319 research outputs found

    Validation and assimilation of Aeolus wind observations

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    Along with scientific and technological developments, the advancement of the Global Observing System (GOS) has been one of the most important factors contributing to the increase in numerical weather forecasting (NWP) skill in recent years. The initial conditions of a forecast are provided by data assimilation systems, combining the latest short-range forecast with a selection of atmospheric observations. One of the current major limitations is the lack of global wind profile observations, particularly in regions and for spatial scales where geostrophic mass-wind coupling is weak. The European Space Agency's (ESA) Doppler Wind Lidar (DWL) satellite mission Aeolus provides a novel data set of wind profiles with quasi-global coverage intended to fill this gap in the GOS. This thesis aims to assess the impact of the Aeolus observations in NWP to demonstrate the potential value of such satellite-based DWL missions. A crucial prerequisite for using meteorological observations in NWP data assimilation systems is the knowledge and characterization of their errors. Therefore, in the first part of this work, a validation study is conducted to investigate the quality of the Aeolus wind profiles. Comparisons with three independent reference data sets - collocated radiosonde observations as well as model equivalents of the global ICOsahedral Nonhydrostatic (ICON) model of the German Weather Service (DWD) and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecasts (ECMWF) - enable a comprehensive estimation of the systematic and random errors of the Aeolus observations. In addition, the systematic errors are examined for their dependencies, and correction approaches that can be used in data assimilation systems as part of quality control are tested. Discrepancies between the radiosonde and model-based validation results that occur in determining the random error are mainly due to differences in spatial and temporal representativeness. The representativeness error components can be estimated using high-resolution regional model simulations and thus can be taken into account in determining the Aeolus observational error. The results provide important information on the magnitude and vertical structure of the Aeolus Rayleigh and Mie wind error, which serves as the basis for the assigned observational error in the data assimilation. The second part of this thesis examines how numerical weather forecasting benefits from the assimilation of the novel DWL observations from the Aeolus satellite. For this purpose, an Observing System Experiment (OSE) based on the operational global assimilation system of ICON at DWD with and without the assimilation of Aeolus observations is analyzed. Besides global impact statistics, regions and periods with particularly pronounced impact are investigated further to understand the underlying dynamics leading to the overall beneficial impact. The largest impact of assimilating Aeolus observations occurs in the 2-3 day wind and temperature forecast in the tropical upper troposphere and lower stratosphere and in the Southern Hemisphere. The influence of the Aeolus observations in the Northern Hemisphere is less pronounced but still relatively large compared to other observing systems. Furthermore, this thesis illustrates three examples of atmospheric phenomena that constitute dynamical scenarios for significant forecast error reduction: the change of the oscillatory phase of two large-scale tropical circulation systems - the quasi-biennial oscillation (QBO) and the El Niño–Southern Oscillation (ENSO) - and the interaction of tropical cyclones undergoing extratropical transition (ET) with the midlatitude waveguide. These indications of dynamical changes and processes related to the particularly high impact of Aeolus on NWP forecasts provide important information for the advancement of observing and NWP systems and will serve as the basis for future studies on opportunities to improve NWP forecasts by additional observations

