Theses and Dissertations

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Open Access
A Lagrangian perspective on deep convective tropical raining systems
(Colorado State University. Libraries, 2013) Duncan, David Ian, author; Kummerow, Christian D., advisor; Thompson, David W. J., committee member; Reising, Steven C., committee member
Deep convective precipitating systems are categorized, tracked, and analyzed in the Tropical Ocean. Precipitating systems are tracked via an algorithm applied to the high-resolution CPC Morphing technique (CMORPH) precipitation product. Systems are categorized with an objective method, using data from the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) and a K-means clustering algorithm that exploits the consistency and similarity of tropical precipitation regimes. Propagation characteristics of these systems are found to be remarkably similar among ocean basins. The raining system's geographic center is calculated at each time step, allowing various ancillary datasets to be co-located with these systems to permit analysis of the effect of deep convective raining systems on local oceanic environments. The ancillary fields examined comprise elements of the water and energy budgets, as well as cloud field information from the International Satellite Cloud Climatology Project (ISCCP). The biggest determinant of a system's environmental impact is its propagation speed. This finding is corroborated by analysis of cloud fields which show that slow-moving systems and their associated deep clouds persist longer in a given location and therefore have a greater impact on the local environment than systems that move through more quickly. In the mean, sea surface temperature (SST) drops by 0.1-0.3°C and total precipitable water (TPW) increases by 5-7kg/m2 due to the passage of a deep convective raining system, with impacts dependent on the ocean basin and system speed. The presence of pervasive, optically thick clouds greatly decreases the net radiative flux at the surface, acting as the key driver of the observed drop in SST. The existence of a possible precipitation feedback based on system propagation speed is also explored.
Open Access
A method to combine spaceborne radar and radiometric observations of precipitation
(Colorado State University. Libraries, 2010) Munchak, Stephen Joseph, author; Kummerow, Christian Detlef, advisor; Chandrasekhar, V., committee member; Rutledge, Steven A., committee member; Stephens, Graeme L., 1952-, committee member
This dissertation describes the development and application of a combined radar-radiometer rainfall retrieval algorithm for the Tropical Rainfall Measuring Mission (TRMM) satellite. A retrieval framework based upon optimal estimation theory is proposed wherein three parameters describing the raindrop size distribution (DSD), ice particle size distribution (PSD), and cloud water path (cLWP) are retrieved for each radar profile. The retrieved rainfall rate is found to be strongly sensitive to the a priori constraints in DSD and cLWP; thus, these parameters are tuned to match polarimetric radar estimates of rainfall near Kwajalein, Republic of Marshall Islands. An independent validation against gauge-tuned radar rainfall estimates at Melbourne, FL shows agreement within 2% which exceeds previous algorithms' ability to match rainfall at these two sites. The algorithm is then applied to two years of TRMM data over oceans to determine the sources of DSD variability. Three correlated sets of variables representing storm dynamics, background environment, and cloud microphysics are found to account for approximately 50% of the variability in the absolute and reflectivity-normalized median drop size. Structures of radar reflectivity are also identified and related to drop size, with these relationships being confirmed by ground-based polarimetric radar data from the North American Monsoon Experiment (NAME). Regional patterns of DSD and the sources of variability identified herein are also shown to be consistent with previous work documenting regional DSD properties. In particular, mid-latitude regions and tropical regions near land tend to have larger drops for a given reflectivity, whereas the smallest drops are found in the eastern Pacific Intertropical Convergence Zone. Due to properties of the DSD and rain water/cloud water partitioning that change with column water vapor, it is shown that increases in water vapor in a global warming scenario could lead to slight (1%) underestimates of a rainfall trends by radar but larger overestimates (5%) by radiometer algorithms. Further analyses are performed to compare tropical oceanic mean rainfall rates between the combined algorithm and other sources. The combined algorithm is 15% higher than the version 6 of the 2A25 radar-only algorithm and 6.6% higher than the Global Precipitation Climatology Project (GPCP) estimate for the same time-space domain. Despite being higher than these two sources, the combined total is not inconsistent with estimates of the other components of the energy budget given their uncertainties.
Open Access
A new look at the Earth's radiation balance from an A-train observational perspective
(Colorado State University. Libraries, 2010) Henderson, David Scott, author; Stephens, Graeme L., 1952-, advisor; Heald, Colette L., committee member; Chandrasekar, V., committee member
The weather and climate of the Earth are driven by interactions of the longwave and shortwave radiation between the Earth's atmosphere and surface. Past studies have tried to derive the Earth radiative budget through the use of models and passive satellite sensors. These past efforts did not have information about the vertical distribution of cloud or aerosols within the atmosphere that significantly influence radiative transfer within the atmosphere. This problem was improved upon with the launch of CloudSat and CALIPSO in 2006. These satellites provide the information on the vertical distribution of clouds. From CloudSat, a fluxes and heating rates product was produced to study the radiative budget, but this was limited to some degree because of undetected clouds and aerosol that have non-negligible effects on the radiative balance. This study addresses these issues by combining CALIPSO and MODIS data with CloudSat to detect and obtain the properties of cloud and aerosol undetected by the CloudSat CPR. The combined data were used to create a cloud and aerosol mask that identified distributions of undetected cloud and aerosol globally and quantified their radiative effects both seasonally and annually. Low clouds were found to have the highest impacts of nearly -6 Wm-2. High clouds globally have little effect, trapping 1 Wm-2, with the majority of the impact in the tropics. Four case studies are presented to show how heating rates change in the vertical due low cloud, cirrus, precipitation, and aerosol. The cloud and aerosol mask was used to create seasonal global distributions of cloud radiative effect using all clouds detected by CloudSat and CALIPSO, and the direct effect of aerosols estimated at the TOA. Using fluxes at the top and bottom of the atmosphere global distributions of outgoing and incoming radiation are shown, and an annual radiation budget of the Earth is derived. Clouds globally are found to have a radiative forcing of -20 Wm-2 at the TOA. The radiative budget of the Earth is calculated in two ways; using normalized shortwave fluxes by the average solar daily insolation, and by changing the solar zenith angle to simulate the diurnal cycle. Finally, the product is validated by comparing the outgoing and surface fluxes with CERES and ISCPP flux products.
