Browsing by Subject "Atmospheric sciences"

Spatial variability of the Earth's surface has a considerable impact on the atmosphere at all scales and understanding the mechanisms involved in land-atmosphere interactions is hindered by the scarcity of appropriate observations. A measurement gap exists between traditional point sensors and large aircraft and satellite-based sensors in collecting measurements of atmospheric quantities. Point sensors are capable of making long time series of measurements, but cannot make measurements of spatial variability. Large aircraft and satellites make measurements over large spatial areas, but with poor spatial and temporal resolution. A helicopter-based platform can make measurements on scales relevant for towers, especially close to the Earth's surface, and can extend these measurements to account for spatial variability. Thus, the Duke University Helicopter Observation Platform (HOP) is designed to fill the existing measurement gap.

Because measurements must be made in such a way that they are as uncontaminated by the platform itself as much as is possible, it is necessary to quantify the aerodynamic envelope of the HOP. The results of an analytical analysis of the location of the main rotor wake at various airspeeds are shown. Similarly, the results of a numerical analysis using the commercial Computational Fluid Dynamics software Fluent are shown. The optimal flight speed for the sampling of turbulent fluxes is found to be around 30 m/s. At this airspeed, the sensors located in front of the nose of the HOP are in advance of the wake generated by the main rotor. This airspeed is also low enough that the region of high pressure due to the stagnation point on the nose of the HOP does not protrude far enough forward to affect the sensors. Measurements of differential pressures, variables and turbulent fluxes made while flying the HOP at different airspeeds support these results. No systematic effects of the platform are seen at airspeeds above about 10 m/s.

Processing of HOP data collected using the current set of sensors is discussed, including the novel use of the Empirical Mode Decomposition (EMD) to detrend and filter the data. The EMD separates the data into a finite number of Impirical Mode Functions (IMFs), each of which is unique and orthogonal. The basis is determined by the data itself, so that it need not be known a priori, and it is adaptive. The EMD is shown to be an ideal tool for the filtering and detrending of HOP data using data gathered during the Cloud and Land Surface Interaction Campaign (CLASIC).

The ability of the HOP to accurately measure atmospheric profiles of potential temperature is demonstrated. During experiments conducted in the marine boundary layer (MBL) and the convective boundary layer (CBL), HOP profiles are evaluated using profiles from an elastic backscatter lidar. The HOP and the lidar agree on the height of the boundary layer in both cases, and the HOP effectively locates other atmospheric structures.

Atmospheric sensible and latent heat fluxes, turbulence kinetic energy (TKE) and horizontal momentum fluxes are also measured, and the resulting information is used to provide context to tower-based data collected concurrently. A brief comparison made over homogeneous ocean conditions yields good results. A more exhaustive evaluation is made using short HOP flights made over an orchard during the Canopy Horizontal Turbulence Study (CHATS).

This study examines whether and how land-atmosphere interactions can have an impact on the nocturnal convection over the Southern Great Plains (SGP) through numerical simulations of an intense nocturnal mesoscale convective system (MCS) on 19-20 June 2007 with the Weather Research and Forecasting (WRF V3.3) model. High-resolution nested simulations were conducted using realistic and idealized land-surfaces and two different planetary boundary layer parameterizations: Yonsei University (YSU) and Mellor-Yamada-Janjic (MYJ). All simulations show a persistent dry layer around 2 km during daytime and, despite ample instability in the boundary layer, the lack of a mesoscale lifting mechanism prevents precipitating convection in the daytime and in the evening ahead of the MCS passage after local midnight. Integral differences in timing and amount of MCS precipitation among observations and model results were examined in the light of daytime land-atmosphere interactions, nocturnal pre-storm environment, cold pool strength, squall line morphology and propagation speed, and storm rainfall. At the meso-gamma scale, differences in land-cover and soil type have as much of an effect on the simulated pre-storm environment as the choice of PBL parameterization: MYJ simulations exhibit strong sensitivity to changes in the land-surface in contrast to negligible impact in the case of YSU. A comparison of one-way and two-way nested MYJ results demonstrates that daytime land-atmosphere interactions modify the pre-storm environment remotely through advection of low-level thermodynamic features, which strongly impact the development phases of the MCS. At the end of the afternoon, as the boundary layer collapses, a more homogenous and deeper PBL (and stronger low level shear) is evident in the case of YSU as compared to MYJ when initial land-surface conditions are the same. For different land-surface conditions, propagation speed is generally faster, and organization (bow echo morphology) and cold pool strength enhanced when nocturnal PBL heights are higher and there is stronger low level shear in the pre-storm environment independently of the boundary layer parameterization. To elucidate the distinct roles of mesoscale transport and redistribution of low level instability (daytime remote feedbacks) and low level shear in the downwind pre-storm environment (nighttime local feedbacks), which is to separate the nonlinear land-atmosphere physical processes from PBL parameterization-specific effects on simulated storm dynamics, requires addressing the phase delay in storm development and propagation between the observed and the simulated MCS.

Another research objective was to examine the contribution of the land surface at short time scales. A second set of experiments was performed in which the land surface properties were homogenized every 5 minutes. The results show that surface effects are most pronounced during periods of insolation and, for the Yonsei University PBL parameterization, effects on the PBL height are most pronounced at the time of PBL collapse. Image processing techniques were found to be a useful measure of the spatial variation within fields. The results of this study show that, for this case, the integrated effect of the land surface can have a noticeable effect on convection, but such effects are not readily discernible at the 5-minute scale. While this study focused on the thermodynamic effects, further work should examine sensitivity to grid spacing and surface roughness.

