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NASA NTRSDate not supplied

Aerosol Wind Profiler (AWP) Doppler Wind Lidar Airborne Observations During the NOAA Joint Venture 3-D Wind Measurement Demonstration and NASA Active-Passive Profiling Experiment (APEX)

The NASA Langley Research Center (LaRC) has recently completed development of the Aerosol Wind Profiler (AWP) airborne Doppler wind lidar (DWL) instrument. AWP was supported by the NASA Earth Science Technology Office and the Earth Science Division, a project that adapted the Wind-Space Pathfinder (Wind-SP) DWL transceiver onto a structure for flight aboard a variety of NASA research aircraft. AWP demonstrates many technologies required for a space DWL mission, including a coherent-detection, optical heterodyne laser transmitter with high pulse energy (up to ~55 mJ) and repetition rate (200 Hz), electronic control of the beam path allowing for multiple viewing angles (allowing vector wind measurements) with no moving parts, compact highly-stable and tunable reference lasers allowing for high-precision measurement of velocity at long ranges while mitigating the impact of satellite platform velocity, and many others. AWP represents NASA’s only currently operational airborne 3-D wind profiling sensor. NASA LaRC was selected by the NOAA Joint Venture (JV) program to conduct a suborbital 3-D Wind measurement campaign demonstrate how data from a coherent-detection DWL like AWP could serve NOAA’s weather analysis and forecasting needs. The NOAA JV program is designed to work with the private sector, academia and other federal agencies to explore the feasibility and capability of emerging technologies spacecraft and other mission-specific tools to meet NOAA’s mission requirements. AWP was initially demonstrated on the NASA DC-8 within this JV program in October 2023, piggybacking on the NASA EcoDemonstrator mission focused on in-situ sampling of jet aircraft emissions and contrail formation from Everett, Washington. The in-situ sampling resulted in very frequent and rapid aircraft attitude changes which unfortunately degraded AWP data quality. But, during times with level flight and AVAPS dropsonde operations, AWP demonstrated excellent precision (< 2 m/s RMS) with high vertical (< 100 meter) resolution and 2 km spacing between profiles. AWP will be flown again on the NASA LaRC Gulfstream-3 from mid-September to mid-October 2024 out of Hampton, VA to complete the NOAA JV 3-D wind demonstration. Additional AWP flights will occur in early November from southern California during the NASA Active-Passive Profiling Experiment (APEX), focused on underflights of the NASA ER-2 equipped with many atmospheric profiling sensors. This presentation will summarize AWP measurements collected during these two fall 2024 flight campaigns, and how the AWP data compares with AVAPS dropsonde, NOAA weather prediction model, and GOES atmospheric motion vector data.

Kristopher Bedka↗
NASA NTRS2017

Limiting Data Friction by Reducing Data Download Using Spatiotemporally Aligned Data Organization Through STARE

Current data processing practice limits the volume and variety of relevant geoscience data that can practically be applied to important problems. File archives in centralized data centers are the principal means by which Earth Science data are accessed. This approach, however, requires laborious search, retrieval, and eventual customization/adaptation for the data to be used. Such fractionation makes it even more difficult to share outcomes, i.e. research artifacts and data products, hampering reusability and repeatability, since end users generally have their own research agenda and preferences as well as scarce resources. Thus, while finding and downloading data files from central data centers are already costly for end users working in their own field, using data products from other disciplines rapidly becomes prohibitive. This curtails scientific productivity, limits avenues of study, and endangers quality and reproducibility. The Spatio-Temporal Adaptive Resolution Encoding (STARE) is a unifying scheme that facilitates the indexing, access, and fusion of diverse Earth Science data. STARE implements an innovative encoding of geo-spatiotemporal information, originally developed for aligning datasets with diverse spatiotemporal characteristics in an array database. The spatial component of STARE recursively quadfurcates a root polyhedron, producing a hierarchical scheme for addressing geographic locations and regions. The temporal component of STARE uses conventional date-time units as an indexing hierarchy. The additional encoding of spatial and temporal resolution information in STARE enables comparisons and conditional selections across diverse datasets. Moreover, spatiotemporal set-operations, e.g. union and intersection, are mapped to efficient integer operations with STARE. Applied to existing data models (point, grid, spacecraft swath) and corresponding granules, STARE indexes provide a streamlined description usable as geo-spatiotemporal metadata. When coupled with large scale, distributed hardware and software, STARE-based data access reduces pre-analysis data preparation costs by offering a convenient means to align different datasets spatiotemporally without specialized effort in parallel computing or distributed data management.

