A Comprehensive Analysis of the Planetary Boundary Layer from 2020 till 2023

A comprehensive analysis of the Planetary Boundary Layer from 2020 till 2023. María Bernabé Morera Tutored by: Dr. Diego Gómez García Acknowledgements. En este apartado dedico mi agradecimiento al profesor, Diego Gómez García, por su gran labor como docente y sobre todo, por sus facultades humanas. A pesar de conocerlo desde hace un solo año, ha hecho que mi pasión por la Meteorología, florezca y haga raíces fuertes. Tal ha sido tal su influencia en mi personalidad y conociemiento que le estaré agradecida por siempre. Gracias también al profesor Jesús de la Rosa Damián por haber sido tan acogedor conmigo desde el inicio. El desarrollo de este proyecto ha sido en parte gracias a tu vocación como docente y apoyo. Aprovecho para dar las gracias a todas las personas que están en mi vida y han estado apoyándome insaciablemente durante todos estos años en las caídas y en los logros, y que lo siguen haciendo diariamente. Dedicarle este proyecto a ellos, es lo mínimo que puedo hacer. A mi familia, por inculcarme que la vida no es fácil y animarme a luchar por mis sueños. A mis amigas, amigos y amigues, y todos los compañeros sin los cuales la carrera no hubiera tenido sentido alguno. Thank you from the very deep of the heart. Contents 1 Abbreviations. 1 English Abstract 3 2 Introduction. 5 2.1 The Planetary Boundary Layer (PBL). . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Theweatherballoon. ................................ 13 2.3 Theemagram. .................................... 15 2.4 Equationofmotion.................................. 17 2.5 Over Planetary Boundary Layer Height: free atmosphere. . . . . . . . . . . . . 17 2.6 Under Planetary Boundary Layer Height: turbulence regime. . . . . . . . . . . 18 2.6.1 Adiabatic approximation. . . . . . . . . . . . . . . . . . . . . . . . . . 21 3 Methodology. 23 3.1 Analysisoftheemagram............................... 24 3.2 The temperature gradient method (TGM). . . . . . . . . . . . . . . . . . . . . . 25 3.3 RStudio: Openair &SmoothTrend.......................... 25 4 Results and discussion. 29 4.1 Evolution average of PBLH. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 4.2 Temperature average evolution of PBL. . . . . . . . . . . . . . . . . . . . . . . 30 4.3 Pressure average evolution of PBL. . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.4 Wind celerity average estimation in the PBL and comparison with winds bursts. 33 4.5 ENSO phenomena evolution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 5 Conclusions. 49 5.1 Futureapplications.................................. 51 6 Bibliography. 53 1 Abbreviations. In this section abbreviations in this report are described. 1. FDP -Final Degree Project 2. PBL -Planetary Boundary Layer 3. PBLH -Planetary Boundary Layer Height 4. CL -Convective Layer 5. CBL -Convective Boundary Layer 6. CBLH -Convective Boundary Layer Height 7. SL -Stable Layer 8. SBL -Stable Boundary Layer 9. SBLH -Stable Boundary Layer Height 10. RL -Residual Layer 11. RBL -Residual Boundary Layer 12. RLH -Residual Layer Height 13. NAF -North AFrican. Describes dust bursts of the wind from North Africa. 14. AEMet -Agencia Estatal de Meterología 15. ENSO -ElNiño-SOthern Oscillation 16. NOAA -National Oceanic and Atmospheric Administration English Abstract In this final Degree report the Planetary Boundary Layer (PBL) is going to be described in terms of the variables: altitude from ground, temperature, pressure and wind speed. The author has made use of data collected through the weather balloon technique at Radiosondes Atmospheric Station (ESAt) of INTA (Huelva). Measurements were made at two particular moments in the day, at 11am and at 11pm from 2020 to 2023. Data were taken using the gradient temperature method (TGM). First, they are plotted in emagrams after sounding and then determined from the table data from the Meteociel French website. The empirical information is then compared with the theoretical assumptions of the PBL. Data analysis was carried out by means of RStudio code by averaging parameters to analyse their tendencies. Climate change consequences on small scale will be assesed as well as the increase of PBLH and turbulence regime on wind. Results are compared with data collected for the analysis of dust events from Africa by professor Jesús de la Rosa Damián, University of Huelva (UHU). Our work will conclude with some features of the evolution of the PBL during the year in terms of the upper limit, local temperature, pressure and wind speed. 2 Introduction. 