Description of Atmospheric Conditions at the Pierre Auger Observatory using the Global Data Assimilation System (GDAS) The Pierre Auger Collaboration P. Abreu75, M. Aglietta58, M. Ahlers110, E.J. Ahn94, I.F.M. Albuquerque20, D. Allard34, I. Allekotte1, J. Allen98, P. Allison100, A. Almela13,9, J. Alvarez Castillo68, J. Alvarez-Mun˜iz85, M. Ambrosio51, A. Aminaei69, L. Anchordoqui111, S. Andringa75, T. Antiˇci’c28, C. Aramo51, E. Arganda6,82, F. Arqueros82, H. Asorey1, P. Assis75, J. Aublin36, M. Ave42, M. Avenier37, 2 G. Avila12, T. B¨acker46, A.M. Badescu78, M. Balzer41, K.B. Barber14, A.F. Barbosa17, 1 0 R. Bardenet35, S.L.C. Barroso23, B. Baughman100 f, J. B¨auml40, J.J. Beatty100, B.R. Becker108, 2 K.H. Becker39, A. Bell´etoile38, J.A. Bellido14, S. BenZvi110, C. Berat37, X. Bertou1, P.L. Biermann43, P. Billoir36, F. Blanco82, M. Blanco36,83, C. Bleve39, H. Blu¨mer42,40, n a M. Boha´ˇcov´a30, D. Boncioli52, C. Bonifazi26,36, R. Bonino58, N. Borodai73, J. Brack92, J I. Brancus76, P. Brogueira75, W.C. Brown93, R. Bruijn88 i, P. Buchholz46, A. Bueno84, 4 R.E. Burton90, K.S. Caballero-Mora101, B. Caccianiga49, L. Caramete43, R. Caruso53, 2 A. Castellina58, O. Catalano57, G. Cataldi50, L. Cazon75, R. Cester54, J. Chauvin37, S.H. Cheng101, A. Chiavassa58, J.A. Chinellato21, J. Chirinos Diaz97, J. Chudoba30, ] E R.W. Clay14, M.R. Coluccia50, R. Concei¸ca˜o75, F. Contreras11, H. Cook88, M.J. Cooper14, H J. Coppens69,71, A. Cordier35, S. Coutu101, C.E. Covault90, A. Creusot34, A. Criss101, J. Cronin103, A. Curutiu43, S. Dagoret-Campagne35, R. Dallier38, B. Daniel21, S. Dasso7,3, . h K. Daumiller40, B.R. Dawson14, R.M. de Almeida27, M. De Domenico53, C. De Donato68, S.J. de p Jong69,71, G. De La Vega10, W.J.M. de Mello Junior21, J.R.T. de Mello Neto26, I. De Mitri50, - o V. de Souza19, K.D. de Vries70, L. del Peral83, M. del R´ıo52,11, O. Deligny33, H. Dembinski42, r N. Dhital97, C. Di Giulio52,48, M.L. D´ıaz Castro17, P.N. Diep112, F. Diogo75, C. Dobrigkeit 21, t s W. Docters70, J.C. D’Olivo68, P.N. Dong112,33, A. Dorofeev92, J.C. dos Anjos17, M.T. Dova6, a [ D. D’Urso51, I. Dutan43, J. Ebr30, R. Engel40, M. Erdmann44, C.O. Escobar94,21, J. Espadanal75, A. Etchegoyen9,13, P. Facal San Luis103, I. Fajardo Tapia68, H. Falcke69,72, 2 G. Farrar98, A.C. Fauth21, N. Fazzini94, A.P. Ferguson90, B. Fick97, A. Filevich9, v 6 A. Filipˇciˇc79,80, S. Fliescher44, C.E. Fracchiolla92, E.D. Fraenkel70, O. Fratu78, U. Fr¨ohlich46, 7 B. Fuchs42, R. Gaior36, R.F. Gamarra9, S. Gambetta47, B. Garc´ıa10, S.T. Garcia Roca85, 2 D. Garcia-Gamez35, D. Garcia-Pinto82, A. Gascon84, H. Gemmeke41, P.L. Ghia36, U. Giaccari50, 2 M. Giller74, H. Glass94, M.S. Gold108, G. Golup1, F. Gomez Albarracin6, M. G´omez Berisso1, . 1 P.F. G´omez Vitale12, P. Gon¸calves75, D. Gonzalez42, J.G. Gonzalez40, B. Gookin92, A. Gorgi58, 0 P. Gouffon20, E. Grashorn100, S. Grebe69,71, N. Griffith100, M. Grigat44, A.F. Grillo59, 2 Y. Guardincerri3, F. Guarino51, G.P. Guedes22, A. Guzman68, P. Hansen6, D. Harari1, 1 T.A. Harrison14, J.L. Harton92, A. Haungs40, T. Hebbeker44, D. Heck40, A.E. Herve14, : v C. Hojvat94, N. Hollon103, V.C. Holmes14, P. Homola73, J.R. Ho¨randel69, A. Horneffer69, Xi P. Horvath31, M. Hrabovsky´31,30, D. Huber42, T. Huege40, A. Insolia53, F. Ionita103, A. Italiano53, C. Jarne6, S. Jiraskova69, M. Josebachuili9, K. Kadija28, K.H. Kampert39, r a P. Karhan29, P. Kasper94, B. K´egl35, B. Keilhauer40, A. Keivani96, J.L. Kelley69, E. Kemp21, R.M. Kieckhafer97, H.O. Klages40, M. Kleifges41, J. Kleinfeller11,40, J. Knapp88, D.-H. Koang37, K. Kotera103, N. Krohm39, O. Kr¨omer41, D. Kruppke-Hansen39, F. Kuehn94, D. Kuempel46,39, J.K. Kulbartz45, N. Kunka41, G. La Rosa57, C. Lachaud34, D. LaHurd90, L. Latronico58, R. Lauer108, P. Lautridou38, S. Le Coz37, M.S.A.B. Le˜ao25, D. Lebrun37, P. Lebrun94, M.A. Leigui de Oliveira25, A. Letessier-Selvon36, I. Lhenry-Yvon33, K. Link42, R. Lo´pez64, A. Lopez Agu¨era85, K. Louedec37,35, J. Lozano Bahilo84, L. Lu88, A. Lucero9, M. Ludwig42, H. Lyberis26,33, M.C. Maccarone57, C. Macolino36, S. Maldera58, D. Mandat30, P. Mantsch94, A.G. Mariazzi6, J. Marin11,58, V. Marin38, I.C. Maris36, H.R. Marquez Falcon67, G. Marsella55, D. Martello50, L. Martin38, H. Martinez65, O. Mart´ınez Bravo64, H.J. Mathes40, J. Matthews96,102, J.A.J. Matthews108, G. Matthiae52, D. Maurel40, D. Maurizio54, P.O. Mazur94, G. Medina-Tanco68, M. Melissas42, D. Melo9, E. Menichetti54, A. Menshikov41, P. Mertsch86, C. Meurer44, S. Mi’canovi’c28, M.I. Micheletti8, I.A. Minaya82, L. Miramonti49, L. Molina-Bueno84, S. Mollerach1, M. Monasor103, D. Monnier