Improving Infrared Based Precipitation Retrieval Algorithms Using Multi Spectral Satellite Imagery PDF Download
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Author | : Nasrin Nasrollahi |
Publisher | : Springer |
Total Pages | : 83 |
Release | : 2014-11-07 |
Genre | : Science |
ISBN | : 3319120816 |
Download Improving Infrared-Based Precipitation Retrieval Algorithms Using Multi-Spectral Satellite Imagery Book in PDF, ePub and Kindle
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.
Author | : Silas Michaelides |
Publisher | : MDPI |
Total Pages | : 318 |
Release | : 2019-07-23 |
Genre | : Science |
ISBN | : 3039212877 |
Download Remote Sensing of Precipitation Book in PDF, ePub and Kindle
Precipitation is a well-recognized pillar in global water and energy balances. An accurate and timely understanding of its characteristics at the global, regional, and local scales is indispensable for a clearer understanding of the mechanisms underlying the Earth’s atmosphere–ocean complex system. Precipitation is one of the elements that is documented to be greatly affected by climate change. In its various forms, precipitation comprises a primary source of freshwater, which is vital for the sustainability of almost all human activities. Its socio-economic significance is fundamental in managing this natural resource effectively, in applications ranging from irrigation to industrial and household usage. Remote sensing of precipitation is pursued through a broad spectrum of continuously enriched and upgraded instrumentation, embracing sensors which can be ground-based (e.g., weather radars), satellite-borne (e.g., passive or active space-borne sensors), underwater (e.g., hydrophones), aerial, or ship-borne.
Author | : Ali Behrangi |
Publisher | : |
Total Pages | : 204 |
Release | : 2009 |
Genre | : |
ISBN | : 9781109513943 |
Download Improved Global High Resolution Precipitation Estimation Using Multi-satellite Multi-spectral Information Book in PDF, ePub and Kindle
In respond to the community demands, combining microwave (MW) and infrared (IR) estimates of precipitation has been an active area of research since past two decades. The anticipated launching of NASA's Global Precipitation Measurement (GPM) mission and the increasing number of spectral bands in recently launched geostationary platforms will provide greater opportunities for investigating new approaches to combine multi-source information towards improved global high resolution precipitation retrievals. After years of the communities' efforts the limitations of the existing techniques are: (1) Drawbacks of IR-only techniques to capture warm rainfall and screen out no-rain thin cirrus clouds; (2) Grid-box- only dependency of many algorithms with not much effort to capture the cloud textures whether in local or cloud patch scale; (3) Assumption of indirect relationship between rain rate and cloud-top temperature that force high intensity precipitation to any cold cloud; (4) Neglecting the dynamics and evolution of cloud in time; (5) Inconsistent combination of MW and IR-based precipitation estimations due to the combination strategies and as a result of above described shortcomings. This PhD dissertation attempts to improve the combination of data from Geostationary Earth Orbit (GEO) and Low-Earth Orbit (LEO) satellites in manners that will allow consistent high resolution integration of the more accurate precipitation estimates, xxii directly observed through LEO's PMW sensors, into the short-term cloud evolution process, which can be inferred from GEO images. A set of novel approaches are introduced to cope with the listed limitations and is consist of the following four consecutive components: (1) starting with the GEO part and by using an artificial-neural network based method it is demonstrated that inclusion of multi-spectral data can ameliorate existing problems associated with IR-only precipitating retrievals; (2) through development of Precipitation Estimation from Remotely Sensed Information using Artificial Neural Network - Multi-Spectral Analysis (PERSIANN-MSA) the effectiveness of using multi-spectral data for precipitation estimation are examined. In comparison to the use of a single thermal infrared channel, using multi-spectral data has a potential to significantly improve rain detection and estimation skills; (3) a method proposed to integrate the previously developed cloud classification system (PERSIANN CCS) with PERSIANN-MSA. Through the integration, PERSIANN-MSA benefits from both cloud-patch classification capability as well as multi-spectral information to culminate the GEO-based precipitation estimation techniques; (4) finally, a new combination technique that incorporates multi-sensor information is developed. The technique is called REFAME, short for Rain Estimation using Forward Adjusted advection of Microwave Estimates. REFAME allows more consistent integration of MW VIS/IR information through hybrid advection and adjustment of MW precipitation rate along cloud motion streamlines obtained from a 2D cloud tracking algorithm using successive GEO/IR images. Evaluated over a range of spatial and temporal scales it is demonstrated that REFAME is a robust technique for real-time high resolution precipitation estimation using multi-satellite information.
