Precipitation characteristics over land from the NOAA‐15 AMSU sensor

Ferraro, Ralph; Weng, Fuzhong; Grody, Norman C.; Zhao, Limin

doi:10.1029/2000gl011665

Cited by 111 publications

(58 citation statements)

References 9 publications

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“…CMORPH integrates multiple satellite-based microwave rain estimates (Ferraro, 1997;Ferraro et al, 2000;Kummerow et al, 2001) in space and time using motion vectors derived from infrared images (Joyce et al, 2004). The newly available gauge-adjusted CMORPH applies daily gauge adjustment using estimates from ∼ 30 000 gauges worldwide (Xie et al, 2011).…”

Section: Satellite Datamentioning

confidence: 99%

Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

Marra

Morin

Peleg

et al. 2017

Hydrol. Earth Syst. Sci.

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Abstract. Intensity-duration-frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2-5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period ( ∼ 50, ∼ 100, and ∼ 150 % for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth.

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Section: Satellite Datamentioning

confidence: 99%

Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

Marra

Morin

Peleg

et al. 2017

Hydrol. Earth Syst. Sci.

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“…Channels 1 and 2 also provide information on precipitation, sea ice, and snow cover. The AMSU-A data have been used extensively in many applications [Ferraro et al, 2000;Prigent et al, 2000;Goldberg et al, 2001;Rosenkranz, 2001] at NOAA and worldwide to generate products for weather forecasting, atmospheric temperature sounding, climate trend analysis, and hydrological studies. The AMSU-A data in radiance, which is the standard output distributed to data users, have significant positive impact on global weather forecast scores [Baker and Campbell, 2004].…”

Section: Introductionmentioning

confidence: 99%

A study of the NOAA 16 AMSU‐A brightness temperatures observed over Amazon rain forest

2005

J. Geophys. Res.

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[1] Angular distributions of brightness temperatures over the Amazon rain forest observed by the NOAA 16 Advanced Microwave Sounding Unit-A (AMSU-A) window channels are studied and simulated with a radiative transfer model. The model is based on a combination of the atmospheric radiative transfer model and a vegetation canopy model which treats the rain forest as a uniform layer with an effective canopy temperature. Since emission from the ground surface is heavily attenuated by the rain forest canopy, only radiations from the rain forest canopy and the atmospheric medium contribute to the measurements. The measured angular distributions of brightness temperatures at the four window channels 1-3 and 15 (with frequencies centered at 23.8, 31.4, 50.3, and 89 GHz, respectively), have relatively small angular dependence that is attributed to the atmospheric path length since the canopy radiation is angular-independent. Data used in this study are collected from April 2001 to October 2002 for 1 month in each season. Angular distributions of the data from each month show remarkably stable patterns which can be well simulated by the model developed in this study. For the four window channels, ascending (day time 2 PM in the Sun) and descending (nighttime 2 AM in dark) measurements differ by 3-8 K, depending on channels. The root-mean-square (RMS) differences between 2 years' angular distributions for all four channels and the three overlapping months (April, July, and October) are 0.82 K for the ascending passes, and 0.47 K for the descending passes, respectively. The stable pattern and small variation of angular distributions can be potentially useful for postlaunch calibration of future microwave instruments.

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“…While precipitation-free areas, as well as heavily precipitating areas (intensity > 5 mm h −1 ), can be identified to a high accuracy, the intermediate classes are more ambiguous. The group at the National Oceanic and Atmospheric Administration (NOAA) National Environmental Satellite, Data and Information Service (NESDIS) has designed a series of algorithms based on cross-track satellite radiometers (AMSR and AMSU) that aim at retrieving precipitation over sea and over land [45][46][47] for operational use and application in hydrology [48,49]. A snowfall detection algorithm was added by Kongoli et al [50].…”

Section: Passive Microwave Methodsmentioning

confidence: 99%

Detection and Measurement of Snowfall from Space

2011

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Snowfall detection and measurement represent highly difficult problems in modern hydrometeorology. Ground measurements are complicated due to detection technology limitations, snow drift and accumulation issues, and error definition. The snowfall detection from space is in turn affected by all detection limitations that characterize the measurement of rainfall with the addition of several complications, such as the indirect character of remote sensing precipitation estimation, the presence of frozen or snow-covered terrain, and the unknown vertical distribution of hydrometeors in the cloud column. Several methods for the retrieval of snowfall intensity from satellite have been proposed in recent times using passive and active sensors. No satisfactory answer to the general problem of quantitative snowfall intensity determination has been found to date, but several studies contribute to delineate a working framework for the future operational retrieval algorithms.

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Precipitation characteristics over land from the NOAA‐15 AMSU sensor

Cited by 111 publications

References 9 publications

Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

Intensity–duration–frequency curves from remote sensing rainfall estimates: comparing satellite and weather radar over the eastern Mediterranean

A study of the NOAA 16 AMSU‐A brightness temperatures observed over Amazon rain forest

Detection and Measurement of Snowfall from Space

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