In high elevated regions, ground-based precipitation measurements are scarce or non-existing. Remote sensing estimates are prone to underestimation due to a lack of sensitivity to light precipitation and ground-clutter contamination of radar signals by steep terrain. The latter creates a blind zone extending up a few kilometers above the ground for the vertical profiling radars onboard the Global Precipitation Measurement mission (GPM) core satellite that serves as a reference standard to unify precipitation measurements from research and operational satellites. Ground radars which are not affected by the blind zone, show an increase in effective reflectivity factor (ERF) from the melting layer below the isotherm of zero towards the surface. The blind zone is critical as rainfall characteristics at the ground results from the vertical evolution of the rain drop-size distributions (RDSDs). Explicit modeling of the rainfall microphysics from the top of the blind zone, if located below the zero-degree isotherm, towards the surface can improve rainfall estimation at the ground, as demonstrated in the Southern Appalachian Mountains. However, if the top of the blind zone is located above the zero-degree isotherm, ice and mix-phase hydrometeors coexist at the top boundary condition precluding explicit modeling because the microphysics of these hydrometeors is yet an active research topic. This work has three parts. The first part is devoted to characterizing precipitating systems and the blind zone in the Central Andes of Peru. Twenty years of TRMM and GPM radar measurements reveal that stratiform precipitation is the most frequent type in the Central Andes. Long-lasting precipitating systems (LDPSs) having a stratiform structure with embedded convection determine the interannual variability of the diurnal cycle of precipitation. Moisture availability at high elevations in the Central Andes is scarce. Moisture sources that sustain LDPSs are located in the adjacent eastern foothills of the Andes (Western Amazon basin) that result from enhanced moisture convergence and low and mid-levels due to the interplay of the South American low-level jet and cold air incursions. In the Central Andes of Peru, the top of the GPM blind zone is 46 % of the time above the zero-degree isotherm located at 5000 masl missing liquid precipitation, and a comparison with ground disdrometer data shows that GPM DPR does not capture the variability of the rain drop size distribution. These findings show that rainfall in the Central Andes depends on the dynamics of atmospheric circulation and microphysics at multiple spatial and temporal scales. These scales cannot be modeled explicitly by one single model nor validated with current observations. However, progress in that direction can be made by using different models to simulate different weather regimes to elucidate the role of different microphysics and dynamic processes. Observations needed to validate the models are lacking in the Andes but available in the Southern Appalachian Mountains (SAM). So, the modeling part of this work is focused on the SAM, and the knowledge gained by modeling the SAM region can be used to improve the representation of dynamical and microphysical processes in numerical models to be used in the Andes.Changes in the cloud drop size distribution ultimately capture the interplay of microphysics, dynamics, and thermodynamics in clouds. The chain of microphysical mechanisms from aerosol activation to cloud drop size distribution (CDSD) evolution during cloud development to the raindrop size distribution (RDSD) dynamics spans a scale range of four orders of magnitude from fractions of micrometers to millimeters. It can be modeled explicitly in cloud parcel and rain-shaft models (for liquid hydrometeors), and it is described using different parameterizations with varying degrees of complexity in Numerical Weather Prediction (NWP) models. However, in the Weather Research and Forecast (WRF) model, parameterized microphysics often fail to capture the diurnal cycle and spatial distribution of precipitation, rainfall intensity, and duration depending on the weather regime, regional topography, and regional aerosol characteristics. Besides, the representation of the dynamical processes affecting the aerosol activation and the CDSDs evolution during cloud formation and of the cloud-droplet-raindrop continuum in precipitating clouds is lacking in NWP models. In the second part of this work, a two-moment bulk microphysics scheme in WRF is modified to add an aerosol activation spectrum from in-situ measurement and compared to the default activation spectrum to characterize the impact of aerosol activation in the onset of precipitation in different weather conditions. WRF simulations show that using the in-situ aerosol activation spectrum yields higher cloud droplet number concentrations (CDNC) than the default WRF aerosol activation spectrum, with smaller cloud droplets and delayed onset of rainfall under weak synoptic forcing conditions. For large-scale systems with strong and sustained moisture convergence at low levels (frontal and tropical systems), mechanically forced rainfall efficiency is enhanced despite high CDNC, there is no delay in the onset of precipitation, and the impact of ACPI on the spatial and temporal variability of rainfall is negligible (significant) at onset (hours later) consistent with rainfall observations. The simulated cloud vertical structure from CDNC indicates that convective development is more intense in the inner SAM region than in the adjacent plains. In the inner region, valley-ridge circulations organize the spatial patterns of cloudiness under weak synoptic forcing conditions. The formation of early afternoon low-level clouds over the ridges in the summertime reflects the high sensitivity of cloud mixing ratios and cloud droplet concentrations to aerosol activation properties. In the third part of this work, the dynamical and microphysical processes not resolved by WRF are modeled using a large eddy simulation coupled with a spectrum bin microphysics that permits aerosol replenishment to characterize the heterogeneity of cloud microstructure associated with cloud circulations and entrainment in non-precipitating cumulus clouds. Furthermore, the sensitivity of cloud variables to different vertical profiles of aerosol loading is tested and validated with aircraft observations. The coupled model simulated a convective case under weak synoptic conditions, and the spatial variability of the LWC, CDNC, and CDSD was characterized. Simulations show large differences between the region around the updraft and regions of entrainment located at cloud edges and preferred locations. The interplay of environmental wind and cloud circulations explains these locations. More and larger droplets towards the location of the updraft and towards but before the cloud's top result in large LWC in the upper half of the cloud. The opposite in regions of entrainment, having small droplets in minor concentrations resulting in low LWC. Different initial vertical profiles of aerosol concentrations result in significantly different values of CDNC; as larger the aerosol loading, the larger the CDNC. The updraft transport aerosols and moisture up to the cloud altitude from near the ground elevation. In the updraft, a sharp CDSD with a small standard deviation has small values of relative dispersion. In regions of entrainment, the CDSD has smaller droplets in minor concentrations due to the evaporation of droplets. Its shape resembles the distribution of interstitial aerosols with a large standard deviation resulting in large values of relative dispersion and smaller values of LWC. Aircraft measurements show agreement with the simulated CDNC and relative dispersion for the simulation with an initial vertical profile of aerosol concentration that decays exponentially with height and has a scale height of 500m. The significant impact of aerosol loading in the CDNC affects the cloud optical thickness (COT). The COT in the cloud mature stage for a scale height of 2000m is approximately 1.75 times the COT for a scale height of 500m.

This dissertation presents the first findings of aerosol indirect effect in the foothills of the Himalayas (Central Nepal), through a systematic research approach involving satellite data analysis, field campaign, growth factor estimation and numerical modeling studies. Satellite retrieved aerosol optical depth data over the region were first used to identify the dominant modes of spatial/temporal variability of aerosols in the region. Based on the observed dominant spatial mode of aerosol in the pre-monsoon season (Shrestha and Barros 2010, ACP), a field campaign was organized under the Joint Aerosol Monsoon Experiment (JAMEX09) at Dhulikhel and Besisahar to simultaneously measure dry and ambient aerosols size spectra using SMPS and chemical composition using filters (Shrestha et al. 2010, ACP). The diurnal cycle of aerosol number concentration exhibited a consistent peak in the morning and evening period, which was found to be associated with increase in local emission and the delay in ventilation of aerosol through upslope flows and mixing (inferred from an idealized numerical study over Besisahar). The aerosol size distribution was mostly unimodal at night and bimodal during the day, with a consistent larger mode around 100nm and a smaller mode located around 20nm. The chemical composition of PM2.5 was dominated by organic matter at both sites. Organic carbon (OC) comprised the major fraction (64~68%) of the aerosol concentration followed by ionic species (24~26%, mainly and ). Elemental Carbon (EC) compromised 7~10% of the total composition and 27% of OC was found to be water soluble at both sites. The aerosol number concentration increased and decreased in the presence of synoptic scale aerosol plumes and after rainfall events respectively.

A simple model based on Köhler theory was used to explain the observed growth factor using an assumption of (NH4)2SO4 aqueous solution including the presence of slightly soluble organic compounds (SSC) with an insoluble core as a function of molality and mass-fraction. The measured growth factors suggest that the aerosols are in metastable state due to the strong diurnal cycle of relative humidity (RH). The bulk hygroscopic parameter estimated from the DGF and chemical composition of aerosols suggests less hygroscopic aerosols at both locations as compared to previous studies. The dry aerosol size distribution and the bulk hygroscopic parameters were used to estimate the cloud condensation nuclei (CCN) spectrum, which was vertically scaled up to lifting condensation level (LCL) assuming that the shape and chemical properties of aerosol remains unchanged (Shrestha et al. 2011, submitted to JGR). Finally, these regional CCN spectra for polluted and clean conditions as well as standard continental and marine spectra used in numerical weather prediction models (Cohard et al. 1998) were used to probe CCN sensitivity for a pre-monsoon storm system in Central Nepal during JAMEX09. A significant shift in the maxima of the accumulated precipitation was observed between the continental aerosol spectra (Cohard et al. 1998) and the polluted spectra for Dhulikhel. This shift caused the displacement of rainfall maximum away from the Kulekhani water reserve catchment, which is key to hydropower in Nepal. Detailed analysis of the simulations suggests that simgnificant differences in the space-time variability and intensity of precipitation, if not areally integrated amounts, can be explained by differences in the timing and intensity of latent heat release and absorption due to freezing/melting of hydrometers and evaporative cooling of droplets, strengthening cold pool formation and associated circulations. This numerical study provides the first look on the aerosol indirect effect over Nepal for a single pre-monsoon rainfall event, and how aerosols can potentially affect the precipitation distribution (to be submitted to JGR). In addition, it shows the importance of using regionally consistent CCN spectra in model parameterizations of aerosol-cloud interactions. At local places, the differences in simulated precipitation between marine, JAMEX09 clean and polluted air spectra were smaller (up tp ± 50%) than the difference between those simulations and the standard continental aerosol spectra (±200%).

