Browsing by Author "Bell, Michael, committee member"
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Item Open Access Aggregator-based residential demand response applications and carbon tax imposition on fossil-fuel generators(Colorado State University. Libraries, 2020) Algarni, Abdullah, author; Suryanarayanan, Siddharth, advisor; Maciejewski, Tony, committee member; Collins, George J., committee member; Siegel, Howard Jay, committee member; Bell, Michael, committee memberSmart Grid Initiative started after realizing the urge for changes in conventional electric power grids. These changes should be made in response to a number of emerging issues in the electricity industry. The increasing involvement of renewable energy technologies, either as large- scale generators or as small-rated distributed generators (DGs), poses a challenge for the grid. The renewable energy generators being intermittent and uncontrollable brings worrying uncertainty at the supply side of the grid. This uncertainty makes the grid's operators anxious about balancing generation with load, which is a necessary condition for the security of the power system. Demand-side management (DSM) offers a promising solution for the uncontrollability of renewable energy. Residential customers, through new entities called demand response (DR) aggregators, can bring DR services for addressing the aforementioned intermittency in supply. A cost-minimization framework is set for power supply-demand adjustment with the involvement of variable resources (i.e., renewable energy generators). The resources in the power supply-demand adjustment problem are demand reduction through aggregators, power flow exchange between areas, and balancing generators' services. The method is simulated in the IEEJ East 30-machine test system after dividing it into 4 areas. The results of the followed method show a lower cost than the traditional method of using only balancing generators' services. This work builds on a previous work of researchers from Keio Univ in Japan. DR aggregators also use the Smart Grid Resource Allocation (SGRA) approach, which is a load shifting technique done by a DR aggregator. The DR aggregator performs a heuristic optimization in order to move part of residential appliances from peak to off-peak times. The effects of integrating multiple aggregators into the transmission level power grid are studied and simulated in the Roy Billinton test system (RBTS) after dividing it into 2 areas. The results show peak demand reductions, electricity prices reduction, and a lower peak-to-average ratio (PAR) for the system under consideration. In line with integrating DR aggregators, a carbon tax function from the work of Prof. W. Nordhaus, a Nobel Memorial Prize winner in economics sciences, is adopted to design a carbon emission-based tax function and apply it to the fossil-fueled generators in the system. The adopted carbon tax leads to less dispatch of coal and natural gas-based generators. As a result, CO2 emissions reduction is achieved and calculated using the set math models. The DR applications prove to represent a complementary element to the imposition of carbon taxation in achieving emissions-reduction. That is, imposing carbon taxation drives increases in electricity prices while applying DR reduces the mean electricity price by lowering the PAR of the system load profile. In addition, a testbed is designed to find a relationship between the aggregator's performance and utility pricing mechanisms. The experiment aims to find how utility pricing mechanisms affect the profitability of the aggregators and peak load shifting. These pricing mechanisms include fixed tariff, time-of-use (TOU) pricing, and real-time pricing (RTP). The simulation-based study shows that aggregators make the highest profits when run in parallel with utilities applying fixed tariffs, while they make the highest shifted peak load when run in parallel with utilities applying RTPs. Furthermore, survey-based data about the use patterns of three smart home appliances are incorporated in the SGRA approach. These three appliances include dishwashers, washing machines, and dryers. Besides using data about these appliances, additional rescheduling constraints are proposed to improve the comfort of participating customers. The results show profitability for the aggregator by using actual data of home appliances in tandem with additional rescheduling constraints to increase the comfort level of participating customers.Item Open Access Analysis of precipitation and convection in the west Pacific during the PISTON field campaign(Colorado State University. Libraries, 2022) Chudler, Kyle, author; Rutledge, Steven, advisor; Bell, Michael, committee member; Maloney, Eric, committee member; Reising, Steven, committee memberTropical convection is a meteorological phenomenon with important impacts on the atmosphere, both locally and globally. Consequently, it has been an intensely studied topic for many years. Importantly, several ship-based field campaigns have taken place over tropical oceans. Such field campaigns are vital to the advancement of knowledge in this field, as meteorological observations over these open oceans are otherwise scant or non-existent. The latest project to examine tropical convection is the Propagation of Intraseasonal Oscillations (PISTON) field campaign, which took place in the western North Pacific in