2.1 The Planetary Boundary Layer (PBL). Definition 2.1. The Planetary Boundary Layer is the lowest part of the troposphere, and reacts to the external forces from the surface with a time scale of an hour or less. (Stull, 1988) PBL is very important in many science fields, as forecast, air-pollution evolution and diffusion, meteorologic study of turbulence or cloud behaviour. (Liu S., &Liang X.-Z., 2010) The PBL exists during the whole day and has two different visible cycles: one daytime and one nighttime. Its most important property is the fact that it contains all turbulent bursts of wind, which makes it so fascinating its study. Above the PBLH, the laminar flow of winds begins, and stability approximations can be applied. Ideally, after sunrise, the heat flux rises to the atmosphere causing the alteration of the air molecules. This alteration of the kinetic energy of the particles produces convective processes and a turbulent flux with eddies of different sizes reaching the troposphere. That atmospheric part is known as convective boundary layer, CBL. The layer gets mixed with the troposphere, and this results in a mixed layer that lasts until sunset. Later on, it remains both a residual layer, RL from that previous daytime. 12 on each year in Spain to demonstrate that the results are coherent. Furthermore comparisons between the ENSO parameters from NOAA, to see how trade winds take place in the southern city, Huelva. A comparison between the variation of the PBLH, temperature, pressure and celerity of the wind, each year with properties of bursts from the wind data obtained by professor de la Rosa, J. University of Huelva (UHU) analysis of air aerosols from 1996 to 2023 is carried out. ∙Finally, conclusions will be developed in Section 7. It will be shown that air components influence the PBLH and its stability as well as future estimation of PBL and ozone layer conditions. 2. introduction. 13 2.2 The weather balloon. Definition 2.2. The weather balloon was first mentioned by Pierre Idrac and Robert Bureu in 1927 (see Figure 2.4) as a device that could obtain meteorological parameters such as temperature, humidity, wind speed, wind orientation, pressure, and height. It consists of a radiosonde that is lifted by a balloon filled with gases that lets it take off from the Earth surface and reaches approximately 30 kilometers, that corresponds to medium Stratosphere. 5(de Arruda Moreira et al.,2020) Figure 2.4: Pierre Idrac and Robert Bureu with their first model of radiosonde, 1928. (Figure taken from Météo Nouvelle-Calédonie website.) The kind of radiosonde capable of obtaining wind speed and orientation is the rawindsonde (radar wind radiosonde), and it is the one considered in this study. (S.J. Fletcher, 2023) The components of the weather balloon are (see Figure 2.5): 1. The balloon. Filled with hellium (He) or hydrogen (𝐻2). It is normally made out of biodegradable latex. They rise until it reaches their maximum diameter, then it bursts, and the parachute is opened. 5Nowadays the technology has developed radiosondes that can reach 53km, as an example of the Japanese balloon BU60-1. 14 2. Recovery parachute. 3. The radiosonde. Modern devices are lightweight enough to reach very high altitudes. (a) Temperature and pressure sensor (b) Humidity sensors (c) GPS receiver (d) Antenna The technological parts must be supplied with a power source provided by an attached battery. Figure 2.5: Radiosonde Parts. (Figure taken from the National Weather Service, USA website) After the rawindsonde, many other instruments have been created, such as the LiDAR (Light detection and Ranging or Laser Imaging Detection and Ranging) sensor included in 6a celiometer. The necessity of improvement for higher temporal and spatial resolution has lead to intense studies on this issue. (Chen, X. et al. 2023) 6The LiDAR uses light in all of the spectrum of and frequency (from ultra-violet to infrared) to detect particles in the air. This sensor detects high-resolution data for pollutants, target clouds, and rain by different methods of scattering. The compound devices make it very expensive and unavailable for many meteorological stations. 2. introduction. 15 Measurement tools are classified in active and passive ones. The passive remote systems obtain their energy from the natural available one, such as the free air. These are typically the the radiosondes. On the other hand, the remote systems or active, emit radiation toward the targets to be measured, these are typically the celiometers. Data from the radiosondes are far less accurate than those from celiometers. However, they are reliable of the time scale of our study. 