Large-scale coherent structures are systematically investigated in terms of their geometric attributes, importance toward describing turbulent exchange of energy, momentum and mass as well as their relationship to landscape features in the context of land-atmosphere interaction. In the first chapter, we present the motivation of this work as well as a background review of large-scale coherent structures in land-atmosphere interaction. In the second chapter, the methodology of large-eddy simulation (LES) and the proper orthogonal decomposition (POD) is introduced. LES was used to serve as a virtual laboratory to simulate typical scenarios in land-atmosphere interaction and the POD was used as the major technique to educe the coherent structures from turbulent flows in land-atmosphere interaction. In the third chapter, we justify the use of the LES to simulate the realistic coherent structures in the atmospheric boundary layer (ABL) by comparing results obtained from LES of the ABL and direct numerical simulation (DNS) of channel flow. In the fourth chapter, we investigate the effects of a wide range of vegetation density on the coherent structures within the air space within and just above the canopy (the so-called canopy sublayer, CSL). The fifth chapter presents an analysis of the coherent structures across a periodic forest-clearing-forest transition in the steamwise direction. The sixth chapter focuses on the role of coherent structures in explaining scalar dissimilarity in the CSL. The seventh chapter summarizes this dissertation and provides suggestions for future study.

Land-surface heterogeneity (LSH) at different scales has significant influence on atmospheric boundary layer (ABL) buoyant and shear turbulence generation and transfers of water, carbon and heat. The extent of proliferation of this influence into larger-scale circulations and atmospheric structures is a topic continually investigated in experimental and numerical studies, in many cases with the hopes of improving land-atmosphere parameterizations for modeling purposes. The blending height is a potential metric for the vertical propagation of LSH effects into the ABL, and has been the subject of study for several decades. Proper assessment of the efficacy of blending height theory invites the combination of observations throughout ABLs above different LSH scales with model simulations of the observed ABL and LSH conditions. The central goal of this project is to develop an apt and thoroughly scrutinized method for procuring ABL observations that are accurately detrended and justifiably relevant for such a study, referred to here as Detrending with Empirical Mode Decomposition (DEMD).

The Duke University helicopter observation platform (HOP) provides ABL data [wind (u, v, and w), temperature (T), moisture (q), and carbon dioxide (CO₂)] at a wide range of altitudes, especially in the lower ABL, where LSH effects are most prominent, and where other aircraft-based platforms cannot fly. Also, lower airspeeds translate to higher resolution of the scalars and fluxes needed to evaluate blending height theory.

To confirm noninterference of the main rotor downwash with the HOP sensors, and also to identify optimal airspeeds, analytical, numerical, and observational studies are presented. Analytical analysis clears the main rotor downwash from the HOP nose at airspeeds above 10 m s-1. Numerical models find an acceptable range from 20-40 m s-1, due to a growing compressed air preceding the HOP nose. The first observational study finds no impact of different HOP airspeeds on measurements from ~18 m s-1 to ~55 m s-1 over a stable marine boundary layer (MBL). Another set of observations studies HOP and tower data, using the Duke University Mobile Micrometeorological Station (MMS) over an MBL, and concludes that HOP sensible heat (SH), latent heat (LE), and carbon dioxide (F_CO2) fluxes align well with MMS findings. The HOP sensors provide ABL data at 40 Hz, as well as a real-time display of θ for in-flight ABL height estimation. Sensor calibration and alignment procedures indicate usable ABL measurements.

HOP data are especially susceptible to the spurious influence of platform motion on ABL data, largely due to the low-altitude and low-airspeed capabilities of the HOP. For example, HOP altitude motion in the presence of a lapse rate can cause spurious T fluctuations. Empirical mode decomposition (EMD) can separate HOP data into a set of adaptive and unique intrinsic mode functions (IMFs), often with physical meaning. DEMD aims to correct for spurious contributions to HOP data, while merging EMD with a correlation analysis to adjust data without eliminating relevant ABL dynamics.

To evaluate DEMD efficacy, two-dimensional synthetic T fields with simulated turbulence over a prescribed lapse rate are sampled with altitude fluctuations similar to HOP flights, and with a wide range of T perturbation and sampling path parameter variations. DEMD recovers the prescribed lapse rate within 1% on average for the 552 test cases passing the filtering criteria. The method is further evaluated via application to vertical cross sections taken from the Ocean-Land-Atmosphere Model (OLAM) large-eddy simulation (LES) results, where DEMD shows improved accuracy of SH recovery.

DEMD is applied to three low-altitude HOP flight legs flown on 19 June 2007 during the Cloud and Land Surface Interaction Campaign (CLASIC), both as an example of practical application and to compare DEMD to the initially proposed method (Holder et al. 2011, hereafter H11). H11 dictates the elimination of correlated IMFs, along with other subtle differences from DEMD, which also eliminates any ABL motions embedded in those IMFs. As suspected, the H11 method produces marked reductions of variances and turbulence kinetic energy (TKE) and substantial deviations in SH, LE, and F_CO2 compared to DEMD. DEMD detrends without unnecessary elimination.

DEMD is vital for ensuring accurate scalars and fluxes from HOP data, and a strategy for future research is presented that integrates properly detrended observations from the CLASIC HOP dataset with OLAM simulations to explore LSH effects on ABL processes and evaluate blending height theory.

This research aims to understand the development of the atmospheric energy spectrum and energy transfer mechanisms across scales. A clear understanding of energy spectrum development and transfer mechanisms is necessary for improving the representation of multiscale processes in the atmosphere, modeling turbulence in the boundary layer, and understanding the limits of atmospheric predictability. This work consists of three parts.

The first part investigates the Navier Stokes Equations (NSE) behavior under idealized conditions relevant to large-scale atmospheric turbulence. This study uses a series of direct numerical simulations (DNS) of two-dimensional (2D) with forcing applied at different scales to investigate energy transfer between the synoptic scale and the mesoscale. The DNS results, forced by a single kinetic energy source at large scales, show that the energy spectra slopes of the direct enstrophy cascade are steeper than the theoretically predicted -3 slope. Next, the presence of two inertial ranges in 2D turbulence at intermediate scales is investigated by introducing a second energy source in the meso-a-scale range. The energy spectra for the simulations with two kinetic energy sources exhibit flatter slopes closer to -5/3, consistent with the observed kinetic energy spectra of horizontal winds in the atmosphere at synoptic scales. Further, the results are independent of model resolution and scale separation between the two energy sources, with a robust transition region between the lower synoptic and the upper meso-a scales in agreement with classical observations in the upper troposphere. These results suggest the existence of mesoscale feedback on synoptic-scale predictability that emerges from the concurrence of the direct (downscale) enstrophy transfer in the synoptic scales and the inverse (upscale) kinetic energy transfer from the mesoscale to the synoptic-scale in the troposphere.

