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The Use of Weather Radar Data: Possibilities, Challenges and Advanced Applications
The Use of Weather Radar Data: Possibilities, Challenges and Advanced Applications
Binetti, Maria Silvia;Campanale, Claudia;Massarelli, Carmine;Uricchio, Vito Felice
Review The Use of Weather Radar Data: Possibilities, Challenges and Advanced Applications Maria Silvia Binetti , Claudia Campanale , Carmine Massarelli * and Vito Felice Uricchio Water Research Institute, Italian National Research Council (IRSA-CNR), 70132 Bari, Italy; email@example.com (M.S.B.); firstname.lastname@example.org (C.C.); email@example.com (V.F.U.) * Correspondence: firstname.lastname@example.org Abstract: The climate in recent decades has aroused interest in the scientiﬁc community, prompting us to analyse the mechanisms that regulate it, to understand the climate change responsible for an increase in extreme phenomena. Consequently, the increase in hydrogeological instability in the Italian territory has led to an in-depth study of atmospheric parameters to understand the variations of the atmospheric system. One tool capable of detecting such variations is the weather radar. The weather radar data available in the area provided by the National Radar Network of the Department of Civil Protection allow the evaluation of variations on a national scale for hydro-meteorological- climatic monitoring as well as the disasters that have occurred. Using open-source programming software, the servers can be queried and data retrieved from a source to perform processing for speciﬁc purposes through data extraction techniques. Keywords: weather radar; extreme events; rainfall; ﬂash ﬂoods; landslide; disaster management; open-source software; python Citation: Binetti, M.S.; Campanale, C.; Massarelli, C.; Uricchio, V.F. The 1. Introduction Use of Weather Radar Data: Climate is the synthesis statistic of atmospheric parameters (temperature, rain, mois- Possibilities, Challenges and ture, pressure, wind) which involve an area over a long time . In recent decades, the Advanced Applications. Earth 2022, 3, climate has aroused interest in the scientiﬁc community pushing us to analyze the mecha- 157–171. https://doi.org/10.3390/ nisms that regulate climate change. In this respect, the Glossary of Meteorology  provides earth3010012 the following deﬁnitions for climate change: any systematic change in the long-term statis- Academic Editor: Charles Jones tics of climate elements (such as temperature, pressure, or winds) sustained over several decades or longer. Climate change is considered responsible for extreme weather events Received: 30 November 2021 Accepted: 31 January 2022 (tornadoes, high winds, hailstorms, ﬂash ﬂoods) due to natural external or anthropogenic Published: 3 February 2022 forcing . Severe convective weather events trigger or have the potential to trigger ﬂash ﬂoods, landslides and potential hazards. The damages associated with extreme weather Publisher’s Note: MDPI stays neutral events are relevant in terms of human and social costs; between 1980 and 2019, the Euro- with regard to jurisdictional claims in pean Environment Agency (EAA) member countries suffered economic losses totaling an published maps and institutional afﬁl- estimated USD 500 billion . In Italy, the situation is made worse by small catchments iations. along coastlines and by the Alps and Apennines Mountain chains. This paper proposes an overview of radar applications, potential uses and a system for the extraction of elaborated weather radar data. Data are based on open-source software, Copyright: © 2022 by the authors. useful for producing new thematic maps, to reconstruct historical data related to a particular Licensee MDPI, Basel, Switzerland. event. The project provides access to information to allow risk assessment. This project is This article is an open access article addressed to administrators, institutional users and citizens. distributed under the terms and This paper is organised as follow: conditions of the Creative Commons Section 1 provides a brief introduction to the theoretical background of Weather radar; Attribution (CC BY) license (https:// Section 2 shows the potential use of weather radar resources in various applications such creativecommons.org/licenses/by/ as military, nautical, aviation, marine, meteorology, biology and weather surveillance; 4.0/). Earth 2022, 3, 157–171. https://doi.org/10.3390/earth3010012 https://www.mdpi.com/journal/earth Earth 2022, 3, FOR PEER REVIEW 2 Earth 2022, 3 158 • Section 2 shows the potential use of weather radar resources in various applications such as military, nautical, aviation, marine, meteorology, biology and weather sur- Section 3 describes the hydrologic applications of weather radar with a focus on veillance; catastrophic impacts caused by ﬂoods; • Section 3 describes the hydrologic applications of weather radar with a focus on Section 4 summarizes the recent advanced worldwide applications and report the catastrophic impacts caused by floods; result of the Italian case history and, in detail, the Puglia Region case study with a • Section 4 summarizes the recent advanced worldwide applications and report the brief discussion; result of the Italian case history and, in detail, the Puglia Region case study with a Section 5 reports conclusions and opportunities for future work. brief discussion; • Section 5 reports conclusions and opportunities for future work. Theoretical Background Theoretical Background The history of Weather Radar (abbreviation of RAdio Detecting And Ranging) begins during World War II, when military radar operators noticed extraneous echoes showing up The history of Weather Radar (abbreviation of RAdio Detecting And Ranging) begins on their display. David Atlas was