Browsing by Author "Kummerow, Christian, committee member"
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Item Open Access A new algorithm for retrieval of tropospheric wet path delay over inland water bodies and coastal zones using brightness temperature deflection ratios(Colorado State University. Libraries, 2013) Gilliam, Kyle L., author; Reising, Steven C., advisor; Notaros, Branislav, committee member; Kummerow, Christian, committee memberAs part of former and current sea-surface altimetry missions, brightness temperatures measured by nadir-viewing 18-34 GHz microwave radiometers are used to determine apparent path delay due to variations in index of refraction caused by changes in the humidity of the troposphere. This tropospheric wet-path delay can be retrieved from these measurements with sufficient accuracy over open oceans. However, in coastal zones and over inland water the highly variable radiometric emission from land surfaces at microwave frequencies has prevented accurate retrieval of wet-path delay using conventional algorithms. To extend wet path delay corrections into the coastal zone (within 25 km of land) and to inland water bodies, a new method is proposed to correct for tropospheric wet-path delay by using higher-frequency radiometer channels from approximately 50-170 GHz to provide sufficiently small fields of view on the surface. A new approach is introduced based on the variability of observations in several millimeter-wave radiometer channels on small spatial scales due to surface emissivity in contrast to the larger-scale variability in atmospheric absorption. The new technique is based on the measurement of deflection ratios among several radiometric bands to estimate the transmissivity of the atmosphere due to water vapor. To this end, the Brightness Temperature Deflection Ratio (BTDR) method is developed starting from a radiative transfer model for a downward-looking microwave radiometer, and is extended to pairs of frequency channels to retrieve the wet path delay. Then a mapping between the wet transmissivity and wet-path delay is performed using atmospheric absorption models. A frequency selection study is presented to determine the suitability of frequency sets for accurate retrieval of tropospheric wet-path delay, and comparisons are made to frequency sets based on currently-available microwave radiometers. Statistical noise analysis results are presented for a number of frequency sets. Additionally, this thesis demonstrates a method of identifying contrasting surface pixels using edge detection algorithms to identify contrasting scenes in brightness temperature images for retrieval with the BTDR method. Finally, retrievals are demonstrated from brightness temperatures measured by Special Sensor Microwave Imager/Sounder (SSMIS) instruments on three satellites for coastal and inland water scenes. For validation, these retrievals are qualitatively compared to independently-derived total precipitable water products from SSMIS, the Tropical Rainfall Measurement Mission (TRMM) Microwave Imager (TMI) and the Advanced Microwave Sounding Radiometer for Earth Observing System (EOS) (AMSR-E). Finally, a quantitative method for analyzing the data consistency of the retrieval is presented as an estimate of the error in the retrieved wet path delay. From these comparisons, one can see that the BTDR method shows promise for retrieving wet path delays over inland water and coastal regions. Finally, several additional future uses for the algorithm are described.Item Open Access Design, fabrication, and demonstration of low-mass, low-power, small-volume, direct detection millimeter-wave radiometers at 92 and 130 GHz(Colorado State University. Libraries, 2012) Albers, Darrin, author; Reising, Steven C., advisor; Kummerow, Christian, committee member; Notaros, Branislav, committee member; Kangaslahti, Pekka, committee memberAdvances in future ocean satellite altimetry missions are needed to meet oceanographic and hydrological objectives. These needs include accurately determining the sea surface height (SSH) on spatial scales of 10 km and larger, as well as monitoring the height of the world's inland bodies of water and the flow rate of rivers. The Surface Water and Ocean Topography (SWOT) mission was recommended by the National Research Council's Earth Science Decadal Survey and selected by the National Aeronautics and Space Administration as an accelerated Tier-2 mission to address these needs. Current surface altimetry missions use nadir pointing 18-37 GHz microwave radiometers to correct for errors in SSH due to wet-tropospheric path delay. Using current antennas at these frequencies, oceanic measurements include significant errors within 50 km of coastlines due to varying emissivity and temperature of land. Higher frequencies (90-170 GHz) can provide proportionally smaller footprints for the same antenna size. In turn, this provides improved retrievals of wet-tropospheric path delay near the coasts. This thesis will focus on the design, fabrication, and testing of two direct detection radiometers with internal calibration at center frequencies of 92 and 130 GHz. Component design, testing and integration of the radiometers using multi-chip modules are discussed. The performance of these radiometers is characterized, including noise figure, internal calibration and long-term stability. These performance parameters, along with their mass, volume, and power consumption, will be used as the basis for the development of future airborne and space-borne millimeter-wave direct detection radiometers with internal calibration.Item Open Access Design, fabrication, and testing of a data acquisition and control system for an internally-calibrated wide-band microwave airborne radiometer(Colorado State University. Libraries, 2014) Nelson, Scott P., author; Reising, Steven C., advisor; Notaros, Branislav, committee