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Saturday, November 14, 2020 | History

4 edition of Automated satellite-based estimates of precipitation found in the catalog.

Automated satellite-based estimates of precipitation

Automated satellite-based estimates of precipitation

an assessment of accuracy

by

  • 210 Want to read
  • 7 Currently reading

Published by U.S. Dept. of Commerce, National Oceanic and Atmospheric Administration, National Environmental Satellite, Data, and Information Service, National Technical Information Service, distributor in Washington, D.C, [Springfield, VA .
Written in English

    Subjects:
  • Precipitation forecasting,
  • Precipitation (Meteorology) -- Mathematical models,
  • Precipitation probabilities,
  • Satellite meteorology

  • Edition Notes

    Other titlesAutomated satellite based estimates of precipitation.
    StatementMichael A. Fortune.
    SeriesNOAA technical report NESDIS -- 90.
    ContributionsUnited States. National Environmental Satellite, Data, and Information Service.
    The Physical Object
    FormatMicroform
    Pagination43 p.
    Number of Pages43
    ID Numbers
    Open LibraryOL17594248M
    OCLC/WorldCa41020757

    A simplified diagnostic model of orographic rainfall for enhancing satellite-based rainfall estimates in data-poor regions: Funk, Christopher C., Michaelsen, J. More Info: The collaborative historical African rainfall model—Description and evaluation.   1 Introduction. Reliable estimates of precipitation are highly essential not only for advancing our scientific knowledge on hydrological cycle and Earth's radiation budget but also for operational utility, like water resources management, hydrological applications, and agricultural forecasts [Jiang et al., ; Zulkafli et al., ].Despite its importance in operational and research fields Cited by: satellite-based estimates of global precipitation, including the GPCP product, is described by Adler et al. (). The general approach with the new GPCP monthly product is to combine the precipitation information availabIe from each source into a final merged product, taking advantage of theFile Size: 2MB. precipitation estimates derived from the TRMM Precipitation Radar (PR, TRMM Product 3A25) are used to combine with the gauge-based analysis described in section 3. Satellite-based precipitation climatology fields are computed on a olat/lon over global land using the TRMM data for an 8-year period from to The TRMM PR-based.


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exercitation answered, in the assertions following made good against it. 1. That the usurpation pretended by the exercitator is really nousurpation, by anything that he hath said to prove it such. 2. That former oaths in controversie oblige not against obedience to present powers. 3. That obedience is due to powers in possession, though unlawfully enterd.

exercitation answered, in the assertions following made good against it. 1. That the usurpation pretended by the exercitator is really nousurpation, by anything that he hath said to prove it such. 2. That former oaths in controversie oblige not against obedience to present powers. 3. That obedience is due to powers in possession, though unlawfully enterd.

Automated satellite-based estimates of precipitation Download PDF EPUB FB2

Get this from a library. Automated satellite-based estimates of precipitation: an assessment of accuracy. [Michael A Fortune; United States. National Environmental Satellite, Data, and Information Service,].

Get this from a library. Automated satellite-based estimates of precipitation: an assessment of accuracy. [Michael A Fortune; United States. National Environmental Satellite, Data, and Information Service.]. The objective of this study was to evaluate the utility of satellite-based precipitation estimates for performing FFA in two presumably ungauged tributaries, the Jialing and Tuojiang Rivers, of the upper Yangtze River.

Annual peak flow series were simulated using the Coupled Routing and Excess STorage (CREST) hydrologic by: Component analysis of errors in satellite-based precipitation estimates Yudong Tian,1,2 Christa D.

Peters-Lidard,1 John B. Eylander,3 Robert J. Joyce,4 George J. Huffman,5,6 Robert F. Adler,5,7 Kuo-lin Hsu,8 F. Joseph Turk,9 Matthew Garcia,1,10 and Jing Zeng1,11 Received 20 February ; revised 10 June ; accepted 25 August ; published 16 December Cited by: mulation estimates extracted from a variety of satellite-based rainfall products.

The approach is especially e ective for correcting rainfall accumulation estimates derived without aid of ground-based rain gauge observations. Special emphasis is placed on demonstrating that the approach can be applied in continental areas. Harsh L. Shah and Vimal Mishra, Uncertainty and Bias in Satellite-Based Precipitation Estimates over Indian Subcontinental Basins: Implications for Real-Time Streamflow Simulation and Flood Prediction*, Automated satellite-based estimates of precipitation book of Hydrometeorology, /JHM-D, 17, 2, (), ().

Improving Satellite-Based Rainfall Accumulation Estimates Using Spaceborne Surface Soil Moisture Retrievals WADE T. CROW USDA/ARS Hydrology and Remote Sensing Laboratory, Beltsville, Maryland GEORGE J. HUFFMAN SSAI, NASA GSFC, Automated satellite-based estimates of precipitation book for.

Evaluation of satellite-based precipitation estimation over Iran Article (PDF Available) in Journal of Arid Environments October with Reads How we measure 'reads'. Component Analysis of Errors in Satellite-Based Precipitation Estimates Article (PDF Available) in Journal of Geophysical Research Atmospheres (D24) December with Reads.

