The present disclosure relates generally to the field of electronics. In particular, the present disclosure relates to analysis of signal for predicting weather. More particularly, the present disclosure relates to systems and methods for forecasting weather.

BACKGROUND
[002] Background description includes information that may be useful in understanding the present invention. It is not an admission that any of the information provided herein is prior art or relevant to the presently claimed invention, or that any publication specifically or implicitly referenced is prior art.
[003] Weather forecasting of storms and other meteorological events is extremely important to aviation, space agencies, emergency response agencies, traffic, and public safety. Moreover, forecasting of meteorological events, such as storm, flood, cyclone, etc., beforehand can provide ample amount of time to people and authorities to take precautionary steps and apply preventive measures, like evacuation of said area, equipping people with appropriate facilities and ingredients, thereby, can facilitate mitigation of adverse effects of said meteorological events, saving of human lives and lives of other animals, and providing them with a substitute as living.
[004] Conventional weather forecasting systems provide weather predictions twelve hours to a few days from the present time by applying mathematical/physical equations using various two-dimensional or three-dimensional physical parameters. Also, the equipment used for forecasting the weather are very costly.
[005] One such technology is weather satellites. The data captured and processed by the weather forecasting satellites is used to get information about the upcoming change in weather and can take certain steps to protect us from the severe weather conditions. But this technique is not precise enough. Also, time range for prediction of the weather for current system is not precise and optimum. For example, during the rainy season, it is not confirmed that when it will rain, therefore people who have to go outdoor does not have the information about the rainfall. Thus, a lot of time is wasted.
[006] There is therefore a need in the art to provide system and method that enable forecasting of weather which is precise, cost effective, time efficient and that can enable predicting the weather for a time range that seeks to overcome or at least ameliorate one or more of the above-mentioned problems and other limitations of the existing solutions and utilize techniques, which are robust, accurate, fast, efficient, cost effective and simple.

OBJECTS OF THE INVENTION
[007] It is an object of the present disclosure to provide a system and method for weather forecasting.
[008] It is another object of the present disclosure to provide a system and method to forecast weather by analysing signal attributes of signals associated with various devices.
[009] It is another object of the present disclosure to provide a system and method to transmit alert signals to a number of people and concerned authority, if an adverse weather condition is forecasted.
[0010] It is another object of the present disclosure to provide a system and method to transmit alert signals to mitigate effects of adverse weather and save human lives and livelihood.
[0011] It is another object of the present disclosure to provide a robust, accurate, fast, efficient, cost effective and simple weather forecasting system and method.
[0012] These and other objects of the present invention will become readily apparent from the following detailed description taken in conjunction with the accompanying drawings.

SUMMARY
[0013] Aspects of the the present disclosure relates generally to the field of electronics. In particular, the present disclosure relates to analysis of signal for predicting weather. More particularly, the present disclosure relates to systems and methods for forecasting weather.
[0014] Various objects, features, aspects and advantages of the inventive subject matter will become more apparent from the following detailed description of preferred embodiments, along with the accompanying drawing figures in which like numerals represent like components.
[0015] An aspect of the present disclosure pertains to a weather forecasting system comprising: one or more detectors operatively coupled to one or more devices, wherein the one or more detectors configured to detect signal attributes associated with the one or more devices, and correspondingly generate a first set of signals; and a monitoring unit operatively coupled to the one or more detectors, the monitoring unit comprising one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors configured to: extract signal attributes associated with at least one of the one or more devices based on the first set of signals generated by at least one of the one or more detectors; match the extracted signal attributes with a first dataset comprising pre-defined limits, and correspondingly determine variances for each of the extracted signal attributes; compute a weighted amalgamation of the determined variances; and generate a set of monitoring signals when the computed weighted amalgamation of the determined variances is within a pre-defined range.
[0016] In an aspect, the one or more detectors comprise any or a combination of Set-top Box (STB) decoder, LNB assembly, RF detector, and dB meters.
[0017] In an aspect, the one or more devices comprise any or a combination of Set-top Box, television, positioning module, GSM module, radio module, Wi-Fi module, Li-Fi module, and radio module.
[0018] In an aspect, the signal attributes associated with the one or more devices comprise any or a combination of strength, intensity, speed, distortion, latency, and signal-to-noise ratio.
[0019] In an aspect, the weighted amalgamation of the determined variances may be computed based on pre-defined weights assigned to each of the signal attributes.
[0020] In an aspect, the monitoring unit may be configured to generate a set of alert signals when the computed weighted amalgamation of the determined variances is beyond the pre-defined range, wherein the alert signals pertain to adverse weather conditions.
[0021] In an aspect, the generated set of alert signals may be transmitted to one or more mobile computing devices.
[0022] In an aspect, the monitoring unit may be configured to update a training-and-testing dataset based on the extracted signal attributes.
[0023] In an aspect, the monitoring unit may be configured to forecast weather based on the updated training-and-testing dataset.
[0024] In an aspect, the monitoring unit may be configured to generate a set of forecast signals, wherein the set of forecast signals pertain to forecast of weather conditions.
[0025] Another aspect of the present disclosure pertains to a weather forecasting method comprising steps of: detecting, through one or more detectors, signal attributes associated with one or more devices, and correspondingly generating a first set of signals; extracting, at one or more processors of a monitoring unit, signal attributes associated with at least one of the one or more devices based on the first set of signals generated by at least one of the one or more detectors; matching, at the one or more processors, the extracted signal attributes with a first dataset comprising pre-defined limits, and correspondingly determining variances for each of the extracted signal attributes; computing, at the one or more processors, a weighted amalgamation of the determined variances; and generating, at the one or more processors, a set of monitoring signals when the computed weighted amalgamation of the determined variances is within a pre-defined range.

