The NESDIS Operational Convective Precipitation- Estimation Technique

Scofield, Roderick A.

doi:10.1175/1520-0493(1987)115<1773:tnocpe>2.0.co;2

Cited by 144 publications

(63 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…At NOAA, the H-E estimates have been used since the late 1970's. At first it was used for the Interactive Flash Flood Analyzer [10], before it was used as fully automated Auto-Estimator Vicente, Scofield and Menzel [11]. The current-generation operational algorithm at NESDIS is the Hydro-Estimator (H-E) which has been used since 2002 [12].…”

Section: Satellite Datamentioning

confidence: 99%

GIS-Based Rainfall Estimator Evaluation and Interpolation Analysis Using ArcGIS

Mambu

2018

CogITo Smart Journal

View full text Add to dashboard Cite

show abstract

Section: Satellite Datamentioning

confidence: 99%

GIS-Based Rainfall Estimator Evaluation and Interpolation Analysis Using ArcGIS

Mambu

2018

CogITo Smart Journal

View full text Add to dashboard Cite

show abstract

“…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%

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.

View full text Add to dashboard Cite

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 %).

show abstract

“…Griffith et al (1978) present the Griffith-Woodley technique based on the study of the life cycle of clouds. The hydroestimator (HE) technique (Vicente et al 1998(Vicente et al , 2002) is based on the methodology developed by Scofield (1987). To compute rainfall rates, it uses a nonlinear power-law relationship between cloud-top temperature and radar-derived rainfall-rate estimates, a gradient and growth-rate mask, and a humidity mask.…”

Section: Introductionmentioning

confidence: 99%

“…RESAT is based on both pixel and cloud-patch properties. Griffith et al (1978), Scofield (1987), Feidas and Cartalis (2001), Mathon and Laurent (2001), and Schumacher and Johnson (2005) point out the importance of the relationship between the life cycle of the MCS and the rainfall it produces. In spite of this, in some cases only qualitative relationships are given and in others cases some problems arise due to the limitations of cloud-patch-based methods.…”

Section: Introductionmentioning

confidence: 99%

“…In spite of this, in some cases only qualitative relationships are given and in others cases some problems arise due to the limitations of cloud-patch-based methods. These limitations include the difficulty in discriminating between cirrus anvils and convective clouds in the Griffith-Woodley technique, and the requirement for the direct interaction of a meteorologist in the case of Scofield (1987). In this work, a cluster rainfall estimation is first computed using radiative and evolu-tion parameters over the life cycle of the MCS and then a pixel-based estimation is derived by adding a correction using cloud-top pixel radiative properties.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Basis for a Rainfall Estimation Technique Using IR–VIS Cloud Classification and Parameters over the Life Cycle of Mesoscale Convective Systems

Delgado

Machado

Angelis

et al. 2008

Journal of Applied Meteorology and Climatology

View full text Add to dashboard Cite

This paper discusses the basis for a new rainfall estimation method using geostationary infrared and visible data. The precipitation radar on board the Tropical Rainfall Measuring Mission satellite is used to train the algorithm presented (which is the basis of the estimation method) and the further intercomparison. The algorithm uses daily Geostationary Operational Environmental Satellite infrared-visible (IR-VIS) cloud classifications together with radiative and evolution properties of clouds over the life cycle of mesoscale convective systems (MCSs) in different brightness temperature (Tb) ranges. Despite recognition of the importance of the relationship between the life cycle of MCSs and the rainfall rate they produce, this relationship has not previously been quantified precisely. An empirical relationship is found between the characteristics that describe the MCSs' life cycle and the magnitude of rainfall rate they produce. Numerous earlier studies focus on this subject using cloud-patch or pixel-based techniques; this work combines the two techniques. The algorithm performs reasonably well in the case of convective systems and also for stratiform clouds, although it tends to overestimate rainfall rates. Despite only using satellite information to initialize the algorithm, satisfactory results were obtained relative to the hydroestimator technique, which in addition to the IR information uses extra satellite data such as moisture and orographic corrections. This shows that the use of IR-VIS cloud classification and MCS properties provides a robust basis for creating a future estimation method incorporating humidity Eta field outputs for a moisture correction, digital elevation models combined with low-level moisture advection for an orographic correction, and a nighttime cloud classification.

show abstract

The NESDIS Operational Convective Precipitation- Estimation Technique

Cited by 144 publications

References 0 publications

GIS-Based Rainfall Estimator Evaluation and Interpolation Analysis Using ArcGIS

GIS-Based Rainfall Estimator Evaluation and Interpolation Analysis Using ArcGIS

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

Basis for a Rainfall Estimation Technique Using IR–VIS Cloud Classification and Parameters over the Life Cycle of Mesoscale Convective Systems

Contact Info

Product

Resources

About