    Long-term monitoring of geodynamic surface deformation using SAR interferometry

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    Thesis (Ph.D.) University of Alaska Fairbanks, 2014Synthetic Aperture Radar Interferometry (InSAR) is a powerful tool to measure surface deformation and is well suited for surveying active volcanoes using historical and existing satellites. However, the value and applicability of InSAR for geodynamic monitoring problems is limited by the influence of temporal decorrelation and electromagnetic path delay variations in the atmosphere, both of which reduce the sensitivity and accuracy of the technique. The aim of this PhD thesis research is: how to optimize the quantity and quality of deformation signals extracted from InSAR stacks that contain only a low number of images in order to facilitate volcano monitoring and the study of their geophysical signatures. In particular, the focus is on methods of mitigating atmospheric artifacts in interferograms by combining time-series InSAR techniques and external atmospheric delay maps derived by Numerical Weather Prediction (NWP) models. In the first chapter of the thesis, the potential of the NWP Weather Research & Forecasting (WRF) model for InSAR data correction has been studied extensively. Forecasted atmospheric delays derived from operational High Resolution Rapid Refresh for the Alaska region (HRRRAK) products have been compared to radiosonding measurements in the first chapter. The result suggests that the HRRR-AK operational products are a good data source for correcting atmospheric delays in spaceborne geodetic radar observations, if the geophysical signal to be observed is larger than 20 mm. In the second chapter, an advanced method for integrating NWP products into the time series InSAR workflow is developed. The efficiency of the algorithm is tested via simulated data experiments, which demonstrate the method outperforms other more conventional methods. In Chapter 3, a geophysical case study is performed by applying the developed algorithm to the active volcanoes of Unimak Island Alaska (Westdahl, Fisher and Shishaldin) for long term volcano deformation monitoring. The volcano source location at Westdahl is determined to be approx. 7 km below sea level and approx. 3.5 km north of the Westdahl peak. This study demonstrates that Fisher caldera has had continuous subsidence over more than 10 years and there is no evident deformation signal around Shishaldin peak.Chapter 1. Performance of the High Resolution Atmospheric Model HRRR-AK for Correcting Geodetic Observations from Spaceborne Radars -- Chapter 2. Robust atmospheric filtering of InSAR data based on numerical weather prediction models -- Chapter 3. Subtle motion long term monitoring of Unimak Island from 2003 to 2010 by advanced time series SAR interferometry -- Chapter 4. Conclusion and future work

    Evaluating the structure and magnitude of the ash plume during the initial phase of the 2010 Eyjafjallajökull eruption using lidar observations and NAME simulations

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    The Eyjafjallajökull volcano in Iceland erupted explosively on 14 April 2010, emitting a plume of ash into the atmosphere. The ash was transported from Iceland toward Europe where mostly cloud-free skies allowed ground-based lidars at Chilbolton in England and Leipzig in Germany to estimate the mass concentration in the ash cloud as it passed overhead. The UK Met Office's Numerical Atmospheric-dispersion Modeling Environment (NAME) has been used to simulate the evolution of the ash cloud from the Eyjafjallajökull volcano during the initial phase of the ash emissions, 14–16 April 2010. NAME captures the timing and sloped structure of the ash layer observed over Leipzig, close to the central axis of the ash cloud. Relatively small errors in the ash cloud position, probably caused by the cumulative effect of errors in the driving meteorology en route, result in a timing error at distances far from the central axis of the ash cloud. Taking the timing error into account, NAME is able to capture the sloped ash layer over the UK. Comparison of the lidar observations and NAME simulations has allowed an estimation of the plume height time series to be made. It is necessary to include in the model input the large variations in plume height in order to accurately predict the ash cloud structure at long range. Quantitative comparison with the mass concentrations at Leipzig and Chilbolton suggest that around 3% of the total emitted mass is transported as far as these sites by small (<100 μm diameter) ash particles

    Observations of aerosol particles and deep convective updrafts and the modeling of their interactions