BRIEF DESCRIPTION OF DRAWINGS
[0026] The accompanying drawings are included to provide a further understanding of the present invention and are incorporated in and constitute a part of this specification. The drawings illustrate exemplary embodiments of the present invention and, together with the description, serve to explain the principles of the present invention.
[0027] FIG. 1 illustrates an exemplary network architecture in which or with which proposed system can be implemented in accordance with an embodiment of the present disclosure.
[0028] FIG. 2 illustrates exemplary functional units of a monitoring unit, in accordance with an exemplary embodiment of the present disclosure.
[0029] FIG. 3 illustrates a weather forecasting method, in accordance with an exemplary embodiment of the present disclosure.
[0030] FIG. 4 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION
[0031] The following is a detailed description of embodiments of the disclosure depicted in the accompanying drawings. The embodiments are in such detail as to clearly communicate the disclosure. However, the amount of detail offered is not intended to limit the anticipated variations of embodiments; on the contrary, the intention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the present disclosure as defined by the appended claims.
[0032] In the following description, numerous specific details are set forth in order to provide a thorough understanding of embodiments of the present invention. It will be apparent to one skilled in the art that embodiments of the present invention may be practiced without some of these specific details.
[0033] Embodiments of the present invention include various steps, which will be described below. The steps may be performed by hardware components or may be embodied in machine-executable instructions, which may be used to cause a general-purpose or special-purpose processor programmed with the instructions to perform the steps. Alternatively, steps may be performed by a combination of hardware, software, and firmware and/or by human operators.
[0034] Various methods described herein may be practiced by combining one or more machine-readable storage media containing the code according to the present invention with appropriate standard computer hardware to execute the code contained therein. An apparatus for practicing various embodiments of the present invention may involve one or more computers (or one or more processors within a single computer) and storage systems containing or having network access to computer program(s) coded in accordance with various methods described herein, and the method steps of the invention could be accomplished by modules, routines, subroutines, or subparts of a computer program product.
[0035] If the specification states a component or feature “may”, “can”, “could”, or “might” be included or have a characteristic, that particular component or feature is not required to be included or have the characteristic.
[0036] Exemplary embodiments will now be described more fully hereinafter with reference to the accompanying drawings, in which exemplary embodiments are shown. These exemplary embodiments are provided only for illustrative purposes and so that this disclosure will be thorough and complete and will fully convey the scope of the invention to those of ordinary skill in the art. The invention disclosed may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Various modifications will be readily apparent to persons skilled in the art. The general principles defined herein may be applied to other embodiments and applications without departing from the spirit and scope of the invention. Moreover, all statements herein reciting embodiments of the invention, as well as specific examples thereof, are intended to encompass both structural and functional equivalents thereof. Additionally, it is intended that such equivalents include both currently known equivalents as well as equivalents developed in the future (i.e., any elements developed that perform the same function, regardless of structure). Also, the terminology and phraseology used is for the purpose of describing exemplary embodiments and should not be considered limiting. Thus, the present invention is to be accorded the widest scope encompassing numerous alternatives, modifications and equivalents consistent with the principles and features disclosed. For purpose of clarity, details relating to technical material that is known in the technical fields related to the invention have not been described in detail so as not to unnecessarily obscure the present invention.
[0037] Thus, for example, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named element.
[0038] Embodiments of the present invention may be provided as a computer program product, which may include a machine-readable storage medium tangibly embodying thereon instructions, which may be used to program a computer (or other electronic devices) to perform a process. The term “machine-readable storage medium” or “computer-readable storage medium” includes, but is not limited to, fixed (hard) drives, magnetic tape, floppy diskettes, optical disks, compact disc read-only memories (CD-ROMs), and magneto-optical disks, semiconductor memories, such as ROMs, PROMs, random access memories (RAMs), programmable read-only memories (PROMs), erasable PROMs (EPROMs), electrically erasable PROMs (EEPROMs), flash memory, magnetic or optical cards, or other type of media/machine-readable medium suitable for storing electronic instructions (e.g., computer programming code, such as software or firmware).A machine-readable medium may include a non-transitory medium in which data may be stored and that does not include carrier waves and/or transitory electronic signals propagating wirelessly or over wired connections. Examples of a non-transitory medium may include, but are not limited to, a magnetic disk or tape, optical storage media such as compact disk (CD) or digital versatile disk (DVD), flash memory, memory or memory devices. A computer-program product may include code and/or machine-executable instructions that may represent a procedure, a function, a subprogram, a program, a routine, a subroutine, a module, a software package, a class, or any combination of instructions, data structures, or program statements. A code segment may be coupled to another code segment or a hardware circuit by passing and/or receiving information, data, arguments, parameters, or memory contents. Information, arguments, parameters, data, etc. may be passed, forwarded, or transmitted via any suitable means including memory sharing, message passing, token passing, network transmission, etc.
[0039] Furthermore, embodiments may be implemented by hardware, software, firmware, middleware, microcode, hardware description languages, or any combination thereof. When implemented in software, firmware, middleware or microcode, the program code or code segments to perform the necessary tasks (e.g., a computer-program product) may be stored in a machine-readable medium. A processor(s) may perform the necessary tasks.
[0040] Systems depicted in some of the figures may be provided in various configurations. In some embodiments, the systems may be configured as a distributed system where one or more components of the system are distributed across one or more networks in a cloud computing system.
[0041] Each of the appended claims defines a separate invention, which for infringement purposes is recognized as including equivalents to the various elements or limitations specified in the claims. Depending on the context, all references below to the "invention" may in some cases refer to certain specific embodiments only. In other cases it will be recognized that references to the "invention" will refer to subject matter recited in one or more, but not necessarily all, of the claims.
[0042] All methods described herein may be performed in any suitable order unless otherwise indicated herein or otherwise clearly contradicted by context. The use of any and all examples, or exemplary language (e.g., “such as”) provided with respect to certain embodiments herein is intended merely to better illuminate the invention and does not pose a limitation on the scope of the invention otherwise claimed. No language in the specification should be construed as indicating any non-claimed element essential to the practice of the invention.
[0043] Various terms as used herein are shown below. To the extent a term used in a claim is not defined below, it should be given the broadest definition persons in the pertinent art have given that term as reflected in printed publications and issued patents at the time of filing.
