=Paper=
{{Paper
|id=Vol-1498/HAICTA_2015_paper71
|storemode=property
|title=A Satellite-based Automated System to Detect and Forecast Cloud Storms Focused on the Protection of the Greek Agricultural Sector
|pdfUrl=https://ceur-ws.org/Vol-1498/HAICTA_2015_paper71.pdf
|volume=Vol-1498
|dblpUrl=https://dblp.org/rec/conf/haicta/Kolios15
}}
==A Satellite-based Automated System to Detect and Forecast Cloud Storms Focused on the Protection of the Greek Agricultural Sector==
https://ceur-ws.org/Vol-1498/HAICTA_2015_paper71.pdf
A Satellite-based Automated System to Detect and
Forecast Cloud Storms Focused on the Protection of the
Greek Agricultural Sector
Stavros Kolios1
1
Hellenic Agricultural Organization "DEMETER", Greece, e-mail: stavroskolios@yahoo.gr
Abstract. This study presents a fully automated system based on Meteosat
multispectral imagery to detect and forecast cloud storms. The first accuracy
assessment results are considered satisfactory, allowing this system to be able
to operate in real-time basis and providing realistic and accurate forecasts for
the storm activity as well as for dangerous phenomena accompany convective
clouds like lightnings, hail and heavy precipitation. The presented system can
operate in a more general point of view, as a driver in the adaptation of
strategies and legislations that concern the crop productivity, reimbursements
for crop losses, the sustainability of the environment and the improvement
quality of lives through the efficient protection from storm effects and their
impacts in the society.
Keywords: Storms, agricultural sector, automated system, satellite images.
1 Introduction
It is well known that the extreme weather phenomena (many of them are direct
effects of the cloud storms) like heavy precipitation, hail, strong winds and lightnings
can often cause disasters in infrastructure, private property and agricultural
production. Therefore, automated systems that provide timely and accurate
information to prevent and reduce disasters caused by such phenomena can be
considered of major importance to the sustainable development of a region. More
specifically, the majority of the cultivate areas is largely exposed to the weather
conditions and often affected by extreme weather events although they comprise a
key factor of economic growth. The agri-food sector (including beverages) accounts
for 14.7 % of total EU manufacturing output, is the third largest employer in Europe
and the second biggest exporter of foodstuffs globally. Moreover, according to the
Hellenic agricultural organization “Demeter”, during the period 1990–2006 after the
frost, hail and heavy precipitation were the most important weather phenomena for
crop losses. Losses in crop production can significantly affect - among others - the
commerce and the economy but the complex nature of extreme weather phenomena
and the need for accurate and early warnings for possible extreme weather events,
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keep the weather forecasting among the challenging issues for the scientific
community.
Especially in the last decade, an important contribution to the improvement in the
weather detection and forecasting, comes from meteorological satellites.
Multispectral images of high spatial and temporal resolution can be used nowadays
in the operational forecasting and provide valuable information and timely warnings
for the protection from the extreme weather events.
In this study, the main stages of the development as well as the first accuracy
assessment results of a fully automated system to detect and forecast convective
clouds (cloud areas that can evolve to storms and produce extreme weather
conditions), are described. The system uses satellite images from the Meteosat
multispectral imagery. The domain of the system includes the greater areas of
Balkan as well as the central and eastern Mediterranean basin but is focused on the
Greek periphery.
2 Data and Methods
2.1 Study Area
The domain of the system (Fig. 1) was choosen to include greater areas around
Greece in order to eraly detect the existence of “signs” that can evolve to storms after
after a few minutes (or hours).
Fig. 1. The red rectangle include the geographical domain of the system operation.
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2.1 Data
There are five channels of the satellite instrument SEVIRI (Spinning Enhanced
Visible and Infrared Imager) on board on Meteosat satellite platform that their
images are used from the system (Table 1). At this point it is noteworthy to pointed
out that there is an extensive use of these channels to detect and estimate
precipitation and hail (e.g Simeonov and Georgiev, 2003; Lazri et al., 2014).