the late-summer and early-fall of 2018 and 2019. On board the PISTON ships was the SEA-POL weather radar, the first polarimetric weather radar designed specifically for deployment at sea. In addition to taking traditional radar measurements of precipitation intensity and velocity, SEA-POL's polarimetric measurements also provide insights into the size, shape, and composition of hydrometeors within precipitating systems. By combining SEA-POL's unique measurements with other meteorological datasets, this work presented in this dissertation provides new insights in tropical convection in the Pacific warm pool. Chapter 2 of this dissertation provides an overview of the variability in convection observed during the PISTON cruises, and relates this variability to large-scale atmospheric conditions. Using an objective classification algorithm, precipitation features are identified and labeled by their size (isolated, sub-MCS, MCS) and degree of convective organization (nonlinear, linear). It is shown that although large mesoscale convective systems (MCSs) occurred infrequently (present in 13% of radar scans), they contributed a disproportionately large portion (56%) of the total rain volume. Conversely, small isolated features were present in 91% of scans, yet these features contributed just 11% of the total rain volume, with the bulk of the rainfall owing to warm rain production. Convective rain rates and 30-dBZ echo-top heights increased with feature size and degree of organization. MCSs occurred more frequently in periods of low-level southwesterly winds, and when low-level wind shear was enhanced. By compositing radar and sounding data by phases of easterly waves (of which there were several in 2018), troughs are shown to be associated with increased precipitation and a higher relative frequency of MCS feature occurrence, while ridges are shown to be associated with decreased precipitation and a higher relative frequency of isolated convective features. During PISTON, SEA-POL routinely measured extreme values of differential reflectivity in the cores of small, isolated convection, owing to the presence of large drops. Chapter 3 examines the structure and frequency of cells containing large drops. Cells with high differential reflectivity (> 3.5 dB) were present in 24% of all radar scans. The cells were typically small (8 km2 mean area), short lived (usually < 10 minutes), and shallow (3.7 km mean height). High differential reflectivity was more often found on the upwind side of these cells, suggesting a size sorting mechanism which establishes a low concentration of large drops on the upwind side. Differential reflectivity also tended to increase at lower altitudes, which is hypothesized to be due to continued drop growth, increasing temperature (dielectric effect), and evaporation of smaller drops. Rapid vertical cross section radar scans, as well as transects made by a Learjet aircraft with on-board particle probes, are also used to analyze these cells, and support the conclusions drawn from statistical analysis. In Chapter 4, the observations of precipitation from spaceborne Ku-Band precipitation radar (KuPR) from the Global Precipitation Mission Dual-Frequency Precipitation Radar is compared surface observations from SEA-POL. Over the 18 instances where KuPR and SEA-POL made concurrent measurements of precipitation, the average rain rate in KuPR was 50% lower than in SEA-POL, but the raining area was 113% higher. The net effect of these two differences of opposite sign was for KUPR to have 23% more rain volume than SEA-POL. The limited resolution of KuPR (5x5 km) causes it to underestimate rain rate in small convective cores, but also over-broaden raining features beyond their true extent. It is also shown that KuPR tends to slightly overestimate rain rate below the melting layer in stratiform rain, likely due to overcorrection of attenuation below radar bright bands. Using a statistical model to simulate KuPR rain volume, it was found that KuPR would theoretically overestimate rain volume during trough phases of the easterly waves observed during PISTON (when there was more precipitating area), and underestimate rainfall during ridge phases (when there was less precipitating area).Item Open Access 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 Comparing precipitation estimates, model forecasts, and random forest based predictions for excessive rainfall(Colorado State University. Libraries, 2023) James, Eric, author; Schumacher, Russ, advisor; Bell, Michael, committee member; Van Leeuwen, Peter Jan, committee member; Morrison, Ryan, committee memberFlash flooding is an important societal challenge, and improved tools are needed for both real-time analysis and short-range forecasts. We present an evaluation of threshold exceedances of quantitative precipitation estimate (QPE) and forecast (QPF) datasets in terms of their degree of correspondence with observed flash flood events over a seven-year period. We find that major uncertainties persist in QPE for heavy rainfall. In general, comparison with flash flood guidance (FFG) thresholds provides the best correspondence, but fixed thresholds and average recurrence interval thresholds provide the best correspondence in certain regions of the contiguous US (CONUS). QPF threshold exceedances from the High-Resolution Rapid Refresh (HRRR) generally do not correspond as well as QPE exceedances with observed