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defesaEvandro

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defesaEvandro

  1. 1. Morfologia das tempestades el´etricas na Am´erica do Sul Evandro M. Anselmo Institute of Astronomy, Geophysics and Atmospheric Science of University of S˜ao Paulo (IAG-USP), S˜ao Paulo-SP, Brazil. IAG/USP – 2015
  2. 2. Introduction South America is one of the 3 lightning chimes (http://thunder.msfc.nasa.gov/data/)
  3. 3. Introduction Infrastructure planning requires knowledge about thunderstorm position, frequency and lightning efficient production
  4. 4. Objectives Create a thunderstorm database based on TRMM measurements Determine the annual cycle and diurnal cycle of thunderstorms in South America Determine the geographical distribution of lightning flashes and thunderstorms Evaluate the thunderstorms severity.
  5. 5. Tropical Rainfall Measuring Mission – TRMM Launched in November 28th, 1997. Orbit with inclination of 35◦ and altitude of 350 km (August, 2011, the altitude was change for 402.5 km) Sensors: Precipitation Radar – PR, TRMM Microwave Imager – TMI, Visible and Infrared Scanner – VIRS, Lightning Imaging Sensor – LIS, Clouds and the Earth’s Radiant Energy System – CERES. Goal: Estimate rainfall over the tropics and subsequently latent heat flux1 1KUMMEROW, C.; BARNES, W.; KOZU, T.; SHIUE, J.; SIMPSON, J. The tropical rainfall measuring mission (TRMM) sensor package. J. Atmos. Oceanic Technol., v. 15, p. 809–817, 1998
  6. 6. Tropical Rainfall Measuring Mission – TRMM
  7. 7. Tropical Rainfall Measuring Mission – TRMM TRMM overpasses
  8. 8. Tropical Rainfall Measuring Mission – TRMM LIS processing
  9. 9. Data and Methodology TRMM: TRMM orbital v7 files: LIS, VIRS (1B01), and PR (2A25), between 1998–2011 ( 30TB of data). During this period we had 79,932 TRMM orbits, but only 63,613 were over South America (SA) 40◦ S–10◦ N and 90◦ –30◦ W. NCEP RII: NCEP RII reanalysis from 1998-2011: geopotential height and temperature at 17 pressure levels. This data is used to convert TRMM PR altitudes levels in temperature. Defining thunderstorms: Thunderstorms have been defined as clouds with brightness temperature below 258 K at the 1B01 10.8 µm channel and had at least one LIS lightning flash [Morales and Anagnostou, 2003]2 . 2Morales, C. A., and E. N. Anagnostou, Extending the capabilities of high-frequency rainfall estimation from geostationary-based satellite infrared via a network of long-range lightning observations, J. Hydrometeor, 4, 141–159, 2003.
  10. 10. TRMM’s thunderstorms (flow chart) Locate thunderstorms clusters (Tb ≤ 258 K and at least one flash) VIRS (1B01) channel 4 (10.8 µm) LIS flash PR (2A25): Ze, sur- face rain, rain type (2A23), geolocation LIS: flashes, events, groups, view time, geolocation VIRS (1B01): chan- nel 4, geolocation Write the TRMM thun- derstorm HDF file read read read
  11. 11. TRMM’s thunderstorms 157,592 thunderstorms were found! Only 94,733 had view time (V Tm) greater than 1 minute and with at least one PR pixel with valid rain.
  12. 12. The onset of the South American thunderstorms Where the thunderstorms are more often in SA? Where are the places with high lightning densities that corresponds with the highest density of thunderstorms? How long does the thunderstorm season last in SA?
  13. 13. Onset: TRMM overpasses and LIS view time (LIS view – VTlis) (VIRS overpasses – VTvirs)
  14. 14. Onset: LIS lightning flashes and number of thunderstorms (lightning – FLlis) (thunderstorms – Pte)
  15. 15. Onset: Defining densities ratios lightning [km−2 year−1 ] DEfl = FLlis VTlisAg 31557600 (1) thunderstorms per orbit [km−2 ] DEte = Pte VTvirsAg (2) lightning per thunderstorms [km−2 year−1 ] DErt = FLlis VTlisAgPte 31557600 (3)
  16. 16. Where the thunderstorms are more often in SA?
  17. 17. Onset: lightning × thunderstorms – Annual
  18. 18. Onset: lightning × thunderstorms – SON
  19. 19. Onset: lightning × thunderstorms – DJF
  20. 20. Onset: lightning × thunderstorms – MAM
  21. 21. Onset: lightning × thunderstorms – JJA
  22. 22. Where are the places with high lightning densities that corresponds with the highest density of thunderstorms?
