Browsing by Author "Kummerow, Christian D., advisor"
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Item 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 memberDeep 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.Item 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; RamÃrez, Jorge A., committee memberA 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).Item 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 memberThe 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.Item Open Access Building the foundations for a physically based passive microwave precipitation retrieval algorithm over the US Southern Great Plains(Colorado State University. Libraries, 2015) Ringerud, Sarah, author; Kummerow, Christian D., advisor; Peters-Lidard, Christa D., advisor; Reising, Steven C., committee member; van den Heever, Susan C., committee member; Vonder Haar, Thomas H., committee memberThe recently launched NASA Global Precipitation Measurement Mission (GPM) offers the opportunity for a greatly increased understanding of global rainfall and the hydrologic cycle. The GPM algorithm team has made improvements in passive microwave remote sensing of precipitation over land a priority for this mission, and implemented a framework allowing for algorithm advancement for individual land surface types as new techniques are developed. In contrast to the radiometrically cold ocean surface, land emissivity in the microwave is large with highly dynamic variability. An accurate understanding of the instantaneous, dynamic emissivity in terms of the associated surface properties is necessary for a physically based retrieval scheme over land, along with realistic profiles of frozen and liquid hydrometeors. In an effort to better simulate land surface microwave emissivity, a combined modeling technique is developed and tested over the US Southern Great Plains (SGP) area. The National Centers for Environmental Prediction (NCEP) Noah land surface model is utilized for surface information, with inputs optimized for SGP. A physical emissivity model, using land surface model data as input, is used to calculate emissivity at the 10 GHz frequency, combining contributions from the underlying soil and vegetation layers, including the dielectric and roughness effects of each medium. An empirical technique is then applied, based upon a robust set of observed channel covariances, extending the emissivity calculations to all channels. The resulting emissivities can then be implemented in calculation of upwelling microwave radiance, and combined with ancillary datasets to compute brightness temperatures (Tbs) at the top of the atmosphere (TOA). For calculation of the hydrometeor contribution, reflectivity profiles from the Tropical Rainfall Measurement Mission Precipitation Radar (TRMM-PR) are utilized along with coincident Tbs from the TRMM radiometer (TMI), and cloud resolving model data from NASA-Goddard's MMF model. Ice profiles are modified to be consistent with the higher frequency microwave Tbs. Resulting modeled TOA Tbs show correlations to observations of 0.9 along with biases 1K or less and small RMS error and show improved agreement over the use of climatological emissivity values. The synthesis of the emissivity and cloud resolving model input with satellite and ancillary datasets leads to creation of a unique Tb database for SGP that includes both dynamic surface and atmospheric information physically consistent with the LSM, emissivity model, and atmospheric information, for use in a Bayesian-type precipitation retrieval scheme utilizing a technique that can easily be applied to GPM as data becomes available.Item Open Access Exploring precipitation processes in stratocumulus clouds from satellite-derived cloud properties(Colorado State University. Libraries, 2021) Murakami, Yasutaka, author; Kummerow, Christian D., advisor; van den Heever, Susan C., advisor; Chiu, Christine, committee member; Venkatachalam, Chandrasekaran, committee memberMarine stratocumulus clouds are low-level convective clouds that develop within the marine atmospheric boundary layer and have a large impact on the global radiation budget and hydrological cycle. Drizzle plays an important but complicated role in their longevity and microphysical properties. Many studies have examined the response of cloud base rain rate to varying cloud droplet number concentrations and cloud thickness, as well as liquid water path (LWP), and found that cloud base rain rates are enhanced with lower cloud droplet number concentrations and greater cloud thickness or LWP. In warm stratocumulus clouds, cloud base rain rate is a combination of raindrop embryo production through collision coalescence (i.e. autoconversion) and raindrop embryo growth by collecting cloud droplets (i.e. accretion). Previous studies have shown that cloud base rain rate depends on LWP or cloud thickness and the geographical location of stratocumulus clouds, but the dependence of the autoconversion process on these variables is not well known because cloud base rain rate represents the effects of both autoconversion and accretion. This two-part dissertation explores the dependence of stratocumulus cloud precipitation processes on cloud thickness and geographical location by examining the cloud properties retrieved by A-Train satellite observations from CloudSat's Cloud Profiling Radar (CPR), CALIPSO's Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and Aqua's Moderate Resolution Imaging Spectroradiometer (MODIS). In the first part, the relations between cloud top properties (radar reflectivity, LWC and cloud droplet number concentration) and cloud geometrical thickness are investigated for subtropical stratocumulus clouds. Satellite-observations show that cloud top LWC and effective radius increase as clouds become thicker. The data also suggest that autoconversion may be more efficient in thicker clouds. These findings are consistent with previous studies that have shown that thicker clouds have larger cloud droplets and thus produce more rain embryos. However, it is also found that clouds separate into two sub-groups as they transition from thick (i.e. geometrical thickness of 384-480m) to very thick clouds (i.e. geometrical thickness of 624-720m). Drizzling clouds have higher LWC and their drops have larger effective radii, whereas