flash floods, except for the 1-h duration in the southwestern CONUS; this suggests that high-resolution model QPF may be a better indicator of flash flooding than QPE in some poorly observed regions. Subsequently, we describe a new random forest (RF) based excessive rainfall forecast system using predictor information from the 3-km operational HRRR. Experiments exploring the use of spatial predictor information reveal the importance of averaging HRRR predictor fields across a spatial radius rather than using only information from sparse input grid points for regimes with small-scale excessive rain events. Tree interpreter results indicate that the forecast benefits of spatial aggregation stem from greater contributions provided by storm attribute predictors. Forecasts are slightly degraded when there is a mismatch between the trained RF model and the daily HRRR forecasts to which the model is applied, both in terms of initialization time and HRRR model version. Use of FFG as an additional predictor leads to forecast improvements, highlighting the potential of hydrologic information to contribute to forecast skill. In addition, averaging predictor information across several HRRR initializations leads to a statistically significant improvement in forecasts relative to using predictor fields from a single HRRR initialization. The HRRR-based RF has been evaluated at the annual Flash Flood and Intense Rainfall Experiment (FFaIR) over the past three years, with year-over-year improvements stemming from the results of sensitivity experiments. The HRRR-based RF represents an important baseline for future machine learning based excessive rainfall forecasts based on convection-allowing models.Item Open Access High-dimensional nonlinear data assimilation with non-Gaussian observation errors for the geosciences(Colorado State University. Libraries, 2023) Hu, Chih-Chi, author; van Leeuwen, Peter Jan, advisor; Kummerow, Christian, committee member; Anderson, Jeffrey, committee member; Bell, Michael, committee member; Kirby, Michael, committee memberData assimilation (DA) plays an indispensable role in modern weather forecasting. DA aims to provide better initial conditions for the model by combining the model forecast and the observations. However, modern DA methods for weather forecasting rely on linear and Gaussian assumptions to seek efficient solutions. These assumptions can be invalid, e.g., for problems associated with clouds, or for the assimilation of remotely-sensed observations. Some of these observations are either discarded, or not used properly due to these inappropriate assumptions in DA. Therefore, the goal of this dissertation is to seek solutions to tackle the issues arising from the linear and Gaussian assumptions in DA. This dissertation can be divided into two parts. In the first part, we explore the potential of the particle flow filter (PFF) in high dimensional systems. First, we tested the PFF in the 1000- dimensional Lorenz 96 model. The key innovation is we find that using a matrix kernel in the PFF can prevent the collapse of particles along the observed directions, for a sparsely observed and high-dimensional system with only a small number of particles. We also demonstrate that the PFF is able to represent a multi-modal posterior distribution in a high-dimensional space. Next, in order to apply the PFF for the atmospheric problem, we devise a parallel algorithm for PFF in the Data Assimilation Research Testbed (DART), called PFF-DART. A two-step PFF was developed that closely resembles the original PFF algorithm. A year-long cycling data assimilation experiment with a simplified atmospheric general circulation model shows PFF-DART is able to produce stable and comparable results to the Ensemble Adjustment Kalman Filter (EAKF) for linear and Gaussian observations. Moreover, PFF-DART can better assimilate the non-linear observations and reduce the errors of the ensemble, compared to the EAKF. In the second part, we shift our focus to the observation error in data assimilation. Traditionally, observation errors have been assumed to follow a Gaussian distribution mainly for two reasons: it is difficult to estimate observation error statistics beyond its second moment, and most of the DA methods assume a Gaussian observation error by construction. We developed the so-called Deconvolution-based Observation Error Estimation (DOEE), that can estimate the full distribution of the observation error. We apply DOEE to the all-sky microwave radiances and show that they indeed have non-Gaussian observation errors, especially in a cloudy and humid environment. Next, in order to incorporate the non-Gaussian observation errors into variational methods, we explore an evolving-Gaussian approach, that essentially uses a state dependent Gaussian observation error in each outer loop of the minimization. We demonstrate the merits of this method in an idealized experiment, and implemented it in the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts. Preliminary results show improvement for the short-term forecast of lower-tropospheric humidity, cloud, and precipitation when the observation error models of a small set of microwave channels are replaced by the non-Gaussian error models. In all, this dissertation provides possible solutions for outstanding non-linear and non-Gaussian data assimilation problems in high-dimension systems. While there are still