Kuo, Kwo-Sen↗
NASA NTRS1983

Fine time structure in the 1979 March 5 gamma ray burst

Data on the impulsive phase of the 1979 March 5 gamma ray burst, taken by three identical detectors aboard the Prognoz 7, Venera 11, and Venera 12 spacecraft with 2 ms time resolution, are presented. The first 200 ms of the time history may be described by an exponential decay with time constant of approximately 120 ms up to approximately 100 ms, and time constant of approximately 30 ms thereafter; at low energies, a pulsating component with a period of approximately 23 ms appears. The data could be consistent with torsional vibrations of a neutron star, the rotation of a rapidly spinning neutron star, or oscillations caused by interactions between hot plasma and a magnetic field.

Barat, C.↗
NASA NTRS1991

Site-certification imaging sensor for the exploration of Mars

A 'time dilation' push-broom imaging CCD sensor is being considered for certification of landing and roving sites for the exploration of Mars. It will be used to certify several 10 x 10 km candidate sites and to identify rocks and holes of 1 m or larger at those sites, using an optical sensor with 0.25 m resolution. This will require a high-resolution large-aperture camera and a camera-pointing system designed for high accuracy and stability. To reduce the data rate and increase the exposure time, the imaging time will be stretched out by a factor of eight over the time required to fly directly over the imaged site. Six parallel data storage strings will be used to reduce the data acquisition rate, allowing each string to process a manageable 25 Mb/s.

Scholl, M. S.↗
NASA NTRS2010

Recent Spatial and Temporal Anomalies and Trends of OLR as Observed by CERES and Computed Based on AIRS Retrievals

We show that a recent CERES-observed negative trend in OLR of approx.-0.1 W/sq m/yr averaged over the globe, for the time period of September 2002 through February 2010 used in this study, is found in the AIRS OLR data as well. Most importantly, even minute details (down to 1 x 1 Degree GCM-scale resolution) of spatial and temporal anomalies and trends of OLR as observed by CERES and computed based on AIRS-retrieved surface and atmospheric geophysical parameters over this time period are essentially the same. We see this correspondence even in the very large spatial variations of these trends with local values ranging from -2.6 W/sq m/yr to +3.0 W/sq m/yr in the tropics. This essentially perfect agreement of OLR anomalies and even local trends derived from observations by two different instruments, in totally independent and different manners, implies that both sets of results must be highly accurate; and indirectly validates the anomalies and trends of other AIRS derived products as well. These products show that global and regional anomalies and trends of OLR, water vapor and cloud cover over the last 7+ years are strongly influenced by El-Nino-La Nina cycles . We use the anomalies and trends of AIRS derived products to explain why the global OLR has a large negative trend over this time period; Global and tropical OLR began to decrease significantly at the onset of a strong La Nina in mid-2007. AIRS products show that cloudiness and mid-tropospheric water vapor began to increase in the tropics at roughly the same time, especially in the region 5degN - 20degS latitude extending eastward from 150degW to 30degE longitude, with a corresponding very large drop in OLR in this region. Late 2009 is characterized by a strong El-Nino, with a corresponding change in sign of observed tropical water vapor, cloud cover, and OLR anomalies. If one excludes the area 5degN - 20degS, 150degW - 30degE from the statistics, area mean OLR trends over the rest of the globe are substantially reduced over the time period under study.

Molnar, Gyula↗
NASA NTRS2014

Assessing the Ability of Instantaneous Aircraft and Sonde Measurements to Characterize Climatological Means and Long-Term Trends in Tropospheric Composition

