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What is radar technology?

Discuss about the Weather Radar Principles And Applications.

Radar refers to the object detection system that makes use of the radio waves for the purpose of determining the range, angle or velocity associated with an object. Radar system consists of transmitter that radiates the radio wave, commonly referred to as radio signal. The signal is emitted in some predefined direction. When the signal encounters an object the signal is reflected or scattered in random directions. However some portion of the signal that is not reflected back or the portion that does not caters following a collision with an object is absorbed by the object. The portion of the signal that is reflected back is very important as it is responsible for the radar to perform the work for which the system is installed (Radartutorial.eu 2018). Radar receiver is located near the transmitter to catch the reflected signal. However in some cases the receiver may be situated far from the receiver.  The weak absorption of radio waves helps the radar to detect the objects that are located in long distances. Other EM waves like visible light, infrared light and ultraviolet light too faces strong attenuation within this long distances.

The weather radar helps to detect rain in the atmosphere. It emits microwave pulses which are reflected back by the raindrops. The reflected signal from the raindrops is then measured by the receiver. The reflected signal gives a lot of useful information about the weather condition. The intensity of the reflected signal is particularly of major interest. It gives information about the intensity of the rain (Wright, D.B., Nielsen, J.E. and Rasmussen, M.R., 2017). The more intense the rain is the more intense the reflected signal will be. Thus by analysing the reflected signal and particularly the intensity factor of the signal, it can be interpreted whether the rain is intense or not. Another factor that is measured is the time of travelling. The time that the reflected signal takes to complete the distance between the targeted object and receiver is calculated. The time that the reflected signal takes helps to measure the distance of the rain like how far the rain is at that instance of time.

Doppler weather radar has become increasingly popular in recent years due to its portability and effectiveness in weather prediction. Doppler radar is an advanced and efficient weather radar system. The radar works on the principle Doppler Effect. According  to the theory of  Doppler effect, based on the relative distance of moving object from the source , the intensity of the sound waves originated from the moving object also changes, the closer the object to the source, the more intense the sound waves seem to be to the source (Westbrook, J.K. and Eyster, R.S., 2017).  The same concept is applied here. Instead of the sound wave, here the intensity of the reflected signal from the rain droplets is measured which gives an estimation of the speed of the rain droplets. The intensity of the rain droplets can also be estimated. The information related to the speed and intensity of the rain droplets plays an important role in weather prediction and serves the information for public use.   

How does weather radar help detect rain and other weather conditions?

Steps of the research methodology:

Step1: choice of weather variables:

Weather data is vast in amount as it contains information about the weather which often ranges from several weeks, months, to several years. Hence it is a huge challenge to analyse the collected data which is so vast in amount. In order to be able to analyse the data it is important to choose the correct analysis tools. It is also important to choose the correct weather variables and also the input and output parameters have to be defined as it has a significant effect on the weather data analysis. There are several weather parameters like “MAX TEMP °C, MIN TEMP °C, AVP (mm) M, AVP (mm) E, RH (%) M, RH (%) E, AVS WS Km/h, Bright Sunshine Hours, Pan Evaporation (mm) and Local Monsoonal Precipitation (mm)”. Proper input and output parameters have to be chosen from the parameters. The input and output parameters for the project have been defined in the table below:

Table 1: Input and output variables

X1

Temperature- TEMP (°C)

Input

X2

Average vapor Pressure- AVP (mm)

Input

X3

Relative Humidity-RH (%)

Input

X4

Pan Evaporation-Pan Evap (mm)

Input

Y1

Local Monsoonal Precipitation- LMP (mm)

output

Step2: Data collection:

The data will be collected from the data base of National Climatic Data Center. The data base provides weather data along with numerical values of the weather parameter. Input Variables include parameters like MAX TEMP °C, MIN TEMP °C, AVP (mm) M, AVP (mm) E, RH (%) M, RH (%) E, AVS WS Km/h, Bright Sunshine Hours, Pan Evaporation (mm) are considered as input variables and the parameter Local Monsoonal Precipitation (mm) has been considered as output variable. The output variable depends on the input variables as mentioned.