2.3 The emagram. Definition 2.3. The emagram or skew-T versus P diagram is a thermodynamic diagram that represents pressure, altitude, and wind direction over temperature in the troposphere. This type of diagrams are common in Meteorological field. Pressure is commonly found on the vertical left axis while pressure height lines and wind direction lines are added on the vertical right axis of the diagram. Sometimes diagrams represent pressure as a function of a natural logarithm (our diagrams use natural pressure). See Figure 2.6 of a Skew-T log (P) typical diagram. See Figure 4.2 as an example of the diagrams analyzed in this studio. The height is usually measured in meters above sea level. Temperature is measured in ºCelsius and pressure, in hPa. It was first used by Heinrich Hertz in 1884 and soon spread all over the world. Three temperatures are represented: the temperature of free air, the temperature taken from a wet bulb, and the dew temperature. 16 Figure 2.6: Parts of a Skew(P) Versus Log(P) diagram. Pressure are represented as isobars: constant lines in orange. Isotherms (for lines connecting points of constant free air temperature) and isodrosotherms (for lines connecting points of constant dew point temperature) are represented as parallel lines of positive slope. Dry adiabats follow parallel lines of negative slope. Their values denote the potential temperature (Equation (2.12)). Moist adiabats or saturated adiabats are dashed-curves up and to the left. This lines will show up the PBLH. When a saturated parcel of air rises to the point where temperature rate happens the height where the planetary layer is displayed. Temperature of free air is the only one plotted on this diagram. However in our diagram we will have the temperature of moisture and the temperature of dew point. (Figure taken from Colorado State University website.) 2. introduction. 17 2.4 Equation of motion. The atmosphere is a fluid, and it follows Navier-Stokes equation for the motion of fluids. Navier-Stokes general continuum equations are given by: 𝐷𝑣 𝐷𝑡 =𝑔 − ∇𝑝 𝜌+ ∇ ⋅𝜏(2.1) Where the functional derivative: 𝐷𝑣 𝐷𝑡 =𝑑𝑣 𝑑𝑡 +𝑣 ⋅ ▿ 𝑣 (2.2) It is expressed in Eulerian formulation, where the time derivative on the celerity is the local derivative and it is used in fluid mechanics for fixed measurements. On the right-side of the formula: 𝑔 is the gravity term,  ∇𝑝 𝜌is the gradient in pressure over the air density commonly known as the baric force, and ∇𝜏denotes the force derived from air viscosity. The viscosity term, 𝜏is a second rank order tensor. There are no analytic solutions for this equations. They must be computed to have numerical solutions. The parameters taking into account are temperature, moisture and wind as well as the boundary conditions on the ground. 2.5 Over Planetary Boundary Layer Height: free atmosphere. Geostrophic approximation: Baric force and Coriolis one compensates at high altitude and there is hydrostatic equilib- 18 rium along the vertical axis. There is no vertical acceleration: the fluid is considered to flow in a stable current because there is a perfect balance in the Gravitational force and the baric (gradient pressure) force. Horizontal acceleration is considered null also. −1 𝜌 𝜕𝑃 𝜕𝑥 + 2𝜔𝑢𝑠𝑒𝑛(𝜃)=0 (2.3) −1 𝜌 𝜕𝑃 𝜕𝑦 − 2𝜔𝑣𝑠𝑒𝑛(𝜃)=0 (2.4) −1 𝜌 𝜕𝑃 𝜕𝑧 −𝑔= 0 (2.5) Here 2𝜔𝑢(𝑣)𝑠𝑒𝑛(𝜃)is the Coriolis parameter 𝑓. 2.6 Under Planetary Boundary Layer Height: turbulence regime. Most processes are turbulent, like: the frictional drag of particles, the transfer of heat, pollutant emissions... et cetera. Studying turbulence demands a wind speed average along a period in between thirty minutes to one hour at least and average is imposed on every parameter, wind, potential temperature, humidity and pressure. The methods exceed the limits of this work but some are briefly explained: Micrometeorology field takes statistical averages made from experiments. Once observations have been made, empirical relationships are applicable. 2. introduction. 19 Developed with the purpose of a suitable treatments of empirical data to make them useful for the Navier-Stokes equations, some theories have been proposed such as the Monin–Obukhov similarity theory. (Beljaars A., 1992) This theory parts from the turbulence as a small scale and it has been used to model hurricanes or fronts. That is the approximation taken into account on the parcel method developed in this project. We are going to describe the temperature flux in the surface under no time dependence and null boundary conditions (wind velocity is zero on the ground). Since the time dependence is assumed to be weak, several conserved quantities can be used. From Navier-Stokes general equations, we first describe the air flux as incrompressible: we assume the density of the fluid to be constant regardless of the variations in pressure. That immediately makes the stress tensor a Galilean invariant so the second rank order tensor becomes a gradient tensor. Incompressible fluid allows to symplify Navier-Stokes equations: 𝐷𝑣 𝐷𝑡 =𝑔 − ∇𝑝 𝜌+𝜇∇2𝑣 (2.6) The left side is the same functional derivative whereas on the right-side appears 𝜇the kinematic viscosity assumed to be constant. Second approximation leads to consider conservative external fields over PBL, that is the fundamental equation of hydrodynamics. 