The second part of this research is devoted to the characterization of atmospheric energy spectra over mountainous terrain under various environmental conditions using the Weather and Research Forecasting (WRF) model. First, a comprehensive analysis of climatology and mesoscale structure of cold air intrusions (CAIs) over the Andes shows that the events are responsible for localized heavy rainfall events (200 mm, less than 6 hours) in the mid-elevations (~1,500) Peruvian Andes. The climatology analysis uses European Center Medium-Range Weather Forecasts (ECMWF) reanalysis, Tropical Rainfall Measurement Mission (TRMM) data products, and rain-gauge observations. This analysis emphasized characterizing year-round CAI frequency, CAI interactions with Andes topography, and their impact on orographic precipitation climatology. The results show a robust enhancement of the diurnal cycle of precipitation during CAI events in all seasons, particularly in the increase in surface rainfall rate during early morning at intermediate elevations (~ 1,500m), the eastern Andes orographic maximum. This link between CAI frequency and rainfall suggests that they play an essential role in maintaining the Andes to Amazon year-round terrestrial connectivity through runoff production and transport by the river networks. Second, the next step examines the transient behavior of horizontal scaling in the vertical during the evolution of extreme orographic precipitation storms. Furthermore, a mechanistic framework to examine the implications of ongoing deforestation in the western Amazon on orographic precipitation in the tropical Andes through land-atmosphere interactions is provided. Understanding the interplay between large-scale dynamics and land-atmosphere interactions is critical to developing an effective boundary layer processes parameterizations for future numerical weather prediction models. The study includes a case over the Appalachians as middle mountains in comparison to high mountains (Andes) highlighting terrain and weather contrasts. Previous work showed that atmospheric model simulations exhibit different scaling behavior of vertically averaged horizontal wind (u, v) and moisture (q) in the mesoscales for convective (spectral slopes β~−5/3) and non-convective (β~−11/5) conditions. Here, the results show that β exhibits a strong diurnal cycle switching between convective and non-convective behavior following the space‐time evolution of atmospheric stability in the lower troposphere (below 700 hPa) depending on latitude, topography, landform, and the synoptic environment. Anomalous flattening of the wind and moisture spectra (i.e., spectral saturation, ∣β ∣ < 5/3) at high wavenumbers and up to 200 hPa is tied to convective activity, where and when strong vertical motions develop, corresponding to an abrupt directional switch from horizontal energy transfer to vertical energy transfer including latent heating release and parameterized microphysical processes. In the small mesoscales (<50 km), β~ − 5/3 at all times up to 200 hPa with nighttime steepening (β~−11/5) below the orographic envelope where cold air pools form at low elevations and vertical motion weakens in the Appalachians. In the Andes, at a high elevation, the scaling behavior exhibits a stronger diurnal cycle at low levels (below 700 hPa) with significant shoaling between tropical and high latitudes. Furthermore, blocking and strong modification of regional circulations result in nighttime anisotropy at midlevels on the altitudinal profile along the North‐South topographic divide.

The third part focuses on modeling turbulent fluxes near the surface, which is essential for an accurate representation of the heterogeneous surface boundary layer. Second-moment turbulent models have been widely used in numerical weather prediction models for parameterizing the planetary boundary layer (PBL). The most commonly used schemes are based on the Mellor and Yamada (1982) model, which are currently implemented to only account for the contribution of the vertical divergences of the vertical turbulent fluxes in the WRF model. Horizontal tendencies are parameterized separately based on a Smagorinsky scheme. Although this approach may be successful in coarse grid models (e.g., dx~12-2 km), the influence of horizontal gradients becomes more important when the grid spacing is smaller (less than 1 km). Recently, a full 3D PBL scheme (3DPBL) has been implemented in WRF to reconcile the representation of the vertical and horizontal turbulent mixing. The model integrates 3DPBL parameterization with a diagnostic model of the three-dimensional second-order turbulent properties of the flow in the surface layer. A set of modifications introduced to the surface parameters provides a better diagnosis of the surface layer covering different flow regimes based on anisotropy and stability conditions. The near-surface diagnostic variables are analyzed and compared with the data from the Weather Forecast Improvement Project II (WFIPII).

Finally, the dissertation discusses and recommends potential directions for future research focusing on boundary layer processes.

Light rainfall is the baseline input to the annual water budget in mountainous landscapes through the tropics and at mid-latitudes. In the Southern Appalachians, the contribution from light rainfall ranges from 50-60% during wet years to 80-90% during dry years, with convective activity and tropical cyclone input providing most of the interannual variability. The Southern Appalachians is a region characterized by rich biodiversity that is vulnerable to land use/land cover changes due to its proximity to a rapidly growing population. Persistent near surface moisture and associated microclimates observed in this region has been well documented since the colonization of the area in terms of species health, fire frequency, and overall biodiversity. The overarching objective of this research is to elucidate the microphysics of light rainfall and the dynamics of low level moisture in the inner region of the Southern Appalachians during the warm season, with a focus on orographically mediated processes. The overarching research hypothesis is that physical processes leading to and governing the life cycle of orographic fog, low level clouds, and precipitation, and their interactions, are strongly tied to landform, land cover, and the diurnal cycles of flow patterns, radiative forcing, and surface fluxes at the ridge-valley scale. The following science questions will be addressed specifically: 1) How do orographic clouds and fog affect the hydrometeorological regime from event to annual scale and as a function of terrain characteristics and land cover?; 2) What are the source areas, governing processes, and relevant time-scales of near surface moisture convergence patterns in the region?; and 3) What are the four dimensional microphysical and dynamical characteristics, including variability and controlling factors and processes, of fog and light rainfall? The research was conducted with two major components: 1) ground-based high-quality observations using multi-sensor platforms and 2) interpretive numerical modeling guided by the analysis of the in situ data collection. Findings illuminate a high level of spatial – down to the ridge scale - and temporal – from event to annual scale - heterogeneity in observations, and a significant impact on the hydrological regime as a result of seeder-feeder interactions among fog, low level clouds, and stratiform rainfall that enhance coalescence efficiency and lead to significantly higher rainfall rates at the land surface. Specifically, results show that enhancement of an event up to one order of magnitude in short-term accumulation can occur as a result of concurrent fog presence. Results also show that events are modulated strongly by terrain characteristics including elevation, slope, geometry, and land cover. These factors produce interactions between highly localized flows and gradients of temperature and moisture with larger scale circulations. Resulting observations of DSD and rainfall patterns are stratified by region and altitude and exhibit clear diurnal and seasonal cycles.

Large areas of the world depend on snowmelt as a freshwater resource and for food production. Space-based remote sensing of seasonal snowpacks provides the only realistic means to monitor and quantify water availability (storage during the accumulation season, and release during the melt season) at global scales. The overarching goal of this study is to elucidate how meteorologic, topographic, and vegetation impact microwave remote-sensing measurements of high-elevation seasonal snowpacks. The working hypothesis is that changes in snowpack microwave behavior can be unambiguously attributed to snow physical processes modulated by meteorology, topography, and vegetation type. The research approach relies on the application of coupled snow hydrology and radiative transfer models to characterize the space-time evolution of snowpacks, and to support the interpretation of satellite-based microwave measurements toward enabling physically-guided estimation of Snow Water Equivalent (SWE). In the first part of this research, ensemble predictions of the seasonal snowpack over Grand Mesa, CO (~ 300 km2) for the hydrologic year 2016-2017 were conducted using a multilayer snow hydrology model. Snowpack ensembles were driven by gridded atmospheric reanalysis and evaluated against SnowEx’17 measurements. The multi-frequency microwave brightness temperatures and backscattering behavior of the snowpack (separate from soil and vegetation contributions) show that at sub-daily time scales, the ensemble standard deviation (i.e., weather variability at 3 × 3 km2) is < 3 dB for dry snow, and increases to 8-10 dB at mid-day when there is surficial melt that also explains the wide ensemble range (~20 dB). The linear relationship of SWE with the mean ensemble backscatter (R2 > 0.95) depends on weather conditions (e.g., 5-6 cm/dB in January; 2-2.5 cm/dB in late February as melt-refreeze cycles modify the microphysics in the top 50 cm of the snowpack). The nonlinear evolution of ensemble snowpack physics translates into seasonal hysteresis in the mesoscale microwave behavior. The backscatter hysteretic offsets between accumulation and melt regimes are robust in the L- and C-bands and collapse for wet, shallow snowpacks at Ku-band. The emissions behave as limit-cycles with weak sensitivity in the accumulation regime, and hysteretic behavior, with offsets increasing with frequency, is different for deep snowpacks at winter-spring transition and shallow ones at spring-summer transition. These findings suggest potential for multi-frequency active-passive remote sensing of high-elevation SWE depending on snowpack regime, particularly suited for data-assimilation via coupled snow hydrology-radiative transfer models extended to include the snow-soil and snow-vegetation interactions. To investigate snowpack microwave behavior in complex topography, an uncalibrated distributed multiphysics snow model driven by downscaled weather forecasts (30-m, 15-min) was implemented as a Radar Observing System Simulator (ROSS) in Senator Beck Basin (SBB), Colorado to elucidate topographic controls on C-, X- and Ku-bands active microwave sensing of mountain snowpacks. Phase-space maps of time-evolving grid-scale ROSS volume backscatter show the accumulation branch of the backscatter-snow water equivalent (σ-SWE) hysteresis seasonal loop that is the physical basis for radar retrieval (direct inference) of SWE and snowpack physical properties. There is good agreement in the accumulation season (R2 ~ 0.7) between Sentinel-1 and ROSS predictions corrected using average Sentinel-1 measurements under snow free conditions to estimate snow-ground backscatter, capturing well spatial patterns tied to elevation, slope, and aspect. Root Mean Square Deviations (RMSDs) do not exceed ±3.2 dB for ripening snowpacks in early spring and ±2.4 dB for dry snowpacks in the accumulation season when the mean absolute bias is < 1 dB for all land-cover types with topographic slopes ≤ 30°. Grid-point RMSDs are attributed to the underestimation of snowfall on upwind slopes compounded with forecast errors for the weather near the ground. Like Sentinel-1, ROSS backscatter fields exhibit frequency-independent single-scaling behavior in the 60-150 m scale range for dry snowpacks in the accumulation season, while frequency-dependent scaling behavior emerges in the ablation season. This study demonstrates skillful physical modeling capabilities to emulate Sentinel-1 observations in complex terrain. Conversely, it suggests high readiness to retrieve snow mass and snowpack properties in mountainous regions from radar measurements at high-spatial resolutions enabled by SAR technology. To estimate vegetation impacts on the snowpack microwave behavior, a coupled snow physics-radiative transfer forward-inversion modeling system was applied over snow-covered terrain in Grand Mesa to estimate vegetation contributions to the total backscatter from the ground-snow-vegetation system via referring to dual-frequency SnowSAR measurements. A simplified but comprehensive first-order microwave emission model (MEMLS-V) was iteratively inverted by a global optimizer – simulated annealing to retrieve unknown parameters and backscatter components from double-bounce, snowpack volume, and snow-ground interface. The retrieved parameters offered the simulations 100% correlation with the observed SnowSAR signal dynamics tied to vegetation and snowpack heterogeneities, which highlights that the forward-inversion system accounting for complex multiple scattering within the ground-snow-vegetation system reliably regulated compensation effects of vegetation and snow-ground interface. To the best of our knowledge, this is the first time that the system, with reduced computational requirements and ancillary data demands, has been successfully operated for SnowSAR data analysis, while maintaining robustness and interpretability. The findings have practical potentials of retrieving large-scale SWE in the northern hemisphere through Earth Observation radars and satellites.