Open Access
A new post-processing paradigm? Improving high-impact weather forecasts with machine learning
(Colorado State University. Libraries, 2018) Herman, Gregory Reid, author; Schumacher, Russ S., advisor; Barnes, Elizabeth A., committee member; van den Heever, Susan C., committee member; Cooley, Daniel S., committee member; Hamill, Thomas M., committee member
High-impact weather comes in many different shapes, sizes, environments, and storm types, but all pose threats to human life, property, and the economy. Because of the significant societal hazards inflicted by these events, having skillful forecasts of the risks with sufficient lead time to make appropriate precautions is critical. In order to occur, these extreme events require a special conglomeration of unusual meteorological conditions. Consequently, effective forecasting of such events often requires different perspectives and tools than routine forecasts. A number of other factors make advance forecasts of rare, high-impact weather events particularly challenging, including the lack of sufficient resolution to adequately simulate the phenomena dynamically in a forecast model; model biases in representing storms, and which often become increasingly pronounced in extreme scenarios; and even difficulty in defining and verifying the high-impact event. This dissertation systematically addresses these recurring challenges for several types of high-impact weather: flash flooding and extreme rainfall, tornadoes, severe hail, and strong convective winds. For each listed phenomenon, research to more concretely define the current state of the science in analyzing, verifying, and forecasting the phenomenon. From there, in order to address the aforementioned persistent limitations with forecasting extreme weather events, machine learning-based post-processing models are developed to generate skillful, calibrated probabilistic forecasts for high-impact weather risk across the United States. Flash flooding is a notoriously challenging forecast problem. But the challenge is rooted even more fundamentally with difficulties in assessing and verifying flash flooding from observations due to the complex combination of hydrometeorological factors affecting flash flood occurrence and intensity. The first study in this dissertation investigates the multi-faceted flash flood analysis problem from a simplified framework considering only quantitative precipitation estimates (QPEs) to assess flash flood risk. Many different QPE-to-flash flood potential frameworks and QPE sources are considered over a multi-year evaluation period and QPE exceedances are compared against flash flood observations and warnings. No conclusive "best" flash flood analysis framework is clearly identified, though specific strengths and weaknesses of different approaches and QPE sources are identified in addition to regional differences in optimal correspondence with observations. The next two-part study accompanies the flash flood analysis investigation by approaching forecasting challenges associated with extreme precipitation. In particular, more than a decade of forecasts from a convection-parameterized global ensemble, the National Oceanic and Atmospheric Administration's Second Generation Global Ensemble Forecast System Reforecast (GEFS/R) model, are used to develop machine learning (ML) models for probabilistic prediction of extreme rainfall across the conterminous United States (CONUS) at Days 2 and 3. Both random forests (RFs) and logistic regression models (LR) are developed, with separate models trained for each lead time and for eight different CONUS regions. Models use the spatiotemporal evolution of a host of different atmospheric fields as predictors in addition to select geographic and climatological predictors. The models are evaluated over four years of withheld forecasts. The models, and particularly the RFs, are found to compare very favorably with both raw GEFS/R ensemble forecasts and those from a superior global ensemble produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) both in terms of forecast skill and reliability. The trained models are also inspected to discern what statistical findings are identified through ML. Many of the findings quantify anecdotal knowledge that is already recognized regarding the forecast problem, such as the relative skill of simulated precipitation in areas where extreme precipitation events are associated with large-scale processes well resolved by the GEFS/R compared with areas where extreme precipitation predominantly occurs in association with convection in the warm-season. But more subtle spatiotemporal biases are also diagnosed, including a northern displacement bias in the placement of convective systems and a southern displacement bias in placing landfalling atmospheric rivers. The final extended study shifts weather phenomenon focus from extreme rainfall to severe weather: tornadoes, large hail, and severe convective winds. While both high-impact, the two classes of weather hazards share some commonalities and contrasts. While rainfall is directly forecast by dynamical weather models, most severe weather occurs on too small of spatial scales to be directly simulated by the same models. Consequently, unlike with extreme precipitation, when developing post-processed severe weather forecasts, there is no obvious benchmark for objectively determining whether and how much improvement the post-processing is yielding. A natural alternative, albeit much more stringent, benchmark is operational forecasts produced by human forecasters. Operational severe weather forecasts are produced by the Storm Prediction Center (SPC), but there is limited published verification of their outlooks quantifying their probabilistic skill. In the first part of this study, an extended record SPC severe weather outlooks were evaluated to quantitatively assess the state of operational severe weather forecasting, including strengths and weaknesses. SPC convective outlooks were found to decrease in skill with increased forecast lead time, and were most skillful for severe winds, with the worst performance for tornado outlooks. Many seasonal and regional variations were also observed, with performance generally best in the North and East and worst in the South and especially West. The second part of the study follows similar methodology to the extreme precipitation models, developing RF-based probabilistic forecast models forced from the GEFS/R for Days 1--3 across CONUS, analogous to the format in which SPC produces its convective outlooks. RF properties are inspected to investigate the statistical relationships identified between GEFS/R fields and severe weather occurrence. Like with the extreme precipitation model, RF severe weather forecasts are generated and evaluated from several years of withheld validation cases. These forecasts are compared alongside SPC outlooks and also blended with them to produce a combined forecast. Overall, by statistically quantifying relationships between the synoptic-scale environment and severe weather in a manner consistent with the community's physical understanding of the forecast problems, the RF models are able to demonstrate skill over SPC outlooks at Days 2 and 3, and can be blended with SPC outlooks to enhance skill at Day 1. Overall, multiple high-impact weather phenomena---extreme precipitation and severe weather---are investigated from verification, analysis, and forecasting standpoints. On verification and analysis, foundations have been laid both to improve existing operational products as well as better frame and contextualize future studies. ML post-processing models developed were highly successful in advancing forecast skill and reliability for these hazardous weather phenomena despite being developed from predictors of a coarse, dated dynamical model in the GEFS/R. The findings also suggest adaptability across a wide array of forecast problems, types of predictor inputs, and lead times, raising the possibility of broader applicability of these methods in operational numerical weather prediction.