non-drizzling clouds have lower cloud top LWC and smaller effective radii. In the second part, the climatology of satellite-derived cloud top properties (radar reflectivity, LWC and cloud droplet number concentration) for 8 stratocumulus cloud regions are presented. While LWP tends to be larger for midlatitude clouds, cloud top LWC tends to be larger at subtropical stratocumulus clouds. Since midlatit0ude stratocumulus clouds are thicker, these results suggests that effective condensation rates are larger for subtropical stratocumulus clouds. Both cloud top and cloud base radar reflectivity also tend to be larger for subtropical stratocumulus clouds. Based on these findings, the sensitivity of cloud top radar reflectivity on LWC and cloud droplet number concentration are examined. Cloud top radar reflectivity is more (less) sensitive to changes in LWC and cloud droplet number concentration for clouds with stronger (weaker) cloud top radar reflectivity. This is consistent with previous findings that collision-coalescence efficiency between liquid water droplets (i.e. approximately 20 μm in diameter) increases non-linearly with droplet size. The overall results presented in this dissertation indicate that the autoconversion process can be represented with a globally applicable function of cloud top LWC and cloud droplet number concentration for all stratocumulus clouds regardless of their geolocation and geometrical thickness. It is also demonstrated that cloud top raindrop embryo generation rate is an important factor for determining the precipitation generation rate for stratocumulus clouds as a whole. In general, accretional growth is controlled by both the total cross-sectional area of rain drops and LWP. By comparing spatial patterns of cloud top radar reflectivity (i.e. total cross-sectional area of rain drops) and radar reflectivity increase from cloud top to bottom (i.e. accretional growth), it is found that accretional growth depends more on total cross-sectional area of rain drops and less on LWP in stratocumulus clouds. These conclusions can explain the findings of previous studies that cloud base rain rate depends on LWP (or cloud thickness) and geographical location of stratocumulus clouds. Cloud base rain rate is dependent on geometrical thickness because cloud top LWC increases as cloud become thicker. Subtropical stratocumulus clouds tend to have stronger precipitation at a given LWP compared to midlatitude stratocumulus clouds because the effective condensation rate of subtropical stratocumulus clouds is greater and so is the cloud top LWC. In this study, the effect of Cloud Condensation Nuclei on warm rain processes is represented by varying cloud droplet number concentration. The results presented in this dissertation represent more than one hundred thousand independent pixels and provide a statistically robust benchmark that numerical models should reproduce.Item Open Access Exploring the limits of variational passive microwave retrievals(Colorado State University. Libraries, 2017) Duncan, David Ian, author; Kummerow, Christian D., advisor; Boukabara, Sid-Ahmed, committee member; O'Dell, Christopher W., committee member; Reising, Steven C., committee member; Rutledge, Steven A., committee member; Schumacher, Russ S., committee memberPassive microwave observations from satellite platforms constitute one of the most important data records of the global observing system. Operational since the late 1970s, passive microwave data underpin climate records of precipitation, sea ice extent, water vapor, and more, and contribute significantly to numerical weather prediction via data assimilation. Detailed understanding of the observation errors in these data is key to maximizing their utility for research and operational applications alike. However, the treatment of observation errors in this data record has been lacking and somewhat divergent when considering the retrieval and data assimilation communities. In this study, some limits of passive microwave imager data are considered in light of more holistic treatment of observation errors. A variational retrieval, named the CSU 1DVAR, was developed for microwave imagers and applied to the GMI and AMSR2 sensors for ocean scenes. Via an innovative method to determine forward model error, this retrieval accounts for error covariances across all channels used in the iteration. This improves validation in more complex scenes such as high wind speed and persistently cloudy regimes. In addition, it validates on par with a benchmark dataset without any tuning to in-situ observations. The algorithm yields full posterior error diagnostics and its physical forward model is applicable to other sensors, pending intercalibration. This retrieval is used to explore the viability of retrieving parameters at the limits of the available information content from a typical microwave imager. Retrieval of warm rain, marginal sea ice, and falling snow are explored with the variational retrieval. Warm rain retrieval shows some promise, with greater sensitivity than operational GPM algorithms due to leveraging CloudSat data and accounting for drop size distribution variability. Marginal sea ice is also detected with greater sensitivity than a standard operational retrieval. These studies ultimately show that while a variational algorithm maximizes the effective signal to noise ratio of these observations, hard limitations exist due to the finite information content afforded by a typical microwave imager.Item Open Access Impacts of assimilating vertical velocity, latent heating, or hydrometeor water contents retrieved from a single reflectivity data set(Colorado State University. Libraries, 2017) Lee, Yoonjin, author; Kummerow, Christian D., advisor; Zupanski, Milija, advisor; Reising, Steven C., committee member; van den Heever, Susan C., committee memberAssimilation of observation data in cloudy regions has been challenging due to the unknown properties of clouds such as cloud depth, cloud vertical profiles, or cloud drop size distributions. Attempts to assimilate data in cloudy regions generally assume a drop size distribution, but most assimilation systems fail to maintain consistency between models and the observation data, as each has its own set of assumptions. This study tries to retain the consistency between the forecast