important remaining issues, we hope this dissertation lays a foundation for the future non-linear and non-Gaussian data assimilation research and practice.Item Open Access Impact of the Boreal Summer Intraseasonal Oscillation on the diurnal cycle of precipitation in the island of Luzon(Colorado State University. Libraries, 2019) Chudler, Kyle, author; Rutledge, Steven, advisor; Xu, Weixin, committee member; Bell, Michael, committee member; Reising, Steven, committee memberThe Asian Summer Monsoon (ASM) is a major component of the global weather system with impacts on multiple scales. Driven by the thermal contrast between the Asian continent and the Indian and Pacific Oceans, the monsoon winds bring warm, moist air into the south Asian and maritime continents. Along with this influx of tropical air often comes copious amounts of rain, which can be both beneficial to agriculture and devastating to flood-prone regions. On a larger scale, the immense amount of latent heat released into the upper troposphere from condensation and deposition of water vapor can impact weather patterns across the globe. One striking feature in rainfall climatology of the ASM is the precipitation maxima located off the western shores of the Western Ghats, Myanmar, and the Philippines. These locations all feature elevated terrain features along their western shores. Many studies have examined why, when monsoon winds impinge upon these mountains, the precipitation preferentially falls off-shore, rather than directly over the mountains where orographic enhancement is strongest. Several theories have been proposed, including convergence of the monsoon winds with a land breeze, afternoon land-based convection which either propagates off-shore or creates a cold pool, and generation of off-shore instability through propagation of gravity waves generated from daytime heating of the boundary layer. Notably, all of these mechanisms are closely tied to the diurnal cycle. The main source of intraseasonal variability during the summer months in the ASM region is the Boreal Summer Intraseasonal Oscillation (BSISO). Characterized by a broad region of convection which propagates SW to NE from the Indian Ocean to the West Pacific, the BSISO brings alternating 2-3-week periods of inactive and active weather conditions to the monsoon region. Inactive periods are characterized by relatively clear skies, weaker winds, and localized but more intense convection over land. Active periods bring an increase in strong low-level monsoon winds and frequent, widespread precipitation and cloud cover. In this study, the impact of the BSISO on the occurrence of off-shore precipitation around the island of Luzon is examined. Satellite precipitation estimates show that off-shore precipitation occurs much more frequently during active BSISO phases. Importantly, results also show that a clear diurnal cycle still exists over land during these phases, despite increased cloud cover and reduced solar heating/instability generation. It is hypothesized that the interaction between strong low-level monsoon winds and the diurnal cycle over land is what promotes off-shore precipitation, either through the generation of wind shear (which supports off-shore propagation), or convergence between these winds and a cold pool or land breeze. The stronger low-level winds also cause greater ocean surface energy fluxes, which further promote precipitation. During inactive phases, despite the stronger diurnal cycle over land, the lack of a strong low-level wind results in an environment less conducive to off-shore rainfall.Item Open Access Influence of the Madden-Julian Oscillation and Caribbean Low-Level Jet on east Pacific easterly waves(Colorado State University. Libraries, 2018) Whitaker, Justin W., author; Maloney, Eric, advisor; Bell, Michael, committee member; Niemann, Jeffrey, committee memberThe east Pacific warm pool exhibits basic state variability associated with the Madden-Julian Oscillation (MJO) and Caribbean Low-Level Jet (CLLJ), which affects the development of easterly waves (EWs). This study compares and contrasts composite changes in the background environment, eddy kinetic energy (EKE) budgets, moisture budgets, and EW tracks during MJO and CLLJ events. While previous studies have shown that the MJO influences jet activity in the east Pacific, the influence of the MJO and CLLJ on EWs is not synonymous. The CLLJ is a stronger modulator of the ITCZ than the MJO, while the MJO has a more expansive influence on the northeastern portion of the basin. Anomalous low-level westerly MJO and CLLJ periods are associated with favorable conditions for EW development paralleling the Central American coast, contrary to previous findings about the relationship of the CLLJ to EWs. Easterly MJO and CLLJ periods support enhanced ITCZ EW development, although the CLLJ is a greater modulator of EW tracks in this region, which is likely associated with stronger moisture and convection variations and their subsequent influence on the EKE budget. ITCZ EW growth during easterly MJO periods is more reliant on barotropic conversion than in strong CLLJ periods, when EAPE to EKE conversion associated with ITCZ convection is more important. Enhanced background state moisture anomalies during strong CLLJ periods lead to stronger diluted CAPE anomalies in the mean state and EWs that support convection. Thus, the influence of these phenomena