Open Access
A parametric optimal estimation retrieval of the non-precipitating parameters over the global oceans
(Colorado State University. Libraries, 2006) Elsaesser, Gregory S., author; Kummerow, Chris, advisor; Reising, Steven C., committee member; Randall, David, committee member
There are a multitude of spacebome microwave sensors in orbit, including the TRMM Microwave Imager (TMI), the Special Sensor Microwave/lmager (SSM/I) onboard the DMSP satellites, the Advanced Microwave Scanning Radiometer - Earth Observing System (AMSR-E), SSMIS, WINDSAT, and others. Future missions, such as the planned Global Precipitation Measurement (GPM) Mission, will incorporate additional spacebome microwave sensors. The need for consistent geophysical parameter retrievals among an ever-increasing number of microwave sensors requires the development of a physical retrieval scheme independent of any particular sensor and flexible enough so that future microwave sensors can be added with relative ease. To this end, we attempt to develop a parametric retrieval algorithm currently applicable to the non-precipitating atmosphere with the goal of having consistent non-precipitating geophysical parameter products. An algorithm of this nature makes is easier to merge separate products, which, when combined, would allow for additional global sampling or longer time series of the retrieved global geophysical parameters for climate purposes. This algorithm is currently applied to TMI, SSM/I and AMSR-E with results that are comparable to other independent microwave retrievals of the non-precipitating parameters designed for specific sensors. The physical retrieval is developed within the optimal estimation framework. The development of the retrieval within this framework ensures that the simulated radiances corresponding to the retrieved geophysical parameters will always agree with observed radiances regardless of the sensor being used. Furthermore, a framework of this nature allows one to easily add additional physics to describe radiation propagation through raining scenes, thus allowing for the merger of cloud and precipitation retrievals, if so desired. Additionally, optimal estimation provides error estimates on the retrieval, a product often not available in other algorithms, information on potential forward model/sensor biases, and a number of useful diagnostics providing information on the validity and significance of the retrieval (such as Chi-Square, indicative of the general "fit" between the model and observations and the A-Matrix, indicating the sensitivity of the model to a change in the geophysical parameters). There is an expected global response of these diagnostics based on the scene being observed, such as in the case of a raining scene. Fortunately, since TRMM has a precipitation radar (TRMM PR) in addition to a radiometer (TMI) flying on-board, the expected response of the retrieval diagnostics to rainfall can be evaluated. It is shown that a potentially powerful rainfall screen can then be developed for use in passive microwave rainfall and cloud property retrieval algorithms with the possibility of discriminating between precipitating and nonprecipitating scenes, and further indicating the possible contamination of rainfall in cloud liquid water path microwave retrievals.
Open Access
A polarimetric radar analysis of convection observed during NAME and TiMREX
(Colorado State University. Libraries, 2011) Rowe, Angela Kaye, author; Rutledge, Steven A., advisor; Johnson, Richard H., committee member; Van den Heever, Susan C., committee member; Lang, Timothy James, committee member; Eykholt, Richard Eric, 1956-, committee member
The mountainous regions of northwestern Mexico and southwestern Taiwan experience periods of intense rainfall associated with the North American and Asian monsoons, respectively, as warm, moist air is ushered onshore due to a reversal of mean low-level winds. Potentially unstable air is lifted along the steep topography, leading to convective initiation over the high peaks and adjacent foothills in both regions. In addition, an enhancement of convection in preexisting systems is observed due to interaction with the terrain, leading to localized heavy rain along the western slopes. The predictability of warm-reason rainfall in these regions is limited by the lack of understanding of the nature of these precipitating features, including the diurnal variability and elevation-dependent trends in microphysical processes. Using polarimetric data from NCAR's S-band, polarimetric radar (S-Pol), deployed during the North American Monsoon Experiment (NAME) and Terrain-influenced Monsoon Rainfall Experiment (TiMREX), individual convective elements were identified and tracked, allowing for an analysis of hydrometeor characteristics within evolving cells. Furthermore, a feature classification algorithm was applied to these datasets to compare characteristics associated with isolated convection to cells contained within organized systems. Examples of isolated cells from a range of topography during NAME revealed the presence of ZDR columns, attributed to the lofting of drops above the melting level, where subsequent freezing and growth by riming led to the production of graupel along the western slopes of the Sierra Madre Occidental (SMO) and adjacent coastal plain. Melting of large ice hydrometeors was also noted over higher terrain, leading to short-lived yet intense rainfall despite truncated warm-cloud depths compared to cells over the lower elevations. Cells embedded within mesoscale convective systems (MCSs) during NAME also displayed the combined roles of warm-rain and ice-based microphysical processes as convection organized along the terrain. In addition to enhancing precipitation along the western slopes of the SMO, melting ice contributed to the production of mesoscale outflow boundaries, which provided an additional focus mechanism for convective initiation over the lower elevations and resulted in propagation of these systems toward the coast. Intense rainfall was also observed along the Central Mountain Range (CMR) in Taiwan; however, in contrast to the systems during NAME, this enhancement occurred as MCSs moved onshore within the southwesterly flow and intercepted the CMR's steep slopes. Elevated maxima in polarimetric variables, similar to observations in convection during NAME, indicated a contribution from melting ice to rainfall at these higher elevations. Vertical profiles of ice mass, however, revealed greater amounts throughout the entire vertical depth of convection during NAME. In addition, isolated cells during TiMREX were relatively shallow compared to organized convection in both regions. Nonetheless, instantaneous rain rates were comparable during both experiments, suggesting efficient warm-rain processes within convection observed in the TiMREX radar domain and emphasizing a range of microphysical processes in these two regions. In addition, the greatest contribution to hourly accumulated rain mass in these regions was associated with deep organized systems along the western slopes, posing threats along the steep topography due to flash flooding and subsequent landslides, emphasizing the need for accurate prediction and understanding of the processes that lead to intense rainfall in these vulnerable regions.
Open Access
A potential vorticity diagnosis of tropical cyclone track forecast errors
(Colorado State University. Libraries, 2023) Barbero, Tyler Warren, author; Bell, Michael M., advisor; Barnes, Elizabeth A., committee member; Chen, Jan-Huey, committee member; Klotzbach, Philip J., committee member; Zhou, Yongcheng, committee member
A tropical cyclone (TC) can cause significant impacts on coastal and near-coastal communities from storm surge, flooding, intense winds, and heavy rainfall. Accurately predicting TC track is crucial to providing affected populations with time to prepare and evacuate. Over the years, advancements in observational quality and quantity, numerical models, and data assimilation techniques have led to a reduction in average track errors. However, large forecast errors still occur, highlighting the need for ongoing research into the causes of track errors in models. We use the piecewise potential vorticity (PV) inversion diagnosis technique to investigate the sources of errors in track forecasts of four high-resolution numerical weather models during the hyperactive 2017 Atlantic hurricane season. The piecewise PV inversion technique is able to quantify the amount of steering, as well as steering errors, on TC track from individual large-scale pressure systems. Through the systematic use of the diagnostic tool, errors that occur consistently (model biases) could also be identified. TC movement generally follows the atmospheric flow generated by large-scale environmental pressure systems, such that errors in the simulated flow cause errors in the TC track forecast. To understand how the environment steers TCs, we use the Shapiro decomposition to remove the TC PV field from the total PV field, and the environmental (i.e., perturbation) PV field is isolated. The perturbation PV field was partitioned into six systems: the Bermuda High and the Continental High, which compose the negative environmental PV, and quadrants to the northwest, northeast, southeast, and southwest of the TC, which compose the positive environmental PV. Each piecewise PV perturbation system was inverted to retrieve the balanced mass and wind fields. To quantify the steering contribution in individual systems to TC movement, a metric called the deep layer mean steering flow (DLMSF) is defined, and errors in the forecast DLMSF were calculated by comparing the forecast to the analysis steering flow. Lag correlation analyses of DLMSF errors and track errors showed moderate-high correlation at -24 to 0 hrs in time, which indicates that track errors are caused in part by DLMSF errors. Three hurricanes (Harvey, Irma, and Maria) were analyzed in-depth and errors in their track forecasts are attributed to errors in the DLMSF. A basin-scale analysis was also performed on all hurricanes in the 2017 Atlantic hurricane season. The DLMSF mean absolute error (MAE) showed the Bermuda High was the highest contributor to error, the Continental High showed moderate error, while the four quadrants showed lower errors. High error cases were composited to examine potential model biases. On average, the composite showed lower balanced geopotential heights around the climatological position of the Bermuda High associated with the recurving of storms in the North Atlantic basin. The analysis techniques developed in this thesis aids in the identification of model biases which will lead to improved track forecasts in the future.
