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Monitoring cumulus clouds by using global horizontal irradiance data

A previously proposed method for the detection of cumulus clouds by using time series of global horizontal irradiance from a pyranometer is developed. A characteristic feature of this method is the acceptance of short 21-minute moving analysis windows as stationary. This approach makes it possible not to construct a clear sky model for transforming a non-stationary daily time series of global horizontal irradiance to stationary time series.


Introduction
Cumulus clouds (Cu) are a basis for the formation of cumulonimbus clouds (Cb) -sources of various dangerous meteorological phenomena such as showers, thunderstorms, squalls, tornadoes, and hail. Monitoring of medium (4-7) Cu amount provides nowcasting information about the possibility of Cb formation. Satellite technologies cannot always provide continuous monitoring of clouds for various reasons. In turn, ground-based identification of cloud types is difficult to automate and it is most often performed by humans. Amplitude and variational analysis of the current actinometric data can be used to supplement or replace human observations of the sky. In the simplest case this can be global horizontal irradiance (GHI) the sum of direct normal S and diffuse horizontal D irradiance measured by an unshaded pyranometer (h is the height of the Sun).
Usually, before amplitude and variational analysis, a non-stationary GHI time series is converted to a stationary time series by using any clear-sky model. Many different methods for constructing such models are known [1][2][3]. In most cases, this requires some set of both: directly measured data (humidity, temperature, pressure, etc.) and reference data (dispersion coefficients of gases and aerosols, local average climatic characteristics, etc.). Measuring and processing such data usually has certain difficulties. Previously, the authors proposed a method for detecting Cu without using a clear sky model [4]. This is possible when short 21-minute samples of the GHI time series (± 10 minutes near the analysis point) are considered as stationary, and only variational analysis is applied to them.

Method
Variational analysis of global irradiance shows the opening and closing frequency of the solar disk by clouds with different densities, and it also shows the speed of these clouds over the observation point. Under stratiform clouds (Cs, As) and clear skies, the coefficient of variation (CV) is close to zero.  This leads to the formation of very heterogeneous time series of GHI ( Figure 1). Using this feature, the detection of cumulus clouds can be reduced only to the search for heterogeneous intervals on the time series of global irradiance using a 21-min moving window. Studies have shown that the variation coefficient of a 21-minute sample of global irradiance V 21 ≥ 0.33 means that there are cumulus clouds Cu hum. or Cu med. in the sky with a probability of 70-80%. Such a coefficient value, V = 0.33, in statistical analysis is a threshold between homogeneous and heterogeneous series. It also turns out that stratus St and nimbostratus Ns clouds have similar CVs. This is due to high sensitivity of the variation coefficient to small changes in the low mean values of the analyzed sample.
To reduce or completely remove such errors, the use of some additional criterion is required. Another characteristic feature of the cumulus clouds is their distinct and unblurred edges and horizontal bases. Therefore, the transition between the states Q max and Q min , as a rule, takes a short time of about 1-2 min ( Figure 2). Thus, if within the analyzed 21-minute sample there is at least one sharp difference between the states Q max and Q min , this is a sign of the presence of Cu hum. and/or Cu med. clouds ( Figure 3). For instance, such differences can be found by analyzing the CV for a 3-min window (±1 min near the analyzed point), which moves within a 21-minute sample, according to the condition V 3 ≥ 0.33.

Results
The comparison of the V 21 and V 21 + V 3 cumulus detector versions was made according to the GHI data for the warm period (May -August) 2018 (h > 30º) from a CM-11 pyranometer (Kipp & Zonen, Netherlands) of the IMCES SB RAS geophysical observatory (Tomsk). The detection results were checked using a binary classifier and visual control by color panoramic images from an All Sky camera MVK-1653c (Byterg, Russia) installed near the pyranometer. In addition, one-hour visual observations of the sky were used.
Using a binary classifier, the state of an object can be attributed to one of two classes, "True" or "False" [5]. When monitoring cumulus clouds, there are four combinations for the result obtained ( Figure 4  Cb was taken as a positive result of the method (TP). It was also not considered as an error to classify a low Cu amount (1-3) as its absence (TN).  Table 1 shows that the use of the additional criterion V 3 increased the detection precision of cumulus clouds PPV by an average of 7%, with a decrease in the completeness of the sample TPR by 6%. At the same time, the number of false points FP decreased by more than 50%, and the harmonic mean F 1 did not change. Table 2 shows that the errors with stratus St and nimbostratus Ns clouds are completely removed. The errors with cirrus clouds Ci decreased almost 7 times. Incorrect identification of cumuliform clouds Ac cuf. and Sc cuf. as Cu decreased by 30% and 50%, respectively. A high number of such errors is caused by the morphological similarity of such clouds and the complexity of their identification from panoramic All Sky images, which leads to subjective errors. Also, one of the detector V 21 + V 3 imperfections is incomplete filtering of the combined sky states Ci + Cu (Cb), which requires additional research.

Conclusions
The above-proposed and improved method allows one to monitor Cu with a medium (4-7) cloud amount in near real time, with a delay of 10 min. The realization of this method does not require considerable computing power, which is often important when creating a monitoring network based on autonomous meters of integral and/or PAR global irradiance. In addition, the existing meteorological observing network can be used where continuous measurements of global irradiance are carried out by an unshaded pyranometer.