on east Pacific EWs should be considered distinct.1 1 This abstract is adapted from the abstract of: Whitaker, J.W., and E. D. Maloney, 2018: Influence of the Madden-Julian Oscillation and Caribbean Low-Level Jet on East Pacific Easterly Wave Dynamics. J. Atmos. Sci., in press. ©American Meteorological Society. Used with permission.Item Open Access Investigations of the uncertainties associated with HID algorithms and guiding input to a novel, synthetic polarimetric radar simulator(Colorado State University. Libraries, 2018) Barnum, Julie I., author; Rutledge, Steven, advisor; Reising, Steven, committee member; Bell, Michael, committee member; Dolan, Brenda, committee memberA methodology for model evaluation against observations is presented. With the advent of polarimetric radars, the need to produce simulated radar observables from model has also become apparent, in order to directly compare the same quantities between observations and models (e.g. rain rate calculations, hydrometeor identification - HID). To the end of evaluating model performance, for both a spectral bin microphysics (SBM) scheme and bulk microphysics scheme (BMS), a novel, synthetic polarimetric radar simulator created by Matsui et al. (2017) was implemented in this study: POLArimetric Radar Retrieval and Instrument Simulator (POLARRIS). POLARRIS takes in model data and simulates polarimetric radar variables in the forward component (POLARRIS-f), and then the inverse component of POLARRIS (iPOLARRIS) utilizes retrieval algorithms that are also employed in observations to make direct 1-to-1 comparisons between model simulations and observations. This inverse component is novel in its ability to help bridge the gap between model output and observations due to the fact that model output and observations without this framework are not directly comparable. The simulation of ice hydrometeors is not straightforward, and several assumptions are required to create polarimetric data for these species, such as the assumption of the size distribution, particle densities, particle melting, the input axis ratio, and canting angle assumptions. The last two variables are notoriously difficult to pin down for ice hydrometeors. This work aims to narrow down the appropriate inputs for axis ratio and canting angle assumptions that create the most comparable results with observations for three ice hydrometeors: aggregates, ice crystals, and graupel for two different meteorological regimes (mid-latitude supercell and tropical, monsoon MCS). Rain was also carried through as a check on model output. Through various sensitivity tests, it was concluded that, when run through the range of potential values, changes in axis ratio had a larger impact on the resulting polarimetric data than did changes in the canting angle assumptions. With this in mind, the 18 Z integrated hour from the 23 January 2006 monsoon MCS TWP – ICE case and the 22 Z integrated hour mid-latitude supercell from the 23 May 2011 MC3E case were simulated to help determine, for each hydrometeor type, the most appropriate axis ratio value(s) and canting angle assumptions that produced comparable results with observations. It was found using co-variance plots that, for 4ICE, the use of a singular axis ratio, mean canting angle, and degree of particle tumbling often produced differential reflectivity and specific differential phase values that converged to one value. While these values were within the observed values, they did not manage to simulate the breadth of observed values. Reflectivity values were also much too low compared to observations. SBM results, regardless of the type of input assumptions, tended to produce broader ranges for these variables, and also managed to better capture the reflectivity range seen in observations than was the case for the BMS. However, the reflectivity ranges seen in SBM were at times too expansive. The differences between SBM output and BMS output is likely due to the differing inherent assumptions in each microphysical scheme. The sensitivity of the simulated hydrometeors' polarimetric data was also probed against changing axis ratio and canting angle input assumptions. It was found that, in particular, BMS differential reflectivity values were quite sensitive to changes in input assumptions, regardless of the regime (tropical MCS vs. mid-latitude supercell). HID was found to be the most effective method to evaluate the performance of the two different model microphysical schemes (SBM vs. BMS) with respect to observations. Input assumptions that produced the most comparable results with respect to observations for each hydrometeor were compared using HID stacked frequency by altitude (SFAD) diagrams for convective and stratiform precipitation. This analysis found that although the co-variance plots revealed many model shortcomings, the HID proved to be fairly robust, especially for MC3E. The sensitivity of the HID retrieval itself was also investigated with respect to changing inputs (i.e. the membership beta functions) to the HID algorithm. The resulting HID was fairly sensitive to changes in the inputs to HID, particularly for model simulations. Observations seemed less responsive to changes in these input assumptions to HID. Longer simulation time frames, the potential inclusion of simulated melting hydrometeors, and investigation of other radar wavelengths are all suggested to help further utilize