one of the pioneers of radar meteorology, along with John during World War II, when military radar operators noticed extraneous echoes showing Stewart Marshall, Walter Palmer and Richard Doviak . After the second world war, John up on their display. David Atlas was one of the pioneers of radar meteorology, along with Stewart John StMarshall ewart Mar and shaW ll, W alter altPalmer er Palmer investigated and Richard Dov Z-R relationships iak . Afte in r t the he Stor secon my d world Weather war, John Stewart Marshall and Walter Palmer investigated Z-R relationships in the Group. Furthermore, Richard Doviak was the father of Doppler radar at the National Stormy Weather Group. Furthermore, Richard Doviak was the father of Doppler radar at Oceanic and Atmospheric Administration (NOAA) for storm forecasting. These are just the National Oceanic and Atmospheric Administration (NOAA) for storm forecasting. some of the milestones in the origins of weather radar. These are just some of the milestones in the origins of weather radar. Weather radars send pulses of electromagnetic energy into the atmosphere, a mi- Weather radars send pulses of electromagnetic energy into the atmosphere, a micro- crosecond of long microwave radiation to identify the presence of hydrometeors . Hy- second of long microwave radiation to identify the presence of hydrometeors . Hydro- drometeors from the Greek hýdor-met ¯ éor ¯ os, which means literally “water that is high in the meteors from the Greek hýdōr-metéōros, which means literally “water that is high in the sky” represent all phenomena of condensation and precipitation of atmospheric humidity sky” represent all phenomena of condensation and precipitation of atmospheric humidity in liquid or solid particles. When the pulses strike an object such as rain, hail, or snow, in liquid or solid particles. When the pulses strike an object such as rain, hail, or snow, Rayleigh scattering occurs and part of the energy is reﬂected back to the radar receiver Rayleigh scattering occurs and part of the energy is reflected back to the radar receiver (Figure 1) . Rayleigh scattering takes place when the wavelengths are greater than the (Figure 1) . Rayleigh scattering takes place when the wavelengths are greater than the diameters of the hydrometeor particles. Different wavelengths identify particles of different diameters of the hydrometeor particles. Different wavelengths identify particles of differ- sizes. Rayleigh scattering starts to become invalid when the hydrometeor diameters are ent sizes. Rayleigh scattering starts to become invalid when the hydrometeor diameters large (e.g., 2–3 cm) compared to the wavelength of the X-band. The X-band has the shortest are large (e.g., 2–3 cm) compared to the wavelength of the X-band. The X-band has the wavelength compared to the S and C bands. shortest wavelength compared to the S and C bands. Figure 1. Weather radar principle of function. Weather radar image reworked from source . Earth 2022, 3 159 Weather radar works in three different frequency bands: S, C and X. S-band radar has a longer wavelength (8–15 cm) and can provide rain detection up to 300 km. C-band radar is medium-range (4–8 cm) and measures up to 200 km, and ﬁnally, the X-band radar is the smallest wavelength (2.5–4 cm). These waves are well-suited for measuring up to a range of 50 km . Further, L-band radar (15–30 cm), with a frequency of 1–2 GHz, is used for clear air turbulence studies, and K-band radar (0.75–1.2 or 1.7–2.5 cm), with a frequency of 27–40 and 12–18 GHz, is similar to the X-band, but more sensitive when compared to them. For example, in heavy rain, due to radar signal attenuation in the X-Band, the reﬂectivity information can be completely hidden from radar scans . This does not happen for K-Bands. Weather radars have ﬁve components: transmitter, antenna, radar processor, receiver, display system. The transmitter generates electromagnetic pulses and the antenna sends pulses into the atmosphere and receives the reﬂected pulses. The antenna dish can rotate 360 degrees horizontally and scan the atmosphere volume using different elevation angles. The radar processor analyses the received data, and the receiver identiﬁes the signal and ampliﬁes the weak signals received. Finally, the data system displays the radar data to their viewers. The precipitation estimate can be driven from different measurement techniques, such as weather radar networks, rain gauge networks and meteorological satellites. The weather radar equation is founded on the basic principles of radar, on the power of transmission, propagation and reception of echo signals: 2 2 P G l s P = (1) (4p) r where P is the received power, P is the transmitted power, G is the antenna gain, s is r t the radar cross-section and l is the transmitter ’s wavelength . The weather radar does not measure rainfall directly but instead uses an algorithm to estimate rainfall from radar observation [10,11]. The radar calculates rainfall intensities, R (mm h ), from the observed 6 3 radar reﬂectivity, Z (mm m ), in single-polarized radar . The rainfall intensity and reﬂectivity are related by a power law . The coefﬁcient of the power law relationship is required to transform reﬂectivity to rainfall rate . Dual-polarized radar produces both horizontal and vertical electromagnetic waves to detect the shapes, size, density and distribution of water droplets in the atmosphere . The radar reﬂectivity Z product displays echo intensity measured in decibels (dBZ). Depending on the software system or user preference, the colors display the different echo intensities, from very weak to very strong hydrometeors. For example, the green– light-blue is related to light rain precipitation when the dBZ value touches 20. The yellow (approximately 35 dBZ value) shows moderate