1,721,003 research outputs found
Development and Implementation of the Hyperbolic Pretracker
Full text linkThis report details the progress made on the design and implementation of a hyperbolic pretracker specifically for the Envisat RA-2 altimeter. First considerable care was taken to construct a waveform simulator whose characteristics matched those of the instrument — specifically, this meant some tuning to get leading edge slope and position of half-power point in exact agreement with the RA-2 altimeter. This simulator was used to generatewaveforms as the virtual instrument overflies a small rectangular patch of enhanced variability (a "bright target") possibly representing glassy seas. From a number of such (noise-free) simulations spanning the narrow altimeter swath it was possible to use mathematical inversion techniques to produce a set of weights for estimating intensities of such hyperbolae, and then remove these features from the 2-D waveform space. This is demonstrated in a number of worked examples (datasets provided for validation). A key aspect of the technique is that it can equally well be used to compensate for weak targets (rain cells or absence of ocean due to land); this is particularly effective in the transits of Pianosa. This technique is much better at coping with multiple discrete targets than an approach that treats each waveform in isolation thus neglecting the contextual information from neighbouring waveforms. The implementation with real RA-2 altimeter data is still problematic, because there are movements in the tracker window, which can still be important at the sub-binwidth scale, yet are hard to correct for. A further challenge is estimating the intensity of hyperbolic features when not all of the feature is present in the waveform anomalies e.g. because ofdata over land generated using a different chirp bandwidth
Improving the intercalibration of ?0 values for the Jason-1 and Jason-2 altimeters
Full text linkThe normalized backscatter from a radar altimeter, ?0, is a measure of the surface roughness at scales of a few
radar wavelengths; over the ocean this is used to infer wind speed. Long-term studies of wind speed rely on consistent
measurements within an altimetric mission and good intercalibration between missions. For the Jason-1 and Jason-2
altimeters the derivation of ?0 from the full waveform data is known to be sensitive to the recovered value for ?2, a
term encompassing both mispointing and inhomogeneities within the altimetric footprint. The six months of data from
the Jason-1/2 tandem mission reveal that different ?0 corrections are needed for these two causes of non-zero ?2
values. With these corrections implemented, the r.m.s. difference of Ku-band ?0 values for Jason-1 and Jason-2 drops
from 0.15 dB to 0.05 dB, with the bias between the two showing a clear trend with wind speed; Jason-1 being 0.04 dB
greater in high winds but 0.19 dB greater in low winds. No clear change in offset is noted during the 6 months of
overlapping data. Implementation of this correction will improve consistency of Jason-1 ?0 values and may impact on
orbit-fitting procedures
Optimizing ?0 information from the Jason-2 altimeter
Full text linkA radar altimeter's normalized backscatter, ?0, is used in many oceanographic applications, to infer values of wind speed, wind stress, rain rate and the presence of biogenic slicks. The waveform retracker used to estimate the key geophysical variables for the altimeters on the Jason-1 and Jason-2 satellites shows increased small-scale variability since the problem is ill-conditioned. A simple empirical adjustment to ?0 improves the separability between various parameters and also improves the along-track profiles of ?0. This leads to i) more realistic wind fields, ii) better discrimination of rain events, and iii) improved comparison between the Jason-1 and Jason-2 altimeters during their tandem mission
Jason-1/Jason-2 metocean comparisons and monitoring
Full text linkThe initial tandem phase of the Jason-2 mission is important for the calibration of the entire altimetric system, not just the records of sea surface height. However, as well as allowing a bulk comparison of metocean parameters such as wave height and backscatter strength (used to infer wind speed), it affords a more detailed opportunity to understand the artefacts within each instrument. The wave height comparison shows no bias between the instruments, with the mismatch error of consecutive points independent of one another. The backscatter difference is not a simple offset, but has a trend with weak non-linear variations. The technique for backscatter monitoring using Ku-/C-band differences is validated during the tandem phase, and extended to show ~59-day oscillations throughout the Jason-1 mission. This effect, which is predominantly in the C-band, is related to changing solar exposure, due to the fraction of orbit in eclipse varying as the orbit precesses. Such anomalies were partially present in TOPEX-B data, but are not noticeable for Jason-2. It is recommended that all TOPEX, Jason-1 and Jason-2 data be transformed to a consistent scale, allowing for the bias and trend terms in the offsets between instruments, and correcting for any long-period drifts in individual calibration; this will enable a single wind speed algorithm to be applied to the combined satellite data
A visible record of eddies in the southern Mozambique Channel
Full text linkThe flows around Madagascar feed into the Agulhas Current, but there have been few hydrographic studies of the flow within the Mozambique Channel. Some cruise and altimetric data point to this being a region of high mesoscale activity, with eddies migrating through the area. Here we show how ocean colour data throw light on the behaviour of eddies in the southern Mozambique Channel
A plankton guide to ocean physics: Colouring in the currents round South Africa and Madagascar
Full text linkThe ocean colour sensor SeaWiFS, launched in August 1997, has been a great boon to those researching large-scale oceanic biological productivity. The sensor can detect variations in the colour of the water due to the presence of chlorophyll in phytoplankton, which essentially changes the water colour from blue to green. SeaWiFS has provided measurements of chlorophyll concentration over nearly all the world’s oceans, and because of their association with fronts, eddies and regions of upwelling, these records of phytoplankton abundance reveal much about physical processes occurring within the ocean
Rain-flagging of the Envisat altimeter
Full text linkAs the goals for altimetric measurements become ever more precise, there is concern about the reliable detection and discarding of rain contaminated data. A dual-frequency rain detection technique developed for the Ku- and C-band TOPEX altimeter, is adapted for the Ku- and S-band RA-2 altimeter on Envisat. Of particular concern is the selection of a suitable threshold to minimise the quantity of good data inadvertently discarded
Ultraplankton distribution in surface waters of the Mozambique Channel – flow cytometry and satellite imagery
Full text linkThe composition of ultraplankton (UP) in near-surface samples collected underway every 1 to 6 h from a ship sailing from Durban to the Seychelles was determined by flow cytometry, using both autofluorescence pigments and fluorescence DNA staining. Prochlorococcus (Pro) (17 to 160 x 103 cells ml-1) numerically dominated the ultraphytoplankton (UPP), followed by Synechococcus (Syn) (4.5 to 57 x 103 cells ml-1) and eukaryotic algae (EA) (0.6 to 4.2 x 103 cells ml-1). The abundance of heterotrophic bacterioplankton (HB) was 0.4 to 1.3 x 106 cells ml-1. A strong correlation (r = 0.8 to 0.97) was observed between sea-viewing wide field of view sensor (SeaWiFS) satellite estimates of total chlorophyll a (chl a) concentration and chl a concentration, abundance and biomass of EA as well as abundance and biomass of HB. This shows the potential for deducing spatial distributions of these 2 groups for ecosystem modelling using satellite data. Although the correlation between satellite chl a estimates and Syn chl a concentration was strong (r = 0.83 to 0.88), the correlation with its abundance and biomass was poor (r < 0.6) due to high variability (factor of 12) in cellular chl a content and to a lesser extent to diurnal cycles. The relationships were similar when either only daytime or all UP measurements were compared with the satellite data. No relationship was found between satellite data and Pro chl a concentration, abundance or biomass, even after correction for a pronounced diel cycle, suggesting that the SeaWiFS instrument might not detect Pro chl a
Introduction to the Special Issue on "Satellite Altimetry: New Sensors and New Applications"
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