 , respectively. Range spread appeared around 2030 LT (0130 UT on 10 Oct).

[26] The value was quite a bit higher on 5 October 2008. Figure 12 (top) shows foF2 on 5 October foF2 at 1900 LT (0000 UT on 5 October) and 01 UT (20 LT on 4 October) were 7.1 MHz and 6.4 MHz, respectively. The corresponding electron densities were n = 6.3 × 10 5 cm −3 and 5.1 × 10 5 cm −3 , respectively. Range spread appeared after 0130 UT (2030 LT on 4 October).

[27] The TEC measurement from the LISN network gives us information on latitudinal profiles of TEC, which can reveal the electrodynamics controlling the formation of the EIA. The TEC data from four stations, Bogota, Popayan, Piura and Jicamarca, are utilized to derive the latitudinal profiles of TEC. The stations are located almost in the same meridional line as Ancon. Absolute values of TEC were derived using the same method as that of Valladares et al. [2009] . Solid lines in Figure 11 (bottom) show TEC against geographic latitude. Vertical dashed line at 12°S represents the magnetic equator. Red, green, blue, and black lines show averaged TEC at hourly intervals during the dusk period on 10 October, as indicated on the top left of Figure 11 (bottom). At each station, TECs are averaged for all satellites for each hour in a bin of one latitudinal degree. Vertical lines for each data point represent the standard deviation in each bin. The latitudinal distribution of TEC clearly shows the northern peak of the EIA. Around 0100–0200 UT (red line), the crest was about 18 TECU and appeared around 2°N (14°N magnetic latitude). The peak decayed and moved toward the magnetic equator gradually. Around 0400–0500 UT, the peak was around 4°S (8°N magnetic latitude) with a value of about 11 TECU.

[28] The TECs and location of EIAs usually have large variation with longitude, season and solar cycle [e.g., Liu et al., 2006 ]. A “typical latitudinal distribution of TEC” from Valladares et al. [2001] shows a similar feature in the EIA. Their TEC profile is based on data of 0100 UT in 1998 (solar flux was 116) in the same latitudinal sector. The EIA crests were displaced 14° away from the magnetic equator and their crest-trough differences were around 75 TECU. The location of the crest is in agreement with that in Figure 11 (bottom). The large reduction in the peak value of TEC probably comes from the current extended solar minimum.

[29] The northern crest of the EIA on 5 October is shown in Figure 12 (bottom). The EIA crests were less evident than that on 10 October as shown in Figure 11. The crest-trough difference was around 15 TECU at 0100–0200 UT, smaller than that seen on 10 October. The most significant difference of the EIA from that on 10 October however, was the location of the crest early in the evening when it was found to be at 3°S (9°N geomagnetic latitude) a full 5° closer to the equator.

[30] Valladares et al. [2004] reported that the ratio of the crest TEC to the trough TEC could be a proxy for scintillation occurrence. They indicated that scintillation activity was higher when the crest-to-trough ratio was larger. In this point of view, scintillation is more likely to occur on 10 October than 5 October because the crest-to-trough ratio was 2.2 on 10 October, while it was 1.5 on October 5. While this may be true for the width of the scintillation belt (recall the bubble at Bogota on 10 October in Figure 2), it is not true for the magnitude of scintillations at the magnetic equator itself. For that it is necessary to consider the density at the IPP of the scintillation measurements. This will be discussed in section 5.

3.3. Pre-reversal Enhancement (PRE) on 5 and 10 October 2008

[31] Vertical plasma drift plays an important role in the formation of EIA [e.g., Basu et al., 2009 ]. The location of the crests, the crest-trough difference of the EIA and the peak-to-trough ratio are all affected by vertical drift [e.g., Heelis, 2004 ]. The vertical motion of the plasma is mainly caused by the zonal electric field. Thus the vertical plasma drift was studied to investigate the difference in the latitudinal distribution of TEC between the two days.