[0044] The present disclosure relates generally to the field of electronics. In particular, the present disclosure relates to analysis of signal for predicting weather. More particularly, the present disclosure relates to systems and methods for forecasting weather.
[0045] In an aspect, the present disclosure pertains to a weather forecasting system including: one or more detectors operatively coupled to one or more devices, wherein the one or more detectors configured to detect signal attributes associated with the one or more devices, and correspondingly generate a first set of signals; and a monitoring unit operatively coupled to the one or more detectors, the monitoring unit including one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors configured to: extract signal attributes associated with at least one of the one or more devices based on the first set of signals generated by at least one of the one or more detectors; match the extracted signal attributes with a first dataset including pre-defined limits, and correspondingly determine variances for each of the extracted signal attributes; compute a weighted amalgamation of the determined variances; and generate a set of monitoring signals when the computed weighted amalgamation of the determined variances is within a pre-defined range.
[0046] In an embodiment, the one or more detectors can include any or a combination of Set-top Box (STB) decoder, LNB assembly, RF detector, and dB meters.
[0047] In an embodiment, the one or more devices can include any or a combination of Set-top Box, television, positioning module, GSM module, radio module, Wi-Fi module, Li-Fi module, and radio module.
[0048] In an embodiment, the signal attributes associated with the one or more devices can include any or a combination of strength, intensity, speed, distortion, latency, and signal-to-noise ratio.
[0049] In an embodiment, the weighted amalgamation of the determined variances can be computed based on pre-defined weights assigned to each of the signal attributes.
[0050] In an embodiment, the monitoring unit can be configured to generate a set of alert signals when the computed weighted amalgamation of the determined variances is found to be beyond the pre-defined range, wherein the alert signals pertain to adverse weather conditions.
[0051] In an embodiment, the generated set of alert signals can be transmitted to one or more mobile computing devices.
[0052] In an embodiment, the monitoring unit can be configured to update a training-and-testing dataset based on the extracted signal attributes.
[0053] In an embodiment, the monitoring unit can be configured to forecast weather based on the updated training-and-testing dataset.
[0054] In an embodiment, the monitoring unit can be configured to generate a set of forecast signals, wherein the set of forecast signals pertain to forecast of weather conditions.
[0055] In another aspect, the present disclosure pertains to weather forecasting method including steps of: detecting, through one or more detectors, signal attributes associated with one or more devices, and correspondingly generating a first set of signals; extracting, at one or more processors of a monitoring unit, signal attributes associated with at least one of the one or more devices based on the first set of signals generated by at least one of the one or more detectors; matching, at the one or more processors, the extracted signal attributes with a first dataset including pre-defined limits, and correspondingly determining variances for each of the extracted signal attributes; computing, at the one or more processors, a weighted amalgamation of the determined variances; and generating, at the one or more processors, a set of monitoring signals when the computed weighted amalgamation of the determined variances is within a pre-defined range.
[0056] FIG. 1 illustrates an exemplary network architecture in which or with which proposed system can be implemented in accordance with an embodiment of the present disclosure.
[0057] According to an embodiment of the present disclosure, the proposed weather forecasting system 100 (interchangeably referred to as weather forecasting system 100 and system 100, hereinafter) can facilitate in forecasting of weather through analysis of signals flowing across various devices. In an embodiment, whenever there is a change in the weather it may result in distortion in signals that are being used by different devices for various purposes. Similarly, satellite TV signals, radio signals, GPS signals, mobile communication signals, VSAT and other signals from satellites face the distortion. Now, based on analysis and comparison of distortion and related pre-stored dataset pertaining to weather related data can enable prediction of data.
[0058] In an embodiment, the system 100 can enable receiving signals from different locations hence prediction can be done in real time from various locations that can also enable creating a spatial dataset that can be stored on a database or server 106 and can be further analysed to enable weather forecasting at enhance accuracy. Further, the system 100 can enable receiving of signals from other sources as well that can be related to weather forecasting, thereby, enhancing accuracy of generated prediction as well as the spatial data.
[0059] In an embodiment, as illustrated, the weather forecasting system 100 can include a monitoring unit 102 that can be communicatively coupled with positioning modules through satellites 108, transmission towers and transmission lines 112, and devices 110, such as, but not limited to, television (TV), Set-top Box, positioning module, GSM module, radio module, Wi-Fi module, Li-Fi module, and radio module, through a network 104. In an embodiment, the monitoring unit 102 can be implemented using any or a combination of hardware components and software components such as a cloud, a server, a computing system, a computing device, a network device and the like. Further, the monitoring unit 102 can interact with the devices 110 through a website or an application that can reside in the proposed system 100. In an implementation, the monitoring unit 102 can be accessed by website or application that can be configured with any operating system, including but not limited to, AndroidTM, iOSTM, and the like.
[0060] Further, the network 104 can be a wireless network, a wired network or a combination thereof that can be implemented as one of the different types of networks, such as Intranet, Local Area Network (LAN), Wide Area Network (WAN), Internet, and the like. Further, the network 104 can either be a dedicated network or a shared network. The shared network can represent an association of the different types of networks that can use variety of protocols, for example, Hypertext Transfer Protocol (HTTP), Transmission Control Protocol/Internet Protocol (TCP/IP), Wireless Application Protocol (WAP), and the like.
[0061] In an embodiment, the weather forecasting system 100 can include one or more detectors (collectively referred to as detectors, and individually referred to as detector, hereinafter) that can be operatively coupled to the devices 110 and the monitoring unit 102, where the detectors can be configured to detect signal attributes associated with the devices 110, and can correspondingly generate a first set of signals. In an exemplary embodiment, the detectors can include, but not limited to, Set-top Box (STB) decoder, LNB assembly, Radio Frequency (RF) detector, and dB meters. In another exemplary embodiment, the signal attributes associated with the devices 110 can include, but not limited to, strength, intensity, speed, distortion, latency, and signal-to-noise ratio.
[0062] In an embodiment, the generated first set of signals can be transmitted to the monitoring unit 102, and on receiving the transmitted first set of signals, the monitoring unit 102 can extract signal attributes associated with at least one of the devices 110, say signal attributes of TV signals, from the first set of signals generated by at least one of the detectors. In an embodiment, the monitoring unit 102 can be configured to match the extracted signal attributes with a first dataset that can be including pre-defined limits of each of the signal attributes. In another embodiment, the monitoring unit 102 can be configured to determine variances for each of the extracted signal attributes by matching the extracted signal attributes with the first dataset.
[0063] In an exemplary embodiment, the following table illustrates variances in signal attributes of TV signals –
Signal Attribute Extracted value (per unit (pu)) Pre-defined Limits (pu) Variance (pu)
Intensity 20 10 1
Speed 20 30 -1
Latency 10 0 1
Distortion 20 0 2