Bellow a sample weather data is provided. The yearly data of the North Carolina monsoon season is separated in accordance with the Standard Meteorological Week or SMW. In the research project, 40 years data of North Carolina monsoon season will be collected. A sample data sheet is shown for reference:

Step3: Data analysis:

The data will be analyzed using the ANFIS editor in the MATLAB. In order to set up the network in the ANIFS editor, at first the Sugeno ANFIS Setup has to be done and then the ANFIS Model has to be trained.

Sugeno ANFIS Setup Model:

In order to design the network in the ANFIS editor, input data has to be loaded in the ANFIS editor. The input data has to be separated as training, checking and testing data set. Sample ANFIS information is:

No. of input 3

No. of output 1

No. of input Mfs: 4 2 2 4

Training of ANFIS model:

The ANFIS model has to be trained with sample input data sets. The training will help the model in learning the overall pattern after analyzing different input data of several years as well as the yearly input data. After the training is completed, the model will be ready for prediction.

Step4: Error calculation in the data:

In order to make effective prediction, it is important to minimize the error in the collected data. There are various types of error calculation tolls that will be used in the experiment. The tolls are:

What is the Doppler weather radar?

Output deviations:

Calculate the error by finding the absolute difference between the observed and predicted value of output variable.

Mean Squared Error:

It Find the squared difference between the forecasted and observed output variable and find the average.

Root Means Squared Error:

It simply finds the square root of mean square error or MSE to calculate the error.

  1. The occurrence of windshear is regarded as one of the primary reason for the aircraft accident. Windshear is referred to the difference of the wind speed and the direction of the wind movement. The difference is calculated within a relatively short distance in the atmospheric conditions. It has both the vertical and horizontal components (Hirth 2015). Horizontal shear is particularly seen near the coast as well as across the weather front. Vertical shear is primarily occurs somewhere near the surface though it can occur at the altitude similar to jet streams. Even it is not very uncommon to observe the vertical windshear near the strong upper level fronts.

Since the 90s, the application of windshear detection radars got widespread attention and several airports around the world adopted the technology commonly known as Terminal Doppler Weather Radars or TDWR. The technology is based on the Doppler Effect. It is very much important to collect the details of windshear in order to avoid any kind of hazards during the aviation. Doppler weather radar helps to collect information about the windshear to effectively schedule the flight time (Edgar and Flyvbjerg 2015). The radar collects the information about the wind speed , wind directions and helps in analyzing information about the windshear.

The strongest windshear is seen due to the microburst, often associated with the thunderstorm that occurs over the areas where the airport terminal is located. In a more intense thunderstorm, intense cold air bursts towards the downward direction and get outspread near the grounds. Abrupt and sudden change in the aircraft speed and wind direction poses threat to aircraft safety.TWDR helps to detect low level windshear near the airport. It is much more technologically advanced than the traditional radar system (Wison et al. 2015). The traditional radars are useful only for monitoring the development and movement of rain areas. With the help of Doppler Effect the TWDR helps in measuring the radial winds originated from the radar. The change in wind speed and direction has a significant effect on the landing and departure of the aircraft. TWDR is placed along the direction of aircraft in the runway so that it measures the windshear and provides the accurate details about the measurement. The accurate measurement is very much important because a little mistake in the calculations will have a larger impact as far as the safety of the aircraft is concerned.

During the thunderstorm, TWWDR scans rapidly in order to detect and measure the rapid changes in the wind speed. It is not only important to measure the rapid changes in the wind speed and direction, fast calculations must be conducted to reflect the changes in the decision making process about the departure and landing of the aircraft. In order to achieve that the signal sent by the radar has to be of high quality. The signal sent by the traditional radar is not very high quality signal. However TWDR has the capability to send signal that are of very high quality and does not affected by high distortions and this makes the Doppler ideal for such application where speed and accuracy has to be maintained at the same time considering the sensitive nature of the application (Browning and Wexler 2015).