[32] Figure 13 shows the vertical drift against local time at Jicamarca for the two days. The blue line shows vertical drift velocity observed on 10 October. The drift velocity from 0700 to 1600 LT, which is shown with triangles in Figure 13 was estimated with two magnetometers: one at Jicamarca and the other at Piura as mentioned in section 2. From 1700 to 2000 LT the ISR at Jicamarca measured vertical drift. The drift velocity was averaged between 368 km and 428 km altitude [ Kelley et al., 2009 ] and shown with dots in Figure 13. The upward drift decreased toward sunset and the PRE started around 1800 LT. The PRE continued after 2000 LT. The upward drift could also be derived from Digisonde h′F data at Jicamarca. The upward motion obtained with h′F agreed with the ISR data.

[33] The red line shows vertical drift velocity on 5 October. Drift velocity during daytime was derived with magnetometer data in the same way as on 10 October. The upward drift was smaller and larger than on 10 October in the morning and around noon, respectively. The difference of the velocity between the two days was less than 5 m s −1 . Drift motion from 1900 to 2000 LT was derived from variation of h′F as ISR measurements were not available. The PRE started around 1800 LT as on 10 October. Notable difference between the two days was the duration of the PRE, which may be due to the day-to-day variability. The upward drift was terminated before 2000 LT on 5 October while it continued after 2000 LT on 10 October. The difference in the duration of the PRE may contribute to the difference in the TEC distribution on the two days.


Variables Describing and Determining Microclimate

As usual in climatology , the microclimate of a particular location is characterized through a number of climate variables. Traditionally, these variables are those describing the thermodynamic and dynamic state of the atmosphere, i.e. radiation, temperature, humidity, wind speed, and pressure (density). Depending on the research focus, other variables, such as trace gas concentrations, should be added to the list. For example, the health and the comfort of the ever increasing number of people living in cities is directly related to the concentration and distribution of air pollutants, which are additionally required to characterize the microclimatic state of this particular environment.

As for the surface properties determining the microclimate, the following groups of variables are relevant in the case of simple, flat surfaces: radiative properties (albedo, emissivity) aerodynamic characteristics (roughness length, zero plane displacement) thermal properties (heat capacity and conductivity), and properties affecting the moisture status (hydraulic characteristics of the soil, type of surface cover). At sites with a more complex geometry, slope and exposition have an important impact, in particular on the components of the radiation balance. These surface properties are not static, but reflect for instance forcing by the large-scale wind field, diurnal changes in the soil moisture conditions, seasonal changes in the vegetation, or the presence or not of snow.

The temporal and spatial distributions of the state variables are governed by the conservation equations for energy, momentum, and mass (dry air, water vapor, tract gases). Owing to the generally turbulent nature of the flow in the vicinity of the Earth's surface, the terms representing turbulent transport in these equations are particularly important and receive much attention in research of microclimate.

1 Introduction

There is concern that mountain areas in comparison to adjacent lowlands may show enhanced warming in response to greenhouse forcing due to factors such as snow-albedo feedback, changes in atmospheric moisture, and the laws of physics [see Rangwala and Miller, 2012 Pepin et al., 2015 ]. However, surface temperature observations are skewed toward lower elevations [Lawrimore et al., 2011 ], and the current observational network is inadequate to determine whether high elevations are warming more rapidly than lower elevations. Numerous studies have investigated observational evidence for elevation-dependent warming, but while some show an increase in warming rates in high mountains [Diaz and Bradley, 1997 Ohmura, 2012 Yan and Liu, 2014 ], others show a decrease [Lu et al., 2010 Vuille and Bradley, 2000 Ceppi et al., 2010 You et al., 2010 ] or a more complex picture [Pepin and Lundquist, 2008 ].

Remotely sensed temperatures have the advantage of extensive spatial coverage, often at fairly high resolution [Merchant et al., 2013 ], but so far, relatively few studies have used this potential data source in the context of temperature trends at high elevations [Qin et al., 2009 ]. Although this is partly because of the short length of the satellite records, this is becoming less relevant with time. However, satellites measure land surface or “skin” temperature (hereafter referred to as LST) [Jin and Dickinson, 2010 ] as opposed to air temperature at screen level (

2 m above ground hereafter referred to as Tair). The latter is commonly used for climate change assessments [Hartmann et al., 2013 ] and is a critical measurement in ecology, hydrology, and climate science. LST is more strongly controlled by the surface radiation balance [Benali et al., 2012 ], which is in turn controlled by the interplay between local land surface characteristics and solar geometry, while Tair tends to be more regional in scope, although rapid variations can occur over short distances due to factors such as cold-air drainage [Daly et al., 2010 Lundquist et al., 2008 ]. Thus, if satellite records are to be applied to examine elevation-dependent warming, studies which compare LST and Tair at extremely high elevations (>4000 m) are required. So far, comparisons have been limited to lowland [Coll et al., 2009 Vancutsem et al., 2010 ] or moderate elevation environments [Wenbin et al., 2013 Shamir and Georgakakos, 2014 ].