Author | : V. Levizzani |
Publisher | : Springer Science & Business Media |
Total Pages | : 738 |
Release | : 2007-05-11 |
Genre | : Science |
ISBN | : 1402058357 |
Download Measuring Precipitation from Space Book in PDF, ePub and Kindle
No other book can offer such a powerful tool to understand the basics of remote sensing for precipitation, to make use of existing products and to have a glimpse of the near future missions and instruments. This book features state-of-the-art rainfall estimation algorithms, validation strategies, and precipitation modeling. More than 20 years after the last book on the subject the worldwide precipitation community has produced a comprehensive overview of its activities, achievements, ongoing research and future plans.
Author | : Mekonnen Gebremichael |
Publisher | : Springer Science & Business Media |
Total Pages | : 327 |
Release | : 2009-12-02 |
Genre | : Science |
ISBN | : 904812915X |
Download Satellite Rainfall Applications for Surface Hydrology Book in PDF, ePub and Kindle
With contributions from a panel of researchers from a wide range of fields, the chapters of this book focus on evaluating the potential, utility and application of high resolution satellite precipitation products in relation to surface hydrology.
Author | : Tianxu Yu |
Publisher | : |
Total Pages | : |
Release | : 2000 |
Genre | : |
ISBN | : |
Download Improved Retrievals for Satellite-based Multispectral Infrared Remote Sensing of Volcanic Clouds Book in PDF, ePub and Kindle
Author | : Alain Chedin |
Publisher | : Springer |
Total Pages | : 492 |
Release | : 2012-01-07 |
Genre | : Science |
ISBN | : 9783642846014 |
Download High Spectral Resolution Infrared Remote Sensing for Earth’s Weather and Climate Studies Book in PDF, ePub and Kindle
One of major challenges facing Earth's science in the next decade and beyondis the development of an accurate long term observational data set to study global change. To accomplish this, a wide range of observations will be required to provide both new measurements, not previously achievable and measurements with a greater degreee of accuracy and resolution than the ones which are presently and currently available. Among the parameters that are currently retrieved from satellite vertical sounding observations, temperature and moisture profiles are the most important for the description of the thermodynamic state of the medium. Other parameters, like those describing the cloud fields, the surface state or the conditions close to the surface are also key parameters for meteorology and climatology. A new generation of high spectral atmospheric sounders in the infrared has recently been designed to provide both new and more accurate data about the atmosphere, land and oceans for application to climate studies. Among the important observations that these instruments should contribute to the climate data set are day and night global measurements of: atmospheric temperature profiles; relative humidity profiles; cloud field parameters; total ozone burden of the atmosphere; distribution of minor atmospehric gases (methane, carbonmonoxide and nitrous oxide).