𝐷𝑣 𝐷𝑡 = −∇ ⋅ℎ+𝜇∇2𝑣 (2.7) 20 Where ℎ=𝜔+𝜙.𝜔represents the thermodynamic work and 𝜙denotes the gravitational potential. If we do not consider friction neither viscosity and static variables (because time is fixed in our experiment and it is not a variable taken on count), then the equation reduces to continuity equation for incompressible fluids. Right at this point, we can define the conserved quantities of the PBL approximation used through this project. 𝐷𝜌 𝐷𝑡 = 0 (2.8) density remains constant over time. Density conservation leads to mass conservation and size of the parcel of air. Divergency of velocity vector, ∇⋅𝑣 =𝜕𝑢 𝜕𝑥 +𝜕𝑣 𝜕𝑦 +𝜕𝑤 𝜕𝑧 = −1 𝜌 𝑑𝜌 𝑑𝑡 = 0 (2.9) The proximity of the sea produces constant humidity during the forecast, that leads to the conservation of moisture, governed by the equation of mass conservation. 2. introduction. 21 2.6.1 Adiabatic approximation. Thermodynamics are based on three fundamental principles that lead to understand the meaning of an adiabatic process. Definition 2.4. Zero Thermodynamic Principle. Having two independent equilibrium systems in contact with a third one means that they are in temperature equilibrium among them. Definition 2.5. First Thermodynamic Principle. 𝑑𝑢 =𝑑𝑞 −𝑑𝑤 (2.10) The differential variation in internal energy of a system, du, is equal to the difference between the variation on heat, dq and the work done, dw. The entropy of the Universe tends to increase. The heat transfer occurs because systems try to find equilibrium of energy, as the first thermodynamic statement establishes. Definition 2.6. Second Thermodynamic Principle. Clausius Statement: There cannot be a process where the only result is the transfer of temperature from a low-temperature body to one of a higher one. Implies that of all of the turbulence on the boundary layer the variations of the entropy tends to zero in addition to the adiabatic character of the atmosphere in most cases. (Decaria, A. 2007) Definition 2.7. The Potential Temperature. The potential temperature is the temperature at which an air parcel at temperature T and pressure p expands to pressure 𝑝0adiabatically. 28 ronment (IDE) that facilitates coding, data analysis, and visualization. The Openair package, offers a versatile toolkit for investigating aerosols and meteorological parameters with accuracy. Among its capabilities, it enables users to conduct comprehensive air quality analyzes, explore atmospheric dynamics, and visualize intricate relationships between pollutants and weather variables. It has a user-friendly interface and robust functionalities. The meteorological variables have been represented with SmoothTrend function. This function finds the tendency of the average parameters and plots both the smoother curve and the average, debugging the points that create peaks (as noise). (Carslaw, D.C., Beevers, S.D., Tate, J.E., 2007) 4 Results and discussion. In this section results are presented, as well as a discussion of them. Plots are presented after the discussion due to their size. 4.1 Evolution average of PBLH. The complete evolution of PBLH estimated from 2020 to 2023 can be seen in Figure 4.1. Same scale is adopted for all figures. To begin with, the data representation has been divided into morning curve Smooth Trend height variation (in purple corresponding to 11am soundings) and night curve Smooth Trend height variation (in yellow corresponding to 11pm soundings). This division has to do with the definition of PBL given above. Ideally, during the day as the surface gets radiated by Sun illumination a CL appears, and during the night by the time Sun downs the RL and the SL. However, in reality, at the morning sounding time, 11am, a mixing layer remains before it is completely convective (CL). The maximum height is approximately at 15pm. We will define it as a "semi-convective layer" (SCL). (Caballero Bravo, C., 2022) At 11pm there is not always a RL, it only appears when the surface is warm enough to radiate and provide kinetic energy to the particles beneath it. SL remains always but it is not 30 detectable with a radiosonde, resides in an average between 700-800 meters. At night the data scattering is relatively higher than that found during the day. At 11am the PBLH shows season periodicity: higher