This paper therefore compares Moderate Resolution Imaging Spectroradiometer (MODIS) LST from the MOD11A2/MYD11A2 products [Wan, 2006 ] with Tair along a transect of 22 weather stations across Kilimanjaro, including sites at elevations up to 5800 m. The elevational range of nearly 5000 m is one of the largest in the world. The main aim is to understand the contrast between LST and Tair and uncover the factors that control this. Previous studies are discussed in section 2. The study area is outlined and methods are explained in section 3. The main behavior of the difference between LST and Tair is examined in section 4, along with the factors that control its variation, before the consequences of our findings are discussed.


Problem 615: Radiation Levels on the Surface of Mars
Students explore radiation dosages on mars and in interplanetary space [Grade: 6-8 | Topics: unit conversions graph analysis rates ] (PDF)

Problem 570: Curiosity Heads for Mt Sharp
Tabular data is used to estimate how long it will take the Curiosity rover to reach the base of Mt Sharp using data from its porevious week travels. [Grade: 3-5 | Topics: averaging numbers in a table time = distance/speed] (PDF)

Problem 536:Exploring a Possible InSight Landing Area on Mars
Students work with latitude and longitude and scaled images of mars to locate the InSight proposed landing area, and describe the terrain of the landing area. [Grade: 6-8 | Topics: degree measure latitude and longitude working with scaled images metric measure] (PDF)

Problem 535:Comparing the InSight Landing Area to a City Block!
Students use scaled images of a proposed InSIght landing area and a scaled image of an urban neighborhood on Earth to compare the sizes of familiar things with the unfamiliar martian landscape. [Grade: 6-8 | Topics: scale proportion metric measurement] (PDF)

Problem 534:Exploring Marsquake Energy with the Moment Magnitude Scale
Students are introduced to the Moment Magnitude marsquake scale which gives a logarithmic index for marsquakes of differing energies. They calculate two examples of marsquakes and meteor impacts and compare their Moment Magnitude. [Grade: 8-10 | Topics: logarithms scientific notation algebra ] (PDF)

Problem 533:Exploring Logarithms and the Richter Magnitude Scale
Students work with a logarithmic scale to estimate how much ground movement occurs for earthquakes of different strengths. [Grade: 8-10 | Topics: logarithms base-ten exponents] (PDF)

Problem 532:The Distance to the Martian Horizon
Students devive a basic equation for the distance to the horizon on a spherical body using the Pythagorean Theorem and a bit of algebra. The estimate the number of cell towers needed to cover Mars. [Grade: 8-10 | Topics: Pythagorean Theorem, Algebra scientific notation areas of spheres and circles ] (PDF)

Problem 531:Exploring the Interior of Mars with Spheres and Shells
Students use the volume properties of spheres and shells along with the relationship mass=densityxvolume to create a model of the interior of mars. [Grade: 8-10 | Topics: formula for volume of spheres and spherical shells mass=densityxvolume scientific notation ] (PDF)

Problem 530:Exploring the Mass of Mars
Students calculate the mass of mars by using satellite data and Keplers Third Law. [Grade: 8-10 | Topics: Algebra scientific notation ] (PDF)

Problem 529:Exploring Impacts and Quakes on Mars
Students work with logarithmic scales to explore the relationship between the energy of an marsquake and its logarithmic index, which is similar to the Richter Scale used for earthquakes. [Grade: 8-10 | Topics: Logarithmic scales scientific notation ] (PDF)

Problem 528:Comparing the Heat Output of Mars and Earth
Students learn about the heat flow formula and use it to explore the properties of Earth and Mars in terms of their crust composition. [Grade: 8-10 | Topics: Algebra temperature gradients] (PDF)

