Structure of Florida Thunderstorms Using High-Altitude Aircraft Radiometer and Radar Observations

Heymsfield, Gerald M.; Shepherd, J. Marshall; Bidwell, S.W.; Boncyk, W.C.; Caylor, I. J.; Ameen, S.; Olson, William S.

doi:10.1175/1520-0450(1996)035<1736:softuh>2.0.co;2

Cited by 28 publications

(20 citation statements)

References 32 publications

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“…With high accuracy and excellent space and time resolutions, radar systems can provide direct measurements of the structure of clouds. Before the launch of TRMM in 1997, ground‐based [e.g., Heymsfield and Fulton , 1988; Cifelli et al , 2002], shipborne [e.g., DeMott and Rutledge , 1998; Rickenbach and Rutledge , 1998], and airborne [e.g., Heymsfield et al , 1996; Geerts et al , 2000] radar systems were used to observe the vertical structure and microphysical characteristics of deep convective clouds. Some of these investigations showed deep convective clouds penetrating into the TTL.…”

Section: Introductionmentioning

confidence: 99%

Detection of tropical deep convective clouds from AMSU‐B water vapor channels measurements

Hong

Heygster

Miao³

et al. 2005

J. Geophys. Res.

165

205

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[1] Methods to detect tropical deep convective clouds and convective overshooting from measurements at the three water vapor channels (183.3 ± 1, 183.3 ± 3, and 183.3 ± 7 GHz) of the Advanced Microwave Sounding Unit-B (AMSU-B) are presented. Thresholds for the brightness temperature differences between the three channels are suggested as criterion to detect deep convective clouds, and an order relation between the differences is used to detect convective overshooting. The procedure is based on an investigation of the influence of deep convective cloud systems on the microwave brightness temperatures at frequencies from 89 to 220 GHz using simultaneous aircraft microwave and radar measurements over two tropical deep convective cloud systems, taken during the Tropical Rainfall Measuring Mission (TRMM) Large Scale Biosphere-Atmosphere Experiment (LBA) campaign. Two other aircraft cases with deep convective cloud systems observed during the Third Convection and Moisture Experiment (CAMEX-3) are used to validate the criteria. Furthermore, a microwave radiative transfer model and simulated mature tropical squall line data derived from the Goddard Cumulus Ensemble (GCE) model are used to validate the procedures and to adapt the criteria to the varying viewing angle of AMSU-B. These methods are employed to investigate the distributions of deep convective clouds and convective overshooting in the tropics (30°S to 30°N) for the four 3-month seasons from March 2003 to February 2004 using the AMSU-B data from NOAA-15, -16, and -17. The distributions show a seasonal variability of shifting from the winter hemisphere to the summer hemisphere. The distributions of deep convective clouds follow the seasonal patterns of the surface rainfall rates. The deep convective clouds over land penetrate more frequently into the tropical tropopause layer than those over ocean. The averaged deep convective cloud fraction is about 0.3% in the tropics, and convective overshooting contributes about 26% to this.Citation: Hong, G., G. Heygster, J. Miao, and K. Kunzi (2005), Detection of tropical deep convective clouds from AMSU-B water vapor channels measurements,

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Section: Introductionmentioning

confidence: 99%

Detection of tropical deep convective clouds from AMSU‐B water vapor channels measurements

Hong

Heygster

Miao³

et al. 2005

J. Geophys. Res.

165

205

View full text Add to dashboard Cite

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“…Also radar observations can be used to detect deep convective clouds. However, passive microwave observations provide the advantage of a much higher global coverage, repeat cycle and total observation period, e.g., with the three sensors AMSU-B aboard the National Oceanic and Atmospheric Administration satellites [15][16][17] [25]. The MIR observed at frequencies from 89-340 GHz.…”

Section: Introductionmentioning

confidence: 99%

Intercomparison of Deep Convective Cloud Fractions From Passive Infrared and Microwave Radiance Measurements

Hong

Heygster

Künzi

2005

IEEE Geosci. Remote Sensing Lett.

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The common method to detect deep convective clouds is from satellite infrared (IR) measurements, which is based on thresholds of cloud-top temperatures. However, thick cirrus clouds with high cloud tops are difficult to screen out using IR methods, resulting in an overestimation of deep convective cloud fractions. Two aircraft cases with simultaneous Millimeter-wave Imaging Radiometer, Multispectral Atmospheric Mapping Sensor, and ER-2 Doppler radar measurements during the Convection and Moisture Experiment 3 in August 1998 are analyzed to investigate the influence of high thick cirrus clouds on two previously developed IR methods. In contrast, a microwave method based on the brightness temperature differences between the three water vapor channels around 183.3 GHz of the Advanced Microwave Sounding Unit-B (AMSU-B) (183 3 1 183 3 3, and 183 3 7 GHz) can screen out high thick cirrus clouds efficiently. The tropical deep convective cloud fractions (30 S-30 N) estimated by the IR methods and the AMSU-B method are compared. Although their geographical distributions are in well agreement with each other, the total fractions detected by the IR methods are about 2-3.5 times greater than that detected by the AMSU-B method. Moreover, the overestimation of deep convective cloud fractions by the IR method (11-m brightness temperature less than 215 K) can result in a displacement in the detected location of the deep convective clouds. The average thick cirrus clouds cover 2.5 times the area of the deep convective clouds that generates them. Index Terms-Deep convective cloud, infrared radiance, microwave radiance, Advanced Microwave Sounding Unit-B (AMSU-B).

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“…It can be also used for modeling rainfall to estimate flood risk (Wheater et al 2005), and for checking suspected rainfall data with great accuracy. With the advance on the observation technology during the recent years, sophisticate hydrometeorologic data have been gathered by means of satellite and remote sensing on a continental or global scale (Heymsfield et al 1996;Levizzani et al 2002;National Research Council 2003). The proposed model has a potential to apply with these external data to estimate rainfalls at missing sites or even at ungaged areas.…”

Section: Discussionmentioning

confidence: 99%

Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

Kim

Ahn

2008

Stoch Environ Res Risk Assess

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Missing data in daily rainfall records are very common in water engineering practice. However, they must be replaced by proper estimates to be reliably used in hydrologic models. Presented herein is an effort to develop a new spatial daily rainfall model that is specifically intended to fill in gaps in a daily rainfall dataset. The proposed model is different from a convectional daily rainfall generation scheme in that it takes advantage of concurrent measurements at the nearby sites to increase the accuracy of estimation. The model is based on a two-step approach to handle the occurrence and the amount of daily rainfalls separately. This study tested four neural network classifiers for a rainfall occurrence processor, and two regression techniques for a rainfall amount processor. The test results revealed that a probabilistic neural network approach is preferred for determining the occurrence of daily rainfalls, and a stepwise regression with a log-transformation is recommended for estimating daily rainfall amounts.

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Structure of Florida Thunderstorms Using High-Altitude Aircraft Radiometer and Radar Observations

Cited by 28 publications

References 32 publications

Detection of tropical deep convective clouds from AMSU‐B water vapor channels measurements

Detection of tropical deep convective clouds from AMSU‐B water vapor channels measurements

Intercomparison of Deep Convective Cloud Fractions From Passive Infrared and Microwave Radiance Measurements

Spatial rainfall model using a pattern classifier for estimating missing daily rainfall data

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