Satellite-based estimates of rainfall have been used operationally at NOAA / NESDIS since the late 's, starting with the largely manual Interactive Flash Flood Analyzer (IFFA; Scofield ), and then progressing to the fully automated Auto-Estimator (Vicente et al.

PERSIANN-MSA: A Precipitation Estimation Method from Satellite-Based Multispectral Analysis ALI BEHRANGI,KUO-LIN HSU,BISHER IMAM, AND SOROOSH SOROOSHIAN Center for Hydrometeorology and Remote Sensing, The Henry Samueli School of Engineering, Department of.

The official website for NASA Precipitation Measurement Missions. These missions study rainfall and other types precipitation around the globe. The Global Precipitation Measurement (GPM) mission provides a new generation of satellite observations of rain and snow worldwide every three hours for scientific research and societal benefits.

The two basins differ from each other with respect to physiographic and climatic conditions as well as the hydrological responses. While the Volta is a medium- to large-size lowland basin, located in the tropical wet and dry zone, with a rather short but pronounced flood period from mid-July to the end of October with inter-annual variable flood peaks exceeding m 3 /s, the Baro-Akobo is a Cited by: Messages containing Satellite-derived Precipitation Estimates (SPE) and satellite based synoptic/mesoscale analyses and trends are issued as guidance to the National Weather Service (NWS) when heavy rain/snow pose a threat to life and property.

Intercomparison of rain gauge, radar, and satellite-based precipitation estimates on hydrologic forecasting. Journal of Hydrometeorology 6(4) Page Share Cite. both satellite estimates and ground‐based measurements and test how well they can predict hydrologic states and fluxes useful for water resource applications, i.e., soil moisture, evapotranspiration, and river streamflow.

A number of satellite‐based precipitation products ranging from retrievals based only on microwave measurements, combined. PERSIANN, an automated system for Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks, has been developed for the estimation of rainfall from geosynchronous satellite longwave infared imagery (GOES-IR) Cited by: An improved procedure for the validation of satellite-based precipitation estimates Ling Tang a,b,⁎,YudongTiana,b,FangYanc,EmadHabibc a Earth System Science Interdisciplinary Center (ESSIC), University of Maryland, College Park, MD,USA b CodeHydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD,USA c Department of Civil Engineering.

Local textural features around each satellite grid box are useful in rainfall estimation (i.e., Wu et al.

).In this study, we extract five features similar to those used in the PERSIANN (Sorooshian et al. ) include the grid-box value itself along with the means and standard deviations of 3 × 3 and 5 × 5 windows of pixels centered on each grid by: Except for TRMM, satellite-based precipitation retrieval techniques perform poorly in complex terrain, because low-level, topographically forced updrafts may trigger heavy instance, the annual rainfall on Mt Waialeale on the island of Kauai, Hawai'i, is nearly 10 m, yet the cloud tops rarely are above ° C.

References. Arkin, P.A. and P.E. Ardanuy Continuous and reliable global precipitation information is crucial for myriad applications ranging from weather to climate, such as flood forecasting, understanding the inner workings of hurricanes and other storm systems, and tracking long-term trends in water supply.

However, measuring. The text product also provides a rainfall forecast from a recent run of the Global Forecast (GFS) model that spans the same 6-hour period as the satellite-based estimates.

The time of the GFS run is specified in the text product. Information on the GFS model can be found here. The location of the tropical cyclone or disturbance at the most. How accurate are satellite estimates of precipitation over the north Indian Ocean. Satya Prakash1,2 & M.

Ramesh Kumar3 & Simi Mathew4 & R. Venkatesan4 Received: 8 June /Accepted: 2 October # Springer-Verlag GmbH Austria Abstract Following the launch of the Global Precipitation Measurement (GPM) Core Observatory in earlymoti.

Thus, satellite-based 59 precipitation estimates play an important role in detecting rainfall distribution and have been 60 complementary to the ground-based rain gauge and radar measurements.

Since the launch of the 61 Tropical Rainfall Measuring Mission [TRMM. product, and the PERSIANN satellite-based system, respectively.

The research questions addressed in this study are as follows. 1) How do precipitation estimates based on the rain gauge network, radar/gauge multisensor product, and satellite-based algorithm compare at the space– time scale currently utilized by the NWS for opera-File Size: 2MB.

Evapotranspiration (ET) represents the consumed component of a liquid water resource and, following precipitation, is the largest component of the hydrologic cycle.

Water consumption by ET for irrigated agriculture in the US is approximately 70 million acre-feet per year (85 million Megaliters (Ml)) (Hutson et al. ).Agriculture in the West is facing severe competition for the limited water. Article Incorporating Satellite Precipitation Estimates into a Radar-Gage Multi-Sensor Precipitation Estimation Algorithm Yuxiang He 1,2, *, Yu Zhang 1, Robert Kuligowski 3, Robert Cifelli 4 and David Kitzmiller 1 1 National Water Center (NWC), National Weather Service (NWS), NOAA, Silver Spring, MLUSA; @ (Y.Z.); [email protected] (D.K.)Cited by: 3.

