Passive Aquatic Listener: Passive Aquatic Listener: A state-of-art system A state-of-art system employed in Atmospheric, employed in Atmospheric, Oceanic and Biological Oceanic and Biological Sciences Sciences 1 M. N. Anagnostou, M. N. Anagnostou, J. A. Nystuen J. A. Nystuen 2 , E. N. , E. N. Anagnostou Anagnostou 1,3 1,3 1 Hellenic Center for Marine Research, Institute of Hellenic Center for Marine Research, Institute of Inland Waters Inland Waters 2 Applied Physics Laboratory, University of Washington, Applied Physics Laboratory, University of Washington, Seattle, Washington, USA Seattle, Washington, USA 3 University of Connecticut, Department of Civil & University of Connecticut, Department of Civil & Environmental Engineering Environmental Engineering
Transcript
Slide 1
Passive Aquatic Listener: A state-of-art system employed in
Atmospheric, Oceanic and Biological Sciences 1 M. N. Anagnostou, J.
A. Nystuen 2, E. N. Anagnostou 1,3 1 Hellenic Center for Marine
Research, Institute of Inland Waters 2 Applied Physics Laboratory,
University of Washington, Seattle, Washington, USA 3 University of
Connecticut, Department of Civil & Environmental
Engineering
Slide 2
Research Questions Passive Aquatic Listeners Underwater Ambient
Sound Sources Can we use Passive Aquatic Listeners (PALs) for
detecting Underwater Ambient Sound Sources generated from
environmental (physical & biological) or geophysical (seismic,
tsunami, Rock dumping, etc.) and man-made sources (Ships, Sonar,
etc.)? Can we use it to detect and classify and then quantify the
above sources? Can we use it to detect and classify and then
quantify the above sources? Can we use it to improve QPF over the
oceans? Can we use it to improve QPF over the oceans? Microphysical
and rainfall estimation over the oceans for satellite validation???
Microphysical and rainfall estimation over the oceans for satellite
validation???
Slide 3
Objectives (1) evaluate the PAL rain classification with a
meteorological radar and assess the PAL rainfall retrieval scheme
based on coincident radar PAL data collected; (2) evaluate the PAL
wind classification and wind speed estimation algorithm with the
Poseidons buoys surface anemometers. To facilitate the research
questions we have employed a series of experiments: (a)ISREX
experiment; (b)PAL integrated to Poseidon system
Slide 4
Technological Overview of PAL Components Low-noise broadband
hydrophone 100 Hz 50,000 Hz TT8 micro-computer processor with 100
kHz A/D sampler 2 Gb memory card 65 amp-hour battery package
Electronic filter and 2-stage amplifier
Slide 5
Sea Level 100-2000m 2000m (d 1 ) d 1 d 2 50m (d 2 ) Surface
sources are assumed to be vertically oriented dipoles, radiating
sound principally vertically. The signal from a non-uniform sound
source at the surface will be smoothed at the deeper hydrophones
The signal from rain changes in both space and time The signal from
wind has a longer space and time scale than rain and will be
assumed to be uniform over the mooring Listening Area of PAL
Spatial Averaging The expectation is that the listening area for
each hydrophone is a function of the depth of the hydrophone.
Roughly half of the energy arriving at the hydrophone comes from an
listening area with radius equal to the depth of the hydrophone and
90% of the energy from an area with radius equal to 3 times the
depth.
Slide 6
Ionian Sea Rainfall Experiment (ISREX): Fall Spring 2004
(Amitai et al. 2006; Anagnostou et al. 2008)
Slide 7
Rainfall Events Storm Dates (mm/dd/yy) PAL (mm) XPOL (mm) Rain
Gauges (mm) Methoni Station (mm) MNOP
01/21-22/0468.567.561.152.4N/A 96.8
02/12/0413.714.614.511.012.122.520.1 03/03/049.99.19.710.32.81.01.4
03/04/044.2 4.73.93.613.413.0 03/08/047.08.912.813.44.011.97.9
03/09/0412.711.810.79.413.014.18.3
03/12/0429.931.230.123.118.15.15.8
04/01/0434.036.331.120.1N/A23.525.5 Legend: M = PAL at 60m depth; N
= PAL at 200m depth; O = PAL at 1000m depth; P = PAL at 2000m
depth.