Previously, we usually analyze climate responses to all the climate drivers combined. However, the climate responses to individual climate drivers are far from well-known, as it is nearly impossible to separate the climate responses to individual climate drivers from the pure observational records. In this dissertation, I analyzed the responses of effective radiative forcing (ERF), precipitation and clouds to five individual climate drivers by using the model output from the Precipitation and Driver Response Model Inter-comparison Project (PDRMIP, consisting of five core experiments: CO2x2, CH4x3, Solar+2%, BCx10, and SO4x5). Firstly, I compared the ERF values estimated by six different methods and demonstrated that the values estimated using fixed sea-surface temperature and linear regression methods are fairly consistent for most climate drivers. For each individual driver, multi-model mean ERF values vary by 10-50% with different methods, and this difference may reach 70-100% for BC. Then, I analyzed the dynamical responses of precipitation in Mediterranean to well-mixed greenhouse gases (WMGHGs) and aerosols and found that precipitation in Mediterranean is more sensitive to BC forcing. When scaled to historical forcing level, WMGHG contributed roughly two-thirds to the Mediterranean drying during the past century and BC aerosol contributed the remaining one-third by causing a northward shift of the jet streams and storm tracks. Lastly, I explored the responses of shortwave cloud radiative effect (SWCRE) to CO2 and the two aerosol species and found that CO2 causes positive SWCRE changes over most of the Northern Hemisphere during boreal summer, and BC causes similar positive responses over North America, Europe and East China but negative SWCRE over India and tropical Africa. When normalized by global ERF, the change of SWCRE from BC forcing is roughly 3-5 times larger than that from CO2. SWCRE change is mainly due to cloud cover changes resulting from the changes in relative humidity, and to a lesser extent, changes in circulation and stability. The SWCRE response to sulfate aerosols, however, is negligible compared to that from CO2 and BC, because the radiation scattered by clouds under all-sky conditions will also be scattered by aerosols under clear-sky conditions. As SW is in effect only during daytime, positive (negative) SWCRE could amplify (dampen) daily maximum temperature (Tmax). Using a multi-linear regression model, I found that Tmax increases by 0.15 K and 0.13 K given unit increase in local SWCRE under the CO2 and BC experiments, respectively. When domain-averaged, SWCRE changes contributed to summer mean Tmax changes by 10-30% under CO2 forcing and by 30-50% under BC forcing, varying by regions, which can have important implications extreme climatic events and socio-economic activities.

The Southeastern United States (SE US) is one of the fastest developing regions of the nation, where summer precipitation becomes increasingly important to sustain population and economic growth. In recent decades, the variability of SE US summer precipitation has significantly intensified, leading to more frequent and severe climate extremes. However, the processes that have caused such enhanced climate variability have been poorly understood. By analyzing atmospheric hydrological cycle, diagnosing atmospheric circulation dynamics, and performing regional climate simulations, this dissertation investigates the mechanisms responsible for SE US summer precipitation variability.

Analysis of regional moisture budget indicates that the variability of SE US summer precipitation is primarily controlled by moisture transport processes associated with the variation of the North Atlantic Subtropical High (NASH) western ridge, while local water recycling is secondary. As the ridge moves northwestward (NW) into the US continent, moisture transport pathway is away from the SE US and the upward motion is depressed. Thus, rainfall decreases over the SE US, leading to dry summers. In contrast, when the ridge moves southwestward (SW), moisture convergence tends to be enhanced over the SE US, facilitating heavier rainfall and causing wetter summers. However, as the ridge is located relatively eastward, its influence on the summer precipitation is weakened. The intensified precipitation variability in recent decades is attributed to the more frequent occurrence of NW- and SW-type ridges, according to the "NASH western ridge - SE US summer precipitation" relationship.

In addition, the "NASH western ridge - SE US summer precipitation" relationship acts as a primary mechanism to determine general circulation model (GCM) and regional climate model (RCM) skill in simulating SE US summer precipitation. Generally, the state-of-the-art GCMs that are capable of representing the abovementioned relationship perform better in simulating the variability of SE US summer precipitation. Similarly, the RCM simulated summer precipitation bias over the SE US is largely caused by the errors in the NASH western ridge circulation, with the physical parameterization playing a secondary role.

Furthermore, the relationship between the NASH western ridge and SE US summer precipitation well explains the projected future precipitation changes. According to the projection by the ensemble of phase-5 of Coupled Model Intercomparison Project (CMIP5) models, summer precipitation over the SE US will become more variable in a warming climate. The enhancement of precipitation variability is due mainly to the atmospheric circulation dynamics, resulting from the pattern shift of the NASH western ridge circulation. In a warming climate, the NASH circulation tends to intensify, which forces its western ridge to extend further westward, exerting stronger impact on the SE US summertime climate. As the ridge extends westward, the NW- and SW-type ridges occur more frequently, resulting in an increased occurrence of extreme summers over the SE US.

In summary, the studies presented in this dissertation identify the NASH western ridge as a primary regulator of SE US summer precipitation at seasonal scale. The "NASH western ridge - SE US summer precipitation" relationship established in this study serves as a first order mechanism for understanding and simulating processes that influence the statistics of extreme events over the SE in the current and future climate.

