Dependence of Satellite Sampling Error on Monthly Averaged Rain Rates:Comparison of Simple Models and Recent Studies

Bell, Thomas L.; Kundu, Prasun K.

doi:10.1175/1520-0442(2000)013<0449:dosseo>2.0.co;2

Cited by 67 publications

(73 citation statements)

References 29 publications

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“…Recent studies have shown that the TRMM microwave imager (TMI) and the Advanced Microwave Scanning Radiometer (AMSR-E) can now replicate the rain rate distributions inferred from gauge-calibrated ground radars with impressive accuracy (Liu andHou 2008, Wolff andFisher 2009). However, significant uncertainties still exist due to the presence of sampling and retrieval errors in the monthly rainfall estimates (Wilheit, 1988, Laughlin 1981, McConnell and North 1987, Shin and North 1988, North 1988, Oki 1994, Steiner 1996, Bell and Kundu 2000, Fisher 2004, 2007.…”

Section: Introductioncontrasting

confidence: 69%

“…Here it is assumed that the observations are independent and that the measurement error does not depend on when the measurement was made (Laughlin 1981 andKundu 2000). Since it is assumed that S 0 and R T are uncorrelated, the covariance term in (5) becomes negligible compared to the other two terms.…”

Section: Introductionmentioning

confidence: 61%

“…Assuming that sampling and retrieval errors are uncorrelated Kundu 2000 andFisher 2007), the random errors in the satellite estimate can be approximated as…”

Section: Error Separation Methodologymentioning

confidence: 99%

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Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR

Fisher¹,

Wolff²

2011

Journal of Applied Meteorology and Climatology

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Passive and active microwave rain sensors onboard earth-orbiting satellites estimate monthly rainfall from the instantaneous rain statistics collected during satellite overpasses. It is well known that climate-scale rain estimates from meteorological satellites incur sampling errors resulting from the process of discrete temporal sampling and statistical averaging. Sampling and retrieval errors ultimately become entangled in the estimation of the mean monthly rain rate. The sampling component of the error budget effectively introduces statistical noise into climate-scale rain estimates that obscure the error component associated with the instantaneous rain retrieval. Estimating the accuracy of the retrievals on monthly scales therefore necessitates a decomposition of the total error budget into sampling and retrieval error quantities. This paper presents results from a statistical evaluation of the sampling and retrieval errors for five different space-borne rain sensors on board nine orbiting satellites. Using an error decomposition methodology developed by one of the authors, sampling and retrieval errors were estimated at 0.25° resolution within 150 km of ground-based weather radars located at Kwajalein, Marshall Islands and Melbourne, Florida. Error and bias statistics were calculated according to the land, ocean and coast classifications of the surface terrain mask developed for the Goddard Profiling (GPROF) rain algorithm. Variations in the comparative error statistics are attributed to various factors related to differences in the swath geometry of each rain sensor, the orbital and instrument characteristics of the satellite and the regional climatology. The most significant result from this study found that each of the satellites incurred negative longterm oceanic retrieval biases of 10 to 30%.3

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Section: Introductioncontrasting

confidence: 69%

Section: Introductionmentioning

confidence: 61%

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Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR

Fisher¹,

Wolff²

2011

Journal of Applied Meteorology and Climatology

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“…A somewhat different explicit form of the constant C was derived by Bell et al (1990) using an approach originally due to Laughlin (1981). Assuming that the entire area A is sampled at regular intervals ⌬t ϭ T/S, they obtained the formula…”

Section: B Model Explanationmentioning

confidence: 99%

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Simple Model

Bell¹,

Kundu²,

Kummerow³

2001

J. Appl. Meteor.

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Quantitative use of satellite-derived maps of monthly rainfall requires some measure of the accuracy of the satellite estimates. The rainfall estimate for a given map grid box is subject to both remote sensing error and, especially in the case of low-orbiting satellites, sampling error due to the limited number of observations of the grid box provided by the satellite. A simple model of rain behavior predicts that rms random error in grid-box averages should depend in a simple way on the local average rain rate, and the predicted behavior has been seen in simulations using surface rain gauge and radar data. This relationship is examined using Special Sensor Microwave Imager (SSM/I) satellite data obtained over the western equatorial Pacific during the Tropical Ocean and Global Atmosphere Coupled Ocean-Atmosphere Response Experiment. Rms error inferred directly from SSM/I rainfall estimates is found to be larger than was predicted from surface data and to depend less on local rain rate than was predicted. Preliminary examination of Tropical Rainfall Measuring Mission (TRMM) microwave estimates shows better agreement with surface data. A simple method of estimating rms error in satellite rainfall estimates is suggested, based on quantities that can be computed directly from the satellite data.

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“…We expect the benefits of having such a high-resolution dataset to outweigh the disadvantage of some statistical fluctuations since the horizontal gradients and temporal scale of precipitation over this region during the monsoon season, are probably much larger than the sampling errors (Anders et al, 2006;Romatschke and Houze, 2011). Also Schumacher and Houze (2003) and Bell and Kundu (2000) have suggested that 0.6 m/year rain accumulation threshold gives less noisy results. Most of the In dian region except the western most part of India receives more than 0.6 m/year (Pokhrel and Sikka, 2013).…”

Section: Trmm Dataset and Its Processingmentioning

confidence: 99%

Intra-seasonal variability of cloud amount over the Indian subcontinent during the monsoon season as observed by TRMM precipitation radar

et al. 2014

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The intra-seasonal variability of the Indian summer monsoon, which manifests in the form of "active" and "break" phases in rainfall, is investigated with respect to the variability of the convective and stratiform precipitating cloud pattern over the region. Long period data from TRMM PR satellite (2A23 and 3B42 datasets) for the monsoon season of 2002 to 2010 over the Indian subcontinent is used for this purpose. The study reveals that the most significant spatial variation in convective and stratiform cloud amount in relation to the active and break phase occurs over the monsoon trough region in central India. The active phase is characterized by positive convective (~5%) and stratiform (~20%) precipitating cloud anomalies over this region. However, the maximum of the former precedes the latter by 1-2 days leading up to the active phase, indicating that the stratiform build up, is due to the gradual organization of the convective cloud systems over the region. The days leading up to the break phase are marked by negative anomalies in the convective and stratiform fractions of cloudiness over this region, which are in phase with each other, unlike the lead-up to the active phase. Analysis of the pattern of atmospheric heat source and sinks over the region from the NCEP-NCAR re-analysis data indicates that the engine for the growth/decay of convection over the monsoon trough region lies primarily in the Bay of Bengal and adjacent east India. The active phase is preceded by a heating pattern that promotes large scale, organized convective cloud growth over the Bay of Bengal preceding the actual onset, while the heating pattern leading up to the break phase promotes the formation of isolated convective clouds and decay of cloud organization over the monsoon trough region.

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Dependence of Satellite Sampling Error on Monthly Averaged Rain Rates:Comparison of Simple Models and Recent Studies

Cited by 67 publications

References 29 publications

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR

Satellite Sampling and Retrieval Errors in Regional Monthly Rain Estimates from TMI, AMSR-E, SSM/I, AMSU-B, and the TRMM PR

Sampling Errors of SSM/I and TRMM Rainfall Averages: Comparison with Error Estimates from Surface Data and a Simple Model

Intra-seasonal variability of cloud amount over the Indian subcontinent during the monsoon season as observed by TRMM precipitation radar

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