Browsing by Author "Kummerow, Christian, advisor"
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Item Open Access Assessing the state-dependency of infrared satellite precipitation errors(Colorado State University. Libraries, 2022) Goldenstern, Eric, author; Kummerow, Christian, advisor; Chiu, Christine, committee member; Ebert-Uphoff, Imme, committee memberThe sensing and prediction of precipitation remains at the forefront of weather forecasting, building upon centuries of measurement and study. While in-situ and ground-based methodologies such as rain gauges and weather radars provide the best assessments of precipitation, they are prone to sampling issues and coverage gaps both over challenging terrain and in developing areas of the world. As a result, the use of remote sensing methodologies, namely satellites, have allowed for the expansion of precipitation measurement to encompass nearly the entire Earth. However, unlike rain gauges, satellites are incapable of directly sensing precipitation; rather, they must infer it from the spectral information that can be captured from space through a mathematical framework known as a retrieval. While satellite precipitation retrievals are a boon to the meteorological community due to their ability to fill in these coverage gaps, their indirect nature inevitably gives rise to errors in the measurements themselves. Furthermore, these errors have historically been specific to their training area and are not directly comparable to the errors in other areas. Therefore, this thesis aims to begin disentangling these errors into more generalizable metrics through known information about the measurements themselves and the environmental state being observed. To do this, a neural-network style retrieval algorithm was developed using infrared and lightning data from the Geostationary Operational Environmental Satellite – 16 (GOES-16) to create a validation statistics study. The error from this retrieval, selected to be its bias statistic, was then analyzed both in the context of the satellite data and ancillary meteorological data. From these analyses, it was shown that an understanding of the satellite data allows for limited reproducibility of the retrieval bias tendencies across multiple areas of study, and that ancillary environmental information can shed additional light on how these errors are influenced by the underlying meteorological state. Though this thesis does not create an exact, quantitative methodology for such an assessment, it does provide a direction in which a framework can be established to predict precipitation uncertainties for a more global perspective.Item Unknown Cloud process information from a fleet of small satellites: synthetic retrievals using an optimal estimation algorithm(Colorado State University. Libraries, 2018) Schulte, Richard M., author; Kummerow, Christian, advisor; Bell, Michael, committee member; Reising, Steven, committee memberThe great importance of clouds in understanding atmospheric phenomena is widely recognized, yet faithful representations of cloud and precipitation processes in models at nearly all scales remain elusive. In order to properly constrain model parameters, it is important to obtain reliable observations of cloud properties in varying atmospheric environments. The Temporal Experiment for Storms and Tropical Systems (TEMPEST) mission was proposed to help address this need by deploying a cluster of CubeSats, each containing an identical, five-frequency passive microwave radiometer, into the same orbit. Doing so would allow for the observation of cloud processes at a high temporal resolution and on a global scale. In order for such a mission to be useful in understanding cloud processes, it is crucial to develop a retrieval algorithm that can distinguish true changes in the atmospheric state from the noise induced by making repeated observations only a few minutes apart at different view angles. To this end, a physical optimal estimation algorithm is developed for the retrieval of water vapor, cloud water, and frozen hydrometeors from cross-track microwave sounders such as the TEMPEST radiometer. The performance of the algorithm is assessed by using high resolution Weather Research and Forecasting (WRF) model output to generate synthetic radiometer observations, while incorporating realistic error estimates, and then comparing the parameters retrieved using the synthetic observations to the actual model parameters. For rapidly changing clouds, differences in parameters retrieved at various view angles, while not trivial, are small enough that changes in cloud properties can be discerned. This is especially true for view angles near nadir, where the field of view is smaller and changes less rapidly with time. Experiments simulating a cluster of TEMPEST instruments successively observing the same cloud system suggest that using the higher-quality retrievals near nadir to constrain preceding and subsequent observations allows for cloud changes to be observed more clearly. An analysis of the contribution of various forward model errors indicates that incorporating more accurate a-priori information about wind speed, cloud