precipitation, while red (approximately 50 dBZ value) is for heavy precipitation. The higher radar reﬂectivity is related to hailstones mixed in with the liquid hydrometeors (approximately 65 dBZ value). The rain gauge network measures the accumulated rainfall as a function of time. This technique uses a point measurement with a temporal resolution of 1 min–1 h. The instruments count drop measure differently to disdrometers, which measure the statistical distribution of drop size . Finally, meteorological satellites for rainfall estimation were developed to strengthen hydrological models and weather forecasting . Rainfall estimates from the radar can also be combined with estimates from meteorological satellites, potentially strengthening the reliability of hydrological models and weather forecasts. There are numerous sources of errors that affect the weather radar measurement. The errors are due to hardware error, radar beam geometry, scan strategy, the distance from the radar site, echoes from the non-meteorological target, orographic obstacles, attenuation signal and anomalous propagation of the radar beam [8,17,18]. It is important to be familiar with the errors of the radar measurement and the processing complexity. Earth 2022, 3 160 2. Potential Use of Weather Radar Resources Radar is used in various applications, such as military, nautical, aviation, marine, meteorology, biology and weather surveillance. Weather radars are essentially employed for measurement and forecasting atmospheric phenomena. The drastic increase of extreme weather events has led to increased frequency and severity of ﬂood events. Hus et al.  developed an automatically combined ground weather radar with images in real-time for ﬂood monitoring. Closed-circuit television systems were combined with automatically combined ground weather radar, providing information regarding the water level in ﬂood monitoring. This system allows one to make a quick evacuation decision to reduce the adverse effect. Rapant et al.  presented a method with a different technique to obtain a dynamic pluvial ﬂash ﬂooding hazard forecast. This approach uses weather radar data to carry information on the current precipitation distribution, watershed and drainage network. In more detail, the 2D weather radar data are transformed into 1D signals related to the section of watercourses. If the system detects danger, it sends a possible warning of ﬂash ﬂooding to neighbouring municipalities. The experimental result showed a substantial reduction in false alarms against imminent ﬂash ﬂoods, including the saturation indicator. Another application of weather radar is wildﬁre monitoring and ﬁlling in the knowl- edge gaps regarding dangerous ﬁre conditions. Wildﬁres constitute considerable nat- ural hazards, and Doppler radar can be used to identify the ﬁre behaviour of wildﬁre plumes . Maki et al.  analyse 3D weather radar data from volcanic eruption clouds to under- stand the ash-fall transportation. It is possible to construct a radar reﬂectivity microphysical model to quantify the eruption regimes . Building off previous experimental observa- tions, it is possible to achieve the classiﬁcation of eruption regime and volcanic ash category and estimate ash concentration. Voormansik et al.  presented a method to detect thunderstorm hail and lightning with C-band dual-polarization Doppler radar. These convective storms, associated with lightning and hail, cause ﬁnancial losses and signiﬁcant damage to infrastructure. The radar lightning estimation identiﬁes which cells are rapidly growing and approaching the measures likely to produce lightning in the foreseeable future. The study is based on four years of the summer periods in Estonia; it was found that 33.9% of the identiﬁed cells produced lightning and 25.9% produced hail. Weather radar can be used in biology to study bird migration at temporal end spatial scales. The detailed information on the areal movements of an organism can be explored to create interpreting regional-scale migration patterns and information in the landscape and aerial environment . Polarimetric radar observation is used for bird detection. Furthermore, recent studies have been conducted using weather radars to explore the harmful inﬂuence of artiﬁcial light on migratory bird populations . Using multi-year weather radar measurement, it has been shown that birds are attracted to artiﬁcial light while in ﬂight, and this inhibits habitat selection. The choice of high-quality stopover habitat is crucial for the conservation of bird populations. Another study evaluated the spatial and temporal variation in nocturnal migration patterns that are affected by winds. This ﬁrst continental-scale study used 70 weather radar stations in Europe for investigating the ecosystem consequences of large-scale bird movements . Several studies report the impressive decline of bird population that deﬁned the global biodiversity crisis . Current weather radar literature research has investigated the offshore wind ﬂuctu- ations for optimizing the administration of wind farms in real-time. Trombe et al.  present an automated decision-support system based on the collection of meteorological observations at high spatio-temporal resolutions to provide relevant inputs to prediction systems. Also of relevance is the interference of echoes from the wind farm to weather radar . The use of weather radar also concerns human health. Using machine learning methods and Next Generation Weather Radar data, it is possible to estimate daily pollen Earth 2022, 3 161 over a 300 km 300 km region at a resolution of 0.5 km 0.5 km . The models are developed using radar measurements of reﬂectivity, direction and speed of the wind, line of sight Doppler velocity and spectral width at the lowest two elevations to