LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration

Bellerby, Tim; Hsu, Kuolin; Sorooshian, Soroosh

doi:10.1175/2009jhm1091.1

Cited by 30 publications

(10 citation statements)

References 52 publications

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“…Examples include the Climate Prediction Center Morphing technique (CMORPH; Joyce et al, 2004). More complex techniques use a combination of satellite observations and modelling to provide rainfall estimates: the Lagrangian Model (LMODEL) proposed by Bellerby et al (2009) is based on a conceptual cloud-development model driven by GEO satellite imagery, locally updated using MW-based rainfall measurements from LEO platforms. More recently Behrangi et al (2010) have developed the REFAME technique that combines advection and cloud development together into one technique.…”

Section: Multi-sensor Techniquesmentioning

confidence: 99%

Status of satellite precipitation retrievals

Kidd

Levizzani

2011

Hydrol. Earth Syst. Sci.

388

207

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Abstract. Satellites offer an unrivalled vantage point to observe and measure Earth system processes and parameters. Precipitation (rain and snow) in particular, benefit from such observations since precipitation is spatially and temporally highly variable and with satellites overcoming some of the deficiencies of conventional gauge and radar measurements. This paper provides an overall review of quantitative precipitation estimation, covering the basis of the satellite systems used in the observation of precipitation, the dissemination and processing of this data, and the generation, availability and validation of these precipitation estimates. A selection of applications utilising these precipitation estimates are then outlined to exemplify the utility of such products.

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Section: Multi-sensor Techniquesmentioning

confidence: 99%

Status of satellite precipitation retrievals

Kidd

Levizzani

2011

Hydrol. Earth Syst. Sci.

388

207

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“…The types of satellite sensors that are commonly used for precipitation estimation are infrared (IR) and microwave (MW) imagers and sounders, and more recently radars. While it is often possible to use individual sensors to estimate global precipitation, combining multiple sensors (e.g., IR, MW) aboard multiple platforms [e.g., Adler et al , 2003; Huffman et al , 2009, 2007; Hsu et al , 1997; Joyce and Xie , 2011; Hong et al , 2004; Bellerby et al , 2009; Turk et al , 2000; Behrangi et al , 2010a] is a common approach to improve the accuracy of rain retrievals and respond to the growing demand for higher spatiotemporal measurement of precipitation globally. Clearly, the accuracy of the combined precipitation products is tied to the observing accuracy that each individual sensor offers.…”

Section: Introductionmentioning

confidence: 99%

On the quantification of oceanic rainfall using spaceborne sensors

Behrangi¹,

Lebsock²,

Wong³

et al. 2012

J. Geophys. Res.

122

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[1] Much of our knowledge about oceanic rainfall comes from spaceborne sensors. These sensors provide direct or indirect information used for precipitation retrievals through various algorithms. A thorough understanding of rain frequency and intensity and its regional distribution, which is especially important in a warming climate, requires an evaluation of the performance of rain-measuring sensors and identification of strengths and limitations offered by each sensor. The Tropical Rainfall Measuring Mission (TRMM) has enabled significant advancement in quantification of moderate to intense rainfall. However, a common limitation of the current suite of rain-measuring sensors is their lack of sensitivity to light rainfall, especially over subtropical and high-latitude oceans. Among various spaceborne sensors, CloudSat enables superior retrieval of light rainfall and drizzle. By using 3 years (2007)(2008)(2009)) of rainfall data from CloudSat and the precipitation radar aboard TRMM, it was determined that the quasi-global (60 S-60 N) oceanic mean rain rate is about 3.05 mm/d, considerably larger than that obtained from any individual sensor product. In the deep tropics, especially within 20 S-20 N, the sensors show the highest agreement, with a large fraction of total rain volume captured by the majority of sensors. However, toward higher latitudes and within the subtropical high-pressure regions, a significant fraction of rainfall, which can exceed 50% or more of total rain volume, is missed by the majority of the sensors.Citation: Behrangi, A., M. Lebsock, S. Wong, and B. Lambrigtsen (2012), On the quantification of oceanic rainfall using spaceborne sensors,

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“…Through continuous efforts and investigations, we have developed the PERSIANN algorithm (Hsu et al, 1997(Hsu et al, , 1999Sorooshian et al, 2000Sorooshian et al, , 2002. Some relevant activities have been: (1) development of an adaptive precipitation retrieval algorithm version using Artificial Neural Networks (Hsu et al, 1997(Hsu et al, , 1999Sorooshian et al, 2000); (2) cloud classifications from individual cloud pixels to individual cloud patches based on the PERSIANN-Cloud Classification System (Hong et al, 2004;Hsu et al, 2007;Behrangi et al, 2010a); (3) improvement of precipitation estimates using enhanced multi-spectral data from the GOES-R satellite (PERSIANN-MSA: Behrangi et al, 2010a,b); (4) development of precipitation estimates based on cloud patch dynamic tracking (LMODEL: Bellerby et al, 2009;Hsu et al, 2009); and (5) development of precipitation estimates based on the combination of Low Earth Orbit (LEO) and Geostationary Earth Orbit (GEO) satellite information (REFAME: Behrangi et al, 2010cBehrangi et al, , 2012.…”

Section: Chrs Global Precipitation Measurementsmentioning

confidence: 99%

G-WADI PERSIANN-CCS GeoServer for extreme precipitation event monitoring

Kuolin¹,

Sellars²,

Phu³

et al. 2013

Sciences in Cold and Arid Regions

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The Center for Hydrometeorology and Remote Sensing at the University of California, Irvine (CHRS) has been collaborating with UNESCO's International Hydrological Program (IHP) to build a facility for forecasting and mitigating hydrological disasters. This collaboration has resulted in the development of the Water and Development Information for Arid Lands--a Global Network (G-WADI) PERSIANN-CCS GeoServer, a near real-time global precipitation visualization and data service. This GeoServer provides to end-users the tools and precipitation data needed to support operational decision making, research and sound water management. This manuscript introduces and demonstrates the practicality of the G-WADI PERSIANN-CCS GeoServer for monitoring extreme precipitation events even over regions where ground measurements are sparse. Two extreme events are analyzed. The first event shows an extreme precipitation event causing widespread flooding in Beijing, China and surrounding districts on July 21, 2012. The second event shows tropical storm Nock-Ten that occurred in late July of 2011 causing widespread flooding in Thailand. Evaluation of PERSIANN-CCS precipitation over Thailand using a rain gauge network is also conducted and discussed.

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LMODEL: A Satellite Precipitation Methodology Using Cloud Development Modeling. Part I: Algorithm Construction and Calibration

Cited by 30 publications

References 52 publications

Status of satellite precipitation retrievals

Status of satellite precipitation retrievals

On the quantification of oceanic rainfall using spaceborne sensors

G-WADI PERSIANN-CCS GeoServer for extreme precipitation event monitoring

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