Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data

Steiner, Matthias; Houze, Robert A.; Yuter, Sandra E.

doi:10.1175/1520-0450(1995)034<1978:ccotds>2.0.co;2

Cited by 858 publications

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“…For example, area averages of near surface observations of rain may also be used for testing the model. Here the convective and stratiform rain amounts and areas have been disaggregated using the texture based algorithm of Steiner et al (1995) (as discussed in Section 3). Figure 5 shows time series of the observed and modelled area averaged total rainfall and the convective rain components of this average.…”

Section: Results and Applicationmentioning

confidence: 99%

“…For example, it is convenient to calculate the area total rainfall as well as the convective and stratiform contributions to the average rain and the relative areas and mean rain rates of the convective and stratiform rain from both the radar and model fields. Here, the convective and stratiform areas are separated using the texture based algorithm described by Steiner et al (1995). The rain rates are converted to a pseudo reflectivity, Z, using standard Z-R relations for this.…”

Section: A Statistical Coverage Product (Scp)mentioning

confidence: 99%

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A method for using weather radar data to test cloud resolving models

May

Lane

2009

Meteorological Applications

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A simple method for extracting useful metrics to characterize cloud fields from volumetric radar data is described along with the use of these metrics to evaluate the performance of cloud resolving models. This method involves the calculation of a 'Statistical Coverage Product', where the fraction of a grid of radar data covered by various reflectivity thresholds and microphysical classifications is calculated, along with profiles of maximum reflectivity, areas of convective and stratiform rain, and the amounts of rainfall in these categories. The application of this method is illustrated, whereby radar observation and a cloud resolving model simulation are compared for a tropical island thunderstorm near Darwin, Australia.

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Section: Results and Applicationmentioning

confidence: 99%

Section: A Statistical Coverage Product (Scp)mentioning

confidence: 99%

A method for using weather radar data to test cloud resolving models

May

Lane

2009

Meteorological Applications

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“…We interpolated the radar reflectivity data from original-resolution Cartesian grid of 4-km 2 horizontal and 1-km vertical spatial resolution. The method used to distinguish between convective and stratiform clouds was based on Steiner et al (1995). Equations (1) and (2) were used to convert radar reflectivity into rainfall intensity for stratiform and convective clouds, respectively, where Z is the reflectivity factor (mm 6 m −3 ), Rr is the rainfall intensity (mm h −1 ), and the constants were generally derived from the distribution of raindrop size (Sauvageot 1994).…”

Section: Combining Rain Gauge Mtsat and Radar Datamentioning

confidence: 99%

“…We adjusted the parameters of a method provided by Steiner et al (1995) to fit with our radar data for Thailand. The parameters obtained were different from the original parameters.…”

Section: Conclusion and Remarksmentioning

confidence: 99%

“…This may result in an underestimation of rainfall by radar (Feng et al 2011). Despite this limitation, radar observations have resulted in a better characterization of precipitation, particularly for mesoscale systems (MSC; see Steiner et al (1995)). An analysis method combining rain gauges, ground-based radar, and geostationary satellites would provide a better alternative, because a geostationary satellite provides almost continuous data with a wide spatial coverage.…”

Section: Introductionmentioning

confidence: 99%

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Retrieval of Rainfall by Combining Rain Gauge, Ground-Based Radar and Satellite Measurements over Phimai, Thailand

Wetchayont

Hayasaka

Satomura

et al. 2013

SOLA

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A classification system for rain clouds was developed using ground-based radar reflectivity and infrared brightness temperature (TBB) data from multifunctional transport satellites (MTSAT) and applied to the Phimai radar station, Thailand. The proposed method can classify cloud types into convective rain, stratiform rain and non-rain for areas covered with cumulus and/or cirrus clouds by applying a statistical integration analysis of rain gauges, ground-based radar, and MTSAT data. The classified precipitation areas were used to estimate quantitative precipitation amounts over Phimai. To merge different rainfall data sets derived from these three sources, the bias among the data must be removed. A combined correction method was developed to estimate the spatially varying multiplicative biases in hourly rainfall obtained from the radar and MTSAT using the rain gauges. This consecutive analysis was applied to the rainy season (July to September) in 2009 to obtain the multiplicative bias correction and to combine the data sets. The correlation coefficient, root mean square error, and mean bias were used as indicators to evaluate the performance of our bias-correction method. The combined method is simple and useful. The combined rainfall data were more useful than the data of TRMM 3B42 V7 and ground-based radar estimates.(Citation: Wetchayont, P., T. Hayasaka, T. Satomura, S. Katagiri, and S. Baimoung, 2013: Retrieval of rainfall by combining rain gauge, ground-based radar and satellite measurements over Phimai, Thailand. SOLA, 9, 166−169,

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Meteorological Radar

Chandrasekar

Baldini

2018

Wiley Encyclopedia of Electrical and Electronics Engineering

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Meteorological radar uses information from the amplitude, and phase, at different polarization states of the scattered electromagnetic wave to reveal relevant properties of clouds and precipitation for weather monitoring and forecasting in short time the behavior of hazardous storms. Since mid‐1950s, many countries have deployed networks of meteorological radars: nowadays, stand‐alone radars or networks of meteorological radars are key tools for most of national weather services. The last couple of decades have been very exciting time for the fields of weather radar science and technology. The major technological advances include operational adaptation of dual‐polarization technology and large‐scale deployment and upgrades with meteorological services, introduction of the concept of dense network of radars through the Collaborative Adaptive Sensing of the Atmosphere (CASA) project, and space‐borne weather radars.This article starts with a simple introduction of the meteorological radar basics, and then describes one of the most important application of weather radars worldwide, namely, the quantitative precipitation estimation. Subsequently, examples of radar observations of winter storms and summer convective storms are presented, followed by one of the popular applications used by operational forecasters, namely, nowcasting. Criteria to design networks of radars, based on two different paradigms (sparse network and dense network), are also presented. Finally, the space‐borne radars for measuring clouds and precipitation from space, introduced through the Tropical Rainfall Measurement Mission (TRMM) program first and followed by theCloudSatand Global Precipitation Measurement (GPM) programs, are described.

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Climatological Characterization of Three-Dimensional Storm Structure from Operational Radar and Rain Gauge Data

Cited by 858 publications

References 0 publications

A method for using weather radar data to test cloud resolving models

A method for using weather radar data to test cloud resolving models

Retrieval of Rainfall by Combining Rain Gauge, Ground-Based Radar and Satellite Measurements over Phimai, Thailand

Meteorological Radar

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