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Climate change has led to an increase in extreme weather conditions such as thunderstorms and lightning events (Barnejee et al. 2014; Reeve & Toumi 1999). Such extreme weather patterns have been shown to cause severe socioeconomic effects (Ding et al. 2015; Karmakar et al. 2016) and destruction to human life and property (Yair 2018).
Consequently, research into accurate models of predicting weather patterns has risen over the years (Harats et al. 2010; Heckman & Liu 2010; Schulze 2007; Tyagi 2000). Such research has changed weather prediction scope from old-fashioned observations to reliable and accurate numerical models. These models can predict thunderstorms (Goodman 1990; Haklander & Van delden, 2003; Kunz 2007; Litta 2013), severe winds (Uman 2001), tornadoes (Darkow 1969), flashfloods (Harats et al. 2010), monsoons (Dudhia et al. 1989), precipitation and lightning (Haby n.d.; Landman et al. 2012; Lynn & Yair 2010; Mazany et al. 2002).
These numerical weather prediction (NWP) models rely on parameters and physical processes like incoming solar radiation, reflection/ absorption from clouds, turbulence, reflection/absorption at the Earth’s surface, precipitation, water/snow/ ice cover, the topography and surface roughness, etc. (Ahrens 2009; Delsole et al. 2012).
Such models have been reliably used by organizations such as the National Centre for Atmospheric Research (NCAR) and National Centers for Environmental Prediction (NCEP), and National Aeronautics and Space Administration (NASA) (Chen et al. 1997; Hodges et al. 2011; McCaul et al. 2011; NCAR 2015).
NCAR uses numerical models such as the Model for Prediction Across Scales (MPAS) to simulate local and global weather patterns simultaneously. These simulations are provided at different resolutions and have been applied to predict hurricanes. This organization also runs the Weather and Forecasting Model (WRF), which provides data about current and future events (Manning 2007; Skamarock et al. 2005; Wang 2012). These models provide adequate time for people to prepare against severe weather conditions’ adverse effects, reducing mortality and property damage.
The prediction of weather is also essential for air and space travel. NASA utilizes space weather forecasts to predict large solar flares, which could release harmful radiation. This radiation would be extremely detrimental to astronauts, satellites, and airplane occupants. Similarly, NASA’s remote sensing technologies are applied in predicting meteorological conditions (Goodman 1990; Scott et al. 2017).
The Antarctic Peninsula is located at the most northerly part of Antarctica. Consequently, although temperature within the continent’s interior can reach lows of -70.6 °F (-57 °C), this region’s climate is milder. In the Peninsular, summer is experienced around January, with temperatures as high as 59 °F (15 °C). During most of the year, the temperature is usually around 32 °F (0 °C) (van Lipzig 2008).
The recorded precipitation in the entire Antarctic region is averaged at 166 millimeters per year (King & Turner 1997). The Peninsular region records a higher than average precipitation of around 380 mm to 640 mm annually (Favier et al. 2017). Most of the region’s precipitation falls as snow, with rainfall mainly occurring during the summer and coastal areas (Bromwich et al. 2011). The low temperatures of the region result in low absolute humidity, with the air being generally dry.
Recent studies show that global warming is rapidly changing the topography of Antarctica. In the past fifty years, the Antarctic Peninsula’s west-coast has been one of the most rapidly warming regions of the planet. A 3°C increase in temperature has been recorded in this region. Similarly, the upper ocean temperatures, close to the Peninsular, have experienced a 1°C increase in average temperature since 1955 (Bromwich et al. 2015).
This temperature rise has resulted in the melting of the glaciers that make up the region’s natural topography (Monaghan & Bromwich 2008b). The melting glaciers have caused a rise in sea-levels globally (Bromwich et al. 2007; DeConto & Pollard 2016). A study showed that the amount of ice lost was 43 gigatons per year between 1992 and 2002. Between 2012 and 2017, the estimated ice loss had increased to 220 gigatons annually (Shepherd et al. 2018; Velicogna & Wahr 2006). This changing topography has also resulted in the colonization of plants as the snow cover melts. Additionally, the penguin colonies have changed with the changes in the sea-ice conditions.
Antarctica experiences severe weather conditions like cyclones and thunderstorms (Monaghan & Bromwich 2008a; Powers et al. 2012). These conditions are caused by the atmospheric circulation of heat and moisture over the Antarctic ice sheet (Reijmer et al. 2005; Sanz Rodrigo et al. 2013; Silber et al. 2018; Walden et al. 2003). Climate change has led to an increase in the occurrence of these extreme weather conditions.
A need for more accurate prediction models has led to the adoption of numerical models to predict these changes (Hines et al. 2019; Kennicutt II et al. 2014; Monaghan et al. 2008; Walsh et al. 2018). Additionally, more research is being carried out to analyze the depletion of the ozone layer in the region (Nadzir et al. 2018; Suparta et al. 2013)
The occurrence of lightning events varies globally. This occurrence is influenced by various factors, including the geographic locations of continents and oceans and the atmospheric circulation patterns (Burrows et al. 2005; Guerova et al. 2019). Antarctica is a frigid, windy, and dry region. Consequently, it was expected that thunderstorms would be a rare occurrence in this region, leading to little research (Suparta 2018). However, recent studies have revealed that thunderstorms and lightning events occur in this region (Nor et al. 2020; Yusop et al. 2019). The low humidity experienced in this region leads to dry thunderstorms, which are often accompanied by lightning.
Various techniques, including nowcasting and remote sensing technologies, numerical weather prediction models such as the WRF model, have been utilized to predict lightning (Clouthier-Lopez et al. 2014; Mostajabi et al. 2019; Price 2008). These predictions rely on parameters such as air temperature, Convective available potential energy (CAPE), the K Index, Lifted Index (LI), Total totals, Convective inhibition (CIN), etc. (Barthe et al. 2010; Burrows et al. 2005; Dowdy 2016; Guerova et al. 2019; Mansell et al. 2002).
The calculations for all these indices are based on the atmosphere’s thermodynamics features (Ajilesh et al. 2020). Despite these advances, the accurate prediction of lightning occurrences has been challenging because of its spatial and temporal inconsistency. Recently, Lynn and Yair (2008) and Yair et al. (2010) defined a new index, the Lightning potential index (LPI), which is based on the microphysical characteristics of thunderstorms. This index is more accurate in predicting lightning (Gharaylou et al. 2019).
Although there is some research into lightning events globally, most of it covers regions like Europe, East Asia, Japan, South America, and the United States. There is limited research into lightning occurrences in the polar regions. This scarcity is mostly attributed to a lack of observational lightning data (Yusop et al. 2019). Inadequate research into the Antarctic has resulted in little information about the occurrence and severity of lightning strikes in this region. The accuracy of lightning prediction models in Antarctica, an area with its unique atmospheric environment, has not been studied.
In addition to the human causalities, mortality, and property destruction, lightning strikes in the Antarctic Peninsula result in ionospheric disturbances. These interruptions disrupt radio signals and affect communication (Nor et al. 2020; Suparta & Nor 2017). Predicting lightning occurrences will provide sufficient time for people to prevent huge losses in terms of mortality and property (Kumar et al. 2012; Kunz 2007).
As more research is carried out in Antarctica and more people travel to the continent, accurate prediction models will be essential for the adequate preparation of all parties involved. Similarly, as lightning occurrences interfere with vertical total electron content (VTEC) activity (Suparta & Nor 2017), accurately predicting lightning strikes will allow the identification of solutions to ionospheric disturbances.
This study aimed to determine the accuracy of the LPI index in predicting lightning occurrence in Peninsular Antarctica. First, prediction data was obtained using the WRF model. These forecasts were then compared with the observed lightning information from World Wide Lightning Location Network (WWLLN) using correlation and regression analysis.