model and the retrieved data by developing a Bayesian retrieval scheme that uses the forecast model itself for the a-priori database. Through the retrieval algorithm, vertical profiles of three variables related to the development of tropical cyclones, including vertical velocity, latent heating, and hydrometeor water contents are derived from the same reflectivity observation. Vertical velocity and latent heating are variables related to dynamical processes of tropical cyclones, whereas hydrometeors are byproducts of those processes. Each retrieved variable is assimilated in the data assimilation system using a flow dependent forecast error covariance matrix. The simulations are compared to evaluate the respective impact of each variable in the assimilation system. In this study, the three assimilation experiments were conducted for two hurricane cases captured by the Global Precipitation Measurement (GPM) satellite: Hurricane Pali and Hurricane Jimena. Analyses from these two hurricane cases suggest that assimilating latent heating and hydrometeor water contents have similar impacts on the assimilation system while vertical velocity has less of an impact than the other two variables. Using these analyses as an initial condition for the forecast model reveals that the assimilations of retrieved latent heating and hydrometeor water contents were also able to improve the track forecast of Hurricane Jimena.Item Open Access Improving the quality of extreme precipitation estimates using satellite passive microwave rainfall retrievals(Colorado State University. Libraries, 2017) Petković, Veljko, author; Kummerow, Christian D., advisor; Vonder Haar, Thomas H., committee member; Rutledge, Steven A., committee member; Niemann, Jeffrey D., committee memberSatellite rainfall estimates are invaluable in assessing global precipitation. As a part of the Global Precipitation Measurement (GPM) mission, a constellation of orbiting sensors, dominated by passive microwave imagers, provides a full coverage of the planet approximately every 2-3 hours. Several decades of development have resulted in passive microwave rainfall retrievals that are indispensable in addressing global precipitation climatology. However, this prominent achievement is often overshadowed by the retrieval's performance at finer spatial and temporal scales, where large variability in cloud morphology poses an obstacle for accurate rainfall measurements. This is especially true over land, where rainfall estimates are based on an observed mean relationship between high frequency (e.g., 89 GHz) brightness temperature (Tb) depression (i.e., the ice-scattering signature) and rainfall rate. In the first part of this study, an extreme precipitation event that caused historical flooding over south-east Europe is analyzed using the GPM constellation. Performance of the rainfall retrieval is evaluated against ground radar and gage reference. It is concluded that satellite observations fully address the temporal evolution of the event but greatly underestimate total rainfall accumulation (by factor of 2.5). A primary limitation of the rainfall algorithm is found to be its inability to recognize variability in precipitating system structure. This variability is closely related to the structure of the precipitation regime and the large-scale environment. To address this influence of rainfall physics on the overall retrieval bias, the second part of this study utilizes TRMM radar (PR) and radiometer (TMI) observations to first confirm that the Tb-to-rain-rate relationship is governed by the amount of ice in the atmospheric column. Then, using the Amazon and Central African regions as testbeds, it demonstrates that the amount of ice aloft is strongly linked to a precipitation regime. A correlation found between the large-scale environment and precipitation regimes is then further examined. Variables such as Convective Available Potential Energy (CAPE), Cloud Condensation Nuclei (CCN), wind shear, and vertical humidity profiles are found to be capable of predicting a precipitation regime and explaining up to 40% of climatological biases. Dry over moist air conditions are favorable for developing intense, well organized systems such as MCSs in West Africa and the Sahel. These systems are characterized by strong Tb depressions and above average amounts of ice aloft. As a consequence, microwave retrieval algorithms misinterpret these non-typical systems assigning them unrealistically high rainfall rates. The opposite is true in the Amazon region, where observed raining systems exhibit relatively little ice while producing high rainfall rates. Based on these findings, in the last part of the study, the GPM operational retrieval (GPROF) for the GMI sensor is modified to offer additional information on atmospheric conditions to its Bayesian-based algorithm. When forming an estimate, the modified algorithm is allowed to use this ancillary information to filter out a priori states that do not match the general environmental condition relevant to the observation and thus reduce the difference between the assumed and observed variability in ice-to-rain ratio. The results are compared to the ground Multi-Radar Multi-Sensor (MRMS) network over the US at various spatial and temporal scales demonstrating outstanding potentials in improving the accuracy of rainfall estimates from satellite-borne passive microwave sensors over land.Item Open Access On the relation between satellite observed liquid water path, cloud droplet number concentration and cloud base rain rate and its implication for the auto-conversion rate in stratocumulus clouds(Colorado State University. Libraries, 2020) Murakami, Yasutaka, author; Kummerow, Christian D., advisor; van den Heever, Susan C., advisor; Venkatachalam, Chandrasekaran, committee memberStratocumulus clouds are low-level convective clouds that develop within the atmospheric boundary layer. Their persistence and broad coverage of the earth's surface produces important impacts on the global radiation energy budget and hydrological cycle. Precipitation processes of these stratocumulus clouds play a large role in their longevity and spatial distribution through their interaction with the vertical profiles of humidity and temperature within the atmospheric boundary layer. This has led to a number of field