estimate the daily pollen dispersal. The study results provide pollen alerts and predict allergic pollen of different species. Recent weather radar literature has investigated the Radar Simulator, which can reproduce realistic weather radar measurement [32–34]. The simulation starts from a known meteorological scenario to build processing algorithms. The increasing use of weather radar generates a large amount of data transmission and storage. Zeng et al.  present a weather radar lossless compression approach called spatial and temporal prediction compression (STPC). Regarding interference in weather radar, the growth of wireless telecommunication systems represents the primary concern with regards to guaranteeing radar data quality. The electromagnetic interference negatively affects the quantitative precipitation estimation and can lead to a biased hydrometeor classiﬁcation . Oh et al.  proposed a clutter elimination algorithm for the non-precipitation echo of radar data, such as anomalous propagation and interference, biological target and sea clutter. Concerning airborne weather radar, the literature is extensive. Airborne weather radar detect potential weather hazards during ﬂight. Li et al.  introduce a microphysics- based simulator applied in different weather scenarios to address the theoretical basis and uncertainties of hydrometeor scattering. Nepal et al.  present a radar implementation on a low-cost weather radar platform for multi-mission applications. Nekrasov et al.  developed a conceptual approach for measuring near-surface wind vectors with airborne weather radar to predict the future sea surface conditions and weather patterns. 3. Hydrologic Applications of Weather Radar Surface precipitation measurements are extremely important in hydrology, climatol- ogy and meteorology studies. These data can be improved by using weather radar and conventional rain and snow gauges. Recent advances in digital radar data management make it possible to provide high-resolution quantitative precipitation information (QPI) for a wide range of hydro- logical applications . With the progress of Geographical Information System (GIS) technology, radar-based quantitative precipitation estimates (QPE) have enabled routine high-resolution hydrologic modeling worldwide . Recent progress and changes in weather radar hydrologic applications make its use a crucial tool for water resource man- agement. The main topics of growth concerning weather radar applied to hydrology related to: (i) radar QPE [42–46], (ii) multi-radar and multi-sensor precipitation analy- sis [47–49], (iii) hydrologic modelling [50–52], (iv) urban hydrologic and hydraulic appli- cations [48,53–56], (v) precipitation frequency analysis [57,58], (vi) hydrometeorological process studies [59,60], (vii) precipitation nowcasting, forecasting , (viii) hydrometeoro- logical applications . Floods Forecasting Catastrophic impacts caused by ﬂoods worldwide will tend to grow due to more frequent climate changes in the next few years . A ﬂood is an excess of water that inun- dates usually dry land, with tragic effects. The factors responsible for ﬂooding include the greenhouse effect, seismic and neotectonics activities, excessive development, soil erosion, damming of rivers, deforestation, riverbed aggradation, subsidence and compaction of sediments, inadequate sediment accumulation and local relative sea-level rise . The negative impact of ﬂooding includes economic damage to structures, roadways and bridges, and especially life loss [65–67]. An essential component of ﬂood management is implementing and improving ﬂood forecasting and warning systems. Earth 2022, 3 162 Over the past years, institutions have adopted different data collection systems for supporting ﬂood management, including rainfall gauges, hydrological stations, humidity sensors, and weather data. Unlike in situ sensors capable of monitoring only a limited region, weather radar is remote sensing-based equipment that measures the volume of rainfall over an entire area instead of measuring a single point. Recently, several countries have investigated using weather radar precipitation to improve situation awareness in a disaster and early warnings (e.g., ﬂoods). In a case study carried out in São Paulo, Brazil, in the period 2017–2018, crowdsensing and weather radar data together helped generate high-quality information at more satisfactory spatial and temporal resolutions to improve the decision-making related to weather-related disaster events. Rainfall data provided by two weather radars located in the city were used to validate ﬂooded areas identiﬁed by volunteered information. Moreover, a clustering approach identiﬁed those ﬂooded areas, which may support more informative decisions in ﬂood management. Based on the soil saturation, the physical-geographical characteristics of an area and QPEs and forecasts (QPFs) is possible to calculate a Flash Flood Indicator (FFI). This system is used at the Czech Hydrometeorological Institute to evaluate the risk of ﬂash ﬂoods resulting from torrential rainfall events over the whole Czech Republic. To improve the accuracy of ﬂash ﬂood forecasting, accurate calculations of QPEs and QPFs are required . Radar data were also used in 2021 to test a new method in the Czech Republic to forecast the ﬂash ﬂooding hazard usually occurring in very small, typically ungauged, watersheds using raw weather radar data and watercourse network. The developed method can provide a map of the ﬂash ﬂooding hazard distribution on the watercourse sections. This result allows an evaluation of the identiﬁed hazard and a risk estimation for inhabitants of the area, with a system of alerts for municipalities . In Taiwan, taking advantage of a dense network of surveillance cameras installed in the city, an automatic ground weather radar (ARMT) and a closed-circuit television system were combined to develop images for real-time ﬂood monitoring. The system integrates real-time ground radar echo images and automatically estimates a rainfall hotspot according to the cloud intensity, providing real-time warning information. The ARMT showed reliability between 83 and 92% using historical data input, while with real-time data, reliability slightly decreased from 79 to 93% . 