To determine the accuracy of the LPI model in predicting lightning occurrence in the Antarctic Peninsula.
The increasing observation of lightning events in the Antarctic Peninsula has garnered interest. This interest was raised, mainly because the poles’ atmospheric conditions were believed to result in little to no lightning events. Since the observation of lightning strikes in this region, there have been studies analyzing the seasonal cloud-to-ground (CG) lightning flash activity (Yusop et al. 2019), short-range weather prediction (Kumar et al. 2012), forecasting model performance (Bromwich et al. 2013), etc.
There is still limited information on the reliability of the suggested LPI model on forecasting lightning strikes in this region. With the data obtained from the study, it will be possible to project thunderstorms and lightning in this region. Such predictions will guide future research, even as more researchers go into the continent of Antarctica to study emerging issues such as ozone depletion and the effects of global warming (Apel et al. 2015; Nadzir et al. 2018; Suparta et al. 2013; Toumi et al. 1996).
The study’s goal was to apply the lightning parameterization options in the LPI model to the WRF system and simulate the region’s lightning occurrence. This data was then compared to observational WWLN data obtained from the closest weather station. The study’s findings will provide essential data vital for understanding the local weather systems in this region.
The findings in this study could also be used to drive meteorological advancements in the Antarctic Peninsula. During the time of the course, there was only one receiver in the region. This station was located in Rothera. The four other receivers used to retrieve the WWLLN data were found in Argentina. This study also aimed to reveal the need for more receivers that can be used to perform more studies for increased data on lightning events in this region.
Lightning is a common occurrence. About 2000 thunderstorms are wandering the Earth’s atmosphere at any given time, with lightning flashing around 100 times every second (Cooray 2015). It was first proposed by Wilson (1916) that thunder clouds have a dipole structure, having both positively and negatively charged regions. He carried out further studies that continued to examine this phenomenon (Wilson 1920).
An extensive study into the variation in electrical fields in New Mexico supported the previous findings (Krehbiel et al. 1979). It was later revealed that thunderstorm clouds are more complicated than dipoles as the main positive charge is generally found at the top of the cloud while the main negative charge is located at the bottom of the cloud (Lang et al. 2004). The presence of a smaller positive charge at the bottom was also discovered, revealing that thunderclouds have tripole structures (Lang et al. 2004).
The central positively charged region of thunderclouds is usually located in heights with a temperature lower than -20°C, while the smaller positive charge is found at temperatures of around 0°C. Meanwhile, the negatively charged region is generally located at heights where the temperature ranges between -10 °C and -20°C (Lang et al. 2004; Price 1993; Stolzenburg et al. 1998; Takahashi 1978).
During the process of electrification, the individual cloud particles are electrified and then separated according to their different polarity in the thunderclouds (Rakov & Uman, 2003). The exact mechanisms that guide this polarity separation were proposed in a study by Takahashi (1984).
A later study by Saunders (2008) provided a detailed description of the charge separation mechanisms in clouds. These mechanisms highlighted in this study include ion charging, inductive charging, drop breakup, ice particle mechanism, graupel/ice-crystal mechanism, and the convection mechanism. Mounting evidence shows that the leading cause of electrification in thunderclouds is the non-inductive collisional ice-graupel mechanism(Rakov & Uman 2003; Gijben 2016). It is non-inductive because it does not require a local electrical field to electrify the thundercloud particles.
In the ice-graupel collision mechanism, electrification of cloud particles occurs when cloud particles, in the form of ice-crystals, and precipitation particles, in the form of graupel, collide. These particles can be distinguished as precipitation particles are more massive and fall at higher speeds (faster than 0.3 ms-1). In contrast, cloud particles are smaller and fall at slower rates (Rakov & Uman 2003). As these sets of particles collide, they transfer electrical current between them, without any influence from the ambient electrical field (Saunders 1993).
Further studies have shown that for significant transfer to occur between these particles, the collision needs to happen in the presence of supercooled water droplets (Rakov & Uman 2003; Takahashi 1978). First, the heavier and larger graupel particles fall rapidly through the supercooled water droplets suspended in the cloud. When the water droplets collide with these ice-crystals or graupel, they freeze onto these larger particles’ surface in a process called rimming.
As the falling graupel particles bump with the ice-crystals (which are suspended), they transfer the charge between them. The polarity and the magnitude of the charge transferred depends on various features such as the cloud water content, droplet sizes, the rate of rime acceleration, the temperature at which this collision occurs (Rakov & Uman 2003).
The polarity of graupel particles can reverse at a temperature called the reversal temperature. At heights where the atmosphere is cooler than the reversal temperature, collisions between ice-crystals and graupel results in the reversal of the charge of the graupel particles to become negatively charged and the ice-crystals become positively charged (Rakov & Uman 2003; Saunders 1993).
In lower heights, where the temperature is higher than the reversal temperature, the reverse is true. The collision of these two particles will result in positively charged graupel particles and negatively charged ice-crystal particles. Although this phenomenon’s exact cause is still debatable, studies have shown that this reversal temperature ranges between -10°C and -20 °C (Gaskell 1981; Rakov & Uman 2003; Saunders 1993; Takahashi 1978; Gijben 2016). Notably, this temperature range is similar to the temperature ranges of the thundercloud’s negatively charged part. cloud
When the charge between oppositely charged particles builds up sufficiently in the thundercloud, an electrical discharge occurs. This discharge is lightning (Cooray 2015). This discharge can occur in various forms: Cloud-to-ground (CG), cloud-to-cloud (intercloud), cloud-to-air (air discharge), or between the oppositely charged sections of the same cloud (intra-cloud). Generally, all the types of cloud lightning, including intercloud, air discharge, and intracloud, are referred to as intra-cloud lightning (Rakov & Uman 2003; Gijben 2016).
CG lightning, also called a ground flash, occurs between the cloud and the Earth’s charged centers. CG is the most studied type of lightning, as it results in property damage and human casualties. Depending on the type of charge brought to the Earth, ground flashes can be classified as positive or negative ground flashes (Cooray 2015).
Around 90% of CG lightning is usually negatively charged and move down from clouds to the ground. On rare occasions, upward negative and positive lightning can transfer from the ground to the clouds, especially from tall buildings and mountain structures (Rakov & Uman 2003).
The detection of lightning in real-time and at great distances is made possible because of the electromagnetic radiation produced during lightning flashes (Price 2008). The electromagnetic radiation emitted from lightning occurs in frequencies ranging from 1Hz to 300MHz (Cooray 2015; Rakov & Uman 2003). Lower frequency waves usually attenuate at a slower rate than high-frequency waves and can consequently be detected at much further distances (Price, 2008).
Lightning discharges generally emit waves of low frequency (LF) or very low frequency (VLF) of between 3 and 300 kHz and very high frequency (VHF) of between 3 and 300 MHz (Betz et al. 2008). Although IC lightning majorly produces relatively small impulses of electromagnetic pulses in the VLF range, CG lightning produces a small number of large amplitude pulses in the VLF range (Betz et al. 2008). Both CG and IC lightning also have many pulses in the VHF range, with the same amplitude (Cummins et al. 2000).
The detection of CG discharges is carried out by targeting the detection and discrimination of VLF/LF waves propagated on the Earth’s surface and ionosphere. The location of CG discharges is identified in terms of their strike-point area using various parameters, including the time-of-arrival (TOA), magnetic direction finding (MDF), or a combination of both (Cummins et al. 2000). Typically, the sensors used in this system are 50- 400 km apart.
One of the techniques used for VLF/LF waves includes gated, Wideband Magnetic Direction-Finders (DFs). This system, adopted in 1976, is an improved DF system that operates on the time domain (targets waves between 1 and 500 kHz). This sensor responds to field waveforms that characterize return strokes in CG flashes (Cummins et al. 2000).