Slide 8
Acoustic Data Wind & Rain classification of PAL Wind and
rain have unique spectral characteristics that allow each sound
source to be identified.
Slide 9
Radar Data Radar data needs to be calibrated and corrected for
atmospheric attenuation (Anagnostou et al.2006) February 12 th
March 8 th March 9 th March 12 th
Slide 10
Radar and PAL Rain estimation algorithms Acoustical Rainfall
Algorithm (Ma and Nystuen, 2005) = 10log 10 () = 42.5 and = 10 b =
15.4 Radar Rainfall Algorithm (Anagnostou et al. 2008)
Slide 11
Spatial averaging effect The rainfall rates from PALs are
correlated to averaged rainfall rates from the radar for different
averaging radii in a circle centered over the mooring location
Slide 12
XPOL/PAL rainfall comparison March 12 th March 9 th March 8 th
February 12 th
Slide 13
PAL integrated with Poseidon System
Slide 14
The marriage of the Year: PAL/Katerina for
Geophysical/Geological Applications
Slide 15
Conclusions High frequency acoustic measurements of the marine
environment at different depths (60, 200, 1000 and 2000 m) are used
to describe the physical, biological and anthropogenic processes
present at a deep water mooring site near Methoni, Greece from
mid-Jan. to mid-April in 2004. XPOL radar reflectivity is then
quality controlled and corrected for attenuation. A combined
rainfall algorithm is then used to average over the mooring site
and compared to PAL. Eight events were recorded from PALs and six
from radar. The radar data were used to verify the acoustic
classification of rainfall, and the acoustic detection of imbedded
shipping noise within a rain event. The comparison shows an
increase in effective listening area with increasing listening
depth. For the highest correlation PAL/XPOL matching values we
determined high rainfall correlations wit the PAL overestimation in
the range of 50%.
Slide 16
Future Work There is a need to continue our experimental effort
to enhance our understanding of acoustic rainfall estimation. New
questions include: (1) is the change in the length scale of maximum
correlation due to the spatial structure of the rain event? If so,
can information about the spatial structure of rain be part of the
acoustic rainfall detection process? (2) What is the influence of
wind on acoustic rainfall classification? Can the wind effect be
incorporated into the acoustic rainfall type classification
algorithms? What is the influence of wind on acoustic rainfall rate
measurement? The combined influence of wind and rain on sound
levels in the ocean has been modeled using data from the tropical
Pacific Ocean (Ma et al. 2005). This model needs to be inverted to
extract the acoustic rainfall signal in the presence of wind. The
calibrated radar data from ISREX will be used to model and
constrain this inversion. (3) Can we use an inverse acoustic
algorithm to estimate DSD retrievals?
Slide 17
Acknowledgments: For the ISREX experiment: E. Boget designed
and deployed the deepwater mooring. The National Observatory of
Athens (NOA) and Dr. Yianni Kalogiro made the XPOL radar available
to the experiment. Prof. G. Chronis and the Hellenic Center for
Marine Research (HCMR) provided vessel Filia used to deploy the
mooring. T. Paganis and A. Gomta, at the Methoni weather station
provided the Methoni met data. The citizens of Finikounda allowed
raingauges to be set up in their yards during the experiment. For
the Poseidon project: The people of the Aegean vessel, Mr. Dionysi
Balla and Mr. Paris Pagonis for the designing and deployment of PAL
to the two Poseidon Buoys. For the PAL/Katerina project: Dr.
Christos Tsambaris for the excelent collaboration, Mr. Nikos and
Stelios Alexakis for the design of the system and the deployment,
and Mr. Leonidas Athinaios for the construction of the
platform.