member; Kummerow, Christian, committee memberThe National Aeronautics and Space Administration (NASA)'s Earth Science Technology Office (ESTO) administers the Instrument Incubator Program (IIP), providing periodic opportunities to develop laboratory, ground-based and airborne instruments to reduce the risk, cost and schedule of future Earth Science missions. The IIP-10 project proposed in 2010 and led by PI S. Reising at Colorado State University focuses on the development of an internally-calibrated, wide-band airborne radiometer to reduce risks associated with wet-path delay correction for the Surface Water and Ocean Topography (SWOT) mission. This airborne radiometer includes microwave channels at 18.7, 23.8, and 34.0 GHz at both H and V polarizations; millimeter-wave window channels at 90.0, 130.0, 168.0 GHz; and temperature and water vapor sounding channels near 118 and 183 GHz, respectively. These microwave, millimeter-wave window and millimeter-wave sounding channels consist of 6, 3 and 16 channels, respectively, for a total of 25 channels in this airborne instrument. Since the instrument is a prototype for space flight, a great deal of effort has been devoted to minimizing the mass, size and power consumption of the radiometer's front-end. Similar goals of minimizing the mass, size and power consumption have driven the design of the radiometer back-end, which performs the data acquisition and control functions for the entire instrument. The signals output from all 25 radiometer channels are conditioned, integrated and digitized on the analog back-end boards. The radiometer system is controlled by a Field Programmable Gate Array (FPGA) and a buffer board. Each analog back-end board conditions and simultaneously samples four signals, performing analog-to-digital conversion. The digital back-end consists of the buffer board and FPGA, which control and accept data from all seven analog back-end boards required to sample all 25 radiometer channels. The digital back-end also controls the radiometer front-end calibration (also called "Dicke") switching and the motor used to perform cross-track scanning and black body target calibration of the airborne radiometer instrument. The design, fabrication, and test results of the data acquisition and control system are discussed in depth. First, a system analysis determines general requirements for the airborne radiometer back-end. In the context of these requirements, the design and function of each component are described, as well as its relationship to the other components in the radiometer back-end. The hardware and software developed as part of this radiometer back-end are described. Finally, the back-end testing and results of these tests are discussed.Item Open Access Drought tolerance and implications for vegetation-climate interactions in the Amazon forest(Colorado State University. Libraries, 2012) Harper, Anna Biagi, author; Denning, Alan Scott, advisor; Randall, David, committee member; Kummerow, Christian, committee member; Cotrufo, Francesca, committee memberOn seasonal and annual timescales, the Amazon forest is resistant to drought, but more severe droughts can have profound effects on ecosystem productivity and tree mortality. The majority of climate models predict decreased rainfall in tropical South America over this century. Until recently, land surface models have not included mechanisms of forest resistance to seasonal drought. In some coupled climate models, the inability of tropical forest to withstand warming and drying leads to replacement of forest by savanna by 2050. The main questions of this research are: What factors affect forest drought tolerance, and what are the implications of drought tolerance mechanisms for climate? Forest adaptations to drought, such as development of deep roots, enable Amazon forests to withstand seasonal droughts, and the maintenance of transpiration during dry periods can affect regional climate. At high levels of water stress, such as those imposed during a multiyear rainfall exclusion experiment or during interannual drought, trees prevent water loss by closing their stomata. We examine forest response to drought in an ecosystem model (SiB3 - the Simple Biosphere model) compared to two rainfall exclusion experiments in the Amazon. SiB3 best reproduces the observed drought response using realistic soil parameters and annual LAI, and by adjusting soil depth. SiB3's optimal soil depth at each site serves as a proxy for forest drought resistance. Based on the results at the exclusion sites, we form the hypothesis that forests with periodic dry conditions are more adapted to drought. We parameterize stress resistance as a function of precipitation climatology, soil texture, and percent forest cover. The parameterization impacts carbon and moisture fluxes during extreme drought events. The loss of productivity is of similar magnitude as plot-based measurements of biomass loss during the 2005 drought. Changing stress resistance in SiB3 also affects surface evapotranspiration during dry periods, which has the potential to affect climate through changing sensible and latent heat fluxes. We examine the effects of forest stress resistance on climate through coupled experiments of SiB3 in a GCM. In a single column model, we find evidence for a more active hydrologic cycle due to increased stress resistance. The boundary layer responds through changes in its depth, relative humidity, and turbulent kinetic energy, and the changes feed back to influence wet season onset and intensity. In a full global GCM, increased stress resistance often decreases drought intensity through enhanced ET and changes to circulation. The circulation responds to changes in atmospheric latent heating and can affect precipitation in the South Atlantic Convergence Zone.Item Open Access High-dimensional nonlinear data assimilation with