FACULTAD DE CIENCIASDEPARTAMENTO DE FÍSICA Y ASTRONOMÍADEPARTAMENTO DE METEOROLOGÍADEPARTAMENTO DE ESTADÍSTICAAnalysis of lo al meteorologi al onditions in Ma ón using theMM5 modeling system

September 28, 2007Omar Cuevas, Mi hel Curé, Carlos AlvarezDepartament of Physi s and Astronomy, Valparaíso UniversityArlette Cha ónDepartament of Meteorology, Valparaíso UniversityAlejandra ChristenDepartament of Statisti s, Valparaíso UniversityUniversidad de Valparaíso

Contents1 Introdu tion 11.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11.1.1 Main objetive . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21.1.2 Spe i� obje tives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 Climatology and orography of Ma ón 32.1 Climatology of Ma ón . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32.1.1 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42.1.2 Geopotential Height . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52.1.3 Relative Humidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62.1.4 Omega (dp / dz) verti al air movement . . . . . . . . . . . . . . . . . . . . . . . 62.1.5 Wind Ve tor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72.1.6 General Des ription . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82.2 Orography . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 Data 113.1 GFS global model data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113.2 Meteorologi al data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123.3 Turbulen e data (MASS-DIMM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 Des ription of the MM5 model system 144.1 TERRAIN module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154.2 REGRID module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.2.1 Pregrid . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.2.2 Regridder . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.3 INTERPF module . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 164.4 MM5 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185 Synopti analisys 195.1 Synopti lassi� ation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195.1.1 Anti y loni Predominan e (AP) . . . . . . . . . . . . . . . . . . . . . . . . . . . 195.1.2 Frontal System (FS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 205.1.3 Cut-o� Low (CL) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 225.1.4 Jet Stream (JS) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 235.1.5 High Trough (HT) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24i

CONTENTS ii5.1.6 Cold Anti y lone (CA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 255.1.7 Synopti pattern in Ma ón . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 265.2 Seasonal Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276 Evaluation of the MM5 model 306.1 Temperature . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306.2 Wind Speed . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 326.3 Humidity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 346.4 Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 367 Traje tory Analysis 387.1 Paranal - Ma ón Traje tories . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387.2 Traje tories that rea h Ma ón . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 407.3 Analysis of altitude traveled by the traje tory . . . . . . . . . . . . . . . . . . . . . . . . 428 Seeing Statisti s 448.1 Distribution Form . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 448.2 Seeing ≤ 1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 468.3 Study of superior outliers (extreme superior values) . . . . . . . . . . . . . . . . . . . . . 498.3.1 Limits for the superior outliers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 498.4 Seeing2 study for periods of 2 hours . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 538.5 A umulated frequen ies of seeing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 539 Seeing Study 569.1 Classi� ation of seeing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 579.1.1 Seeing at Tolar . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589.1.2 Seeing at Ma on . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589.2 Isotherm 320 K . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 589.3 Seeing and wind . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 639.4 TKE and the Ri hardson number (Ri) . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6810 Con lusions 7610.1 Lo al meteorology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7610.2 Seeing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7711 Appendix 82

Chapter 1Introdu tionThe Ma ón zone (24oS, 66oW) in the northeast of Argentina, is an area pre-sele ted for the onstru tionof the ELT (Extremely Large Teles ope), whi h the ESO1 is in harge of. Preliminary analysis hasdes ribed this se tor as one of the optimal zones of our plant for this a tivity. These studies weredone within the framework of sele ting a site for ESO astronomi observation [Sarazin et al., 2000℄.Due to interest in this zone, in situ measuring ampaigns have been ondu ted of variables whi h ondition the pla e, among those meteorologi al and turbulen e information is onsidered importantfor astronomi observation. This information will be analyzed relating seeing (turbulen e) ases tolo al and synopti meteorology, in order to identify the �ow pattern in an area that is onsidered tobe orographi ally omplex.In order to study the meteorology of this pla e the MM5 modeling system will be used, whose hara teristi s make it possible to do small s ale analysis (∼1 km), and it integrates the most mod-ern and realisti parameterization s hemes of a variety of atmospheri physi al pro esses, whi h areappropriate to the onditions present in Ma ón.In this report, limati and orthographi des riptions were done �rst and are des ribed in hapter2. Chapter 3 shows the data used in this study. The on�guration of the MM5 modeling systems forMa ón is des ribed in hapter 4. A synopti analysis and lassi� ation of �ow patterns of the Ma ónzone were ondu ted and are des ribed in hapter 5. Chapter 6 shows the MM5 model validation thatwas performed using real data registered for the meteorologi al station in Ma ón. Next, in hapter7 traje tories of di�erent spatial s ales were analyzed in order to determine the origin of the air �owthat rea hes Ma ón. In the following hapter (8), a statisti al analysis was done of the seeing variablefor Ma ón, and then a study of seeing was ondu ted where lo al meteorologi al variables were relatedto turbulen e indi es, su h as the TKE and the Ri hardson number (Ri). Chapter 10 dis ussed themost relevant on lusions of this study.1.1 MotivationIn Ma ón turbulen e data was olle ted and saved over a total of 158 nights using the MASS (MultiAperture S intillation Sensor) and DIMM (Di�erential Image Motion Monitors) instruments. Thisinformation forms part of the preliminary studies ondu ted by the ESO when sear hing for a site to1European Southern Observatory 1

CHAPTER 1. INTRODUCTION 2build the ELT. To analysis this data as well as the numeri al simulations using the MM5 model andthus understand the meteorologi al onditions in Ma ón are the main motivations for this study.1.1.1 Main objetiveTo analyze the lo al meteorology of Ma ón using the MM5 modeling system, along with data obtainedin di�erent ampaigns.1.1.2 Spe i� obje tives1. Implement the MM5 model for the Ma ón zone.2. Statisti ally analyze the measured turbulen e data (Cn2) and seeing through time and identifyrelevant episodes as ase sele tions.3. Do simulations for the ases sele ted.4. Analyze the synopti onditions of the simulations arried out.5. Analyze and ompare the turbulen e indi es TKE and the Ri hardson number (Ri) and othermeteorologi al variables with turbulen e data (Cn2) and seeing.

Chapter 2Climatology and orography of Ma ón2.1 Climatology of Ma ónThe majority of the literature reviewed (Rutllant, 1982; Ulriksen, 2001a) expressed that the mostimportant fa tors that ontrol the atmospheri weather and onditions the limate throughout on-tinental Chile are the Subtropi al Anti y lone of the South Pa i� , the old o eani ir ulation ofHumboldt, the winds from the West zone where the frontal systems and ut of low �ow travel, andfurther South are the low pressure zones whi h surround the Antar ti , known as ir umpolar trough.Furthermore, in the North of Chile, in the summertime the monzoni ir ulation developed on theEast side of the Andes Cordillera generates pre ipitation in the altiplane.For the Ma ón zone, lo ated in the middle of the Andes Cordillera in Argentine territory, one musttake spe ial onsideration of the predominan e of warm anti y lone of the Subtropi al Anti y lone ofthe Pa i� South (PA), the zone of the West Winds (WW) that onstantly adve t masses of old air,indu ing frequently dominated as high trough (HT) and migratory old anti y lones (CA). Further-more, it is important to onsider the altitudes e�e ts on jet streams both subtropi al and polar (JS),and during the summertime periods, the monozoni ir ulation of the altiplane and Altiplane Winter(AW) have an in�uen e.Other ir ulations to onsider are more losely related to South Ameri an limatology where theContinental Warm Low (WL) is onsidered the most important, whi h is developed at low levels likethe warm and humid tongue in the entral Brazil and that moves approximately from North to Southfor the enter of the ontinent, with a maximum intensity in the summertime period. On the otherside, the lo al limate of Ma ón orresponds to the old desert of the mountain whose most relevant hara teristi is very dry air and where the rhythm of the temperatures is regulated by the altitude.The type of ground is hara terized by the dominan e of more drasti onditions of dryness.(Alvarez,2004).There is big thermal amplitude in the levels losest to the surfa e in the desert, with strong ontrastof temperature extreme between day and night. Additionally, in the valleys and ou�n appear valley-mountain breezes. In atmospheri movements on a small s ale, lo al e�e ts predominate su h as theintera tion with the surfa e, relief and obsta les.In order to analyze the Ma ón zone, it is ne essary to identify the most in�uential synopti lima-tology, whi h entails looking for ir ulation patrons in South Ameri a at synopti s ale. Following arethe limatologi ally omposites of the most relevant variables.3

CHAPTER 2. CLIMATOLOGY AND OROGRAPHY OF MACÓN 42.1.1 TemperatureIn analyzing the limatologi ally variable of temperature, it an be observed that at low levels (925hPa), a warming elongation from the enter of Brazil toward the South is learly delineated, des ribingthe ir ulation of the WL. In the se tor the Pa i� O ean it an be observed that the in ursion of old air re�e ting the in�uen e of the o ean urrent of Humboldt, helping the limati stability of theNorth of Chile (�gure 2.1a). It an be assumed that the temperature ontributions that arrived atMa ón, from low levels, depends on the behavior of the WL and PA.In the 500 hPa, it an be observed that limati ally over Ma ón soft in ursions of HT are elongated,where in the trough se tor enters slightly old air and in the ridge enters warm air with a limatologi- ally isotherm of -8 degrees Celsius (�gure 2.1 b). For the 250 hPa there is a very established marginzone with an isothermal mean of -43 degrees Celsius (�gure 2.1 ).

a) b)

)Figure 2.1: Temperature limatology (a, b, ) in 950, 500 and 250 hPa. Reanalysis images taken(NCEP).

CHAPTER 2. CLIMATOLOGY AND OROGRAPHY OF MACÓN 52.1.2 Geopotential HeightFor the level of the 925hPa, above the Pa i� O ean, the predominan e subtropi al anti y loni semipermanent in the zone an be observed (AP), whi h is related to the elongation of low pres-sure values of the oastal low (CL), patterns that ir ulate from north to south along the oast ofChile. In addition, in the southern zone the WW are shown, patterns where the frontal systems �owtravel, and where the entran e of these frontal system toward the north barely entering to ontinent.Also, the prolongation above the ontinent of the WL an be observed (Figure 2.2a). On the 500 hPais an approximate average of 5850 mgp (geopotential meters) for Ma ón with in ursions of HT (Figure2.2b). On the 250hPa zonality is observed (Figure 2.2 ).

a) b)

)Figure 2.2: Climatology (a, b, ) and anomaly (d, e, f) of geopotential height at 950, 500 and 250 hPa.Images taken from reanalysis (NCEP).

