Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities

Smith, Travis; Lakshmanan, Valliappa; Stumpf, Gregory J.; Ortega, Kiel L.; Hondl, Kurt; Cooper, Karen; Calhoun, Kristin M.; Kingfield, Darrel M.; Manross, Kevin L.; Toomey, Robert; Brogden, Jeff W.

doi:10.1175/bams-d-14-00173.1

Cited by 161 publications

(111 citation statements)

References 32 publications

(1 reference statement)

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“…The MRMS is a collaboration project between the NSSL of the NOAA and the University of Oklahoma. The system started operation in 2014 at the National Center for Environmental Prediction (Smith et al ). Precipitation data from the MRMS system that cover a similar time period (May 2015) were used.…”

Section: Methodsmentioning

confidence: 99%

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Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

Bayabil

Fares

Sharif

et al. 2019

J American Water Resour Assoc

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The objectives of this study were to (1) evaluate the performance of the Multi‐Radar Multi‐Sensor (MRMS) system in capturing precipitation compared to gauge data, and (2) assess the effects of spatial (1–50 km) and temporal (15–120 min) data aggregation scales on the performance of the MRMS system. Point‐to‐grid comparisons were conducted between 215 rain gauges and the MRMS system. The MRMS system at 1 km spatial and 15 min temporal resolutions captured precipitation reasonably well with average R2, root mean square error (RMSE), and percent bias (PBIAS) values of 0.65, 0.5 mm, and 11.9 mm; whereas Threat Score, probability of detection, and false alarm ratio were 0.57, 0.92, and 0.40, respectively. Decreasing temporal resolution from 15 min to two hours resulted in an increase in R2 and a decrease in RMSE, whereas PBIAS was not affected. Reducing spatial resolution from 1 to 50 km resulted in increases in R2 and PBIAS, whereas RMSE was decreased. Increasing spatial aggregation scale from 1 to 50 km resulted in an R2 increase of only 0.08. Similarly, improvement in R2 was only modest (0.17) compared to an eightfold reduction in temporal resolution (from 15 min to two hours). While aggregating data at coarser temporal resolutions resolved some of the under/overestimation issues of the MRMS system, it was apparent even at coarser spatial and temporal resolutions the MRMS system inherently overestimated smaller precipitation events while underestimated bigger events.

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Section: Methodsmentioning

confidence: 99%

“…in 2014 at the National Center for Environmental Prediction (Smith et al 2016). Precipitation data from the MRMS system that cover a similar time period (May 2015) were used.…”

Section: Precipitation Datamentioning

confidence: 99%

Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

Bayabil

Fares

Sharif

et al. 2019

J American Water Resour Assoc

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“…Whereas previous studies used reflectivity-based thresholds for thunderstorm tracking (35 dBZ at -15°C, S09), this study uses vertically integrated liquid (VIL) in combination with lightning data. VIL is calculated from the merged and gridded radar data following the same method for single radar quality control and multi-radar blending as the national Multi-Radar Multi-Sensor system at the National Centers for Environmental Prediction, and provided to the National Weather Service (NWS) in real-time (Smith et al 2016).…”

Section: Methodsmentioning

confidence: 99%

Automated storm tracking and the lightning jump algorithm using GOES-R Geostationary Lightning Mapper (GLM) proxy data

Schultz¹,

Schultz²,

Carey³

et al. 2016

J. Operational Meteor.

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This study develops a fully automated lightning jump system encompassing objective storm tracking, Geostationary Lightning Mapper proxy data, and the lightning jump algorithm (LJA)-which are important elements in the transition of the LJA concept from a research to an operational-based algorithm. Storm cluster tracking is based on a product created from the combination of a radar parameter (vertically integrated liquid) and lightning information (flash rate density). Evaluations show that the spatial scale of tracked features or storm clusters has a large impact on the lightning jump system performance, where increasing spatial scale results in a decreased dynamic range of the system's performance. This framework also serves as a means to refine the LJA itself to enhance its operational applicability. Parameters within the system are isolated and the system's performance is evaluated with adjustments to parameter sensitivity. The system's performance is evaluated using the probability of detection and false alarm ratio statistics. Of the algorithm parameters tested, the sigma-level (i.e., a metric of lightning jump strength) and flash rate threshold influence the system's performance the most. Finally, minor changes in verification methods can dramatically impact the evaluation of the lightning jump system.

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“…The position of surface frontal zones was determined by manual analysis of surface observations and surface analyses provided by the National Oceanic and Atmospheric Administration (NOAA)/National Weather Service (NWS)/Ocean Prediction Center (OPC) unified surface analysis. Observed radar reflectivity were obtained from the Multi-Radar Multi-Sensor (MRMS) gridded composite reflectivity dataset (Lakshmanan et al 2007;Smith et al 2016). Observed positions of Harvey were derived from the Hurricane Best Track Database (HURDAT2; Landsea and Franklin 2013).…”

Section: Synoptic Analysis a Data And Computation Of Steering Layer mentioning

confidence: 99%

The Hurricane Harvey (2017) Texas Rainstorm: Synoptic Analysis and Sensitivity to Soil Moisture

Galarneau

Zeng

2020

Monthly Weather Review

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A synoptic analysis and soil moisture (SM) sensitivity experiment is conducted on the record-breaking rainstorm in Texas associated with Hurricane Harvey on 26-30 August 2017. The rainstorm occurred as the moist tropical air mass associated with Harvey was lifted along a frontogenetical near-surface coastal baroclinic zone beneath the equatorward entrance region of an upper-level jet streak. The weak steering winds in Harvey's environment, allowing Harvey to remain nearly stationary, were associated with a deformation steering flow pattern characterized by a trough to the north and flanking ridges to the west and east. This pattern has occurred with other notable tropical cyclone rainstorms along the Gulf Coast, except in Harvey's case it contributed to the collocation of deep tropical moisture and a persistent midlatitude lifting mechanism. Motivated by marked increases in SM during the rainstorm, a suite of six numerical simulations is used to test the sensitivity of the Harvey rainstorm (track, intensity, and rainfall) to varying SM. These simulations include dry, realistic, and wet SM conditions and an additional three runs with the initial SM held constant throughout the simulations. The results showed that track and prelandfall intensity were most sensitive to SM. Decreased SM resulted in the 1) development of an anticyclone in the southern plains that steered Harvey farther southwest in Texas, and 2) interruption in the intensification of Harvey in the Gulf of Mexico as dry air in the Yucatan Peninsula was entrained into Harvey's circulation, contributing to a weaker system at landfall. Implications of these findings on the evolution of tropical systems are also discussed.

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Multi-Radar Multi-Sensor (MRMS) Severe Weather and Aviation Products: Initial Operating Capabilities

Cited by 161 publications

References 32 publications

Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

Effects of Spatial and Temporal Data Aggregation on the Performance of the Multi‐Radar Multi‐Sensor System

Automated storm tracking and the lightning jump algorithm using GOES-R Geostationary Lightning Mapper (GLM) proxy data

The Hurricane Harvey (2017) Texas Rainstorm: Synoptic Analysis and Sensitivity to Soil Moisture

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