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16
Mesoscale gravity wave variances from AMSU-A radiances
- Geophys. Res. Lett
"... [1] A variance analysis technique is developed here to extract gravity wave (GW) induced temperature fluctuations from NOAA AMSU-A (Advanced Microwave Sounding Unit-A) radiance measurements. By carefully removing the instrument/measurement noise, the algorithm can produce reliable GW variances with ..."
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[1] A variance analysis technique is developed here to extract gravity wave (GW) induced temperature fluctuations from NOAA AMSU-A (Advanced Microwave Sounding Unit-A) radiance measurements. By carefully removing the instrument/measurement noise, the algorithm can produce reliable GW variances with the minimum detectable value as small as 0.1 K2. Preliminary analyses with AMSU-A data showGWvariancemaps in the stratosphere have very similar distributions to those found with the UARS MLS (Upper Atmosphere Research Satellite Microwave Limb Sounder). However, the AMSU-A offers better horizontal and temporal resolution for observing regional GW variability, such as activity over sub-Antarctic islands. INDEX TERMS: 0350
Q.: An example of temperature structure differences in two cyclone systems derived from the Advanced Microwave Sounder Unit, Wea
- Forecast
"... The Advanced Microwave Sounding Unit (AMSU) has better horizontal resolution and vertical temperature sounding abilities than its predecessor, the Microwave Sounding Unit (MSU). Those improved capabilities are demonstrated with observations of two cyclonic weather systems located in the South Pacifi ..."
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Cited by 10 (3 self)
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The Advanced Microwave Sounding Unit (AMSU) has better horizontal resolution and vertical temperature sounding abilities than its predecessor, the Microwave Sounding Unit (MSU). Those improved capabilities are demonstrated with observations of two cyclonic weather systems located in the South Pacific Ocean on 1 March 1999. These weather systems appear quite similar in conventional infrared satellite imagery, suggesting that they are comparable in structure and intensity. However, an analysis using temperature retrievals created from the AMSU shows that their vertical thermal structure is quite different. This is just one example of an application highlighting the improved sounding capabilities available with the AMSU instrument suite. A preliminary look at what the AMSU can provide in data-void regions and a discussion of future plans to create AMSU-based products to better diagnose synoptic-scale weather systems are presented.
Determination of precipitable water and cloud liquid water over oceans from
- the NOAA 15 Advanced Microwave Sounding Unit,” J. Geophys. Res.,
, 2001
"... Abstract. The advanced microwave sounding unit (AMSU) was finally launched in May 1998 aboard the NOAA 15 satellite. Algorithms are provided for retrieving the total precipitable water (TPW) and cloud liquid water (CLW) over oceans using the AMSU measurements at 23.8 and 31.4 GHz. Extensive compari ..."
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Abstract. The advanced microwave sounding unit (AMSU) was finally launched in May 1998 aboard the NOAA 15 satellite. Algorithms are provided for retrieving the total precipitable water (TPW) and cloud liquid water (CLW) over oceans using the AMSU measurements at 23.8 and 31.4 GHz. Extensive comparisons are made between the AMSU retrievals of CLW and TPW and those obtained using other satellite instruments (Special Sensor Microwave Imager (SSM/I) and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI)) and ground-based radiometers. The AMSU TPW is also compared against radiosonde data, where all of the results are in good agreement with rms differences less than 3 mm and biases less than 1 mm over the range between 5 and 60 mm. The CLW comparisons show greater variability, although the time series of the AMSU and ground-based sensors follow each other and cover the same dynamic range of 0 -0.5 mm. The AMSU CLW also compares well with the other satellite measurements, although a bias exists between AMSU and TMI when the CLW exceeds 0.5 mm.
Potential of Advanced Microwave Sounding Unit-A (AMSU-A) and AMSU-B measurements for atmospheric temperature and humidity profiling over
, 2005
"... [1] A neural network retrieval method has been applied to investigate AMSU-A/ AMSU-B atmospheric temperature and humidity profiling capabilities over land. The retrieval method benefits from a reliable estimate of the land emissivity and skin temperature as well as first guess information regarding ..."