coverage, and cloud heights, perhaps obtained from coincident measurements by other spaceborne instruments, would further constrain the retrieval and mitigate some of the view angle induced biases.Item Open Access Consistency in the AMSR-E snow products: groundwork for a coupled snowfall and SWE algorithm(Colorado State University. Libraries, 2019) Gonzalez, Ryan L., author; Kummerow, Christian, advisor; Liston, Glen, committee member; Chiu, Christine, committee member; Notaros, Branislav, committee memberSnow is an important wintertime property because it is a source of freshwater, regulates land-atmosphere exchanges, and increases the surface albedo of snow-covered regions. Unfortunately, in-situ observations of both snowfall and snow water equivalent (SWE) are globally sparse and point measurements are not representative of the surrounding area, especially in mountainous regions. The total amount of land covered by snow, which is climatologically important, is fairly straightforward to measure using satellite remote sensing. The total SWE is hydrologically more useful, but significantly more difficult to measure. Accurately measuring snowfall and SWE is an important first step toward a better understanding of the impacts snow has for hydrological and climatological purposes. Satellite passive microwave retrievals of snow offer potential due to consistent overpasses and the capability to make measurements during the day, night, and cloudy conditions. However, passive microwave snow retrievals are less mature than precipitation retrievals and have been an ongoing area of research. Exacerbating the problem, communities that remotely sense snowfall and SWE from passive microwave sensors have historically operated independently while the accuracy of the products has suffered because of the physical and radiometric dependency between the two. In this study, we assessed the relationship between the Northern Hemisphere snowfall and SWE products from the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E). This assessment provides insight into regimes that can be used as a starting point for future improvements using coupled snowfall and SWE algorithm. SnowModel, a physically-based snow evolution modeling system driven by the Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA-2) reanalysis, was employed to consistently compare snowfall and SWE by accounting for snow evolution. SnowModel has the ability to assimilate observed SWE values to scale the amount of snow that must have fallen to match the observed SWE. Assimilation was performed using AMSR-E, Canadian Meteorological Centre (CMC) Snow Analysis, and Snow Data Assimilation System (SNODAS) SWE to infer the required snowfall for each dataset. Observed AMSR-E snowfall and SWE were then compared to the MERRA-2 snowfall and SnowModel-produced SWE as well as SNODAS and CMC inferred snowfall and observed SWE. Results from the study showed significantly different snowfall and SWE bias patterns observed by AMSR-E. Specifically, snowfall was underestimated nearly globally and SWE had pronounced regions of over and underestimation. Snowfall and SWE biases were found to differ as a function of surface temperature, snow class, and elevation.Item Open Access Evaluation of inter-annual variability and trends of cloud liquid water path in climate models using a multi-decadal record of passive microwave observations(Colorado State University. Libraries, 2016) Manaster, Andrew, author; Kummerow, Christian, advisor; O'Dell, Christopher W., advisor; Randall, David, committee member; Reising, Steven, committee memberLong term satellite records of cloud changes have only been available for the past several decades and have just recently been used to diagnose cloud-climate feedbacks. However, due to issues with satellite drift, calibration, and other artifacts, the validity of these cloud changes has been called into question. It is therefore pertinent that we look for other observational datasets that can help to diagnose changes in variables relevant to cloud-radiation feedbacks. One such dataset is the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP), which blends cloud liquid water path (LWP) observations from 12 different passive microwave sensors over the past 27 years. In this study, observed LWP trends from the MAC-LWP dataset are compared to LWP trends from 16 models in the Coupled Model Intercomparison Project 5 (CMIP5) in order to assess how well the models capture these trends and thus related radiative forcing variables (e.g., cloud radiative forcing). Mean state values of observed LWP are compared to those of previous observed climatologies and are found to have relatively good quantitative and qualitative agreements. Mean state observed LWP variables are compared both qualitatively and quantitatively to our suite of CMIP5 models. These models tend to capture mean state and mean seasonal cycle LWP features, but the magnitudes exhibit large variations from model to model. Several metrics were used to compare observed mean state LWP and mean seasonal cycle amplitude and the mean state LWP and mean seasonal cycle amplitude in each model. However, the models' performance in