Open Access
A simple ice phase parameterization
(Colorado State University. Libraries, 1979) Stephens, Mark Argyle, author; Cotton, William R., advisor; Keefe, Thomas J., committee member; Orville, Harold D., committee member
A two variable ice parameterization was developed for use in three dimensional models of cumulonimbus clouds and mesoscale squall lines. Bulk water techniques were employed to simulate the growth and decay of snow crystals and of graupel in order to keep the use of computer resources to a minimum. An externally specified concentration of ice crystals was used to initiate snow. Graupel was assumed to follow the Marshall-Palmer distribution with a constant total concentration. Microphysical growth processes for snow included initiation from the vapor at liquid water saturation, riming, melting, vapor deposition and conversion of rimed crystals into graupel. The graupel microphysical processes that were modeled included raindrop freezing by contact with snow crystals, accretion of raindrops, vapor deposition, riming of cloud droplets and melting. Both types of ice were allowed to precipitate. Sensitivity tests and internal consistency checks on the parameterization were done using a one-dimensional, time-dependent cloud model. Results suggested that the parameterization should simulate adequately the ice phase evolution in higher dimensional models. The parameterization is most suitable for modeling studies in which the major emphasis is on exploring the dynamic consequences of the ice phase rather than exploratory studies in cloud microphysics. Several deficiencies of the parameterization were commented on, specifically: the use of an externally specified snow concentration and its influence on the conversion of snow into graupel. Comments were also made on how local changes in the snow concentration brought about by seeding, ice multiplication and aggregation could be handled in' higher dimensional models.
Open Access
A simple parameterization of aerosol emissions in RAMS
(Colorado State University. Libraries, 2013) Letcher, Theodore, author; Cotton, William, advisor; Kreidenweis, Sonia, committee member; Ramirez, Jorge, committee member
Throughout the past decade, a high degree of attention has been focused on determining the microphysical impact of anthropogenically enhanced concentrations of Cloud Condensation Nuclei (CCN) on orographic snowfall in the mountains of the western United States. This area has garnered a lot of attention due to the implications this effect may have on local water resource distribution within the Region. Recent advances in computing power and the development of highly advanced microphysical schemes within numerical models have provided an estimation of the sensitivity that orographic snowfall has to changes in atmospheric CCN concentrations. However, what is still lacking is a coupling between these advanced microphysical schemes and a real-world representation of CCN sources. Previously, an attempt to representation the heterogeneous evolution of aerosol was made by coupling three-dimensional aerosol output from the WRF Chemistry model to the Colorado State University (CSU) Regional Atmospheric Modeling System (RAMS) (Ward et al. 2011). The biggest problem associated with this scheme was the computational expense. In fact, the computational expense associated with this scheme was so high, that it was prohibitive for simulations with fine enough resolution to accurately represent microphysical processes. To improve upon this method, a new parameterization for aerosol emission was developed in such a way that it was fully contained within RAMS. Several assumptions went into generating a computationally efficient aerosol emissions parameterization in RAMS. The most notable assumption was the decision to neglect the chemical processes in formed in the formation of Secondary Aerosol (SA), and instead treat SA as primary aerosol via short-term WRF-CHEM simulations. While, SA makes up a substantial portion of the total aerosol burden (much of which is made up of organic material), the representation of this process is highly complex and highly expensive within a numerical model. Furthermore, SA formation is greatly reduced during the winter months due to the lack of naturally produced organic VOC's. Because of these reasons, it was felt that neglecting SOA within the model was the best course of action. The actual parameterization uses a prescribed source map to add aerosol to the model at two vertical levels that surround an arbitrary height decided by the user. To best represent the real-world, the WRF Chemistry model was run using the National Emissions Inventory (NEI2005) to represent anthropogenic emissions and the Model Emissions of Gases and Aerosols from Nature (MEGAN) to represent natural contributions to aerosol. WRF Chemistry was run for one hour, after which the aerosol output along with the hygroscopicity parameter (κ) were saved into a data file that had the capacity to be interpolated to an arbitrary grid used in RAMS. The comparison of this parameterization to observations collected at Mesa Verde National Park (MVNP) during the Inhibition of Snowfall from Pollution Aerosol (ISPA-III) field campaign yielded promising results. The model was able to simulate the variability in near surface aerosol concentration with reasonable accuracy, though with a general low bias. Furthermore, this model compared much better to the observations than did the WRF Chemistry model using a fraction of the computational expense. This emissions scheme was able to show reasonable solutions regarding the aerosol concentrations and can therefore be used to provide an estimate of the seasonal impact of increased CCN on water resources in Western Colorado with relatively low computational expense.