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[1] A neural network retrieval method has been applied to investigate AMSU-A/ AMSU-B atmospheric temperature and humidity profiling capabilities over land. The retrieval method benefits from a reliable estimate of the land emissivity and skin temperature as well as first guess information regarding the temperature-humidity profiles. It has been applied on a large geographic area (60°W–60°E, 60°S–60°N) and atmospheric situations (winter and summer). The retrieved RMS errors are within 2 K and 9 % in temperature and relative humidity, respectively. Regardless of scanning conditions, vegetation types, and atmospheric situations, the algorithm retrieval results are satisfactory for both temperature and relative humidity. The retrieval approach has been evaluated by comparison with available in situ measurements.
Using Opaque Microwave Bands
, 2004
"... This thesis describes the use of opaque microwave bands for global estimation of precipitation rate. An algorithm was developed for estimating instantaneous precip-itation rate for the Advanced Microwave Sounding Unit (AMSU) on the NOAA-15, NOAA-16, and NOAA-17 satellites, and the Advanced Microwave ..."
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This thesis describes the use of opaque microwave bands for global estimation of precipitation rate. An algorithm was developed for estimating instantaneous precip-itation rate for the Advanced Microwave Sounding Unit (AMSU) on the NOAA-15, NOAA-16, and NOAA-17 satellites, and the Advanced Microwave Sounding Unit and Humidity Sounder for Brazil (AMSU/HSB) aboard the NASA Aqua satellite. The algorithm relies primarily on channels in the opaque 54-GHz oxygen and 183-GHz water vapor resonance bands. Many methods for estimating precipitation rate using surface-sensitive microwave window channels have been developed by others. The algorithm involves a set of signal processing components whose outputs are fed into a neural net to produce a rain rate estimate for each 15-km spot. The signal processing components utilize techniques such as principal component analysis for characterizing groups of channels, spatial filtering for cloud-clearing brightness tem-
unknown title
, 2008
"... A neural network approach for temperature retrieval from AMSU-A measurements onboard NOAA-15 and NOAA-16 satellites and a case study during Gonu cyclone ..."
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A neural network approach for temperature retrieval from AMSU-A measurements onboard NOAA-15 and NOAA-16 satellites and a case study during Gonu cyclone
unknown title
, 2008
"... A neural network approach for temperature retrieval from AMSU-A measurements onboard NOAA-15 and NOAA-16 satellites and a case study during Gonu cyclone Atmósfera, vol. 23, núm. 3, enero, 2010, pp. 225-239 ..."
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A neural network approach for temperature retrieval from AMSU-A measurements onboard NOAA-15 and NOAA-16 satellites and a case study during Gonu cyclone Atmósfera, vol. 23, núm. 3, enero, 2010, pp. 225-239
Observation of polar lows by the Advanced Microwave Sounding Unit: potential and limitations
, 2008
"... The potential of the Advanced Microwave Sounding Unit (AMSU) observations for the depiction and tracking of intense high-latitude mesoscale maritime weather systems, called polar lows, is explored. Since a variety of mechanisms are important for their development and maintenance, this investigation ..."
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The potential of the Advanced Microwave Sounding Unit (AMSU) observations for the depiction and tracking of intense high-latitude mesoscale maritime weather systems, called polar lows, is explored. Since a variety of mechanisms are important for their development and maintenance, this investigation is based on three polar low cases of different types. The AMSU-B channels at 183 GHz are able to locate convective polar lows (PL) even in their incipient stage, at a time when there is considerable uncertainty as to the nature of the cloud structures seen in the visible or infrared imagery. This detection is based on temperature depression due to scattering by hydrometeors, as confirmed by comparison with radar data. These same channels will, however, fail to unambiguously detect weakly convective and mainly baroclinic PL. The AMSU-A channels help documenting the large-scale environment in which PL develop. Channel 5 clearly shows the cold air outbreaks associated with these developments, whereas the difference between channels 7 and 5 can be used to detect and locate positive upper-level potential vorticity anomalies. Because of the high temporal availability of AMSU observations and despite some limitations pointed out in this study, these results are relevant for PL forecasting and monitoring. 1.
CORRECTING FOR PRECIPITATION EFFECTS IN SATELLITE-BASED PASSIVE MICROWAVE TROPICAL CYCLONE INTENSITY ESTIMATES
, 2005
"... Public reporting burden for this collection of Information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments ..."
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Public reporting burden for this collection of Information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and maintaining the data needed, and completing and reviewing the collection of information. Send comments regarding this burden estimate or any other aspect of this