Another method makes use of time-of-arrival (TOA) sensors to locate lightning. This system measures the TOA of radio signals in several synchronized stations. The hyperbola is the constant difference in the arrival between two stations. When comparing several synchronized stations, the intersection of multiple hyperbolas provides an accurate source location (Lewis et al. 1960; Cummins et al. 2000).
Similarly, the Improved Accuracy Using Combined Technology (IMPACT) method is used to detect VLF/LF waves. This method combines TOA and MDF to locate lightning source locations. Using this model produces three estimated parameters, the discharge time, latitude, and longitude. These three models can also detect long-range VLF waves (Cummins et al. 2000; Lee 1989).
The detection of VHF waves has been made possible by using two- or three- dimensional models. These models reconstruct (in two- or three-dimensions) the path taken by the cloud discharge. These ‘lightning mapping’ systems tend to focus on the details of the discharge structure. They do not provide broad information on the discharge’s polarity or magnitude (Cummins et al. 2000).
Another technique used is direction-finding based on VHF Interferometry. This technique allows radio interferometer to measure the elevation angles and azimuth of lightning sources with VHF frequencies (Cummins et al. 2000; Hayenga & Warwick 1981). This system is commercially available for the location of both CG and IC flashes.
There are also TOA methods that operate at VHF. This location method is an extension of the two-dimensional hyperbole intersections used for VLF/LF waves. The NASA Kennedy Space Center recently developed a Lightning Detection and Ranging (LDAR) System, which provides three-dimensional locations of more than a thousand RF pulses that come with a single lightning flash (Cummins et al. 2000; Lennon & Maier 1991).
Additionally, lightning can be detected from space through Earth-orbiting satellites that can catch either the electromagnetic waves or light emitted during a lightning flash (Gijben 2016; Rakov & Uman 2003). Examples of such lightning detectors include the Lightning Imaging System (LIS), which is located on the Tropical Rainfall Measuring Mission (TRMM) system, and NASA’s Optical Transient Detector (OTD) (Gijben 2016).
The prediction of lightning and thunderstorms is becoming increasingly crucial for reducing human casualties and property destruction. However, forecasting thunderstorms is still challenging due to the variability in the processes involved in their formation and their small temporal and spatial scales (Blyth et al. 2001; Gijben 2016; Huntsville et al. 2001). The three main requirements for a thunderstorm to occur are a trigger mechanism, moisture, and instability. The kinetic and thermodynamic methods for predicting thunderstorms analyze these three (Kunz 2007).
Predicting lightning in a thunderstorm is even more challenging as some of the processes involved in thunderclouds’ electrification are still poorly understood. This challenge has led to the development of various techniques for predicting lightning (Dementyeva et al. 2015; Heath et al. 2016; Mazany et al. 2002; Lynn et al. 2010).
The use of weather radar and sophisticated LDNs has made nowcasting of lightning possible (Gijben 2016). Nowcasting refers to the detailed description of the current weather conditions and extrapolation for a period of up to six hours ahead (Price 2008; Mostajabi et al. 2019). The use of statistical techniques such as binary logistic regression and multiple linear regression has been broadly adopted to predict thunderstorms and lightning (Murphy & Wilks 1998; Shafer & Fuelberg 2008).
Remote sensing relies on the link between lightning occurrence and the pre-storm environment to forecast the possibility of a lightning event (McCaul et al. 2009). The parameters used for these predictions are usually obtained from atmospheric soundings (Shafer & Fuelberg 2008). Although this has been applied in various schemes, these forecasts may not be accurate as the sounding is typically only done twice a day and at limited locations. Consequently, soundings taken in the morning and used to predict thunderstorms during the day may not yield any storms due to atmospheric changes over the day (Gijben 2016; Shafer & Fuelberg 2008).
NWP models offer more accurate lightning occurrence predictions due to higher spatial and temporal resolution (Shafer & Fuelberg 2008). Consequently, more organizations that rely heavily on weather forecasts embrace NWP models and move away from the use of empirical forecasting techniques (Goodman et al. 2013).
NWP models of weather prediction are advantageous as they can accurately provide data on very short-range and short-range forecast scales. They can be used to predict accurate parameters that are related to lightning many hours ahead (McCaul et al. 2009). The use of pre-storm parameter data has been incorporated into the statistical analysis carried out in NWP models to forecast lightning (Burrows et al., 2005; Shafer & Fuelberg 2008; Lynn & Yair 2010).
Thermodynamic parameters are used by many of the statistical models to predict lightning (Lynn & Yair 2010; Lynn et al. 2012). However, although these parameters can accurately predict lightning risk, they cannot forecast the magnitude of the lighting event (Gijben 2016; McCaul et al. 2009). Currently, NWP models can indicate microphysical parameters that are linked to thunderstorm and lightning events. These parameters relate to charge separation in thunderstorms, e.g., hydrometeors’ mixing ratio (McCaul et al. 2009).
The lifted index was a modification of a previous index, the Showalter index. LI was developed to act as a predictor of latent instability, which would accurately forecast thunderstorms (Peppler 1988). This index is calculated by considering the temperature an air parcel will have when it is lifted adiabatically from the atmosphere’s surface layer to the 500 mb level. This temperature value is then subtracted from the value of the environment’s temperature at 500 mb (Galway 1956; Pepper 1988). The Lifted Index can be represented by equation 2-1:
LI = T500 – Tp500 (2-1)
Where: T500 – Temperature of the environment at 500 hPa
Tp500 – Temperature of an air parcel at 500 hPa
The LI measures the latent instability of the atmosphere. This latent instability measures the stability of the section of a conditionally unstable column of air above the level of free convection (LFC) (Peppler 1988; Gijben 2016). This measure is used to evaluate whether an air parcel is positively or negatively buoyant.
CAPE is the total amount of energy available in an air parcel when it is lifted from the LFC to the level of neutral buoyancy (LNB). It is calculated by vertical integration of the environment and parcel virtual temperatures between these different levels. CAPE can be represented by equation 2-2:
CAPE = (2-2)
Where: Tvp – the virtual temperature of the parcel
Tve – the virtual temperature of the environment
LNB – Level of neutral buoyancy
LFC – Level of free convection
– gravity
Although many computations use the average temperature, Doswell & Rasmussen (1994) showed the importance of using virtual temperatures. Virtual temperature refers to the temperature that dry air must reach to have the same density as moist air in the same at the same pressure. CAPE has proven to be an essential parameter for predicting thunderstorms. It measures the conditional instability in the atmosphere. The term instability refers to when an air column’s temperature lapse rate is higher than the moist- adiabatic lapse rate but lower than the dry adiabatic lapse rate (Peppler 1988; Gijben 2016).
This parameter provides an estimation of the amount of water vapor available in the atmosphere. It considers the mass of water vapor in a column of the unit cross-sectional area between two layers in the atmosphere (Duplika & Reuter 2006).
One of the requirements for the occurrence of a thunderstorm is moisture (Burrows et al. 2005). The PW measures the amount of moisture in the atmosphere, allowing for thunderstorms prediction (Duplika & Reuter 2006). Studies have shown that days, where higher PW measures were recorded, had more lightning flash rates (Colson 1960).
The PW can be represented using equation 2-3:
PW = (2-3)
Where: – acceleration due to gravity
– starting pressure level
– stopping pressure level
– specific humidity
The RH is the ratio between the vapour pressure and the saturation vapor pressure of water. This measure shows how close to saturation the air is. According to Houze (1993), a RH of 100% shows that the atmosphere is saturated. The RH can be represented by the equation 2-4.