campaigns to understand the precipitation processes of stratocumulus clouds. However, because of the limited field campaign domains and limited amount of these observations, it is difficult to draw statistically significant conclusions on the precipitation processes of global stratocumulus clouds from these data. In this study, space-borne observations from A-Train satellites are utilized to obtain robust relations among the liquid water path, cloud droplet number concentration and cloud base rain rate for three geographical regions with similar large-scale environments, namely the north east Pacific off the coast of California, the south east Pacific off the coast of Peru and the south east Atlantic off the coast of Namibia, where strong subsidence flow from the subtropical-high is observed. Radar reflectivity from CloudSat's Cloud Profiling Radar (CPR) is employed to estimate the cloud base rain rate (Rcb). Liquid water path (LWP) and cloud droplet number concentration (Nd) are estimated from Moderate Resolution Imaging Spectroradiometer (MODIS) cloud optical thickness and effective radius. The relation between cloud base rain rate (Rcb) and the ratio of liquid water path to cloud droplet number concentrations (LWP/Nd) are obtained from a large number of A-train observations that show similar probability density distribution for all three target areas in this study. Rcb has a positive correlation with LWP/Nd and the increase in Rcb becomes larger as LWP/Nd increases, which is consistent with the results from previous ground-based observations. The research presented here also shows that the increase of Rcb in respect to LWP/Nd become more gradual in larger Nd regions, which suggests that the relation between Rcb and LWP/Nd changes with different cloud droplet number concentrations. These findings are consistent with our theoretical understanding of cloud physics processes in that 1) auto-conversion and accretion growth of rain embryos becomes more effective as cloud droplet number concentrations near cloud top decrease, and 2) auto-conversion is suppressed when the cloud droplet radius is small enough. The sensitivity of the auto-conversion rate to cloud droplet number concentration is investigated by examining pixels with small LWP in which the accretion process is assumed to have little influence on Rcb. The upper limit of the dependency of auto-conversion on the cloud droplet number concentration is assessed from the relation between cloud base rain rate and cloud top droplet number concentration since the sensitivity is exaggerated by the accretion process. The upper limit of the sensitivity of auto-conversion found in this study was found to be a cloud droplet number concentration to the power of -1.44±0.12. This study demonstrates that satellite observations are capable of detecting the average manner in which precipitation processes are modulated by the liquid water path and drop number concentrations.Item Open Access On the role of warm rain clouds in the tropics(Colorado State University. Libraries, 2008) Rapp, Anita Denise, author; Kummerow, Christian D., advisorA combined optimal estimation retrieval algorithm has been developed for warm rain clouds using Tropical Rainfall Measuring Mission (TRMM) satellite measurements. The algorithm uses TRMM Microwave Imager (TMI) brightness temperatures that have been deconvolved to the 19-GHz field-of-view (FOV) to retrieve cloud liquid water path (LWP), total precipitable water, and wind speed. Resampling the TMI measurements to a common FOV is found to decrease retrieved LWP by 30%. These deconvolved brightness temperatures are combined with cloud fraction from the Visible Infrared Scanner (VIRS) to overcome the beam-filling effects and with rainwater estimates from the TRMM Precipitation Radar (PR). This algorithm is novel in that it takes into account the water in the rain and retrieves the LWP due to only the cloud water in a raining cloud, thus allowing the investigation of the effects of precipitation on cloud properties. The uncertainties due to forward model parameters and assumptions are computed and range from 1.7 K at 10 GHz to about 6K at the 37 and 85 GHz TMI channels. Examination of the sensitivities in the LWP retrieval shows that the cloud fraction information increases the retrieved LWP with decreasing cloud fraction and that the PR rainwater reduces retrieved LWP. Retrieval algorithm results from December 2005 to January 2006 show that warm rain cloud LWP and the ratio of warm rain cloud LWP to rainwater both decrease by 50% over sea surface temperatures (SST) ranging from 292 to 302 K in the tropical western Pacific due to increased precipitation efficiency depleting more of the cloud water at higher SSTs. The LWP retrieval developed in this study is also applied to study the influence of warm rain clouds on atmospheric preconditioning for deep convection associated with tropical depression-type disturbances (TDs). Results show that positive warm rain cloud LWP anomalies occur with positive low-level moistening and heating anomalies prior to TD events, but that there is little variation in the properties of non-raining clouds. The moistening by these clouds is also shown to influence the generation of convective available potential energy (CAPE) prior to deep convection.Item Open Access Quantitative comparisons of satellite observations and cloud models(Colorado State University. Libraries, 2011) Wang, Fang, author; Kummerow, Christian D., advisor; Vonder Haar, Thomas H., committee member; Cotton, William R., committee member; Ramirez, Jorge A., committee memberMicrowave radiation interacts directly with precipitating particles and can therefore be used to compare microphysical properties found in models with those found in nature. Lower frequencies (< 37 GHz) can detect the emission signals from the raining clouds over radiometrically cold ocean surfaces while higher frequencies (≥ 37 GHz) are more sensitive to the scattering of the precipitating-sized ice particles in the convective storms over high-emissivity land, which lend them particular capabilities for different applications. Both are explored with a different scenario for each case: a comparison of two rainfall retrievals over ocean and a comparison of a cloud model simulation to satellite observations over land. Both the Goddard