An Examination of the Dynamics of a Rear-inflow Jet Associated with an Idealized Mesoscale Convective System

This study evaluates the main controls on the descent of the rear-inflow jet (RIJ), associated with a mesoscale convective system (MCS), toward the surface. This study employs the Cloud Model 1 (CM1), release 18.3, to simulate idealized MCSs. The model has a horizontal grid spacing of 1 km with 100 vertical levels, and utilizes doubly periodic lateral boundary conditions. The Morrison double-moment explicit moisture scheme is used and Coriolis accelerations are ignored. To initiate convection, a 2 K warm bubble is applied over a limited subset of the domain. Simulations in which the magnitude of vertical wind shear is perturbed, using base-state substitution, are then considered to examine how the descent of the RIJ is impacted. It was found that for greater magnitudes of 2.5 km vertical wind shear, the RIJ associated with the simulated MCS is more elevated and stronger than with weaker wind shear over the same layer. This can be attributed to better balance between the cold pool, line-normal vertical wind shear, and RIJ. Future work includes extending the wind shear-RIJ phase space to include other magnitudes, depths, and directions of wind shear as well as comparing this implementation of base-state substitution to other applications

Evaluation of daily precipitation from the era5 global reanalysis against ghcn observations in the northeastern united states

Licensee MDPI, Basel, Switzerland. Precipitation is a primary input for hydrologic, agricultural, and engineering models, so making accurate estimates of it across the landscape is critically important. While the distribution of in-situ measurements of precipitation can lead to challenges in spatial interpolation, gridded precipitation information is designed to produce a full coverage product. In this study, we compare daily precipitation accumulations from the ERA5 Global Reanalysis (hereafter ERA5) and the US Global Historical Climate Network (hereafter GHCN) across the northeastern United States. We find that both the distance from the Atlantic Coast and elevation difference between ERA5 estimates and GHCN observations affect precipitation relationships between the two datasets. ERA5 has less precipitation along the coast than GHCN observations but more precipitation inland. Elevation differences between ERA5 and GHCN observations are positively correlated with precipitation differences. Isolated GHCN stations on mountain peaks, with elevations well above the ERA5 model grid elevation, have much higher precipitation. Summer months (June, July, and August) have slightly less precipitation in ERA5 than GHCN observations, perhaps due to the ERA5 convective parameterization scheme. The heavy precipitation accumulation above the 90th, 95th, and 99th percentile thresholds are very similar for ERA5 and the GHCN. We find that daily precipitation in the ERA5 dataset is comparable to GHCN observations in the northeastern United States and its gridded spatial continuity has advantages over in-situ point precipitation measurements for regional modeling applications

Hydroclimatic Variability of the Northeastern United States

Over the historical record, the climate of the Northeastern United States (hereafter Northeast) has moved in and out of periods of drought and pluvial conditions. Precipitation has increased drastically over the past three decades but, within the same time frame, the Northeast has also experienced several significant drought events. If we can more fully understand the processes and drivers behind extreme hydroclimatic events in the historical record we may be able to gain a better understanding of the dynamics of why things are changing, aiding not only in our interpretation of global climate models but in the processes themselves. A multi-scale (regional-to-local), meteorological analysis of heavy precipitation events and an in-depth analysis on the meteorological drivers of soil moisture variability in the Northeast allows for the analysis and quantification of the various feedbacks between scales and between the atmosphere and land surface. First, I compare daily precipitation accumulations from the ERA5 Global Reanalysis (hereafter ERA5) and the US Global Historical Climate Network (hereafter GHCN) across the Northeast to evaluate the use of precipitation from ERA5 in applications across the Northeast. I find that the distance from the Atlantic Coast, elevation difference between ERA5 estimates and GHCN observations, and seasonality affect precipitation relationships between the two datasets. Then, I used a multi-duration (1, 2, 3, 7, 14, and 30 days), multi-return interval (2, 5, 10, and 50 years) observational precipitation dataset to diagnose changes in, and synoptic patterns associated with the identified precipitation events across the Northeast from 1895 to 2017. I found increasing trends in all duration and return-interval event combinations and that the rarest, longest duration events are increasing at faster rates than more frequent, shorter duration events. Additionally, daily synoptic patterns associated with events are similar across all durations and return-intervals and include coastal low, deep trough, east coast trough, zonal, and high pressure patterns. Finally, I sought to assess the meteorological drivers in soil moisture variability over the growing season at in-situ soil moisture observing sites across the Northeast. I found that seasonality and geographic location of stations lead to differences in the importance of meteorological drivers on surface soil moisture observations. This dissertation identifies useful tools for the estimation of precipitation across the Northeast, expands knowledge on trends and synoptic patterns associated with a spectrum of impactful precipitation events, and introduces new methods for the analysis of the meteorological drivers of soil moisture variability over the growing season