Inter-comparison of automatic rain gauges

The Ocean Acoustics Division (OAD) of the Atlantic Oceanographic and Meteorological Laboratory (AOML), in cooperation with NOAA/NESDIS and NASA, has deployed six rain gauges for calibration and intercomparison purposes. These instruments include: (1) a weighing rain gauge, (2) a RM Young Model 50202 capacitance rain gauge, (3) a ScTI ORG-705 (long path) optical rain gauge, (4) a ScTI ORG-105 (mini-ORG) optical rain gauge, (5) a Belfort Model 382 tipping bucket rain gauge, and (6) a Distromet RD-69 disdrometer. The system has been running continuously since July 1993. During this time period, roughly 150 events with maximum rainfall rate over 10 mm/hr and 25 events with maximum rainfall rates over 100 mm/hr have been recorded. All rain gauge types have performed well, with intercorrelations 0.9 or higher. However, limitations for each type of rain gauge have been observed

Listening to Raindrops

The sound of rain underwater is loud and distinctive. It can be used as a signal to detect and measure oceanic rainfall. These measurements are needed to support climatological studies of the distribution and intensity of global rainfall patterns. Individual raindrops produce sound underwater by their impacts onto the ocean surface and, more importantly, by sound radiation from any bubbles trapped underwater during their splashes. Because different raindrop sizes produce distinctive sounds, the underwater sound can be inverted to quantitatively measure drop size distribution in the rain. Acoustical Rain Gauges (ARGs) are being deployed on oceanic moorings to make long-term measurements of rainfall using this acoustical technique.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/60267/1/raindrops.pd

Solstice: An Electronic Journal of Geography and Mathematics, Volume X, Number 2.

This document was delivered over the internet.The purpose of Solstice is to promote interaction between geography and mathematics. Articles in which elements of one discipline are used to shed light on the other are particularly sought. Also welcome, are original contributions that are purely geographical or purely mathematical. These may be prefaced (by editor or author) with commentary suggesting directions that might lead toward the desired interaction. Contributed articles will be refereed by geographers and/or mathematicians. Invited articles will be screened by suitable members of the editorial board. IMaGe is open to having authors suggest, and furnish material for, new regular features.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/2/jackson.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/3/1_jackson.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/4/solsw.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/5/sols299.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/6/jackson5.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/7/jackson4.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/8/jackson2a.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/9/jackson3.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/10/jackson2.htmlhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/11/jackson1.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/12/jackson0.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/13/image696.JPGhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/14/Image13.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/15/Image12.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/16/Image11.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/17/Image10.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/18/Image9.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/19/Image8.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/20/Image7.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/21/Colorbar.gifhttp://deepblue.lib.umich.edu/bitstream/2027.42/58246/43/1999SolsticeVol10.pdfDescription of 1999SolsticeVol10.pdf : Solstice, 1999, Numbers 1 and 2. Contains attachments

Assessing the cross platform performance of marine mammal indicators between two collocated acoustic recorders

Equipment and deployment strategies for remote passive acoustic sensing of marine environments must balance memory capacity, power requirements, sampling rate, duty-cycle, deployment duration, instrument size, and environmental concerns. The impact of different parameters on the data and applicability of the data to the specific questions being asked should be considered before deployment. Here we explore the effect of recording and detection parameters on marine mammal acoustic data across two platforms. Daily classifications of marine mammal vocalizations from two passive acoustic monitors with different subsampling parameters, an AURAL and a Passive Aquatic Listener (PAL), collocated in the Bering Sea were compared. The AURAL subsampled on a preset schedule, whereas the PAL sampled via an adaptive protocol. Detected signals of interest were manually classified in each dataset independently. The daily classification rates of vocalizations were similar. Detections from the higher duty-cycle but lower sample rate AURAL were limited to species and vocalizations with energy below 4 kHz precluding detection of echolocation signals. Temporal coverage from the PAL audio files was limited by the adaptive sub-sampling protocol. A method for classifying ribbon (Histriophoca fasciata) and bearded seal (Erignathus barbatus) vocalizations from the sparse spectral time histories of the PAL was developed. Although application of the acoustic entropy as a rapid assessment of biodiversity was not reflective of the number of species detected, acoustic entropy was robust to changes in sample rate and window length.Keywords: Acoustic diversity, Marine mammal classification, Spectral detection, Acoustic entropy, Ocean acousticsKeywords: Acoustic diversity, Marine mammal classification, Spectral detection, Acoustic entropy, Ocean acousticsKeywords: Acoustic diversity, Marine mammal classification, Spectral detection, Acoustic entropy, Ocean acoustic

Noise level correlates with manatee use of foraging habitats

Author Posting. © Acoustical Society of America, 2007. This article is posted here by permission of Acoustical Society of America for personal use, not for redistribution. The definitive version was published in Journal of the Acoustical Society of America 121 (2007): 3011-3020, doi:10.1121/1.2713555.The introduction of anthropogenic sound to coastal waters is a negative side effect of population growth. As noise from boats, marine construction, and coastal dredging increases, environmental and behavioral monitoring is needed to directly assess the effect these phenomena have on marine animals. Acoustic recordings, providing information on ambient noise levels and transient noise sources, were made in two manatee habitats: grassbeds and dredged habitats. Recordings were made over two 6-month periods from April to September in 2003 and 2004. Noise levels were calculated in one-third octave bands at nine center frequencies ranging from 250 Hz to 64 kHz. Manatee habitat usage, as a function of noise level, was examined during four time periods: morning, noon, afternoon, and night. Analysis of sightings data in a variety of grassbeds of equal species composition and density indicate that manatees select grassbeds with lower ambient noise for frequencies below 1 kHz. Additionally, grassbed usage was negatively correlated with concentrated boat presence in the morning hours; no correlation was observed during noon and afternoon hours. This suggests that morning boat presence and its associated noise may affect the use of foraging habitat on a daily time scale.This research was supported by a P.E.O. Scholar Award and National Defense Science and Engineering Graduate Fellowship awarded to Jennifer Miksis

TRY plant trait database – enhanced coverage and open access

Plant traits - the morphological, anatomical, physiological, biochemical and phenological characteristics of plants - determine how plants respond to environmental factors, affect other trophic levels, and influence ecosystem properties and their benefits and detriments to people. Plant trait data thus represent the basis for a vast area of research spanning from evolutionary biology, community and functional ecology, to biodiversity conservation, ecosystem and landscape management, restoration, biogeography and earth system modelling. Since its foundation in 2007, the TRY database of plant traits has grown continuously. It now provides unprecedented data coverage under an open access data policy and is the main plant trait database used by the research community worldwide. Increasingly, the TRY database also supports new frontiers of trait‐based plant research, including the identification of data gaps and the subsequent mobilization or measurement of new data. To support this development, in this article we evaluate the extent of the trait data compiled in TRY and analyse emerging patterns of data coverage and representativeness. Best species coverage is achieved for categorical traits - almost complete coverage for ‘plant growth form’. However, most traits relevant for ecology and vegetation modelling are characterized by continuous intraspecific variation and trait–environmental relationships. These traits have to be measured on individual plants in their respective environment. Despite unprecedented data coverage, we observe a humbling lack of completeness and representativeness of these continuous traits in many aspects. We, therefore, conclude that reducing data gaps and biases in the TRY database remains a key challenge and requires a coordinated approach to data mobilization and trait measurements. This can only be achieved in collaboration with other initiatives