heights on summer and lowers heights during winter. In spite of the dispersion during summer PBLH has relative maxima both for night and day, see Figure 4.1. Meanwhile, in winter, it highlights adverse weather, so the CL is weaker during the day, which is represented with relative minima. In general terms, it shows a general increase of PBLH during the day of 1000m (CL), and 500m during the night (SL + RL). In 2020 PBLH plot, oscillation is divided into summer and winter with a difference of 1500m, see Figure 4.2. However, the PBLH plot observed in 2021 has increased, see Figure 4.3 and the difference between summer are winter is smaller in averagae 1000m. CL in 2021 shows a lower amplitude oscillation, decreasing 500m from the previous year. However the layers at night behave similarly. Not only 2021, but 2022 and 2023 also showed the same average in altitude of their CL, which means more space for turbulence and aerosol propagation. See Figure 4.4 and Figure 4.5 where the PBLH tends to be almost continuous during summer and only decreases in autumn and spring. The PBLH in 2023 shows an outstanding contrast: spring minimum is almost disappearing, and the height in winter is the highest compared with the previous years. 4.2 Temperature average evolution of PBL. Figure 4.6 shows the evolution of the daily temperature profile from 2020 to 2023. Yellow curve describes the periodic tendency for the morning values, showing relative maxima in summer and relative minima in winter. Purple line has not been adjusted to the periodicity of the values 4. results and discussion. 31 due to the fact that RStudio finds data noise and represents no curve tendency. With that on mind, even though the height has grown in 4 years the temperature has not, because the PBL parameters have changed analogously, corresponding to the prediction done on equation (2.12). Potential temperature is proportional to height and temperature, that is why the evolution of temperature shows a periodic behaviour as well as the height. The point is determining how all the variables affect PBLH from the empirical experiment. From August 2019 to February 2020, the WMO (World Meteorological Organisation) indexed ENOS as neutral in terms of ENOS strength. From May on, the temperature on the Pacific Ocean surface was similar to or lower than the average and the formation of La Niña cycle happened in September 2020. See decrease of temperatures in September 2020. Same profile presented 2021, from May on, the temperatures decreased from the average, Figure 4.8. Temperature resembles in every season, with the only difference that in 2021 La Niña began later than 2020, in December. They resemble each other and leads to admit that the summer is characterized by the increase of temperature between June and August and the decrease of temperatures in September. Decrease showed in Figure 4.7 and Figure 4.8 from autumn to winter, has to do with the increase of height observed in Figure 4.2 and Figure 4.3 because not only in summer there is a high PBLH, but in winter as it was said, takes longer to grow. The PBL temperature in 2022 shows an abnormal heat oscillation from September to October, and it is reflected in the variables that predict La Niña (see Figure 4.9.) and a possible transition to El Niño cycle. However, La Niña was active from winter 2022 until 2023 spring, observing a temperature increase until summer 2023. 2023 was the warmest year ever registered, reported from the advance AEMet’s archive of the following year. It could be observed temperature anomalies of 2 °C over average in 32 Andalusia, Catalonia, Aragon, and La Rioja, while in the rest of the country the increase was approximately 1°C. Although winter temperatures (from 21𝑠𝑡 of December 2022 to 2023) were within average intervals, winter 2023 was warmer. Furthermore, 2023 spring start (from 1𝑠𝑡 March to 31𝑠𝑡 May) particularly hot compared to 2022, being 1,8ºC higher, according to AEMet’s report. Temperature profiles of 2022 and 2023 show opposite behavior, intimately related to the ENSO phenomena. A “Triple-dip” of La Niña occured through this years but after summer 2023 El Niño cycle begun. Regarding summer 2023, it was was found to be the hottest one ever since 1961 first report. Similar temperatures were registered in 2022 and 2003. It was characterized by four heat waves, two in July and the other two in August. A set of 9 extremely hot years have taken place in XXI century. (See Figure 4.11) For the next variables, a decrease in pressure due to the increase in height and an increase in wind velocity can be predicted, in addition to the same fact: having more altitude leads to a more intense turbulence. 