non-Gaussian observation errors for the geosciences(Colorado State University. Libraries, 2023) Hu, Chih-Chi, author; van Leeuwen, Peter Jan, advisor; Kummerow, Christian, committee member; Anderson, Jeffrey, committee member; Bell, Michael, committee member; Kirby, Michael, committee memberData assimilation (DA) plays an indispensable role in modern weather forecasting. DA aims to provide better initial conditions for the model by combining the model forecast and the observations. However, modern DA methods for weather forecasting rely on linear and Gaussian assumptions to seek efficient solutions. These assumptions can be invalid, e.g., for problems associated with clouds, or for the assimilation of remotely-sensed observations. Some of these observations are either discarded, or not used properly due to these inappropriate assumptions in DA. Therefore, the goal of this dissertation is to seek solutions to tackle the issues arising from the linear and Gaussian assumptions in DA. This dissertation can be divided into two parts. In the first part, we explore the potential of the particle flow filter (PFF) in high dimensional systems. First, we tested the PFF in the 1000- dimensional Lorenz 96 model. The key innovation is we find that using a matrix kernel in the PFF can prevent the collapse of particles along the observed directions, for a sparsely observed and high-dimensional system with only a small number of particles. We also demonstrate that the PFF is able to represent a multi-modal posterior distribution in a high-dimensional space. Next, in order to apply the PFF for the atmospheric problem, we devise a parallel algorithm for PFF in the Data Assimilation Research Testbed (DART), called PFF-DART. A two-step PFF was developed that closely resembles the original PFF algorithm. A year-long cycling data assimilation experiment with a simplified atmospheric general circulation model shows PFF-DART is able to produce stable and comparable results to the Ensemble Adjustment Kalman Filter (EAKF) for linear and Gaussian observations. Moreover, PFF-DART can better assimilate the non-linear observations and reduce the errors of the ensemble, compared to the EAKF. In the second part, we shift our focus to the observation error in data assimilation. Traditionally, observation errors have been assumed to follow a Gaussian distribution mainly for two reasons: it is difficult to estimate observation error statistics beyond its second moment, and most of the DA methods assume a Gaussian observation error by construction. We developed the so-called Deconvolution-based Observation Error Estimation (DOEE), that can estimate the full distribution of the observation error. We apply DOEE to the all-sky microwave radiances and show that they indeed have non-Gaussian observation errors, especially in a cloudy and humid environment. Next, in order to incorporate the non-Gaussian observation errors into variational methods, we explore an evolving-Gaussian approach, that essentially uses a state dependent Gaussian observation error in each outer loop of the minimization. We demonstrate the merits of this method in an idealized experiment, and implemented it in the Integrated Forecasting System of the European Centre for Medium-Range Weather Forecasts. Preliminary results show improvement for the short-term forecast of lower-tropospheric humidity, cloud, and precipitation when the observation error models of a small set of microwave channels are replaced by the non-Gaussian error models. In all, this dissertation provides possible solutions for outstanding non-linear and non-Gaussian data assimilation problems in high-dimension systems. While there are still important remaining issues, we hope this dissertation lays a foundation for the future non-linear and non-Gaussian data assimilation research and practice.Item Open Access Machine-learned gas optics with a focus on geostationary extended observations (GeoXO) for improving water vapor observations in the lower atmosphere(Colorado State University. Libraries, 2023) Solanki, Dhyey, author; Chiu, Christine, advisor; Kummerow, Christian, committee member; Jathar, Shantanu, committee memberIn the grand scheme of the earth-atmosphere system, there are few constituents more vital and mysterious than water vapor. Vital because of its interwoven thermodynamic, radiative, and dynamic influence on the weather and climate of the planet, and mysterious because of our limited capacity in observing its time evolution in horizontal and vertical space. The advancements in the spectral and radiometric accuracy of next-generation infrared sounders are expected to bring unprecedented value to our observational capability with improved profiling of lower tropospheric water vapor where it is most abundant. Essential to performing satellite observations and their assimilation to dynamical models is the accurate and efficient radiative transfer calculations. In this process, calculating the atmospheric absorption by various gases is one of the most important steps. The 'line-by-line' approach of computing the influence of every absorption and emission line is operationally impractical for many observations that can contain hundreds of absorption lines. The existing radiative transfer models, therefore, use parameterized gaseous absorption using methods like pre-computed lookup tables or regression methods. The conventional methods compute channel values and can only be used for a specific sensor and channel. Here, we present a new method of performing gas absorption calculations using machine learning that can be applied to the spectral interval of any channel. With an example spectral interval of the new water vapor channel on the upcoming GeoXO infrared sounder, we train neural networks to emulate the line-by-line layer optical depths on a consistent grid of 100 atmospheric layers defined by 101 