Finally, the observed cloud cover is the hourly sum of the observed low and medium cloud cover. Note that here the observed cloud cover is a summation since it is calculated from time series of independent columns for which only the first cloud layer contributes to the cloud cover calculation the lowest layer.
The forecast LCC and MCC represent the fractional cloud cover from 0 to 1 over the grid point, and combining these requires an assumed overlap factor. It should be noted that the model radiative transfer scheme is unlikely to completely account for the three-dimensional nature of radiative transfer as experienced by the observations. Cloud cover forecasts are evaluated with 2-D histograms and skill scores.
MAESS uses the absolute difference between the forecast and observed value, and MSESS uses the squared difference, which for two forecasts with the same absolute error will penalize the forecast with one or two large errors more than the forecast with many small errors.
For example, a hit occurs when both forecast and observed cloud cover are above a given cloud cover threshold. Here, the threshold for cloud cover is set to 0. Therefore, a hit means that some amount of cloud is both forecast and observed; however, a hit does not yet imply a perfect forecast. ME is the only error metric that shows the sign of the error. A positive bias is seen when the model overestimates the incoming surface solar radiation, whereas a negative bias is when the model underestimates the incoming solar radiation.
Coastal effects, such as sea breezes, are common. There are no large variations in topography around the site. There is an annual variation in the observed cloudiness at the site Fig. In contrast, broken cloud 0. The variation in cloudiness is quite high from year to year, especially in summer, but in winter the most probable sky condition contains cloud. Bars show yearly variation min, max. Shaded area represents the year-to-year variation in monthly means.
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The amount of solar radiation at the top of the atmosphere is much higher during summer when the solar zenith angle is also much higher Fig. This signal is also clear in the amount of solar radiation reaching the ground, the measured GHI Fig. The year-to-year variation in the monthly mean of measured GHI is much greater during summer months lighter shaded area in Fig.
To investigate the seasonal variation, we define seasons based on the annual variation in the solar resource Fig. The summer season is defined as May to July when the solar resource is at a maximum, and winter is defined as November to January when the solar resource is at a minimum spring is February to April, autumn is August to October. In Fig. For a perfect forecast, all values would lie on the diagonal dashed line in each scatter plot. For all seasons, the majority of cloud cover values are concentrated around clear conditions pair 0;0 and overcast conditions pair 1;1 for both observations and forecasts.
This suggests that not only are clear and overcast conditions the most commonly observed, but also most skilfully forecast in all seasons. During winter the vast majority of cloud cover observations and forecasts are at or close to overcast Fig. Clear-sky conditions are more common in other seasons both observations and model. The large spread for both observed and forecast cloud cover values between 0. It is also notable that, during all seasons, there are values on the boundaries of the scatter plot away from the diagonal, for example, where the model is incorrectly forecasting clear sky during cloudy conditions or overcast conditions during clear or broken skies.
Summer and autumn seasons Fig. Skill scores represent the model's ability to forecast a given variable. To calculate skill scores, we generate a contingency table for cloud cover. This requires a binary forecast so we use a threshold cloud cover value of 0. The annual relative occurrences of contingency table elements hit, false alarm, miss, correct negative are shown in Fig. The hit occurrence is greatest between October and February when overcast conditions are also most common Fig. Note that a hit requires that both observations and model have some cloud, but it does not necessarily represent a perfect forecast.
Similarly, the relative occurrence of correct negative is highest during spring and summer months. This information is important, especially for solar energy purposes, as it shows that clouds are forecast less skilfully in summer, which is when the solar resource is greatest.
There are also notable variations in skill scores from year to year, especially in October and December. The absolute error in the solar radiation forecast can therefore potentially be much higher in summer and is evident in the potential range of scatter between observed GHI and forecast GHI for each season Fig. The forecast of solar radiation is usually overestimated in all seasons Fig.
Solar radiation forecast MAE Fig. Positive bias red and negative bias blue are shown separately; shaded area represents year-to-year variation. Throughout the year, the positive bias both absolute and relative is greater than the negative bias, and thus the model overestimates solar radiation more than it underestimates.
Solar radiation and human health.
The year-to-year variation in relative positive bias is also larger than the relative negative bias. Overestimates are also more common than underestimates not shown. The result is an overall positive bias in forecast GHI. Assuming the correct representation of radiative transfer in the atmosphere, with only the forecast of cloud impacting the solar radiation forecast at the surface no change in aerosol or humidity , then an increase in forecast cloud cover would be expected to result in a reduction in the amount of forecast solar radiation.
However, the amount of cloud may be correctly forecast, but not the cloud properties. Since cloud properties are directly responsible for the cloud radiative effect, both cloud amount and properties should be correctly forecast in order to obtain a reliable solar radiation forecast.
The four bars in each month represent individual years — It can be seen that months with a large accumulated negative bias in cloud cover forecasts e. However, not all months show a clear correlation between a negative bias in the cloud cover forecast and a positive bias in the solar radiation forecast. This is most probably due to compensating effects whereby, for example, the cloud cover forecast could be overestimated positive bias in cloud cover but the liquid water content forecast is underestimated which would result in positive bias in solar radiation forecasts.