Problem 527:Exploring Heat Flow and Insulation
Students explore how insulation works to reduce heat flow. They convert a verbal description of a formula expressed in proportions, and use it to calculate why aluminum pots heat faster than steel pots, and how we can determine the properties of martian sooil from heat flow and temperature changes. [Grade: 8-10 | Topics: algebra rates of change ] (PDF)

Problem 526:Exploring Temperature Change in Earth?s Outer Crust
Students explore the rate of temperature change in the crust of Earth and Mars and learn about units expressed as degrees C/km. They calculate how hot the ground will be at various depths, and how gold miners must deal with extreme heat. [Grade: 6-8 | Topics: fahrenheit and celsius degrees rates of change] (PDF)

Problem 525:Exploring the InSight Lander Telemetry Data Flow
Students explore how long it takes to transmit digital data using examples from downloading songs from their computer to their ipod. [Grade: 6-8 | Topics: working with kilo, mega and rates of data transfer in bytes/sec. ] (PDF)

Problem 524:Seeing the Martian Surface with IDC
Students learn about the IDC camera and calculate resolution and how many images are needed to map the InSight landing area. [Grade: 6-8 | Topics: ANgular measurfe, degrees and seconds image scal tiling an area with overlap. ] (PDF)

Problem 523:Telling Time on Mars - Earth Days and Mars Sols
Students work with two clocks on Earth and Mars and learn about earth and mars time given that a day on Mars is 40 minutes longer than an Earth day. [Grade: 6-8 | Topics: time calculations, hours, minutes, seconds length of day ] (PDF)

Problem 522:Radio Communications with Earth ? The Earth-Sun Angle
The earth-sun angle is given in tabular form in degrees. Students graph the data and find the dates when transmissions to Earth cannot occur. [Grade: 8-10 | Topics: Interpreting tabular data rates and slopes ] (PDF)

Problem 521:Estimating the Mass of a Martian Dust Devil!
Students estimate the mass of a martian dust devil using the approximation that it is a cylinder with a fixed density of dust. [Grade: 8-10 | Topics: Volume of a cylinder mass = density x volume ] (PDF)

Problem 520:The Work Area In Front of the Lander
Students estimate the area in front of the InSight lander where experiments will be conducted and instruments moved with a single robotic arm. [Grade: 6-8 | Topics: Area of a circle segment Area common to two intersecting circles] (PDF)

Problem 519:Scheduling Events in Time for Launch
Students learn about scheduling many events along a timeline (breakfast, packing, driving, etc ) by planning a family trip where the family members have to arrive at the airport for a flight that leaves at a specific date and time. [Grade: 5-7 | Topics: working with time units creating a timeline] (PDF)

Problem 518:The InSight Seismographic Station Solar Power System
Students explore the properties of decagons to determine the area of the solar panels used on the InSight lander. [Grade: 7-9 | Topics: area of regular polygons estimating areas of non-square shapes] (PDF) Problem 508: The InSight Seismographic Station - Wave arrival times
Students work with the circumference of Mars and the speed of shock waves in the martian crust to estimate the arrival times of the waves at the InSight Lander. [Grade: 6-8 | Topics: speed=distance/time Time calculations circumference of a circle] (PDF)

Problem 500: Curiosity Uses X-Ray DIffraction to Identify Minerals on Mars
Students learn about diffraction geometry and then estimate the distance between crystal planes in a mars rock sample. [Grade: 10-12 | Topics: geometry trigonometry] (PDF)

Problem 491: The Curiosity Rover on the Move.
Students plot the position of the Curiosity Rover on a cartesian grid covering the satellite image of the landing area. They use the 2-point distance formula to determine how far the rover traveled between stops, and determine it speed. [Grade: 6-8 | Topics: Cartseian graphs ordered pairs and coordinates distance = speed x time metric measure ] (PDF)

Problem 485: Curiosity Discovers Ancient Mars River
Students estimate the speed of an ancient mars river using images from the CUriosity rover. [Grade: 9-12 | Topics: Algebra trigonometry evaluating functions ] (PDF)

Problem 479: Exploring Gale Crater with the Curiosity Rover
Students explore the Gale Crater landing area and calculate rover distances to various way stations to determine the round trip distance and travel time. [Grade: 9-12 | Topics: Pythagorean Distance Formula Coordinate geometry ] (PDF)