Ragaigne35, F. Montanet37, Preprint accepted for publication inAstroparticle Physics December 27, 2011 B. Morales68, C. Morello58, E. Moreno64, J.C. Moreno6, M. Mostaf´a92, C.A. Moura25, M.A. Muller21, G. Mu¨ller44, M. Mu¨nchmeyer36, R. Mussa54, G. Navarra58†, J.L. Navarro84, S. Navas84, P. Necesal30, L. Nellen68, A. Nelles69,71, J. Neuser39, P.T. Nhung112, M. Niechciol46, L. Niemietz39, N. Nierstenhoefer39, D. Nitz97, D. Nosek29, L. Noˇzka30, J. Oehlschl¨ager40, A. Olinto103, M. Ortiz82, N. Pacheco83, D. Pakk Selmi-Dei21, M. Palatka30, J. Pallotta2, N. Palmieri42, G. Parente85, E. Parizot34, A. Parra85, S. Pastor81, T. Paul99, M. Pech30, J. Pe¸kala73, R. Pelayo64,85, I.M. Pepe24, L. Perrone55, R. Pesce47, E. Petermann107, S. Petrera48, P. Petrinca52, A. Petrolini47, Y. Petrov92, C. Pfendner110, R. Piegaia3, T. Pierog40, P. Pieroni3, M. Pimenta75, V. Pirronello53, M. Platino9, V.H. Ponce1, M. Pontz46, A. Porcelli40, P. Privitera103, M. Prouza30, E.J. Quel2, S. Querchfeld39, J. Rautenberg39, O. Ravel38, D. Ravignani9, B. Revenu38, J. Ridky30, S. Riggi85, M. Risse46, P. Ristori2, H. Rivera49, V. Rizi48, J. Roberts98, W. Rodrigues de Carvalho85, G. Rodriguez85, J. Rodriguez Martino11, J. Rodriguez Rojo11, I. Rodriguez-Cabo85, M.D. Rodr´ıguez-Fr´ıas83, G. Ros83, J. Rosado82, T. Rossler31, M. Roth40, B. Rouill´e-d’Orfeuil103, E. Roulet1, A.C. Rovero7, C. Ru¨hle41, A. Saftoiu76, F. Salamida33, H. Salazar64, F. Salesa Greus92, G. Salina52, F. Sa´nchez9, C.E. Santo75, E. Santos75, E.M. Santos26, F. Sarazin91, B. Sarkar39, S. Sarkar86, R. Sato11, N. Scharf44, V. Scherini49, H. Schieler40, P. Schiffer45,44, A. Schmidt41, O. Scholten70, H. Schoorlemmer69,71, J. Schovancova30, P. Schov´anek30, F. Schr¨oder40, S. Schulte44, D. Schuster91, S.J. Sciutto6, M. Scuderi53, A. Segreto57, M. Settimo46, A. Shadkam96, R.C. Shellard17, I. Sidelnik9, G. Sigl45, H.H. Silva Lopez68, O. Sima77, A. ’Smial kowski74, R. Sˇm´ıda40, G.R. Snow107, P. Sommers101, J. Sorokin14, H. Spinka89,94, R. Squartini11, Y.N. Srivastava99, S. Stanic80, J. Stapleton100, J. Stasielak73, M. Stephan44, A. Stutz37, F. Suarez9, T. Suomija¨rvi33, A.D. Supanitsky7, T. Sˇuˇsa28, M.S. Sutherland96, J. Swain99, Z. Szadkowski74, M. Szuba40, A. Tapia9, M. Tartare37, O. Ta¸sc˘au39, C.G. Tavera Ruiz68, R. Tcaciuc46, N.T. Thao112, D. Thomas92, J. Tiffenberg3, C. 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Ziolkowski46 1 Centro Ato´mico Bariloche and Instituto Balseiro (CNEA-UNCuyo-CONICET), San Carlos de Bariloche, Argentina 2 Centro de Investigaciones en La´seres y Aplicaciones, CITEDEF and CONICET, Argentina 3 Departamento de F´ısica, FCEyN, Universidad de Buenos Aires y CONICET, Argentina 6 IFLP, Universidad Nacional de La Plata and CONICET, La Plata, Argentina 7 Instituto de Astronom´ıa y F´ısica del Espacio (CONICET-UBA), Buenos Aires, Argentina 8 Instituto de F´ısica de Rosario (IFIR) - CONICET/U.N.R. and Facultad de Ciencias Bioqu´ımicas y Farmac´euticas U.N.R., Rosario, Argentina 9 Instituto de Tecnolog´ıasen Deteccio´n y Astropart´ıculas(CNEA, CONICET, UNSAM), Buenos Aires, Argentina 10 NationalTechnologicalUniversity,Faculty Mendoza(CONICET/CNEA),Mendoza,Argentina 11 Observatorio Pierre Auger, Malargu¨e, 12 ObservatorioPierre Auger and Comisi´on Nacional de Energ´ıa Ato´mica, Malargu¨e, Argentina 13 Universidad Tecnol´ogica Nacional - Facultad Regional Buenos Aires, Buenos Aires, Argentina 14 University of Adelaide, Adelaide, S.A., Australia 17 Centro Brasileiro de Pesquisas Fisicas, Rio de Janeiro, RJ, Brazil 19 Universidade de Sa˜o Paulo, Instituto de F´ısica, Sa˜o Carlos, SP, Brazil 20 Universidade de Sa˜o Paulo, Instituto de F´ısica, Sa˜o Paulo, SP, Brazil 2 21 Universidade Estadual de Campinas, IFGW, Campinas, SP, Brazil 22 Universidade Estadual de Feira de Santana, Brazil 23 Universidade Estadual do Sudoeste da Bahia, Vitoria da Conquista, BA, Brazil 24 Universidade Federal da Bahia, Salvador, BA, Brazil 25 Universidade Federal do ABC, Santo Andr´e, SP, Brazil 26 Universidade Federal do Rio de Janeiro, Instituto de F´ısica, Rio de Janeiro, RJ, Brazil 27 Universidade Federal Fluminense, EEIMVR, Volta Redonda, RJ, Brazil 28 Rudjer Boˇskovi’cInstitute, 10000 Zagreb, 29 Charles University, Faculty of Mathematics and Physics, Institute of Particle and Nuclear Physics, Prague, Czech Republic 30 Institute of Physics of the Academy of Sciences of the Czech Republic, Prague,Czech 31 Palacky University, RCPTM, Olomouc, Czech Republic 33 