Here, Variance is calculated by the formula -
In case of intensity, variance = (20-10)/10 = 1,
and similarly, can be calculated by the above formula for other signal attributes.
[0064] In an embodiment, the monitoring unit 102 can be configured to compute a weighted amalgamation of the determined variances, where the weighed amalgamation can be computed based on pre-defined weights assigned to each of the signal attributes, and the determined variances. In an embodiment, when the computed weighted amalgamation of the determined variances is found to be within a pre-defined range, the monitoring unit 102 can generate a set of monitoring signals that can pertain to normal weather conditions, and the system 100 continues monitoring. In another embodiment, when the computed weighted amalgamation of the determined variances is found to be beyond the pre-defined range, the monitoring unit 102 can generate a set of alert signals that can pertain to adverse weather conditions.
[0065] In an exemplary embodiment, the following table illustrates computation of weighted amalgamation –
Signal Attribute Variance (v) Pre-determined Weights (w) Weighted Amalgamation
Intensity 1 5
15
Speed -1 4
Latency 1 1
Distortion 2 4

Here, Weighted Amalgamation is calculated by the formula -

Therefore, Weighted Amalgamation = [5*4 + (-1)*4 + 1*1 + 2*4]
= [20 – 4 + 1 + 8]
= 15

Now, for example, pre-defined range can be –
Pre-defined Range Weather Condition
Less than or Equal to 20 Normal
21 – 30 Yellow Alert
31 – 40 Orange Alert
More than 40 Red Alert

As for this condition, the computed weighted amalgamation, i.e., 15 falls in the normal weather condition, therefore, the monitoring unit 102 can generate the set of monitoring signals.
However, if the computed weighted amalgamation had been beyond 20 then the monitoring unit 102 have generated a set of alert signals based on the weather condition, for example, in case of yellow alert, the monitoring unit 102 can generate a first set of alert signals, in case of orange alert, the monitoring unit 102 can generate a second set of alert signals, whereas in case of red alert, the monitoring unit 102 can generate a third set of alert signals.
[0066] In an embodiment, the weather forecasting system 100 can store the detected signal attributes at the server 106, and can correspondingly update itself. The weather forecasting system 100 can further facilitate in forecasting of weather based on the updation, and can correspondingly generate a set of forecast signals.
[0067] FIG. 2 illustrates exemplary functional units of a monitoring unit 102, in accordance with an exemplary embodiment of the present disclosure.
[0068] As illustrated, the monitoring unit 102 can include one or more processor(s) 202. One or more processor(s) 202 can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, logic circuitries, and/or any devices that manipulate data based on operational instructions. Among other capabilities, one or more processor(s) 202 are configured to fetch and execute computer-readable instructions stored in a memory 204 of the monitoring unit 102. The memory 204 can store one or more computer-readable instructions or routines, which may be fetched and executed to create or share the data units over a network service. The memory 204 can include any non-transitory storage device including, for example, volatile memory such as RAM, or non-volatile memory such as EPROM, flash memory, and the like.
[0069] In an embodiment, the monitoring unit 102 can also include an interface(s) 206. The interface(s) 206 may include a variety of interfaces, for example, interfaces for data input and output devices, referred to as I/O devices, storage devices, and the like. The interface(s) 206 may facilitate communication of the monitoring unit 102 with various devices coupled to the monitoring unit 102. The interface(s) 206 may also provide a communication pathway for one or more components of the monitoring unit 102. Examples of such components include, but are not limited to, processing engines(s) 208 and database 210.
[0070] In an embodiment, the processing engine(s) 208 can be implemented as a combination of hardware and programming (for example, programmable instructions) to implement one or more functionalities of the processing engine(s) 208. In examples described herein, such combinations of hardware and programming may be implemented in several different ways. For example, the programming for the processing engine(s) 208 may be processor executable instructions stored on a non-transitory machine-readable storage medium and the hardware for the processing engine(s) 208 may include a processing resource (for example, one or more processors), to execute such instructions. In the present examples, the machine-readable storage medium may store instructions that, when executed by the processing resource, implement the processing engine(s) 208. In such examples, the monitoring unit 102 can include the machine-readable storage medium storing the instructions and the processing resource to execute the instructions, or the machine-readable storage medium may be separate but accessible to the monitoring unit 102 and the processing resource. In other examples, the processing engine(s) 208 may be implemented by electronic circuitry. The database 210 can include data that is either stored or generated as a result of functionalities implemented by any of the components of the processing engine(s) 208.
[0071] In an embodiment, as illustrated in FIG. 2, processing engine(s) 208 can include a matching unit 212, an amalgamation unit 214, and other unit(s) 218. The other unit(s) 218 can implement functionalities that supplement applications or functions performed by the monitoring unit 102 or the processing engine(s) 208.
[0072] In an embodiment, the matching unit 212 associated with the monitoring unit 102 can facilitate matching of signal attributes, associated with devices 110, with a first dataset. In an exemplary embodiment, the signal attributes can include, but not limited to, strength, intensity, speed, distortion, latency, and signal-to-noise ratio. In an embodiment, detectors such as, but not limited to, Set-top Box(STB)decoder, LNB assembly, Radio Frequency (RF) detector, and dB meters, can detect signal attributes associated with the devices 110, , such as, but not limited to, television (TV), Set-top Box, positioning module, GSM module, radio module, Wi-Fi module, Li-Fi module, and radio module. In an embodiment, based on the detection of signal attributes, detectors can generate a first set of signals. The first set of signals can be further transmitted to the monitoring unit 102, where signal attributes associated with at least one of the devices 110, say signal attributes of a GSM module, can be extracted from the first set of signals generated by at least one of the detectors, which can be matched with the first dataset. In another embodiment, the matching unit 212 can determine variances for each of the extracted signal attributes by matching the extracted signal attributes with the first dataset.
[0073] In an exemplary embodiment, the following table illustrates variances in signal attributes of a GSM module –
Signal Attribute Extracted value (per unit (pu)) Pre-defined Limits (pu) Variance (pu)
Intensity 20 40 -1
Speed 30 50 -1
Latency 20 0 1
Distortion 20 0 1