RH= (2-4)
Where: – Vapour pressure
– Saturation Vapour pressure
This parameter, denoted by Ɵe, refers to the temperature that an air parcel has as it is lifted to the lifting condensation level dry adiabatically, then wet adiabatically to a height where all od the water vapor condenses out of the parcel, and eventually back to the surface dry adiabatically (Houze 1993; Gijben 2016). The following equation (2-5) represents this parameter:
(2-5)
Where: Ɵe – Equivalent potential temperature
Lc – Latent heat of condensation
Qs – Saturation mixing ratio
Cp – Specific heat of dry air at constant pressure
T – Temperature
This parameter measures the potential instability of a layer in the atmosphere. An unsaturated section of the atmosphere is considered unstable when Ɵe decreases with height (Kuntz 2007). The equivalent potential temperature lapse rate (ƟeΓ) is used to assess the instabilities required for the occurrence of thunderstorms (Gijben 2016; Madhulatha 2013).
When the sun heats up the Earth’s surface, our weather systems, through the atmosphere transfers this heat to other parts of the globe. These convective processes result in atmospheric instabilities (Engels 1996; Silber et al 2018). Consequently, air temperature plays a significant role in the occurrence of thunderstorms.
This index, which is based on the microphysical characteristics of thunderstorms, was proposed Lynn and Yair (2008) and Yair et al. (2010). This index takes into account various meteorological parameters, including the Lifted Index, CAPE, and Theta-E lapse rates, which measure the instability necessary for thunderstorms.
The LPI also takes into account parameters that measure precipitation, like precipitable water and relative humidity at -10°C. The 850 mb temperature, which negates any overestimations of lightning during the cold season, is also included in the calculations.
LPI is calculated at a temperature span of 0°C and -20°C. At this temperature, the collisions between graupel particles and ice are the most effective in the presence of supercooled water (Lagasio et al. 2017). The clouds’ microphysical characters may result in precipitation particles and possible separate charges between clouds, building up electric fields in convective clouds. This index measures the kinetic energy created in the updraft of the developing thunderstorm that has the potential of charge separation, depending on the ratios of liquid and ice water calculated between 0°C and -20°C (the central charge zone in the clouds) (Lagasio et al. 2017; Gharaylou et al. 2019).
For LPI calculation, the modeled grid-scale updraft velocity and simulated hydrometer mass mixing ratios of cloud ice, snow, liquid water, and graupel are utilized. Research indicates that LPI shows a positive correlation between heavy rainfall and lightning density (Yair 2010). Consequently, LPI can be used as a measure to predict the occurrence of heavy rain and potential lightning.
The primary tool used for simulating LPI is the Weather Research and Forecasting Model (i.e., WRF; Skamarock, 2005). This model is a non-hydrostatic atmospheric model, which is entirely compressible, that utilizes a terrain-following hydrostatic vertical pressure coordinate. Although it is mainly used in medium- and short-range weather forecasting and research, combining it with various physics options and parameterization schemes makes it a valuable research tool. The availability of lightning parameterization in later versions of WRF (version 3.5 and onwards) makes predicting potential lightning useful.
The LPI index can be represented with equation 2-6.
The LPI value has been determined using the mixed ration of vertical velocity and hydrometers (Yair et al. 2010).
LPI = 1/V (2-6i)
Where, V – the model unit volume
– is the vertical wind component in ms−1
The computation of integral within the cloud volume in this relation (1) is done from freezing point to more than -20°C isotherm. In relation (1), has a value between 0 1, which can be calculated using the below formula and is a dimensionless number.
(2-6ii)
Where, Q1 – the total liquid water mass mixing ratio in (kg/kg)
Qi – the ice fractional mixing ratio in (kg/kg)
As defined (Yair et al. 2010):
Qi = (2-6iii)
Where the qs is a model computed mass mixing ratio for snow and qi is for ice, and qg for graupel expressed in kg/kg.
The study was carried out on the continent of Antarctica. The specific regions were the Antarctica Peninsular (Domain 01) and the Carlini Base (Domain 02). The Carlini base contains the lightning detector that was used during the study. Simulations were carried out on this site using WRF (Figure 1).
Figure 1. Map showing the WRF Modeling Domain 02. The image shows the Carlini base in the Antarctic peninsula region.
The data collection was carried out between 2013 and 2016. For all these years, lightning data in the form of CAPE, LI, KI, LPI, RH, PW, Total Totals, and precipitation was simulated using microphysical parameters. The data was collected for the four seasons, autumn, winter, spring and summer, as they were experienced in the region.
The WRF version 4.0 was used to create the models used in this study. The model is nested two-ways, with a coarse grid resolution of 25×25 km grids and a finer grid resolution of 5×5 km. The model is bounded as -60.7629 to -55.94W and -63.705 to 61.34S. Each simulation was carried out for 36 hours, with the first 12 hours designated for model spin-up time and the following 24 hours used in the analysis. Yearly and seasonal simulations were carried out.
The Global Forecast System (GFS) reanalysis data with 0.25×0.25 grid resolution was used to initialize the model. The model configurations utilized in the study were carefully chosen based on previous, related reviews (Table 1).
Table 1. Model Configuration and Physical Parametrizations
| Parameters | Property |
| Horizontal resolution (m) | 25 and 5km |
| Number of vertices level | 35 |
| NX x NY | 51 x 51 D01 |
| NX x NY | 41 x 41 D02 |
| Boundary layer scheme | MYJ |
| Microphysics scheme | NSSL double moment |
| Cumulus parameterization scheme | Kain-Fritsch for two outer domains |
| Radiation scheme | RRTM |
These configurations included the Grell- Devenyi cumulus parameterization scheme, Planetary boundary layer, Mellor Yamada-Janjic (MYJ), and Thompson Microphysics. The Dudhia Scheme (Dudhia, 1989) and the Rapid Radiation Transfer Model (RRTM) (Mlawer et al., 1997) were used in the modeling of short- and long-wave radiation, respectively. The Monin-Obukhov scheme (Janjic, 1996) was utilized to simulate fluctuations in the water, whereas the Mellor-Yamada-Janjic Turbulent Kinetic Energy (TKE) scheme (Mellor and Yamada, 1982; Janji c, 1990; 1994) was used in simulating changes in the boundary layer. The Cumulus parametrization scheme, used for the two outer domain models, was initialized by the Global Forecast System (GFS) reanalysis Planetary boundary layer.
The measurements were obtained using a Boltek Lightning Detector EFM-100 and LD-350 that was installed on the roof of the Calbido Laboratory Building, located in the Carlini Base (CARL: 62.23 S, 58.63 W) as shown in the studies by Yusop et al., 2019 and Nor et al., 2020. The LD-350 relies on the Magnetic Direction Finder (MDF) system to identify lightning pulses sources. This technique allows it to detect strike locations and provide relevant data about a lightning strike in a range of almost 480km. When the storm is intense, the LD-350 can sense lightning up to 960km away, making it crucial for the long-range detection of lightning occurrences. The EFM-100 is used for the detection of short-range thunderstorms, located up to 30km away. This tool acts as a high-quality detector that monitors and records data about electric fields.
In this study, we utilize data from the Worldwide Lightning Location Network (WWLLN, http://wwlln.net/). This network, which hosts cloud-to-air databases, is housed in the University of Washington. It is made up of numerous sensors in global testing facilities and universities. Each sensing system can detect a signal equivalent to VLF (3-30 kHz). This VLF signal needs to be detected by a minimum of five sensors to establish a lightning occurrence’s spatial location. This data consists of information on the time and date of the lightning occurrence in Universal Time Coordinated (UTC), longitude and latitude in fractional degrees, lightning energy (Joules), and the total of weather stations involved in the observation. For our study, the Rothera station (67.57S, 68.13W), the only one in the Antarctica peninsula, and four other sensors found in Argentina provided the data from lightning sensors.