4. Overview of Recent Advanced Worldwide Applications There are many weather radar applications in multiple areas. Table 1 reports the most signiﬁcant studies, with their reference, for applications that we consider most rep- resentative, with a summary regarding the principal aims. The applications are divided into two main categories: natural disasters (ﬂood events, wildﬁres, volcanic ash) and the enhancement of ecosystem services (airborne, urban hydrology, bird migration), which indicate the many advantages to humans furnished by the natural environment and from healthy ecosystems, as popularized in the Millennium Ecosystem Assessment . Table 1. Recent advanced worldwide applications. Year of Application Principal Aim Application Reference Publication Flood events Substantial reduction in false alarms 2021  Understanding of volcanic eruption column dynamics and horizontal Volcanic ash 2021  Natural ash-fall transportation with three-dimensional analyses disasters Wildﬁres A better understanding of ﬁre behaviour and ﬁre atmosphere interaction 2019  Reduced ﬁnancial losses and signiﬁcant damage to infrastructure products Thunderstorm hail and 2017  lightning by thunderstorm hail and lightning Detailed information on bird movements in the landscape and aerial Bird migration 2020  environment Wind farms Detecting interference from wind farm echoes 2019  Enhancement of ecosystem Pollen Providing pollen alerts and predicting allergic pollen of different species 2019  services concentration Urban hydrology Improve the applicability of radar and rain gauge rainfall estimates 2019  Airborne Radar implementation low-cost for multi-mission applications 2017  Earth 2022, 3 163 4.1. Italian Case Studies The Italian Civil Protection Department is designed to monitor weather-hydrological and volcanic ash fallout. The Department has the fundamental role of gathering and coordinating the national resources necessary and ensuring assistance to the population in case of emergency. The Italian national weather radar network is coordinated by the Civil Protection De- partment (DPC), in collaboration with the Air Trafﬁc Control service (ENAV), the Weather Service of the Air Force (CNMCA), regional authorities and research centers. The Ital- ian radar network includes both C-band radars and ﬁve dual-polarized X-band radars, deployed throughout the country . The Civil Protection Department provides a plat- form called Radar-DPC  that allows seeing both ongoing phenomena and those of the previous six days on a national scale. This platform provides basic products, previously processed data from ground stations networks, satellite and lightning . We focused on Vertical Maximum Intensity (VMI), which represents the maximum reﬂectivity value present in the vertical column above each point. The VMI can be used for general monitor- ing, as it allows one to distinguish very well the areas in which signiﬁcant phenomena are underway and to classify them according to their type (fronts, convection systems, etc.). The reﬂectivity dBZ is a quantity directly related to the cloud’s density, and therefore to the water content. Reﬂectivity values higher than 10 dBZ usually (but not necessarily) indi- cate the presence of precipitation on the ground (values lower are typically not displayed to avoid disturbances and/or residues of unwanted clutter, increasing the data quality threshold). Reﬂectivity values above 40 dBZ are considered very intense, and when value of 45 dBZ are reached and exceeded, there is a high probability of extreme phenomena dangerous for people, animals or things, and these values typically indicate the presence of convective storms with high rainfall rates. 4.2. The Puglia Region Case Study The Puglia region is located in southern Italy and shows landform depending on struc- tural and geolithological factors. The prevailing morphologic characteristic of the Apulian region is the presence of plains and hills with highly diversiﬁed climatic conditions . It is possible to discern the area in ﬁve main physiographic areas: Daunian Appennines, Gargano, Tavoliere, high and low Murge and Salento. The region is segmented by a NW-SE trending normal fault, divided into three structural blocks of Mesozoic limestone with different degrees of uplift: Gargano, Murge and Salento. Daunian Appennines, the eastern boundary of the southern Appenninic chain, is characterized by hilly landscapes, with the highest peak being approximately 1150 m. The Tavoliere, the northern part of the Bradanic Trough domain, is the wildest alluvial plain in south Italy (Figure 2). In recent years, there have been more and more frequent phenomena of soil instability related to the evolution of the slopes, the collapses along the high coast and the sinking of underground cavities as well as ﬂoods for which, given the heterogeneity of the territory, it is always difﬁcult to correctly identify the areas involved as it is necessary to process the data further. So, considering the particular characteristics of the territory and above all its heterogeneity, in order to insert a further phase of data processing that could also include these geomorphological aspects of the territory, a system for data extraction and use of weather data in real-time has been implemented, followed by a speciﬁc processing workﬂow. This application