Doppler radar has been widely used in the development of the Advanced Weather Interactive Processing System or commonly known as AWIPS. It is an advanced weather information processing system where highly advanced and powerful processors display and communication equipment is used. It is operated by the United States National Weather Service or NWS.It is basically a complex network that integrates several metrological, hydrological and satellite data collected from various weather radar or the Doppler radar to find important details about the weather system (Birkenheuer 2015). This data, after insightful analysis, is sent to the various weather and river forecast centers. This data are then used by the centers to gather important information about the weather conditions like humidity, temperature and various other weather related details. This helps the centers to make accurate forecast about the weather, water and climate conditions. In 2015, Raytheon, the architect responsible for the AWIPS revolution , brought the AWIPS II in the market to mark the up gradation to the system. With the AWIPS II, several new features were added to the system which made the system even more powerful in terms of capabilities and accuracy in the prediction. It integrated several new hardware and software to the design to make it more powerful compared to the previous editions. It now allows to forecast in the field and respond right from the location to deal with emergency conditions. AWPS make the data processing automatic and accurate. In order to get details about the weather, water and climate conditions and make effective decision based on that, the data must be very accurate and reliable and for that very high quality radar is required to collect the information and help the station to remain alert about the various conditions of the weather and the climate changes (Birkenheuer 2015). In order to collect such important and sensitive data, there should be absolute no scope of error and even if there is any that must be well within the accepted limit. It is true that AWPS uses vey high quality of processor and analyzing tools to collect information from the raw data. However there will be no use of those advanced system if there is error in the collected data. The advanced analyzing tolls will only be useful if they produce accurate information. Still it has nothing to do with the accuracy of the raw data. The raw data has to be accurate if the result needs to correct. In order to collect accurate raw data the radar needs to be very efficient as the data will be collected first by the radar and then only those data can be analyzed by advanced processing systems to generate useful information from the collected data. Doppler radar as compared to the traditional data is much more powerful and advanced and can reflect the changes in the weather and climate very effectively, thus helps to realize the full potentials of the analyzing tools to make predictions more correctly. The ability of the weather radar to make correct measurement about wind conditions and the weather condition as a whole makes it suitable in the field of agriculture for weather condition measurement.

Steps involved in a research project for analyzing weather data to predict local monsoonal precipitation

Weather prediction plays an important role in the field of agriculture. The weather conditions as well the climate conditions needs to be measured accurately to provide correct assistance to the farmer for their agricultural decision.  The capability of the weather radar to measure all type of weather conditions and provide day long weather monitoring information make the weather radar the perfect choice for agricultural applications (Friday 2014). Traditional remote sensing devices use the natural wave. The traditional remote sensing device is also known as the passive device. However the radar remote sensing system uses artificial wave, hence it is known as active device. This allows the operators to select the necessary wave band as per the need of the application and also offers flexibility in terms of choosing the correct polarization model. The weather radar also provides information about the growth of the crops and helps to monitor the growth effectively.  The radar use the information related to the moister content of the crop. The information about the moist content of the crop helps to measure the growth of the crops. During the time of crop growing, the moister content is very helpful in knowing the details about crop growth. It is impossible for traditional RS devices to measure the moist content and hence it is not suitable for the application. However, the radar RS helps to conduct the measurement in these conditions due to the ability of the radar to measure the moist content.

Strom and bad weather also hampers the agricultural works. Hence accurate measurement of the weather is necessary. Weather radar helps to collect information about the conditions of the weather and based on that the agricultural centers monitors various factors related to the data which helps them to provide assistance to the farmers and the agricultural firms. The wind direction and speed is another important aspect of measuring the weather conditions. The weather radar has the capability to sense the variation of wind speed. It can also measure the direction of the wind flow. This information is sent to the centers that are assigned with the responsibility to analyze the data as provided by the radar system. Perfect analyze of this data helps in correct measurement of the weather conditions which in turn helps in making effective predictions (Meischner 2015). The predictions may not be always correct and error free. However the error can be minimized by making improvement in the data collection process itself so that the data is very accurate and correct. In order to do that the data collection process has to be very smart and advanced. Compared to the traditional remote sensing devices, the weather radar is much more advanced and provides correct data about the weather condition so that data analysis becomes more reliable and effective. 

