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A Study of Precipitation Occurrence Using Visual and Infrared Satellite Data

A Study of Precipitation Occurrence Using Visual and Infrared Satellite Data
Author: Linda Sue Paul
Publisher:
Total Pages: 238
Release: 1983
Genre: Meteorology
ISBN:

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Bi-spectral satellite thresholds for precipitation specification are explored with visual and infrared satellite data collocated with Service-A hourly observations for 137 surface stations in the southeastern United States. The data span the month of August 1979 and total 70,623 observations, including 538 daylight precipitation observations. The distributional and statistical differences of four satellite resolution sizes ranging from 484 to 2025 nmi2 are explored and determined to be significant in the representation of weather conditions. Precipitation and no-precipitation data can be statistically differentiated with the visual and infrared mean and standard deviation values. For overcast ceiling reports, a simple linear bi-spectral threshold based on a 50% probability of precipitation is defined as extending from albedo 1.00 to 0.60 with associated cloud top temperatures 290K and 210K, respectively. For overcast and broken ceiling reports, and albedo greater than 0.80 specifies a 50% probability of precipitation. (Author).


Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery

Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery
Author: Nasrin Nasrollahi
Publisher: Springer
Total Pages: 83
Release: 2014-11-07
Genre: Science
ISBN: 3319120816

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This thesis transforms satellite precipitation estimation through the integration of a multi-sensor, multi-channel approach to current precipitation estimation algorithms, and provides more accurate readings of precipitation data from space. Using satellite data to estimate precipitation from space overcomes the limitation of ground-based observations in terms of availability over remote areas and oceans as well as spatial coverage. However, the accuracy of satellite-based estimates still need to be improved. The approach introduced in this thesis takes advantage of the recent NASA satellites in observing clouds and precipitation. In addition, machine-learning techniques are also employed to make the best use of remotely-sensed "big data." The results provide a significant improvement in detecting non-precipitating areas and reducing false identification of precipitation.


Remote Sensing of the Terrestrial Water Cycle

Remote Sensing of the Terrestrial Water Cycle
Author: Venkataraman Lakshmi
Publisher: John Wiley & Sons
Total Pages: 572
Release: 2014-10-31
Genre: Science
ISBN: 1118872266

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Remote Sensing of the Terrestrial Water Cycle is an outcome of the AGU Chapman Conference held in February 2012. This is a comprehensive volume that examines the use of available remote sensing satellite data as well as data from future missions that can be used to expand our knowledge in quantifying the spatial and temporal variations in the terrestrial water cycle. Volume highlights include: An in-depth discussion of the global water cycle Approaches to various problems in climate, weather, hydrology, and agriculture Applications of satellite remote sensing in measuring precipitation, surface water, snow, soil moisture, groundwater, modeling, and data assimilation A description of the use of satellite data for accurately estimating and monitoring the components of the hydrological cycle Discussion of the measurement of multiple geophysical variables and properties over different landscapes on a temporal and a regional scale


Remote Sensing of Clouds and Precipitation

Remote Sensing of Clouds and Precipitation
Author: Constantin Andronache
Publisher: Springer
Total Pages: 288
Release: 2018-02-21
Genre: Technology & Engineering
ISBN: 3319725831

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This book presents current applications of remote sensing techniques for clouds and precipitation for the benefit of students, educators, and scientists. It covers ground-based systems such as weather radars and spaceborne instruments on satellites. Measurements and modeling of precipitation are at the core of weather forecasting, and long-term observations of the cloud system are vital to improving atmospheric models and climate projections. The first section of the book focuses on the use of ground-based weather radars to observe and measure precipitation and to detect and forecast storms, thunderstorms, and tornadoes. It also discusses the observation of clouds using ground-based millimeter radar. The second part of the book concentrates on spaceborne remote sensing of clouds and precipitation. It includes cases from the Tropical Rainfall Measuring Mission (TRMM) and the Global Precipitation Measurement (GPM) mission, using satellite radars to observe precipitation systems. Then, the focus is on global cloud observations from the ClaudSat, Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO), including a perspective on the Earth Clouds, Aerosols, and Radiation Explorer (EarthCARE) satellite. It also addresses global atmospheric water vapor profiling for clear and cloudy conditions using microwave observations. The final part of this volume provides a perspective into advances in cloud modeling using remote sensing observations.


Monthly Weather Review

Monthly Weather Review
Author:
Publisher:
Total Pages: 702
Release: 2004
Genre: Meteorology
ISBN:

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Precipitation Estimation Using Collocated Goes Satellite and Surface Data

Precipitation Estimation Using Collocated Goes Satellite and Surface Data
Author: D. W. Rust
Publisher:
Total Pages: 71
Release: 1984
Genre:
ISBN:

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The separation of precipitation from non-precipitation events using a matrix of GOES-E digitized infrared and visual satellite data was studied. Precipitation verification was conducted with collocated surface observations. The data set consists of 70,623 surface observations of which 29,342 have collocated satellite data. The visual data were normalizd and converted to albedos using the Muench and Keegan (1979) normalization scheme. The data set was separated into four categories (precipitation/no-precipitation and infrared/visual) and after testing for normality, it was determined that none of the categories were normally distributed. Using histograms, a distinct separation between the peaks of precipitating and non-precipitating events was found, but some overlap does exist. Testing of infrared/visual thresholds for precipitation/no-precipitation events used in automated cloud and precipitation research yielded a correct estimation rate of 92% when the infrared and visual thresholds were combined.