Profiling algorithm (GPROF) and European Centre for Medium-Range Weather Forecasts (ECMWF) one-dimensional + four-dimensional variational analysis (1D+4D-Var) rainfall retrievals are inversion algorithms based on the Bayes' theorem. Differences stem primarily from the a-priori information. GPROF uses an observationally generated a-priori database while ECMWF 1D-Var uses the model forecast First Guess (FG) fields. The relative similarity in the two approaches means that comparisons can shed light on the differences that are produced by the a-priori information. Case studies have found that differences can be classified into four categories based upon the agreement in the brightness temperatures (Tbs) and in the microphysical properties of Cloud Water Path (CWP) and Rain Water Path (RWP) space. We found a category of special interest in which both retrievals converge to similar Tb through minimization procedures but produce different CWP and RWP. The similarity in Tb can be attributed to comparable Total Water Path (TWP) between the two retrievals while the disagreement in the microphysics is caused by their different degrees of constraint of the cloud/rain ratio by the observations. This situation occurs frequently and takes up 46.9% in the one month 1D-Var retrievals examined. To attain better constrained cloud/rain ratios and improved retrieval quality, this study suggests the implementation of higher microwave frequency channels in the 1D-Var algorithm. Cloud Resolving Models (CRMs) offer an important pathway to interpret satellite observations of microphysical properties of storms. High frequency microwave brightness temperatures (Tbs) respond to precipitating-sized ice particles and can, therefore, be compared with simulated Tbs at the same frequencies. By clustering the Tb vectors at these frequencies, the scene can be classified into distinct microphysical regimes, in other words, cloud types. The properties for each cloud type in the simulated scene are compared to those in the observation scene to identify the discrepancies in microphysics within that cloud type. A convective storm over the Amazon observed by the Tropical Rainfall Measuring Mission (TRMM) is simulated using the Regional Atmospheric Modeling System (RAMS) in a semi-ideal setting, and four regimes are defined within the scene using cluster analysis: the 'clear sky/thin cirrus' cluster, the 'cloudy' cluster, the 'stratiform anvil' cluster and the 'convective' cluster. The relationship between Tb difference of 37 and 85 GHz and Tb at 85 GHz is found to contain important information of microphysical properties such as hydrometeor species and size distributions. Cluster-by-cluster comparison between the observations and the simulations discloses biases in the model including overproduction of supercooled water and large hail particles. The detected biases shed light on how the model should be adjusted to generate more realistic microphysical relationships for each cluster. Guided by the model/observation discrepancies in the 'convective' cloud cluster, a new simulation is performed to provide dynamic adjustments by generating more but smaller hail particles.Item Open Access Reconciling TRMM precipitation estimates related to El Niño Southern Oscillation variability(Colorado State University. Libraries, 2017) Henderson, David S., author; Kummerow, Christian D., advisor; van den Heever, Susan C., committee member; Rutledge, Steven, committee member; Notaros, Branislav, committee memberOver the tropical oceans, large discrepancies in TRMM passive and active microwave rainfall retrievals become apparent during El Niño-Southern Oscillation (ENSO) events, where TMI retrievals exhibit a systematic shift in precipitation seemingly correlated with ENSO phase, while the PR does not. To investigate the causality of this relationship, this dissertation focuses, both spatially and temporally, on the evolution of precipitation organization between El Niño and La Niña conditions and their impacts on TRMM TMI and PR retrieved precipitation through the use of ground validation (GV) and satellite-based sources. The precipitation validation is performed as a function of convective organization through implementation of defined precipitation regimes, which have physical characteristics consistent across meteorological regimes. Before a full evaluation of TRMM retrieved rain rates is completed, an assessment of TRMM ground validation (GV) oceanic rain rate estimates is necessary. The robustness of radar-based GV rainfall estimates from the Kwajalein S-band KPOL radar are examined through comparisons with the Kwajalein rain gauge network. The TRMM-GV 2A53 rainfall product is found to heavily underestimate convective rain types, where prominent biases occur as precipitation becomes more organized. To further examine these rainfall biases, GV and polarimetrically-tuned rain rates are compared, where GV biases in both the 2A53 product and convective and stratiform Z-R relationships are minimized when the rain rate relationships are developed specifically as a function of precipitation regime. The results demonstrate that exploration into precipitation regimes should be considered when deriving and evaluating rain relationships to establish the source and range of uncertainties existing within different precipitating systems. TRMM radar (PR) and radiometer (TMI) rain rates are then evaluated though multiple case studies of collocated TRMM and KPOL rain rates at the 1°x1° and TMI footprint scale. The results of this study indicate that TRMM TMI and PR rainfall biases are best explained when derived as a function of organization and convective fraction. Large underestimates in both TMI and PR rain rates are associated with predominately convective rainfall across all regimes, where TMI rainfall underestimates both PR and GV rain rates. While PR rain rate estimates typically underestimate GV rainfall, TMI rain rates are heavily overestimated in rainfall regimes containing predominantly stratiform precipitation. Over the Kwajalein region, differences in TMI and PR rain rates seem to be driven by the occurrence of organized precipitation, where TMI-PR differences during El Niño conditions largely derive from MCS-like precipitating systems containing large stratiform precipitating regions. Application of the resultant