Over four decades of measurements exist that sample the 3-D composition of reactive trace gases in the troposphere from approximately weekly ozone sondes, instrumentation on civil aircraft, and individual comprehensive aircraft field campaigns. An obstacle to using these data to evaluate coupled chemistry-climate models (CCMs)the models used to project future changes in atmospheric composition and climateis that exact space-time matching between model fields and observations cannot be done, as CCMs generate their own meteorology. Evaluation typically involves averaging over large spatiotemporal regions, which may not reflect a true average due to limited or biased sampling. This averaging approach generally loses information regarding specific processes. Here we aim to identify where discrete sampling may be indicative of long-term mean conditions, using the GEOS-Chem global chemical-transport model (CTM) driven by the MERRA reanalysis to reflect historical meteorology from 2003 to 2012 at 2o by 2.5o resolution. The model has been sampled at the time and location of every ozone sonde profile available from the Would Ozone and Ultraviolet Radiation Data Centre (WOUDC), along the flight tracks of the IAGOSMOZAICCARABIC civil aircraft campaigns, as well as those from over 20 individual field campaigns performed by NASA, NOAA, DOE, NSF, NERC (UK), and DLR (Germany) during the simulation period. Focusing on ozone, carbon monoxide and reactive nitrogen species, we assess where aggregates of the in situ data are representative of the decadal mean vertical, spatial and temporal distributions that would be appropriate for evaluating CCMs. Next, we identically sample a series of parallel sensitivity simulations in which individual emission sources (e.g., lightning, biogenic VOCs, wildfires, US anthropogenic) have been removed one by one, to assess where and when the aggregated observations may offer constraints on these processes within CCMs. Lastly, we show results of an additional 31-year simulation from 1980-2010 of GEOS-Chem driven by the MACCity emissions inventory and MERRA reanalysis at 4o by 5o. We sample the model at every WOUDC sonde and flight track from MOZAIC and NASA field campaigns to evaluate which aggregate observations are statistically reflective of long-term trends over the period.

Atmospheric composition↗
NASA NTRS1972

The radiation budget of the earth-atmosphere system as measured from the Nimbus 3 satellite /1969-1970/.

Data from the Medium Resolution Infrared Radiometer (MRIR) experiment on Nimbus 3 have provided global radiance measurements during four seasons (April 1969-February 1970). From these data, values of the total radiation budget of the earth-atmosphere system were derived on varying time and space scales and were compared with previous satellite measurements and theoretical computations. Results confirm earlier satellite measurements. They show a warmer and darker planet than was previously believed. In addition, the Nimbus 3 data offer the first medium resolution views of both the Northern and Southern Hemispheres' radiation budgets during all seasons.

Vonder Haar, T. H.↗
NASA NTRS2009

SPoRT - An End-to-End R2O Activity

Established in 2002 to demonstrate the weather and forecasting application of real-time EOS measurements, the Short-term Prediction Research and Transition (SPoRT) program has grown to be an end-to-end research to operations activity focused on the use of advanced NASA modeling and data assimilation approaches, nowcasting techniques, and unique high-resolution multispectral observational data applications from EOS satellites to improve short-term weather forecasts on a regional and local scale. SPoRT currently partners with several universities and other government agencies for access to real-time data and products, and works collaboratively with them and operational end users at 13 WFOs to develop and test the new products and capabilities in a "test-bed" mode. The test-bed simulates key aspects of the operational environment without putting constraints on the forecaster workload. Products and capabilities which show utility in the test-bed environment are then transitioned experimentally into the operational environment for further evaluation and assessment. SPoRT focuses on a suite of data and products from MODIS, AMSR-E, and AIRS on the NASA Terra and Aqua satellites, and total lightning measurements from ground-based networks. Some of the observations are assimilated into or used with various versions of the WRF model to provide supplemental forecast guidance to operational end users. SPoRT is enhancing partnerships with NOAA / NESDIS for new product development and data access to exploit the remote sensing capabilities of instruments on the NPOESS satellites to address short term weather forecasting problems. The VIIRS and CrIS instruments on the NPP and follow-on NPOESS satellites provide similar observing capabilities to the MODIS and AIRS instruments on Terra and Aqua. SPoRT will be transitioning existing and new capabilities into the AWIIPS II environment to continue the continuity of its activities.