CHAPTER 2. CLIMATOLOGY AND OROGRAPHY OF MACÓN 62.1.3 Relative HumidityClimati ally speaking, it an be observed in the zone of Ma ón is espe ially dry with very low relativehumidity in the 925 hPa (Figure 2.3a). Analyzing the level of 500 hPa, it shows a minimum relativehumidity from the Northwest asso iated mainly with PA, whose in�uen e is noti eable (Figure 2.3b).For the level of the 300 hPa the same pattern of the 500 hPa an be observed, with the di�eren ebeing that it is slightly moved North (Figure 2.3 ).

a) b)

)Figure 2.3: Climatology (a, b, ) of relative humidity 950, 500, and 300 hPa.. Images taken fromreanalysis (NCEP).2.1.4 Omega (dp / dz) verti al air movementHere the pattern of the 925 hPa shows us a zone with slightly negative values, implying elevation of airby orographi hara ter by way of air �ow from the East, along with the monozoni ir ulation of AW ofthe summer (Figure 2.4a), while in the 500 hPa there exist a se tor in a great portion of the ordillera,

CHAPTER 2. CLIMATOLOGY AND OROGRAPHY OF MACÓN 7in luding Ma ón, with maintained elevations, whi h are asso iaded again with the orographi e�e tsand the ontributions of air from lower levels of the WL and the AW, also the PA with its maintainedair des ents (Figure 2.4b). The level of the 250 hPa ontinue to show the PA although with greaterintensity, whi h guarantees general ir ulation model of the atmosphere yielding limati ally speakinga se tor of air des ent over the Pa i� O ean, ommonly known as the limit between the ir ulationsof the ells of Ferrel and Hadley (Holton, 1990), while for Ma ón there are slight elevations (Figure2.4 ).

a) b)

)Figure 2.4: Climatology (a, b, ) of Omega (dp/dz) at 950, 500 y 300 hPa. Images taken from reanalysis(NCEP).2.1.5 Wind Ve torFor the 925 hPa a enter of y loni ir ulation exists limati ally speaking in the Ma ón zone, whereair omes from the West, produ t of the PA, and from the East due to the ir ulation of the WT(Figure 2.5a). In the 500 hPa the e�e t of the HT is observed as a hange in the wind intensity and

CHAPTER 2. CLIMATOLOGY AND OROGRAPHY OF MACÓN 8dire tion, the limatologi al value of the module of the wind over Ma ón approximates the 10 m/s(Figure 2.5b). In the level of the 250 hPa a semipermanent JS an be identi�ed that is elongated fromthe West � Northwest to the East � Southeast asso iated to the birthing zone of the frontal systems,whi h are also alled frontalgeneti zones, whose nu leus is approximately between Uruguay and theSouth of Brazil, whose e�e ts spread to the Ma ón zone (Figure 2.5 ).

a) b)

)Figure 2.5: Wind limatology in 950, 500 y 250 hPa. Images taken from reanalysis (NCEP).2.1.6 General Des riptionWith the lo al hara teristi s of the Ma ón zone, we an highlight that the radiative balan e andtherefore the temperature, are fundamental in the limati modulation. This means that the limatehas a radiative hara ter, whi h means that within a normal year there are not great variationsasso iated with adve tions of air form the other zones.Despite this, in the analysis of the synopti limatology you an identify the di�erent ir ulationsthat dominate above the South Ameri an ontinent, the most in�uential over Ma ón, of semipermanent

CHAPTER 2. CLIMATOLOGY AND OROGRAPHY OF MACÓN 9 hara ter are: the PA asso iated with the subtropi al anti y lone of the Pa i� , the ir ulation of lowlevels of theWL, and the monozoni ir ulation of AW. Now, in synopti patterns that move, we �nd the HTwhi h o ur often, JS and CA that have a lesser presen e. In addition, there exist very few in ursionsof frontal systems and segregated lows during the year.In fun tion of lo al fa tor and of synopti limatology, we an sum up by saying that the Ma ónzone has many thermal variables, whi h bring onsequen es that the type of ground onditions a lo al,daily (day and night). In addition, if we add up the patterns of ir ulation that move, the urve ofnormal limatologi al variables of the Ma ón zone, su�ers small hanges throughout the year, mainlyby HT and JS, patterns that present interseasonal periods. The variable relative humidity is the mostregular of all. Its extension temporary and spe ial in luding verti ally, sin e it is an extremely dryzone.It is important to emphasize the semipermanent patterns also su�er hanges in fun tion of moreextensive ir ulations both temporarily and spe ial, just like the Niño and the Southern Os ilation(ENSO) or the Antar ti O eani Cir ulation (SSA). These patterns present periods within de ades,whi h is out of the s ope of this study.It is important to point out that the semipermanent patterns also su�er hanges in fun tion ofmore extensive ir ulations both temporarily and spe ial, just like el Niño and the Southern Os ilation(ENSO) or the Antar ti O eani Cir ulation (SSA). These patterns present periods within de ades,whi h is out of the s ope of this study.2.2 OrographyMa ón is lo ated to the northwest of Argentina, almost at the border with Chile on the west side ofthe Andes Cordillera. In Figure 2.6 the mountain hain of the Ma ón zone an be seen, whi h extendswest to the salt lake Arizaro. In Ma ón summits are found until 5,000 m.a.s.l. and meteorologi alstation is at 4,6000 m.a.s.l. Toward the southwest is Tolar, a pla e where turbulen e measurementswere taken by the MASS instrument, whi h serve as a bases for this study. This station is found at aheight of 3,500 m.a.s.l. with approximate di�eren e of 1 km with respe t to Ma ón.Figure 2.6 shows the omparison between two sour es of data that des ribe the zone, to the left adigital pi ture from Google Earth is shown, while on the right a digital pi ture of the zone used bythe MM5 model. This omparison re�e ts the detail that the MM5 model provides, be ause of thein�uen e of the omplex orograph and lo al ir ulation is aptured and it is possible to establish betterperforman e of previous analyses.

CHAPTER 2. CLIMATOLOGY AND OROGRAPHY OF MACÓN 10

Figure 2.6: Orograph of the zone studies, above an digital image by Google Earth, below domain 4utilized by the MM5 model.

Chapter 3DataFor this study, tree main sour es of data orresponding to meteorologi al information of global models(GFS model and mesoes ale model MM5), data of data of atmospheri turbulen e and seeing takenby ESO in the Ma ón zone are used. Meteorologi al data of an automati station implemented in thezone and infrared satelital images by GOES-12.3.1 GFS global model dataThe NCEP1 has developed a numeri al model of the planetary atmosphere, with the purpose beingto support the work done in fore asting and resear h in meteorology. This model has be ome afundamental tool in the area of meteorology world wide. In addition, this information has served as abasis for the development of more sophisti ated tools in the area of atmospheri s ien es.The global numeri al model of the NCEP is urrently the GFS2, whi h has be programmed tomodel the earth's atmosphere in horizontal resolution grids of 1.25 degree and a verti al resolutiongrid of 12 pressure levels starting from 1000 hPa to 70 hPa. Temporarily this data rea hes 120 hoursof fore asting. The model is run four times a day every six hours (00Z, 06Z, 12Z, 18Z). The runs arestarted based on the observations registered for the majority of the reports that exist o� ially aroundthe world at earth, sea and air meteorologi al stations.For this study the data available from the ftp site of the NCEP orresponding to the o tal half ofsouth Ameri a situated between 0oS to 90oS latitude and 120oW to 30oW longitude was used. TheUniversidad de Valparaíso has a database for this model starting in June 2004 and going throughAugust 2006.The GFS data will serve as boundary and initial onditions for the high resolution MM5 modelingover the Ma ón area (Argentina, 24oS, 66oW). Due to its losest geographi ally lo ation, these al- ulations an be applied to Paranal (Chile, 24.6o, 70.5oW) and ALMA (Chile, Chajnantor, Ata amaDesert) as well.In the appendix, the s ript whi h is urrently used to download this information automati ally anddaily is shown.1National Center for Environmental Predi tion. www.n ep.noaa.gov2Global Fore ast System. ftpprd.n ep.noaa.gov 11

CHAPTER 3. DATA 123.2 Meteorologi al dataIn Ma ón one �nds a weather station installed near the summit lo ated al 4600 m.a.s.l. and registersthe following meteorologi al variables: temperature at 2 metres, intensity and wing dire tion at 10metres and relative humidity. These re ords will serve to be ompared with the results of the modelMM5 applied to the zone. In addition we used infrared satelites images of GOES-12 for 00z and 09zin the period of the study.3.3 Turbulen e data (MASS-DIMM)In Ma ón, verti al atmospheri turbulen e data has been registered, using the MASS instrument (MultiAperture S intillation Sensor). This instrument registers turbulen e (C2

n) at six di�erent height levels(0.5, 1, 2, 4, 8, 16 KM) as they related to time. These registries allows also to obtain the value ofseeing, variable analized in later hapters.

Figure 3.1: A nigth of turbulen e pro�le by MASS (26/04/06).ESO in Ma on has made 6 ampaigns of measurament of turbulen e and seeing with MASS instru-ment, during the period between Mar h 10, 2005 and April 28, 2006 whith a total of 158 night of dataof atmospheri turbulen e.Also in Ma on, were made ampaigns of measurement with DIMM (Diferen ial Image MotionMonitors) with a total of 30 nights of registration od data, between May 30, 2005 and April 28, 2006.The data used in this study orrespond to the registered by MASS instrument in the zone of Tolar(24o 35'S, 67o 24'W) orresponding to 126 nights of re ord between the months of Mar h to De emberof 2005.The following table summarizes the number of re ords of data available and used in this study.