biases helps mitigate the TMI-PR differences occurring between the ENSO phases and explain uncertainties introduced by the TMI Bayesian retrieval. Expanding the analysis tropics-wide, TRMM discrepancies directly relate to a shift from isolated deep convection during La Niña events toward organized precipitation during El Niño events with the largest variability occurring in the Pacific basins. During El Niño conditions, an increase in stratiform raining fraction leads to an increase in TMI rain rates that is less prevalent in PR rain rate retrievals. Reanalysis and AIRS data indicate that higher occurrences in organized systems are aided by increased mid- and upper-tropospheric moisture accompanied by more frequent deep convection. During La Niña events tropical rainfall is dominated by isolated deep convective regimes associated with drier mid-tropospheric conditions and strong mid- and upper level zonal wind shear. Application of the known TMI and PR biases yields increased consistency in PR rainfall with the radiometer-based TMI and GPCP rainfall estimates. The resultant satellite-based rainfall estimates are in general agreement when describing the response of tropical precipitation to ENSO induced variability in tropical SSTs.Item Open Access Relationships between aerosol, cloud, and precipitation as observed from the A-train constellation of spaceborne sensors(Colorado State University. Libraries, 2009) Lebsock, Matthew David, author; Kummerow, Christian D., advisor; Stephens, Graeme L., 1952-, advisor; Randall, David A. (David Allan), 1948-, committee member; Reising, Steven C., committee memberData from several sensors flying in NASA's A-train constellation of satellites are analyzed to examine global relationships between aerosol, clouds and precipitation with particular emphasis placed on the Earth's radiation budget. The multi-sensor data are applied to two specific studies. The first addresses the response of cloud water path to atmospheric aerosol burden and the second quantifies relationships between tropical precipitation and radiation within the context of radiative-convective equilibrium. The first focused study presents a global multi-sensor satellite examination of aerosol indirect effects on warm oceanic clouds. The study centers on the water path response of cloud to aerosol burden. It is demonstrated that high aerosol environments are associated with reduced liquid water path in nonprecipitating clouds and that the reduction in liquid water path reduces the albedo enhancement expected from decreasing effective radius. Furthermore the reduction in liquid water path is greater in thermodynamically unstable environments than in stable environments, suggesting a greater sensitivity of liquid water path to aerosol in cumulus clouds than stratus clouds. In sharp contrast with nonprecipitating clouds, the cloud liquid water path of transitional and precipitating clouds increases dramatically with aerosol, which may be indicative of an inhibited coalescence process. Following from these observations, the magnitude of the aerosol indirect albedo sensitivity (IAS) is calculated as the sum of distinct cloud regimes over the global oceans. Selection of the cloud regimes is guided by the observation that both thermodynamic stability and the presence of precipitation affect the sensitivity of cloud albedo to aerosol concentrations. The IAS, defined as the change in warm cloud albedo for a fractional change in aerosol burden, is found to be -0.42 ±0.38 Wm-2 over the global oceans. Twenty five percent of the effect is due to precipitating clouds despite the fact that only eight percent of clouds are identified as precipitating. An additional assumption of the anthropogenic aerosol fraction provides an estimate of the indirect albedo forcing (IAF) of -0.13 ± 0.14 Wm-2, which is significantly lower than the range provided by climate model estimates. The second focused study presents an analysis of anomalous precipitation, cloud, thermodynamic, and radiation variables on the tropics-wide mean spatial scale. In particular, relationships between the mean tropical oceanic precipitation anomaly and radiative anomalies are examined. It is found that tropical mean precipitation is well correlated with cloud properties and radiative fields. In particular, the tropical mean precipitation anomaly is positively correlated with the top of the atmosphere reflected shortwave anomaly and negatively correlated with the emitted longwave anomaly. The tropical mean relationships are found to primarily result from a coherent oscillation of precipitation and the area of high-level cloudiness. The correlations manifest themselves radiatively as a modest cooling at the top of the atmosphere and a redistribution of energy from the surface to the atmosphere through reduced solar radiation to the surface and decreased longwave emission to space. The anomalous atmospheric column radiative heating is found to be about 10% of the magnitude of the anomalous latent heating. The temporal signature of the radiative heating is observed in the column mean temperature that indicates a coherent phase-lagged oscillation between atmospheric stability and convection. These relationships are identified as a radiative-convective cloud feedback that is observed on intra-seasonal timescales associated with the Madden-Julian oscillation in the tropical atmosphere. A composite analysis showing the spatial patterns of the anomalies provides evidence that the feedback mechanism works through a modulation of the strength of the large-scale tropical overturning circulations.Item Open Access The hydroclimate of the Upper Colorado River Basin and the western United States(Colorado State University. Libraries, 2014) Bolinger, Rebecca A., author; Kummerow, Christian D., advisor; Doesken, Nolan, committee member; Ramirez, Jorge, committee member; Rutledge, Steven, committee member; Vonder Haar, Tom, committee memberUnderstanding water budget variability of the Upper Colorado River Basin (UCRB) is critical, as changes can have major impacts on the region's vulnerable water resources. Using in situ, reanalysis, and satellite-derived datasets, surface and atmospheric water budgets of the UCRB are analyzed. All datasets capture the seasonal cycle for each water budget component. Most products capture the interannual