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    2020 Spring.Includes bibliographical references.Within cloud updrafts, cloud droplets form on aerosol particles that serve as cloud condensation nuclei (CCN). Varying the concentrations of CCN alters the concentrations of cloud droplets, which in turn modifies subsequent microphysical processes within clouds. In this dissertation, both observational and modeling studies are presented that reduce the uncertainties associated with these aerosol-induced feedback processes in deep convective clouds. In the first study, five years of observations of aerosol particle size distributions from central Oklahoma are compared, and useful metrics are provided for implementing aerosol size distributions into models. Using these unique, long-term observations, power spectra analyses are also completed to determine the most relevant cycles (from hours to weeks) for different aerosol particle sizes. Diurnal cycles produce the strongest signals in every season, most consistently in the accumulation mode and the smallest (diameters < 30 nm) particles. The latter result suggests that these smallest particles may play a more important role in the CCN budget than previously thought. Ultimately, in understanding which, when and why different aerosol particles are present in the atmosphere, we can better assess the impacts that they have on clouds. The types and number of aerosol particles that can serve as CCN depend on the amount of supersaturation, and thus the magnitude of the cloud updraft vertical velocities. However, in situ updraft observations in deep convective clouds are scarce, and other vertical velocity estimates often have uncertainties that are difficult to characterize. In the next study, novel, in situ observations of deep convective updraft vertical velocities from targeted radiosonde launches during the CSU Convective Cloud Outflows and Updrafts Experiment (C3LOUD-Ex) are presented. Vertical velocities of over 50 m s-1 are estimated from radiosonde observations taken in Colorado. Radar data are used to contextualize the radiosonde measurements and to provide an independent estimate of the updraft magnitudes for comparison. These observations are valuable in that they: 1) contribute novel estimates of the vertical velocities within deep convective clouds, 2) demonstrate that in situ observations of vertical velocities complement estimates from other platforms and 3) will allow for better assessments of the supersaturation magnitudes, and thus the amount of CCN that are present within deep convective clouds. While the first two studies focus on observing aerosol particles and updrafts separately, the third study within this dissertation presents simulations of their interactions from an international model intercomparison project. Seven models from different institutions simulated the same case study of isolated deep convective clouds with both high and low CCN concentrations. The range of the responses in updrafts to varying CCN concentrations are calculated for this model suite. Despite the various physical parameterizations that these models utilize, all the models simulate stronger updrafts in the High-CCN simulations from near cloud base through ~8 km AGL, with diverging results above this altitude. The vertical velocity tendency equation is analyzed to explain which processes are causing the consistent and inconsistent updraft responses to varying CCN concentrations amongst the models. The three studies in this dissertation each reduce the uncertainties related to aerosol effects on deep convective cloud updrafts. This work also assisted in motivating the DOE Tracking Aerosol Convection Interactions Experiment (TRACER), which will further connect observational and modeling research to reduce the uncertainties in aerosol-cloud interactions

    Estimating Wind Velocities in Atmospheric Mountain Waves Using Sailplane Flight Data

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    Atmospheric mountain waves form in the lee of mountainous terrain under appropriate conditions of the vertical structure of wind speed and atmospheric stability. Trapped lee waves can extend hundreds of kilometers downwind from the mountain range, and they can extend tens of kilometers vertically into the stratosphere. Mountain waves are of importance in meteorology as they affect the general circulation of the atmosphere, can influence the vertical structure of wind speed and temperature fields, produce turbulence and downdrafts that can be an aviation hazard, and affect the vertical transport of aerosols and trace gasses, and ozone concentration. Sailplane pilots make extensive use of mountain lee waves as a source of energy with which to climb. There are many sailplane wave flights conducted every year throughout the world and they frequently cover large distances and reach high altitudes. Modern sailplanes frequently carry flight recorders that record their position at regular intervals during the flight. There is therefore potential to use this recorded data to determine the 3D wind velocity at positions on the sailplane flight path. This would provide an additional source of information on mountain waves to supplement other measurement techniques that might be useful for studies on mountain waves. The recorded data are limited however, and determination of wind velocities is not straightforward. This thesis is concerned with the development and application of techniques to determine the vector wind field in atmospheric mountain waves using the limited flight data collected during sailplane flights. A detailed study is made of the characteristics, uniqueness, and sensitivity to errors in the data, of the problem of estimating the wind velocities from limited flight data consisting of ground velocities, possibly supplemented by air speed or heading data. A heuristic algorithm is developed for estimating 3D wind velocities in mountain waves from ground velocity and air speed data, and the algorithm is applied to flight data collected during “Perlan Project” flights. The problem is then posed as a statistical estimation problem and maximum likelihood and maximum a posteriori estimators are developed for a variety of different kinds of flight data. These estimators are tested on simulated flight data and data from Perlan Project flights

    New GPS Time Series Analysis and a Simplified Model to Compute an Accurate Seasonal Amplitude of Tropospheric Delay

