GOES Multispectral Rainfall Algorithm (GMSRA)

Ba, Mamoudou B.; Gruber, Arnold

doi:10.1175/1520-0450(2001)040<1500:gmrag>2.0.co;2

Cited by 185 publications

(125 citation statements)

References 26 publications

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“…In particular, information about environmental moisture is used to obtain a more correct estimation of rainfall as well as for the screening of the non-rainy pixels. Ba and Gruber (2001) used the GOES visible (0.65 µm), near-infrared (3.9 µm), water vapour (6.7 µm) and window channels (10.7 and 12.0 µm) to estimate rainfall rate, distinguishing raining from non-raining clouds by taking into account the cloud top temperature, the effective radius of cloud particles and the temperature gradient. Moreover, in an attempt to give more reliable values of rain rates, Ba and Gruber (2001) used the moisture factor correction developed by Scofield (1987) and modified by Vicente et al (1998).…”

Section: E Ricciardelli Et Al: a Statistical Approach For Rain Intementioning

confidence: 99%

“…Ba and Gruber (2001) used the GOES visible (0.65 µm), near-infrared (3.9 µm), water vapour (6.7 µm) and window channels (10.7 and 12.0 µm) to estimate rainfall rate, distinguishing raining from non-raining clouds by taking into account the cloud top temperature, the effective radius of cloud particles and the temperature gradient. Moreover, in an attempt to give more reliable values of rain rates, Ba and Gruber (2001) used the moisture factor correction developed by Scofield (1987) and modified by Vicente et al (1998). Other authors used artificial neural networks to derive precipitation estimates using satellite IR images (Hsu et al, 1997;Behrangi et al, 2009;Capacci and Porcù, 2009).…”

Section: E Ricciardelli Et Al: a Statistical Approach For Rain Intementioning

confidence: 99%

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A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

Ricciardelli

Cimini

Paola

et al. 2014

Hydrol. Earth Syst. Sci.

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Abstract. This study exploits the Meteosat Second Generation (MSG)-Spinning Enhanced Visible and Infrared Imager (SEVIRI) observations to evaluate the rain class at high spatial and temporal resolutions and, to this aim, proposes the Rain Class Evaluation from Infrared and Visible observation (RainCEIV) technique. RainCEIV is composed of two modules: a cloud classification algorithm which individuates and characterizes the cloudy pixels, and a supervised classifier that delineates the rainy areas according to the three rainfall intensity classes, the non-rainy (rain rate value < 0.5 mm h −1 ) class, the light-to-moderate rainy class (0.5 mm h −1 ≤ rain rate value < 4 mm h −1 ), and the heavyto-very-heavy-rainy class (rain rate value ≥ 4 mm h −1 ). The second module considers as input the spectral and textural features of the infrared and visible SEVIRI observations for the cloudy pixels detected by the first module. It also takes the temporal differences of the brightness temperatures linked to the SEVIRI water vapour channels as indicative of the atmospheric instability strongly related to the occurrence of rainfall events.The rainfall rates used in the training phase are obtained through the Precipitation Estimation at Microwave frequencies, PEMW (an algorithm for rain rate retrievals based on Atmospheric Microwave Sounder Unit (AMSU)-B observations). RainCEIV's principal aim is that of supplying preliminary qualitative information on the rainy areas within the Mediterranean Basin where there is no radar network coverage. The results of RainCEIV have been validated against radar-derived rainfall measurements from the Italian Operational Weather Radar Network for some case studies limited to the Mediterranean area. The dichotomous assessment related to daytime (nighttime) validation shows that RainCEIV is able to detect rainy/non-rainy areas with an accuracy of about 97 % (96 %), and when all the rainy classes are considered, it shows a Heidke skill score of 67 % (62 %), a bias score of 1.36 (1.58), and a probability of detection of rainy areas of 81 % (81 %).

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Section: E Ricciardelli Et Al: a Statistical Approach For Rain Intementioning

confidence: 99%

Section: E Ricciardelli Et Al: a Statistical Approach For Rain Intementioning

confidence: 99%

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

Ricciardelli

Cimini

Paola

et al. 2014

Hydrol. Earth Syst. Sci.