Open Access
A simplified approach to understanding boundary layer structure impacts on tropical cyclone intensity
(Colorado State University. Libraries, 2018) Delap, Eleanor G., author; Bell, Michael M., advisor; Maloney, Eric D., committee member; Venayagamoorthy, Subhas Karan, committee member
The relationship between tropical cyclone boundary layer (TCBL) structure and tropical cyclone (TC) intensity change is difficult to understand due to limited observations of the complex, non-linear interactions at both the top and bottom boundaries of the TCBL. Consequently, there are debates on how the TCBL interacts with surface friction and how these interactions affect TC intensity change. To begin to address these questions, a conceptual framework of how axisymmetric dynamics within the TCBL can impact TC intensity change is developed from first principles in the form of a new, simple logistic growth equation (LGE). Although this LGE bears some similarities to the operational LGE Model (LGEM; DeMaria 2009), the difference is that our growth-limiting term incorporates TCBL structure and surface drag. The carrying capacity of the LGE—termed the instantaneous logistic potential intensity (ILPI) in this study—is used to explore the relationship between TCBL structure and TC intensity. The LGE is also further solved for the drag coefficient (CD) to explore the relationships between it and both TCBL structure and TC intensity. The validity of this new LGE framework is then explored in idealized numerical modeling using the axisymmetric version of Cloud Model 1 (CM1; Bryan and Fritsch 2002). Results show that CM1 exhibits changes to TCBL structure and TC intensity that are consistent with the LGE framework. Sensitivity of these results to the turbulent mixing lengths, Lh and Lv, are also explored, and general LGE relation- ships still hold as CD is increased. Finally, the LGE framework is applied to observations, and initial CD retrievals indicate that while this new method is low compared to Bell et al. (2012), they are still plausible estimates.
Open Access
A spatio-temporal correlation technique to improve satellite rainfall accumulation
(Colorado State University. Libraries, 2011) Petković, Veljko, author; Kummerow, Christian D., advisor; Vonder Haar, Thomas H., committee member; Ramírez, Jorge A., committee member
A spatio-temporal correlation technique has been developed to combine satellite rainfall measurements using the spatial and temporal correlation of the rainfall fields to overcome problems of sparse and infrequent measurements, while at the same time accounting for the measurements' accuracies. This technique estimates instantaneous rainfall with desired temporal sampling using only currently available satellite measurements with the goal of estimating 3-hour total rainfall accumulations at various spatial scales. The technique uses weighted mean to combine the measurements, adjusting the weights to the temporal correlation length of the measured rainfall field, and to the instrument accuracies. The relationship between the temporal and spatial correlation of the rainfall field is exploited to provide information about rainfall beyond instantaneous measurements. This information, depending on the nature of the rainfall field, can be accurate for prolonged time periods. It is shown that slow changing rainfall fields (i.e. stratiform-like rain) have high values of spatial correlation coefficients, and temporal correlation lengths as long as 60min. While, on the other hand, fast changing rainfall fields (i.e. convective-like rain) tend to have low spatial correlations, and temporal correlation lengths as short as 20min. This technique is developed using synthetic radar data. Nine months of the Operational Program for the Exchange of weather RAdar (OPERA) data is used on grid sizes of 100km, 250km and 500km with pixel resolutions of 8km, 12km and 24km to simulate satellite FOVs, and then applied to the real satellite data over the Southwest region of USA to calculate 3-hour rainfall accumulations. The results are then compared to the simple averaging technique , which takes a simple mean of the measurements as a constant rainfall rate over the entire accumulation period. The comparison is presented as improvements of the total absolute and RMS errors. Using synthetic data, depending on the time separation of the measurements and their accuracy, the technique has shown the potential to bring improvements of up to 40% in absolute, and up to 25% in RMS error. When applied to the real satellite data over the SE-USA, the technique has shown less skill, only 2% to 6% error improvement, which can be explained by the poor temporal sampling of the reference measurements. This technique is computationally inexpensive and easily applicable to currently used rainfall accumulation methods with linear interpolation between measurements such as CMORPH (Climate Prediction Center's Morphing Technique) and TMPA (The Tropical Rainfall Measuring Mission Multi-satellite Precipitation Analysis).
Open Access
A statistical prediction model for east Pacific and Atlantic tropical cyclone genesis
(Colorado State University. Libraries, 2012) Slade, Stephanie A., author; Maloney, Eric D., advisor; Thompson, David, committee member; Chong, Edwin, committee member
A statistical model is developed via multiple logistic regression for the prediction of weekly tropical cyclone activity over the East Pacific and Atlantic Ocean regions using data from 1975 to 2009. The predictors used in the model include a climatology of tropical cyclone genesis for each ocean basin, an El Niño-Southern Oscillation (ENSO) index derived from the first principal component of sea surface temperature over the Equatorial East Pacific region, and two indices representing the propagating Madden-Julian Oscillation (MJO). These predictors are suggested as useful for the prediction of East Pacific and Atlantic cyclogenesis based on previous work in the literature and are further confirmed in this study using basic statistics. Univariate logistic regression models are generated for each predictor in each region to ensure the choice of prediction scheme. Using all predictors, cross-validated hindcasts are developed out to a seven week forecast lead. A formal stepwise predictor selection procedure is implemented to select the predictors used in each region at each forecast lead. Brier skill scores and reliability diagrams are used to assess the skill and dependability of the models. Results show a significant increase in model skill at predicting tropical cyclogenesis by the inclusion of the MJO out to a three week forecast lead for the East Pacific and a two week forecast lead for the Atlantic. The importance of ENSO for Atlantic genesis prediction is highlighted, and the uncertain effects of ENSO on East Pacific tropical cyclogenesis are re-visited using the prediction scheme. Future work to extend the prediction model with other predictors is discussed.
Open Access
A study of low cloud climate feedbacks using a generalized higher-order closure subgrid model
(Colorado State University. Libraries, 2013) Firl, Grant J., author; Randall, David A., advisor; Denning, Scott, committee member; Johnson, Richard, committee member; Evangelista, Paul, committee member
One of the biggest uncertainties in projections of future climate is whether and how low cloudiness will change and whether that change will feed back on the climate system. Much of the uncertainty revolves around the difference in scales between the processes that govern low cloudiness and the processes that can be resolved in climate models, a fact that relegates shallow convection to the parameterization realm with varying levels of success. A new subgrid-scale parameterization, named THOR, has been developed in an effort to improve the representation of low cloudiness via parameterization in climate models. THOR uses the higher-order closure approach to determine the statistics describing subgrid-scale processes. These statistics are used to determine a trivariate double-Gaussian PDF among vertical velocity, ice-liquid water potential temperature, and total water specific humidity. With this information, one can diagnose what portion of the grid cell is cloudy, subgrid-scale cloud water content, and subgrid-scale vertical cloud water flux. In addition, samples are drawn from the trivariate PDF in order to drive the microphysics and radiation schemes. Although schemes similar to THOR have been developed over the past decade, THOR includes several novel concepts, like the generalization of the saturation curve to include condensation over both ice and liquid substrates, the determination of the PDF parameters from the given turbulence statistics, the introduction of a stochastic parcel entrainment process for the turbulence length scale, and a sub-column approach for calculating radiative transfer using the PDF. The new model is validated by simulating five test cases spanning a wide range of boundary layer cloud types, from stratocumulus to cumulus and the transition between the two. The results are compared to an ensemble of LES models running the same cases, with particular attention paid to turbulence statistics and cloud structure. For all cloud types tested, THOR produces results that are generally within the range of LES results, indicating that the single-column THOR is able to reproduce the gross characteristics of boundary layer clouds nearly as well as three-dimensional LES. Sensitivity to vertical grid spacing, diagnostic/prognostic third- order moments, choice of turbulence length scale entrainment process, and whether or not PDF sampling is used to drive the microphysics and radiation schemes is assessed for all test cases. Simulation of the cumulus regime was degraded when vertical grid spacing exceeded 200 m, when more third-order moments were predicted, when higher parcel entrainment rates were assumed, and when PDF sampling for the microphysics scheme was omitted. Simulation of stratocumulus was degraded with grid spacing larger than 100 m, when PDF sampling for microphysics was omitted, and when PDF sampling for radiation was included. Lastly, THOR is used to study low cloud climate feedbacks in the northeastern Pacific Ocean in the context of the CGILS project. Initial conditions and forcings are supplied at 13 points along the GPCI cross-section that spans from the ITCZ northeast to the coast of California transecting regions of shallow cumuli and stratocumuli, for both the current climate and a climate with a +2K SST perturbation. A change in net cloud radiative forcing of 0-8 W/m2 was simulated along the cross-section for the perturbed climate, representing neutral to weak positive feedback. The responsible mechanism appeared to be increased boundary layer entrainment and stratocumulus decoupling leading to reduced maximum cloud cover in the cumulus regime and reduced liquid water path in the stratocumulus regime.