CHAPTER 3. DATA 13Turbulen e (Cn2) Global Model (GFS) Weather station126 nights 118 days 113 daysTable 3.1: Nigths sample

Chapter 4Des ription of the MM5 model systemThe MM5 modeling system, urrently in its �fth generation, was developed by Pennsylvania StateUniversity (PSU) and the NCAR1. This model is used in several universities and institutions aroundthe world be ause of its high de�nition in mesos ale atmospheri systems. The model solves numeri allythe equations of the atmosphere, whi h are equations of momentum, mass, and energy onservation.Satisfa tory results have been found in the southern hemisphere [see eg., Garreaud, 1999℄, thus thismodel an be applied it in the se tor under study in this resear h.Important aspe ts of the model are [MM5 home page2℄:1. Multiple nesting apa ity with �two-way� intera tion between domains. This fa ilitates the studyof atmospheri phenomena using di�erent spa e s ales and the design at very high resolutionsfore ast.2. Formulation of a non-hydrostati dynami , whi h alows the model to be used e� iently to rep-resent phenomena with small spatial dimensions, that is, of very few kilometers.3. Adaptation for multiple plataforms and in omputers of shared or distributed memory.4. Automati ingest of data from di�erent meteorologi al analisys and observation sour es, in ludingthe apa ity to assimilate data in 4-dimensions.5. Variational assimilation of onventional data and satellites during fore asting.6. In orporation of the most modern and realisti parameterization s hemes of physi al pro essesrelated to atmospheri radiation, louds and pre ipitation mi rophysi s, onve tion by umulus,turbulen e, energy �ow and momentum on land surfa e.The MM5 suite has pre and post pro ess programs, in the following se tion the pre-pro ess modulesare des ribed brie�y. They are fundamental to the fun tioning of the suite and are the same ones thatare implemented in developing this resear h.1National Center for Atmospheri Resear h2www.mmm.u ar.edu/mm5 14

CHAPTER 4. DESCRIPTION OF THE MM5 MODEL SYSTEM 154.1 TERRAIN moduleThis module belongs to the MM5 suite, and it is in harge of on�guring the domains for the simulationsthat follow. The main hara teristi s of this module are the land on�guration with di�erent horizontalresolutions and the formation of grids for the posterior pro ess. It is also here where the type of landelevation, ground type and use, vegetation, and other types are sele ted. It has resolutions that go hashigh as 900 m for land information. For us what is essential is the sele tion of the type of land surfa euse (LSM3) in order to integrate the variable TKE (Turbulent Kineti Energy) whi h is important forposterior seeing analysis.Four domains are on�gured for this study (Fig. 3.1):

Figure 4.1: Domain maked whith TERRAIN for simulations of the MM5 model1. D1: 80x80 grid points and 27 km of horizontal resolution. Use of global land and ground to 19km. (Mother Domain)2. D2: 70x97 grid points and 9 km of horizontal resolution. Use of global land and ground to 9 km.3. D3: 100x181 grid points and 3 km of horizontal resolution. Use of global land and ground to 4km.4. D4: 73x85 grid points and 1 km of horizontal resolution. Use of global land and ground to 1 km.3Land-Surfa e Model

CHAPTER 4. DESCRIPTION OF THE MM5 MODEL SYSTEM 164.2 REGRID moduleThis module belongs to the pre-pro edural entran e data used for simulation in MM5. It is dividedin two submodules whi h ful�ll di�erent fun tions, assigned to pro ess the type of entran e data withthe di�erent meteorologi al information it possesses.4.2.1 PregridThis module reads the data in GRIB format (format of available data from GFS and ECMWF). Inour ase it is on�gured for the GFS and ECMWF input data.The data reated for this module are of type:• FILE: (date) = meteorologi al data orresponding to height levels.• SST_FILE: (date) = surfa e sea temperature data.4.2.2 RegridderThis module is in harge of re al ulating the GRIB data to the grids generated with TERRAIN. Theexits of this module are an example of type:• REGRID_DOMAIN1 (for domain 1)This data has surfa e and pressure level (3d) intergrated information with a time frequen y of 6 hoursin this ase.4.3 INTERPF moduleThis module is in harge of generating the initial onditions in order to start the numeri al simulationswith MM5, in our ase starting with the GFS global model. In this model the �sigma� (σ) levels forheight (Fig 3.2) are added repla ing the pressure oordinates for the GFS data heights . The sigmalevels have the advantage of following the orography and are related to the pressure at di�erent heightlevels by the following equation:

σ = (p − pt)/(ps − pt)Where p is the a tual pressure, pt onstant pressure of the top referen e, ps is the surfa e referen epressure.

CHAPTER 4. DESCRIPTION OF THE MM5 MODEL SYSTEM 17

Figure 4.2: Sigma levels integrated by MM5 modelIn our ase 30 sigma levels were used. They are des ribed as follows.1.00, 0.99, 0.98, 0.96, 0.93, 0.89, 0.87,0.85, 0.80, 0.75, 0.70, 0.65, 0.60, 0.57,0.55, 0.50, 0.45, 0.40, 0.35, 0.32, 0.30, 0.27,0.25, 0.23, 0.20, 0.17, 0.15, 0.10, 0.07, 0.05, 0.00Subsequently, this MM5 module reads the �les pro essed in regridder and reates the following exit�les:• MMINPUT_DOMAIN• BDYOUT_DOMAIN• LOWBDY_DOMAINThese �les are those needed to start a simulations with the MM5 model.

CHAPTER 4. DESCRIPTION OF THE MM5 MODEL SYSTEM 184.4 MM5The model for this study has been on�gured using parameterizations that adapt better to the oro-graphi onditions of the zone and also have a higher horizontal resolution. (∼1 km).1. S hultz mi rophysi s: A highly e� ient and simpli�ed s heme (based on S hultz 1995 with somefurther hanges), designed for running fast and being easy to tune for real-time fore ast system.It ontains i e and graupel/hil pro esses. [Internet referen e 1℄.2. Kain-Frits h s heme umulus: Similar to Frits h-Chappell, but using a sophisti ated loud-mixing s heme to determine antrainment/detrainment, and removing all available bouyant energyin the relaxation time. This s heme predi ts both updraft and downdraft properties and also de-trains louds and pre ipitation. Shear e�e ts on pre ipitation e� ien y are also onsidered.[Kainand Frits h, 1993℄.3. Eta Planetary boundary layer parameterization: This is the Mellor-Yamada s heme as used inthe Eta model, Janji (1990, MWR) and Janji (1994, MWR). It predi ts TKE and has lo alverti al mixing. The s heme alls the SLAB routine or the LSM for surfa e temperature and hasto use ISOLI=1 or 2 (not 0) be ause of its long time step. Its ost is between the MRFPBL andHIRPBL s hemes. Before SLAB or the LSM the s heme al ulates ex hage oe� ients usingsimilarity theory, and after SLAB/LSM it al ulates verti al �uxes with an impli it di�usions heme. [Internet referen e 1℄.4. RRTM4 longwave s heme: This is ombined with the loud-radiation shortwave s heme whenIFRAD=4 is hosen. This longwave s heme is a new highly a urate and e� ient method pro-vided by AER INC. (Mlawer et al. 1997). It is the RRTM and uses a orrelated-K model torepresent the e�e ts of the detailed absoption ape trum taking into a ount water vapor, ar-bon dioxide and ozone. It is implemented in MM5 to also intera t with the model loud andpre ipitation �elds in a similar way to IFRAD=2.5. Five-Layer Soil model: Temperature predi ted in 1,2,4,8,16 m layers (approx.). with �xedsubstrate below using verti al di�usion equation. Thermal inertia same as for e/restore s heme,but verti ally resolves diurnal temperature variation allowing for more rapid response of surfa etemperature.

4Rapid Radiative Transfer Model

Chapter 5Synopti analisysIn this hapter the synopti analysis of the period used in this study (Chapter 3) is des ribed startingwith the synopti lassi� ation done using the results of the MM5 model and the support of infraredsatellite images GOES -12.5.1 Synopti lassi� ationThe lassi� ation is based on the hara teristi synopti episodes that are found in the entral �western part of South Ameri a, whi h ontrol the limate of this region and whi h form part of theMa ón zone. Six hara teristi synopti episodes where lassi�ed using the results of domain 1 of thesimulations done with the MM5.5.1.1 Anti y loni Predominan e (AP)The semi-permanent subtropi al anti y lone of the Pa i� is the great limate regulator hara teristi of the west ost of the entral and northern part of South Ameri a. This anti y lone, having warm hara teristi s, is present until the boundary of the troposphere (tropopause) and has slow, loudydes ending movements, hara teristi of stratus y strato umulus louds at low levels. At Ma ón'slatitude, it is one of the great limate regulators present almost year round.

19

CHAPTER 5. SYNOPTIC ANALISYS 20

Figure 5.1: AP for the 15/03/05. Above, hart of 500 HPa en domain 1 of the MM5, bla k lines are thegeopotential height, wind is represented in ve tor form and shaded olors represent relative vorti ity.Below, infrared satellite image of GOES - 12. Images representative of the 00Z.5.1.2 Frontal System (FS)The frontal systems are formed mainly due to the thermal, humidity, and density di�eren es betweentwo air masses of distin t origins. The fronts that a�e t Chile are formed in the Pa i� O ean by the ollision of masses of dry and old air having subpolar origins and masses of warm, humid air having

CHAPTER 5. SYNOPTIC ANALISYS 21subtropi al origins. The frontal systems are the major sour e of pre ipitation in the entral part ofChile [Garreaud, 1999℄ and part of the atmospheri al perturbation an rea h areas like the Ma ónzone.

Figure 5.2: FS for the 12/06/05. Above, hart of 500 HPa en domain 1 of the MM5, bla k lines are thegeopotential height, wind is represented in ve tor form and shaded olors represent relative vorti ity.Below, infrared satellite image of GOES - 12. Images representative of the 00Z.

CHAPTER 5. SYNOPTIC ANALISYS 225.1.3 Cut-o� Low (CL)The old nu leus in ut-o� low orresponds to air that is older than its surroundings, whi h be auseof ir ulation ontinues turning yloni ly forming trough and fronts in altitude. Su h manifestations an be generally be seen in the middle and high atmosphere, where a major sour e of bad weather is inthe ordillera arriving further north than the frontal systems that generally rea h the entral part ofChile. It is ommon to see su h nu leus in the harts of 500 HPa lo ated 5.500 geopontential meters.