variability, although there are some discrepancies with atmospheric divergence estimates. Variability and magnitude among storage volume change products also vary widely. With regards to the surface budget, the strongest connections exist between precipitation, evapotranspiration (ET), and soil moisture, while snow water equivalent is best correlated with runoff. Using the most ideal datasets for each component, the atmospheric water budget balances with 73 mm leftover. Increasing the best estimate of ET by 5% leads to a better long-term balance between surface storage changes, runoff, and atmospheric convergence. It also brings long-term atmospheric storage changes to a better balance of 13 mm. A statistical analysis and case study are performed to better understand the variability and predictability of the UCRB's hydroclimate. Results show significant correlations (at the 90% confidence level) between UCRB temperature and precipitation, and El Nino - Southern Oscillation (ENSO) and Pacific Decadal Oscillation (PDO) during the fall. However, correlations are typically not greater than 0.4. ENSO and PDO are associated with the second mode of variability in a Principal Component analysis, while the first mode of variability (57% of variance for precipitation and 74% of variance for temperature) displays a high year-to-year variability. A case study of a wet and a dry year (with similar ENSO/PDO conditions) shows that a few large accumulation events is what drives the seasonal variability. These large accumulation events are more dependent on a variety of more local synoptic conditions (e.g., location of low pressure, direction and speed of local winds, amount of moisture available). An analysis of ten winters shows that there are generally less than five large accumulating events in drier winters, with closer to ten in wetter winters. Overall, the statistics and case study show that a consistently accurate seasonal forecast for the UCRB is not achievable at this time. Expanding the ideal datasets selected over the UCRB, an analysis of the errors in atmospheric and surface water budgets is performed for every individual HUC4 basin over the western U.S. Surface water budgets show overall much smaller residual errors than the atmospheric water budgets over the region. Visually analyzing the balances and imbalances, we see that several different areas around the Continental Divide and the Great Basin balance well at the surface, but not as well in the atmosphere; around Arizona, most basins don't balance at either the surface or atmosphere; many of the Pacific coastal basins and basins in the northern Rocky mountains balance well at the surface and in the atmosphere. These balances/imbalances, climate variability, land cover, and topography are combined to delineate five hydroclimate zones. Seasonal and interannual variability is analyzed for each zone. The Pacific Coast zone shows strong agreement amongst the seasonal cycles of all the water budget components, while most of the other zones show an offset in peaks between components during the winter and summer.Item Open Access The spatial and temporal properties of precipitation uncertainty structures over tropical oceans(Colorado State University. Libraries, 2015) Liu, Jianbo, author; Kummerow, Christian D., advisor; O'Dell, Christopher W., committee member; Reising, Steven C., committee memberThe global distribution of precipitation has been measured from space using a series of passive microwave radiometers for over 40 years. However, our knowledge of precipitation uncertainty is still limited. While previous studies have shown that the uncertainty associated with the surface rain rate tends to vary with geographic location and season, most likely as a consequence of inappropriate and inaccurate microphysical assumptions in the forward model, the internal uncertainty structure remains largely unknown. Hence, a classification scheme is introduced, in which the overall precipitation uncertainty consists of random noise, constant biases, and region-dependent cyclic patterns. It is hypothesized that those cyclic patterns are the result of an imperfect forward model simulation of precipitation variation associated with regional atmospheric cycles. To investigate the hypothesis, differences from ten years of collocated surface rain rate measurements from TRMM Microwave Imager and Precipitation Radar are used as a proxy to characterize the precipitation uncertainty structure. The results show that the recurring uncertainty patterns over tropical ocean basins are clearly impacted by a hierarchy of regionally prominent atmospheric cycles with multiple time scales, from the diurnal cycle to multi-annual oscillation. Spectral analyses of the uncertainty time series have also confirmed the same argument. Moreover, the relative importance of major uncertainty sources varies drastically not only from one basin to another, but also with different choices of sampling resolutions. Following the classification scheme and hypothesis proposed in this study, the magnitudes of un-explained precipitation uncertainty can be reduced up to 68% and 63% over the equatorial central Pacific and eastern Atlantic, respectively.Item Open Access Tropical rainfall regimes and their evolution on hourly to daily timescales(Colorado State University. Libraries, 2011) Elsaesser, Gregory Scott, author; Kummerow, Christian D., advisor; Maloney, Eric D., committee member; Moncrieff, Mitchell W., committee member; Randall, David A., committee member; Reising, Steven C., committee memberData from multiple satellite and in situ sources are used to investigate the dominant raining cloud populations in the tropics, with the purpose of documenting how diverse the raining cloud populations are at any given time over a scale similar in size to the grid-box (~100 - 200 km) of a present-day global climate model (GCM). For all locations in the tropics, three similar rainfall clusters (defined according to their ensemble of clouds) are found. Differences in mean-state rainfall (e.g. East versus West Pacific Ocean) are largely the result of similar rainfall clusters occurring at ocean basin-dependent relative frequencies of occurrence. Area-average rainfall rates are substantially different for each cluster. While each rainfall cluster is observed in all tropical