Table 1. Spectral characteristics of the Meteosat channels are used for the system.
Channel Spectral region Spectral center
(Band) (μm) (μm)
5 5.35 - 7.15 6.2
6 6.85 - 7.85 7.3
7 8.3 - 9.1 8.7
9 9.8 - 11.8 10.8
10 11-13 12.0
2.2 Characteristics of the system
The system comprises an algorithm written in Visual Basic 2012 programming
language. The system consists of two main modules, the detection module and the
forecasting module. In the detection module, a set of criteria is used (Table 2) to
detect all the cloud pixels belong to storms (or can evolve in the next minutes or
hours to storms). Hereinafter, these pixels are referred as convective cloud pixels.
These criteria comprise a combination well known and recent thresholding
techniques for the detection of convective cloud patterns in the satellite imagery
(Bedka, Mecicalski 2011; Merk and Zinner, 2013; Kolios and Stylios, 2014).
Table 2. The five citeria are used for the detection of the cloud pixels of interest in the
Meteosat multispectral imagery.
Criteria
T6.2μm < 240 K
(ΔT10.8μm/ΔΤ) < -6 K (15 min)-1
(ΔT(6.2μm - 10.8μm) /ΔΤ) > 3 K (15 min)-1
ΔT(6.2μm - 7.3μm) >-20 K
ΔT(12.0μm - 10.8μm) >-3 Κ
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In the Fig. 2, it can be seen how the detection module of the system isolates the
cloud pixels of interest (convective cloud pixels). The white colored areas refer to
cloud areas. The whiter they are seen in the color composite of the Fig. 2, the most
possible to evolve to cloud storm areas, are. All the pixels that fulfill the set of
criteria of the Table 2, are stored in relative image files and in a central internal
database of the system.
Fig. 2. Detection of pixels of interest from the system. On the left, the greater area of Greece
can be seen. On the right, it can be seen, zoomed, the area inside the red rectangle of the left
panel. The yellow colored pixels refer to the pixels of interest (convective cloud pixels).
For the accuracy assessment regarding the efficiency of the criteria in the
detection of the convective cloud pixels, free datasets with lightnings from ZEUS
system, were used (Chronis and Anagnostou, 2006). The lightnings are considered a
good indicator for the detection of storm activity (e.g. Williams, 2005; Katsanos et
al., 2006). For this reason, a spatiotemporal correlation between the available
lightnings datasets and the relative satellite images, was conducted. More
specifically, during a two-hour period, for every lightning event, the channel
temperatures for the most relative pixel in time and space, was connected. As a
result, it was collected a set of 3593 pixels with lightning events along with their
relative temperature values in all the used channels. Considering that the lightnings
are mainly located in cloud areas with intense storm activity, the threshold values
were evaluated regarding their capability to isolate such cloud areas. In this first
evaluation of the system detection procedure, three (out of five) criteria of the Table
2 were checked. The results show that there is a tendency for the pixels with
lightnings to be connected with low temperatures in the 6.2 μm channel (Table 3 and
Fig 3). There is also a second maximum in the distribution of the 6.2 μm channel
(Fig. 3) that is connected with stratiform cloud regions where lightnings can also
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occur. The same reason can explain the significant number of lightnings that not
fulfill the “ΔT(6.2μm - 7.3μm)” criterion. Conclusively, comparing the values of the
distributions of the Fig. 3 along with their cumulative distributions, it is noted that at
least the 50% of the total number of the pixels with lightnings, fulfill the threshold
values of the relative parameter (Table 2). This result, highlight a satisfactory and
efficient detection procedure for the convective cloud pixels.
Table 3. Basic statistics for the pixels with lightnings (K is “Kelvin” unit).Error! Not a valid
link.
Fig. 3. Graphs that depict the distribution of the values for three parameters (T(6.2μm - 7.3μm)
above left, T(10.8μm - 12.0μm) above right and T6.2μm down n the center) for the pixels with
lightnings. The blue line represent the cumulative distribution (blue y-axis) and the black
columns represent the number of lightnings pixels (black y-axis) for the different parameter
values (x-axis).