Open Access
A study of the relationship between thunderstorm processes and cloud-top ice crystal size
(Colorado State University. Libraries, 2008) Lindsey, Daniel T., author; Johnson, Richard H., advisor
Satellite observations and numerical models are used to understand the physical mechanisms responsible for thunderstorms with varying cloud-top ice crystal sizes. Geostationary Operational Environmental Satellite (GOES) data are used to create a three-year climatology of cloud-top 3.9 µm reflectivity, a quantity which is closely correlated with particle size. Maximum mean values are found over the High Plains and Rocky Mountain regions of the U.S., suggesting that convection over that region tends to generate smaller anvil ice crystals than areas throughout much of the eastern U.S. To correct for preferred forward scattering by the cloud-top ice crystals, an effective radius retrieval using GOES is developed. Forward radiative transfer simulations are run for a wide range of cloud-top ice crystal sizes and sun-cloud-satellite scattering angles. The output is used to generate a lookup table, so that GOES-measured radiances may be used along with sun-satellite geometry to obtain an estimate for particle size. Validation of the retrieval shows that the assumed scattering properties perform quite well. To help explain the geographical variation in cloud-top ice crystal size, a composite analysis is performed in the High Plains region by averaging environmental conditions for days which produced both small and large ice crystal storms. Small ice is found to occur with relatively high based storms and steep mid-level lapse rates. Additionally, observational evidence from a pyrocumulonimbus event is presented to show the effect of low-level cloud condensation nuclei (CCN) on cloud-top ice crystal size. Model simulations using the Colorado State University Regional Atmospheric Modeling System (RAMS) are performed to help understand the physical mechanisms responsible for cloud-top ice crystal size. Through a series of sensitivity tests, it is found that larger low-level CCN concentrations lead to smaller anvil ice. In addition, as cloud-base temperature decreases (and cloud-base height increases), storm-top ice crystals get smaller. A weaker updraft strength is found to have very little effect on ice crystal size.
Open Access
A theoretical and numerical investigation of warm-phase microphysical processes
(Colorado State University. Libraries, 2015) Igel, Adele, author; van den Heever, Susan, advisor; Kreidenweis, Sonia, committee member; Rutledge, Steven, committee member; Oprea, Iuliana, committee member
Several studies examining microphysical processes are conducted with an emphasis on further understanding warm-phase processes, particularly condensation. In general, these studies progress from simple to complex representations of microphysical processes in models. In the first study, a theoretical, analytical expression for the condensational invigoration, that is the invigoration in the warm-phase of a cloud due to changes in the condensation rate, of a polluted, cloudy parcel of air relative to a clean, cloudy parcel of air is developed. The expression is shown to perform well compared to parcel model simulations, and to accurately predict the invigoration to within 30% or less. The expression is then used to explore the sensitivity of invigoration to a range of initial conditions. It is found that the invigoration, in terms of added kinetic energy, is more sensitive to the cloud base temperature than to the initial buoyancy of the parcels. Changes in vertical velocity between clean and polluted parcels of up to 4.5 m s−1 at 1 km above cloud base are theoretically possible, and the difference in vertical velocity decreases when the initial vertical velocity of either parcel is large. These theoretical predictions are expected to represent an upper limit to the magnitude of condensational invigoration and should be applicable to both shallow cumulus clouds as well as the warm phase of deep convection. In the second study, the focus shifts to the comparison of the representation of microphysical processes in single- and double-moment microphysics schemes. Single-moment microphysics schemes have long enjoyed popularity for their simplicity and efficiency. However, it is argued that the assumptions inherent in these parameterizations can induce large errors in the proper representation of clouds and their feedbacks to the atmosphere. For example, precipitation is shown to increase by 200% through changes to fixed parameters in a single-moment scheme and low cloud fraction in the RCE simulations drops from ~15% in double-moment simulations to ~2% in single-moment simulations. This study adds to the large body of work that has shown that double-moment schemes generally outperform single-moment schemes. It is recommended that future studies, especially those employing cloud-resolving models, strongly consider moving to the exclusive use of multi-moment microphysics schemes. An alternative to multi-moment schemes is a bin scheme. In the third study, the condensation rates predicted by bin and bulk microphysics schemes in the same model framework are compared in a novel way using simulations of non-precipitating shallow cumulus clouds. The bulk scheme generally predicts lower condensation rates than does the bin scheme when the saturation ratio and the integrated diameter of the droplet distribution are identical. Despite other fundamental disparities between the bin and bulk condensation parameterizations, the differences in condensation rates are predominantly explained by accounting for the width of the cloud droplet size distributions simulated by the bin scheme which can alter the rates by 50% or more in some cases. The simulations are used again in the fourth study in order to further investigate the dependency of condensation and evaporation rates to the shape parameter and how this dependency impacts the microphysical and optical properties of clouds. The double-moment bulk microphysics simulations reveal that the shape parameter can lead to large changes in the average condensation rates, particularly in evaporating regions of the cloud where feedbacks between evaporation and the depletion of individual droplets magnify the dependency of the evaporation rate on the shape parameter. As a result the average droplet number concentration increases as the shape parameter increases, but changes to the cloud water content are small. Taken together, these impacts lead to a decrease in the average cloud albedo. Finally, the simulations indicate that the value of the shape parameter in subsaturated cloudy air is more important than the value in supersaturated cloudy air, and that a constant shape parameter may not be a poor assumption for simulations of non-precipitating shallow cumulus clouds.