The EMF-100 and LD-350 data were initially converted, using the Windows command line prompt (DOS) software, from *.nex files to *.txt files. This data included the bearing to strike, timestamp (in seconds), corrected distance to strike, and the strike’s type and polarity. These parameters are all necessary for the comprehensive understanding of lightning occurrences in the target region, the Antarctica Peninsula. Data from the EMF-100 was first filtered, to ensure no missing data, before being analyzed in the MATLAB software. This data was then used to analyze the diurnal variation in the atmospheric electric fields and support the study’s statistical analysis of lightning events.
The LPI data collected daily revealed the expected lightning occurrence for the region from the year 2013 to 2016 (Figure 2). The data for the year 2013 was only collected up to October. Hence November and December of this month were not considered in statistical analysis. The data showed that the lightning events greatly varied, with some days having LPI frequencies closer to zero, and others having up to 800 J/Kg.
Lightning was experienced all through the year, with a few months having more LPI values of more than 600 J/Kg. Notably, in the year 2014, there were high scales of lightning events in the months of August and October. In the year 2015, there was a lower incidence, with LPI values of over 600 J/Kg being predicted only around November.
Figure 2: Daily LPI values of predicted lightning events in Peninsula Antarctica in the years 2013-2016.
The expected daily precipitation was modeled over the years using nowcasting techniques. As atmospheric moisture is an essential requirement for the occurrence of lightning, this data would also show the likelihood of experiencing lightning. The daily precipitation is shown in Figure 3.
Figure 3: Daily precipitation recorded from the year 2013 to 2016.
The predicted precipitation over the years showed no season pattern. The precipitation in 2013 was relatively low, being less than 20 mm over the year, except for two occasions. The year 2014 also began with relatively low precipitation before steadily climbing from the end of April until the end of the year.
The year 2015 began with low precipitation levels, which rose around September and plateaued at around 30 mm until the end of the year. In 2016, The forecast precipitation was around 20 mm although the year, except for the months of June to August.
The seasonal CAPE simulated over the years showed the variation experienced in the different seasons in the Antarctic region. Figure 4 shows the CAPE predictions of the year 2014. This year was selected through randomization.
Figure 4: CAPE data for the year 2014. The figure shows the predictions for the different seasons of A) Summer B) Autumn C) Winter and D) Spring.
Higher CAPE values show that the atmosphere would be more unstable, and therefore more likely to experience a stronger updraft. Severe weather like thunderstorms tend to occur when the CAPE values are higher. In the year 2014, the CAPE values were higher in Spring. This prediction showed that more lightning events were expected during that season.
The Precipitable water (PW) shows the measure of the amount of water in the atmosphere. The PW predictions for the year 2016 were as shown in Figure 5.
Figure 5: PW values for the year 2016. The predictions were collected for the four seasons; A) Summer B) Autumn C) Winter and D) Spring.
The PW value does not predict how much it will rain, but rather how much water is in the atmosphere. Therefore, the measures show how much the condensed water would be in inches. The data for the year 2016 showed that higher PW would be experienced in Spring and Autumn. This shows that there was more moisture in the atmosphere during this time. High levels of moisture are one of the ingredients necessary for thunderstorms, and hence, lightning. If experienced in a high CAPE environment, high PW values will lead to storms that produce an abundant amount of lightning.
The Lifted index data, which predicts the stability was also obtained. A positive index demonstrates that the air is stable, where as a negative index shows that there is instability in the air parcels. Instability is one of the key ingredients for thunderstorms, and hence lightning. Notably, the area experienced negative LI values all through the different seasons.
Figure 5: LI values for the year 2013. Predictions were made for the seasons A) Summer B) Autumn C) Winter D) Spring
The risk of total lightning flashes increases as the relative humidity increases. When the RH is higher than 90%, the chances of CG lighting, both positively and negatively charged, increase. Figure 6 shows that in this region, the RH ranges around 90% and above, regardless of the season.
Figure 6: Relative humidity for the year 2014. These measurements were taken for all seasons A) Summer B) Autumn C) Winter D) Spring
The seasonal LPI values were also obtained to determine the estimated lightning events experienced during the various seasons. Higher LPI Values, indicated with the colour red indicated that those regions had a higher risk of experiencing lightning events.
Figure 5: LPI values for the different seasons in the year 2015. A) Summer, B) Autumn C) Winter D) Spring
The seasonal WWLLN data was obtained for the years 2013 to 2016. This data captures the convective activity of the region. The number of observed lightning events, recorded by the weather station was as shown in Table 2. On average, the year 2016 had a higher number of lightning strikes, with a mean of around 47 strikes per month. On the other hand, the year 2014 had the lowest number of strikes recorded on average. In the year 2016, the months of January and June had the highest lightning occurrences, with 211 and 127 being recorded. The lowest recorded month was November 2015.
Table 2: The monthly number of lightning flashes observed from 2013 to 2016
| Number of lightning flashes per month | ||||
| Year | 2013 | 2014 | 2015 | 2016 |
| January | 81 | 19 | 15 | 211 |
| February | 40 | 14 | 16 | 19 |
| March | 21 | 10 | 18 | 14 |
| April | 42 | 16 | 22 | 17 |
| May | 53 | 12 | 58 | 13 |
| June | 7 | 16 | 13 | 127 |
| July | 11 | 14 | 27 | 17 |
| August | 15 | 11 | 55 | 13 |
| September | 10 | 22 | 7 | 93 |
| October | 14 | 20 | 16 | 14 |
| November | 13 | 13 | 5 | 11 |
| December | 7 | 14 | 19 | 17 |
The WWLLN data for the year 2015 is shown in Figure 6. This shows the total lightning that was observed in the year 2015. This year had a low occurrence of lightning, with a total of 271 lightning flashes all through the year.
Figure 6: Total Lightning WWLLN data for the different seasons in the year 2015.
The Lightning potential index provided predictions for various microphysical factors that are relevant to the occurrence of thunderstorms and lightning. The WWLLN data showing the total number of flashes observed corresponded with the LPI data of predicted lightning events. The other factors, such as CAPE and PW corresponded with the expected data. The seasons with high levels of instability and moisture in the air were also the seasons with high lightning incidence.
Using Lynn and Yair’s (2008) numerical simulations, it was evident that the vertically high reflective core is considerably lower than -25°C. The regions showing the maximum LPI values are consistent with the locations indicating lightning occurrences (World Wide Lighting Location Network) WWLLN data. This WWLLN data, which usually includes the CG flashes, is comprehensively used in studying the cases without taking CG and the exact amount of lightning. For each season, both the observed lightning and predicted LPI was noted.
The use of 5km- and 25-km grids was crucial to show the exact locations that experienced thunderstorms. Similarly, calculating the overlapping areas from the data provided the actual sites. The correlations in the Carlini base was relatively lower than expected. This low correlation could be attributed to the great distance between the WWLLN station and the base. This station is located in Rothera, which is 730 km away from the base. However, the second site’s data showed a higher correlation between the projected LPI and precipitation data and the WWLLN station’s values.
There is a linear correlation between the lightning rate for both sites, measured as flash units per km2 per hour, and the time-averaged values or measured LPIs. This linear relationship corresponds to the recorded behavior of lightning. This correlation can again be explained by the distances between the sites and the WWLLN station. The 25 km grid shows a higher correlation between the projected LPIs and the weather station data because it is closer to the station.
Overall, there is a relative agreement between the simulated data (LPI) and the observational data (WWLLN) of spatial and temporal distributions. However, there are some exceptions where inconsistencies can be observed. These exceptions have been previously discussed by Collier et al. (2009), and Rodger et al. (2006), and where their studies examined the recognition efficiency of WWLLN data. They revealed that receivers’ non-uniform distribution caused variation in the spatial and temporal data in WWLLN data. This variation implies that although weather stations’ data can be used as reference data, it will vary based on the receivers. Consequently, our study site’s limited number of receivers could affect the referenced WWLLN data in our domain.