Figure 5.3: CL for 16/09/05. Above, hart of 500 HPa en domain 1 of the MM5, bla k lines are thegeopotential height, wind is represented in ve tor form and shaded olors represent relative vorti ity.Below, infrared satellite image of GOES - 12. Images representative of the 00Z.

CHAPTER 5. SYNOPTIC ANALISYS 235.1.4 Jet Stream (JS)The jet stream is lo ated almost at the upper boundary layer of the troposphere (tropopause) andJS is generated mainly due to the thermal di�eren es between the air masses that are found at highaltitudes. In the southern hemisphere two jet streams an be distinguished: one being subpolar andthe other subtropi al, both determined by its formation origin. It is known that the maximum speed ofwind of the jet stream generate turbulen e in its nu leus surrounding (Holton, 1996). The jet streamis present at low and middle latitudes, being observed in the Ma ón zone.

Figure 5.4: JS for the day of 14/09/05. Above is the wind speed maximum in dark olors, the blue linesare the lines of the wind urrent. Below infrared satellite image of GOES - 12. Images representativeof the 00Z.

CHAPTER 5. SYNOPTIC ANALISYS 245.1.5 High Trough (HT)The troughs are wave prolongations of y loni ir ulation, whi h an be asso iated with ut-o� lowor frontal systems. These waves an be of subsynopti s ales both spatial on temporily (less than24 hours). These show up in the mid and high troposphere, and are quite ommon in the ordilleraarea, and an a�e t the Ma ón area. These bring onsequently as ending movements and thereforeinestability in the front part of the trough and des ending movements (stability) in the posterior partof trough. In addition, they an ome up during short weather periods ( ir a one day).

CHAPTER 5. SYNOPTIC ANALISYS 25

Figure 5.5: HT for the day of 20/09/05. Above, hart of 500 HPa en domain 1 of the MM5, bla k linesare the geopotential height, wind is represented in ve tor form and shaded olors represent relativevorti ity. Below, infrared satellite image of GOES - 12. Images representative of the 00Z.5.1.6 Cold Anti y lone (CA)The CA are old air masses from high pressures that move behind a old frontal systems and have old, dry thermal hara teristi s and greater density than its surroundings. They migrate travelinga ross thousands of kilometers and are asso iated with low temperatures and loudy stratiform lo atedat low levels lose to the ground. Some of them an rea h the Ma ón area.

CHAPTER 5. SYNOPTIC ANALISYS 26

Figure 5.6: CA for the day of 180605. Above, hart of 500 HPa en domain 1 of the MM5, bla k linesare the geopotential height, wind is represented in ve tor form and shaded olors represent relativevorti ity. Below, infrared satellite image of GOES - 12. Images representative of the 00Z.5.1.7 Synopti pattern in Ma ónApplying this lassi� ation for the 118 ases modeled by MM5 and orresponding to the days formeasuring turbulen e and seeing for the MASS station in Tolar, what was found was that for mostdays the anti y loni predominan e was present (AP), followed by high trough (HT), and the jet stream

CHAPTER 5. SYNOPTIC ANALISYS 27(JS) as possible atmospheri disturban es. It is important to note that more than one synopti episode an o ur in a night, in some ases even simultaneously whi h means that they are not ex lusive withinthemselves. This explains, then, why the sum of the number of events might be more than the totalnumber of nigth analyzed. Figure 5.6 shows the distribution of events in the period.

0

20

40

60

80

100

AFVAJSBSSFPA

Nro

. epi

sodi

os

Patrones sinopticos

Episodios sinopticos casos modelados

58.47%

11%

1.7%

34.7%

39.8%

4.2%Figure 5.7: Synopti episodes found during the period studied.5.2 Seasonal AnalysisIn order to di�erentiate the synopti patterns during di�erent times of the year, the events are separatedseasonally into during of fall, winter, and spring (see Table 5.1).E1 Mar h - April - MayE2 June and AugustE3 September - O tober - November - De emberTable 5.1: Groups of synopti episodes separated by seasonsThus, we �nd the following distribution of episodes throughout the three groups de�ned.

CHAPTER 5. SYNOPTIC ANALISYS 28

0

5

10

15

20

25

30

AFVAJSBSSFPA

Casos por periodos E1, E2 y E3

52.9%

5.8%2.9%

41.2%38.2%

5.8%

0

5

10

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25

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AFVAJSBSSFPA

Nro

. Cas

os

51.5%

21.2%

0%

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33.3%

6.06%

0

10

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30

40

50

AFVAJSBSSFPA

Patrones Sinópticos

66.6%

7.8%

1.9%

39.2%

45%

1.9%Figure 5.8: Synopti episodes distributed in E1, E2, and E3 seasonal groups.The anti y loni predominan e (AP) is present in a greater per entage in all of the periods ina ordan e with the limatology des ribed in Chapter 2, where the anti y lone is des ribed as a majorregulatory fa tor of the limate in these latitudes. In the E2 group a greater presen e of frontal systemsthat a�e t this area are present. Likewise, atmospheri instability is asso iated with these events. It

CHAPTER 5. SYNOPTIC ANALISYS 29is important to note, the high per entage of high trough (HT) in every period. These events provokeinstability as well as stability in a relatively short time, like during a night.

Chapter 6Evaluation of the MM5 modelThe validation of the MM5 modeling system was performed by ontrasting the data from domain 4of the model with the real (observed) data registered by the meteorologi al station at the summitof Ma ón. In order to do this, standar meteorologi al variables registered in situ are ompared withsimulated ones al ulated at the same pla e and time by the MM5 model. The data output from themodel has a frequen y of one hour, thus they were ompared with the data from the station between00Z and 09Z. Therefore, the following variables were analyzed:6.1 TemperatureThe temperature at 2 meters over the surfa e at Ma ón registered by the meteorologi al station andthat simulated by the MM5 model were ompared. The data re�e t that a relationship between thedata exists, but it is on entrated below the line 1:1 (Figure 6.1). This means that the simulated datahave lower values than those of the real data, but there exists a good orrelation in their tenden y.To quantify this relationship the BIAS error analysis was used, whi h al ulates the error betweenfore asted data and data observed by using their di�eren e (BIAS = fore asted data � observed data)[White et al, 1999℄. A plot of observed versus fore ast temperature an be seen in Table 6.1, here theresults with a negative value indi ate that the fore asted temperature data is less than the observeddata. This an also be seen in the histograms showing the temperature data (Figure 6.2). Whileweather station temperature show an average around -2 oC, the modeled temperature show an averageof -8 oC.

30

CHAPTER 6. EVALUATION OF THE MM5 MODEL 31

-16

-13

-10

-7

-4

-1

2

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-16 -13 -10 -7 -4 -1 2 5 8

MM

5 m

odel

tem

pera

ture

(de

gree

s C

elci

us)

Station temperature (degrees Celcius)

Comparison of temperature at 2 meters

Figure 6.1: Comparison between simulated data of temperatures at 2 meters with observed data.BIAS - Variable Minimum Maximum Average MedianTemperature 2m -16.1 -0.54 -6.56 -6.12Wind Int. 10m -9.61 11.47 1.42 1.56RelativeHumidity -23.41 38.71 10.11 9.46Table 6.1: BIAS error analysis for the simulated data by MM5 and the data observed by the meteo-rologi al station at Ma ón.

CHAPTER 6. EVALUATION OF THE MM5 MODEL 32

0

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-16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8

No.

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sode

Station Temperature al 2 meters (degrees Celcius)

Histogram of Station Temperature at 2 meters

0

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-16 -14 -12 -10 -8 -6 -4 -2 0 2 4 6 8

N´°

Epi

sode

MM5 model Temperature at 2 meters (degrees Celcius)

Histogram of MM5 model Temperature at 2 meters

Figure 6.2: Histogram of the temperatures: above observed data; below simulated data.6.2 Wind SpeedThe magnitude of the wind is registered by the station at 10 meters over the surfa e of Ma ón andis ompared to the data from the MM5 model at the same altitude. This shows a on entration ofpoints above the line 1:1 (Figure 6.3), whi h indi ates an overestimation of the model with respe t tothat observed. Table 6.1 shows the results of the BIAS error analysis, yielding positive values, whi hre�e ts this overestimation. The histogram of the data (Figure 6.4) shows that the station data andthe model data have a the median around 5 ms−1, but the observed values have a grates s atter.

CHAPTER 6. EVALUATION OF THE MM5 MODEL 33

0

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0 5 10 15 20 25 30

Win

d in

tens

ity o

f MM

5 m

odel

(m

/s)

Wind intensity of station (m/s)

Comparison of wind intensity at 10 meters

Figure 6.3: Comparison of the wind at 10 m from the surfa e of Ma ón observed by the station withthose simulated by the model.

CHAPTER 6. EVALUATION OF THE MM5 MODEL 34

0

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No.

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Wind intensity of Station at 10 meters (m/s)

Histogram of Station wind intensity at 10 meters

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0 5 10 15 20 25 30

No.

Epi

sode

Wind intensity of MM5 model at 10 meters (m/s)

Histogram of MM5 model wind intensity at 10 meters

Figure 6.4: Histograms of wind intensity: above for the meteorologi al station; below for the MM5model.6.3 HumidityThe humidity sensor of the meteorologi al station has had problems in its alibration [privat omuni- ation with Mar Sarazin℄, thus, its observed data is not orre t and are not representative sample.This justi�es the little variability and the pattern marked around 10% and 25%, whi h the �gure 6.5and �gure 6.6 show. Under su h onditions, it is impossible to validate the relative humidity that themodel al ulate.

CHAPTER 6. EVALUATION OF THE MM5 MODEL 35

0

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0 5 10 15 20 25 30 35 40 45 50

MM

5 m

odel

Rel

ativ

e H

umid

ity (

%)

Station Relative Humidity (%)

Comparison of Relative Humidity at 2 meters

Figure 6.5: Comparison of relative humidity of the meteorologi al station in Ma ón and the MM5model.