Open Access
A theory of topographically bound balanced motions and application to atmospheric low-level jets
(Colorado State University. Libraries, 2011) Silvers, Levi Glenn, author; Schubert, Wayne H., advisor; Randall, David A., committee member; Thompson, David W. J., committee member; Eykholt, Richard E., committee member
The response of a stratified fluid to forcing from the lower boundary is studied both analytically and numerically. The lower boundary forces a flow field through orographic obstacles and potential vorticity anomalies. It is argued that these mechanisms contribute to the maintenance of low-level jets that are observed regularly in the vicinity of the Rocky Mountains and the Andes. Low-level jets function as one of the primary mechanisms through which topography and surface heating influence regional and global climates. On the ƒ-plane a horizontal transform of the governing equation for potential vorticity leads to a vertical structure equation that is solved using Green's functions. On the sphere a vertical transform of this system leads to a horizontal structure equation that is solved using spheroidal harmonics. These analytic solutions lead to a conceptually simple picture of the fluid response to forcing. However, these derivations only lead to closed analytic solutions for the case of an isentropic lower boundary. When the lower boundary is not isentropic a massless layer must be included in the domain and the solution is then found iteratively. For the cases including a massless layer the system is approximated using finite differences and solved with an over-relaxation procedure. Solutions are presented for the geostrophically balanced, steady response of the fluid to three idealized lower boundaries. An isentropic ridge is studied to determine the role non-heated orography plays on the wind field. Then a flat heated lower boundary and a non-isentropic ridge are studied. The cases with a heated lower surface result in a cyclonic wind field that is anchored over the topography. Observations show a prominent cyclonic wind field centered on both the Rocky Mountains and the Andes. The idealized cases studied in this work allow for the examination of fluid systems analogous to the Great Plains low-level jet and the South American LLJ. Both the mean behavior of these jets and their variability have important climatological and economic impacts on the plains regions of North and South America. One of the purposes of this work is to interpret particular low-level jet systems as part of the orographically bound, balanced motion associated with the potential vorticity anomalies produced by solar heating. This research proposes the jets on opposite sides of the mountains to be a single response to potential vorticity forcing that is the result of radiative heating on the Rockies and the Andes. The orographically bound circulations can also impact monsoon circulations. Although the importance of heated orography to LLJs has tended to be downplayed in the literature, it is shown here to be a significant component in the maintenance of LLJs.
Open Access
A triple-moment bulk hail microphysics scheme to investigate the sensitivities of hail to aerosols
(Colorado State University. Libraries, 2012) Loftus, Adrian Matthew, author; Cotton, William R., advisor; Rutledge, Steven A., committee member; van den Heever, Susan C., committee member; Bringi, Viswanathan N., committee member
Hail is a frequent occurrence in warm season deep convection in many mid-latitude regions and causes significant damage to property and agricultural interests every year. Hail can also have a substantial impact on the precipitation characteristics of deep convection as well as on the dynamic and thermodynamic properties of convective downdrafts and cold-pools, which in turn can affect storm evolution and propagation. In addition, large and often destructive hail commonly occurs in severe convection, yet most one- (1M) and two-moment (2M) bulk microphysics schemes in cloud-resolving numerical models are incapable of producing large hail (diameter D ≥ 2 cm). The limits imposed by fixing one or two of the distribution parameters in these schemes often lead to particularly poor representations of particles within the tails of size distribution spectra; an especially important consideration for hail, which covers a broad range of sizes in nature. In order to improve the representation of hail distributions in simulations of deep moist convection in a cloud-resolving numerical model, a new triple-moment bulk hail microphysics scheme (3MHAIL) is presented and evaluated. The 3MHAIL scheme predicts the relative dispersion parameter for a gamma distribution function via the prediction of the sixth moment (related to the reflectivity factor) of the distribution in addition to the mass mixing ratio and number concentration (third and zeroeth moments, respectively) thereby allowing for a fully prognostic distribution function. Initial testing of this scheme reveals significant improvement in the representation of sedimentation, melting, and formation processes of hail compared to lower-order moment schemes. The 3MHAIL scheme is verified in simulations of a well-observed supercell storm that occurred over northwest Kansas on 29 June 2000 during the Severe Thunderstorm and Electrification and Precipitation Study (STEPS). Comparisons of the simulation results with the observations for this case, as well as with results of simulations using two different 2M microphysics schemes, suggest a significant improvement of the simulated storm structure and evolution is achieved with the 3MHAIL scheme. The generation of large hail and subsequent fallout in the simulation using 3MHAIL microphysics show particularly good agreement with surface hail reports for this storm as well as with previous studies of hail in supercell storms. On the other hand, the simulation with 2M microphysics produces only small hail aloft and virtually no hail at the surface, whereas a two-moment version of the 3MHAIL scheme (with a fixed relative dispersion parameter) produces unrealistically high amounts of large hail at low levels as a result of artificial shifts in the hail size spectra towards larger diameter hail during the melting process. The 3MHAIL scheme is also used to investigate the impact of changing the concentrations of aerosols that act as cloud condensation nuclei (CCN) on hail for the 29 June 2000 supercell case. For the simulated supercells in the particular environment examined, an increase in CCN from 100 to 3000 cm-3 leads to an increase in the numbers and a decrease in the sizes of cloud droplets, as expected, yet the overall storm dynamics and evolution are largely unaffected. Increases in CCN lead to non-monotonic responses in the bulk characteristics of nearly all hydrometeor fields, surface precipitation, and cold-pool strength. However, higher concentrations of CCN also result in larger hail sizes and greater amounts of large diameter (≥ 2 cm) hail both aloft as well as at the surface. Analyses of the hail formation and growth mechanisms for these simulations suggest that the combination of increased sizes of new hail particles and localized reductions in numbers of new hailstones forming near maximum growth regions with increasing CCN tends to promote conditions that lead to increased hail sizes and amounts of large hail.