regards to these metrics is found to not be indicative of their abilities to accurately reproduce trends on a regional or global scale. Global trends in the observations and the model means are compared. It is found that observational trends are roughly 2-3 times larger in magnitude in most regions globally when compared to the model mean although this is thought to be at least partly caused by cancellation effects due to differing inter-annual variability and physics between models. Several regions (e.g., the Southern Ocean) have consistent signs in trends between the observations and the model mean while others do not due to spatial inconsistencies in certain trend features in the model mean relative to the observations. Trends are examined in individual regions. In four of the six regions analyzed, the observational trends are statistically different from zero, while, in most regions, very few models have trends that are statistically significant. In certain regions, the majority of modeled trends are statistically consistent with the observed trends although this is typically due to large estimated errors in the observations and/or models, most likely caused by large inter-annual variability. The Southern Ocean and globally averaged trends show the strongest similarities to the observed trends. Almost all Southern Ocean trends are robustly positive and statistically significant with the majority of models being statistically consistent with the observations. Similarly, the observed and global trends are all positive with the majority being statistically significant and statistically consistent. We discuss why a large positive Southern Ocean trend is unlikely to be due to a trend in cloud phase. CMIP5 model mean and observational LWP trends are compared regionally to Atmospheric Model Intercomparison Project (AMIP) and ERA-interim reanalysis trends. It is found that AMIP model mean and ERA LWPs are better than the CMIP5 model mean at capturing the inter-annual variability in the observed time series in most of the regions examined. The AMIP model mean better replicates the observed trends when the inter-annual variability is better captured. The ERA reanalysis tends to better reproduce the observed inter-annual variability when compared to the AMIP model mean in almost every region, but, surprisingly, it is either worse or roughly the same in regards to matching observed trends. Our results suggest that observed trends are due to a combination of inter-annual and decadal-scale internal variability, in addition to external forced trends due to anthropogenic influences on the climate system. With a record spanning three decades, many modeled trends are statistically consistent with the observed trends, but a true climatically forced signal is not yet apparent in the models that agrees with the observations. The primary exception to this is in the Southern Ocean, where virtually all models and observations indicate an increasing amount of cloud liquid water path.Item Unknown Evaluation of OCO-2 small-scale XCO2 variability using lidar retrievals from the ACT-America flight campaign(Colorado State University. Libraries, 2018) Bell, Emily, author; Kummerow, Christian, advisor; O'Dell, Christopher, advisor; Denning, Scott, committee member; Cooley, Daniel, committee memberWith eight 1.25 x 3 kilometer footprints across its swath and nearly 1 million observations of column-mean carbon dioxide concentration (XCO2) per day, the Orbiting Carbon Observatory (OCO-2) presents exciting possibilities for monitoring the global carbon cycle, including the detection of small-scale column CO2 variations. While the global OCO-2 dataset has been shown to be quite robust, and case studies have shown successful observation of CO2 plumes from power plants and cities, the validation of XCO2 gradients on small spatial scales remains challenging: ground-based measurements, while extremely precise, are sparsely scattered and often geographically stationary. In this work, we investigate the use of an integrated path differential absorption (IPDA) lidar as a source for OCO-2 small-scale validation. As part of NASA's ACT-America project, several campaigns over North America have included a number of direct underflights of OCO-2 tracks with the Multi-Functional Fiber Laser Lidar (MFLL), as well as a set of in situ instruments, to provide a precisely collocated, high-resolution validation dataset. We explore the challenges involved in comparing the MFLL and OCO-2 datasets, from instrument principles to retrieval differences, and develop a method of correcting for some of these differences. After nine underflights, a combination of lidar data and a novel in situ-derived CO2 "curtain" have helped us to identify systematic spurious small-scale features in the OCO-2 dataset due to both surface and cloud effects. We show that though real XCO2 features on scales of tens of kilometers remain challenging to observe and validate, the lidar and OCO-2 generally have comparable spatial gradients on synoptic scales.Item Unknown Features based assessments of warm season convective precipitation forecasts from the high resolution rapid refresh model(Colorado State