The hydrologic cycle may be described in essence as the process of water rising and falling in its various phases between land and atmosphere. In this minimal description of the hydrologic cycle two features come into focus: intermittency and irreversibility. In this dissertation intermittency and irreversibility are investigated broadly in the soil-plant-atmosphere system. The theory of intermittency and irreversibility is addressed here in three ways: (1) through its effect on components of the soil-plant-atmosphere system, (2) through development of a measure of the degree of irreversibility in time-series, and (3) by the investigation of the dynamical sources of this intermittency. First, soil infiltration and spring frost risk are treated as two examples of hydrologic intermittency with very different characters and implications for the soil plant system. An investigation of the water budget in simplified soil moisture models reveals that simple bucket models of infiltration perform well against more accurate representation of intra-storm infiltration dynamics in determining the surface water partitioning. Damaging spring frost is presented as a ``biologically-defined extreme event'' and thus as a more subtle form of hydrologic intermittency. This work represents the first theoretical development of a biologically-defined extreme and highlights the importance of the interplay between daily temperature mean and variance in determining the changes in damaging frost risk in a warming climate. Second, a statistical measure of directionality/asymmetry is developed for stationary time-series based on analogies with the theory of nonequilibrium thermodynamics. This measure is then applied to a set of DNA sequences as an example of a discrete sequence with limited state-space. The DNA sequences are found to be statistically asymmetric and further that the local degree of asymmetry is a reliable indicator of the coding/noncoding status of the DNA segment. Third, the phenomenology of rainfall occurrence is compared with canonical examples of dynamical intermittency to determine whether these simple dynamical features may display a dominant signature in rainfall processes. Summer convective rainfall is found to be broadly consistent with Type-III intermittency. Following on this result we studied daytime atmospheric boundary layer dynamics with a view toward developing simplified models that may further elucidate the interaction the interaction between land surface conditions and convective rainfall triggering.

Accurate modeling of land-atmosphere interaction would help us understand the persistent weather conditions and further contribute to the skill of seasonal climate prediction. In this study, seasonal variations in radiation and precipitation forcing are included in a stochastic soil water balance model to explore the seasonal evolution of soil moisture probabilistic structure. The theoretical results show soil moisture tends to exhibit bimodal behavior only in summer when there are strong positive feedback from soil moisture to subsequent rainfall. Besides the statistical analysis of soil moisture – rainfall feedback, simplified mixed-layer models, coupled with soil-plant-atmosphere continuum, are also used to study heat flux partitioning, cloud initiation, and strength of moist convection. Approximate analytical solutions to the mixed-layer model are derived by applying Penman-Monteith approach, which help explain the roles of equilibrium evaporation and vapor pressure deficit in controlling the diurnal evolution of boundary layer. Results from mixed-layer model also define four regimes for possible convection in terms of cloud/no-cloud formation and low/high convection intensity. Finally, cloud-topped mixed-layer model is developed to simulate the boundary-layer dynamics after the cloud formation, when the evaporative and radiative cooling other than surface heat flux may significantly contribute to the growth of the boundary layer.

Long-term exposure to ambient ozone (O3) is associated with a variety of impacts, including adverse health effects in humans (U.S. EPA 2013) and reduced yields in commercial crops (Chameides et al., 1994; Mauzerall and Wang, 2001). Due to such impacts, efforts have been undertaken in recent decades to reduce ground-level O3 through public policy regulating the emission of anthropogenic precursor emissions, such as nitrogen oxides (NOx) and volatile organic compounds (VOCs). These efforts have been widely successful in reducing peak concentrations (Simon et al., 2015; Lefohn et al., 2017; Fleming et al., 2018), but impacts related to both human-health and crop yields nonetheless persist (Cohen et al., 2017; Seltzer et al., 2018; Zhang et al., 2018; Shindell et al., 2019). Of particular importance, the maginute and trend of impacts reported in the literature often feature substantial differences (Seltzer et al., 2018; Zhang et al., 2018; Stanaway et al., 2018). As climate change is anticipated to exacerbate O3 pollution (Leibensperger et al., 2008; Jacob and Winner 2009; Nolte et al., 2018) and the emission of O3 precursors are projected to vary dramatically in both direction and region over the coming decades (Rao et al., 2017), there is a growing need to better constrain the magnitude and trends of, as well as illuminate the reason for the persistent differences in, impacts attributable to long-term O3 exposure. Here, I used a variety of modeling methods to explore the strengths and weaknesses of standard methods that are frequently used to simulate impact metrics related to air quality, generate a measurement-based estimate of the magnitude of O3 exposure and subsequent impacts within several populous regions of the world, and use machine learning to predict the trends in O3 exposure and subsequent impacts within the United States over an extended period.

First, I use the NASA GISS ModelE2 and GEOS-Chem models, each setup in a number of configurations, to simulate the near-present chemistry of the atmosphere and predict a number of impact metrics. Results featured minor differences due to the model resolution, whereas model, meteorology, and emissions inventory each drove large variances. Surface metrics related to O3 were consistently high biased and capturing the change in O3 metrics over time proved difficult, demonstrating the need to evaluate particular modeling frameworks before O3 impacts are quantified. Oftentimes, the configuration that captured the change of a metric best over time differed from the configuration that captured the magnitude of the same metric best, illustrating the difficulty in skillfully simulating and evaluating predicted impacts.

Then, I use data solely from dense ground-based monitoring networks in the United States, Europe, and China for 2015 to estimate long-term O3 exposure and calculate premature respiratory mortality using exposure-response relationships derived from two separate analyses of the American Cancer Society Cancer Prevention Study-II (ACS CPS-II) cohort. Results show that estimated impacts were quite different when using the two cohort analyses, with the analysis using the older ACS CPS-II cohort yielding approximately 32%–50% lower health impacts. In addition, both sets of results are lower (∼20%–60%) on a region-by-region basis than analogous prior studies based solely on CTM predicted O3, due in large part to the fact that the latter tends to be high biased in estimating exposure. I also demonstrate how small biases in modeled results of long-term O3 exposure can amplify health impacts due to nonlinear exposure-response relationships.

Finally, I develop and apply artificial neural networks to empirically model long-term O3 exposure over the continental United States from 2000-2015, generating a 16-year measurement-based assessment of impacts on human-health and crop yields. I again find that the impacts are quite different when using the two ACS CPS-II cohort studies, but I notably also report that the results differ in their trends over the study period due to the seasons included in each averaging metric. When using the older averaging metric and concentration-response function, there was a ~18% decrease in normalized human-health impacts. In contrast, there was little change in the newer averaging metric between 2000-2015, which resulted in a ~5% increase in normalized human-health impacts. In both cases, an aging population structure played a substantial role in modulating these trends. All agriculture-weighted crop-loss metrics indicate yield improvements over this period, with reductions in the estimated national relative yield loss ranging from 1.7-1.9 % for maize, 5.1-7.1% for soybeans, and 2.7% for wheat. Overall, the results from this study illustrate how different conclusions regarding historical impacts can be made through the use of varying metrics.

Global mean surface air temperature (GMST) is one of the most well-known and robust measures of global climate change both contemporarily as well as through deep time. In contemporary climate science, the most often discussed causes of GMST change are referred to as external radiative forcings, which are considered to be exogenous to the land-atmosphere-ocean system and which impose a radiative energy imbalance (N) at the top of the earth’s atmosphere. Examples of external radiative forcings include changes in well-mixed greenhouse gas concentrations, changes in volcanic or anthropogenic aerosol loading, anthropogenic changes in land use, and changes in incoming solar radiation. The climate system can also produce unforced variability in GMST that spontaneously emerges from the internal dynamics of the land-atmosphere-ocean system. Unforced GMST variability can emerge via a vertical redistribution of heat within the climate system. For example, there can be a net transport of energy from below the ocean’s mixed layer to the surface during an El-Niño event. Additionally, unforced GMST variability can be due to an unforced change in N. For example, an internally generated change in the strength of an ocean circulation could alter the extent of sea ice and thus change the Earth’s albedo.

Understanding the magnitude and mechanisms underlying unforced GMST variability is relevant for both the attribution of past climate change to various causes, as well to the prediction of future changes on policy-relevant timescales. However, the literature on unforced GMST variability, particularly at interdecadal and longer timescales, is inconsistent and there is significant disagreement on its magnitude, on its primary geographic origins, and on the physical mechanisms that are most responsible.