CHAPTER 6. EVALUATION OF THE MM5 MODEL 36

0

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Station Relative Humidity at 2 meters (%)

Histogram of Station Relative Humidity at 2 meters

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0 5 10 15 20 25 30 35 40 45 50

No.

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MM5 model Relative Humidity at 2 meters (%)

Histogram of MM56 model Relative Humidity at 2 meters

Figure 6.6: Histogram of relative humidity: above from the meteorologi al station; below from theMM5 model.6.4 AnalysisIn order to onsider the errors of the model, it is important to take into a ount some relevant fa torsthat a�e t the results. Mainly, it is the s ar ity of the data in the zone and generally speaking, inSouth Ameri a, where the integration of real information by means of the global models is a boundary ondition for simulations with the MM5 are minimum. Despite these di� ulties, errors an be de reasedusing te hniques to improve the fore asted data and the systemati errors that are found to be present

CHAPTER 6. EVALUATION OF THE MM5 MODEL 37in the simulations an be lowered. One of the te hniques used that yields good results is the appli ationof the Kalman �lter whi h in previous studies [Ramos, 2003; internet referen e 2℄ has shown substantialimprovements of the fore asted data.

Chapter 7Traje tory AnalysisTraje tory analysis was done for the purpose of evaluating where the air �ow during the night passingMa on omes from. Previous statisti al studies of turbulen e analysis show that the greatest on-tribution to seeing is produ ed by turbulen e at lower levels near the surfa e [internet referen e 4℄.Considering this, traje tories were al ulated that rea h Ma ón, paying attention to those that startat levels lower than the Ma ón summit, espe ially, from the salt marsh of Arizaro, lose to where theturbulen e was registered by the MASS instrument. Also, we want to �nd out if the air �ow thatpasses trough Paranal an arrive at Ma ón the same night, as well as �nd out if the seeing measuredin Paranal is related to that observed in Ma ón. Therefore, traje tories from MM5 domains 2, 3, and4 were al ulated every night.7.1 Paranal - Ma ón Traje toriesTo �nd out if a relation exists between the air�ow that passes trough Paranal and the air�ow thatrea hes Ma ón a traje tory analysis was done, starting at Paranal (forward traje tories), ontinuingthroughout the night (00Z to 09Z) and that that rea hes Ma ón (ba kward traje tories) in the sameperiod of time. This relation an be made sin e both sites are at very similar latitudes, trying to seeif seeing events that are measured at Paranal an be transported toward Ma ón.Greater importan e was given to the traje tories that �ow from Paranal from 0.5 km and 1 kmof altitude above the surfa e. This was also done for Ma ón from 0.5 km and 1 km, sin e theselevels, based on previous analysis [internet referen e 4℄, is where the greatest amount of turbulen e ontribution for seeing is produ ed.Next, by studying ea h ase, it was found that only during two nights the air that �owed fromParanal passed trough Ma ón (Figure 7.1). This orresponded to June 11, 2005 and November 19,2005. From this, it was found that the air�ow that passed at the levels lose to the surfa e is not apable to travel in one night passing the Andes mountain altitude range from Paranal to Ma ón. Inthese ases, a relation having a ommon meteorologi al pattern annot be found, due to the fa t thaton June 11 there were a frontal system episode and on November 19 there was a jet stream present. Itis important to onsider though, that for both ases wind speed intensity should be high so that theair is apable of traveling the same night from Paranal to Ma ón.38

CHAPTER 7. TRAJECTORY ANALYSIS 39

Figure 7.1: Traje tories from Paranal to Ma ón. Abobe air�ow traveled from 00Z to 09Z for June 11,below: November 19.

CHAPTER 7. TRAJECTORY ANALYSIS 407.2 Traje tories that rea h Ma ónThe analysis of the air�ow that rea hes Ma ón was done with ba kward traje tories from 00Z to 09Z.To al ulate this, domain 4 was used preferablly, but there are ases in whi h the velo ity of the winddid not allow the traje tory found in the domain to be al ulated. In this ase, the data from domain3 was used. Analyzing the traje tories that arrive at the surfa e of Ma on (4.600 m.a.s.l.) and 1/2km of altitude (5.000 m.a.s.l.), it was found that the wind velo ity in 97% of the ases has a West omponent.There are di�eren es between the air that rea hes the surfa e at Ma ón and an altitude of 1/2 km.This an be seen in Table 7.1 where the arrival of air�ow is distributed quite homogeneously betweenthe dire tions from the NO - O - SO in the ase of the 1/2 km of altitude over Ma ón. This is di�erentfrom the air�ow that rea hes the surfa e, whi h ome mainly from the NO - O.N NO O SO S SE E NESurfa e 1% 43% 33% 21% 1% 2% 0% 0%1/2 km Altitude 1% 31% 37% 30% 2% 2% 0% 0%Table 7.1: Traje tory dire tions that rea h Ma ón at the surfa e and at an altitude of 1/2 km.

CHAPTER 7. TRAJECTORY ANALYSIS 41

Figure 7.2: Traje tories that rea h Ma ón. From top to bottom and from left to right the traje toriesare onsidered from: NO, O, SO, S, SE, N.

CHAPTER 7. TRAJECTORY ANALYSIS 427.3 Analysis of altitude traveled by the traje toryThe purpose of analyzing the altitude of the traje tory (air �ow) is to see the in�uen e that the saltmarsh of Arizaro has over the �ow pattern and if the turbulen e from low levels an as end slope upand rea hes Ma ón.It was found that over 90% the traje tories do not travel from low levels to the summit of Ma ón.The air�ow that rea hes the summit of Ma on ome from altitudes over 4.500 (m.a.s.l) and the air�owthat passes trough over Ma on ome from altitudes in the range of 5.000 to 5.500 (m.a.s.l.) for 1 kmover the summit. Over 90% of the traye tories ases was there and in�uen e from lower levels, and5 of whi h the wind dire tion omes from the northeast, 2 from the west and 2 from the southeast.Thus, the ontribution of turbulen e from lower levels from Tolar that rea hes Ma ón is almost null.The reason of this behaviour is that in Arizaro's salt marsh a se undary ir ulation ( ell) is formedand stay during the night. This ir ulation is ontroled by the high of the thermal invertion layer, anddoes not allow for �ow ex hange forward higher altitudes. This se ondary ir ulation is learly seenin the latitudinal � verti al ross se tion pro�le above Ma ón and the salt marsh of Arizaro. Most ofthe wind that arrives is from the west and northeast and does not intera t with the surfa e levels ofthe salt marsh at Arizaro.

Figure 7.3: Altitude traveled by traje tories in one night. Air that rea hes the top of Ma ón (4.6 km),0.5 km above the summit (5 km) and 1 km above the summit of Ma ón (5.5 km), for the night ofMar h 15, 2005.

CHAPTER 7. TRAJECTORY ANALYSIS 43

Figure 7.4: Verti al pro�le above the latitude of Ma ón, wind velo ity in ve torform.

Chapter 8Seeing Statisti sIn this se tion we show the results of a statisti al analysis was done of the seeing data. The ampaignswere done using the MASS instrument whi h yields information about turbulen e and seeing from 0.5km over the surfa e of Tolar. To estimate the seeing at Ma ón, the following riteria were onsidered:1. The re ord were taken at the station lo ated in Tolar (3,500 m.a.s.l.) one kilometer lower thanthe summit of Ma ón (4,600 m.a.s.l.).2. The traje tory analysis ( hapter 7) showed that the air�ow that arrives to Ma ón is not in�uen esby lower levels at the Arizaro salt marsh.3. The air�ow that rea hes Ma ón during one night is not related with the air�ow that omes fromParanal.Consequently, to infer the seeing of Ma ón, these riteria imply that in a �rst approximation, theseeing (here after seeing2) an be obtained using the measurements of C2n: with the MASS instrumentat Tolar from 2 km and higher, that is to say:seeing2 = ((C2n3 + C2n4 + C2n5 + C2n6)/6.8e − 13)0.6where C2n3 orresponds to the level at 2.0 km and C2n4, C2n5

, C2n6 orresponds to the levels at 4, 8 and16 km respe tively.The seeing and turbulen e database has a total of 42672 re ords.8.1 Distribution FormTo know the form that the data are distributed, we used a boxplot s heme. A boxplot is a graph with5 measurements from bottoms to top: relative minimum (inferior extreme of the previous tail of the�rst quartile), the �rst quartile (25% of the data), the se ond quartile or median (50%), third quartile(75%), and the relative maximum (superior extreme of the posterior tail of the third quartile). Thedata above the relative maximum ( ir les) orresponds to the extreme values or �outliers� measuredusing ut o� points for this purpose. In Figure 8.1 an abundan e of extreme values an be observed.The obje tive of this summary is to al ulate the quantity of these extreme values and show the ut o� points that individualize the extreme values or outliers.The ut o� points separate the data that, due to its distan e from the entral measurements(median), are strangely distributed. We found two superior ut o� points that mark two se tors, one44

CHAPTER 8. SEEING STATISTICS 45being of moderate outliners (values not so distant) between the �rst threshold and the se ond; andsevere outliers beyond the se ond threshold.

Figure 8.1: Boxplot for the seeing data.The boxplot of the data (Figure 8.1) shows an asymmetri distribution, the right side having agreat amount of outliers.Table 8 .1 shows the summary measurements of the seeing data.Data valid 43672missing 0Mean 0.956Median 0.821Std. devia-tion 0.528Varian e 0.279Minimun 0.09Maximun 3.09Per entil 25 0.53250 0.82175 1.302Table 8.1: Statisti information of seeing data.

CHAPTER 8. SEEING STATISTICS 46The seeing data range is between 0.09 and 3.09. The average is 0.96, the median is 0.82 and 75%of the measurements are at most 1.30.