Author | : National Aeronautics and Space Administration (NASA) |
Publisher | : Createspace Independent Publishing Platform |
Total Pages | : 32 |
Release | : 2018-07-11 |
Genre | : |
ISBN | : 9781722796792 |
Download Ssm/I Rain Retrievals Within a Unified All-Weather Ocean Algorithm Book in PDF, ePub and Kindle
A new method for the physical retrieval of rain rates from satellite microwave radiometers is presented and compared to two other rainfall climatologies derived from satellites. The method is part of a unified ocean parameter retrieval algorithm that is based on the fundamental principles of radiative transfer. The algorithm simultaneously finds near-surface wind speed W, columnar water vapor V, columnar cloud liquid water L, rain rate R, and effective radiating temperature T(sub U) for the upwelling radiation. The performance of the algorithm in the absence of rain is discussed in Wentz, and this paper focuses on the rain component of the algorithm. A particular strength of the unified algorithm is its ability to 'orthogonalize' the retrievals so that there is minimum cross-talk between the retrieved parameters. For example, comparisons of the retrieved water vapor with radiosonde observations show that there is very little correlation between the water vapor retrieval error and rain rate. For rain rates from 1 to 15 mm/h, the rms difference between the retrieved water vapor and the radiosonde value is 5 mm. A novel feature of the rain retrieval method is a beamfilling correction that is based upon the ratio of the retrieved liquid water absorption coefficients at 37 GHz and 19.35 GHz. This ratio decreases by about 40% when heavy and light rain co-exist within the SSM/I footprint as compared to the case of uniform rain. This correction has the effect of increasing the rain rate when the spectral ratio of the absorption coefficients is small. Even with this beamfilling correction, tropical rainfall is still unrealistically low when the freezing level in the tropics (approx. 5 km) is used to specify the rain layer thickness. We restore realism by reducing the assumed averaged tropical rain layer thickness to 3 km, thereby accounting for the existence of warm rain processes in which the rain layer does not extend to the freezing level. Global rain rates are produced f...
Author | : Ali S. Amirrezvani |
Publisher | : |
Total Pages | : 224 |
Release | : 2008 |
Genre | : |
ISBN | : |
Download Improving a Rainfall-retrieval Algorithm for Summer Thunderstorms Using GOES IR & Lightning Data Book in PDF, ePub and Kindle
Author | : Negar Karbalaee |
Publisher | : |
Total Pages | : 114 |
Release | : 2017 |
Genre | : |
ISBN | : 9780355260717 |
Download Improving Warm Rainfall Detection and Rainfall Estimation of a Multiple Satellite-based Rainfall Retrieval Algorithm Book in PDF, ePub and Kindle
Precipitation as an essential component of the hydrologic cycle has a great importance to be measured accurately due to various applications such as hydrologic modeling, extreme weather analysis, and water resources management. Among different methods, meteorological satellites are one of the instruments that are widely used for precipitation estimation in fine spatial and temporal resolution. Precipitation Estimation from Remotely Sensed Imagery using Artificial Neural Network Cloud Classification System (PERSIANN-CCS) uses infrared (IR) data from Geostationary Earth Orbit (GEO) satellites to retrieve precipitation based on relationship between clout top temperature (Tb) and rainfall rate (RR) using a neural network technique. The complexity of Tb-RR relationship for estimating precipitation causes uncertainty in PERSIANN-CCS rainfall product. This research is focused on improving PERSIANN-CCS rainfall retrieval using several approaches:1) Bias adjustment of PERSIANN-CCS rainfall estimates using PMW satellite rainfall data: Using multi satellite data can enhance the quality of rainfall estimation considerably; in this research we have combined the rainfall data from PERSIANN-CCS and PMW rainfall to enhance the bias of PERSIANN-CCS precipitation estimates. The results showed improvement of rainfall estimation during summer and winter time.2) Increasing the rainfall detection by including warm clouds rainfall: PERSIANN-CCS currently cannot detect rainfall from clouds with temperature warmer than 253 K. This study explores the impacts of increasing the temperature threshold on precipitation estimation. The results show that increasing the threshold level can improve the PERSIANN-CCS rainfall detection.3) Generating a probabilistic framework for precipitation retrieval: The current version of PERSIANN-CCS retrieves precipitation based on the exponential function fitted to Tb-RR. The major assumption behind this relationship is that the heavier rainfalls are associated with colder clouds which cause underestimation of warmer clouds and overestimation of colder clouds rainfall. The probabilistic approach uses the corresponding sample relationship between cloud temperature and rainfall rate. The model is evaluated during a full summer season which showed improvement in both detection and estimation of rainfall in compare with the current PERSIANN-CCS algorithm.