An analysis of the variability of the critical frequency of the F2 region on quiet conditions M. Mosert 1, D. Bilitza 2, E. Gularte 3, D. Altadill 4, D. Buresova, K.Alazo 6, R.Ezquer 7,8, M.A.Cabrera 7,9, E. Zuccheretti, M. Pezzopane, A.M. Gulisano 11,12, P. Marcó 13, L A. McKinnell 14 1 Instituto de Ciencias Astronómicas, de la Tierra y del Espacio, CONICET UNSJ, CC 49 400 San Juan, (Argentina); mmosert@icate-conicet.gob.ar 2 George Mason University, Fairfax, Virginia (USA). 3 FCAG, UNLP, La Plata (Argentina). 4 Observatori de l Ebre, (OE), CSIC, Univ. Ramon Llull, Roquetes (Spain). Institute of Atmospheric Physics, ASCR, Prague (Czech Republic). 6 Instituto de Geofísica y Astronomía, La Habana (Cuba). 7 Laboratorio de Ionosfera, UNT CONICET, Tucumán - (Argentina). 8 CIASUR, FRT, UTN (Argentina). 9 Laboratorio de Telecomunicaciones, DEEC, FACET, UNT (Argentina). INGV, Rome (Italy) 11 IAA - DNA, Buenos Aires (Argentina). 12 IAFE - CONICET, Buenos Aires (Argentina) 13 Dirección de Investigación de la Armada, Buenos Aires (Argentina). 14 SANSA, Hermanus (South Africa). IRI Workshop 2013, Olsztyn, Poland, June 24 28, 2013
Introduction In the framework of the IRI Task Force Activities and IRI Workshops the need of a good description of the variability of ionospheric magnitudes has been pointed (Bilitza, 2000). A model of ionospheric variability would be useful for the user of the ionospheric model. An operator or satellite designer needs to know not only the monthly mean value of the magnitude but also the expected deviations from that monthly value. Many efforts are devoted to get knowledge on the variability of different ionospheric characteristics (Alazo, et al. 2003; Mosert and Radicella, 199; Gulyaeva et.al, 2008; Bradley, 2000; Kouris and Fotiadis, 2000; Mosert et al, 2002; Ezquer et al 2002a; Ezquer et al 2002b, among others). However the IRI model does not provide yet information on the variability. This topic was particularly discussed during the international RAPEAS (Red Argentina Para el Estudio de la Atmósfera Superior) meeting organized by CONICET and La Plata University during November 2012. The present paper is a result of the contributions of some colleagues participating in that meeting and its objective is to contribute to the formulation of a model of the variability of fof2 for quiet conditions.
Data used Table 1. Location of Data used in the study Station Lat. Geog. Long. Geog. Pruhonice 0.0 1.0 4.8 Ebro 40.4 0.30 48.1 El Arenosillo 37.1 33.3 4. La Habana 22.9 277.8 44.0 Jicamarca 12.0 283.0 2.73 Tucumán 26.9 294.6 21.3 San Juan 31. 290.4 27.9 Buenos Aires 34.6 301.7 31.7 Concepción 36.8 287.0 34.4 Trelew 43.2 294.7 41. Ushuaia 4.8 291.7 49.8 San Martin 68.1 293.0 60.2
Table 2. Time periods of data used Years 2007/08/09 1989/90/91/00/07/08/09 197/76/81/82/2000/07/08/09 1964 1970; 73 78: 81/84/89; 90 9; 2002/2007 2007/08/09//74/82 1971 1981 1977/81/82/2000/01 198/9/64/6/79/80 196/7/80/81/82 96/97/2000/01/07/08
We have use only fof2 data from days with a daily ap at or below 1 We have used representative months for each season: NH/SH Winter (12,01,02/ 06,07,08) Spring (03,04/09,) Summer (06,07,08/12,01,02) Fall (09,/ 03,04) LT periods Noon: 3 hours centered around local noon. Midnight: 3 hours centered around local midnight. Solar Activity index used is Rz12 (Table 2). For all the data sets we have computed the SD% for the noon and the midnight (LT) period in each season Assuming a linear variation with the 12 month running mean of sunspot(rz12), we calculated the SD% values for Rz12= and Rz12=0, based on the low and high solar activity periods given for each location in Table 2.