Jedlovec, Gary J.↗
NASA NTRSDate not supplied

FloodPlanet: High-Resolution Commercial Imagery for Training and Validation of Deep Learning-Based Models of Inundation Extent

Flooding events are becoming increasingly frequent worldwide and are known to cause extensive damage. Public optical and radar satellite imagery can be used to detect large areas of inundation in rural areas, however, long revisit times and coarse spatial resolution limit applications for short-lived events and urban areas. Commercial constellations such as those operated by Planet offer increased spatial and temporal resolution and can supplement mapping efforts to provide more information to disaster response, relief, and mitigation efforts. Deep learning requires high quality labeled data for training across coincident sensors. The FloodPlanet dataset presented here contains labeled surface water for 18 events across the world based on Planetscope imagery with coincident Harmonized Landsat Sentinel-2 ( HLS) or Sentinel-1 and builds upon the previously existing Sen1Floods11, xBD, and NASA Sentinel-1 datasets. Sen1Floods11 includes 4,831 512x512 pixel overlapping tiles of coincident Sentinel-1 and Sentinel-2 data observing 11 flood events across the world from 2017-2019. The dataset contains a combination of automated and hand-labeled surface water for use in training and validation of inundation modeling efforts. The xBD dataset identifies flood-damaged buildings and indicates the scale of damage to each (none, minor, moderate, and major) from four flood events which occurred in the United States, India, Nepal, and Bangladesh from the same time period. The NASA dataset contains hand-labeled water bodies observed in Sentinel-1 imagery during five flood events within the 2017-2019 period. The effort presented here utilizes observations from these previously investigated flood events to generate labels of surface water at the 3-5m spatial resolution provided by Planetscope and facilitate the comparison between public and commercial data. A data pipeline was built which uses clustering algorithms to pick the most suitable overlapping chips between the public data and PlanetScope data for manual labeling. Labels were created manually using NASA’s ImageLabeler tool and include areas of high- and low-confidence water. The high confidence designation is reserved for areas of open, unobstructed water while low confidence is used for areas of suspected water beneath vegetation, clouds, or cloud shadows. Expected to be released in late 2022, the FloodPlanet dataset will include tiled imagery with a unique ID for each 1024x1024 pixel tile, 7 bands of HLS data, and high- and low-confidence flood labels in both shapefile and tiff formats. The authors will follow Spatial Temporal Access Catalog (STAC) guidelines to release FloodPlanet on the Radiant Earth ML hub, which hosts public datasets for machine learning.

Alexander Melancon↗
NASA NTRS2010

Implications of Microwave Holography Using Minimum Required Frequency Samples for Weakly- and Strongly-Scattering Indications

Microwave imaging techniques, an integral component of nondestructive testing and evaluation (NDTE), have received significant attention in the past decade. These techniques have included the implementation of synthetic aperture focusing (SAF) algorithms for obtaining high spatial resolution images. The next important step in these developments is the implementation of 3-D holographic imaging algorithms. These are well-known wideband imaging technique requiring a swept-frequency (i.e., wideband), which unlike SAF that is a single frequency technique, are not easily performed on a real-time basis. This is due to the fact that a significant number of data points (in the frequency domain) must be obtained within the frequency band of interest. This not only makes for a complex imaging system design, it also significantly increases the image-production time. Consequently in an attempt to reduce the measurement time and system complexity, an investigation was conducted to determine the minimum required number of frequency samples needed to image a specific object while preserving a desired maximum measurement range and range resolution. To this end the 3-D holographic algorithm was modified to use properlyinterpolated frequency data. Measurements of the complex reflection coefficient for several samples were conducted using a swept-frequency approach. Subsequently, holographical images were generated using data containing a relatively large number of frequency samples and were compared with images generated by the reduced data set data. Quantitative metrics such as average, contrast, and signal-to-noise ratio were used to evaluate the quality of images generated using reduced data sets. Furthermore, this approach was applied to both weakly- and strongly-scattering indications. This paper presents the methods used and the results of this investigation.

Fallahpour, M.↗
NASA NTRS1998

Neutral Networks for Subpixel Classification of Multispectral Images

In this work, the implementation and use of AVHRR (Advanced Very High Resolution Radiometer) images for subpixel analysis is studied. The work consists of two parts, the first is making training data, the second is using the training data to design a neural net subpixel analyzer. Most work on subpixel analysis has been done with images with more spectral bands. AVHRR images were chosen because of their easy acquisition, and because the five spectral bands allow investigation into the development of training data. The first step in subpixel analysis is the development of training data. This consists of image to be classified, and the classification of each pixel. In order to do the classification, a high spatial resolution image is typically needed in order to manually create a classified image. It is difficult to have both the image of interest, and a high spatial resolution image of the same area taken at the same time. Thus it was studied whether a subsampled image taken from the image of interest could serve as the training data. Statistical work has been done showing the unusefulness of this approach. In the second part of the analysis, a feedforward neural net was trained and used to classify the AVHRR images. Results of these tests, comparing the neural net with typical statistical based schemes are shown.