Institut de Physique Nucl´eaire d’Orsay (IPNO), Universit´e Paris 11, CNRS-IN2P3, Orsay, 34 Laboratoire AstroParticule et Cosmologie (APC), Universit´e Paris 7, CNRS-IN2P3, Paris, France 35 Laboratoire de l’Acc´el´erateurLin´eaire (LAL), Universit´e Paris 11, CNRS-IN2P3, Orsay, France 36 Laboratoire de Physique Nucl´eaire et de Hautes Energies (LPNHE), Universit´es Paris 6 et Paris 7, CNRS-IN2P3, Paris,France 37 Laboratoire de Physique Subatomique et de Cosmologie (LPSC), Universit´e Joseph Fourier, INPG, CNRS-IN2P3, Grenoble, France 38 SUBATECH, E´cole des Mines de Nantes, CNRS- IN2P3,Universit´e de Nantes, Nantes, France 39 Bergische Universit¨at Wuppertal, Wuppertal, Germany 40 Karlsruhe Institute of Technology - Campus North - Institut fu¨r Kernphysik, Karlsruhe, Germany 41 Karlsruhe Institute of Technology - Campus North - Institut fu¨r Prozessdatenverarbeitung und Elektronik, Karlsruhe, Germany 42 Karlsruhe Institute of Technology - Campus South - Institut fu¨r Experimentelle Kernphysik (IEKP), Karlsruhe, Germany 43 Max-Planck-Institut fu¨r Radioastronomie,Bonn, Germany 44 RWTH Aachen University, III. Physikalisches Institut A, Aachen, Germany 45 Universit¨at Hamburg, Hamburg, Germany 46 Universit¨at Siegen, Siegen, Germany 47 Dipartimento di Fisica dell’Universit`a and INFN, Genova, Italy 48 Universit`a dell’Aquila and INFN, L’Aquila, 49 Universit`a di Milano and Sezione INFN, Milan, Italy 50 Dipartimento di Fisica dell’Universit`a del Salento and Sezione INFN, Lecce, Italy 51 Universit`a di Napoli ”Federico II” and Sezione INFN, Napoli, Italy 52 Universit`a di Roma II ”Tor Vergata” and Sezione INFN, Roma, Italy 53 Universit`a di Catania and Sezione INFN, Catania, Italy 54 Universit`a di Torino and Sezione INFN, Torino, Italy 55 Dipartimento di Ingegneria dell’Innovazione dell’Universit`a del Salento and Sezione INFN, Lecce, Italy 57 Istituto di Astrofisica Spaziale e Fisica Cosmica di Palermo (INAF), Palermo,Italy 58 Istituto di Fisica dello Spazio Interplanetario (INAF), Universit`a di Torino and Sezione INFN, Torino, Italy 59 INFN, Laboratori Nazionali del Gran Sasso, Assergi (L’Aquila), Italy 64 Benem´erita Universidad Aut´onoma de Puebla, Puebla, Mexico 65 Centro de Investigaci´on y de Estudios Avanzados del IPN (CINVESTAV), M´exico, D.F., Mexico 67 Universidad Michoacana de San Nicolas de Hidalgo, Morelia, Michoacan, Mexico 68 Universidad Nacional Autonoma de Mexico, Mexico, D.F., Mexico 69 IMAPP, Radboud University Nijmegen, 70 Kernfysisch Versneller Instituut, University of Groningen, Groningen, Netherlands 71 Nikhef, Science Park, Amsterdam, Netherlands 72 ASTRON, Dwingeloo, Netherlands 73 Institute of Nuclear Physics PAN, Krakow, 74 University of L o´d´z, L o´d´z, Poland 3 75 LIP and Instituto Superior T´ecnico, Technical University of Lisbon, Portugal 76 ’Horia Hulubei’ National Institute for Physics and Nuclear Engineering, Bucharest-Magurele, 77 University of Bucharest, Physics Department, Romania 78 University Politehnica of Bucharest, Romania 79 J. Stefan Institute, Ljubljana, Slovenia 80 Laboratory for Astroparticle Physics, University of Nova Gorica, Slovenia 81 Instituto de F´ısica Corpuscular, CSIC- Universitat de Val`encia, Valencia, Spain 82 Universidad Complutense de Madrid, Madrid, 83 Universidad de Alcala´, Alcala´ de Henares (Madrid), Spain 84 Universidad de Granada & C.A.F.P.E., Granada, Spain 85 Universidad de Santiago de Compostela, Spain 86 Rudolf Peierls Centre for Theoretical Physics, University of Oxford, Oxford, United Kingdom 88 School of Physics and Astronomy, University of Leeds, United Kingdom 89 Argonne National Laboratory,Argonne, IL, USA 90 Case Western Reserve University, Cleveland, OH, USA 91 Colorado School of Mines, Golden, CO, USA 92 Colorado State University, Fort Collins, CO, 93 Colorado State University, Pueblo, CO, USA 94 Fermilab, Batavia, IL, USA 95 Los Alamos National Laboratory,Los Alamos, NM, USA 96 Louisiana State University, Baton Rouge, LA, 97 Michigan Technological University, Houghton, MI, USA 98 New York University, New York, NY, USA 99 Northeastern University, Boston, MA, USA 100 Ohio State University, Columbus, OH, USA 101 Pennsylvania State University, University Park, PA, USA 102 Southern University, Baton Rouge, LA, USA 103 University of Chicago, Enrico Fermi Institute, Chicago, IL, USA 107 University of Nebraska, Lincoln, NE, USA 108 UniversityofNew Mexico,Albuquerque,NM, 110 UniversityofWisconsin,Madison,WI,USA 111 University of Wisconsin, Milwaukee, WI, USA 112 Institute for Nuclear