University. Libraries, 2017) Bytheway, Janice L., author; Kummerow, Christian, advisor; Schumacher, Russ, committee member; Randall, David, committee member; Chandrasekar, V., committee member; Alexander, Curtis, committee memberForecast models have seen vast improvements in recent years, via increased spatial and temporal resolution, rapid updating, assimilation of more observational data, and continued development and improvement of the representation of the atmosphere. One such model is the High Resolution Rapid Refresh (HRRR) model, a 3 km, hourly-updated, convection-allowing model that has been in development since 2010 and running operationally over the contiguous US since 2014. In 2013, the HRRR became the only US model to assimilate radar reflectivity via diabatic assimilation, a process in which the observed reflectivity is used to induce a latent heating perturbation in the model initial state in order to produce precipitation in those areas where it is indicated by the radar. In order to support the continued development and improvement of the HRRR model with regard to forecasts of convective precipitation, the concept of an assessment is introduced. The assessment process aims to connect model output with observations by first validating model performance then attempting to connect that performance to model assumptions, parameterizations and processes to identify areas for improvement. Observations from remote sensing platforms such as radar and satellite can provide valuable information about three-dimensional storm structure and microphysical properties for use in the assessment, including estimates of surface rainfall, hydrometeor types and size distributions, and column moisture content. A features-based methodology is used to identify warm season convective precipitating objects in the 2013, 2014, and 2015 versions of HRRR precipitation forecasts, Stage IV multisensor precipitation products, and Global Precipitation Measurement (GPM) core satellite observations. Quantitative precipitation forecasts (QPFs) are evaluated for biases in hourly rainfall intensity, total rainfall, and areal coverage in both the US Central Plains (29-49N, 85-105W) and US Mountain West (29-49N, 105-125W). Features identified in the model and Stage IV were tracked through time in order to evaluate forecasts through several hours of the forecast period. The 2013 version of the model was found to produce significantly stronger convective storms than observed, with a slight southerly displacement from the observed storms during the peak hours of convective activity (17-00 UTC). This version of the model also displayed a strong relationship between atmospheric water vapor content and cloud thickness over the central plains. In the 2014 and 2015 versions of the model, storms in the western US were found to be smaller and weaker than the observed, and satellite products (brightness temperatures and reflectivities) simulated using model output indicated that many of the forecast storms contained too much ice above the freezing level. Model upgrades intended to decrease the biases seen in early versions include changes to the reflectivity assimilation, the addition of sub-grid scale cloud parameterizations, changes to the representation of surface processes and the addition of aerosol processes to the microphysics. The effects of these changes are evident in each successive version of the model, with reduced biases in intensity, elimination of the southerly bias, and improved representation of the onset of convection.Item Open Access On modeled and observed warm rainfall occurrence and its relationships with cloud macrophysical properties(Colorado State University. Libraries, 2014) King, Joshua Matthew, author; Kummerow, Christian, advisor; van den Heever, Susan, advisor; Notaros, Branislav, committee memberRainfall from low-level, liquid-phase ("warm") clouds over the global oceans is ubiquitous and contributes non-negligibly to the total amount of precipitation that falls to the globe. In this study, modeled and observed warm rainfall occurrence and its bulk statistical relationships with cloud macrophysical properties are analyzed independently and directly compared with one another. Rain is found to fall from ~25% of the warm, maritime clouds observed from space by CloudSat and from ~27% of the warm clouds simulated within a large-scale, fine-resolution radiative convective equilibrium experiment performed with the Regional Atmospheric Modeling System (RAMS). Within both the model and the observations, the fractional occurrence of warm rainfall is found to increase with both column-integrated liquid water mass and cloud geometric depth, two cloud-scale properties that are shown to be directly related to one another. However, warm rain within RAMS is more likely with lower amounts of column water mass than observations indicate, suggesting that the parameterized cloud-to-rain conversion processes within RAMS produce rainfall too efficiently. To gain insight into the relationships between warm rainfall production and the concentration of liquid water within a cloud layer, warm rainfall occurrence is subsequently investigated as a