Doppler weather radar is used in the command ship. It is used to assist the satellite launches at sea. The radar is used mainly in commercial satellite launches. It operates on board of the command ship. The satellite is launched from a location at sea. The radar is installed on board of the sea launch commander. The radar supports launch related activities. The radar is also used in helping the launch team to track storm while crossing between ports. The radar is stabilized in such a way that it can provide accurate data even if there is pitch and roll motion exhibited by the ship. The radar also provides velocity products which helps in correcting the speed and direction of the ship. It has major benefits in the weather prediction technique. It also allows using the full capability of the radar in any conditions. The result does not depend on the motion of the ship (Xiaoding 2014). Whether the ship is in motion or it is still, the accuracy of the radar is not affected. The radar helps in obtaining full weather related data. It provides data related to the volume scan products. The volume control product is very helpful in weather predictions, especially in the context of satellite launching activities.  

The TDR 3070-C is widely used Doppler weather radar that is used in the satellite launching in the ships. The radar works with the IRIS software made by Sigmet Inc. the radar along with the software helps in obtaining the weather related products including the data about the reflectivity, velocity and spectrum width products. It also provides the volume scan products. The TDR 3070-C is highly sensitive that allows in detecting the ice crystals of high altitude. The crystals are highly effective in predicting the weather condition like where there is any possible chance of triggered lighting. The lighting is one of the major factors of concern that is associated with the satellite launch decision.      

Weather radar is used in storm prediction centres. Weather radar has the ability of locating precipitation. The along with detecting the precipitation, also helps in calculating the motion of the precipitation. The radar is also capable of estimating the type of the precipitation like whether it is rain or snow or storm.  In order to increase the accuracy in prediction today almost every weather radar that is used is pulse-Doppler radar. In order to make prediction about storm, it is very much important to know the motion of rain droplets. Additionally the intensity of precipitation is also an important factor to determine the probability of storm. Correct information about both the factors is necessary to effectively make the prediction. To collect the data the radar should be highly adaptive to the conditions of the weather (Krikorian 2014). Doppler weather radar can accurately measure the motion of the rain droplets and also the intensity of the precipitation is measured accurately. These data are sent to the storm prediction and disaster recovery centres. The data are then analyzed with advanced software and analyzing tolls to make prediction about storm and provide alert based on the conditions so that effective measure can be taken against the natural calamities.

Today most of the weather radars are Doppler radar. The radar uses Doppler shift method to recognize the moving object by the phase variation technique.

The Doppler radar can be classified into two broad categories (Novak 2015):

  • Continuous wave Doppler radar
  • Pulse Doppler radar

 Pulse Doppler radar can be further classified into two categories:

  • High pulse Doppler radar
  • Low pulse Doppler radar.

Continuous wave Doppler radar:

Continuous wave Doppler radar makes use of the electromagnetic wave as opposed to the pulse Doppler radar which uses pulses to measure the Doppler shift. This type of radar does not measure the range of the targeted object, rather measures the rate at which the range of the object changes.  The radar sends electromagnetic wave towards the object and then waits for the arrival of the reflected signal. Once the reflected signal is picked up by the receiver of the radar, then the signal is compared with the transmitted signal to measure the changes in the phase (Novak 2015). The transmitted signal is radio frequency signal. The RF signal or the radio frequency signal is mixed with the intermediate frequency signal or the IF signal and the mixing is performed in the mixing stage. The mixing signal is then passed to the IF filter to produce local oscillator frequency which is again mixed with the received signal by another mixer in the next mixing stage. The received signal picked up by the receiver consists of RF signal frequency and the Doppler frequency. The output signal of the second mixer is then amplified by the IF amplifier. The amplified signal is then given to another mixer where it is mixed with the IF signal to get the Doppler frequency. The Doppler frequency or the Doppler shift is the measurement of the change at which the range of the target changes, thus gives information about the velocity of the target.