This study shows the reliability of LPI data, as it is consistent with the lightening occurrence provided in the WWLLN data. The LPI indices, representing the total probability of lightning events, have shown to be reliable parameters to predict the occurrence and severity of rain and lightning. Compared to the Number of Lightning events (NOL), the different time durations examined have shown the varying correlations in different scenarios.
The Lightning Potential Index is generally calculated using the WRF model targeting the dynamic and microphysical field. The LPI measures the probability for the generation and separation of charges resulting in lightning flashes in convectional storms. Combining the LPI and the NOL will provide more accurate and consistent results. In our study, the WRF model was used to simulate two major lightning activities. This model relied on the general features of lightning spatial distribution and convection, greatly simplifying lightning behavior. As the LPI is seen as the direct output from the WRF model, it has been identified to have lower calculative costs.
The study shows a substantial similarity between the data predicted using LPI and those obtained from WWLLN when they are combined over a total area of 25×25 sq km. LPI can, therefore, be reliably used for the prediction of lightning events. These findings also reveal that the Antarctic peninsula is affected by lightning events. Knowing when and where lightning will strike will be essential in reducing socioeconomic losses and interpreting radio signals during such storms in Antarctica. However, these findings are preliminary, and further investigation, over many seasonal conditions, is required. Additionally, there is no receiver in Antarctica Peninsular linked with the WWLL network that would show significant variation from the numerical estimates provided. Overall estimates show that any event related to a lightening event would collectively have fewer arithmetic costs.
Ajilesh, P., Rakesh, V., Sahoo, S.K. and Himesh, S., 2020. Observed and model‐simulated thermodynamic processes associated with urban heavy rainfall events over Bangalore, India. Meteorological Applications, 27(1), p.e1854.
Ahrens, C.D., 2009. Meteorology Today 9th Edition: An Introduction to Weather, Climate, and The Environment.
Apel, E.C., Hornbrook, R.S., Hills, A.J., Blake, N.J., Barth, M.C., Weinheimer, A., Cantrell, C., Rutledge, S.A., Basarab, B., Crawford, J. and Diskin, G., 2015. Upper tropospheric ozone production from lightning NOx‐impacted convection: Smoke ingestion case study from the DC3 campaign. Journal of Geophysical Research: Atmospheres, 120(6), pp.2505-2523.
Banerjee, A., Archibald, A.T., Maycock, A.C., Telford, P., Abraham, N.L., Yang, X., Braesicke, P. and Pyle, J.A., 2014. Lightning NO x, a key chemistry–climate interaction: impacts of future climate change and consequences for tropospheric oxidising capacity. Atmospheric Chemistry and Physics, 14(18), pp.9871-9881.
Barthe, C., Deierling, W. and Barth, M.C., 2010. Estimation of total lightning from various storm parameters: A cloud‐resolving model study. Journal of Geophysical Research: Atmospheres, 115(D24).
Betz, H.D., Schumann, U. and Laroche, P. eds., 2008. Lightning: principles, instruments and applications: review of modern lightning research. Springer Science & Business Media.
Blyth, A.M., Christian Jr, H.J., Driscoll, K., Gadian, A.M. and Latham, J., 2001. Determination of ice precipitation rates and thunderstorm anvil ice contents from satellite observations of lightning. Atmospheric Research, 59, pp.217-229.
Bromwich, D. H., J. P. Nicolas, A. J. Monaghan, M. A. Lazzara, L. M. Keller, G. A. Weidner, and A. B. Wilson, 2014: Corrigendum: Central West Antarctica among the most rapidly warming regions on Earth. Nature Geoscience, 7, p76, doi: 10.1038/ngeo2016
Bromwich, D. H., Nicolas, J. P., and Monaghan, A. J., 2011: An Assessment of Precipitation Changes over Antarctica and the Southern Ocean since 1989 in Contemporary Global Reanalyses, Journal of Climate, 24, 4189–4209.
Bromwich, D. H., J. E. Box, R. L. Fogt, and A. J. Monaghan, 2007: Contributors to the Antarctic and Nordic sections of ‘State of the Climate in 2006’. Bull. American Meteorology Society, 88, s72-s74, s104-s106.
Burrows, W.R., Price, C. and Wilson, L.J., 2005. Warm season lightning probability prediction for Canada and the northern United States. Weather and Forecasting, 20(6), pp.971-988.
Chen, F., Janjić, Z. and Mitchell, K., 1997. Impact of atmospheric surface-layer parameterizations in the new land-surface scheme of the NCEP mesoscale Eta model. Boundary-Layer Meteorology, 85(3), pp.391-421.
Colson, D., 1960. High level thunderstorms of July 31–August 1, 1959. Mon. Wea. Rev, 88, pp.279-285.
Cummins, K.L., Murphy, M.J. and Tuel, J.V., 2000, September. Lightning detection methods and meteorological applications. In IV International Symposium on Military Meteorology (pp. 26-28).
Cooray, V. 2015. An introduction to lightning. An Introduction to Lightning. doi:10.1007/978-94-017-8938-7
Clouthier-Lopez, J. and Raga, G.B., 2014, Model estimates of lightning in convective systems in southern Mexico and the Gulf of Tehuantepec.
Darkow, G. L., 1969: An analysis of over sixty tornado proximity soundings. Preprints, 6th Conf. on Severe Local Storms, Chicago, IL, Amer. Meteor. Soc., 218-221.
DeConto, R. M. and Pollard, D., 2016: Contribution of Antarctica to past and future sea-level rise, Nature, 531, 591–597.
DelSole, T. and Shukla, J., 2012. Climate models produce skillful predictions of Indian summer monsoon rainfall. Geophysical Research Letters, 39(9).
Dementyeva, S.O., Ilin, N.V. and Mareev, E.A., 2015. Calculation of the lightning potential index and electric field in numerical weather prediction models. Izvestiya, Atmospheric and oceanic physics, 51(2), pp.186-192.
Ding, W. and Rakas, J., 2015. Economic impact of a lightning strike–induced outage of air traffic control tower: case study of Baltimore–Washington International Airport. Transportation research record, 2501(1), pp.76-84.
Doswell III, C.A. and Rasmussen, E.N., 1994. The effect of neglecting the virtual temperature correction on CAPE calculations. Weather and forecasting, 9(4), pp.625-629.
Dowdy, A.J., 2016. Seasonal forecasting of lightning and thunderstorm activity in tropical and temperate regions of the world. Scientific reports, 6, p.20874.
Dudhia, J., 1989. Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. Journal of the atmospheric sciences, 46(20), pp.3077-3107.
Dupilka, M.L. and Reuter, G.W., 2006. Forecasting tornadic thunderstorm potential in Alberta using environmental sounding data. Part II: Helicity, precipitable water, and storm convergence. Weather and forecasting, 21(3), pp.336-346.
Engels, R. and Heinemann, G., 1996. Three-dimensional structures of summertime Antarctic meso-scale cyclones: Part 2: Numerical simulations with a limited area model. Global Atmosphere and Ocean System. 1996.
Favier, V., Krinner, G., Amory, C., Gallée, H., Beaumet, J. and Agosta, C., 2017. Antarctica-regional climate and surface mass budget. Current Climate Change Reports, 3(4), pp.303-315.
Galway, J.G., 1956. The lifted index as a predictor of latent instability. Bulletin of the American Meteorological Society, 37(10), pp.528-529.
Gaskell, W., 1981. A laboratory study of the inductive theory of thunderstorm electrification. Quarterly Journal of the Royal Meteorological Society, 107(454), pp.955-966.
Gharaylou, M., Farahani, M.M., Hosseini, M. and Mahmoudian, A., 2019. Numerical study of performance of two lightning prediction methods based on: Lightning Potential Index (LPI) and electric POTential difference (POT) over Tehran area. Journal of Atmospheric and Solar-Terrestrial Physics, 193, p.105067.