joint, simultaneous function of both cloud depth and column-integrated water mass. While rainfall production within RAMS is largely governed by the availability of liquid water within the cloud volume, rain from observed warm clouds with relatively little column water mass is actually more likely to fall from deeper clouds with lower cloud-mean water contents. The latter, CloudSat-derived trend is shown to be robust across different seasons and environmental conditions; it varies little when the warm cloud distribution is stratified into ascending (day) and descending (night) CloudSat overpass groups. Using temperature differences between RAMS cloud tops and their immediate, surrounding environments as a proxy for cloud-top buoyancy, an attempt is then made to quantitatively investigate simulated warm rain occurrence within the broader context of cloud life cycle. It is found that rainfall likelihoods from RAMS-simulated warm clouds with cloud top temperatures warmer than their surrounding environments more closely resemble the overall CloudSat-derived rainfall occurrence trends. This result suggests that the CloudSat-observed warm cloud distribution is characterized by increased numbers of positively buoyant, developing clouds.Item Open Access Regional aerosol effects on precipitation: an observational study(Colorado State University. Libraries, 2011) Boyd, Kathryn J., author; Kummerow, Christian, advisor; van den Heever, Susan, committee member; Reising, Steven, committee memberThere have been a multitude of studies on the effects increased amounts of aerosols may have on clouds. The connection between increased cloud condensation nuclei (CCN) and cloud microphysics has been established by in situ observations as well as modeling studies. However, the impact on precipitation is less well established. Of the studies that have assessed aerosol effects on precipitation most have been limited to modeling studies or global studies using satellite data. The few observational studies that have examined these relationships have been mainly limited to data from short-lived field campaign, such as oceanic stratocumulus decks or biomass burning areas. This study attempts to examine regional aerosol effects on precipitation in areas not previously examined in field campaigns, using data from two different sites, one from an Atmospheric Radiation Measurement (ARM) Program permanent facility in Oklahoma and the other from a mobile facility located in the Azores. These two sites were chosen in order to illustrate the differences between a marine and a continental location. Meteorological conditions were taken into account in both locations through surface and sounding data and trends in precipitation were found with increasing aerosol concentrations. The marine site witnessed a suppression of precipitation, consistent with past studies and proposed theories of aerosol effects. This was not true for clouds with liquid water paths exceeding 200g/m2. These clouds appear to contain sufficient amounts of water to overcome the aerosol effect. The continental site, however, experienced an opposite trend, with enhancement of precipitation witnessed in all clouds examined in this study. This is thought to be due to a buffering mechanism in these types of clouds, as introduced by Stevens and Feingold (2009). Results were separated by season and cloud type using the horizontal variability of radar reflectivity at cloud top height. The seasonal results generally either were in line with the year round results or were too noisy to interpret. The results separated by cloud type give a concrete result, illustrating the fact that differing cloud dynamics may lead to opposing trends in precipitation with increasing aerosols. Competing effects of aerosols within clouds appear to dampen any effect on precipitation to the point that it is not detectable from the in-situ observations considered here.Item Open Access The changing nature of convection over Earth's tropical oceans from a water budget perspective(Colorado State University. Libraries, 2023) Leitmann-Niimi, Nicolas, author; Kummerow, Christian, advisor; Maloney, Eric, committee member; Arabi, Mazdak, committee memberConsistent spatiotemporal hydrologic measurements over Earth's oceans are only feasible with satellite remote sensing. The water budget components of an atmospheric column are precipitation (P), evaporation (E) and horizontal water vapor divergence (divQ). Physically, the sum of these components leaves a residual term: the amount of water vapor stored inside the atmospheric column. When time series of the water budget components are made using independent data products, this residual term is unphysical, which must be a result of measurement error in one or more of the water variables. This study finds that variations in lack of closure are not random, and seeks to reveal underlying sources for long-term, high amplitude trends so that errors in observations may be better understood as the climate system evolves and assumptions built into the algorithms today may bias results into the future. Trends in the residual are particularly significant over the Tropical West Pacific (TWP), Southern