Figueroa, Ricardo R.↗
DOE OSTI2024

Identifying Climate Patterns Using Clustering Autoencoder Techniques

Abstract The complexity of growing spatiotemporal resolution of climate simulations produces a variety of climate patterns under different projection scenarios. This paper proposes a new data-driven climate classification workflow via an unsupervised deep learning technique that can dimensionally reduce the vast volume of spatiotemporal numerical climate projection data into a compact representation. We aim to identify distinct zones that capture multiple climate variables as well as their future changes under different climate change scenarios. Our approach leverages convolutional autoencoders combined with k -means clustering (standard autoencoder) and online clustering based on the Sinkhorn–Knopp algorithm (clustering autoencoder) across the conterminous United States (CONUS) to capture unique climate patterns in a data-driven fashion from the Geophysical Fluid Dynamics Laboratory Earth System Model with GOLD component (GFDL-ESM2G). The developed approach compresses 70 years of GFDL-ESM2G simulation at 0.125° spatial resolution across the CONUS under multiple warming scenarios to a lower-dimensional space by a factor of 660 000 and then tested on 150 years of GFDL-ESM2G simulation data. The results show that five climate clusters capture physically reasonable and spatially stable climatological patterns matched to known climate classes defined by human experts. Results also show that using a clustering autoencoder can reduce the computational time for clustering by up to 9.2 times when compared to using a standard autoencoder. Our five unique climate patterns resulting from the deep learning–based clustering of the lower-dimensional space thereby enable us to provide insights on hydrometeorology and its spatial heterogeneity across the conterminous United States immediately without downloading large climate datasets. Significance Statement This paper presents a data-driven climate classification approach using unsupervised deep learning to dimensionally reduce climate model outputs and to identify distinct climate regions for their future changes. Our approach compresses climate information for 70 years of Geophysical Fluid Dynamics Laboratory Earth System Model data across the conterminous United States (CONUS) at 0.125° spatial resolution. The results reveal that five climate clusters capture reasonable and stable climatological patterns matched to known climate patterns. The embedded clustering process in deep learning provides ×9.2 times faster execution than the k -means clustering technique. These results give us insight about climate spatial patterns and heterogeneity of hydrological patterns across the conterminous United States without downloading large climate datasets.

Kurihana, Takuya↗
NASA NTRSDate not supplied

Modeling Methods for 3D Lightning Mapping from Space

Global lightning detection has advanced greatly over the past few decades both on ground and from orbit. Ground-based systems like the Lightning Mapping Array (LMA) excel at high-precision 3D reconstruction of local flashes, while spaceborne lightning sensors have much larger potential coverage but have been limited by coarser resolution and often rely on data from other instruments to determine flash location. The primary focus of this study is to examine the level of flash detail that can be expected from one or more low-Earth orbiting small satellites that combine VHF and optical measurements of lightning, which is a new mission concept called CubeSpark. The goal of CubeSpark is to map the 3D charge structure of thunderstorms at 1-2 km spatial resolution on a global scale, enabling a host of new atmospheric and space electricity studies. In order to maximize the potential data quality and minimize potential cost, flashes were simulated beneath single- and multi-satellite configurations. In the multi-satellite approach, RF detectors on each of the six satellites would measure the arrival times of impulsive VHF sources to collectively pinpoint their locations in 3D and reconstruct flashes with higher resolution than has been achieved from space. A single-station approach for 3D observation of lightning would follow the method of determining altitudes of strong VHF sources that produce trans-ionospheric pulse pairs (TIPPs) while relying on an on-board high-resolution optical day/night lightning mapper for approximate horizontal flash locations. Here we present preliminary results comparing the expected data fidelity and accuracy between these methods to inform potential satellite missions like CubeSpark.