Here, Variance is calculated by the formula -
In case of intensity, variance = (20-40)/40 = -1,
and similarly, can be calculated by the above formula for other signal attributes.
[0074] In an embodiment, the amalgamation unit 214 associated with the monitoring unit 102 can facilitate computation of a weighted amalgamation of the determined variances, where the weighed amalgamation can be computed based on pre-defined weights assigned to each of the signal attributes, and the determined variances. In an embodiment, when the computed weighted amalgamation of the determined variances is found to be within a pre-defined range, the amalgamation unit 214 can facilitate generation of a set of monitoring signals that can pertain to normal weather conditions. In another embodiment, when the computed weighted amalgamation of the determined variances is found to be beyond the pre-defined range, the amalgamation unit 214 can facilitate generation of a set of alert signals that can pertain to adverse weather conditions. In an exemplary embodiment, the generated set of alert signals can be transmitted to one or more mobile computing devices to alert people regarding adverse weather conditions.
[0075] In an exemplary embodiment, the following table illustrates computation of weighted amalgamation –
Signal Attribute Variance (v) Pre-determined Weights (w) Weighted Amalgamation
Intensity -1 1
25
Speed -1 2
Latency 1 10
Distortion 1 20

Here, Weighted Amalgamation is calculated by the formula -

Therefore, Weighted Amalgamation = [(-1)*1 + (-1)*2 + 1*10 + 1*20]
= [- 1 - 4 + 10 + 20]
= 25
Now, for example, pre-defined range can be –
Pre-defined Range Weather Condition
Less than or Equal to 10 Normal
11 – 20 Yellow Alert
21 – 30 Orange Alert
More than 30 Red Alert