pressure levels spanning from 1100 hPa to 0.005 hPa. We sample a diverse set of 8640 profiles around the globe for the year 2014 from the Medium-Range Weather Forecasts (ECMWF) atmospheric reanalyses dataset (ERA5) and use 80% of these profiles as training data and 20% of the profiles as validation data. We test the performance of the emulators using a completely independent set of 83 profiles from ECMWF for the year 2006-2007, known as ECMWF83 profiles that have been widely used for training the atmospheric transmittance due to gas absorptions. The atmospheric optical depth used as the truth in all datasets is calculated from the line-by-line Monochromatic Radiative Transfer Model (MonoRTM). The evaluation results from the testing dataset show that the trained neural networks are able to predict line-by-line layer optical depths with a mean percent error of 0.47%. Radiative transfer models used for simulating satellite radiances, like Community Radiative Transfer Model (CRTM), require channel layer-to-space transmittance profiles for solving the radiative transfer equation. Transmittance profiles were calculated using the predicted line-by-line layer optical depths with a mean percent error of 0.02%. Further, the predicted values are also able to accurately calculate the channel weighting functions with the mean percent error of 0.13%. The results show the feasibility of utilizing neural networks in predicting line-by-line optical depths that can be applied for any spectral interval and can be highly useful for the designing of future sensors.Item Open Access Microphysical and macrophysical responses of marine stratocumulus polluted by underlying ships(Colorado State University. Libraries, 2012) Christensen, Matthew Wells, author; Stephens, Graeme, advisor; Kummerow, Christian, committee member; van den Heever, Susan C., committee member; Reising, Steven, committee memberMultiple sensors flying in the A-train constellation of satellites were used to determine the extent to which aerosol plumes from ships passing below marine stratocumulus alter the microphysical and macrophysical properties of the clouds. Aerosol plumes generated by ships sometimes influence cloud microphysical properties (effective radius) and, to a largely undetermined extent, cloud macrophysical properties (liquid water path, coverage, depth, precipitation, and longevity). Aerosol indirect effects were brought into focus, using observations from the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) and the 94-GHZ radar onboard CloudSat. To assess local cloud scale responses to aerosol, the locations of over one thousand ship tracks coinciding with the radar were meticulously logged by hand from the Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. MODIS imagery was used to distinguish ship tracks that were embedded in closed, open, and unclassifiable mesoscale cellular cloud structures. The impact of aerosol on the microphysical cloud properties in both the closed and open cell regimes were consistent with the changes predicted by the Twomey hypothesis. For the macrophysical changes, differences in the sign and magnitude of these properties were observed between cloud regimes. The results demonstrate that the spatial extent of rainfall (rain cover fraction) and intensity decrease in the clouds contaminated by the ship plume compared to the ambient pristine clouds. Although reductions of precipitation were common amongst the clouds with detectable rainfall (72% of cases), a substantial fraction of ship tracks (28% of cases) exhibited the opposite response. The sign and strength of the response was tied to the type of stratocumulus (e.g., closed vs open cells), depth of the boundary layer, and humidity in the free-troposphere. When closed cellular clouds were identified, liquid water path, drizzle rate, and rain cover fraction (an average relative decrease of 61%) was significantly smaller in the ship-contaminated clouds. Differences in drizzle rate resulted primarily from the reductions in rain cover fraction (i.e., fewer pixels were identified with rain in the clouds polluted by the ship). The opposite occurred in the open cell regime. Ship plumes ingested into this regime resulted in significantly deeper and brighter clouds with higher liquid water amounts and rain rates. Enhanced rain rates (average relative increase of 89%) were primarily due to the changes in intensity (i.e., rain rates on the 1.1 km pixel scale were higher in the ship contaminated clouds) and, to a lesser extent, rain cover fraction. One implication for these differences is that the local aerosol indirect radiative forcing was more than five times larger for ship tracks observed in the open cell regime (-59 W m-2) compared to those identified in the closed cell regime (-12 W m-2). The results presented here underline the need to consider the mesoscale structure of stratocumulus when examining the cloud dynamic response to changes in aerosol concentration. In the final part of the dissertation, the focus shifted to the climate scale to examine the impact of shipping on the Earth's radiation budget. Two studies were employed, in the first; changes to the radiative properties of boundary layer clouds (i.e., cloud top heights less than 3 km) were examined in response to the substantial decreases in ship traffic that resulted from the recent world economic recession in 2008. Differences in the annually averaged droplet effective radius and top of atmosphere outgoing shortwave radiative flux between 2007 and 2009 did not manifest as a clear response in the climate system and, was probably masked either due to competing aerosol cloud feedbacks or by interannual climate variability. In the second study, a method was developed to estimate the radiative forcing from shipping by convolving lanes of densely populated ships onto the global distributions of closed and open cell