  23. 23. Onset: lightning per thunderstorms – Annual
  24. 24. How long does the thunderstorm season last in SA?
  25. 25. Onset: Diurnal and Annual Cycle
  26. 26. Annual cycle
  27. 27. Diurnal cycle
  28. 28. Thunderstorms Severity
  29. 29. Thunderstorms Severity: Defining severity index Flash rate (FT) is defined as the ratio of number of lightning flashes (Nfl) by the mean view time (V Tm) in the thunderstorm area extracted. FT = Nfl V Tm 60 [min−1 ] (4) Flash rate normalized by thunderstorm area (FTA) is defined as the ratio of number of lightning flashes (Nfl) by the mean view time (V Tm) and thunderstorm area (At). FTA = Nfl V TmAt 60 [min−1 km−2 ] (5)
  30. 30. Thunderstorms Severity: FTA and FT pdf distribution
  31. 31. Thunderstorms Severity: defining the extreme FTA and FT thunderstorms Extreme: 90th percentile FTA: 29.3 to 1,258.7 × 10−4 fl min−1 km−2 FT: 47.2 to 1,283.6 fl min−1
  32. 32. Thunderstorms Severity: Rain and CV/ST fraction Thunderstorms with extreme FTA: rain areas Thunderstorms with extreme FT: rain areas
  33. 33. Thunderstorms Severity: Top severe FTA index
  34. 34. Thunderstorms Severity: Top severe FT index
  35. 35. Thunderstorms Severity: CFAD – FTA (w/o lightning)
  36. 36. Thunderstorms Severity: CFAD – FT (w/o lightning)
  37. 37. Thunderstorms Severity: CFAD – FTA (with lightning)
  38. 38. Thunderstorms Severity: CFAD - FT (with lightning)
  39. 39. Thunderstorms Severity: CFADs FTA pixels are taller than FT. FTA pixels have higher Zc values than FT, especially above 5 km.
  40. 40. Thunderstorms Severity: temperature dependency It is know that the mixed phase region regulates the electrification charge process.
  41. 41. Thunderstorms Severity: temperature dependency As a consequence, we converted the PR altitude levels into temperature levels based on RII reanalysis. Now we created the Contoured Frequency by Temperature Diagram (CFTD) in addition to the cumulative CFTD (CCFTD).
  42. 42. Thunderstorms Severity: CFTD - FTA (with lightning)
  43. 43. Thunderstorms Severity: CFTD - FT (with lightning)
  44. 44. Thunderstorms Severity: CCFTD-FTA (with lightning)
  45. 45. Thunderstorms Severity: CCFTD-FT (with lightning)
  46. 46. Thunderstorms Severity: Rate of Zc change with temperature Above 0◦ C we might have ice and supercooled water droplets. The rate of change of Zc with temperature might give an idea about of extend of mixed phase layer. We computed this rate for 30%, 50%, 70% and 95%. We compare tropical versus sub-tropical regions (Amazon × La Plata basin)
  47. 47. Thunderstorms Severity: Zc change – La Plata basin (30S-40S and 60W-70W)
  48. 48. Thunderstorms Severity: Zc change – Amazon (00N-10N and 60W-70W)
  49. 49. Thunderstorms Severity: identifying the efficient and severe regions For each grid box with 2.5◦ × 2.5◦ we obtained the FTA and FT pdf. For each grid box, we extracted the 99th percentile value. Based on 99th percentile FTA and FT values, we build a spatial distribution map that show where are the most efficient (FTA) and severe (FT).
  50. 50. Thunderstorms Severity: the most efficient thunderstorms
  51. 51. Thunderstorms Severity: the most severe thunderstorms
  52. 52. Thunderstorms Severity: where are the most efficient and severe thunderstorms Find the thunderstorms that have FTA and FT above 90th percentile. Compute the geographical densities of these thunderstorms (normalized by TRMM overpasses).
  53. 53. Thunderstorms Severity: the most severe and efficient thunderstorms
  54. 54. Conclusions This study characterized the severe thunderstorms over South America by employing 14 years of TRMM measurements. The thunderstorms were characterized by integrating TRMM LIS, PR and VIRS measurements. A total of 157,592 electrified clouds have been observed in South America (40S-10N and 90W-30W). For the severe thunderstorms database, only 94,733 thunderstorms were used. Those thunderstorms had LIS view time greater and equal to 60 seconds and at least one valid PR raining pixel .
  55. 55. The onset of the thunderstorms Diurnal cycle: 40% of thunderstorm occurred between 13h–17h (LT) In the ocean, we found one maximum at 20h (LT) and another between 4–5 (LT) The nocturnal peak of thunderstorms were found in extreme North of Andes Mountains (Paramillo National Natural Park, Maracaibo lake). 630 systems where observed in 14 years, only between 0h e 00:59h The thunderstorms were more frequent in center of South America (10◦ –0◦ S and 70◦ –50◦ W and 20◦ –10◦ S and 60◦ –50◦ W), between 14h–16h (LT).