To investigate how well the forecast cloud cover corresponds to the observed cloud cover, the counts of hourly observed and forecast cloud cover values are paired together in 2-D histograms Fig. For perfect forecasts, all counts would lie on the diagonal. However, it is clear that there are many values on the boundaries, which means that cloud is either observed and not forecast miss or cloud is forecast but not observed false alarm. The solar radiation forecast ME for concurrent pairs of cloud cover values in Fig.
ME values below the diagonal, for which the forecast cloud cover is underestimated, are mostly positive; similarly, ME values above the diagonal are mostly negative, and the forecast cloud cover is overestimated.
Note that the change from positive to negative ME does not quite follow the diagonal, with minimal bias appearing to follow a line from 0. This negative bias during clear-sky situations over Helsinki was also observed by Rontu and Lindfors and is most likely due to the aerosol climatology implemented in the model having too much aerosol. Another possible source of negative bias during clear-sky situations would be too much water vapour in the atmosphere. Forecasting individual clouds in the right place at the right time is challenging and here we investigate whether temporal averaging improves the cloud forecast and therefore the radiation forecast.
Different averaging windows 3-hourly, 6-hourly, hourly, daily are used in preparing the data for evaluation in the same manner as for Fig. The agreement between observed and forecast cloud cover improves with increasing averaging windows, and the number of cases of extreme misses and false alarms corners 1;0 and 0;1 decreases.
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When calculated separately, the magnitudes of the positive and negative solar radiation forecast biases for concurrent pairs of cloud cover values decrease with increasing averaging time. Increasing the averaging window does not alter the pattern in which the change from positive to negative ME is away from the diagonal.
The negative bias in clear-sky conditions and positive bias in overcast conditions are still present, suggesting that the bias is likely to be due to cloud properties rather than the cloud presence. Extreme misses and false alarms for cloud cover are reduced, and for GHI MAE, the individual absolute errors are reduced with temporal averaging.
Persistence forecasts were also investigated; a persistence forecast uses the hourly forecast values from the day before. The skill for the cloud cover persistence forecast also increases with increasing temporal averaging, as does the reduction of error in the persistence GHI forecast; however, these are not as good as the actual forecasts at this location.
As the cloud amount is correctly forecast, this suggests that the bias must be due to cloud properties. The clouds are classified as warm or cold supercooled , depending on their cloud base temperature, using the temperature profile from the IFS as no observed temperature profiles are available. These values were selected based on the range of optical depths that would be expected for each LWP range bin. Unfortunately, there was no observed LWP available for this measurement site. Colour scale indicates LWP values. Note that the response is similar for both warm and cold liquid clouds.
This suggests that either forecast clouds do not have enough LWP or that the optical properties of clouds with low LWP are not properly modelled. The first conclusion, that forecast clouds do not have enough LWP, is consistent with the findings of Ahlgrimm and Forbes Furthermore, they found that IFS overestimates the occurrence of clouds with low LWP and underestimates the number of clouds with high LWP, which also results in a positive bias in solar radiation forecasts. Challenges in correctly modelling supercooled liquid clouds have previously been reported, but our results suggest that the issue of a positive bias in GHI is more pronounced for warm clouds and not just an issue for supercooled liquid clouds.
Solar Radiation & Photosynthetically Active Radiation
The new algorithms are widely applicable for both operational use and research, e. Over Helsinki, both observed and forecast cloud cover distributions are U-shaped, indicating that most of the time the sky is either clear or overcast. Overcast conditions are most common in winter, whereas clear and broken cloud conditions are more common in summer. Cloud cover is better forecast in winter; however, this is when the solar resource is lower. The measured GHI is strongly influenced by the annual solar resource characterized by the northern latitude and annual variations in cloudiness; the absolute solar radiation forecast error tracks GHI, but the relative error is more or less constant throughout the year.
As expected, the bias in forecast GHI is negative when the model overestimates cloud cover incoming solar radiation is underestimated by the model and positive when the model underestimates cloud cover. However, these wavelength ranges need to be sub-divided depending on the individual application fields. Best known are the prismatic colours of visible light, the colours of the rainbow. Shorter wavelengths higher frequency have higher energy, thus increasing the effect on biological and chemical systems. The attenuation of solar radiation passing through our atmosphere is due to the following processes:.
Molecular Ozone in the upper layers of our atmosphere functions as a filter for ultraviolet radiation and the effect increases with shorter wavelengths. Changes of the atmospheric Ozone content will especially affect the amount of UV-B. An increasing Ozone layer concentration means decreasing UV-B radiation and a decreasing Ozone layer means increasing UV-B radiation reaching the surface. I consent to having this website collect my personal data via this form. The authors suggest that a research programme should be collaboratively designed in such a way that is has social value as well as scientific benefits, it adopts a risk view, transparency is guaranteed, vested interests are identified and do not bias evaluation, and regulatory frameworks are established.