stratocumulus clouds. Closed cells were observed more than twice as often as open cells. Despite the smaller abundance of open cells, a significant portion of the radiative forcing from shipping was claimed by this regime. On the whole, the global radiative forcing from ship tracks was small (approximately -0.45 mW m-2) compared to the radiative forcing associated with the atmospheric buildup of anthropogenic CO2.Item Open Access Millimeter and sub-millimeter wave radiometers for atmospheric remote sensing from CubeSat platforms(Colorado State University. Libraries, 2018) Ogut, Mehmet, author; Reising, Steven C., advisor; Chandrasekar, V., committee member; Kummerow, Christian, committee member; Vivekanandan, Jothiram, committee memberTo view the abstract, please see the full text of the document.Item Open Access Optimizing remote sensing data for actual crop evapotranspiration mapping at different resolutions(Colorado State University. Libraries, 2024) Costa Filho, Edson, author; Chávez, José L., advisor; Venayagamoorthy, Karan, committee member; Niemann, Jeffrey, committee member; Kummerow, Christian, committee memberThis study aimed to advance irrigation water management by developing and evaluating a procedure to improve the multispectral data from sub-optimal remote sensing sensors when using the optimal spectral resolution for a given remote sensing (RS) of crop actual evapotranspiration (ETa) algorithm. Data have been collected at three research sites in Colorado under different irrigation systems, soil textures, and vegetation types. The research site in Greeley (CO) has a five-year dataset (2017-2018 and 2020-2022). The fields in Fort Collins and Rocky Ford (CO) have data from 2020 and 2021. Three categories of ETa algorithms were evaluated in the study: The reflectance-based crop coefficient (RBCC) with three different models based on the normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), and fractional vegetation canopy cover (fc), the one-source simplified surface energy balance (OSEB) based on a surface aerodynamic temperature approach, and the two-source surface energy balance algorithm (TSEB) using two different resistance approaches (parallel and series). All three ETa modeling categories use either just surface reflectance in the visible and invisible light spectrum (e.g., RED, BLUE, GREEN, Near-infrared) or a combination of multispectral and thermal data as inputs to predict crop ETa, alongside local micrometeorological data from nearby agricultural weather stations. A total of six RS of ETa algorithms were evaluated in this study. A total of five RS sensors were evaluated: three spaceborne sensors (e.g., Landsat-8, Sentinel-2, and Planet CubeSat), one proximal device (multispectral radiometer), and an uncrewed aerial vehicle (UAS). The spatial resolution of the RS sensors varied from 30 m to 0.03 m. The accuracy assessment of the crop ETa predictions considered a statistical performance analysis using, among several statistical metrics, the mean bias error (MBE) and root mean square error (RMSE), and compared estimated ETa values from all seven RS ETa algorithms with observed ETa values obtained from the Eddy Covariance Energy Balance System (Greeley and Fort Collins sites) and a weighing lysimeter (Rocky Ford). The study was divided into three stages: a) the evaluation of different remote sensing (RS) pixel spatial resolutions (scales) as inputs on the estimation of different types of data needed for estimating ETa in hourly and daily time frames; b) the development of a calibration protocol and standards for the use of different imagery spatial resolutions (scales) in RS of ETa algorithms. The calibration approach involved a novel two-source pixel decomposition approach for partitioning surface reflectance into soil and vegetation using a non-linear, physically based spectral model, machine-learning regression, and a novel spatial light extinction model (kp); c) the accuracy evaluation of resulting ETa rates from calibrated/standardized data (for each selected RS of ETa algorithms). Results of stage one of the study indicated that depending on the RS of ETa and RS sensor data (spatial and spectral resolutions), the accuracy (MBE ± RMSE) of estimated ETa predictions varied. For the NDVI and fc RBCC ETa algorithms, Sentinel-2 provided the best RS data for predicting daily maize ETa. Errors were 0.21 (5%) ± 0.78 (18%) mm/d and 0.59 (14%) ± 1.07 (25%) mm/d, respectively. For the OSEB algorithm, Planet CubeSat gave the best RS data since it provided the smallest error for hourly maize ETa, -0.02 (-3%) ± 0.07 (13%) mm/h. For the SAVI RBCC model, the MSR data provided the best results since the maize ETa error was -0.13 (-3%) ± 0.67 (16%) mm/d. For the TSEB in series and parallel, the errors when estimating hourly maize ETa were -0.02 (-3%) ± 0.07 (11%) mm/h and -0.02 (-4%) ± 0.09 (14%) mm/h, respectively when using MSR data. For stage two of the study, the best machine learning regression model for a given RS sensor data and RS of the ETa algorithm depended on the surface reflectance composite (plant or bare soil values). The best machine-learning models for adjusting RS data were the regression tree and the Gaussian Process Regression. Regarding the pixel decomposition approach based on the novel spatial light extinction coefficient model, the novel approach provided reliable predictions of kp using the different RS sensor data. The error in predicting kp was -0.01 (-2%) ± 0.05 (10%) when combining all RS sensor data for the two-year data set at LIRF (years 2018 and 2022). For stage three of the study, results showed improvements in the accuracy of crop ETa estimation after adjusting the RS data using the proposed calibration protocol. At the Greeley site, regarding the RBCC RS of ETa algorithm, adjusted data from Planet CubeSat had better performance when estimating daily crop ETa since