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“…Over the last few decades many satellite-based precipitation algorithms have been developed (Ba and Gruber, 2001, Huffman et al, 2002, Joyce et al, 2004, Negri et al, 2002, Sorooshian et al, 2000, Vicente et al, 1998, Weng et al, 2003, Xie and Arkin, 1998. Satellite methods can be used to generate precipitation products at various spatial and temporal resolutions.…”

Section: Introductionmentioning

confidence: 99%

An adaptive spatial model for precipitation data from multiple satellites over large regions

Chakraborty

De²,

Bowman

et al. 2013

Stat Comput

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Satellite measurements have of late become an important source of information for climate features such as precipitation due to their near-global coverage. In this article, we look at a precipitation dataset during a 3-hour window over tropical South America that has information from two satellites. We develop a flexible hierarchical model to combine instantaneous rainrate measurements from those satellites while accounting for their potential heterogeneity. Conceptually, we envision an underlying precipitation surface that influences the observed rain as well as absence of it. The surface is specified using a mean function centered at a set of knot locations, to capture the local patterns in the rainrate, combined with a residual Gaussian process to account for global correlation across sites. To improve over the commonly used pre-fixed knot choices, an efficient reversible jump scheme is used to allow the number of such knots as well as the order and support of associated polynomial terms to be chosen adaptively.To facilitate computation over a large region, a reduced rank approximation for the parent Gaussian process is employed.

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“…This overestimation due to attributing rain rates to non-precipitating cirrus is partly compensated for by an underestimation of rain rates from shallow convection. Despite these drawbacks, various rainfall retrieval techniques have been based on thermal infrared (TIR) temperatures only (mostly using the 10-12 µm atmospheric window spectrum), assuming that the amount of non-precipitating cirrus clouds is only minor (Negri et al, 1984;Arkin and Meisner, 1987;Adler and Negri, 1988;Negri and Adler, 1993;Ba and Gruber, 2001). An advantage of TIR data is the availability during both day and night.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH

Wolters

Hurk

Roebeling

2011

Hydrol. Earth Syst. Sci.

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Abstract. This paper describes the evaluation of the KNMI Cloud Physical Properties -Precipitation Properties (CPP-PP) algorithm over West Africa. The algorithm combines condensed water path (CWP), cloud phase (CPH), cloud particle effective radius (r e ), and cloud-top temperature (CTT) retrievals from visible, near-infrared and thermal infrared observations of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) satellites to estimate rain occurrence frequency and rain rate. For the 2005 and 2006 monsoon seasons, it is investigated whether the CPP-PP algorithm is capable of retrieving rain occurrence frequency and rain rate over West Africa with sufficient accuracy, using Tropical Monsoon Measurement Mission Precipitation Radar (TRMM-PR) as reference. As a second goal, it is assessed whether SEVIRI is capable of monitoring the seasonal and daytime evolution of rainfall during the West African monsoon (WAM), using Climate Prediction Center Morphing Technique (CMORPH) rainfall observations. The SEVIRI-detected rainfall area agrees well with TRMM-PR, with the areal extent of rainfall by SE-VIRI being ∼10% larger than from TRMM-PR. The mean retrieved rain rate from CPP-PP is about 8% higher than from TRMM-PR. Examination of the TRMM-PR and CPP-PP cumulative frequency distributions revealed that differences between CPP-PP and TRMM-PR are generally within +/−10%. Relative to the AMMA rain gauge observations, CPP-PP shows very good agreement up to 5 mm h −1 . However, at higher rain rates (5-16 mm h −1 ) CPP-PP overestimates compared to the rain gauges. With respect to the second goal of this paper, it was shown that both the accumulated precipitation and the seasonal progression of rainfall throughout the WAM is in good agreement with CMORPH, although CPP-PP retrieves higher amounts in the coastal reCorrespondence to: E. L. A. Wolters (wolterse@knmi.nl) gion of West Africa. Using latitudinal Hovmüller diagrams, a fair correspondence between CPP-PP and CMORPH was found, which is reflected by high correlation coefficients (∼0.7) for both rain rate and rain occurrence frequency. The daytime cycle of rainfall from CPP-PP shows distinctly different patterns for three different regions in West Africa throughout the WAM, with a decrease in dynamical range of rainfall near the Inter Tropical Convergence Zone (ITCZ). The dynamical range as retrieved from CPP-PP is larger than that from CMORPH. It is suggested that this results from both the better spatio-temporal resolution of SEVIRI, as well as from thermal infrared radiances being partly used by CMORPH, which likely smoothes the daytime precipitation signal, especially in case of cold anvils from convective systems. The promising results show that the CPP-PP algorithm, taking advantage of the high spatio-temporal resolution of SEVIRI, is of added value for monitoring daytime precipitation patterns in tropical areas.

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GOES Multispectral Rainfall Algorithm (GMSRA)

Cited by 185 publications

References 26 publications

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

A statistical approach for rain intensity differentiation using Meteosat Second Generation–Spinning Enhanced Visible and InfraRed Imager observations

An adaptive spatial model for precipitation data from multiple satellites over large regions

Evaluation of rainfall retrievals from SEVIRI reflectances over West Africa using TRMM-PR and CMORPH

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