Gijben, M., 2016. The lightning climatology of South Africa. South African Journal of Science,108(3-4),pp. 44-53
Goodman, S.J., 1990. Predicting thunderstorm evolution using ground-based lightning detection networks.
Goodman, S.J., Blakeslee, R.J., Koshak, W.J., Mach, D., Bailey, J., Buechler, D., Carey, L., Schultz, C., Bateman, M., McCaul Jr, E. and Stano, G., 2013. The GOES-R geostationary lightning mapper (GLM). Atmospheric research, 125, pp.34-49.
Guerova, G., Dimitrova, T. and Georgiev, S., 2019. Thunderstorm classification functions based on instability indices and GNSS IWV for the Sofia Plain. Remote Sensing, 11(24), p.2988.
HABY, J., Severe Weather Indices Page [online]. The Weather Prediction Webpage. [viewed 3 August 2015]. Available from: http://www.theweatherprediction.com/severe/indices/
Harats, N., Ziv, B., Yair, Y., Kotroni, V. and Dayan, U., 2010. Lightning and rain dynamic indices as predictors for flash floods events in the Mediterranean. Advances in Geosciences, 23.
Haklander, A.J. and Van Delden, A., 2003. Thunderstorm predictors and their forecast skill for the Netherlands. Atmospheric Research, 67, pp. 273-299.
Hayenga, C.O. and Warwick, J.W., 1981. Two‐dimensional interferometric positions of VHF lightning sources. Journal of Geophysical Research: Oceans, 86(C8), pp.7451-7462.
Heath, N.K., Pleim, J.E., Gilliam, R.C. and Kang, D., 2016. A simple lightning assimilation technique for improving retrospective WRF simulations. Journal of advances in modeling earth systems, 8(4), pp.1806-1824.
Heckman, S. and Liu, C., 2010. The application of total lightning detection for severe storm prediction. ems, pp.EMS2010-623.
Hines, K. M., D. H. Bromwich, S.-H. Wang, I. Silber, J. Verlinde, and D. Lubin, 2019. Microphysics of summer clouds in central west Antarctica simulated by Polar Weather Research and Forecasting Model (WRF) and the Antarctic Mesoscale Prediction System (AMPS). Atmospheric Phys., 19, 12431-12452, doi: 10.5194/acp-19-12431-2019.
Hodges, K.I., Lee, R.W. and Bengtsson, L., 2011. A comparison of extratropical cyclones in recent reanalyses ERA-Interim, NASA MERRA, NCEP CFSR, and JRA-25. Journal of Climate, 24(18), pp.4888-4906.
Houze Jr, R.A., Schmid, W., Fovell, R.G. and Schiesser, H.H., 1993. Hailstorms in Switzerland: Left movers, right movers, and false hooks. Monthly weather review, 121(12), pp.3345-3370.
Huntsville, A. 2001. Precipitation Ice and Lightning: From Global to Cell Scales Walter A. Petersen, Wiebke K. Deierling, Michael L. Gauthier, Hugh J. Christian. Cell, (August), 1–4.
Janjić, Z.I., 1990. The step-mountain coordinate: Physical package. Monthly Weather Review, 118(7), pp.1429-1443.
Janjić, Z.I., 1994. The step-mountain eta coordinate model: Further developments of the convection, viscous sublayer, and turbulence closure schemes. Monthly weather review, 122(5), pp.927-945.
Kennicutt, M.C., Chown, S.L., Cassano, J.J., Liggett, D., Massom, R., Peck, L.S., Rintoul, S.R., Storey, J.W., Vaughan, D.G., Wilson, T.J. and Sutherland, W.J., 2014. Polar research: six priorities for Antarctic science. Nature News, 512(7512), p.23.
Krehbiel, P. R., Brook, M., & McCrory, R. A.,1979. An analysis of the charge structure of lightning discharges to ground. Journal of Geophysical Research: Oceans, 84(C5), 2432-2456.
King, J. C. and Turner, J., 1997. Antarctic meteorology and climatology, Cambridge University Press
Kumar, A., Bhowmik, S.R. and Das, A.K., 2012. Implementation of Polar WRF for short range prediction of weather over Maitri region in Antarctica. Journal of earth system science, 121(5), pp.1125-1143.
Kunz, M. 2007. The skill of convective parameters and indices to predict isolated and severe thunderstorms. Natural Hazards and Earth System Science, 7(2), 327–342. doi:10.5194/nhess-7-327-2007
Lang, T. J., Rutledge, S. A., & Wiens, K. C., 2004. Origins of positive cloud‐to‐ground lightning flashes in the stratiform region of a mesoscale convective system. Geophysical research letters, 31(10).
Lee, A.C.L., 1989. Ground truth confirmation and theoretical limits of an experimental VLF arrival time difference lightning flash locating system. Quarterly Journal of the Royal Meteorological Society, 115(489), pp.1147-1166.
Lennon C. and Maier L., 1991. Lightning mapping system, in Proc. Int. Aerospace and Ground Conf. on Lightning and Static Electricity, Cocoa Beach, FL, NASA Conf. Pub. 3106, (II), (pp. 89- 1 – 89-10).
Litta, A. J. 2013. COMPUTATIONAL MODELS FOR THE PREDICTION OF SEVERE THUNDERSTORMS OVER EAST INDIAN REGION Thesis submitted by LITTA A J in partial fulfilment of the requirements for the award of the degree of Under the Faculty of Technology DEPARTMENT OF COMPUTER SCIENCE C (November).
Lynn, B. and Yair, Y., 2010. Prediction of lightning flash density with the WRF model. Advances in Geosciences, 23.
Madhulatha, A., Rajeevan, M., Venkat Ratnam, M., Bhate, J. and Naidu, C.V., 2013. Nowcasting severe convective activity over southeast India using ground‐based microwave radiometer observations. Journal of Geophysical Research: Atmospheres, 118(1), pp.1-13.
Mansell, E. R., MacGorman D. R., Ziegler C. L., and Straka J. M. , 2002. Simulated three-dimensional branched lightning in a numerical thunderstorm model, Journal of Geophysics Research, 107, 4075, doi:10.1029/2000JD000244.
Manning, K. W., 2007. Polar WRF testing in Antarctica. Extended Abstracts, Eighth WRF Users’ Workshop, Boulder,CO, NCAR, P.12.
Murphy, A.H. and Wilks, D.S., 1998. A Case Study of the Use of Statistical Models in Forecast Verification: Precipitation Probability Forecasts. Weather and Forecasting, 13(3), pp. 795-810.
Peppler, R.A., 1988. A review of static stability indices and related thermodynamic parameters. Illinois State Water Survey.
Silber, I., Verlinde, J., Eloranta, E. W., and Cadeddu, M., 2018: Antarctic Cloud Macrophysical, Thermodynamic Phase, and Atmospheric Inversion Coupling Properties at McMurdo Station: I. Principal Data Processing and Climatology, Journal of Geophysical Research: Atmospheres, 123, 6099–6121.
Saunders , C. P. R., 1993: A review of thunderstorm electrification processes. Journal of Applied Meteorology., 32, 642–655.
Saunders, C. 2008. Charge separation mechanisms in clouds. Space Science Reviews, 137(1-4), 335.
TAKAHASHI, T., 1984. Thunderstorm Electrification—A Numerical Study. Journal of the Atmospheric Sciences, 41(17), pp. 2541-2558.
RAKOV, V.A. and UMAN, M.A., 2003. Lightning: Physics and Effects. Cambridge, UK: Cambridge University Press.
Takahashi, T. (1978), Riming electrification as a charge generation mechanism in thunderstorms, Journal of Atmospheric Science, 35(8), 1536-1548.