Pulse Doppler radar:

It uses the pulse timing techniques to determine the range to a target. In pulse radar the frequency of the carrier is modulated using rectangular pulses. The pulses are repetitive in nature and they are sent in sequence to perform the modulation of the carrier. The modulation technique here used is the frequency modulation where the frequency of the carrier signal is modulated in accordance with the frequency of the modulating signal. In this context the rectangular pulses serve as the modulating signal

High pulse Doppler radar:

Pulsed radar sends pulses of high frequency and intensity towards the target object. The radar then waits for the reflection of the signal. The reflected signal is also known as echo signal. The radar waits for this eco signal as the next pulse will be sent only after the echo signal is received from the targeted object. The frequency at which the pulse is repeatedly sent to the objects is known as pulse repetition frequency. The range and resolution of the radar depends on the frequency. Hence the frequency is very much important. In order to avoid the Doppler ambiguities, the pulse Doppler transmits pulses of high frequency and high repetition. The frequency spectrum associated with the signal transmitted by the transmitter of the radar is line spectrum that is comb-shaped. The spacing of the line in the line spectrum is determined by the Pulse Repetition Frequency or PRF. The lines cannot be separated by applying simple amplitude variation technique. The received frequency can only be used for correctly predicting the velocity of the target object if the received spectrum has smaller displacement than the spacing of the lines in the line spectrum. Otherwise the velocity will be subject to error. It means that the Doppler frequency has to be smaller than the pulse repetition frequency or the pulse repetition frequency has to be higher than the Doppler frequency. It is true that the electromagnetic wave travels at the speed of light, still the radar needs time to receive the reflected signal. In case the interpulse period is longer, then that is not a problem. When the interpulse duration is shorter which means that the time period of the previous pulse is shorter than the actual time frame of the pulse then it gives a shorter range of the targeted object which is not only false but ambiguous as well. Hence the range of the targeted object cannot measure accurately.

The high pulse Doppler radar mix the transmitted signal with the received echo signal and the mixing is done by the detector of the radar to measure the Doppler shift. The difference signal is further filtered by a Doppler filter for rejecting the unwanted signal or the noise from the signal

Low pulse Doppler radar:

Low pulse Doppler radar is also known as Moving Target Indicator RADAR or MTI radar. It sends pulses that have low repetition frequency. The low frequency is used for avoiding Doppler ambiguity. The radar first transmits a low frequency signal towards the targeted object. The reflected signal or the echo signal is received by the receiver of the radar. The receiver then directs the received signal towards the mixer. The mixer then mix the signal with another signal that is produced by a stable oscillator connected locally. The oscillator is also known as STALO. The oscillator produces infrared signal or IF signal after performing the signal mixing. In the next step the IF signal produced in the signal mixing technique the IF signal is then amplified. The amplified signal is then sent to the phase detector where the detector measures the phase difference between the amplified IF signal and the signal that is produced by a Coherent Oscillator, also referred to as COHO. The phase detector after measuring the phase difference produces the difference signal.  There is no phase variation between the coherent signal and the transmitted signal. The coherent signal and the signal that is produced by the STALO are mixed to turn on the amplifier. The amplifier is turned on and off with the pulse produced by the pulse modulator.

Sustainability aspect of the project:

In the modern era of 21st century, the fact that global climate is changing is not a matter of scientific research and debate anymore. Rather it is widely accepted by the scientific community all over the world. It is also accepted that the changes are very much forced by the modern activities which has only increased the level pollution over the years. With the advancement of modern lifestyle, the pollution level has only increased. The weather prediction has become complex process and it has been a major challenge to maintain accuracy in the weather prediction. It is not only important to maintain the accuracy in the weather related data, but the data has to be highly sustainable, so that it can be referenced for further research purpose. .  In order to make effective assessment of the present situation of the weather conditions, it is very much necessary to produce data that are of high quality. The quality of the data is necessary not only for the present assessment purpose, but to make the data sustainable in the coming years as well so that it remains valuable for future use as well. Most of the weather measuring system that were established in the past years had limited scope in terms of purpose and the range of uses. Those systems were only used for performing predefined tasks like prediction of daily weather, advising farmers about rain and the climate conditions to assist them in better decision making. The requirement and purpose of the observations is evolving continuously with emergence of new technologies and also restriction on the budget has to be taken into account while designing such weather measurement system (Lopez 2014). The requirement is such that the system must be technologically advanced, but at the same time should be cost efficient.