lightning↗
NASA NTRS2020

Telescopic Observations of Lunar Hydration: Variations and Abundance

Prior to 2009, the Moon was believed to be anhydrous. However, observations by three spacecraft revealed a hydrated surface by reporting a 3 μm absorption band attributed to hydroxyl and possibly molecular water. The Moon Mineralogy Mapper (M3) spectrometer, onboard the Chandrayaan-1 spacecraft is mainly used to study the lunar 3 μm band but its spectral range ends at 3 μm. The limited wavelength range of M3 has allowed observed variations in the 3 μm band to be called into question due to uncertainties in thermal corrections. To investigate the validity of variations in the lunar 3 μm band, we used the SpeX infrared spectrograph at the NASA InfraRed Telescope Facility at Maunakea Observatory in Hawaiʻi. With SpeX, we are able to obtain lunar data over a wavelength range of 1.67 to 4.2 μm at 1 – 2 km spatial resolution. The long wavelengths provide strong constraints on thermal emission corrections. We confirm that the 3 μm band varies with lunar time of day as well as with latitude and composition. Each observation reveals strong variations in abundances of hydroxyl and possibly molecular water. The data reveal a decrease in abundance with increasing lunar local time, an asymmetric trend about the equator that favors the southern latitudes, and higher concentrations in highland regions. The longer wavelengths provided by SpeX have allowed us to examine variations in the 3 μm band and provide definitive evidence that the variations are due to changes in hydration.

C. I. Honniball↗
NASA NTRS2019

UAVSAR real-time embedded GPU processor

Synthetic aperture radar (SAR) can provide high-resolution imagery regardless of cloud cover or lighting conditions. These qualities make SAR potentially well-suited for informing response efforts to natural and man-made disasters, but such applications require data products with minimal latency. To meet this challenge, we implemented a real-time SAR processor capable of producing 10 m imagery using an NVIDIA Jetson TX2 embedded GPU module. With its low mass (87 g module) and power consumption under 8 W, the system also holds promise for spaceborne applications.

Tung, Wayne↗
NASA NTRS2001

Validation of the galactic cosmic ray and geomagnetic transmission models

A very high-momentum resolution particle spectrometer called the Alpha Magnetic Spectrometer (AMS) was flown in the payload bay of the Space Shuttle in a 51.65 degrees x 380-km orbit during the last solar minimum. This spectrometer has provided the first high statistics data set for galactic cosmic radiation protons, and helium, as well as limited spectral data on carbon and oxygen nuclei in the International Space Station orbit. First measurements of the albedo protons at this inclination were also made. Because of the high-momentum resolution and high statistics, the data can be separated as a function of magnetic latitude. A related investigation, the balloon borne experiment with a superconducting solenoid spectrometer (BESS), has been flown from Lynn Lake, Canada and has also provided excellent high-resolution data on protons and helium. These two data sets have been used here to study the validity of two galactic cosmic ray models and the geomagnetic transmission function developed from the 1990 geomagnetic reference field model. The predictions of both the CREME96 and NASA/JSC models are in good agreement with the AMS data. The shape of the AMS measured albedo proton spectrum, up to 2 GeV, is in excellent agreement with the previous balloon and satellite observations. A new LIS spectrum was developed that is consistent with both previous and new BESS 3He observations. Because the astronaut radiation exposures onboard ISS will be highest around the time of the solar minimum, these AMS measurements and these models provide important benchmarks for future radiation studies. AMS-02 slated for launch in September 2003, will provide even better momentum resolution and higher statistics data. Published by Elsevier Science Ltd.

unmanned↗
DOE OSTI2024

Commissioning of the Mu2e tracker DAQ, planning for the Vertical Slice Test and pre-pattern recognition studies