This dissertation seeks to advance the scientific understanding of unforced GMST variability by addressing seven primary scientific goals: 1) To identify the geographic locations (and by proxy modes of variability) that are most responsible for unforced GMST variability in both the instrumental record and in climate models. 2) To identify the primary reasons why AOGCMs disagree on the magnitude of interdecadal unforced GMST variability. 3) To quantify the magnitude of unforced GMST variability in observations over the instrumental record as well as in multi-proxy reconstructions over the past millennium. 4) To quantify the degree to which unforced GMST variability is influenced by internally generated N energy imbalances. 5) To understand how anomalous N fluxes can influence large scale modes of surface temperature variability that affect GMST, such as the Atlantic Multidecadal Oscillation (AMO). 6) To understand the nature of the restoring force responsible for returning a perturbed GMST anomaly back to equilibrium; and 7) To understand how the magnitude and mechanisms of GMST variability might change in the future as the climate warms.

This research relies on the analysis of coupled Atmosphere-Ocean general circulation models (AOGCMs) that participated in Phase 5 of the Coupled Model Intercomparison Project (CMIP5), satellite observations of the Earth’s energy budget from the Clouds and Earth’s Radiant Energy System (CERES), instrumental surface temperature observations from NASA GISS Surface Temperature Analysis (GISTEMP), atmospheric reanalysis data from the European Center for Medium-Range Weather Forecasts interim reanalysis (ERA-I) and surface temperature reconstructions over the past millennium from numerous multiproxy archives.

This work has yielded six primary conclusions: I) Dynamics over the tropical Pacific Ocean represent the primary contributor to unforced GMST variability at interdecadal and longer timescales with lesser contributions from dynamics in the subpolar north Atlantic and Southern Ocean. II) AOGCMs tend to underestimate the magnitude of unforced GMST variability at interdecadal and longer timescales relative to both instrumental and reconstructed surface temperature datasets. III) N imbalances can act to significantly enhance interdecadal GMST variability. IV) GMST is able to restore equilibrium after an internally generated perturbation via the transport of energy to high-latitude locations and via the rearrangement the atmospheric circulation; both of which allow for much more efficient release of outgoing longwave radiation (OLR) than would otherwise be expected. V) N imbalances can significantly enhance internal modes of variability such as the AMO; and VI) The magnitude of interdecadal GMST variability is likely to decline and the generating mechanisms of such variability may be fundamentally altered as climate warms over the 21st century. These results advance our understanding of unforced GMST variability and they have implications for attribution studies and may inform projections of climate change on interdecadal timescales.

Light rainfall (< 3 mm/hr) amounts to 30–70% of the annual water budget in the Southern Appalachian Mountains (SAM), a mid-latitude mid-mountain system in the SE CONUS. Topographic complexity favors the diurnal development of regional-scale convergence patterns that provide the moisture source for low-level clouds and fog (LLCF). Low-level moisture and cloud condensation nuclei (CCN) are distributed by ridge-valley circulations favoring LLCF formation that modulate the diurnal cycle of rainfall especially the mid-day peak. The overarching objective of this dissertation is to advance the quantitative understanding of the indirect effect of aerosols on the diurnal cycle of LLCF and warm-season precipitation in mountainous regions generally, and in the SAM in particular, for the purpose of improving the representation of orographic precipitation processes in remote sensing retrievals and physically-based models.

The research approach consists of integrating analysis of in situ observations from long-term observation networks and an intensive field campaign, multi-sensor satellite data, and modeling studies. In the first part of this dissertation, long-term satellite observations are analyzed to characterize the spatial and temporal variability of LLCF and to elucidate the physical basis of the space-time error structure in precipitation retrievals. Significantly underestimated precipitation errors are attributed to variations in low-level rainfall microstructure undetected by satellites. Column model simulations including observed LLCF microphysics demonstrate that seeder-feeder interactions (SFI) among upper-level precipitation and LLCF contribute to an three-fold increase in observed rainfall accumulation and can enhance surface rainfall by up to ten-fold. The second part of this dissertation examines the indirect effect of aerosols on cloud formation and warm-season daytime precipitation in the SAM. A new entraining spectral cloud parcel model was developed and applied to provide the first assessment of aerosol-cloud interactions in the early development of mid-day cumulus congestus over the inner SAM. Leveraging comprehensive measurements from the Integrated Precipitation and Hydrology Experiment (IPHEx) in 2014, model results indicate that simulated spectra with a low value of condensation coefficient (0.01) are in good agreement with IPHEx aircraft observations. Further, to explore sensitivity of warm-season precipitation processes to CCN characteristics, detailed intercomparisons of Weather Research and Forecasting (WRF) model simulations using IPHEx and standard continental CCN spectra were conducted. The simulated CDNC using the local spectrum show better agreement with IPHEx airborne observations and better replicate the widespread low-level cloudiness around mid-day over the inner region. The local spectrum simulation also indicate suppressed early precipitation, enhanced ice processes tied to more vigorous vertical development of individual storm cells. The studied processes here are representative of dominant moist atmospheric processes in complex terrain and cloud forests in the humid tropics and extra-tropics, thus findings from this research in the SAM are transferable to mountainous areas elsewhere.

Environmental sensing is experiencing tremendous development due largely to the advancement of sensor technology and wireless technology/internet that connects them and enable data exchange. Environmental monitoring sensor systems range from satellites that continuously monitor earth surface to miniature wearable devices that track local environment and people's activities. However, transforming these data into knowledge of the underlying physical and/or chemical processes remains a big challenge given the spatial, temporal scale, and heterogeneity of the relevant natural phenomena. This research focuses on the development and application of novel sensing and inference techniques in air and water environments. The overall goal is to infer the state and dynamics of some key environmental variables by building various models: either a sensor system or numerical simulations that capture the physical processes.

This dissertation is divided into five chapters. Chapter 1 introduces the background and motivation of this research. Chapter 2 focuses on the evaluation of different models (physically-based versus empirical) and remote sensing data (multispectral versus hyperspectral) for suspended sediment concentration (SSC) retrieval in shallow water environments. The study site is the Venice lagoon (Italy), where we compare the estimated SSC from various models and datasets against in situ probe measurements. The results showed that the physically-based model provides more robust estimate of SSC compared against empirical models when evaluated using the cross-validation method (leave-one-out). Despite the finer spectral resolution and the choice of optimal combinations of bands, the hyperspectral data is less reliable for SSC retrieval comparing to multispectral data due to its limited amount of historical dataset, information redundancy, and cross-band correlation.

Chapter 3 introduces a multipollutant sensor/sampler system that developed for use on mobile applications including aerostats and unmanned aerial vehicles (UAVs). The system is particularly applicable to open area sources such as forest fires, due to its light weight (3.5 kg), compact size (6.75 L), and internal power supply. The sensor system, termed “Kolibri”, consists of low-cost sensors measuring CO2 and CO, and samplers for particulate matter and volatile organic compounds (VOCs). The Kolibri is controlled by a microcontroller, which can record and transfer data in real time using a radio module. Selection of the sensors was based on laboratory testing for accuracy, response delay and recovery, cross-sensitivity, and precision. The Kolibri was compared against rack-mounted continuous emission monitors (CEMs) and another mobile sampling instrument (the ``Flyer'') that had been used in over ten open area pollutant sampling events. Our results showed that the time series of CO, CO2, and PM2.5 concentrations measured by the Kolibri agreed well with those from the CEMs and the Flyer. The VOC emission factors obtained using the Kolibri are comparable to existing literature values. The Kolibri system can be applied to various open area sampling challenging situations such as fires, lagoons, flares, and landfills.

Chapter 4 evaluates the trade-off between sensor quality and quantity for fenceline monitoring of fugitive emissions. This research is motivated by the new air quality standard that requires continuous monitoring of hazardous air pollutants (HAPs) along the fenceline of oil and gas refineries. Recently, the emergence of low-cost sensors enables the implementation of spatially-dense sensor network that can potentially compensate for the low quality of individual sensors. To quantify sensor inaccuracy and uncertainty of describing gas concentration that is governed by turbulent air flow, a Bayesian approach is applied to probabilistically infer the leak source and strength. Our results show that a dense sensor network can partly compensate for low-sensitivity or high noise of individual sensors. However, the fenceline monitoring approach fails to make an accurate leak detection when sensor/wind bias exists even with a dense sensor network.