1 1 Real-time Global Flood Estimation using 2 Satellite-based Precipitation and a Coupled Land 3 Surface and Routing Model 4 Huan Wu1, 2*, Robert F. Adler1, 2, Yudong Tian1, 2, George J.

Huffman2, Hongyi Li3, and 5 JianJian Wang1, 2 6 1 Earth System Science Interdisciplinary Center, University of Maryland, College Park, MD 7 2 NASA Goddard Space Flight Center, Greenbelt, MD   Satellite-based precipitation estimates represent a potential alternative source of input data in a plethora of meteorological and hydrological applications, especially in regions characterized by a low density of rain gauge stations.

Paraguay provides a good example of a case where the use of satellite-based precipitation could be advantageous. This study aims to evaluate the version 7 of the Cited by: 2. uncertainty of the satellite-based rainfall estimates and also that long, homogeneous rainfall time series can be generated for critical areas such as sub-Saharan Africa.

This makes satellite-based rainfall indices an attractive 1 I nt ernati o alR src h iufo C md S y(I), E b U v ity 2 Cl imat eH aza rd G oup.

Two versions of Global Satellite Mapping of Precipitation (GSMaP) products (GSMaP-V4 and GSMaP-V5) are validated both in a single grid scale and in contiguous China by comparing to gauge-based rainfall analysis dataset.

GSMaP products can capture spatial patterns and magnitude of rainfall in daily mean precipitation. They perform better in summer than in winter over the Chinese by: Landslides can be triggered by intense or prolonged rainfall.

Rain gauge measurements are commonly used to predict landslides even if satellite rainfall estimates are available. Recent research focuses on the comparison of satellite estimates and gauge measurements. The rain gauge data from the Italian network (collected in the system database “Verifica Rischio Frana”, VRF) are compared Cited by: 5.

The precipitation estimates are first compared and then used to drive the Sacramento Soil Moisture Accounting hydrological model for runoff forecasting.

The results provide insight into the potential utility of satellite-based precipitation estimates for water resources management of ungauged by: 2. Currently, satellite-derived precipitation estimates and 3-hour precipitation trends for convective systems, extratropical cyclones and tropical cyclones are computed on the NESDIS Interactive Flash Flood Analyzer and transmitted via AFOS to Weather Service Forecast Offices, Weather Service Offices and River Forecast by: 3.

Abstract. In this chapter, we present an overview of the role of satellite-based rainfall estimates (SREs) in drought risk management applications, ranging from simple anomaly and index-based approaches to cross-cutting drought early warning systems (EWS) and financial instruments such as weather index-based insurance (WII) schemes.

In recent years, satellite-based precipitation estimates have been developed on subdaily time resolution over the globe by combining information from microwave (MW) and infrared (IR) observations (Hsu et al. ; Sorooshian et al. ; Kidd et al. ; Hong et al. ; Joyce et al.

; Turk and Miller ; Huffman et al. ).Two operational quasi-global satellite rainfall products Cited by: STAR Satellite Rainfall Estimates Validation Algorithms. Since Aprilpersonnel at the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data, and Information Service (NESDIS) Center for Satellite Applications and Resarch (STAR) have been archiving data from six different algorithms from estimating precipitation using data from the Geostationary.

Its societal importance cannot be over stated. For climate research, precipitation is one of the key terms for balancing the energy budget, and one of the most challenging aspects of climate modeling. Hence, high quality estimates of precipitation's distribution, amounts and intensity are essential.

Rank correlation of daily precipitation between the satellite-based 3B42RT product and APHRODITE as a function of both station data coverage (expressed as the number of °subcells in a °grid cell with precipitation gauges) and satellite data coverage (availability of higher-quality microwave (MW) sounder precipitation estimates for at Cited by: Yan Shen, Anyuan Xiong, Yang Hong, Jingjing Yu, Yang Pan, Zhuoqi Chen and Manabendra Saharia, Uncertainty analysis of five satellite-based precipitation products and evaluation of three optimally merged multi-algorithm products over the Tibetan Plateau, International Journal of Remote Sensing, /, 35, 19, (.

ESTIMATING BIAS OF SATELLITE-BASED PRECIPITATION ESTIMATES, RELATIVE TO IN SITU MEASUREMENTS Thomas M. Smith1, Phillip A. Arkin2, George J. Huffman3, John J. Bates1 There are several possible sources of errors in satellite-based precipitation estimates.

They include random errors, sampling errors, and systematic errors. Random errors may be greatly.Satellite based rainfall estimation gives a real-time broader overview of rainfall that is useful for agriculture, famine warning, hydrology, pest monitoring and erosion.

More specifically, this information is useful to know whether the rain is sufficient for crop growth or not; and to predict river flow, erosion and potential locust outbreak.Intercomparison of Rainfall Estimates from Radar, Satellite, Gauge, Automated Data System (HADS) network operated by the Office of Hydrologic Development of the U.S.

Na-tional Weather Service (NWS). The red triangles show In recent years, satellite-based precipitation estimates.