The primary objective of the Mu2e experiment at Fermilab is to search for the neutrino-less coherent $\mu \rightarrow e$ conversion in the field of an aluminum nucleus ($\mu^- \text{Al} \rightarrow e^- \text{Al}$). The signature of this process is a monochromatic Conversion Electron (CE) with an energy of approximately 104.97 MeV \cite{bartoszek2015mu2e}. Within the Standard Model (SM), the branching ratio for this process, including neutrino masses and oscillation, is expected to be less than $\mathcal{O}(10^{-50})$. This value is far beyond current experimental capabilities. However, models of physics beyond the SM predict much higher relative rates, approaching an observable level. The SINDRUM II experiment set an upper limit on muon conversion at $7 \times 10^{-13}$ (90\% CL) on Au target \cite{SINDRUMII:2006dvw}, and the Mu2e collaboration aims to improve this limit by four orders of magnitude. Observing this process would provide a clear evidence of physics beyond the Standard Model. A brief discussion of the theoretical and experimental aspects is provided in Chapter \ref{intr}. Mu2e adopts a sophisticated experimental setup to achieve its goals, further described in Chapter \ref{mu2echapter}. The central part of the Mu2e detector is the tracker, that consists of 18 tracking stations. The tracker must provide excellent momentum resolution, approximately 1 MeV/c, to distinguish the monochromatic CE signal from the background. To minimize the energy losses, a straw tube tracker will be used \cite{bobbb}. Chapter \ref{chaptertrk} provides an overview of the straw tracker design and its working principles. This Thesis presents a comprehensive study of the Mu2e tracker, covering complementary aspects from initial commissioning to optimization and first steps of the calibration processes. My work at Fermilab has been focused on the complete Data Acquisition (DAQ) testing from both hardware and software perspectives. I was involved in the commissioning of the Mu2e DAQ system and the Vertical Slice Test (VST) of the tracker. The VST encompasses the entire testing chain, from the straws to the readout, and to processed data on disk. I was also focused on the offline analysis, especially on pre-pattern recognition studies, to explore the best methods for identifying $\delta$-electrons during the data taking. Chapter \ref{commissioning} details the commissioning of the tracker DAQ system, emphasizing the importance of understanding of the readout process before the data acquisition. This includes validating the readout logic and firmware through Monte Carlo simulations to confirm functionality and buffering, monitoring the quality of the data from the tracker preamplifiers and front-end electronics, and assessing overall DAQ performance to ensure reliability during future calibration and data-taking. Chapter \ref{planning} discusses the initial steps towards the tracker calibration. The ultimate goal is to perform a time calibration of the first assembled station of the tracker using cosmic muons, aiming for a longitudinal hit position resolution better than 4 cm. This involves determining the signal propagation times and channel-to-channel delays. I performed a Monte Carlo study to determine the impact of the station orientation on the quality of the calibration, in particular on the cosmic track reconstruction, focusing on potential biases that could arise. These studies provide essential insights into the operation, optimization, and calibration of the Mu2e tracker system. Given the high data volume expected during Mu2e operations, estimated at approximately 7 PBytes per year, optimizing memory usage and minimizing CPU consumption are critical. A significant challenge lies in effectively flagging $\delta$-electron hits, which are the primary source of hits in the tracker, without compromising the efficiency of CE hit detection and track reconstruction. A detailed study of pre-pattern recognition and a thorough comparison of two $\delta$-electron flagging algorithms is provided in Chapter \ref{delta}. In Chapter \ref{conclusions}, the findings are concisely summarized, offering a comprehensive synthesis of the research and emphasizing the key insights derived from this study.

43 PARTICLE ACCELERATORS↗
NASA NTRS2007

A First Approach to Global Runoff Simulation using Satellite Rainfall Estimation

Many hydrological models have been introduced in the hydrological literature to predict runoff but few of these have become common planning or decision-making tools, either because the data requirements are substantial or because the modeling processes are too complicated for operational application. On the other hand, progress in regional or global rainfall-runoff simulation has been constrained by the difficulty of measuring spatiotemporal variability of the primary causative factor, i.e. rainfall fluxes, continuously over space and time. Building on progress in remote sensing technology, researchers have improved the accuracy, coverage, and resolution of rainfall estimates by combining imagery from infrared, passive microwave, and space-borne radar sensors. Motivated by the recent increasing availability of global remote sensing data for estimating precipitation and describing land surface characteristics, this note reports a ballpark assessment of quasi-global runoff computed by incorporating satellite rainfall data and other remote sensing products in a relatively simple rainfall-runoff simulation approach: the Natural Resources Conservation Service (NRCS) runoff Curve Number (CN) method. Using an Antecedent Precipitation Index (API) as a proxy of antecedent moisture conditions, this note estimates time-varying NRCS-CN values determined by the 5-day normalized API. Driven by multi-year (1998-2006) Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis, quasi-global runoff was retrospectively simulated with the NRCS-CN method and compared to Global Runoff Data Centre data at global and catchment scales. Results demonstrated the potential for using this simple method when diagnosing runoff values from satellite rainfall for the globe and for medium to large river basins. This work was done with the simple NRCS-CN method as a first-cut approach to understanding the challenges that lie ahead in advancing the satellite-based inference of global runoff. We expect that the successes and limitations revealed in this study will lay the basis for applying more advanced methods to capture the dynamic variability of the global hydrologic process for global runoff monlto~ngin real time. The essential ingredient in this work is the use of global satellite-based rainfall estimation.

Hong, Yang↗