As for this condition, the computed weighted amalgamation, i.e., 25 falls in the range associated with Orange alert, therefore, a first set of alert signals can be generated.
However, if the computed weighted amalgamation had been below 10 then a set of monitoring signals might have been generated. Moreover, if the computed weighted amalgamation had been between 11-20 then a second set of alert signals might have been generated, which can pertain to Yellow alert, and if the computed weighted amalgamation had been more than 30, then a third set of alert signals might have been generated, which pertain to Red alert.
[0076] In an embodiment, pre-defined ranges, pre-defined limits, and pre-defined weights may vary for different devices 110, such as smart phones, TV sets, and the likes, based on priority of signal attributes required by said device 110.
[0077] In an embodiment, a training-and-testing dataset, stored in database 210, can be updated based on the extracted signal attributes, and said updated training-and-testing dataset along with the matching unit 212 and the amalgamation unit 214 can facilitate in weather forecasting via technologies like machine learning and artificial intelligence. In an exemplary embodiment, a set of forecast signals can be generated based on analysis of detected signal attributes and the updated training-and-testing dataset, where the set of forecast signals can pertain to forecast of weather conditions.
[0078] In an embodiment, in a very similar manner, signal attributes of signals associated with satellites 108 and transmission lines 112 can be detected and further analysed to determine variances and weighted amalgamation of said variations, and accordingly weather condition can be forecasted.
[0079] In another embodiment, based on analysis of weighted amalgamation of signal attributes associated with various devices 110, satellites 108 and transmission lines 112, weather forecasting can be done more accurately. For example, if weighted amalgamation of signal attributes associated with TV illustrates a normal weather condition, whereas weighted amalgamation of signal attributes associated with GSM module depicts an Orange alert, moreover, if weighted amalgamation of signal attributes associated with satellites 108 depicts a Yellow alert, whereas weighted amalgamation of signal attributes associated with transmission lines 112 depicts an Orange alert. Then, all the said weighted amalgamations and depicted weather conditions are analysed, and correspondingly the system 100 can generate a set of signals pertaining to an specific weather condition determined through accumulation of said weighted amalgamations and depicted weather conditions, for example, in this case, a set of first alert signals can be generated and transmitted, where the set of first alert signals can pertain to Yellow alert.
[0080] FIG. 3 illustrates a weather forecasting method, in accordance with an exemplary embodiment of the present disclosure.
[0081] In an embodiment, as illustrated in FIG. 3, the weather forecasting method 300 can include a step 302 of detecting, through one or more detectors, signal attributes associated with one or more devices 110, and correspondingly generating a first set of signals.
[0082] In an embodiment, the weather forecasting method 300 can include a step 304 of extracting, at one or more processors of a monitoring unit 102, signal attributes associated with at least one of the one or more devices 110 based on the first set of signals generated in the step 302 by at least one of the one or more detectors.
[0083] In an embodiment, the weather forecasting method 300 can include a step 306 of matching, at the one or more processors, the signal attributes that are being extracted in the step 304 with a first dataset that can be including pre-defined limits, and correspondingly determining variances for each of the extracted signal attributes.
[0084] In an embodiment, the weather forecasting method 300 can include a step 308 of computing, at the one or more processors, a weighted amalgamation of the variances that are being determined in the step 306.
[0085] In an embodiment, the weather forecasting method 300 can include a step 310 of generating, at the one or more processors, a set of monitoring signals when the weighted amalgamation of the determined variances that is being computed in the step 308 is within a pre-defined range.
[0086] FIG. 4 illustrates an exemplary computer system in which or with which embodiments of the present invention can be utilized in accordance with embodiments of the present disclosure.
[0087] As shown in FIG. 4, the computer system 400 can include an external storage device 410, a bus 420, a main memory 430, a read only memory 440, a mass storage device 450, communication port 460, and a processor 470. A person skilled in the art will appreciate that computer system may include more than one processor and communication ports. Examples of processor 470 include, but are not limited to, an Intel® Itanium® or Itanium 4 processor(s), or AMD® Opteron® or Athlon MP® processor(s), Motorola® lines of processors, FortiSOC™ system on a chip processors or other future processors. The processor 470 may include various modules associated with embodiments of the present invention. The communication port 460 can be any of an RS-232 port for use with a modem-based dialup connection, a 10/100 Ethernet port, a Gigabit or 10 Gigabit port using copper or fiber, a serial port, a parallel port, or other existing or future ports. The communication port 460 may be chosen depending on a network, such a Local Area Network (LAN), Wide Area Network (WAN), or any network to which computer system connects.