Open Access
A tropical radiation and cloud system feedback modulated by sea surface temperature
(Colorado State University. Libraries, 2011) Igel, Matthew R., author; Stephens, Graeme, advisor; van den Heever, Susan, committee member; Eykholt, Richard, committee member
A large domain, high resolution cloud system resolving model set up in the tropics over fixed sea surface temperatures (SST) of 298 K and 302 K and run to radiative convective equilibrium has been analyzed with the focus on well equilibrated, domain mean results. The Regional Atmospheric Modeling System (RAMS) is used. The modeled convection organizes into disturbed, convective and undisturbed, subsidence regions. The mean profiles of state variables such as temperature, relative humidity (RH), and convective mass flux are analyzed and found to depend on SST in both predictable and unpredictable ways. The characteristics of rain depend on SST such that higher surface temperatures produce greater variability in intensity and lesser frequency. Next, the large-scale mean state is used to understand the convective system-scale setup. A focus is on the controls in the undisturbed regions of the disturbed region, deep convective anvil detrainment. Upper tropospheric radiation, through diabatic convergence, is used as a paradigm to understand the height at which detrainment occurs. The dependence of upper tropospheric radiation on RH is derived explicitly for the first time. From this new equation, temperature and RH are found to control anvil detrainment. The addition of RH as an anvil detrainment control explains why the model leads to an understanding of cooler anvils with higher SST - a positive climate feedback on the system. Other anvil feedbacks exhibited by the model are similar to those proposed in the Iris and Thermostat hypotheses. The convective system components are shown to enhance one another such that the overall system dependence on SST is nonlinear. To understand the circulation system, a heat engine analogue is made that shows the warmer state is able to more efficiently circulate or move heat. Finally, observational evidence from Cloudsat and CALIPSO shows that some of the modeled results are also apparent in nature.
Open Access
Aerosol impacts on deep convective storms in the tropics: a combination of modeling and observations
(Colorado State University. Libraries, 2012) Storer, Rachel Lynn, author; van den Heever, Susan C., advisor; Stephens, Graeme L., committee member; Johnson, Richard H., committee member; Eykholt, Richard, committee member
It is widely accepted that increasing the number of aerosols available to act as cloud condensation nuclei (CCN) will have significant effects on cloud properties, both microphysical and dynamical. This work focuses on the impacts of aerosols on deep convective clouds (DCCs), which experience more complicated responses than warm clouds due to their strong dynamical forcing and the presence of ice processes. Several previous studies have seen that DCCs may be invigorated by increasing aerosols, though this is not the case in all scenarios. The precipitation response to increased aerosol concentrations is also mixed. Often precipitation is thought to decrease due to a less efficient warm rain process in polluted clouds, yet convective invigoration would lead to an overall increase in surface precipitation. In this work, modeling and observations are both used in order to enhance our understanding regarding the effects of aerosols on DCCs. Specifically, the area investigated is the tropical East Atlantic, where dust from the coast of Africa frequently is available to interact with convective storms over the ocean. The first study investigates the effects of aerosols on tropical DCCs through the use of numerical modeling. A series of large-scale, two-dimensional cloud-resolving model simulations was completed, differing only in the concentration of aerosols available to act as CCN. Polluted simulations contained more deep convective clouds, wider storms, higher cloud tops and more convective precipitation across the entire domain. Differences in the warm cloud microphysical processes were largely consistent with aerosol indirect theory, and the average precipitation produced in each DCC column decreased with increasing aerosol concentration. A detailed microphysical budget analysis showed that the reduction in collision and coalescence largely dominated the trend in surface precipitation; however the production of rain through the melting of ice, though it also decreased, became more important as the aerosol concentration increased. The DCCs in polluted simulations contained more frequent, stronger updrafts and downdrafts, but the average updraft speed decreased with increasing aerosols in DCCs above 6 km. An examination of the buoyancy term of the vertical velocity equation demonstrates that the drag associated with condensate loading is an important factor in determining the average updraft strength. The largest contributions to latent heating in DCCs were cloud nucleation and vapor deposition onto water and ice, but changes in latent heating were, on average, an order of magnitude smaller than those in the condensate loading term. It is suggested that the average updraft is largely influenced by condensate loading in the more extensive stratiform regions of the polluted storms, while invigoration in the convective core leads to stronger updrafts and higher cloud tops. The goal of the second study was to examine observational data for evidence that would support the findings of the modeling work. In order to do this, four years of CloudSat data were analyzed over a region of the East Atlantic, chosen for the similarity (in meteorology and the presence of aerosols) to the modeling study. The satellite data were combined with information about aerosols taken from the output of a global transport model, and only those profiles fitting the definition of deep convective clouds were analyzed. Overall, the cloud center of gravity, cloud top, rain top, and ice water path were all found to increase with increased aerosol loading. These findings are in agreement with what was found in the modeling work, and are suggestive of convective invigoration with increased aerosols. In order to separate environmental effects from that due to aerosols, the data were sorted by environmental convective available potential energy (CAPE) and lower tropospheric static stability (LTSS). The aerosol effects were found to be largely independent of the environment. A simple statistical test suggests that the difference between the cleanest and most polluted clouds sampled are significant, lending credence to the hypothesis of convective invigoration. This is the first time evidence of deep convective invigoration has been demonstrated within a large region and over a long time period, and it is quite promising that there are many similarities between the modeling and observational results.
Open Access
Aerosol parameterizations in space-based near-infrared retrievals of carbon dioxide
(Colorado State University. Libraries, 2019) Nelson, Robert Roland, author; Kummerow, Christian D., advisor; O'Dell, Christopher W., advisor; Denning, A. Scott, committee member; Pierce, Jeffrey R., committee member; Hoeting, Jennifer A., committee member
The scattering effects of clouds and aerosols are one of the primary sources of error when making space-based measurements of carbon dioxide. This work describes multiple investigations into optimizing how aerosols are parameterized in retrievals of the column-averaged dry-air mole fraction of carbon dioxide (XCO2) performed on near-infrared measurements of reflected sunlight from the Orbiting Carbon Observatory-2 (OCO-2). The primary goal is to enhance both the precision and accuracy of the XCO2 measurements by improving the way aerosols are handled in the NASA Atmospheric CO2 Observations from Space (ACOS) retrieval algorithm. Two studies were performed: one on using better informed aerosol priors in the retrieval and another on reducing the complexity of the aerosol parameterization. It was found that using ancillary aerosol information from the Goddard Earth Observing System Model, Version 5 (GEOS-5) resulted in a small improvement against multiple validation sources but that the improvements were restricted by the accuracy and limitations of the model. Implementing simplified aerosol parameterizations that allowed for the retrieval of fewer parameters sometimes resulted in small improvements in XCO2, but further work is needed to determine the optimal way to handle the scattering effects of clouds and aerosols in near-infrared measurements of XCO2. With several multi-million dollar space-based greenhouse gas measurement missions scheduled and in development, the massive amount of measurements will be an incredible boon to the global scientific community, but only if the precision and accuracy of the data are sufficient.