STOLZENBURG, M., RUST, W.D. and MARSHALL, T.C., 1998a. Electrical structure in thunderstorm convective regions: 2. Isolated storms. Journal of Geophysical Research: Atmospheres, 103(D12), pp. 14079-14096.
PRICE, C., 1993. Global surface temperatures and the atmospheric electrical circuit. Geophysical Research Letters, 20(13), pp. 1363-1366.
Wilson, C. T. R., 1920: Investigations of lightning discharges and on the electric field of thunderstorms. Philosophical transactions of the Royal Society of London, 221, 73–115.
Wilson, C. T. R. 1916. On some determinations of the sign and magnitude of electric discharges in lightning flashes. Proceedings of the Royal Society of London. Series A, Containing Papers of a Mathematical and Physical Character, 92(644), 555-574.
Suparta, W., & Nor, W. N. A. (2017, May). Irregularities of ionospheric VTEC during lightning activity over Antarctic Peninsula. In Journal of Physics. Conference Series (Online) (Vol. 852, No. 1).
Yusop, N. et al. Seasonal Analysis of Cloud-To-Ground Lightning Flash Activity in the Western Antarctica. Atmosphere 10, 744 (2019).
Nor, W. N. A. W. M. et al. The response between VTEC and lightning activity over the Antarctic Peninsula. Polar Science 25, 100563 (2020)
Suparta, W. (2018). Monitoring of thunderstorms activity over the Antarctic Peninsula based on GPS sensing. E&ES, 169(1), 012001.
Nadzir, M. S. M., Khan, M. F., Suparta, W. & Zainudin, S. K. Comparison of In situ Observation, NOAA-AIRS Satellite and MACC Model on Surface Ozone Over the Ushuaia, Southern Ocean and Antarctic Peninsula Region. in Space Science and Communication for Sustainability (eds. Suparta, W., Abdullah, M. & Ismail, M.) 37–45 (Springer, 2018). doi:10.1007/978-981-10-6574-3_4.
Suparta, W., Bakar, F. N. A. & Abdullah, M. Remote sensing of Antarctic ozone depletion using GPS meteorology. International Journal of Remote Sensing 34, 2519–2530 (2013).
Reijmer, C. H., van Meijgaard, E., and van den Broeke, M. R., 2005: Evaluation of temperature and wind over Antarctica in a Regional Atmospheric Climate Model using 1 year of automatic weather station data and upper air observations, Journal of Geophysical Research, 110, D04 103.
Sanz Rodrigo, J., Buchlin, J. M., van Beeck, J., Lenaerts, J. T. M., and van den Broeke, M. R., 2013: Evaluation of the antarctic surface wind climate from ERA reanalyses and RACMO2/ANT simulations based on automatic weather stations, Climate Dynamics, 40, 353–376.
Silber, I., Verlinde, J., Eloranta, E. W., and Cadeddu, M., 2018: Antarctic Cloud Macrophysical, Thermodynamic Phase, and Atmospheric Inversion Coupling Properties at McMurdo Station: I. Principal Data Processing and Climatology, Journal of Geophysical Research: Atmospheres, 123, 6099–6121.
Walden, V. P., Warren, S. G., and Tuttle, E., 2003: Atmospheric ice crystals over the Antarctic Plateau in winter, Journal of Applied Meteorology, 42, 1391–1405.
Monaghan, A. J., D. H. Bromwich, and D. P. Schneider, 2008: 20th century Antarctic air temperature and snowfall simulations by IPCC climate models. Geophys. Res. Letts., 35, L07502, doi:10.1029/2007GL0326.
Walsh, J. E., D. H. Bromwich, J. E. Overland, M. C. Serreze, and K. R. Wood, 2018: 100 Years of Progress in Polar Meteorology. Meteorology Monogram., 59, 21.1-https://doi.org/10.1175/AMSMONOGRAPHS-D-18-0
Powers, J. G., K. W. Manning, D. H. Bromwich, J. J. Cassano, and A. M. Cayette, 2012: A decade of Antarctic science support through AMPS. Bull. Amer. Meteor. Soc., 93, 1699–1712, doi:10.1175/BAMS-D-11-00186.1.
Velicogna, I. and Wahr, J., 2006: Measurements of time-variable gravity show mass loss in Antarctica, Science, 311, 1754–1756.
Shepherd, Andrew; Ivins, Erik; et al. (IMBIE team) (2018-06-13). “Mass balance of the Antarctic Ice Sheet from 1992 to 2017” (PDF). Nature. 558 (7709): 219–222. Bibcode:2018Natur.558..219I. doi:10.1038/s41586-018-0179-y. PMID 29899482. Lay summary – Ars Technica(2018-06-13).
Monaghan, A. J., and D. H. Bromwich, 2008b: Global warming at the poles. Nature Geoscience, 1, 728-729, doi: 10.1038/ngeo338.
Monaghan, A. J., and D. H. Bromwich, 2008a: Advances in describing recent Antarctic climate variability. Bull. American Meteorology Society, 89, 1295-1306
NCAR – Research Applications Laboratory, 2015. Verification: Weather Forecast Verification Utilities. R package version 1.41, http://CRAN.R-project.org/package=verification.
McCaul, Jr., E. W., K. Fuell, G. Stano, and J. Case (2011), Lightning Forecast Algorithm (LFA) overview, NASA Short-term Prediction Research and Transition Center (SPoRT) Training, October 2011.
McCaul, E. W., S. J. Goodman, K. M. LaCasse, and D. J. Cecil (2009), Forecasting lightning threat using cloud-resolved model simulations, Weather Forecasting, 24, 709-729, doi:10.1175/2008WAF222215
van Lipzig, N. P. M. et al. 2008. The Relationship between the Southern Hemisphere Annular Mode and Antarctic Peninsula Summer Temperatures: Analysis of a High-Resolution Model Climatology. Journal of Climate, 21(8), 1649–1668. doi:10.1175/2007JCLI1695.1
Scott, R. C., Lubin, D., Vogelmann, A. M., and Kato, S., 2017: West Antarctic Ice Sheet Cloud Cover and Surface Radiation Budget from NASA A-Train Satellites, Journal of Climate, 30, 6151–6170.
Wang, W. 2012. Set Up and Run WRF WRF System Flowchart. Presentation, (January), 1–51.
Karmakar, S., Quadir, D., Alam, R. & Das, M. Impact of a thunderstorm on the biodiversity and socioeconomic conditions of the people in Kushtia and Jhenaidah districts in Bangladesh. (2016).
Yair, Y. Lightning hazards to human societies in a changing climate. Environ. Res. Lett. 13, 123002 (2018)
SCHULZE, G.C., 2007. Atmospheric observations and numerical weather prediction. South African Journal of Science, 103(7-8), pp. 318-323.
LANDMAN, S., ENGELBRECHT, F.A., ENGELBRECHT, C.J., DYSON, L.L. and LANDMAN, W.A., 2012. A short-range weather prediction system for South Africa based on a multi model approach. Water SA, 38(5), pp. 765-774.
MAZANY, R.A., BUSINGER, S., GUTMAN, S.I. and ROEDER, W., 2002. A Lightning Prediction Index that Utilises GPS Integrated Precipitable Water Vapor. Weather and Forecasting, 17(5), pp. 1034-1047.
Uman, M. A. (2001), The Lightning Discharge, Dover Publications, Inc., Mineola, New York, 377 pp. Powers, J. G., 2007: Numerical prediction of an Antarctic severe wind event with the Weather Research and Forecasting (WRF) model. Monthly Weather Review, 135, 3134–3157.
REEVE, N. and TOUMI, R., 1999. Lightning activity as an indicator of climate change. Quarterly Journal of the Royal Meteorological Society, 125(555), pp. 893-903.
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