Figure 8.2: Histogram and distribution of the seeing2 data.In the histogram of seeing (Figure 8.2) the asymmetry is learly observed, but also something thatseems to be two di�erent groupings; one that is very asymmetri with a maximum nearing the valueof 0.5 of seeing and the other with a maximum around 1.7.8.2 Seeing ≤ 1In this se tion we studied the ases that have a value of seeing2 less than 1. This values was hosenbe ause empiri ally it is a value that limits the good and bad seeing. Furthermore, it is lose the averagevalue of distribution that is 0.956. Table 8.2 shows the number of ases with seeing2 lower than 1. Here61.2% of the data have seeing2 less that 1 and orresponds to 77 nigths of the sample of 126 nigths thatwere measured in Tolar. A ording to this riterion, the remaining 38.8% of the measurements had a

CHAPTER 8. SEEING STATISTICS 47seeing2 greater than one. Con erning the number of nights this 38.8% of measurements orrespondsto 109 nights (of 126), where a seeing2 was measured at > 1. This tells us that mostly there areno omplete nights with nigths less than one in Ma ón. This on lusion an be observed in Figure8.3, whi h shows ea h night measurement per entages with seeing2 greater or less than one, where inalmost every day there is a portion of seeing2 > 1 (in pink).Frequen y Per ent Valid Per ent Cumulative Per- entHasta 1 26728 61.2 61.2 61.2Más de 1 16944 38.8 38.8 100.0Total 43672 100.0 100.0Table 8.2: Table of seeing2 ategorized by values greater or less than one.

CHAPTER 8. SEEING STATISTICS 48

Figure 8.3: Nights with seeing2 measurements greater (pink) or less (blue) than one.

CHAPTER 8. SEEING STATISTICS 49

Figure 8.4: Average seeing per night.In �gure 8.4 the seeing2 average for ea h night is ploted. The average values vary between 0.31and 2.13. The highest are found between June and September and the lower mostly in the summer.8.3 Study of superior outliers (extreme superior values)Due to the asymmetry of the distribution fun tion of the data (Figure 8.2), we did a study of outlierswith the purpose to know the in�uen e these have in global behavior of seeing2 above Ma ón.8.3.1 Limits for the superior outliersTable 8.3 shows the superior level.Superior limit 1(ls1) 2.46Superior limit 2(ls2) 3.61Table 8.3: Superior level of outliners.Between 2.46 and 3.61 the superior moderate outliners an be found. The data with seeing > 3.61 orresponds to severe outliers (superiors).The answer to the question. How mu h data is there with seeing > ls1 (2.46)? is shown in Table8.4 where the per entage of outliers is to minimum and orresponds to a 0.4% and to 13 nigths of thetotal 126 nigths. Table 8.5 shows in detail these 13 days.

CHAPTER 8. SEEING STATISTICS 50Seeing2 Frequen y Per ent Valid Per ent CumulativePer ent< 2.46 43484 99.6 99.6 99.6≥ 2.46 188 0.4 0.4 100.0Total 43672 100.0 100.0Table 8.4: Table of greater and lesser values at the ls1 limit.Month-day < 2.46 ≥ 2.46 Total0411 391 7 3980613 420 2 4220815 323 1 3240816 434 7 4410822 414 9 4230829 196 76 2720830 415 6 4210903 299 1 3000908 154 68 2220916 400 1 4010916 345 3 3481116 126 5 1311118 260 2 262Total 188Table 8.5: Month � night that orresponds to outliers of the seeing2 variable.The nigths with a greater quantity of superior outliers were the 29 of August (40.4% of the totaland 27.9% of the measurements of the nigth) and the 8 of September (36.2% of the total and 30.6%of the measurements of the nigth). The remaining nigths represent 23% of the outliers.What nigths orrespond to the values of seeing2 > ls1 (2.46)? The nigths with outliers of greatermagnitud were: 11/04/05, 29/08705 and the 08/09/05, whi h an be observed in the boxplot of seeing2for the nigths with extreme values (Figure 8.5).

CHAPTER 8. SEEING STATISTICS 51

Figure 8.5: Boxplot of seeing2 on nights with outliers.And for ea h nigth, what is the maximum seeing2 value? Figures 8.6 and 8.7 show the maximumseeing value per nigth, as well as the maximum value and the median respe tively.

CHAPTER 8. SEEING STATISTICS 52

Figure 8.6: Maximum seeing2 per nigth.

Figure 8.7: Maximum and median seeing2 per nigth.

CHAPTER 8. SEEING STATISTICS 538.4 Seeing2 study for periods of 2 hoursThe data is divided by nigth and at every 2 hours. Figure 8.8 shows that the median (wide line inea h boxplot) of seeing drops as the night progresses.Just as with the maximum values of seeing2, Figure 8.9 shows the minimum, median, and maximumof seeing2 per hour.We on lude that the seeing2 de ay during the night and the most probable reason for this is dueto the ooling of the atmosphere.

Figure 8.8: Boxplot every 2 hours for every night.8.5 A umulated frequen ies of seeingFigure 8.10 show a umulated frequen ies of seeing2 at Ma ón. This, when ompared to other astro-nomi sites (Figure 8.11) shows that the bad seeing has greater frequen y in Ma ón. If we evaluatethe value of seeing = 1 in Figure 8.11a and 8.11b, we see that in Ma ón this value is less reo urringthan at other astronomi sites, and observing the urve there are greater values of seeing a umulatedin Ma ón.

CHAPTER 8. SEEING STATISTICS 54

Figure 8.9: Minimum, maximum, and median for seeing2 per night.

Figure 8.10: A umulated per entages of seeing2.

CHAPTER 8. SEEING STATISTICS 55a)

b)

Figure 8.11: A umulated frequen y of seeing for di�erent astronomi sites (above) and a umlatedfrequen ies of seeing2 for Ma ón (below) ompared with the same s ale.

Chapter 9Seeing StudyIn this hapter the results of the sear h for a relationship between synopti patterns and good or badseeing are shown. For this purpose the previous studies (Chapters 5, 7 and 8) of seeing for Ma ón andfor Tolar were used.The statisti al study previously done for Ma ón, where the �rst 2 levels of the turbulen e entriestaken with the MASS instrument in Tolar to al ulate the seeing were not onsidered, served as a basefor the lassi� ation of good and bad nights of seeing.

56

CHAPTER 9. SEEING STUDY 57

Figure 9.1: Altitude di�eren es between Ma ón and Tolar, nivel at whi h the seeing is al ulated.9.1 Classi� ation of seeingTo lassify the seeing in Ma on, we applied the lasi� ation used in Paranal, proposed Julio Navarrete[internet referen e 3℄, whi h is based on the adaptive opti . Table 9.1 show the lassi� ation of seeingbyeand adaptive opti used for these ases.Range Type Adaptive Opti < 0.5� Super System of adaptive opti , maximum di�ra tion, ex ellentimage quality with normal instruments0.5�< 0.8� Good Adaptive opti works well, infrared instruments (20mi rons) as VISIR a omplishes di�ra tion limit0.8� < 1.2� Default1.2� Bad The adaptive opti begins to have problems orre tingthe atmosphereTable 9.1: Classi� ation of seeing based on adaptive opti .

CHAPTER 9. SEEING STUDY 589.1.1 Seeing at TolarUsing these ranges to lassify the seeing (using all levels of C2n) for the data at Tolar and using themedian of ea h night, it was found that 3% of the ases were good and 47% if the ases were bad (Table9.2). Seeing Super Good Default Bad TotalCasos 4 31 32 59 126% 3% 24.6% 35.4% 47% 100%Table 9.2: Clasi� ation of seeing for Tolar, seeing al ulated using all the turbulen e measurementlevels.9.1.2 Seeing at Ma onThe same statisti al study of seeing2 for Ma on, where the �rst two turbulen e levels were not onsid-ered in the al ulation.The bad ases are redu ed to 24% and the good ases in rease to 18% (Table9.3). Seeing Super Good Default Bad TotalCasos 22 37 37 30 126% 18% 29% 29% 24% 100%Table 9.3: Clasi� ation of seeing2 for Ma ón, not onsidering the �rst two levels of the turbulen edata.9.2 Isotherm 320 KA way of relating the good and bad seeing with meteorologi al ondition was to perform a verti alanalysis of wind velo ity and potential temperature. Our preliminary analyses [Report I and II previousto this study, internet referen e 4℄ had suggested to related seeing to thermal thi kness (see �gure 9.2).

CHAPTER 9. SEEING STUDY 59

Figure 9.2: Comparison between a ase of bad seeing and good seeing. Above, verti al ut of potentialtemperture (red lines) and wind velo ity (ve tors), the verti al bla k bars show the altitude di�eren eof the isotherms (thermal thi kness) between a ase of high turbulen e (lower left) and low turbulen e(lower right).Analizing �gure 9.2 we on lud that the seeing is related to the verti al distan e between isotherms.The higher is this distan e, the high is the vañue of the seeing.We de ided to analize the altitud of the isotherm T = 320 K ba ause the Ma on summit high liesin the range of altitude of this isotherm. Figures 9.5 and 9.6 show two ases of good seeing and badseeing and the altitude of the isotherm 320K. This suggests that there ould be a relationship betweenthermal thinkness (verti al distan e between isotherm) and seeing, but analyzing the mean value ofseeing and the altitude of the isotherm per night this relationship was not found (Figure 9.4).

CHAPTER 9. SEEING STUDY 60

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ght k

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line 1

Figure 9.3: Average altitude of the isotherm 320K.

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tude

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mt)

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Comparison of Seeing with the Altitude of isotherm 320 K

Seeing v/s Altitude

Figure 9.4: Seeing versus altitude of isotherm 320K.

CHAPTER 9. SEEING STUDY 61

Figure 9.5: Altitude of isotherm 320K for ases of good seeing.

CHAPTER 9. SEEING STUDY 62

Figure 9.6: Altitude of isotherm 320K for bad seeing.

CHAPTER 9. SEEING STUDY 639.3 Seeing and windThe wind velo ity registered at 10 m of altitude by the meteorologi al station at Ma ón was omparedwith the seeing lassi� ation from se tion 9.1. These entries show that the predominate wind dire tionin Ma ón is from NW �W � SW and relating it with seeing it an be found that for the lassi� ationsuper (seeing < 0.5�) the wind mostly has a dire tion between 270o and 345o (W � NW) with intensitiesless than 8 ms−1 . For episodes of seeing default (0.8� < seeing < 1.2�) and bad (1.2� < seeing) thedire tion of the wind in mostly on entrated between 240o and 135o (SW-S-SE) with intensities greaterthan 6 ms−1 (Figure 9.7).