Tropical East Indian (STEI) and Tropical Central Pacific (TCP), where there are multi-year residual trends that maintain a consistent magnitude of 1 mm/day. While there are still residual discrepancies over the Tropical Western Indian (TWI), Tropical Eastern Pacific (TEP) and Tropical Atlantic Ocean (TAO), closure is overall better, as residual trends are more annual in variation and less unphysical in magnitude. This study hypothesizes that the first-order explanation for potential long-term biases lies in shifting convective organization. Convective organization changes are quantified using the amount of rain explained by three different regimes of convection (shallow, deep isolated and deep organized), which are dubbed convective rain states (CRS). A second-order explanation lies in relative ice amount. Relative ice amount is represented by ice-rain ratio (IRR), the amount of ice per amount of rain present in the atmospheric volume as determined from spaceborne radars. Changes in CRS can cause biases because rainfall spatial correlations related to well-known errors (e.g. beam-filling, convective/stratiform microphysics) are likely responsible for over-and underestimation of precipitation, while changes in the relative ice amount in individual convective rain states can cause the precipitation to be under or over-estimated due to scattering effects. Over the TCP these changes are purely dictated by the El-Nino Southern Oscillation (ENSO), with organization becoming a clear function of SST. Over the STEI there is a circulation that stems from the Indian Ocean Dipole (IOD), leading to a CRS and IRR dependence on vertical wind shear. Finally, over the TWP, CRS is neither a simple function of SST nor shear, but rather seems to arise from a deeper ocean change of state: a coupling/decoupling of west Pacific SST with central and east Pacific SSTs that coincides with the global warming hiatus period. Two different mechanisms may be at play when it comes to shifts in convective organization during this period. Outside of 2001-2007, the regular mechanism sees low-level shear directing convective organization. During 2001-2007, TWP SSTs are much warmer than surrounding SSTs, leading to an anomalous mechanism that sees water vapor convergence and atmospheric instability favoring isolated convection. The purpose of this study is not to locate specific algorithm retrieval or model deficiencies, but rather to connect long-term measurement errors with fundamental changes in dynamic characteristics of ocean environments. While these proposed mechanisms would explain much of the observed biases, this study cannot address quantitative biases, as these depend on algorithm details and vary from one algorithm to another – although the qualitative trends are consistent among them.Item Unknown The hydrometeorological sustainability of Miscanthus × giganteus as a biofuel crop in the US Midwest(Colorado State University. Libraries, 2016) Roy, Gavin R., author; Kummerow, Christian, advisor; Randall, David, committee member; Barnes, Elizabeth, committee member; Niemann, Jeffrey, committee member; Peters-Lidard, Christa, committee memberMiscanthus × giganteus (M. × giganteus) is a dense, 3-5 m tall, productive perennial grass that has been suggested to replace corn as the principal source of biofuel for the US transportation industry. However, cultivating a regime of this water-intensive rhizomatous crop across the US Midwest may not be agronomically realistic if it is unable to survive years of low precipitation or extreme cold wintertime soil temperatures, both of which have previously killed experimental crops. The goal of this research was to use a third-generation land surface model (LSM) to provide a new assessment of the hypothetical biogeophysical sustainability of a regime of M. × giganteus across the US Midwest given that, for the first time, a robust and near-complete dataset over a large area of mature M. × giganteus was available for model validation. Modifications to the local hydrology and microclimate would necessarily occur in areas where M. × giganteus is adapted, but a switch to this biofuel crop can only occur where its intense growing season water usage (up to 600 mm) and wintertime soil temperature requirements (no less than -6° C) are feasibly sustainable without irrigation. The first step was to interpret the observed turbulent and ecosystem flux behavior over an extant area of mature M. × giganteus and replicate this behavior within the SiB3 third-generation LSM (Simple Biosphere Model, version 3). A new vegetation parameterization was developed in SiB3 using several previous empirical studies of M. × giganteus as a foundation. The simulation results were validated against a new, robust series of turbulent and ecosystem flux data taken over a four-hectare experimental crop of M. × giganteus in Champaign, IL, USA from 2011-2013. Wintertime mortality of M. × giganteus was subsequently assessed. It was proposed that areas with higher seasonal snowfall in the US Midwest may be favorable for M. × giganteus sustainability and expansion due to the significant insulating effect of snow cover. Observations of snow cover and air and soil