Science and Technology (INST), Hanoi, Vietnam (†) Deceased (a) at Konan University, Kobe, Japan (f) now at University of Maryland (h) now at NYU Abu Dhabi (i) now at Universit´e de Lausanne Abstract Atmospheric conditions at the site of a cosmic ray observatory must be known for reconstructing observed extensive air showers. The Global Data Assimilation System (GDAS) is a global at- mospheric model predicated on meteorologicalmeasurements and numerical weather predictions. GDAS provides altitude-dependent profiles of the main state variables of the atmosphere like temperature, pressure, and humidity. The original data and their application to the air shower reconstruction of the Pierre Auger Observatory are described. By comparisons with radiosonde and weather station measurements obtained on-site in Malargu¨e and averaged monthly models, the utility of the GDAS data is shown. Keywords: Cosmic rays, extensive air showers, atmospheric monitoring, atmospheric models 4 1. Introduction The Pierre Auger Observatory [1, 2] is located near the town of Malargu¨e in the province of Mendoza, Argentina. At the site, at the base of the Andes mountains, two well-established measurementtechniques arecombinedto measureextensive airshowerswith energiesabovesome 1017 eV. The hybrid detector consists of a Surface Detector (SD) arrayand five Fluorescence De- tector(FD)buildings. Eachoftheslightlymorethan1600SDstationsisawater-filledCherenkov detector, measuring the secondary particles of air showers that reach the ground. The detectors of the array are spaced by 1.5 km (750 m in a small infill area in the western part of the array) and provide the lateral particle distribution around a shower core. Four FD buildings comprise six telescopes each and one FD enhancement installation consists of three telescopes. In each FD telescope, the UV light emitted by excited nitrogen molecules along the shower track is collected by a large segmented mirror and reflected onto a camera composed of 440 PMTs. With this measurement, the geometry and the longitudinal profile of the shower can be obtained. For the reconstruction of extensive air showers, the optical properties of the atmosphere at the site of the observatory have to be known. This is particularly true for reconstructions based on data obtained with the fluorescence technique [3], but also impacts upon data collected with the surface detectors [4]. The detection of clouds is an important task of the atmospheric mon- itoring systems. Clouds can obstruct or – through scattering of the intense Cherenkov light – amplify the apparent fluorescence light before it reaches the FD. To eliminate data recorded in cloudy conditions from physics analyses, lidar stations and infrared cloud cameras are installed at each FD station of the Pierre Auger Observatory. These instruments scan the fields of view of the fluorescence detectors several times per hour during data taking periods to measure the cloud coverage and the base height of clouds [5]. The vertical profile of the aerosol optical depth is measured once every hour using vertical laser shots from two facilities near the center of the array. Usingthecalibratedlaserenergyandtheamountoflightscatteredoutofthebeamtowards the FDs, the amount of aerosols can be estimated [3]. Weather conditions near ground, and the height-dependentatmosphericprofilesoftemperature,pressureandwatervaporpressurearerele- vantforseveralAugerObservatorymeasurements. E.g.,theseparametersaffectthe productionof fluorescencelightbyexcitednitrogenmoleculesattheshowertrack,andtheRayleighscatteringof the light between the air shower and detector. Atmospheric conditions are measured by intermit- tentmeteorologicalballoonradiosoundings. Additionally,ground-basedweatherstationsmeasure surface data continuously. The profiles from the weather balloons were averaged to obtain local models, called (new) Malargu¨e Monthly Models [3]. Since March 2009,the atmospheric monitor- ing system has been upgraded with the implementation of a rapid monitoring system [6]. Part of the new programwas the measurement of atmospheric profiles with radio soundings shortly after the detection of particularly high-energy air showers, a system called Balloon-the-Shower (BtS). This enables a high-quality reconstruction of the most interesting events. However, performing radio soundings and applying these data to air shower analyses is not straightforward. Very critical aspects are the time of the weather balloon ascent and the data validity