Table 3. Standard deviations in percentage (SD%) for different conditions LT= 00 LT= 12 Winter Spring Summer Fall Winter Spring Summer Fall Station /Rz12 N 0 0 0 0 N 0 0 0 0 Pruhonice 4.8 794 14 13 12 96 7 7 9 Ebro 48.1 199 13 21 21 11 1 1883 11 9 18 19 9 14 12 Arenosillo 4. 9 14 22 36 22 29 11 8 9 12 9 13 26 7 16 6 La Habana 44 1116 11 13 13 12 19 13 11 1374 11 8 13 8 18 12 7 Jicamarca 2.7 48 21 1 32 20 19 23 14 399 13 7 3 12 12 8 Tucumán 21.3 1313 24 20 24 20 17 14 26 11 1869 18 12 16 7 9 8 13 San Juan 27.9 1122 19 22 2 19 11 1 40 23 1178 17 1 18 8 30 13 Buenos Aires 31.7 613 14 1 14 13 13 12 8 14 94 19 11 13 8 13 7 1 Concepción 34.4 3713 13 38 14 13 14 11 17 16 439 13 11 18 11 17 11 17 13 Ushuaia 49.4 1173 11 11 17 16 14 13 16 877 11 13 8 11 12 12 San Martín 60 904 8 13 17 12 14 1 23 818 17 12 8 9 8 12 20
Fig. 1. Winter - Rz12= /0; LT= 00, 12 Day/night variation variation 40 3 Winter Rz= - LT: 00, 12 SD0 SD12 40 3 Winter Rz=0 - LT: 00, 12 SD0 SD12 30 30 SD% 2 20 SD% 2 20 1 1-60 -40-20 0 20 40 60-60 -40-20 0 20 40 60
Fig.2. Spring - Rz12= /0; LT= 00, 12 Day/night variation- variation Spring Rz12= - LT: 00, 12 Spring Rz12=0 - LT: 00, 12 40 3 SD% 00 SD% 12 40 3 SD% 00 SD% 12 30 30 2 2 SD% 20 SD% 20 1 1 0-60 -40-20 0 20 40 60 0-60 -40-20 0 20 40 60
Fig. 3 Summer - Rz12= /0; LT= 00, 12 Day/night variation- variation Summer Rz12= - LT: 00, 12 Summer Rz12=0 - LT: 00, 12 40 3 SD% 00 SD% 12 40 3 SD% 00 SD% 12 30 30 2 2 SD% 20 SD% 20 1 1 0-60 -40-20 0 20 40 60 0-60 -40-20 0 20 40 60
Fig.4 Fall - Rz12= /0; LT= 00, 12 Day/night variation- variation Fall Rz12= - LT: 00, 12 Fall Rz12=0 - LT: 00, 12 40 3 SD% 00 SD% 12 40 3 SD% 00 SD% 12 30 30 2 SD% 2 20 SD% 20 1 1-60 -40-20 0 20 40 60 0-60 -40-20 0 20 40 60
Fig.11 SD% values Seasonal variation Rz12=, Rz12= 0 40 Noon, Rz= 0 40 Noon, Rz= 3 3 30 30 2 2 SD% 20 1 SD% 20 1 0-60 -40-20 0 20 40 60 0-60 -40-20 0 20 40 60 40 3 30 Midnight, Rz= 0 0 4 40 3 Midnight, Rz= Summer Fall Winter Spring 2 30 SD% 20 SD% 2 20 1 1 0-60 -40-20 0 20 40 60 0-60 -40-20 0 20 40 60
Results From the analysis of the data the following features are observed: Midnight values are generally larger than noon values. (70% of the cases analized) The seasonal behavior of the variability depends on the modip and solar activity but a trend to be larger in winter than in summer is observed at noon in the South hemisphere. Tha largest values of the variability are generally found at equinox at midnight and low solar activity. The variation of the SD% values with the solar activity is not clear.however a trend to greater at low solar activity than at high solar activity is observed at noon. At midnight the highest values of the variability are observed at low latitude, near the region of the equatorial anomaly.
In next figures we have plotted the SD% values from Table 3 versus modip for the different seasons (winter,spring, summer and fall) and solar activities considered (Rz= and Rz12=0).Based in these plots we have deduced (by a polynomial fitting ) a set of SD% values shown in Table 4.
