Global Millimeter-Wave Precipitation Retrievals Trained With a Cloud-Resolving Numerical Weather Prediction Model, Part I: Retrieval Design

@article{Surussavadee2008GlobalMP,
  title={Global Millimeter-Wave Precipitation Retrievals Trained With a Cloud-Resolving Numerical Weather Prediction Model, Part I: Retrieval Design},
  author={C. Surussavadee and D. Staelin},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  year={2008},
  volume={46},
  pages={99-108}
}
  • C. Surussavadee, D. Staelin
  • Published 2008
  • Environmental Science, Computer Science
  • IEEE Transactions on Geoscience and Remote Sensing
This paper develops a global precipitation rate retrieval algorithm for the advanced microwave sounding unit (AMSU), which observes 23-191 GHz. The algorithm was trained using a numerical weather prediction (NWP) model (MM5) for 106 globally distributed storms that predicted brightness temperatures consistent with those observed simultaneously by AMSU. Neural networks were trained to retrieve hydrometeor water-paths, peak vertical wind, and 15-min average surface precipitation rates for rain… Expand
Global satellite millimeter-wave precipitation retrievals trained with a cloud-resolving numerical weather prediction model
  • C. Surussavadee, D. Staelin
  • Environmental Science, Computer Science
  • 2007 IEEE International Geoscience and Remote Sensing Symposium
  • 2007
TLDR
This paper develops global retrieval algorithms for surface precipitation rate, peak vertical wind, and water-paths, for the Advanced Microwave Sounding Unit (AMSU) aboard the NOAA-15, -16, and -17 satellites, trained using numerical weather prediction model (MM5). Expand
Global Precipitation Retrievals Using the NOAA AMSU Millimeter-Wave Channels: Comparisons with Rain Gauges
A surface-precipitation-rate retrieval algorithm for 13-channel Advanced Microwave Sounding Unit (AMSU) millimeter-wave spectral observations from 23 to 191 GHz is described. It was trained usingExpand
Global precipitation retrieval algorithm trained for SSMIS using a Numerical Weather Prediction Model: Design and evaluation
  • C. Surussavadee, D. Staelin
  • Environmental Science, Geography
  • 2010 IEEE International Geoscience and Remote Sensing Symposium
  • 2010
TLDR
A global precipitation retrieval algorithm for the Special Sensor Microwave Imager/Sounder (SSMIS) and employs neural networks trained with 122 global storms that spanned a year and was simulated using the fifth-generation National Center for Atmospheric Research/Penn State Mesoscale Model (MM5) and a radiative transfer program validated using AMSU observations. Expand
Global Millimeter-Wave Precipitation Retrievals Trained With A Cloud-Resolving Numerical Weather-Prediction Model, Part II: Performance Evaluation
TLDR
Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are characterized. Expand
Passive millimeter-wave retrieval of global precipitation utilizing satellites and a numerical weather prediction model
TLDR
The model’s predicted millimeter-wave atmospheric radiances were found to statistically agree with those observed by satellite instruments on the United States National Ocean and Atmospheric Administration NOAA-15, -16, and -17 satellites over 122 global representative storms. Expand
Satellite Retrievals of Arctic and Equatorial Rain and Snowfall Rates Using Millimeter Wavelengths
A new global precipitation retrieval algorithm for the millimeter-wave Advanced Microwave Sounding Unit is presented that also retrieves Arctic precipitation rates over surface snow and ice. ThisExpand
Global Observations of Precipitation Using Satellite Passive Millimeter-Wave Sensors
  • C. Surussavadee
  • Environmental Science, Computer Science
  • IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium
  • 2008
TLDR
Developing and validating a global model that simulates ground-truth and predicts millimeter-wave radiances consistent with those coincidentally observed by AMSU aboard NOAA satellites and developing a series of precipitation retrieval algorithms, AMSU/MM5, where the latest version is able to estimate surface precipitation rates over snow-covered land and sea ice. Expand
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NPOESS Precipitation Retrievals Using the ATMS Passive Microwave Spectrometer
TLDR
Image sharpening of the ATMS Nyquist-sampled observations below 90 GHz further improves the recovery of small features but amplifies noise so that the benefits are restricted primarily to finely structured convective systems. Expand
Precipitation Retrievals Employing GOES Imager Infrared Channels and AMSU MIT Precipitation Retrieval Products
This paper develops a neural network based precipitation retrieval algorithm called the PSU Infrared Precipitation retrieval algorithm version 1 (PIP-1), which employs infrared observations from theExpand
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Global Millimeter-Wave Precipitation Retrievals Trained With A Cloud-Resolving Numerical Weather-Prediction Model, Part II: Performance Evaluation
TLDR
Differences between these retrievals and those from the conically scanned Advanced Microwave Scanning Radiometer for the Earth Observing System instrument and an alternate NOAA AMSU algorithm are characterized. Expand
Passive millimeter-wave retrieval of global precipitation utilizing satellites and a numerical weather prediction model
TLDR
The model’s predicted millimeter-wave atmospheric radiances were found to statistically agree with those observed by satellite instruments on the United States National Ocean and Atmospheric Administration NOAA-15, -16, and -17 satellites over 122 global representative storms. Expand
Millimeter-Wave Precipitation Retrievals and Observed-versus-Simulated Radiance Distributions: Sensitivity to Assumptions
Abstract Brightness temperature histograms observed at 50–191 GHz by the Advanced Microwave Sounding Unit (AMSU) on operational NOAA satellites are shown to be consistent with predictions made usingExpand
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The precipitation-rate estimation method presented is based on the opaque-channel approach described by Staelin and Chen (2000), but it utilizes more channels and training data and infers 54-GHz band radiance perturbations at 15-km resolution and the dynamic range now reaches 100 mm/h. Expand
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Promising agreement over land and sea has been obtained between NEXRAD 3-GHz radar observations of precipitation rate and retrievals based on simultaneous passive observations at 50-191 GHz from theExpand
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In this study, the authors show that good results may be obtained by weighting profiles from the prior probability density function according to their deviation from the observed brightness temperatures, and present a computationally simple technique for retrieving the precipitation and vertical hydrometeor profiles from downward viewing radiometers. Expand
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A new suite of products that are generated through the Microwave Surface and Precipitation Products System (MSPPS) includes precipitation rate, total precipitable water, land surface emissivity, and snow cover, which demonstrate their importance to weather forecasting and analysis, and climate monitoring. Expand
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In recent year san increasingly diverse range of passive microwave satellite data has become available for applications in numerical weather forecasting, climate studies and environmental monitoring.Expand
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The design and validation of the FSU precipitation profile retrieval algorithm for applications with SSM/I passive microwave measurements are described, and solid evidence that the profile approach is returning credible rainfall estimates whose uncertainnes are commensurate with those of current validation data is presented. Expand
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