Figure 9.7: A winds ompass rose ompared with lassi�ed seeing.The histogram of the wind dire tion shows that the wind on average has a dire tion between 220oand 290o (SW � W � NW) (Figure 9.8). Referring to wind intensity the histogram shows that theaverage of wind is 6 ms−1 (Figure 9.9).Seasonally the wind rose for winds for the group E1 (Table 5.1) show that the wind is ommingfrom the northwest (NW) dire tion with average intensities of 8 ms−1(Figure 9.10).

CHAPTER 9. SEEING STUDY 64

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0 60 120 180 240 300 360

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Distribution of wind direction

Figure 9.8: Histogram of wind dire tion for the Ma ón station for the period analyzed (Mar h toDe ember).

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Distribution of wind intencity

Figure 9.9: Histogram of the wind speed for the Ma ón weather station for the period analyzed (Mar hto De ember).

CHAPTER 9. SEEING STUDY 65

Figure 9.10: A wind ompass rose for the E1 ases.During this time of the year (fall, E1 group) the predominate wind dire tion is from northwest(NW) whi h is di�erent from other seasons (E2 and E3 group, Table 5.1), onsistent with episodes ofgood seeing, it has a NW dire tion.For the E2 ases, it an be noted that default and bad seeing is when the wind has a southwest �south omponent (SW � S; 225o to 180o) with an intensity over 8 ms−1. Good seeing is noted withthe wind omes from the northwest (NW) with intensities between 0 a 8 ms−1approximately (Figure9.11).

CHAPTER 9. SEEING STUDY 66

Figure 9.11: Wind and seeing ompass rose for E2 ases.In the E3 period the default and bad seeing have a wind dire tion between 240o to 120o (SW � S �SE) approximately with speed over 8 ms−1. For good and very good seeing it has a dire tion of 270oto 0o with speed lower than 6 ms−1 approximately (Figure 9.12).

CHAPTER 9. SEEING STUDY 67

Figure 9.12: Wind and seeing ompass rose for E3 ases.A seasonally analyzis, showed that bad seeing related the wind dire tion has a rotation from west(W) for the E1 ases until ontribution from the southeast (SE) in the E3 ase. From this we see thein�uen e of the time of year for the wind dire tion and speed related to bad seeing. For the ase ofgood seeing the low wind intensities (less than 8 ms−1) and the dire tion from the northwest (NW)are predominant.Figure 9.13 shows that there exist a relationship between wind speed averaged every 10 minutesand seeing averaged every 10 minutes in Ma ón. In this relationship we an see wind speed up to 35ms−1. From the �gure we an distinguish between 0 and 10 ms−1 and there is not a lear orrelationsin e the data is dispersed. Between 10 and 18 ms−1 appox., a zone with lineal tenden ies betweenseeing and wind intensity an be distinguish. The data is very well related linearly. For the asebetween 18 to 35 ms−1, the orrelation between the data represents a linear tenden y, although lessthan the 10 to 18 ms−1 range.

CHAPTER 9. SEEING STUDY 68

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ng (

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Seeing v/s v. wind

Figure 9.13: Comparison between seeing and wind intensity for the meteorologi al station at Ma ón.9.4 TKE and the Ri hardson number (Ri)The TKE (Turbulent Kineti Energy) and Ri hardson indi es were al ulated and ompared with theseeing meassured at Tolar and at Ma ón (Chapter 8). In the �rst approximation both the TKE andRi harson showed that the greatest amount of turbulen e is found at the lower layers near the surfa e(Figure 9.14 and 9.15). This ompared with seeing and the turbulen e measured at Tolar with theMASS instrument might be a good indi ator for good and bad seeing.

CHAPTER 9. SEEING STUDY 69

Figure 9.14: Comparison between TKE for a bad and good ase of seeing. On the above line, ahorizontal ut of domain 4; the bottom line shows a verti al ut above the Ma ón latitude.

CHAPTER 9. SEEING STUDY 70

Figure 9.15: Ri hardson number for a bad ase (above) and a good ase (below) of seeing.

CHAPTER 9. SEEING STUDY 71Later, the olumn total for the simulated TKE by the MM5 model was ompared with seeing al ulated for Ma ón (Chapter 8). To do this the TKE values entered for the model at the 30 levelsof altitude at oordinates sigma were added, thus there was an attempt to relate these two variable.Figure 9.16 shows that the TKE values have a base lose to value 6 with les variability than seeing.This �gure shows that thee does not exist a lear relationship between the data.

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E

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Comparison of Seeing with TKE

Seeing v/s TKE

Figure 9.16: Comparison between TKE and seeing for Ma ón.Also, an analysis of turbulen e data (C2

n) registered for MASS at di�erent levels was done. Figure9.17 shows the TKE ompared at 2,4, and 8 Km from the altitude above Ma ón. The variability thatTKE has is very low, almost nil at all levels. This is mainly due to the fa t that MM5 estimates theturbulen e (TKE) lose to the surfa e; towards higher altitudes this has a onstant value of 0.2. Itshould be mentioned that the parameterization used to al ulate TKE with MM5 (se tion 4.4) was onlyone of the three that exist for this purpose. Due to te hni al reasons of ompilation we ould manageto use only this option. In a future study we will evaluate the other two options of parameterizationof TKE of the MM5 modeling system.

CHAPTER 9. SEEING STUDY 72

Figure 9.17: TKE ompared with C2

n at 2, 4 and 8 km above Ma ónFor the Ri hardson number (Ri) ase, two al ulation methods were used: one made by the RIP

CHAPTER 9. SEEING STUDY 73program as part of the suite of the MM5 modeling system, whi h yields verti al graphs as in Figure9.15. The se ond way of al ulating was with the data from the model using the following relationship:Ri =

g

T(∇Tp

∇V 2)where g is gravity� T is the layer temperature referent; ∇Tp is the poten ial temperture gradient in onelayer, and ∇V 2 is the wind module gradient in a layer using this relationship. With this last formulawe ompare the Ri versus the Seeing. Figure 9.18 shows this relationship where Ri has a on entrationvery lose to 0 (zero) and values very s attered. There is no relationship between the data of thevariables, whi h is due to the fa t that Ri stays at a osntant value of 0 when the seeing varies.

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icha

rdso

n

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Comparison of Seeing with Richardson Number

Seeing v/s N. Ri

Figure 9.18: Comparison of the Ri hardson number with seeing in Ma ón.In addition, a omparison between Ri and turbulen e data (C2

n) from the MASS instrument at 2, 4and 8 km above Ma ón was ondu ted. Figure 9.19 shows that the values fo Ri at 2 km is on entratedbelow 60 (adimensional), with small variability. For the 4 km ase, the vaules are very similar to thoseat 2 km, on entrated below 60 (adimensional) of the Ri. For the 8 km ase at 8, the variability is on entrated at these values and they are not orelated. As with the previous ase, the values of Riat altitudes do not show a relationship with the turbulen e measured by MASS.

CHAPTER 9. SEEING STUDY 74

Figure 9.19: Comparison of Ri with turbulen e data (C2

n) at 2, 4 and 8 km above Ma ón.The omparison of these indi es (TKE and Ri) with the real data (seeing and turbulen e(C2

n))

CHAPTER 9. SEEING STUDY 75show a good approximation with the turbulen e that o urs at lover levels lose to the surfa e. But,at higher altitudes the values are onstant. The small variability indi ates that as an estimation toolfrom turbulen e at high levels, it is not optimal.

Chapter 10Con lusionsThe Ma ón area, site pre-sele ted for the onstru tion of the ELT (Extremely Large Teles ope), is onsidered one of the optimal zones for astronomy development and the a tivity that this aids in. Atthis site, during 2005, a total of 126 measured nights of atmospheri turbulen e (C2

n) and seeing bythe MASS instrument were registered, also measurements of standard meteorologi al variables froma weather station that was lo ated at the summit of Ma ón (4,600 m.a.s.l.). These measurementsserved as a basis for this study to analyze and ompare the meteorology onditions of Ma ón andits relationship to the seeing. The use of meteorologi al information from numeri al models wasfundamental to this study. This in fa t is how the MM5 modeling system has beeing used to analyzethe lo al meteorology onditions of the Ma ón zone.10.1 Lo al meteorologyMa ón is surrounded by mountains, on the west by the Andes Mountains and on the east by moremountain hains. The omplex orography has made it a fundamental fa tor for analyzing the lo almeteorology of this zone Climatologi ally, there is a marked pattern of ir ulation from the westpredominan e of semipermanent and not very humid subtropi al anti y lones from the south pa i� ,whi h are altered by synopti situation like HV and JS, whi h have a duration of time less than thesynopti patterns of PA or BT. The synopti analysis for the ases studied on�rm this situation,where the major instability ontribution are the HT, whi h an last a day. The HT are present allover the year. Its seasonal analysis for fall, winter, and spring (E1, E2, E3) show that the samepattern is present in every period with subsynopti development time (≥ day). There exists a lo almeteorologi al omponent, ontrolled by the orography, that makes this pla e a very notably unstablezone. The air�ow is strongly altered by mountain summits forming mountain waves [Whiteman, 2000℄that disrupt the atmosphere from the west to Ma ón, on the surfa e leading to the salt marsh ofArizaro, a region with an average altitude of 3,500 m.a.s.l. extensive and �at whi h shows thermalstability lose to the surfa e dominated mainly by the radiation. Here a se ondary ir ulation existthat is formed in the �rst 500 m approximately over the surfa e. This se ondary ir ulation does nothave a strong verti al omponent in a way that intera ts with higher levels.The analysis showed that Ma ón lo al meteorology is dominated by winds oming from the west,rotating throughout the year toward the southwest from fall to spring. The wind that generallyrea hes Ma ón is parallel to its summit (4,600 m.a.s.l) with wind speed that average 6 ms−1. This76

