Rain volume estimation over areas using satellite and radar data

semiannual report on grant no. NAG 5-396, period covered: 1 January 1985 - 30 June 1985
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National Aeronautics and Space Administration? , [Washington, D.C.?
Rain and rainfall -- Measurement., Radar meteoro
Statementby Andre A. Doneaud and Thomas H. Vonder Haar.
Series[NASA-CR] -- 176050., NASA contractor report -- NASA CR-176050.
ContributionsVonder Haar, Thomas H., United States. National Aeronautics and Space Administration.
The Physical Object
Pagination1 v.
ID Numbers
Open LibraryOL15396138M

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Rain volume estimation over areas using satellite and radar data: semi-annual report on grant no. NAGperiod covered 1 July December [Andre A Doneaud; Thomas H Vonder Haar; United States.

National. Get this from a library. Rain volume estimation over areas using satellite and radar data: semiannual report on grant no. NAGperiod covered: 1 January June [Andre A Doneaud; Thomas H Vonder Haar; United. rainfall against rain-gauge data is attractive because of the direct nature of the rain-gauge measurements.

Comparisons of satellite estimates to rain-gauge data are difficult, however, because of the extreme variability of rain and the fact that satellites view large areas over a short time while rain gauges monitor small areas Size: 1MB.

In November the TRMM (Tropical Rainfall Measuring Mission) satellite was launched (3). This satellite carries all the above (passive microwave, infrared, and visible), plus an active radar.

This combination provides a far superior estimate of rainfall within 35° from the equator. The Figure on the right shows a 3D slice through tropical cyclone Susan east of Fiji (the green. Optimization of radar rainfall estimation using line, area, and volume integrations of gauge and radar data Conference Paper (PDF Available) January.

Precipitation estimation over radar gap areas based on satellite and adjacent radar observations Yu-Ri Lee and Dong-Bin Shin Department of Atmospheric Sciences, Yonsei University, Seoul, South Korea 37th Conference on Radar Meteorology.

The most important reason for using radar to estimate precipitation is the fact that, compared to a network of rain gauges, the radar (or combinations of radars) can sample a large area (> 30, km2 for a weather radar sampling out to km) over a short period of time (Cited by: 8.

Description Rain volume estimation over areas using satellite and radar data FB2

The correlation between gage-estimated rain volume and radar estimated area covered with showers is also studied, focusing on the possible estimation of rain volume values using a. Satellite estimation cannot be evaluated with confidence unless the validation data set is itself known to be very well calibrated in an absolute sense.

For these reasons, radar data processing plays an important part in the assessment of satellite estimates. Radar data processing The radar-derived data sequences selected for the pre-Cited by: Rain gauge data are vital in our quest for improved understanding of radar-based rainfall estimation technologies.

Radar estimation of extreme rainfall. Radar estimation of extreme rainfall rates plays an important role in a range of applications dealing with the hydrology and hydraulics of by: Comparing satellite rainfall estimates with rain gauge data: Optimal strategies suggested by a spectral model the statistics of radar-derived rain rates over the eastern tropical Atlantic (from GATE) and the western tropical Pacific (from TOGA COARE).

The model predicts that there is anCited by: Precipitation estimation with satellites and radar. LWP=2/3 spectively. 1* *T,+ variance. accuracy is comparable to that of precipitation estimation from radar data over the investigated area found earlier (Clarmathy, ).

G.L. AustinThe delineation of rain areas from visible and IR satellite data for GATE Cited by: 5. The resulting rainfall data is equivalent in accuracy to having 2, rain gauges placed across the County.

Start the Calibrated Radar Rainfall Tool (for Data through February )»» Note: historic gauge and calibrated radar rainfall data is currently only available in this tool through February For data from March present, see.

The great advantage of using satellite data for rainfall estimation is that satellite coverage is continuous over most regions of the earth.

Thus, satellite data can be used to estimate rainfall in areas which are difficult or expensive to monitor by conventional surface weather stations and/or weather radar. A variety of rainfall estimation. Consequently, a research work on satellite based rainfall estimation over Bangladesh has been done using Infra-red (IR) satellite data.

It is well documented that IR based algorithm for rainfall estimation has a lot of uncertainty and biases in the result due to the lack of direct physical.

Rainfall Estimation Techniques; Background; Different methods are available for estimating area rainfall using images of visible (wavelength between and mm) and infrared (wavelength between and mm) electromagnetic radiation from geostationary visible channel measures the short wave radiation backscattered by the atmosphere and the earth.