4.3 Pressure average evolution of PBL. We could estimate the behaviour of pressure in addition to the increased observed in PBLH. When altitude increases, pressure decreases. That is seen in the inversion of the curves (purple represents the 11am profile pressures and yellow the 11pm profile pressures.) 4. results and discussion. 33 4.4 Wind celerity average estimation in the PBL and comparison with winds bursts. Higher heights in PBL are associated with high wind speed (Chan, R., Lee, O., &Cheng, A. 2006.) that is why, the average increase observed in PBLH is the same as the observed in wind speed. There is no way Smooth Trends could estimate a smooth curve, rather than a line with positive slope. The data varies from complete alteration in CL (Figure 4.13), during the morning the turbulence prevails. In winter velocity terms are higher than summer values. During the night, the SL tends to have lower winds and the curve is almost oscillatory. The result of a study of the winds in Huelva from 1996 to 2022 of professor de la Rosa, J. from UHU is relevant in the discussion of wind celerity PBL. Desertification was previously explained as one of the consequences of climate change. Figure 4.14 shows that since 2016 until 2022 index number of NAF winds have doubled its regular values from previous years. This fact matches to the increase in wind celerity in Figure 4.12 plot and the increase in height found in Figure 4.1. 4.5 ENSO phenomena evolution. ENSO phenomena is related with the increase of wind speed too, since Huelva is located just above the limit of northern easterlies. Studies have shown the strength of winds coming from NAF since 2016. From the Figure 4.14 bursts winds from Air Quality and Meteorology Blog of Andalucia, Associate Unit CSIC-University of Huelva "Atmospheric Pollution" CIQSO, Center for 34 Research in Sustainable Chemistry (by professor de la Rosa) we can conclude wind celerity increases in winter. In addition to the appearance of strong NAF episodes since 2023 of the examination of PBL variables, it is clear a tendency of turbulence wind regime. They have become faster through time, about 15km/s on average. In contrast with the evolution until 2022, 2016 represents a negative value of the normalization in Figure 4.14. The end of La Niña episode has produced the beginning of El Niño. Scientists are now concerned because the last episode of La Niña did not follow the theories conventionally made. There has not been a stop of la Niña in three consecutive years and it was only recorded in 2000. The impacts have made extreme climate changes all over the World. Atlantic ocean has had a greater behavior on hurricanes never seen before because the jet stream of cold winds from La Niña moves northward, making south parts of Earth warmer and drier. After this episode, El Niño has not been as strong as predicted, and it has make scientists suggest ENSO phenomena maybe not only a Pacific Ocean phenomenom, but a basin general issue. By general conclusion, multi-year La Niña is predicted to become more common while no laws on greenhouse gasses emission are established. Climate change is a serious problem to take on account and even though is not reversible, at least consequences could be not that extreme if we act now. 4. results and discussion. 35 Figure 4.1: Complete PBLH profile daily average variation from 2020 to 2023. 36 Figure 4.2: Height daily variation in 2020. 4. results and discussion. 37 Figure 4.3: Height daily variation in 2021. 44 Figure 4.10: Temperature daily variation in 2023. 4. results and discussion. 45 Figure 4.11: Anomalies in temperature from the average temperature from 1991 to 2020. In blue the coldest years and in red the hottest ones. (Figure taken from the Climate Advance AEMet, 2023) 46 Figure 4.12: Complete pressure profile daily average variation from 2020 to 2023. 4. results and discussion. 47 Figure 4.13: Complete wind celerity profile daily average variation from 2020 to 2023. 48 Figure 4.14: Bursts of wind from NAF in Huelva. Evolution from 1996 to 2023. Winds normalized to the average temperature since 1996. 