basins, differing relative frequencies of occurrence imply that rainfall lifecycles (i.e. the time duration for transition from light to deep rainfall) vary as a function of basin. Among the processes influencing this transition, both mesoscale cold pools (inferred from QuikSCAT surface wind field retrievals) and convective inhibition (CIN, derived from radiosonde-observations) emerge as important parameters driving the transition from light rainfall to deep convection at the spatial scale of 100 - 200 km. Associated with significant increases in rainfall are substantial decreases (40%) in convective available potential energy (CAPE). The temporal evolution of rainfall clusters is derived for different lifecycle stages of a composite Madden-Julian Oscillation (MJO) event. It is found that the rainfall cluster consisting of shallow (<3 km) and congestus raining clouds exhibits little temporal variation for all stages of the composite event, while non-raining scenes and deeper clouds are modulated as a function of time for all stages. Instead of a "transition" from shallow to deep convection, the results suggest an "addition" of deep convection at the expense of non-raining scenes. Unique to the initiation stage, deep organized convective systems are rare until 1 - 5 days before the development of a convective anomaly that finally begins propagating eastward. The lack of deep convection during the initiation stage relative to other stages is associated with both decreased values of columnar water vapor (TPW) and increased stability in the lower-troposphere. Both are hypothesized to preclude the development of deeper convection, thus allowing for the slow (10-30 day) increase in TPW by evaporation to continue, in contrast to later stages of the MJO when moisture convergence serves as the largest contributor to moistening. The analyses described above are applied to output from a novel multiscale-modeling framework (MMF) coupled with a slab ocean model. The extent to which the MMF yields results similar to the observational depictions outlined above is discussed in great detail.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%.Item Open Access Using GOES-16 ABI data to detect convection, estimate latent heating, and initiate convection in a high resolution model(Colorado State University. Libraries, 2021) Lee, Yoonjin, author; Kummerow, Christian D., advisor; Zupanski, Milija, advisor; Bell, Michael B., committee member; Chandrasekar, V., committee member; Chiu, Christine, committee memberConvective-scale data assimilation has received more attention in recent years as spatial resolution of forecast models has become finer and more observation data are available at such fine scale. Significant amounts of observation data are available over the globe, but only a limited number of observations are assimilated in operational forecast models in the most effective way. One of the most important observation data for predicting precipitation is radar reflectivity from ground-based radars as it provides three-dimensional structure of precipitation. Many operational models use these data to create cloud analysis and initiate convection. In High-Resolution Rapid Refresh (HRRR), the cloud permitting operational model at National Oceanic and Atmospheric Administration (NOAA) that is responsible for short term forecasts over the Contiguous United States (CONUS), latent heating is derived from ground-based radars and added in the observed convective regions to initiate convection. Even though adding heating is shown to improve forecasts of convection, this cannot be done over ocean or mountainous regions where radar data is not available. Geostationary data are available regardless of radar coverage and its data are provided in similar spatial and temporal resolution as ground-based radar. Currently, geostationary data are only used as a source of cloud top information or atmospheric motion vectors due to lack of vertical information. However, Geostationary Operational Environmental Satellites (GOES)-16 and -17 have high temporal resolution data that can compensate the lack of vertical information. From loops of one-minute visible images, convective clouds can be detected by finding a region with a constant bubbling. Therefore, this dissertation seeks a way to use these high temporal resolution GOES-16 data to mimic what radars do over land. In the first two papers presented in the dissertation, two methods are proposed to detect convection using one-minute GOES-16 Advanced Baseline Imager (ABI) data. The first method explicitly calculates Tb decrease or lumpiness of reflectance data and finds convective regions. The second paper tries to automate this process using machine learning method. Results from both methods are comparable to radar product, but the machine learning model seems to detect more convective regions than the conventional method. In the third paper, latent heating profiles for convective clouds are estimated from GOES-16. Once a convective cloud is detected, latent heating profiles corresponding to cloud top temperature of the convective cloud is searched from the lookup table created using model simulations. This technique is similar to spaceborne radar inferred latent heating developed for National Aeronautics and Space Administration (NASA)'s Global Precipitation Measurement Mission (GPM). Latent heating assigned from GOES-16 is shown to be similar to latent heating derived from Next-Generation Radar (NEXRAD) once they are summed up over each cloud. Finally in the last paper, latent heating estimated by using the method from the third paper are assimilated into the Weather Research and Forecasting (WRF) model to examine impacts of using GOES-16 derived latent heating in initiating convection in the forecast model. Two case studies are presented to compare results using GOES-16 derived heating and NEXRAD derived heating. Results show that using GOES-16 derived heating sometimes produce deeper convection than it should, but it improves overall precipitation forecasts. This appears related to the much deeper column of heating assigned by GOES than the empirical relation used by the HRRR operational scheme. In addition, in a case when storms developed over Gulf of Mexico where radar data are not available, forecasts are improved using GOES-16 latent heating.