CHAPTER 10. CONCLUSIONS 77was on luded based on the analysis of registered wind by the meteorologi al station at Ma ón andthe analysis of traje tories from the MM5 model, where in more than 90% of the ases studied, theair�ow from levels below the summit of Ma ón did not rea h it.On the other hand, the air�ow that rea hes the summit and 1/2km of altitude above Ma ón do not ome, during one night interval, from Paranal. This dis ard the idea that the turbulen e measured atParanal in low levels an rea h the summits of Ma ón.10.2 SeeingTwo riteria were onsidered for the al ulation and later analysis of seeing; one above Tolar that theMASS instrument measures and the other for Ma ón, eliminating the two �rst levels of measurement ofatmospheri turbulen e by the MASS instrument (0.5 and 1 km respe tively) adequate for estimatingthe atmospheri thi kness from the summit of Ma ón. This riterion was adopted onsidering theprevious traje tory analysis, where it was shown that the air that rea hes Ma ón and 1/2 km aboveit, does not in�uen e low levels (salt marsh of Arizaro). The seeing measured in Ma ón has a medianof 0.82� and a superior maximum of 3�. The statisti al analysis showed that 61.2% of the data withseeing < 1, orrespond to 77 days of the total 126, but on 109 days there were entries of seeing =>1 orresponding to 38% of the data. It was also estimated that the seeing would diminish on averageas the night progressed, due to the ooling of the atmosphere lose to the surfa e by radiation andthe lower thermal ontrast that is produ ed at these levels. Compared with other atmospheri sites,Ma ón has a seeing on average almost similar to La Silla, with a median lose to 0.8�, but with a less umulative urve.To analyze the seeing a lassi� ation of episodes was done. We adopted the one used in Paranal fromadaptive opti . We found that 47% of the nights measured had a value of the median orrespondingto a bad night (seeing > 1.2�), this ompared with the wind speed, shows that the bad seeing eventsare present when the wind velo ity has a southwest � south � southeast omponent, whi h variesseasonally. The bad seeing in fall is on entrated from the southwest in order to arrive in spring withwind dire tion from the south � southeast with values above the 20 ms−1.Also, a good relationship with wind speed below 35 ms−1 was found. This justi�es what was shownby the wind rose, where the bad seeing relates to major intensities of wind speed.In order to �nd a relationship between seeing and a meteorologi al variable, the isotherm of 320K of potential temperature was analyzed, with the purpose to relate the thermal thi kness (verti aldistan e between isotherms) and the seeing. Unfortunately, no relationship was obtained.Previous studies showed that the major ontribution of seeing is turbulen e from levels near thesurfa e. This is in ontrast with indi es of atmospheri turbulen e simulated by the MM5 model.Analysis of TKE was done ontrasting the turbulen e measured by MASS at di�erent levels of altitude.The orrelation found for higher levels (2, 4, and 8 km) were not good, sin e the TKE index is apableof showing turbulen e at levels lose to the surfa e. The analysis using the Ri hardson number (Ri)also shows that it is sensitive for turbulen e produ ed at low levels, ompared with the turbulen e athigh levels su h as 2, 4, and 8 km.The MM5 modeling system ontrasted with the data observed in Ma ón showed a negative errorfor temperature and a positive for the wind speed. In order to improve these values, applying theKalman �lter was suggested in order to eliminate systemati errors of the model, also suggested wasthe integration of simulations with the data observed through the meteorologi al station. Evaluatingthe parameterization that TKE al ulates in order to implement the one that is the most adequatefor this type of omplex terrain was also suggested. There are experien es that exist at Mauna Kea

CHAPTER 10. CONCLUSIONS 78Weather Center, Hawaii, where they simulate using the MM5 the verti al turbulen e pro�le(C2

n). This an also be implemented in future studies.In summary, the meteorologi al onditions and the seeing at Ma ón have a very onditioned rela-tionship by the omplexity of the terrain. There is a very good relationship between measured variablesu h as wind speed with respe t to good and bad seeing. On the other hand, the MM5 is apable of apturing many details of the onditions on synopti and lo al s ales, but the indi es of turbulen esimulated have greater exa titude only at lower levels lose to the surfa e.

Bibliography[Alvarez, C., 2005℄ Alvarez, C., 2005: Análisis multivariable en meteorología de ontamina iónatmosferi a para fundi ión Potrerillos. Tesis para el grado de Li en iatura enMeteorología. Universidad de Valparaíso.[Ballard et al., 1991℄ Ballard, S. P., Golding, B. W., Smith, R. N., 1999: Mesos ale model experi-mental Fores asts of the Haar of Northeast S otland. Nom. Wea. Rev., 119,2107-2123.[Banta et al, 2003℄ Banta, R., Pi hugina, Y., Newsom, R., 2003: Relationship between Low-Level Jet Properties an Turbulen e Kineti Energy in the No turnal StableBoundary Layer. J. Atmos. S i. 60, 2549-2555.[Cuevas, 2005℄ Cuevas, O., 2005: Análisis de ozonosondas en Rapanui: Climatología, masasde aire y su impá to en el ozono troposféri o. Tesis para el grado de Li en- iatura en Meteorología. Universidad de Valparaíso.[Garreaud, 1999℄ Garreaud, R., 1999: Multies ale analysis of the sumertime pre ipitation overof the entral Andes. Mon. Wea. Rev., 127, 901-921.[Grell et al., 1994℄ Grell, G. A., Dudhia, J. and Stau�er, D. R., 1994: A des ription of the �fth-generation Penn State/NCAR mesoes ale model (MM5). NCAR Te hni alNote, NCAR/TN-398+SRT, 117 pp.[Kain and Frits h, 1993℄ Kain, J. S., Frits h, J. M.;1993: Conve tive parameterization for mesos alemodel: the Kain-Frits h s heme. The representation of umulus in numeri almodels; Meteor. Monogr. Amer. Metor. So .[Mlawer et al., 1997℄ Mlawer, E. J., Taubman, S. J., Brown P. D., Ia ono, M. J., and Clough S. A.,1997: Radiative transfer for inhomogeneous atmosphere: RRTM, a validated orrelated-k model for longwave. J. Geophys. Res., 102 (D14), 16663-16682.[Ramos, 2003℄ Ramos, I., 2003: Valida ión del modelo European Centre for Medium RangeWeather Fore asts (ECMWF) para el pronósti o de temperatura en la VRegión. Tesis para el grado de Lin en iado en Meteorología. Universidad deValparaíso.[Rutlland, J., 1982℄ Rutlland J., Salinas H., 1982: Fre uen ia de o urren ia de una ondi iónmeteorológi a desfavorable para la disfusión de ontaminantes atmosféri osen al zona entral de Chile. Tralka. Depto. de Geofísi a, Universidad de Chile.79

BIBLIOGRAPHY 80[Sarazin et al., 2002℄ Sarazin, M., Graham, E., Beniston, M., Riemer, M., 2002: The New Toolsfor a Global Survey of Poten ial Sites for the Future Gigant Teles opes. SPIEConf. on astronomi al Teles ope and Instrumentation, Waikoloa, Hawaii,USA, paper SPIE-4840-54.[S hultz, 1995℄ S hultz, P., 1995: An expli it loud physi s parameterization for operationalnumeri al waether predi tion. Mon. Wea. Rev., 123, 3331-3343.[Shafran et al., 2000℄ Shafran, P. C., Seaman, N. L., and Gayno, A., 2000: Evaluation of numeri alpredi tion of boundary layer stru ture during the lake-Mi higan Ozone Study.J. Appl. Meteor., 39, 412-426.[Ulriksen P., 2001a℄ Ulriksen P., 2001: Meteorología apli ada a ontamina ión atmosféri a. Es- uela de postgrado y diplomado en ontamina ión atmosféri a. Fa ultad deCien ias Físi as y Matemáti as, Universidad de Chile. Centro Na ional deMedio Ambiente (CENMA).[Ulriksen P., 2001b℄ Ulriksen P., 2001: Instru tivo para la apli a ión de modelos de difusión at-mosféri as a e�uentes gaseosos. Es uela de postgrado y diplomado en onta-mina ión atmosféri a. Fa ultad de Cien ias Físi as y Matemáti as, Universi-dad de Chile. Centro Na ional de Medio Ambiente (CENMA).[White et al., 1999℄ White, B. G, J. Paegle, W. J. Steenburgh, J. D. Horel, R. T. Swanson, L. K.Cook, D. J. Onton and J. G. Miles, 1999: Short-Term fore ast validation ofsix models. Wea. Fore ast, 14, 84-108.[Whiteman, 2000℄ Whiteman, D. C., 2000: Mountain Meteorology: Fundamentals and appli a-tions. Oxford University Press.

Internet referen e1. Internet referen e 1 : http://www.mmm.u ar.edu/mm5/do uments/MM5_tut_Web_notes/MM5/mm5.htm2. Internet referen e 2 : http://www.eso.org/gen-fa /pubs/astro lim/fore ast/meteo/veri� ation/LaSilla/index.html3. Internet referen e 3 : http://www.eso.org/gen-fa /pubs/astro lim/paranal/seeing/seewind/4. Internet referen e 4 : http://www.eso.org/gen-fa /pubs/ast lim/paranal/asm/mass/MASS-Argentina/MM5/ma on_proj-18apr07_eng.pdf

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Chapter 11AppendixProgram for download GFS data daily and automati allyA program written in PERL language that downloads from the ftp of the NCEP the data of the globalGFS model, every 6 hours until 72 hours of fore asting is rea hed. This data orresponds to ea h day's00Z.#!/usr/bin/perl$fe ha="gfs.".`date +20%y%m%d00`; homp($fe ha);mkdir($fe ha); hdir($fe ha);use Net::FTP;$ftp = Net::FTP->new("ftpprd.n ep.noaa.gov", Debug => 1);$ftp->login("anonymous",'-');$ftp-> wd("/pub/data/n f/ om/gfs/prod/$fe ha");$ftp->get("wafsgfs_P_t00z_intdsk00");$ftp->get("wafsgfs_P_t00z_intdsk06");$ftp->get("wafsgfs_P_t00z_intdsk12");$ftp->get("wafsgfs_P_t00z_intdsk18");$ftp->get("wafsgfs_P_t00z_intdsk24");$ftp->get("wafsgfs_P_t00z_intdsk30");$ftp->get("wafsgfs_P_t00z_intdsk36");$ftp->get("wafsgfs_P_t00z_intdsk42");$ftp->get("wafsgfs_P_t00z_intdsk48");$ftp->get("wafsgfs_P_t00z_intdsk60");$ftp->get("wafsgfs_P_t00z_intdsk72");$ftp->quit;82