the error was reduced from 21% to 16% for the fc-input model. For the SAVI-input model, the RS data that performed better was the UAS. Errors were reduced from -0.42 (-11%) ± 0.76 (20%) mm/d to -0.21 (-5%) ± 0.41 (11%) mm/d. For the NDVI-input model, the adjusted UAS data performed better when estimating daily maize ETa. The improved accuracy was 0.32 (8%) ± 0.40 (10%) mm/d. At the Rocky Ford site, for the fc-input model, adjusted RS optical data from the MSR performed better. Daily maize ETa error was reduced from 17% to 15%. For the SAVI-input model, the RS data that performed better was the Landsat-8, with errors being reduced from -1.84 (-28%) ± 2.61 (39%) mm/d to -1.14 (-17%) ± 1.79 (27%) mm/d. The NDVI-based RBCC model had better performance when using adjusted MSR data daily maize ETa. Regarding the OSEB RS of crop ETa approach, at the Greeley site, the OSEB-adjusted data from UAS performed better. Hourly maize ETa error was reduced from 0.11 (19%) mm/h to 0.07 (13%) mm/h for the OSEB algorithm. For the TSEB parallel algorithm, the RS data that had better performance was the Landsat-8/9 since the error was reduced from 0.19 (34%) mm/h to 0.11 (20%) mm/h. For the TSEB series algorithm, the adjusted UAS data performed better. Daily maize ETa errors decreased from 0.10 (18%) mm/h to 0.05 (9%) mm/h. In summary, this study provided an RS calibration approach to support irrigation water management through the development and evaluation of a method for enhancing optical multispectral data sourced from various RS sensors. This study also highlighted the efficacy of machine learning models, like regression tree and Gaussian Process Regression, in adjusting RS data based on surface reflectance composites. Furthermore, a novel pixel decomposition approach utilizing a spatial light extinction model effectively predicted the light extinction coefficient. Overall, this research showcases the potential of RS data adjustments in improving the accuracy of ETa estimates, which is crucial for optimizing irrigation practices in agricultural settings.Item Open Access Regional analysis of convective systems during the West African monsoon(Colorado State University. Libraries, 2012) Guy, Bradley Nicholas, author; Rutledge, Steven, advisor; Tao, Wei-Kuo, advisor; Kummerow, Christian, committee member; van den Heever, Sue, committee member; Cifelli, Rob, committee member; Reising, Steven, committee memberThe West African monsoon (WAM) occurs during the boreal summer and is responsible for a majority of precipitation in the northern portion of West Africa. A distinct shift of precipitation, often driven by large propagating mesoscale convective systems, is indicated from satellite observations. Excepting the coarser satellite observations, sparse data across the continent has prevented understanding of mesoscale variability of these important systems. The interaction between synoptic and mesoscale features appears to be an important part of the WAM system. Without an understanding of the mesoscale properties of precipitating systems, improved understanding of the feedback mechanism between spatial scales cannot be attained. Convective and microphysical characteristics of West African convective systems are explored using various observational data sets. Focus is directed toward meso -α and -β scale convective systems to improve our understanding of characteristics at this spatial scale and contextualize their interaction with the larger-scale. Ground-based radar observations at three distinct geographical locations in West Africa along a common latitudinal band (Niamey, Niger [continental], Kawsara, Senegal [coastal], and Praia, Republic of Cape Verde [maritime]) are analyzed to determine convective system characteristics in each domain during a 29 day period in 2006. Ancillary datasets provided by the African Monsoon Multidisciplinary Analyses (AMMA) and NASA-AMMA (NAMMA) field campaigns are also used to place the radar observations in context. Results show that the total precipitation is dominated by propagating mesoscale convective systems. Convective characteristics vary according to environmental properties, such as vertical shear, CAPE, and the degree of synoptic forcing. Data are bifurcated based on the presence or absence of African easterly waves. In general, African easterly waves appear to enhance mesoscale convective system strength characteristics (e.g. total precipitation and vertical reflectivity profiles) at the inland and maritime sites. The wave regime also resulted in an increased population of the largest observed mesoscale convective systems observed near the coast, which led to an increase in stratiform precipitation. Despite this increase, differentiation of convective strength characteristics was less obvious between wave and no-wave regimes at the coast. Due to the propagating nature of these advecting mesoscale convective systems, interaction with the regional thermodynamic and dynamic environment appears to result in more variability than enhancements due to the wave regime, independent of location. A 13-year (1998-2010) climatology of mesoscale convective characteristics associated with the West African monsoon are also investigated using precipitation radar and passive microwave data from the NASA Tropical Rainfall Measuring Mission satellite. Seven regions defined as continental northeast and northwest, southeast and southwest, coastal, and maritime north and south are compared to analyze zonal and meridional differences. Data are categorized according to identified African easterly wave (AEW) phase and when no wave is present. While some enhancements are observed in association with AEW regimes, regional differences were generally more apparent than wave vs. no-wave differences. Convective intensity metrics confirm that land-based systems exhibit stronger characteristics, such as higher storm top and