There are some monitoring principles that should be addressed to make a weather monitoring system that is not only of high quality but also meet the sustainability requirement (Silvius and Schipper 2014).

It should be measured that how the weather monitoring system can influence the existing measurement system so that it can make sustainable weather prediction for the future. The assessment should be performed in context of the climate variability and climate change.

Parallel testing:

The measurement produced by the old and existing system should be compared with the result obtained from the new and improved system. The measurement should be performed in the long time period in order to have better view about the quality of the data produced by each system. This comparison will give a better insight about the modification and changes that is required for the new system to make it more efficient in weather measurement.

Metadata:

Each observing system as well as the operating procedures should be documented. This is very important to follow immediately prior to and also after the changes that is contemplated.  

Integrated Environmental Assessment:

The environment assessment should be integrated with the weather assessment as the integration is very important for maintaining climate relevancy that is very important for sustainable weather measurement.

Continuity in purpose:

It is important to have a long-term commitment to the quality of the observation techniques to maintain the stability of the system. Along with the commitment it is also very important that a clear transition plan is developed so that the system can serve research need and the operational purposes at the same time without compromising in the quality.

Climate requirements:

It is important that the networking and the design team, responsible for designing the weather monitoring system, understand the requirements that should be fulfilled by the climate monitoring system. In order to facilitate the design team with the requirements, the climate monitoring requirements should be clearly outlined to the team so that they can maintain the required quality in producing the instruments for the system integration. The accuracy is the major aspect of any system so that it can predict accurate results and this requirement becomes particularly important in the context of weather monitoring system.

Data and metadata access:

The data should be easy to discover and also it is accessible whenever required. In order to facilitate the data access, the data management should be such that it offers freedom in access and also support cost effective mechanisms with proper quality control on the data and the data management process simultaneously.  

Characteristic of sustainable weather monitoring system: 

Observational system consists of process steps arranged in sequence. The system will only be effective for use in the climate monitoring purposes if the entire process exists in robust condition. This paper analysis the various conditions and characteristics that makes a climate observing system sustainable (Sanchez 2015).

The requirements that must be addressed by the system are outlined in the following section:

  • Measurements must be taken with instruments that are accurate and perfectly calibrated. The data that are produced by these instruments must be converted in the geophysical data and the data must be stored in a database that is robust and secure.
  • It is not only sufficient to collect the data, but it is equally important to check the quality of the data,
  • Data should be stored in proper format and efficiently complied
  • Data and the metadata that is stored in the database must be available for use whenever it is necessary.
  • Data must be precise and accurate enough so that it can be used over the next decade and provide useful reference for continued monitoring
  • Data should be homogeneous irrespective of time, location and method.

Conclusion

The advancement of the satellite technology has contributed to the advancement of the various sectors that uses the satellite technology. One such sector where the satellite technology is extensively used is the weather prediction sector. The report describes the radar systems technology and with reference to that the project topic has been briefly described. In the introduction sector the application of the radar technology in designing the weather system has been described with enough details to present adequate information about the topic. The report after introducing the topic, describes the methodology which has been used for conducting the case study. The section has been dedicated for choosing the appropriate methodology that best suits the case study topic. The research design that has been chosen for the case study topic follows descriptive style. The report follows both primary and secondary type of data collection method. The primary source of data that has been used here is the weather data base of National Climatic Data Center which offers free access to the weather for research purpose. The collected data has been tabulated using the Matlab Data Editor. Various statistical models like Linear, Quadratic, Pure Quadratic, and Interactive Model has been used for the data analysis. In order to calculate the error in the collected data, various techniques like RMSE, output deviation has been used. In the literature review section application of the weather radar technology has been described.  At first the application of the technology in predicting weather for air traffic control has been described with details that how the radar is exactly used for predicting the weather using the terminal Doppler weather radar. Then the application of the weather radar technology has been described for designing the Advanced Weather Interactive Processing System or commonly known as AWIPS. Applications of the technology in the agricultural, marine and storm prediction sector have also been described. The sections not only focus on the technology itself, but the way the technology has revolutionized the particular section of application has been given equal importance while describing the application s of the technology in the literature review section. The literature review section is followed by the classification of weather radar. Each type of radar is described with the basic principle and technology used for the radar. Proper diagram has been provided for better understanding. The sustainability aspect of the project has also been described. The factors that contribute to the sustainability aspect have been described with adequate details with characteristics for the sustainable weather monitoring system. 