  56. 56. Onset: Annual cycle: Thunderstorms season in South America was between October and March with two peaks: January and October. Northeast of South America (0◦ –10◦ N and 70◦ –50◦ W) the peak of thunderstorm was found in August. In South of South America (40◦ –20◦ S and 70◦ –60◦ W, Plata basin) was found a shorter thunderstorm season with 2 months. The longer season was found in Colombia and West of Venezuela with 9 months.
  57. 57. Onset: Numbers of thunderstorm associate with seasonal geographical densities of lightning and thunderstorms 1◦ Spring = 57,861 2◦ Summer = 46,077 3◦ Autumn = 36,804 4◦ Winter = 16,850
  58. 58. Onset: Total geographical densities Thunderstorms: North and Northwest of South America (Colombia, Panama, Northwest of Amazon): 3.5–4.7 × 10−4 km−2 Regions with high topography: Northeast of Titicaca Lake in Peru, mountain range in Santa Catarina, Emas National Park in Goias: 2.5 × 10−4 km−2 Lightning: Mouth of the Catatumbo River: 148.1 fl year−1 km−2 Cochabamba, Bolivia: 60 fl year−1 km−2 The Black Needles peak, Matiqueira mountain range, Neblina peak, La Plata basin: 30–60 fl year−1 km−2 . Lightning per thunderstorm: 11.73 × 10−2 ano−1 km−2 (Maracaibo lake). In this grid point (772 km2 ) each thunderstorm had in average 91 fl year−1 .
  59. 59. Severity of Thunderstorms in South America Two groups of severe systems are found: FTA 29.3 to 1,258.7 × 10−4 fl min−1 km−2 FT 47.2 to 1,283.6 fl min−1 (above category 3 of Cecil et al. [2005] and Zipser et al. [2006]) Cloud top Tb: FTA thunderstorms are 10 K colder than FT thunderstorms Convective × Stratiform rain fraction: FTA thunderstorms have 72% of convective rain fraction and 32% stratiform and FT systems 22% convective and 65% stratiform. Lightning × non Lightning pixels (CFAD): profiles with lightning pixels have higher Zc values, and are taller. In the layer between 5 and 7 km height, the Zc difference can reach 5 to 10 dBZ. At lower levels (2–3km) Zc is above 50 dBZ compared to 40 dBZ for non-lightning pixels; Lightning pixels (CFAD): FTA thunderstorms show 1-3 dBZ higher Zc values than FT systems, specially above 5 km height;
  60. 60. Top 1% FTA thunderstorms: More than 148.93 × 10−4 fl min−1 km−2 . Associated with orography in the Pantanal Matogrossense, Central Plateau of Brazil, and the Xingu and Araguaia basin and Tocantins rivers in the Amazon basin, the meridional Plateau of Brazil in Paran´a basin and the Andes foothill – eastward of the Sierra of Cordoba in Argentina. Top 1% severe FT thunderstorms: More than 272.88 fl min−1 and are located in the elevated topography areas like Andes foothill and Sierra of Cordoba in Argentina and also at less pronounced orography at Tocantins central of Brazil, Sierras Gauchas and Catarinenses at South of Brazil to more flat areas that have a water and vegetation contrast like south of Amazon, Rio Branco in Acre, in the boundaries of the states of Acre, Amazon with Peru, central Bolivia, Pantanal Matogrossense, Paran´a state, Paraguay and a Plata basin.
  61. 61. Severity: Temperature dependence – CFTD Zc distribution: FTA thunderstorms show broader Zc distribution than FT systems for the same temperature level, specially above 0◦ C and they have 1–3 dBZ higher Zc values; Latitudinal variation: FT thunderstorm show an enhancement of the collision-coalescence process towards the sub-tropics. Zc change: the maximum Zc decrease with temperature for FTA(FT) thunderstorms is at -2◦ C (0◦ C) and -12◦ C(-8◦ C) for 50% and 95% levels profiles respectively in the Amazon and -6◦ C(-2◦ C) and -14◦ C (-6◦ C) for 50% and 95% respectively for the La Plata Basin.
  62. 62. ACKNOWLEDGEMENTS This work is part of PhD project that is supported by CNPq grant 140842/2011-0. This work is partially supported by CAPES PROEX program and COELCE. I would like to thank MSFCNASA for providing LIS data set and GSFCNASA for providing TRMM dataset. Finally I would like to thank Dra. Rachel Albrecht for the LIS lightning and view time reprocessed dataset.

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