period. Furthermore, performing radio soundings, in particular within BtS, imposes a largeburdenonthe collaboration. Therefore,we investigatethe possibility ofusing datafromthe Global Data Assimilation System (GDAS), a global atmospheric model, for the site of the Auger Observatory. ThedataarepubliclyavailablefreeofchargeviaREADY(Real-timeEnvironmental Applications and Display sYstem). Each data set contains all the main state variables with their dependence on altitude with a validity period of 180 minutes for each data set. Key aspects of the impact of the profiles of atmospheric state variables on the development and detection of extensive air showers are discussed briefly (Sec. 2). We motivate the necessity of more reliable atmospheric profiles by a discussion about the data validity period of weather balloons (Sec. 3), describe the content and processing of the GDAS data (Sec. 4) and compare them to local measurements (Sec. 5). The new atmospheric data are implemented in the data processing and simulation framework of the Auger Observatory for an analysis of reconstructed air showers (Sec. 6). 5 2. Impact of Atmospheric State Variables on the Development and Detection of Ex- tensive Air Showers Varying atmospheric conditions in terms of state variables like temperature, pressure and humidity, may alter the development and, in particular, the detection of extensive air showers. Here, different aspects relevantto the analysis ofair showersat the PierreAuger Observatoryare discussed. The air fluorescence emission excited by the passage of an air shower depends on pressure, temperature, and humidity [7]. The collisional de-excitation of excited nitrogen molecules by other molecules of the atmosphere like nitrogen, oxygen, and water vapor counteracts the de- excitationofthemoleculesviaradiation. Thesequenchingprocessesarepressureandtemperature dependent as described by kinetic gas theory, and dependent on the water vapor content in air. Furthermore, the collisional cross sections for nitrogen-nitrogen and nitrogen-oxygen collisions follows a power law in temperature, σ ∝ Tα. Most recent experimental data indicate a negative exponent α. In reconstructions of air shower data from the Auger Observatory, the fluorescence yield with its dependence on atmospheric conditions is described using experimental results from the AIRFLY experiment [8, 9]. The absolute calibration of the main fluorescence emission at 337.1nmistakenfromNaganoetal.[10]. Thedependenceofthefluorescenceyieldonatmospheric conditions translates to an atmospheric dependence of the reconstructed cosmic ray energy and the depth of shower maximum, the latter being an indicator for the mass of the primary cosmic ray particle. Even short-term variations of the atmosphere may introduce noticeable effects on these reconstructed parameters. Besides the fluorescence emission, the pressure, temperature and humidity profiles of the at- mosphereareimportantforotheraspectsofthe reconstructionofdatacollectedbythe FD.These include the conversion between geometrical altitudes and atmospheric depth; the treatment of Cherenkov emission from air showers; and the transmission of the produced photons from the air shower to the FD: • The air shower development is governed by the interactions and decays of the secondary particles. These processes are largely determined by the atmospheric depth X, the total column density of atmospheric matter traversed by the air shower at a given point. X is calculated by integrating the density of air from the top of the atmosphere, along the trajectoryoftheshowerthroughthegas. Theobservationofthelongitudinalshowerprofiles by fluorescence telescopes is based on geometrical altitudes h. Thus, geometrical altitudes must be converted into atmospheric depth by taking into account the actual air density profile ρ(h) at the site of the Observatory, and the zenith angle θ of the trajectory of the shower, 1 ∞ X(h )= ρ(h)dh. (1) 0 cosθ Z h0 • The secondary particles in extensive air showers travel faster than the speed of light in air. As a result, they induce the emission of Cherenkovlight in a narrow,forward-beamedcone. Some of this light in the UV range may be – depending on the shower geometry relative to the FD telescope – detected together with the fluorescencelight. To effectively subtract the Cherenkov photons from the total number of photons detected, the amount of Cherenkov light emitted by the air shower must be estimated. The Cherenkov yield depends on the refractive index n of the