has been tested at a regional scale. The technique, called data extraction, refers to the process, put into practice by a user, of extracting and retrieving data from a data source to perform further processing or storage. Earth 2022, 3, FOR PEER REVIEW 8 The Puglia region is located in southern Italy and shows landform depending on structural and geolithological factors. The prevailing morphologic characteristic of the Apulian region is the presence of plains and hills with highly diversified climatic condi- tions . It is possible to discern the area in five main physiographic areas: Daunian Ap- pennines, Gargano, Tavoliere, high and low Murge and Salento. The region is segmented by a NW-SE trending normal fault, divided into three structural blocks of Mesozoic lime- stone with different degrees of uplift: Gargano, Murge and Salento. Daunian Appennines, the eastern boundary of the southern Appenninic chain, is characterized by hilly land- Earth 2022, 3 164 scapes, with the highest peak being approximately 1150 m. The Tavoliere, the northern part of the Bradanic Trough domain, is the wildest alluvial plain in south Italy (Figure 2). Earth 2022, 3, FOR PEER REVIEW 9 (a) (b) Figure 2. (a) Administrative limits of the Puglia region (Base map from Bing Maps). (b) Geograph- Figure 2. (a) Administrative limits of the Puglia region (Base map from Bing Maps). (b) Geographical ical subdivisions based on the morphology of the territory of the Puglia region (Base map from SIT subdivisions based on the morphology of the territory of the Puglia region (Base map from SIT Puglia ). Puglia ). In recent years, there have been more and more frequent phenomena of soil instabil- ity related to the evolution of the slopes, the collapses along the high coast and the sinking of underground cavities as well as floods for which, given the heterogeneity of the terri- tory, it is always difficult to correctly identify the areas involved as it is necessary to pro- cess the data further. So, considering the particular characteristics of the territory and above all its heterogeneity, in order to insert a further phase of data processing that could also include these geomorphological aspects of the territory, a system for data extraction and use of weather data in real-time has been implemented, followed by a specific pro- cessing workflow. This application has been tested at a regional scale. The technique, called data extraction, refers to the process, put into practice by a user, of extracting and retrieving data from a data source to perform further processing or storage. The created real-time radar weather data system extracts the weather-climatic data from DPC. Its data structures are implemented through relational databases and are based on a complex system architecture ; the services  are provided through an open- source server for sharing geospatial data designed for interoperability . These services are provided according to the standards . Consultation of the DPC platform  takes place through interactive maps and al- lows access to weather-radar products compatible with international standards to ex- change geo-related data. The spread of web databases has made numerous data accessible to users . As mentioned, the DPC radar platform allows the visualization of radar weather data over the previous seven days; on this assumption, a specific data extraction procedure has been implemented based on the open-source scripting language python , as shown below. Earth 2022, 3 165 The created real-time radar weather data system extracts the weather-climatic data from DPC. Its data structures are implemented through relational databases and are based on a complex system architecture ; the services  are provided through an open- source server for sharing geospatial data designed for interoperability . These services are provided according to the standards . Consultation of the DPC platform  takes place through interactive maps and allows access to weather-radar products compatible with international standards to exchange geo-related data. The spread of web databases has made numerous data accessible to users . As mentioned, the DPC radar platform allows the visualization of radar weather data over the previous seven days; on this assumption, a speciﬁc data extraction procedure has been implemented based on the open-source scripting language python , as shown below. Python was chosen because it is considered very robust and highly versatile and has a consistent number of python bindings , which are packages and extensions tools for programming and manipulating the Geospatial Data Abstraction Library (GDAL) . First of all, it is important to be aware of the territory extension to extrapolate the data, the resolution with which these are provided and, consequently, the coordinates of the bounding box based on information . The data extraction system has been implemented through python scripts based mainly on the ogr, gdal, pyproj, NumPy and PIL libraries and allows the geo-referencing of the information, which can be viewed on the radar platform available to the user. Below we report the rules used for data extraction and georeferencing in pseudocode format; this is a way of expressing an algorithm without conforming to speciﬁc syntax rules and is an efﬁcient way to communicate ideas and concepts (note that indentation represents nested code). The purpose of inserting this pseudocode is to make people understand the operating principles of the extraction technique implemented in order to be able to replicate it. START defining bounding box defining time of the query executing query via urllib.urlretrieve method saving the tiles provided as the response from DPC servers load the image tiles in the memory transform the images into NumPy array for each X Y dimension