maximum 30-dBZ heights and significant 85-GHz brightness temperature depressions. Continental systems also contain a lower fraction of points identified as stratiform. Results suggest that precipitation processes also varied depending upon region and AEW regime, with warm-rain processes more apparent over the ocean and the southwest continental region and ice-based microphysics more dominant over land, including mixed-phase processes. AEW regimes did show variability in stratiform fraction and ice and liquid water content, suggesting modulation of mesoscale characteristics possibly through feedback with the synoptic environment. Two mesoscale convective systems (MCSs) observed during the African Monsoon Multidisciplinary Analyses (AMMA) experiment are simulated using the three-dimensional (3D) Goddard Cumulus Ensemble model. One of the MCSs, the 8 September 2006 system, is associated with the passage of an African easterly wave trough while the other, the 14 July 2006 case, is not. Simulations are performed using 1 km horizontal grid spacing, a lower limit on current embedded cloud resolving models within a multi-scale modeling framework. Simulated system structure is compared to radar observations using contoured frequency-by-altitude diagrams (CFADs), calculated ice and water mass, and identified hydrometeor variables. Results indicate general agreement in the temporal distribution of hydrometeors. Vertical distributions show that ice hydrometeors are often underestimated at mid- and upper-levels, partially due to the inability of the model to produce adequate system heights. Abundance of high reflectivity values below and near the melting level in the simulation led to a broadening of the CFAD distributions. Observed vertical reflectivity profiles indicate larger reflectivities aloft compared to simulated values. Despite these differences and biases, the radar-observed differences between the two cases are noticeable in the simulations as well, suggesting that the model is able to capture gross observed differences between the two MCSs.Item Open Access Satellite-based investigation of convection and precipitation in tropical cyclone intensity change(Colorado State University. Libraries, 2022) Razin, Muhammad Naufal Bin, author; Bell, Michael, advisor; Kummerow, Christian, committee member; Rasmussen, Kristen, committee member; Kirby, Michael, committee memberMultiple hypotheses on the role of convection and precipitation in tropical cyclone intensity change have been proposed, but a scientific consensus has not yet been obtained. Some recent studies emphasize the importance of asymmetric deep convection in intensification, while others emphasize the importance of more symmetric precipitation that is not necessarily deep. To help provide clarity on this issue, this dissertation analyzes a large dataset of satellite passive microwave observations in order to obtain a sufficient sample size, which enables us to minimize the impact of the tropical cyclone environmental variability on intensity. The first part of this dissertation involves compiling an extensive research-quality and open-access dataset of low-Earth orbit (LEO) satellite passive microwave observations centered on tropical cyclones, called the Tropical Cyclone Precipitation, Infrared, Microwave, and Environmental Dataset (TC PRIMED). TC PRIMED consists of tropical cyclone-centric 1) inter-calibrated, multi-channel, multi-imager microwave brightness temperatures, 2) retrieved rainfall from NASA's Goddard Profiling algorithm (GPROF), 3) nearly coincident geostationary satellite infrared imagery, and 4) auxiliary data such as tropical cyclone position and intensity, ERA5 fields and derived environmental diagnostics, and satellite precipitation radar variables. TC PRIMED includes observations from over 168,000 LEO satellite overpasses of 2,101 tropical cyclones from 1998 to 2019. A simple composite analysis demonstrates the huge potential of TC PRIMED. The second part of this dissertation uses Global Precipitation Measurement (GPM) satellite observations from TC PRIMED to train a random forest model to classify the precipitation type (i.e., convective and stratiform). The model uses raw and derived passive microwave brightness temperature variables as input predictors and observations from the GPM dual-frequency precipitation radar as reference. This approach leverages the wider swath of the GPM passive microwave observations to obtain a larger sample size of precipitation type observation for the subsequent study on the importance of convection and precipitation in tropical cyclone intensity change. The random forest model performs very well at delineating clear air from stratiform and convective precipitation, and captures key features like the tropical cyclone eye, eyewall, and primary rainbands. However, the model struggles to detect some finer features like randomly scattered convection. Analysis of the model's performance demonstrates the importance of passive microwave texture information for precipitation type classification. The final part of this dissertation uses the tools developed in parts one and two to investigate the distribution of convection and precipitation in tropical cyclone intensity change. This analysis minimizes the impacts of the environmental variability on intensity by only selecting observations of tropical cyclones in favorable environments. Results reveal that intensifying minor tropical cyclones have a more symmetric distribution of rainfall that is not necessarily convective, while intensifying major tropical cyclones have more numerous and intense deep convection in the upshear quadrants. The results lead to the following hypotheses: i) for minor tropical cyclones, a more symmetric distribution of rainfall is more efficient at intensifying the tropical cyclone, and ii) the occurrence of deep convection in the upshear