temperatures from small experimental plots of M. × giganteus in Illinois, Wisconsin, and the lake effect snowbelt of southern Michigan were analyzed during several anomalously cold winters. While a large insulating effect was observed, shallow soil temperatures were still observed to drop below laboratory mortality temperature thresholds of M. × giganteus during periods of snow cover. Despite this, M. × giganteus often survived these low temperatures, and it is hypothesized that the rate of soil temperature decrease might play a role in wintertime rhizome survival. The domain was expanded in SiB3 to cover the US Midwest, and areas defined as cropland were replaced with the developed M. × giganteus surface parameterization. A 14-year uncoupled simulation was carried out and compared to an unmodified simulation in order to gauge the first-order hydrometeorological sustainability of a large-scale M. × giganteus regime in this area in terms of simulated productivity, evapotranspiration, soil water content, and wintertime cold soil temperature. It was found that M. × giganteus was biogeophysically sustainable and productive in a relatively small portion of the domain in southern Indiana and Ohio, consistent with a small set of previous studies and ultimately in disagreement with the theory that M. × giganteus could reliably replace corn in areas such as Illinois and Iowa as a profitable and sustainable biofuel crop.Item Open Access Trends in regional atmospheric water cycles across ocean basins diagnosed using multiple products(Colorado State University. Libraries, 2021) Koeritzer, Drew W., author; Kummerow, Christian, advisor; Chiu, Christine, committee member; Niemann, Jeffrey, committee memberThe importance of water within the earth system, especially its direct impacts on weather and climate through its presence and transport in the atmosphere, cannot be overstated. Accordingly, it is critical to obtain an accurate baseline understanding of the current state of the atmospheric branch of the water cycle if we are to infer future changes to the water cycle and associated influences on weather and climate. Technological advances in both remote and in-situ observing systems have made it possible to characterize water and energy budgets on global scales. However, relatively little work has been done to study the degree of closure, and thus the accuracy of these methods, at regional scales – especially over the oceans. This is a task complicated by the lack of long-term continuous data records of the variables of interest, including ocean surface evaporation, atmospheric water vapor flux divergence, and precipitation. This work aims to fill these gaps and contribute to the establishment of a baseline understanding of the water cycle within the current TRMM and GPM era. The evolution of water cycle closure within five independent regions in the equatorial Pacific, Atlantic, and Indian Oceans has been established previously using atmospheric reanalysis and gridded observational and pseudo-observational data products. That research found that while the water budgets closed extremely well in most basins, the water cycle within the West Pacific was found to trend out of closure within the first decade of the 21st century. The current study aims to extend this analysis temporally, in addition to including a wider variety of independent data sources to confirm the presence of this emerging lack of closure and hypothesize the reason for its existence. Differences between independent products are used within the context of each region to infer whether the emerging lack of closure is a data artifact or is a result of a more fundamental shift in the physical mechanisms and characteristics of the evaporation, precipitation, or water vapor flux divergence within a specific region. Results confirm an initial hypothesis that the emerging lack of water cycle closure in the West Pacific is not due to satellite or instrument drift. Rather, it appears to be related to changes in the prevalence of deep isolated versus deep organized convection in the West Pacific region and its associated impact on passive microwave precipitation retrieval algorithms.Item Open Access Turning night into day: the creation, validation, and application of synthetic lunar reflectance values from the day-night band and infrared sensors for use with JPSS VIIRS and GOES ABI(Colorado State University. Libraries, 2023) Pasillas, Chandra M., author; Kummerow, Christian, advisor; Bell, Michael, advisor; Miller, Steven D, committee member; Rasmussen, Kristen, committee member; Reising, Steven, committee memberInvestigation of the dynamics of tropical cyclone precipitation structure using radar observations and numerical modeling Satellite remote sensing revolutionized weather forecasting and observing in the 1960s providing a true bird's eye view of the weather beyond what could be achieved from balloon and aircraft reconnaissance. With advances in observing systems came the desire for more capabilities and a better understanding of the Earth system, leading to rapid increases in satellite imaging capabilities. The most popular imager products come from solar reflective radiation