The brightness temperature values, the channel differences and the cooling
(warming) rates for all the pixels of the study area, are automatically calculated and
stored in an internal database of the system. The forecasting methodology produces
forecasts every 15 min and is based in linear multivariate functions (in its current
version). More specifically, for a defined period, all the Meteosat images were
selected and all the appropriate parameters were computed in order to construct the
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analytical linear multivariate functions (regression analysis). These functions are
referred to the temperature values of all the used Meteosat channels (Table 1). The
dependent variable is the pixel temperature of a channel and the independent
variables as well as their coefficients (Eq.1) is defined from the regression analysis
and the evaluation results. Conclusively, there were developed five different
analytical functions (for each of the channel temperatures). Each of them is used to
forecast the relative channel temperature on a pixel basis. The coefficients of the
functions are remaining constant in every forecast and the values of the independent
variables are the relative mean values (estimates or observations) of the previous four
timesteps (typical one hour before). For example, for the forecast one hour after the
current time (to), the mean values (at pixel basis) from the three previous timesteps.
y = A0+A1x1+…..+Anxn (1)
Where “y” is the dependent variable (pixel temperature of a specific channel), A0
is a specific constant and An (n=1, 2, 3, …) is the coefficient of the relative
independent variable xn .
Fig. 4. Schematic diagram of the forecasting methodology of the system. Every cube
represents a timestep of 15 min (same as the typical temporal resolution of Meteosat).
In the Table 4, basic statistics [Mean Absolute Error (MAE), Mean Error (ME)
and correlation coefficient] of the forecasting procedure regarding the brightness
temperature values of 6.2 μm channel in four different trimesters for a selected case
study. The statistics were calculated using 603.841 pixel values. As initial time, the
Meteosat image channels at 04:00 UTC (25/08/2006) were used.
Table 4.The accuracy of the forecasting procedure about the brightness temperature values of
6.2 μm channel in four different trimesters for a selected case study. As initial time, the
Meteosat image channels at 04:00 UTC (25/08/2006) were used.
Forecast MAE ME Adjusted R
15min 0.42 -0.013 0.99
30 min 0.59 0.0004 00.98
60 min 0.87 0.015 0.95
120 min 1.17 0.014 0.94
180 min 1.52 0.057 0.92
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In the Table 4, it can be seen that there is a very small overestimation for the
temperature pixel values in the 6.2 μm channel (the ME values are positive). The
MAE is also very small in the first forecasting timesteps but it is gradually increasing
for the next forecast and exceeds 1K after the first hour of the forecasts.
3 Conclusions
A fully automated system for the detection and forecast of the storms in the
greater area of Greece was developed. The system is using image data from the
operational meteorological European satellite, called “Meteosat”. This system can be
established for operational use, having as basic scope the provision of accurate and
timely warnings about extreme weather phenomena like hail, strong winds and
heavy precipitation that can cause significant losses in the agricultural sector.
The first accuracy assessment results are satisfactory and the overall efficiency of
the system for its potentially operational use seems promising for the protection of
the agricultural sector.
Future steps include a more quantitative evaluation of the accuracy of the system
and the use of nonlinear algorithms in an effort to be provided even more accurate
predictions and the further extension of the forecasting capabilities, in time. A
detailed digital maps with recent land use and land cover information are also
intended to be integrated in the system, in order to provide more detailed warnings,
aiming also to operate additional as an autonomous decision support system.
Acknowledgments. This research project is funded under the Action “Research &
Technology Development Innovation projects (AgroETAK)”, MIS 453350, in the
framework of the Operational Program “Human Resources Development”. It is co-
funded by the European Social Fund and by National Resources through the National
Strategic Reference Framework 2007-2013 (NSRF 2007-2013) coordinated by the
Hellenic Agricultural Organisation "DEMETER" (Institute of Agricultural Research,
Ionnnina, Greece / Scientific supervisor: Dr. Panagiotis Platis).
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