this methodology for evaluating model microphysical schemes' abilities to accurately simulate polarimetric data and HID retrievals with respect to observations.Item Open Access Rain and RELAMPAGO: analysis of the deep convective storms of central Argentina(Colorado State University. Libraries, 2023) Kelly, Nathan Robert, author; Schumacher, Russ, advisor; Rasmussen, Kristen, committee member; Bell, Michael, committee member; Nelson, Peter, committee memberWhen, where and how much precipitation falls are fundamental questions to research interests spanning the weather to climate spectrum, yet are difficult to solve. The various methods used to answer "how much" each have sources of error, making it important to obtain knowledge about the characteristics of an individual dataset. This is especially true for rare events such as extreme precipitation. IMERG, TRMM 3B42, MERRA2 and ERA5 precipitation datasets were regridded to the same resolution and compared for 3-hourly heavy rainfall (99th and 99.9th percentile) in subtropical South America, which has some of the strongest convective storms on Earth. Seasonal and dirunal distribution are compared, with similar seasonal distributions between the datasets but at the diurnal scale MERRA2 and ERA5 show more afternoon events than TRMM and IMERG. Thermodynamic environments were compared with MERRA2 events tending to occur in more marginal environments than TRMM 3B42 and ERA5 environments over much of the analyzed region. Overall the satellite datasets showed the highest amounts. Brief case studies are included to illustrate these differences, which reinforce that choice of dataset can be an important factor in precipitation research. How the precipitation falls is also addressed using a case study from the RELAMPAGO field program in Argentina. Many observations are available of this case, which occurred during the mobile operations period of the field program. Mobile surface stations, increased temporal resolution from fixed sounding sites, and six mobile sounding systems provide a high level of detail on the evolution of this storm system. Additionally, a trove of radar data and a GOES mesoscale sector are available. This case is demonstrative of a common occurrence in the region: a strong MCS (Mesoscale Convective System) over the Sierras de Córdoba mountain range. The extent of the backbuilding observed with this MCS was not predicted by the operation convective allowing models used for field program forecasting. To study this event two simulations are presented: one in which backbuilding of the MCS occurs and one where such backbuilding does not occur. The difference between these simulations is the number of vertical levels used in the model which impacts moisture availability upstream of the system via the effect of mountain wave downslope winds.Item Open Access Understanding and quantifying the uncertainties in satellite warm rain retrievals(Colorado State University. Libraries, 2022) Schulte, Richard, author; Kummerow, Christian D., advisor; Bell, Michael, committee member; Boukabara, Sid, committee member; Reising, Steven, committee member; van Leeuwen, Peter Jan, committee memberSatellite-based oceanic precipitation estimates, particularly those derived from the Global Precipitation Measurement (GPM) satellite and CloudSat, suffer from significant disagreement over regions of the globe where warm rain processes are dominant. Part of the uncertainty stems from differing assumptions about drop size distributions (DSDs). Satellite radar-based retrieval algorithms rely on DSD assumptions that may be overly simplistic, while radiometers further struggle to distinguish cloud water from rain. The aim of this study is to quantify uncertainties related to DSD assumptions in satellite precipitation retrievals, contextualize these uncertainties by comparing them to the uncertainty caused by other important factors like nonuniform beam filling, surface clutter, and vertical variability, and to see if GPM and CloudSat warm rainfall estimates can be partially reconciled if a consistent DSD model is assumed. Surface disdrometer data are used to examine the impact of DSD variability on the ability of three satellite architectures to accurately estimate warm rainfall rates. Two architectures are similar to existing instrument combinations on the GPM Core Observatory and CloudSat, while the third is a theoretical triple frequency radar/radiometer architecture. An optimal estimation algorithm is developed to retrieve rain rates from synthetic satellite measurements, and it is found that the assumed DSD shape can have a large impact on retrieved rain rate, with biases on the order of 100% in some cases. To compare these uncertainties against the effects of horizontal and vertical inhomogeneity, satellite measurements are also simulated using output from a high-resolution cloud resolving model. Finally, the optimal estimation algorithm is used to retrieve rain rates from near-coincident observations made by GPM and CloudSat. The algorithm retrieves more rain from the CloudSat observations than from the GPM observations, due in large part to GPM's insensitivity to light rain. However, the results also suggest an important role for DSD assumptions in explaining the discrepancy. When DSD assumptions are made consistent between the two retrievals, the gap in total accumulation between GPM and CloudSat is reduced by about 25%.