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Retrievals of Convective Detrainment Heights Using Ground-Based Radar Observations.

ABSTRACT: To better constrain model simulations, more observations of convective detrainment heights are needed. For the first time, ground-based S band radar observations are utilized to create a comprehensive view of irreversible convective transport over a 7-year period for the months of May and July across the United States. The radar observations are coupled with a volumetric radar echo classification scheme and a methodology that uses the convective anvil as proxy for convective detrainment to determine the level of maximum detrainment (LMD) for deep moist convection. The LMD height retrievals are subset by month (i.e., May and July), by morphology (i.e., mesoscale convective system, MCS, and quasi-isolated strong convection, QISC), and region (i.e., northcentral, southcentral, northeast, and southeast). Overall, 135,890 deep convective storms were successfully sampled and had a mean LMD height of 8.6 km or tropopause-relative mean LMD height of -4.3 km; however, LMD heights were found to extend up to 2 km above the tropopause. May storms had higher mean tropopause-relative LMD heights, but July storms contained the highest overall LMD heights that more commonly extended above the tropopause. QISC had higher mean tropopause-relative LMD heights and more commonly had LMD heights above the tropopause while only a few MCSs had LMD heights above the tropopause. The regional analysis showed that northern regions have higher mean LMD heights due to large amounts of diurnally driven convection being sampled in the southern regions. By using the anvil top, the highest possible convective detrainment heights extended up to 6 km above the tropopause.

SUBMITTER: Starzec M

PROVIDER: S-EPMC8098003 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Agricultural subsurface drainage systems are commonly installed on farmland to remove the excess water from poorly drained soils. Conventional methods for drainage mapping such as tile probes and trenching equipment are laborious, cause pipe damage, and are often inefficient to apply at large spatial scales. Knowledge of locations of an existing drainage network is crucial to understand the increased leaching and offsite release of drainage discharge and to retrofit the new drain lines within the existing drainage system. Recent technological developments in non-destructive techniques might provide a potential alternative solution. The objective of this study was to determine the suitability of unmanned aerial vehicle (UAV) imagery collected using three different cameras (visible-color, multispectral, and thermal infrared) and ground penetrating radar (GPR) for subsurface drainage mapping. Both the techniques are complementary in terms of their usage, applicability, and the properties they measure and were applied at four different sites in the Midwest USA. At Site-1, both the UAV imagery and GPR were equally successful across the entire field, while at Site-2, the UAV imagery was successful in one section of the field, and GPR proved to be useful in the other section where the UAV imagery failed to capture the drainage pipes' location. At Site-3, less to no success was observed in finding the drain lines using UAV imagery captured on bare ground conditions, whereas good success was achieved using GPR. Conversely, at Site-4, the UAV imagery was successful and GPR failed to capture the drainage pipes' location. Although UAV imagery seems to be an attractive solution for mapping agricultural subsurface drainage systems as it is cost-effective and can cover large field areas, the results suggest the usefulness of GPR to complement the former as both a mapping and validation technique. Hence, this case study compares and contrasts the suitability of both the methods, provides guidance on the optimal survey timing, and recommends their combined usage given both the technologies are available to deploy for drainage mapping purposes.

Dataset Information

Retrievals of Convective Detrainment Heights Using Ground-Based Radar Observations.

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