[0088] The memory 430 can be Random Access Memory (RAM), or any other dynamic storage device commonly known in the art. Read only memory 440 can be any static storage device(s) e.g., but not limited to, a Programmable Read Only Memory (PROM) chips for storing static information e.g., start-up or BIOS instructions for processor 470. Mass storage 450 may be any current or future mass storage solution, which can be used to store information and/or instructions. Exemplary mass storage solutions include, but are not limited to, Parallel Advanced Technology Attachment (PATA) or Serial Advanced Technology Attachment (SATA) hard disk drives or solid-state drives (internal or external, e.g., having Universal Serial Bus (USB) and/or Firewire interfaces), e.g. those available from Seagate (e.g., the Seagate Barracuda 7102 family) or Hitachi (e.g., the Hitachi Deskstar 7K1000), one or more optical discs, Redundant Array of Independent Disks (RAID) storage, e.g. an array of disks (e.g., SATA arrays), available from various vendors including Dot Hill Systems Corp., LaCie, Nexsan Technologies, Inc. and Enhance Technology, Inc.
[0089] The bus 420 communicatively couples the processor(s) 470 with the other memory, storage and communication blocks. Bus 420 can be, e.g. a Peripheral Component Interconnect (PCI) / PCI Extended (PCI-X) bus, Small Computer System Interface (SCSI), USB or the like, for connecting expansion cards, drives and other subsystems as well as other buses, such a front side bus (FSB), which connects the processor 470 to software system.
[0090] Optionally, operator and administrative interfaces, e.g. a display, keyboard, and a cursor control device, may also be coupled to the bus 420 to support direct operator interaction with computer system. Other operator and administrative interfaces can be provided through network connections connected through the communication port 460. External storage device 410 can be any kind of external hard-drives, floppy drives, IOMEGA® Zip Drives, Compact Disc - Read Only Memory (CD-ROM), Compact Disc - Re-Writable (CD-RW), Digital Video Disk - Read Only Memory (DVD-ROM). Components described above are meant only to exemplify various possibilities. In no way should the aforementioned exemplary computer system limit the scope of the present disclosure.
[0091] Embodiments of the present disclosure may be implemented entirely hardware, entirely software (including firmware, resident software, micro-code, etc.) or combining software and hardware implementation that may all generally be referred to herein as a “circuit,” “module,” “component,” or “system.” Furthermore, aspects of the present disclosure may take the form of a computer program product comprising one or more computer readable media having computer readable program code embodied thereon.
[0092] Thus, it will be appreciated by those of ordinary skill in the art that the diagrams, schematics, illustrations, and the like represent conceptual views or processes illustrating systems and methods embodying this invention. The functions of the various elements shown in the figures may be provided through the use of dedicated hardware as well as hardware capable of executing associated software. Similarly, any switches shown in the figures are conceptual only. Their function may be carried out through the operation of program logic, through dedicated logic, through the interaction of program control and dedicated logic, or even manually, the particular technique being selectable by the entity implementing this invention. Those of ordinary skill in the art further understand that the exemplary hardware, software, processes, methods, and/or operating systems described herein are for illustrative purposes and, thus, are not intended to be limited to any particular named.
[0093] As used herein, and unless the context dictates otherwise, the term "coupled to" is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms "coupled to" and "coupled with" are used synonymously. Within the context of this document terms "coupled to" and "coupled with" are also used euphemistically to mean “communicatively coupled with” over a network, where two or more devices are able to exchange data with each other over the network, possibly via one or more intermediary device.
[0094] It should be apparent to those skilled in the art that many more modifications besides those already described are possible without departing from the inventive concepts herein. The inventive subject matter, therefore, is not to be restricted except in the spirit of the appended claims. Moreover, in interpreting both the specification and the claims, all terms should be interpreted in the broadest possible manner consistent with the context. In particular, the terms “comprises” and “comprising” should be interpreted as referring to elements, components, or steps in a non-exclusive manner, indicating that the referenced elements, components, or steps may be present, or utilized, or combined with other elements, components, or steps that are not expressly referenced. Where the specification claims refers to at least one of something selected from the group consisting of A, B, C …. and N, the text should be interpreted as requiring only one element from the group, not A plus N, or B plus N, etc.
[0095] While the foregoing describes various embodiments of the invention, other and further embodiments of the invention may be devised without departing from the basic scope thereof. The scope of the invention is determined by the claims that follow. The invention is not limited to the described embodiments, versions or examples, which are included to enable a person having ordinary skill in the art to make and use the invention when combined with information and knowledge available to the person having ordinary skill in the art.

