Estimating Tropical Pacific Rainfall Using Digital Satellite Data

Motell, Craig; Weare, Bryan C.

doi:10.1175/1520-0450(1987)026<1436:etprud>2.0.co;2

Cited by 21 publications

(7 citation statements)

References 11 publications

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“…Also shown in the third and fourth rows in Figure 10 are the OLR map obtained from the NOAA Climate Diagnostics Center and the precipitation pattern derived from the NOAA Climate Prediction Center Merged Analysis of Precipitation (CMAP); both the OLR and CMAP date are also averaged for January 1992–1994 and July 1992–1994. The OLR data have been widely used to indicate regions of deep convection [e.g., Motell and Weare , 1987; Horel et al , 1989; Murakami and Wang , 1993; Mitchell and Wallace , 1992]. The CMAP blends station rain observations and five different types of satellite products to estimate global precipitations [ Huffman et al , 1997; Xie and Arkin , 1997].…”

Section: Other Preliminary Modeling and Data Analysesmentioning

confidence: 99%

Geographical distribution and interseasonal variability of tropical deep convection: UARS MLS observations and analyses

et al. 2004

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[1] Tropical deep convection and its dynamical effect on the tropopause and stratosphere are investigated using a suite of data from the Upper Atmospheric Research Satellite (UARS) Microwave Limb Sounder (MLS), including upper tropospheric humidity, cloud radiance, and gravity wave measurements. For this purpose, geographical distributions of temperature, water vapor, and cloudiness in the tropical tropopause layer (TTL) are compared with corresponding maps of gravity wave variance in the stratosphere. In addition, ECMWF global wind divergent and velocity potential fields as well as NOAA outgoing longwave radiation and CMAP rainfall data are analyzed to help pinpoint the locations of deep convection. We found that high-altitude clouds near the bottom of TTL ($147 hPa) are usually surrounded by high-humidity air, and their spatial pattern and seasonal variability are closely associated with regions of vigorous summertime deep convection. Upward propagating gravity waves generated from these convection regions are shifted poleward by prevailing stratospheric winds. We estimate that tropical deep convection lifts $5% of the cloud tops to altitudes above 100 hPa and that most of the extreme deep convection events occur in the Western Pacific and Indian monsoon regions. Lowtemperature regions in the TTL are associated with, but often drift away from, the center of deep convection. Regions of water vapor maxima near the bottom of TTL are located directly above the deep convection centers, but this moisture behavior is somewhat reversed at the top of the TTL. The integrated picture derived from this study implies that convective scale motions could be important in affecting short-term dehydration processes in the TTL. Our results also suggest that the spatial organization and temporal development of tropical convective systems will be better monitored with the follow-on Earth Observing System (EOS) Aura satellite instruments and lead to improved understanding of the complex interaction of tropical convection with largescale dynamic and thermodynamic conditions.

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Section: Other Preliminary Modeling and Data Analysesmentioning

confidence: 99%

Geographical distribution and interseasonal variability of tropical deep convection: UARS MLS observations and analyses

et al. 2004

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“…OLR data have been used extensively to study tropical convection (e.g., Lau and Chan 1983;Graham and Barnett 1987) and even to estimate tropical rainfall (e.g., Motell and Weare 1987;Yoo and Carton 1988). Recently, Waliser et al (1993) compared OLR data to the frequency of the deep convective cloud type of the shorter series of the International Satellite Cloud Climatology Project stage C2 dataset (Rossow and Schiffer 1991).…”

Section: Observationsmentioning

confidence: 99%

Detecting Climate Change Concurrent with Deforestation in the Amazon Basin: Which Way Has It Gone?

Chu¹,

Zhang²,

Hastenrath³

1994

Bull. Amer. Meteor. Soc.

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To detect climate change in the Amazon Basin, as possibly induced by deforestation, time series of monthly mean outgoing longwave radiation (OLR), an index of tropical convection, and monthly rainfall totals at Belem and Manaus forthe past 15 years are analyzed. A systematic bias in the original OLR series was removed priorto the analysis. Linear regression analysis and nonlinear Mann-Kendall rank statistic are employed to detect trends. Over almost all of the basin, the OLR trend values are negative, indicating an increase of convection with time.

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“…In this study, a pattern that shows smaller OLR and greater rainfall occurred in the area was obtained. This result is line with research [24] that OLR is negatively correlated with rainfall, and also research [25] suggests that OLR is negatively correlated with the ITCZ (Inter Tropical Convergence Zone). …”

Section: Resultsmentioning

confidence: 99%

Analysis of El Niño and IOD Phenomenon 2015/2016 and Their Impact on Rainfall Variability in Indonesia

Avia

Sofiati

2018

IOP Conf. Ser.: Earth Environ. Sci.

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Estimating Tropical Pacific Rainfall Using Digital Satellite Data

Cited by 21 publications

References 11 publications

Geographical distribution and interseasonal variability of tropical deep convection: UARS MLS observations and analyses

Geographical distribution and interseasonal variability of tropical deep convection: UARS MLS observations and analyses

Detecting Climate Change Concurrent with Deforestation in the Amazon Basin: Which Way Has It Gone?

Analysis of El Niño and IOD Phenomenon 2015/2016 and Their Impact on Rainfall Variability in Indonesia

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