(), and Bell et al. (), using ground-based rainfall data, have shown that this uncertainty is expected to decrease for higher rainfall rates, larger domain sizes, and longer time integration. This has also been seen in studies using satellite data, for example, by Chang et al. (), WengCited by: Rain rate () estimation from radar measurements is based of S-Pol is and the radar data were collected at 1 eleva-tion angle.

The disdrometer was located at an azimuth of relation between rain rate and median volume diameter (MVD), are shown in Fig. Again, there is little correlation betweenFile Size: KB.

A satellite-based rainfall estimation algorithm, Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) Cloud Classification System (CCS), is described. This algorithm extracts local and regional cloud features from infrared ( mm) geostationary satellite imagery in estimating.

When the obtained ratio between the monthly mean 3B42RT rain rate and GPCP surface rain gauge data is equally applied to the 3-hourly rainfall estimate (Huffman et al.

), weak–moderate rain events become far heavier rain events, as shown in Fig. By contrast, the adjustment of heavy rain events is by: Sets of parameters have been obtained that fit the statistics of radar‐derived rain rates over the eastern tropical Atlantic (from GATE) and the western tropical Pacific (from TOGA COARE).

The model predicts that there is an optimal averaging time for gauge data when gauge averages are compared to average satellite rain‐rates for a Cited by: In this study, we present rain estimation using a Bayesian approach to retrieve DSD parameters from Z H and Z DR at S band ( cm).

In addition to the eval-uation by simulated radar data, a rain event on 13 May in central Oklahoma is analyzed to verify the algo-rithm by comparing radar retrievals with in situ mea-surements. space volume affected by the liquid precipitation.

In [8], [9] rainfall rate estimates are obtained from measurements of satellite signal attenuation and compared with the corresponding data generated by radar sensors. Moreover, [10] discusses a technique for rainfall rate estimation using satellite links for internet services.

The ra in rate data retrieved from radar is used to estimate the max RRmax (rain rate). Then it is inserted in IDV as shown in Fig. 2 and 4 is detail of own IDV script follow by blocks of processing data on IDV. In This study, 12 months for the year of radar data use to simulate rain rate estimation.

The data of RR are collect every ten. rain Weather Morocco, Satellite Weather Morocco, Weather Forecast, Rainfall, Clouds, Sun in Morocco - Source: SATcom.

In this study, a framework is given by which air/space-borne dual-wavelength radar data can be used to estimate the characteristic parameters of hydrometeors. The focus of the study is on the Global Precipitation Measurement (GPM) precipitation radar, a dual-wavelength radar that will operate in the Ku ( GHz) and Ka (35 GHz) by:   This paper presents an assessment of the 3B42 research version rainfall product from NASA’s Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis (TMPA).

The study provides new results of TMPA data accuracy in urban areas and highlights trends associated with the climatological indicators of temperature and relative humidity in by: Each array is centered over one of Service A weather stations scattered over southeastern United States.

Surface reports from these stations obtained during a 30 day period in August of were used to ground truth the technique. The technique produced rain rate estimates for four classes: none, light, moderate and heavy. Chulsang Yoo, Jungsoo Yoon and Eunho Ha, Detection of mean-field bias of the radar rain rate using rain gauges available within a small portion of radar umbrella: a case study of the Donghae (East Sea) radar in Korea, Stochastic Environmental Research and.

disadvantage for studies of convective rain events. In addition, satellite-measured microwave radiances are also influenced by soil and vegetation effects over land surfaces (including mixed pixel effects in coastal areas).

Despite the disadvantages of the rainfall estimates inferred by IR satellite data, interest in making.

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enhance short-term (2- to day) satellite-based rainfall accumulation products. Using ground rain gauge data as a validation source, and a soil moisture product derived from the Advanced Microwave Scanning Radiometer aboard the NASA Aqua satellite, the approach is evaluated over the contiguous United States.The rain attenuation measurement studies using satellite signal have been carried out using their own satellite beacon signal at advanced countries such as U.S.A., EU, Italy, Japan, etc.

In United States, NASA is performing the study of the radio characteristics of the satellite link in Ku and Ka band receiving satellite signal such as CTS.instruments (i.e., X-band radar, C-band radar and rain gauges), small areas centred in the 6 rain gauge loca-tions were selected and radar rainfall estimates of those areas were compared to rain gauge measurements.

Each one of the six areas is about 1 km²; the size of the areas was chosen especially to represent the typical.