air, which itself depends on the wavelength of the emitted light as well as the temperature, pressure and humidity [11, 12]. Parameterized formulae for the refractive index of dry air, CO and water vapor are used to calculate a total refractive 2 index, ρ ρ ρ n −1=(n −1)· dry +(n −1)· CO2 +(n −1)· w . (2) tot dry ρ CO2 ρ w ρ air air air Therefractiveindexofeachcomponentisweightedwithitsdensity, whichcanbecalculated using the number density and the molar mass of the constituent. Finally, the effect of the 6 decreasing number density with altitude is parameterized [13] as a function of pressure p and temperature ϑ in ◦C, 1+p·(61.3−ϑ)·10−10 n −1=(n −1)·p· . (3) air tot 96095.4·(1+0.003661·ϑ) • Between the production of fluorescence and Cherenkov light in the air shower and the de- tection at the FD telescope, the light is scattered by molecules in the atmosphere. The transmission of light depends on the Rayleigh cross section [14], 24π3 n2 −1 2 σ (λ,p,T,e)= · air ·F (λ,p,e). (4) R λ4·N2 (cid:18)n2 +2(cid:19) air air where λ is the wavelength in m and N the atmospheric molecular density, measured in moleculesperm−3. F isthe Kingfactorthataccountsfor the anisotropyinthe scattering air introducedbynon-sphericalscattercenters,whichdependsslightlyonpressureandhumidity. The refractive index n depends on several atmospheric state variables, see Eq. 3. air The last three itemized effects on the reconstruction of extensive air showers can be taken sufficiently into account by using a proper description of the atmospheric state, e.g., the local atmospheric monthly models derived from multi-year meteorologicalballoonradio soundings (see Sec.3). Theyaffectthereconstructionresultsofairshowerdata,mainlyprimaryenergyandposi- tionofshowermaximum, onlyby marginallybroadeningthe uncertaintieswithout anysignificant systematic shifts. However,in the case of the earlierdiscussed fluorescenceemission processand its atmospheric variability,systematicalterationsofthereconstructionresultsmaybeseentogetherwithincreased uncertainties, even for short-term variations of the atmospheric parameters. Finally, after this discussion on atmospheric influences on FD analysis, it should be noted that uncertainties in the surfacedetectorsignalsintroducedbyvaryingatmosphericconditionsclosetothe groundarewell understood and quantified [4]. 3. Validity of Radio Soundings SinceAugust2002,meteorologicalradiosoundingshavebeenperformedabovethePierreAuger Observatory to measure altitude-dependent profiles of atmospheric variables, mainly pressure, temperature, and relative humidity. Regular measurements were done until December 2008 in orderto collectdatafor allmonths. After applying selectioncriteria,261profilesfromthe middle of2002untiltheendof2008couldbeusedtobuildthenewMalargu¨eMonthlyModels[3]. Starting in March 2009, the radio soundings became part of the rapid atmospheric monitoring system known as the Balloon-the-Shower (BtS) program [6, 15]. A fast online air shower reconstruction with subsequent quality selections is used to trigger the launch of a weather balloon by a local technician. A procedure was developed to find the period of time for which the data measured during the ascent of a weather balloon give a good description of the atmospheric conditions at the Pierre Auger Observatory. The 3-dimensional atmospheric conditions before and after a weather balloon ascent are unknown but data from local weather stations may help to identify stable periods or trends towardsrapidly changing conditions. Everyactive weatherstation is used as an independentsourceofdata,nomatterhowmanystationscontributeinformationduringtheperiod of the weather balloon ascent. For each station, the maximum variations of the temperature, the pressure, and the humidity are obtained for the duration of the corresponding weather balloon launch defined as the time between the start of the weather balloon and the burst of the balloon, see Fig. 1. The difference ∆Q – with Q being temperature T, pressure p, or water vapor pressure e – between maximum (Q ) and minimum (Q ) values of every station during weather balloon max min 7 Q Q max Q min tvalidity tballoon tballoon tvalidity t start start burst end Figure 1: Schematic drawing of the procedure to find an extended time period of validity for data from weather balloons based on data measured by ground-based weather stations for typical atmospheric con- ditions. For details see text. flights can be seen in Fig. 2. From these histograms, periods with very stable conditions (∆Q < ∆Q ),withtypicalconditions,andwithunstableconditionsaredefinedforeachquantityQ(see low caption of Fig. 2). entries 112400 entries 450000 entries 345000 100 300 80 300 250 200 60 200 150 40 100 100 20 50 00 2 4 6 8 10 12 14 16 18 20 00 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 00 1 2 3 4 5 6 7 8 9 ∆T (K) ∆p (hPa) ∆e (hPa) Figure 2: Differences between maximum and minimum values for temperature T, pressure p, and water vapor pressure e as found in weather station data during a weather balloon launch. For temperature data (left), typical conditions are defined as variances between 4 K and 9 K, with 4 K being ∆Qlow for the quantity temperature. Periods with ∆T < 4 K indicate very stable conditions while periods with ∆T >9 K are unstable. For pressure data (middle), typical conditions are defined between 1 and 3 hPa. Forwatervaporpressuredata(right),thetypicalconditionsarebetween0.8and4.0hPa. Theverystable and unstable conditions of thelast two quantitiesare defined accordingly. For typical conditions, the data of the weather stations are scanned before and after the time of the balloon ascent for each quantity Q, and the time at which any quantity leaves the range between Q and Q is determined. This time period gives the validity time period of min max the radio sounding for every active weather station. For launches performed during very stable conditions, the differences in weather station data are quite small. Thus, only small variations beyond the narrow interval would indicate the end of validity, imposing very strict cuts on this type of launch. For unstable conditions, the large ∆Q values could result in quite long extended periods of validity. Since both cases result in inappropriate validity periods, two special criteria for each quantity are found in addition to the typical case. For very stable conditions, Q max/min are redefined to Q˜ = Q±Q /2, where Q is the mean of the interval Q to Q and max/min low min max Q is 4 K in the case of temperature data, 1 hPa for pressuredata, and0.8 hPa for water vapor low pressure data. After definition of Q˜ , the same procedure as for the typical conditions is max/min applied. In case of unstable conditions, the validity time period is set to the time period during 8 which the weather balloon ascended. The average duration of a weather balloon ascent was about 100 minutes. A validity time period of 200 minutes on average is given by the local weather station data as described above. Applyingthis procedure,abouthalfofthecosmicrayeventswhichtriggeredtheBtS programare observed at times not covered by the period of validity of the corresponding balloon launch. Until its termination at the end of 2010, many details of local atmospheric conditions could be studied with the BtS program. The obtained atmospheric profiles can be applied to improve the reconstruction of the most interesting, high-energy air showers. However, the data are not suitable for application to the standard reconstruction because of their short period of validity. Only very few air shower events would be covered by atmospheric profiles from radio soundings. 4. Global Data Assimilation System (GDAS) In the field of Numerical Weather Prediction, data assimilation is the adjustment of the de- velopment within a model to the real behavior of the atmosphere as found in meteorological observations [16]. The atmospheric models describe the atmospheric state at a given time and position. Three steps are needed to perform a full data assimilation: 1. Collect data from meteorological measuring instruments placed all over the world. These instruments include weather stations on land, ships, and airplanes as well as radiosondes and weather satellites. 2. Use a short-term forecast from a previous iteration of the numerical weather prediction to- getherwiththemeasurementstodescribethecurrentsituation. Thisadditionalinformation is needed because the available observations alone are not sufficient. The forecast or first guess adds more information to the system, namely all knowledge of atmospheric behavior expressedinmathematicalmodelequations. Themodelsusenon-lineardifferentialequations based on thermodynamics and fluid dynamics. 3. Adjust the model output to the measured atmospheric state. The resulting 3-dimensional image of the atmosphere is called analysis. A schematic showing the principle of data assimilation is given in Fig. 3. At a given time t , 0 the observations provide the value of a state variable. A model forecast for this variable from a previous iteration exists for the same time. The analysis step combines observation and forecast to describe the current state better than the forecast. This analysis is the initial point for the weather prediction model to create the forecast for a later time t . 