Calibration of weather radar using region probability matching method (RPMM)

Ayat, Hooman; Kavianpour, Mohammad Reza; Moazami, Saber; Hong, Yang; Ghaemi, Esmail

doi:10.1007/s00704-017-2266-7

Cited by 15 publications

(15 citation statements)

References 25 publications

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“…In all seasons, land and ocean-originating storms tend to peak early in their lifetimes (fig 6) consistent with the findings of Ayat et. al.…”

Section: Discussionsupporting

confidence: 90%

“…Although gauge and reported data provide us with valuable information, the 79 distribution of recorded data is highly influenced by the local population (Allen 2018). In addition, gauge instruments are point measurements and sparse rain gauge networks often fail to observe the maximum rainfall (Ayat et al 2018).…”

Section: Introductionmentioning

confidence: 99%

“…Some studies (Cecil and Blankenship 2012;Ni et al 2017) have applied a satellite-based approach using passive and active microwave sensors to estimate the global climatology of hail storms. However, images from these sensors are available only for a few overpasses per day (Ayat et al 2021a), which may result in biasing the results depending on the diurnal cycle of convection in the respective regions (Punge and Kunz 2016). In addition, passive microwave products are capturing the upper part of the storms in which there might be more hail particles that melt/evaporate at lower altitudes, leading to false estimation of hail on the ground surface (Ayat et al 2021b).…”

Section: Introductionmentioning

confidence: 99%

“…High spatio-temporal resolution radar rainfall estimates offer the potential to study the thunderstorms occurring over their coverage areas, particularly over the regions in which gauge observations are usually sparse or unevenly distributed (Ghaemi et al 2017;Ayat et al 2018;Moazami et al 2014). Radar records in many parts of the globe are temporally limited to just over a decade (Allen 2018).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

A Radar Object-Based Examination of Rain System Climatology and Including Climate Variability

Ayat¹,

Evans²,

Sherwood³

et al. 2021

Preprint

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We know the climate is warming and this is changing some aspects of storms, but we have little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional storm climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~10am and ~8pm (AEST), peaking in the afternoon. Storms are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five storm types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some storm types using a multivariate approach. This climatology for a variety of storm characteristics will allow future study of any changes in these characteristics due to climate change.

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“…In all seasons, land and ocean-originating storms tend to peak early in their lifetimes (fig 6) consistent with the findings of Ayat et. al.…”

Section: Discussionsupporting

confidence: 90%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Radar Object-Based Examination of Rain System Climatology and Including Climate Variability

Ayat¹,

Evans²,

Sherwood³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Ramli and Tahir (2013) in Malaysia developed new Z/R ratios for a Doppler radar using an optimal reflectivity and rain curve for different rainfall types based on their intensity, but this method did not include errors of location or timing. More recently, Ayat et al (2018) developed the Region Probability Matching Method (RPMM) to calibrate the Amir-Abad weather radar located in northern Iran. This method overcame the applicability limitations of previous methods in regions with scattered monitoring, light rains, and poor space-time resolutions.…”

Section: Introductionmentioning

confidence: 99%

New approach for local C-band weather radar precipitation calibration

Ávila-Carrasco

Herrera

Júnez-Ferrerira

et al. 2021

ATM

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Weather radar calibration is a topic of great current interest because it is useful for various hydrological applications. Several methods have been developed for adjusting the relation between reflectivity data Z and rainfall intensity R (Z/R) because droplet size distributions in different storm events are unknown and highly variable in time and space. The present study developed and tested a new space and time window-based procedure for optimal local calibration of weather radar using Z/R relations and applying it to convective and stratiform storms in the lower Grijalva river basin in Mexico. Improving rain estimates from the Sabancuy, Campeche radar is essential because it monitors this basin, which is prone to floods. The resulting estimates of the optimal power-law (Z = ARb) window-based procedure (OP) are compared with those of the default Marshall and Palmer (MP) relation using the observed rain gauge records. The appropriate window was selected using a criterion that considers factors affecting the free fall of raindrops. For most of the storms tested, metrics for the OP models showed better values than those calculated for the MP ones. The best MP performance is when using smooth calibration data, achieving similar metric results to that of the OP. The proposed observed calibration method could be useful to improve the default MP model estimates at any weather radar with similar characteristics to the ones analyzed in this work. The resulting Z/R relations could improve precipitation radar estimates for hydrologic model inputs.

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An object-based climatology of precipitation systems in Sydney, Australia

et al. 2022

Self Cite

View full text Add to dashboard Cite

The climate is warming and this is changing some aspects of storms, but we have relatively little knowledge of storm characteristics beyond intensity, which limits our understanding of storms overall. In this study, we apply a cell-tracking algorithm to 20 years of radar data at a mid-latitude coastal-site (Sydney, Australia), to establish a regional precipitation system climatology. The results show that extreme storms in terms of translation-speed, size and rainfall intensity usually occur in the warm season, and are slower and more intense over land between ~ 10 am and ~ 8 pm (AEST), peaking in the afternoon. Precipitation systems are more frequent in the cold season and often initiate over the ocean and move northward, leading to precipitation mostly over the ocean. Using clustering algorithms, we have found five precipitation system types with distinct properties, occurring throughout the year but peaking in different seasons. While overall rainfall statistics don't show any link to climate modes, links do appear for some system types using a multivariate approach. This climatology for a variety of precipitation system characteristics will allow future study of any changes in these characteristics due to climate change.

show abstract

Calibration of weather radar using region probability matching method (RPMM)

Cited by 15 publications

References 25 publications

A Radar Object-Based Examination of Rain System Climatology and Including Climate Variability

A Radar Object-Based Examination of Rain System Climatology and Including Climate Variability

New approach for local C-band weather radar precipitation calibration

An object-based climatology of precipitation systems in Sydney, Australia

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