quadrants of major tropical cyclones is optimal for intensification. This dissertation provides the scientific community with an extensive set of tools to investigate tropical cyclones using satellite passive microwave observations and ancillary data in the form of TC PRIMED. Subsequently, the second part of this dissertation demonstrates the utility of a machine learning model in classifying multiple precipitation types from satellite passive microwave observations, with discussions on model deficiencies providing a framework to further improve such approach. Finally, this dissertation leverages a large dataset of tropical cyclone observations to clarify the role of convection and precipitation in tropical cyclone intensification.Item Open Access Using machine learning to improve vertical profiles of temperature and moisture for severe weather nowcasting(Colorado State University. Libraries, 2021) Stock, Jason D., author; Anderson, Charles, advisor; Ebert-Uphoff, Imme, advisor; Pallickara, Shrideep, committee member; Kummerow, Christian, committee memberVertical profiles of temperature and moisture as provided by radiosondes are of paramount importance to forecasting convective activity, yet the National Weather Service radiosonde network is spatially coarse and suffers from temporal paucity. Supplementary information generated by numerical weather prediction (NWP) models is invaluable---analysis and forecast profiles are available at a high sampling frequency and horizontal resolution. However, numerical models contain inherent errors and inaccuracies, and many of these errors occur near the surface and influence the short-term prediction of high impact events such as severe thunderstorms. For example, the convective available potential energy and the convective inhibition are highly dependent on the near-surface values of temperature and moisture. To address these errors and to create the most useful vertical profiles of temperature and moisture for severe weather nowcasting, we explore a machine learning approach to combine satellite and surface observations with an initial NWP profile. In particular, we explore deep learning to improve vertical profiles from an NWP model, which is the first known work to do so. Using initial profile predictions from the Rapid Refresh (RAP) model, corresponding surface products from the Real-Time Mesoscale Analysis (RTMA), and satellite data from the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager, we train variations of fully-connected and convolutional neural networks with custom knowledge guided loss functions to produce enhanced profiles. We evaluate the success of our approach by comparing estimates with ground truth radiosonde observations (RAOB)s and their derived indices for samples collected between January 1, 2017 and August 31, 2020. The proposed Residual U-Net architecture shows a 26.15% reduction in error over the profiles relative to the RAP errors, with the greatest improvements in the mid- to upper-level moisture. Furthermore, we detail the importance of the GOES-16 channels and assess our model under different meteorological conditions, finding: 1) no bias of seasonality; 2) training with additional samples, even in cloudy conditions, to be beneficial; and 3) sounding locations with more samples and higher initial errors to have greater improvement. As such, this work is targeted to aid forecasters concerned with severe convection make more precise predictions, thereby enhancing the nation's readiness, responsiveness, and resilience to high-impact weather events.Item Open Access Using remotely sensed fluorescence and soil moisture to better understand the seasonal cycle of tropical grasslands(Colorado State University. Libraries, 2017) Smith, Dakota Carlysle, author; Denning, A. Scott, advisor; Smith, Melinda, committee member; O'Dell, Christopher, committee member; Kummerow, Christian, committee memberSeasonal grasslands account for a large area of Earth's land cover. Annual and seasonal changes in these grasslands have profound impacts on Earth's carbon, energy, and water cycles. In tropical grasslands, growth is commonly water-limited and the landscape oscillates between highly productive and unproductive. As the monsoon begins, soils moisten providing dry grasses the water necessary to photosynthesize. However, along with the rain come clouds that obscure satellite products that are commonly used to study productivity in these areas. To navigate this issue, we used solar induced fluorescence (SIF) products from OCO-2 along with soil moisture products from the Soil Moisture Active Passive satellite (SMAP) to "see through" the clouds to monitor grassland productivity. To get a broader understanding of the vegetation dynamics, we used the Simple Biosphere Model (SiB4) to simulate the seasonal cycles of vegetation. In conjunction with SiB4, the remotely sensed SIF and soil moisture observations were utilized to paint a clearer picture of seasonal productivity in tropical grasslands. The remotely sensed data is not available for every place at one time or at every time for one place. Thus, the study was focused on a large area from 15° E to 35° W and from 8°S to 20°N in the African Sahel. Instead of studying productivity relative to time, we studied it relative to soil moisture. Through this investigation we found soil moisture thresholds for the emergence of grassland growth, near linear grassland growth, and maturity of grassland growth. We also found that SiB4 overestimates SIF by about a factor of two for nearly every value of soil moisture. On the whole, SiB4 does a surprisingly good job of predicting the response of seasonal growth in tropical grasslands to soil moisture. Future work will continue to integrate remotely sensed SIF & soil moisture with SiB4 to add to our growing knowledge of carbon, water, and energy cycling in tropical grasslands.