Problem 457: The Interplanetary Voyage of MSL
Students use the properties of ellipses to determine the formula for the Hohmann Transfer Orbit taking the Mars Science Laboratory to Mars in 2012 [Grade: 10-11 | Topics: time=distance/speed scale models metric math properties of ellipses] (PDF)

Problem 456: The Launch of the Mars Science Laboratory (MSL) in 2011
Students use a sequence of launch images to determine the Atlas V's launch speed and acceleration. By determining the scale of each image, they estimate average speeds during the first 4 seconds after lift-off. [Grade: 8-10 | Topics: time=distance/speed scale models metric math] (PDF)

Problem 393: Taking a stroll around a martian crater! Students use a recent photograph of a crater on Mars to estimate its circumference and the time it will take NASAs Opportunity Rover to travel once around its edge. [Grade: 6-8 | Topics: scale model distance = speedxtime metric measure] (PDF)

Problem 237: The Martian Dust Devils Students determine the speed and acceleration of a martian dust devil from time laps images and information about the scale of the image. [Grade: 6-8 | Topics: scales Determining speed from sequential images V = D/T (PDF)

Problem 139: How Big Is It? - Mars Students use an image of a crater wall on mars to investigate ancient water gullies discovered in 2008 by the Mars Orbiter. [Grade: 4 - 7 | Topics:image scales metric measurement division and multiplication decimals] (PDF)

Problem 133: How Big is It? - The Mars Rover. Students work with an image taken by the Mars Orbiter satellite of the Spirit landing site. They determine the image scale, and calculate the sizes of various surface features from the image. [Grade: 4 - 7 | Topics:image scaling multiply, divide, work with millimeter ruler] (PDF)

Problem 126 : How Big is It? - A Martian Avalanche! Students work with a Mars reconnissance Orbiter image to determine image scale, and search for the smallest things seen in a photograph.This avalanche was caught as it occurred on February 19, 2008! [Grade: 4 - 7 | Topics:image scaling multiply, divide, work with millimeter ruler] (PDF)

Problem 74: A Hot Time on Mars - The NASA Mars Radiation Environment (MARIE) experiment has created a map of the surface of mars, and measu black the ground-level radiation background that astronauts would be exposed to. This math problem lets students examine the total radiation dosage that these explorers would receive on a series of 1000 km journeys across the martian surface. The students will compare this dosage to typical background conditions on earth and in the International Space Station to get a sense of perspective [Grade level: 6-8 | Topics: decimals, unit conversion, graphing and analysis ] (PDF)

Problem 70: Calculating Total Radiation Dosages at Mars - This problem uses data from the Mars Radiation Environment Experiment (MARIE) which is orbiting Mars, and measures the daily radiation dosage that an astronaut would experience in orbit around Mars. Students will use actual plotted data to calculate the total dosage by adding up the areas under the data curve. This requires knowledge of the area of a rectangle, and an appreciation of the fact that the product of a rate (rems per day) times the time duration (days) gives a total dose (Rems), much like the product of speed times time gives distance. Both represent the areas under their appropriate curves. Students will calculate the dosages for cosmic radiation and solar proton flares, and decide which component produces the most severe radiation problem. [Grade level: 6-8 | Topics: decimals, area of rectangle, graph analysis] (PDF)

Outlook and Novel Technical Opportunities

The future directions in photobiology are bright and spread far outside the scope of the small review. Evident progress of optogenetics is expressed nowadays in potential medical applications. Further and deeper understanding of photobiological processes including leap to spatial nanoscale and temporal femtoscale in combination with new approaches of molecular biology and genetics needs also integrative and synthetic way of seeing. The new and more detailed picture with higher resolution will rise. More knowledge is gained from different species, so details of phototransduction may vary and leave plenty of space for future research.

Emerging new sources of light with different statistics of photons (lasers), light-emitting diods with unusual spectral properties offer valuable tools for re-questioning old problems and posing new ones. Recent interest in quantum dots was rewarded by opportunity to get single photons using quantum dots. 104 - 106 The source of single photons might be valuable for determining sensitivity of photoreception, for providing exact number of photons of certain energy (wavelength) and seems to be very promising for the future research.

Watch the video: Ποια ήταν η τελευταία εντύπωση που άφησα σαυτο το άτομο? (September 2021).