in the form of visible imagery as they are the most intuitive to users. Similar benefits were later made possible by equivalent nighttime imagery; first available through the operational lines can system (OLS) and then the Day/Night Band (DNB), but these sensors have limited revisit time due to their low Earth orbits. A day-night band sensor in geostationary orbit would greatly enhance the utility of this measurement for now casters, but it does not exist. Work towards a pseudo-nighttime visible imagery to fill this gap has been done with varying results (Chirokova et al., 2018; Kim et al., 2019; Kim and Hong, 2019; Mohandoss et al., 2020; Harder et al., 2020). This dissertation demonstrates the creation and implementation of a machine learning model to turn night into day by transforming satellite radiance observations into representative full moon lunar reflectance values that provide quantifiable metrics and visible-like imagery to its users. In Chapter 2, a method is described that utilizes a feed-forward neural network model to replicate DNB lunar reflectance using brightness temperatures and brightness temperature differences in the short and long-wave infrared (IR) spectrum as the primary input. The goal was to improve upon the performance of the DNB during new moon periods, and lay the foundation for transitioning the algorithm to the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI). Results from this method are the first to quantitatively validate low-light visible nighttime imagery with lunar reflectance calculated from DNB radiances. This work further demonstrated that there is a relationship between full moon lunar reflectance and IR that can be captured to create imagery that is visually consistent across the full lunar cycle regardless of moon phase and angle. In Chapter 3, the machine learning (ML) nighttime visible imagery (NVI) model is applied to the GOES ABI utilizing wavelength relationships and satellite inter-calibrations information. This demonstrates that a model trained and validated on VIIRS polar orbiting imagery can work on sensors aboard geostationary satellites. It also confirms why the 10.3μm channel is the preferred substitution for the 10.7μm centered band over the 11.2μm channel. Furthermore, it demonstrates that lunar reflectance derived from IR can be replicated across sensors with similar spectral response functions providing enhanced geographic and temporal resolution that is not possible on the JPSS platforms. The final section of the dissertation transitions into forecaster applications by examining case studies concerning tropical cyclones and fog in greater detail. Focused on low cloud detection, NVI provides additional information not possible from IR and current analysis products available. It can detect tropical cyclone low-level circulations through cirrus cloud and identify fog extent more readily. The findings in this doctoral study will advance remote sensing of clouds at night, further reducing weather now-casting errors and increasing weather-related safety. In Chapter 2, a method is described that utilizes a feed-forward neural network model to replicate DNB lunar reflectance using brightness temperatures and brightness temperature differences in the short and long-wave infrared (IR) spectrum as the primary input. The goal was to improve upon the performance of the DNB during new moon periods, and lay the foundation for transitioning the algorithm to the Geostationary Operational Environmental Satellite (GOES) Advanced Baseline Imager (ABI). Results from this method are the first to quantitatively validate low-light visible nighttime imagery with lunar reflectance calculated from DNB radiances. This work further demonstrated that there is a relationship between full moon lunar reflectance and IR that can be captured to create imagery that is visually consistent across the full lunar cycle regardless of moon phase and angle. In Chapter 3, the machine learning (ML) nighttime visible imagery (NVI) model is applied to the GOES ABI utilizing wavelength relationships and satellite inter-calibrations information. This demonstrates that a model trained and validated on VIIRS polar orbiting imagery can work on sensors aboard geostationary satellites. It also confirms why the 10.3 µm channel is the preferred substitution for the 10.7 µm centered band over the 11.2 µm channel. Furthermore, it demonstrates that lunar reflectance derived from IR can be replicated across sensors with similar spectral response functions providing enhanced geographic and temporal resolution that is not possible on the JPSS platforms. The final section of the dissertation transitions into forecaster applications by examining case studies concerning tropical cyclones and fog in greater detail. Focused on low cloud detection, NVI provides additional information not possible from IR and current analysis products available. It can detect tropical cyclone low-level circulations through cirrus cloud and identify fog extent more readily. The findings in this doctoral study will advance remote sensing of clouds at night, further reducing weather now-casting errors and increasing weather-related safety.