1 4.1. GDAS Data The Global Data Assimilation System [18] is an atmospheric model developed at NOAA’s1 National Centers for Environmental Prediction (NCEP). It provides an analysis four times a day (0, 6, 12, and18 UTC) and a 3-, 6- and 9-hourforecast. The numericalweather prediction model used in the GDAS is the Global Forecast System (GFS). 3-hourly data are available at 23 constant pressure levels – from 1000 hPa (roughly sea level) to 20 hPa (≈ 26 km) – on a global 1◦-spaced latitude-longitude grid (180◦ by 360◦). Each data set is complemented by data for the surface level. The data are stored in weekly files and made available online [18]. In Table 1, the level indices corresponding to each data level are listed. For reference, the altitude from the US Standard Atmosphere 1976 (USStdA) [19] is also given in the table. The actual height of the pressure level is stored in the data file. GDAS data are available startingJanuary2005. Therearetwoperiodswithoutdatainthesets. ThefirsttwoweeksofMay 2005 and weeks 3 and 4 of November 2005 are missing. Other than that, the record is complete up to the present time (end of November 2011). 1NationalOceanicandAtmosphericAdministration. 9 e bl a ri a v state at stepanalysis forecast m step o s p h er e observation with uncertainty analysis with uncertainty forecast with uncertainty to t1 time Figure3: Schematic of thedata assimilation process. Figure adopted in a modified form from [17]. Because of the lateral homogeneity of the atmospheric variables across the Auger array [3], only one location point is needed to describe the atmospheric conditions. In Fig. 4, the available GDAS grid points are marked as red crosses on a map together with a map of the surface and fluorescence detectors of the Auger Observatory. The grid point at 35◦ south and 69◦ west was chosen, at the north-eastern edge of the surface detector array. The two points to the west of the array are in the foothills of the Andes mountains and therefore not suitable. The point to the south-east of the array is quite far away and with a surface height of 1685 m a.s.l., it is also too high. Nevertheless, the profiles at this point are very similar to those at the chosen point, on average differing by less than 1 ◦C in temperature and less than 0.3 hPa in water vapor pressure at all altitudes, confirming the homogeneity. The height at which the surface data are given changes over the years for the selected grid point. Starting in January 2005, the surface altitude is 1831.29 m above sea level. On May 31, 2005, the surface height changes to 1403.38 m, and on August 22, 2006 it goes down further to 1328.68 m and stays within a few centimeters of this value until July 27, 2010, when it changes to 1404.65 m. In Fig. 5, the surface height provided by the GDAS data sets is shown between January2005 and December 2010. For reference, the altitudes of the lowestSD tank (1331.05m) andthehighestandlowestFDbuildings(1712.3mand1416.2m)arealsoshown. Thereasonsfor thesechangesareregularimprovementsofthemodelsandcalculationsusedtoproducetheGDAS profiles, or resolutionchanges in the meteorologicalmodel [20]. These changes can occur againin the future, so the surface height of the data has to be monitored for undesired changes [21]. For air shower analysis, only data above ground level in Malargu¨e are interesting. Therefore, we onlyuse data fromthe surfaceandfrompressurelevels 6andabove. The datafrombeginning ofJanuarytotheendofMay2005haveasurfaceheightofaround1800m. Thisisevenabovethe heightofthe highestFDbuilding atCoihueco. We decidednotto attemptanextrapolationdown to the actual ground level of around 1300 m and discard these data. Therefore, the first data set we use is from June 1, 2005 at 0:00 UTC. 4.2. Preprocessing of Data For air shower analyses, several types of information are stored in databases such as the one describing the state variables of the atmosphere. It contains values for temperature, pressure, relative humidity, air density, and atmospheric depth at several altitude levels. The first three quantities and the altitude are directly available in the GDAS data. Air density and atmospheric depth must be calculated. The surface data contain ground height, pressure at the ground, and 10