Table 3. Standard deviations in percentage (SD%) for different conditions LT= 00 LT= 12 Winter Spring Summer Fall Winter Spring Summer Fall Station /Rz12 N 0 0 0 0 N 0 0 0 0 Pruhonice 4.8 794 14 13 12 96 7 7 9 Ebro 48.1 199 13 21 21 11 1 1883 11 9 18 19 9 14 12 Arenosillo 4. 9 14 22 36 22 29 11 8 9 12 9 13 26 7 16 6 La Habana 44 1116 11 13 13 12 19 13 11 1374 11 8 13 8 18 12 7 Jicamarca 2.7 48 21 1 32 20 19 23 14 399 13 7 3 12 12 8 Tucumán 21.3 1313 24 20 24 20 17 14 26 11 1869 18 12 16 7 9 8 13 San Juan 27.9 1122 19 22 2 19 11 1 40 23 1178 17 1 18 8 30 13 Buenos Aires 31.7 613 14 1 14 13 13 12 8 14 94 19 11 13 8 13 7 1 Concepción 34.4 3713 13 38 14 13 14 11 17 16 439 13 11 18 11 17 11 17 13 Ushuaia 49.4 1173 11 11 17 16 14 13 16 877 11 13 8 11 12 12 San Martín 60 904 8 13 17 12 14 1 23 818 17 12 8 9 8 12 20
Fig. 12
Fig. 13
Fig.14
Fig. 1
Table 4. Preliminary table of S% values for different conditions Winter Spring Summer Fall Rz12 LT=00 LT=12 LT=00 LT=12 LT=00 LT=12 LT=00 LT=12 4 8 7 17 17 19 1 0 14 17 16 13 7 20 1 8 30 22 14 14 6 0 11 7 24 14 8 19 32 21 13 19 7 0 12 8 21 13 12 6 0 22 14 31 20 11 2 11 0 1 8 12 18 11 1 8 23 18 28 13 18 29 1 0 19 1 1 9 19 20 21 19 24 1 16 11 28 18 0 22 12 17 13 8 16 4 11 14 13 1 12 8 9 0 1 11 16 11 16 13
Final comments The objective of the present paper has been to contribute to the formulation of a model of the variability of fof2 for quiet condition. The variability index used in our analysis is the relative standard deviation STD/mean (in units of %). The fof2 database includes quiet time hourly values from stations located between 0.0 N and 68.1 S. The variability of the parameter has been analyzed as a function of local time, season, solar activity and modip. The study shows that is not easy to model the variability taking into account that it depends of several factors (modip, solar activity, season, local time, longitude?, ). Anyway we have presented a preliminary table based on ionosonde data measurements from stations located between +0 and 60 degrees (modip). Additional efforts are needed in order to establish a reliable representation of the fof2 variability using a larger data base (Digisonde data, RAPEAS data base,...). This work is in process.
American sector. Alazo et al., 2003 Station 1. ARGENTINE IS. 7.7 2. PT. STANLEY 46.8 3. CONCEPCION 3.1 4. BUENOS AIRES 31.6. SAN PABLO 23.2 6. TUCUMAN 23.1 7. NATAL 7.4 8. LA PAZ.6 9. HUANCAYO 2.1. CHICLAYO 11.3 11. TALARA 14.1 12. PARAMARIBO 26.3 13. BOGOTA 28.7 14. PANAMA 33.7 1. PUERTO RICO 41.3 16. HAVANA 44.7 17. GRAND BAHAMA 47.0 18. CAPE KENNEDY 48.1 19. BERMUDA 49.3 20. EGLIN AFB 49.1 21. La Plata (new ionosonde) Dip (IGRF 1980) 8.7 48.0 36.0 32.0 23. 23.1 7.47.4.6 2.1 11.4 14.4 28.3 31.4 37.9 49.0 4.4 8.2 9.9 61.4 61.4 Geomag. Lat ( o ) 3.9 40. 2. 23.3 13.0 1.6 3.7.2 0.7 4.4 6. 16.7 1.8 20.6 29.8 34.6 37.8 39. 43.3 41.1 Geogr. Lat ( o ) 6.2 1.7 36.6 34. 23. 26.9.7 16. 12.0 6.8 4.6.8 4. 9.4 18. 23 26.6 28.4 32.2 30.4
Asia & Oceania Region Name 1. SALISBURY 2. MUNDARING 3. WATHEROO 4. LEARMONTH. DARWIN 6. VANIMO 7. SINGAPORE 8. MANILA 9. GUANGZHOU. CHUNG LI 11. OKINAWA 12. CHONGQING 13. YAMAGAWA 14. KOKUBUNJI 1. AKITA 16. BEIJING 17. WAKKANAI 18. KHABAROVSK 19. MANZHOULI 2.2 1. 0.6 4.3 3.6 21.4 17.1 13.8 29.8 32.2 33.7 38.7 39.4 43.1 46.6 48.7 0.9 3.6.4 Dip (IGRF 1980) 67.1 66.4 64.8.8 40.6 22. 17.6 13.9 31.1 34.4 36.2 42.9 43.6 48.4 3.2 7.0 9.1 63.2 67.0 Geomag. Lat ( o ) 44.4 43.2 41. 33.0 23.0 12.3.0 3.6 11.8 13.8 1. 18.1 20.6 2.7 29.8 28.8 3. 38.1 38.4 Geogr. Lat ( o ) 34.7 32.0 30.3 21.9 12.4 2.7 1.3 14.6 23.1 24.9 26.3 29. 31.2 3.7 39.7 40.0 4.4 48. 49.6
Acknowledgments The authors are grateful to the ionospheric groups at El Arenosillo INTA (Huelva, Spain),Concepcion University (Chile) and JRO, Jicamarca (Peru) for providing data used in this study.
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Valle de la luna, San Juan Obelisco, Buenos Aires Thanks for your attention Muchas Gracias