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    Horizontal and vertical deformation of the Earth’s crust is due to a variety of different geophysical processes that take place on various spatiotemporal scales. The quality of the observations from spaced-based geodesy instruments such as Global Positioning System (GPS) and differential interferometric synthetic aperture radar (DInSAR) data for monitoring these deformations are dependent on numerous error sources. Therefore, accurately identifying and eliminating the dominant sources of the error, such as troposphere error in GPS signals, is fundamental to obtain high quality, sub-centimeter accuracy levels in positioning results. In this work, I present the results of double-differenced processing of five years of GPS data, between 2008 and 2012, for sparsely distributed GPS stations in southeastern Ontario and western Québec. I employ Bernese GPS Software Version 5.0 (BSW5.0) and found two optimal sub-networks which can provide high accuracy estimation of the position changes. I demonstrate good agreement between the resulted coordinate time series and the estimates of the crustal motions obtained from a global solution. In addition, I analyzed the GPS position time series by using a complex noise model, a combination of white and power-law noises. The estimated spectral index of the noise model demonstrates that the flicker noise is the dominant noise in most GPS stations in our study area. The interpretation of the observed velocities suggests that they provide an accurate constraint on glacial isostatic adjustment (GIA) prediction models. Based on a deeper analysis of these same GPS stations, I propose a model that accurately estimates the seasonal amplitude of zenith tropospheric delay (ZTD) error in the GPS data on local and regional spatial scales. I process the data for the period 2008 through 2012 from eight GPS stations in eastern Ontario and western Québec using precise point positioning (PPP) online analysis available from Natural Resource Canada (NRCan) (https://webapp.geod.nrcan.gc.ca/geod/tools-outils/ppp.php). The model is an elevation-dependent model and is a function of the decay parameter of refractivity with altitude and the seasonal amplitude of refractivity computed from atmospheric data (pressure, temperature, and water vapor pressure) at a given reference station. I demonstrate that it can accurately estimate the seasonal amplitude of ZTD signals for the GPS stations at any altitude relative to that reference station. Based on the comparison of the observed seasonal amplitudes of the differenced ZTD at each station and the estimates from the proposed model, it can provide an accurate estimation for the stations under normal atmospheric conditions. The differenced ZTD is defined as the differences of ZTD derived from PPP at each station and ZTD at the reference station. Moreover, I successfully compute a five-year precipitable water vapor (PWV) at each GPS site, based on the ZTD derived from meteorological data and GPS processing. The results provide an accurate platform to monitor long-term climate changes and inform future weather predictions. In an extension of this research, I analyze DInSAR data between 2014 and 2017 with high temporal and spatial resolution, from Kilauea volcano in Hawaii in order to derive the spatial and temporal pattern of the seasonal amplitude of ZTD. I propose an elevation-dependent model by the data from a radiosonde station and observations at a surface weather station for modeling the seasonal amplitudes of ZTD at any arbitrary elevation. The results obtained from this model fit the vertical profile of the observed seasonal amplitude of ZTD in DInSAR data, increasing systematically from the elevation of the DInSAR reference point. I demonstrate that the proposed model could be used to estimate the seasonal amplitude of the differenced ZTD at each GPS station within a local network with high accuracy. The results of this study concluded that, employing this model in GPS processing applications eliminates the need for the meteorological observations at each GPS site