of the NumPy array do this: if parameters of X and value of Y is more than 250 for component Red and equal to 0 for the components Green and Blue of the colours: save these coordinates in another NumPy array open this second array of coordinates for each couple of coordinates memorised and considering the X and Y coordinates of the bounding box: add a line to a file with attributes coordinate_X and coordinate_Y save the file END (An array is a grid of values and it contains information about the raw data, how to locate an element and how to interpret an element. It has a grid of elements that can be indexed in various ways.) The DPC servers are queried by executing the script, the data of interest selected and the radar weather data tiles for the area of interest saved in the PC. The areas with powerful phenomena in progress are then selected, processed and classiﬁed as fronts or convection systems, and their coordinates are saved. Below are the reported results of n; two tests were performed on the whole Puglia territory. It is possible to view the perimeter of the Puglia Region, the coverage map of the radar services provided with the VMI parameter and the result of the script execution. The red dots represent points of a shapeﬁle imported into a Geographical Information System (GIS) of very heavy rain cores present at that time (Figure 3). Earth 2022, 3, FOR PEER REVIEW 11 Below are the reported results of n; two tests were performed on the whole Puglia territory. It is possible to view the perimeter of the Puglia Region, the coverage map of the radar services provided with the VMI parameter and the result of the script execution. Earth 2022, 3 166 The red dots represent points of a shapefile imported into a Geographical Information System (GIS) of very heavy rain cores present at that time (Figure 3). (a) (b) (c) (d) Legend Colour bar related to the intensity of the phenomena in progress from DPC. Earth 2022, 3, FOR PEER REVIEW 12 weak moderate intense strong very strong Vertical Maximum Intensity Points obtained after executing the script. Administrative limits of Puglia Region. Figure 3. Example of two tests carried out: (a) and (c) are data from DPC servers, respectively, on 4 Figure 3. Example of two tests carried out: (a) and (c) are data from DPC servers, respectively, on July 2020 and at 21:00 and 14 October 2021 at 17:00, (b) and (d) are the same data with the point 4 July 2020 and at 21:00 and 14 October 2021 at 17:00, (b) and (d) are the same data with the point obtained after running the script and imported into a GIS system. obtained after running the script and imported into a GIS system. Figure 3 shows how the data extraction technique outputs a reliable result. Other tests have been carried out, but the results have always only been satisfactory. The points with a red circle (on the right of the figures), obtained from data on the left of the figure, have been imported into a GIS system and superimposed on the official data of the DPC. Therefore, we can assert that the extraction technique works well as we can now use the data provided according to our needs. Moreover, this can be done every time the DPC data is updated, i.e., every 15 min. The use of the processed data, made possible by implementing this ICT tool through the scripting language in python, allows any user to query the databases and obtain solid rainfall values related to a specific territory point in a given moment. Following the query, the result could also be provided in the form of a summary report or information sheet containing a series of useful information relating to the calculation and validation process already carried out by the DPC, which is very useful for understanding the phenomena in progress and those that have previously taken place, and also for insurance purposes. Pursuing this goal, where the areas of interest and the reasons require it, we could apply geostatistical techniques and algorithms to obtain more precise results according to the peculiarities of the considered area. This result could also be pursued by integrating the data with other available data sources, such as the weather stations of the Regional Agro- meteorological Service (ARIF)  and those provided by the Regional Agency for the Prevention and Protection of the Environment (ARPA) . Regarding this, it seems rele- vant to us to implement an Inverse Distance Weighted (IDW) based spatialization algo- rithm contemplating other factors such as the altitudes, the distance from the sea and riv- ers (the five largest in Puglia) to obtain measurements more accurate by weighing the recorded values with the mutual distances. The IDW algorithm represents a deterministic spatial interpolation method (it pro- duces the same results if the input data are the same) proposed by Shepard . It is one of the more popular methods used by hydrologists and earth scientists, it is easy to im- plement, and most specialized software integrates it among the basic options. It is applied to estimate unknown values based on known values, assuming closer values are more related than further values. It uses only distance to make estimates. For this reason, we believe it necessary to implement it by integrating the calculating of the weighted values of other variables. Finally, working exclusively with open-source software, the goal is to develop in- teroperability services and tools between databases. This makes the maximum diffusion and use of high value-added services for the Public Administration possible, increasing the potential of the interoperability standards that can be implemented, for which the Ital- ian Agency has recently adopted the Guidelines . 