References:

Birkenheuer, D.,2015. Exploiting available satellite data in AWIPS-ERA workstations(Advanced Weather Interactive Processing System). In Conference on Satellite Meteorology and Oceanography, 8 th, Atlanta, GA (pp. 46-49).

Browning, K.A. and Wexler, R., 2015. The determination of kinematic properties of a wind field using Doppler radar. Journal of Applied Meteorology, 7(1), pp.105-113.

Edgar, R.C. and Flyvbjerg, H., 2015. Error filtering, pair assembly and error correction for next-generation sequencing reads. Bioinformatics, 31(21), pp.3476-3482.

Friday Jr, E.W., 2014. The modernization and associated restructuring of the National Weather Service: An overview. Bulletin of the American Meteorological Society, 75(1), pp.43-52.

Hirth, B.D., Schroeder, J.L., Gunter, W.S. and Guynes, J.G., 2015. Coupling Doppler radar?derived wind maps with operational turbine data to document wind farm complex flows. Wind Energy, 18(3), pp.529-540.

Krikorian, K.V. and Rosen, R.A., Raytheon Co, 2015. All weather precision guidance of distributed projectiles. U.S. Patent 6,653,972.

López Álvarez, D., Sánchez Carracedo, F., Vidal López, E.M., Pegueroles Vallés, J.R., Alier Forment, M., Cabré Garcia, J.M., García Almiñana, J. and García, H., 2014. A methodology to introduce sustainability into the weather radar Project to foster sustainable engineering projects. In Frontiers in Education Conference: opening innovations and internationalization in engineering education: october 22-25, 2014, Madrid-Spain: proceedings (pp. 2360-2366). Institute of weather data collection.

Meischner, P. ed., 2015. Weather radar: principles and advanced applications. Springer Science & Business Media.

Novak, L.M., 2015, August. A comparison of 1-D and 2-D algorithms for radar target classification. In IEEE International Conference on Systems Engineering (Vol. 10).

Radartutorial.eu. (2018).[online] Available at: https://www.radartutorial.eu/druck/Book1.pdf [Accessed 19 Apr. 2018].

Sánchez, M.A., 2015. Integrating sustainability issues into weather project management. Journal of Cleaner Production, 96, pp.319-330.

Silvius, A.J. and Schipper, R.P., 2014. Sustainability aspect in weather project management A literature review and impact analysis. Social Business, 4(1), pp.63-96.

Sloss, P.W. and Atlas, D., 2014. Wind shear and reflectivity gradient effects on Doppler radar spectra. Journal of the Atmospheric Sciences, 25(6), pp.1080-1089.

Westbrook, J.K. and Eyster, R.S., 2017. Doppler weather radar detects emigratory flights of noctuids during a major pest outbreak. Remote Sensing Applications: Society and Environment, 8, pp.64-70.

Wilson, J.W., Roberts, R.D., Kessinger, C. and McCarthy, J., 2015. Microburst wind structure and evaluation of Doppler radar for airport wind shear detection. Journal of Climate and Applied Meteorology, 23(6), pp.898-915.

Wright, D.B., Nielsen, J.E. and Rasmussen, M.R., 2017. ICUD-0471 Weather radar rainfall for design of urban storm water systems. In 14th International Conference on Urban Drainage (pp. 178-181). IWA/IAHR International Conference on Urban Drainage.

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