    Utilization of drones in vertical profile measurements of the atmosphere

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    Numerical weather predicition and climate models require accurate and continuous measurements of the atmosphere. Radiosoundings conducted daily all over the world provide the backbone for these measurements thanks to their accuracy and high spatial resolution. However they are expensive and thus are limited to only a few profile measurements per day. Especially within boundary layer this is not enough and to fill this gap a new type of drone-based measurement system has been developed. The recent emergence of drones has brought new opportunities in atmospheric research and in this study their utilization in meteorological profiling is investigated. The measurement system consists of an octocopter with a Vaisala RD41 dropsonde attached underneath for temperature and humidity measurements. This drone is accompanied by a custom-build ground station that allows autonomous operation. With the drone measurements up to 450m were possible. To investigate the capabilities of drone-borne setups for vertical profiling, the temperature and humidity measurements were compared between ascending and descending legs of the flight as well as collocated radiosonde measurements. Statistical analysis on the differences between the measured profiles was conducted and individual case studies were performed for better understanding of the effects caused by the drone and the different atmospheric conditions. The results indicate a warm bias in the drone measurements when compared against radiosonde measurements, and this bias is higher during the ascend leg. Ascend leg shows a bias of 0.4 !C when compared against the radiosonde measurements and the descend leg shows a bias of 0.2 !C. The ascend leg shows a bias of 0.3 !C when compared against the descend leg. The relative humidity measurements with the drone show a dry bias when compared against radiosonde measurements. The ascending leg has a bias of −1.9% and the descending leg −0.3 %. The difference between ascend and descend legs is −1.4 %. Thus the descending leg agrees better with the radiosonde measurements, but also the ascending leg generally agrees with the radiosonde measurements within half a degree in temperature and two percentage relative humidity

    BOMEX bulletin, no. 10

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    Radiosonde and radar echo measurements on area of air/sea interactions over tropical ocean surface with numerical weather forecastin

    Design and Implementation of a Novel Multicopter Unmanned Aircraft System for Quantitative Studies of the Atmosphere

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    The call for creating new innovative meteorological instruments to help fulfill observational gaps in the atmospheric sciences has been gaining strength in the past few years. This comes along with the urgent need to increase the understanding of fast-evolving atmospheric processes to subsequently provide accurate and reliable weather forecasts in a timely manner. The increased interest in obtaining atmospheric observations with higher spatio-temporal resolution pushed scientists to begin exploring and harnessing new leading-edge engineering technology. For instance, affordable and accessible Unmanned Aircraft Systems (UASs) technology emerged within this timeframe and has since evolved rapidly. Many researchers and institutions have agreed that UASs are promising technology candidates for targeted in situ weather sampling, which has the potential to meet the stringent meteorological measurement requirements. However, the current market has shifted and shaped UASs for other applications that may be unsuitable or suboptimal for weather sampling. Special considerations were examined in this study to conceptualize a specialized weather UAS (WxUAS) capable of collecting reliable thermodynamic and kinematic measurements. While also performing similarly to conventional weather instruments, such as radiosondes, Doppler wind lidars, and meteorological towers, as well as providing a complementary role whenever measurement limitations arise. Therefore, given that the exploration of integrating weather instrumentation into UAS is rare, it is hypothesized that atmospheric measurements of a modified multicopter UAS that minimizes platform-induced errors can fill the thermodynamic and kinematic data gap in the planetary boundary layer (PBL). The proposed solution is a UAS-based in situ vertical profiler system, dubbed the CopterSonde, with necessary weather instrumentation, adequate sensor placement, and useful flight functions for optimal sampling of undisturbed air. This solution attempts to provide a holistic WxUAS design where the UAS itself was adapted to become not just a payload carrier but also part of the weather instrumentation system. Flow simulation studies backed with observations in the field were used to address sensor siting and mitigate sources of thermodynamic errors. Moreover, techniques for thermodynamic measurement correction, adaptable flight behavior, and 3D wind estimation were implemented using the experimental CopterSonde concept with results comparable to widely accepted conventional weather instruments. Additionally, the platform reliability was successfully demonstrated in different challenging environments, from freezing temperatures in Hailuoto, Finland, to high elevations in Colorado, USA. A robust concept of operation and decision-making algorithms were established to ensure safe flights during demanding field campaigns. As a result, the National Oceanic and Atmospheric Administration (NOAA) in the USA has recognized the CopterSonde as part of the approved UAS fleet for NOAA-related missions. Overall, the engineering advances shown in this work helped to produce an optimized UAS capable of collecting targeted and reliable weather observations. Even though the CopterSonde is an experimental design, this work can be used as a guideline to define future standards for WxUAS development and deployment
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