Ground and satellite-based night-time cloud identification techniques at the Pierre Auger Observatory Johana Chirinos Michigan Technological University IS@AO Workshop Cloud pic

Identifying Clouds over the Pierre Auger Observatory As part of atmospheric studies for Pierre Auger Observatory: Identifying night-time clouds in the field of view of the Fluorescence Detectors(FD) is important for getting correct shower profiles. Ground-based night-time cloud identification techniques: XLF and CLF laser shots seen by FDs Infrared Cloud Cameras LIDARs (previous talk by Aurelio) Satellite-based night-time cloud identification technique: GOES12/13 satellite images over Southern Hemisphere 04/18/11 IS@AO Workshop

Shower profile from cosmic rays Shower profile:particles along shower path correct? Shower development viewed by 4FD FD pixels seeing shower profile 04/18/11 IS@AO Workshop

Clouds affecting shower profile Attenuation of light by cloud Cosmic Ray Shower Decrease or absence of fluorescence light Hacer un mejor dibujo FD 04/18/11 IS@AO Workshop

Clouds affecting shower profile Forward directed light cone Forward directed light scattered towards FD Cosmic Ray Shower Hacer un mejor dibujo FD 04/18/11 IS@AO Workshop

CLF/XLF FD 04/18/11 IS@AO Workshop

Cloud over CLF Cloud between CLF&Eye P r o f i l e 04/18/11 IS@AO Workshop

IR Clouds Cameras 4 IR cameras: one at each FD site. During FD operation: -Full sky mosaic 27 images every 15' 04/18/11 IS@AO Workshop

5 images across FDs field-of-view every 5' IR Clouds Cameras 5 images across FDs field-of-view every 5' Cloud Camera Images Image Processing FD Pixels Cloud Index 04/18/11 IS@AO Workshop

IR Clouds Cameras Cloud-affected event Cloud Camera Image Image Processed 04/18/11 IS@AO Workshop

Satellite GOES12/13 Extensive atmospheric monitoring task for many topics over 3000km2: Great effort for constructing, running, maintaining equipment/software. For cloud identification: Satellite data will eliminate these worries. Better resolution in space and time, especially to identify exotic events. Double bang Cloud 04/18/11 IS@AO Workshop

Satellite GOES12/13 GOES (Geostationary Operational Environmental Satellite) at 35,800 km. Full-disc view of Earth: 1 visible band: 1km by 1km. 4 IR bands: 4km by 4km. GOES-12/13 Imager at 75º W: Southern Hemisphere Scan Sector 68-70º W longitude, 34-36º S latitude each 30' (hh09ss, hh39ss) at night 04/18/11 IS@AO Workshop

4 Satellite IR Bands Emission spectrum for a 280 K black-body at Earth's surface with absorption effects of atmospheric water vapor: Cloud identification during FD shifts. Satellite dark time: 00:09am / 03:09am / 06:09am / 09:09am - Band 2 & 4: unaffected, none of earth's atmospheric gases absorb very well. Able to sense earth's surface and clouds. - Bigger effect in band 3:responds to water vapor at middle and upper layers. 04/18/11 IS@AO Workshop

Ground-truthing cloud identification technique To ground-truth algorithm: comparison with instruments at Auger Observatory. : IR Cloud Camera and LIDARs: Different field of view/timing: not easy/perfect comparison. CLF: Cloudy/clear state of CLF pixel is regularly monitored by CLF. Every 15' during FD nights: CLF produces 50 laser shots seen by FD. For 2007 data, we plot all 50 laser profiles in one plot: If peak over laser profile: Cloud over CLF 04/18/11 IS@AO Workshop

CLF(1 hour data) 07:09 07:00 Clear 07:15 Clear Satellite 07:39 Clear/Cloudy tag of CLF pixel: If CLF shots 9' before & 6' after both clear/cloudy. CLF pixel clear 07:30 Cloudy over CLF 07:45 Cloudy between CLF & Eye Satellite 07:39 CLF pixel undefined 04/18/11 IS@AO Workshop

Ground Temperature Correlation T2 & T4: sensitive to temperature of emitting surface. For clear pixels: T2 & T4 should be correlated with ground T. For CLF pixel: T4 vs. T of ground from weather station at CLF(Tclf). Correlation for clear nights: T4 ~ Tclf T4 < Tclf when cloudy. Linear fit for clear nights: Small residual: CLF pixel clear. Large residual: CLF pixel cloudy. 04/18/11 IS@AO Workshop

Residuals animated gif Assuming a relative uniform T of region: T of all pixels from region ~ Tclf For every pixel: When residual small, pixel is clear(clear blue). When residual large: pixel is cloudy(dark blue). http://befnet.auger.mtu.edu/satellite/output/gif/All/Anim_gif_nightly/Residuals/ Better model of T over array: WS at CLF, at 4 FD, at BS and nearby towns. 04/18/11 IS@AO Workshop

Satellite info only: T3 vs T2-T4 T3 & T2-T4: independent of current ground T. Clear by CLF : condensed blob Cloudy by CLF: anti-correlated line Greatest separation between clear and cloudy. We project data onto this line. We fit to an empirical function of cloud probability. 04/18/11 IS@AO Workshop 18

Satellite info only: maps Pixels with cloud probability < 20%: light green, clear. Pixels with higher cloud probabilities in grey scale. http://befnet.auger.mtu.edu/befnet_html/Satellite.html ATMON 2010 04/18/11 IS@AO Workshop

Satellite info only: maps Cloudiness since 2007 04/18/11 IS@AO Workshop

Ideas for a collaborative work : We have more than 5 years of different types of atmospheric data. These could be useful to other scientists. For example: Ground-truthing MODIS satellite results for a CTA site search. 04/18/11 IS@AO Workshop

Cloud Identification Principles Clouds are typically colder than Earth's surface: T2&T4(non-absorbing): Sense unattenuated radiation from emitting surface. Lower T2 or T4 than Earth T: marker for clouds. Wavelength dependence in emissivity of clouds, but not for Earth. Depends on relationship between cloud droplet size and wavelength. T2-T4:sensitive to emissivity differences for clouds between the bands. T2-T4:  for clouds. T2 ~T4: Earth surface. Clouds: mixture of water vapor and liquid water droplets. T3 varies with water vapor: fraction of cloud in a pixel. Cloud identification algorithms with T2, T4, T2-T4, and T3 appear promising. 04/18/11 IS@AO Workshop