5. Conclusions and Future Challenges This study proposes an overview of possibilities, challenges and advanced applica- tions in weather radar. Weather radar measurements have enormous prospective in vari- ous applications, in particular hydrological ones. As a result of straightforward rework- ing, the weather radar data of the Department of Civil Protection can be used and en- hanced in the different regional territories for different purposes. The approach identified and described in this report allows the extraction of weather radar data. These data evolve into information and then into knowledge. This is easily divulged for the provision of a series of predictive services, emergency management and the assessment of damage re- sulting from extreme events. In addition, python proves to be a versatile and powerful Earth 2022, 3 167 Figure 3 shows how the data extraction technique outputs a reliable result. Other tests have been carried out, but the results have always only been satisfactory. The points with a red circle (on the right of the ﬁgures), obtained from data on the left of the ﬁgure, have been imported into a GIS system and superimposed on the ofﬁcial data of the DPC. Therefore, we can assert that the extraction technique works well as we can now use the data provided according to our needs. Moreover, this can be done every time the DPC data is updated, i.e., every 15 min. The use of the processed data, made possible by implementing this ICT tool through the scripting language in python, allows any user to query the databases and obtain solid rainfall values related to a speciﬁc territory point in a given moment. Following the query, the result could also be provided in the form of a summary report or information sheet containing a series of useful information relating to the calculation and validation process already carried out by the DPC, which is very useful for understanding the phenomena in progress and those that have previously taken place, and also for insurance purposes. Pursuing this goal, where the areas of interest and the reasons require it, we could apply geostatistical techniques and algorithms to obtain more precise results according to the peculiarities of the considered area. This result could also be pursued by integrating the data with other available data sources, such as the weather stations of the Regional Agrometeorological Service (ARIF)  and those provided by the Regional Agency for the Prevention and Protection of the Environment (ARPA) . Regarding this, it seems relevant to us to implement an Inverse Distance Weighted (IDW) based spatialization algorithm contemplating other factors such as the altitudes, the distance from the sea and rivers (the ﬁve largest in Puglia) to obtain measurements more accurate by weighing the recorded values with the mutual distances. The IDW algorithm represents a deterministic spatial interpolation method (it pro- duces the same results if the input data are the same) proposed by Shepard . It is one of the more popular methods used by hydrologists and earth scientists, it is easy to imple- ment, and most specialized software integrates it among the basic options. It is applied to estimate unknown values based on known values, assuming closer values are more related than further values. It uses only distance to make estimates. For this reason, we believe it necessary to implement it by integrating the calculating of the weighted values of other variables. Finally, working exclusively with open-source software, the goal is to develop interop- erability services and tools between databases. This makes the maximum diffusion and use of high value-added services for the Public Administration possible, increasing the potential of the interoperability standards that can be implemented, for which the Italian Agency has recently adopted the Guidelines . 5. Conclusions and Future Challenges This study proposes an overview of possibilities, challenges and advanced applications in weather radar. Weather radar measurements have enormous prospective in various applications, in particular hydrological ones. As a result of straightforward reworking, the weather radar data of the Department of Civil Protection can be used and enhanced in the different regional territories for different purposes. The approach identiﬁed and described in this report allows the extraction of weather radar data. These data evolve into information and then into knowledge. This is easily divulged for the provision of a series of predictive services, emergency management and the assessment of damage resulting from extreme events. In addition, python proves to be a versatile and powerful tool for managing data with a strong space-time connotation. Because of this, it was possible to develop a new ICT tool that was able to use information already available but in a different way from how it is currently used, for example, for insurance purposes. This can ensure fair rewards for citizens affected by extreme events and identify the illegal conduct of undue claims. Earth 2022, 3 168 Author Contributions: Conceptualization, M.S.B., C.M. and C.C.; methodology, M.S.B. and C.M.; software, C.M.; validation, M.S.B., C.M. and C.C.; formal analysis, M.S.B., C.M. and C.C.; investi- gation, M.S.B.; resources, C.M. and V.F.U.; data curation, M.S.B. and C.C.; writing—original draft preparation, M.S.B. and C.C.; writing—review and editing, M.S.B., C.M. and C.C.; visualization, M.S.B., C.M. and C.C.; supervision, C.M.; project administration, V.F.U.; funding acquisition, V.F.U. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by Omninext Group. 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Multidisciplinary Digital Publishing Institute
The Use of Weather Radar Data: Possibilities, Challenges and Advanced Applications
Binetti, Maria Silvia
Uricchio, Vito Felice
, Volume 3 (1) –
Feb 3, 2022
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