5 Conclusions. ∙The first objective was to understand PBL from its theoretical definition (Stull, 1988). Defining two visible cycles of the layer, one at night and another one completely different at daytime and each name: CL for the layer at daytime and RL, SL for the nighttime. Both cycles are typical during a turbulence regime. Introduction was developed to define the importance of PBL in meteorology field for future predictions on climate change. We try to prove that PBL behavior correlates with ENSO cycles and global warming. ∙Weather balloon and Skew-T versus Pressure meteorological devices were described by their physical meanings in Methodology 4𝑡ℎ section. Relation between variables inside PBL: altitude, pressure at the concrete altitude over sea surface, temperature and wind speed respectively with the potential temperature formula. Determining PBLH on the diagram by following the saturated adiabatic lines was necessary to prove adiabatic approximation. Data was taken for 8 months (from October 2023 to May 2024) intensively studying the emagrams from sounding weather balloons previously realised at El Arenosillo station from INTA, Huelva. RStudio application was used to plot PBL associated parameters with Smooth Trend curves tool. Every figure has a diurnal and nocturnal plot corresponding to 11am and 11pm respectively and each parameter its own color to attend difference. 50 ∙Discussion summed up in a general increase of PBLH and wind celerity and a decrease on pressure for both diurnal and nocturnal layers presented on PBL. Meanwhile temperature oscillations show periodic tendency because of the "Triple-dip" of La Niña in the period of study of the project. The increase average on height, CL rises to 1500m while RL and SL at night stands an average of 500m. Summing up, the higher heights are shown in summer during the presence of a convective layer at 11am. Relation between altitude over sea surface and pressure is positive for both nocturnal and diurnal data, since the rise of heights means lower pressure and along with that, a descrease of air density. Diurnal temperature oscillation have positive correlation with PBLH. It does not mean that the approximation made was wrong, it actually makes sense that the oscillations on temperature are constant in amplitude. PBL temperature is constant at its height meaning that the ground temperature has increased in the 3 years period, and so the altitude has grown. At night, PBL temperature has no trend, showing uncorrelated peaks. A general decrease in diurnal temperature in 2023 can possibly be related to El Niño beginning. Wind speed lines for both diurnal and nocturnal data present oscillation but frequently random peaks break the tendency and no periodicity seen. However, the correlation is positive between wind speed and PBLH, they both increasing with time showing more turbulence regime. It is necessary to emphasize that the PBL at sea level is very different from the ones we established. Although the data were taken over the land, the station is near the sea level (20m) and boundary conditions are in the limit of acceptance. Taken that into account, the results are reliable to the climate change predictions. 5. conclusions. 51 We have proved that PBL parameters show a tendency in agreement with the global phenomena and despite experimental and human error, emagrams and weather balloons are reliable tools. 5.1 Future applications. Future work can be addressed as follows: - Developing an IA code to determines the height by analyzing directly environmental and moisture rate as well as the changes in wind direction. - Sounding radiosondes daily to study the PBL. This process would be slower but the human error could be avoided. -Analyzing the relation of PBLH, temperature, pressure and wind speed to match them with the spread of aerosols and pollutants. -Correlation of wind direction with Ekmann diagrams at the lowest troposphere layer. (Beljaars, A., 1992) - Study of storm formation over CBL variation. (Groenemeijer, P. 2009) 6 Bibliography. Beljaars, A. May 1992. European Centee for Medium-Range Weather Forecasts. The parametrization of the planetary boundary layer. From Pages 2 to 12. Caballero Bravo, C. 2022. Estudio de la capa límite planetaria y su influencia en la contaminación atmosférica. URI: https://uvadoc.uva.es/handle/10324/58312 Carslaw, D. C., Beevers, S. D., Tate, J. E., Institute for Transport Studies, University of Leed, UK, 2007. Modelling and assessing trends in traffic-related emissions using a generalised additive modelling approach. Atmospheric Environment 41, 5289–5299. DOI: 10.1016/j.atmosenv.2007.02.032 Chan, R., Lee, O., &Cheng, A. 2006. Laboratory for Atmospheric Research, Dept. of Physics and Materials Science, City University of Hong Kong. Diurnal variation of mixing height in Hong Kong. Chen, X., Yang, T., Wang, Z., Wang, F., and Wang, H.: An ensemble method for improving the estimation of planetary boundary layer height from radiosonde data., 2023. Atmos. Meas. Tech., 16, 4289–4302. DOI: https://doi.org/10.5194/amt-16-4289-2023 DeCaria, A. J. (2007). Department of Earth Sciences, Millersville University, Millersville, Pennsylvania. Relating static energy to potential temperature: A caution. Journal of the Atmospheric Sciences. DOI: https://doi.org/10.1175/jas3906.1