ADVANTAGES OF THE INVENTION
[0096] The present disclosure provides a system and method for weather forecasting.
[0097] The present disclosure provides a system and method to forecast weather by analysing signal attributes of signals associated with various devices.
[0098] The present disclosure provides a system and method to transmit alert signals to a number of people and concerned authority, if an adverse weather condition is forecasted.
[0099] The present disclosure provides a system and method to transmit to mitigate effects of adverse weather and save human lives and livelihood.
[00100] The present disclosure provides a robust, accurate, fast, efficient, cost effective and simple system and method.

CLAIMS:1.
A weather forecasting system comprising:
one or more detectors operatively coupled to one or more devices, wherein the one or more detectors configured to detect signal attributes associated with the one or more devices, and correspondingly generate a first set of signals; and
a monitoring unit operatively coupled to the one or more detectors, the monitoring unit comprising one or more processors coupled with a memory, the memory storing instructions executable by the one or more processors configured to:
extract signal attributes associated with at least one of the one or more devices based on the first set of signals generated by at least one of the one or more detectors;
match the extracted signal attributes with a first dataset comprising pre-defined limits, and correspondingly determine variances for each of the extracted signal attributes;
compute a weighted amalgamation of the determined variances; and
generate a set of monitoring signals when the computed weighted amalgamation of the determined variances is within a pre-defined range.
2. The weather forecasting system as claimed in claim 1, wherein the one or more detectors comprise any or a combination of Set-top Box (STB) decoder, LNB assembly, RF detector, and dB meters, and wherein the one or more devices comprise any or a combination of Set-top Box, television, positioning module, GSM module, radio module, Wi-Fi module, Li-Fi module, and radio module.
3. The weather forecasting system as claimed in claim 1, wherein the signal attributes associated with the one or more devices comprise any or a combination of strength, intensity, speed, distortion, latency, and signal-to-noise ratio.
4. The weather forecasting system as claimed in claim 1, wherein the weighted amalgamation of the determined variances is computed based on pre-defined weights assigned to each of the signal attributes.
5. The weather forecasting system as claimed in claim 1, wherein the monitoring unit is configured to generate a set of alert signals when the computed weighted amalgamation of the determined variances is beyond the pre-defined range, wherein the alert signals pertain to adverse weather conditions.
6. The weather forecasting system as claimed in claim 1, wherein the generated set of alert signals are transmitted to one or more mobile computing devices.
7. The weather forecasting system as claimed in claim 1, wherein the monitoring unit is configured to update a training-and-testing dataset based on the extracted signal attributes.
8. The weather forecasting system as claimed in claim 7, wherein the monitoring unit is configured to forecast weather based on the updated training-and-testing dataset.
9. The weather forecasting system as claimed in claim 8, wherein the monitoring unit is configured to generate a set of forecast signals, wherein the set of forecast signals pertain to forecast of weather conditions.
10. A weather forecasting method comprising steps of:
detecting, through one or more detectors, signal attributes associated with one or more devices, and correspondingly generating a first set of signals;
extracting, at one or more processors of a monitoring unit, signal attributes associated with at least one of the one or more devices based on the first set of signals generated by at least one of the one or more detectors;
matching, at the one or more processors, the extracted signal attributes with a first dataset comprising pre-defined limits, and correspondingly determining variances for each of the extracted signal attributes;
computing, at the one or more processors, a weighted amalgamation of the determined variances; and
generating, at the one or more processors, a set of monitoring signals when the computed weighted amalgamation of the determined variances is within a pre-defined range.