Chapter 5 explores the feasibility of applying a mobile sensing approach to estimate fugitive methane emissions in suburban and rural environments. We first compare the mobile approach against a stationary method (OTM33A) proposed by the US EPA using a series of controlled release tests. Analysis shows that the mobile sensing approach can reduce estimation bias and uncertainty compared against the OTM33A method. Then, we apply this mobile sensing approach to quantify fugitive emissions from several ammonia fertilizer plants in rural areas. Significant methane emission was identified from one plant while the other two shows relatively low emissions. Sensitivity analysis of several model parameters shows that the error term in the Bayesian inference is vital for the determination of model uncertainty while others are less influential. Overall, this mobile sensing approach shows promising results for future applications of quantifying fugitive methane emission in suburban and rural environments.

While very important from an economical and societal point of view, estimating precipitation in the western United States remains an unsolved and challenging problem. This is due to difficulties in observing and modeling precipitation in complex terrain. This research examines this issue by (i) providing a systematic evaluation of precipitation observations to quantify data uncertainty and (ii) investigating the ability of the Ocean-Land-Atmosphere Model (OLAM) to simulate the winter hydroclimate in this region. This state-of-the-art, non-hydrostatic model has the capability of simulating simultaneously all scales of motions at various resolutions.

This research intercompares nine precipitation datasets commonly used in hydrometeorological research in two ways. First, using principal component analysis, a precipitation climatology is conducted for the western U.S. from which five unique precipitation climates are identified. From this analysis, data uncertainty is shown to be primarily due to differences in (i) precipitation over the Rocky Mountains, (ii) the eastward wet-to-dry precipitation gradient during the cold season, (iii) the North American Monsoon signal, and (iv) precipitation in the desert southwest during spring and summer. The second intercomparison uses these five precipitation regions to provide location-specific assessments of uncertainty, which is shown to be dependent on season and location.

Long-range weather forecasts on the order of a season are important for water-scarce regions such as the western U.S. The modeling component of this research looks at the ability of the OLAM to simulate the hydroclimate in the western U.S. during the winter of 1999. Six global simulations are run, each with a different spatial resolution over the western U.S. (360 km down to 11 km). For this study, OLAM is configured as for a long-range seasonal hindcast but with observed sea surface temperatures. OLAM precipitation compares well against observations, and is generally within the range of data uncertainty. Observed and simulated synoptic meteorological conditions are examined during the wettest and driest events. OLAM is shown to reproduce the appropriate anomaly fields, which is encouraging since it demonstrates the capability of a global climate model, driven only by SSTs and initial conditions, to represent meteorological features associated with daily precipitation variability.

Quantitative precipitation estimation (QPE) in mountainous regions remains a challenging task owing to its high spatial and temporal variability. Satellite-based radar observations at high resolution have the best potential to capture the spatial patterns of precipitation, but there is high uncertainty in the interpretation of low-level measurements due to ground clutter effects, observing geometry, and sub-grid scale vertical and horizontal heterogeneity of precipitation systems that result from interactions among orographic clouds and propagating storm systems. In the high elevation tropics and in middle mountains everywhere, the landscape is often immersed in multi-layered cloud systems that modify precipitation significantly at low levels in a complex manner depending on time of day and location that is very different from the classical understanding of orographic precipitation enhancement with elevation, and are not easily parameterized or corrected for in QPE algorithms. The overall objective of this proposal is to characterize and elucidate the physical basis of uncertainty in Global Precipitation Measurement Mission (GPM) Dual-frequency Precipitation Radar (DPR) QPE in mountainous regions and develop an improved retrieval framework for orographic precipitation. The following science objectives will be addressed specifically: i) to characterize the dependencies among the spatial and temporal variability of errors in orographic QPE and associated hydrometeorological regimes; ii) to characterize the vertical structure of radar reflectivity associated with QPE retrieval error and establish a physics-based retrieval model; and iii) to develop an operational framework to integrate DPR observations and Numerical Weather Prediction (NWP) model toward improving the retrieval of orographic QPE. The research hypothesis are two folds: a) satellite QPE errors (false alarms, missed detections, underestimations and overestimations) exhibit a robust spatial and temporal organization that is explained by the spatial and temporal variability in the vertical microstructure of precipitation; and b) current satellite-based QPE algorithms fail because the vertical structure of precipitation cannot be detected. If the vertical structure of precipitation systems can be predicted, then the key microphysical processes can be modelled to improve QPE. The study will be conducted using observations from the Southern Appalachian Mountains (SAM) region including a high-density rain gauge network, Micro Rain Radars (MRR), and Parsivel disdrometers since the launch of GPM as well as Integrated Precipitation and Hydrology Experiment (IPHEx) 2014 data.

This research approach consists of integrating ground-based point measurements from long-term observation networks, fields campaign (IPHEx), multi-satellite data, and modeling studies to develop a physically-based retrieval framework for orographic precipitation. To characterize the dependencies among the spatial and temporal variability of errors in orographic QPE, GPM estimations were evaluated using the ground-based precipitation observations and investigated for the robust organization of uncertainty. The physics-based retrieval of near-surface rain-rates was demonstrated through explicit modeling of rain shaft microphysical processes constrained by GPM-DPR observations. Finally, a general data-driven operational framework was developed to improve the detection and to predict the vertical structure of precipitation systems by integrating GPM observations with NWP model simulations.

Lightning is one of the most familiar, impressive, but catastrophic natural phenomena that occur commonly on Earth. It produces perhaps the loudest sound, the most broadband radio emission, and the brightest light in the atmosphere. However, lightning remains relatively poorly understood since it is so transient (usually < 1 second) and so unpredictable that hinders direct measurements inside thunderstorms. For these reasons, radio remote sensing has been widely used for lightning studies. With recent advances in instrumentation and remote sensing technique, some basic problems like how lightning initiates inside the thundercloud begin to be addressed, and new challenging scientific problems are being discovered, such as the Terrestrial Gamma-ray Flashes (TGFs, energy > 20 MeV) associated with lightning, photonuclear reactions triggered by lightning, and needle-like plasma structures on the positive lightning leader, connecting lightning as part of atmospheric physics to high-energy physics, plasma physics, etc.

This dissertation aims to address fundamental questions like how lightning initiates and propagates, and how are TGFs related to lightning processes, by applying state-of-the-art radio remote sensing and imaging techniques. We measure and analyze electromagnetic signals produced by lightning from the vicinity to more than a thousand miles away, at radio frequencies from VLF, LF, to VHF and UHF. First, we investigated LF/VLF lightning sferics at the time of TGFs and found a statistically consistent connection between a slow LF pulse (~80 $\mu s$ duration) and TGFs, suggesting that the radio pulse is produced directly by the TGF production process. Second, in light of the slow pulse-TGF connection, we discovered a new type of downward CG-TGF with a reverse positron beam detected by Fermi GBM on the orbit, which could constitute 5--10 % of the previously known TGF population. Third, we employed supervised and unsupervised machine learning approaches to classify energetic lightning radio pulses for unprecedented ground detection of TGFs as well as understanding lightning sferics and ionospheric effects. In the meanwhile, we developed a short-baseline VHF interferometer with 200 MHz bandwidth to image lightning channels in high spatiotemporal resolution, shedding new insights into needles and lightning leader dynamics. Last but not least, we demonstrated and applied a new approach to indirectly measuring electric fields in the discharge region during lightning initiation and positive leader propagation using VHF-UHF radio spectrum, enabling an entirely new and useful capability for probing the ambient condition during lightning discharge processes. Implications of the estimated electric fields for lightning physics and high-energy physics are discussed.