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Computer software for ensembling all the space vehicle (SV) and ground clocks of the Global Positioning Systems (GPS) was implemented at the Master Control Station (MCS) on 17 June 1990. Improved GPS time stability and steering control are predicted. The perceptions of both Air Force and outside users are assessed. Current performance is compared with theoretical predictions.
Results are reported for computer simulation experiments in which a statistical ensemble of random magnetic field realizations is generated, orbits of charged particles in the random fields are followed, and a pitch-angle diffusion coefficient is derived from the temporal evolution of the orbits. Diffusion coefficients predicted by three nonlinear theories are compared with the derived coefficients for the standard quasilinear theory of velocity diffusion, and the goals of future simulations are outlined.
Some studies suggest that the proper initialization of soil moisture in a forecasting model may contribute significantly to the accurate prediction of seasonal precipitation, especially over mid-latitude continents. In order for the initialization to have any impact at all, however, two conditions must be satisfied: (1) the initial soil moisture anomaly must be "remembered" into the forecasted season, and (2) the atmosphere must respond in a predictable way to the soil moisture anomaly. In our previous studies, we identified the key land surface and atmospheric properties needed to satisfy each condition. Here, we tie these studies together with an analysis of an ensemble of seasonal forecasts. Initial soil moisture conditions for the forecasts are established by forcing the land surface model with realistic precipitation prior to the start of the forecast period. As expected, the impacts on forecasted precipitation (relative to an ensemble of runs that do not utilize soil moisture information) tend to be localized over the small fraction of the earth with all of the required land and atmosphere properties.
El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite Sea Surface Salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (National Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF ( Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive) V4 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by the satellite at approximately 1 centimeter) and in situ measurements (typically measured by Argo floats at 3 meters). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from the GMAO S2S reanalyses that assimilates/withholds along-track (L2) SSS. In particular, we contrast forecasts of the overestimated 2014 El Nino, the big 2015 El Nino, and the minor 2016 La Nina. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrades the mixed layer depth leading to more accurate coupled air/sea interaction.
The combination of the Wang-Sheeley-Arge (WSA) coronal model, ENLIL heliospherical model version 2.7, and CONED Model version 1.3 (WSA-ENLIL with CONED Model) was employed to form ensemble forecasts for 15 halo coronal mass ejections (halo CMEs). The input parameter distributions were formed from 100 sets of CME cone parameters derived from the CONED Model. The CONED Model used image processing along with the bootstrap approach to automatically calculate cone parameter distributions from SOHO/LASCO imagery based on techniques described by Pulkkinen et al. (2010). The input parameter distributions were used as input to WSA-ENLIL to calculate the temporal evolution of the CMEs, which were analyzed to determine the propagation times to the L1 Lagrangian point and the maximum Kp indices due to the impact of the CMEs on the Earth's magnetosphere. The Newell et al. (2007) Kp index formula was employed to calculate the maximum Kp indices based on the predicted solar wind parameters near Earth assuming two magnetic field orientations: a completely southward magnetic field and a uniformly distributed clock-angle in the Newell et al. (2007) Kp index formula. The forecasts for 5 of the 15 events had accuracy such that the actual propagation time was within the ensemble average plus or minus one standard deviation. Using the completely southward magnetic field assumption, 10 of the 15 events contained the actual maximum Kp index within the range of the ensemble forecast, compared to 9 of the 15 events when using a uniformly distributed clock angle.
El Nino/Southern Oscillation (ENSO) has far reaching global climatic impacts and so extending useful ENSO forecasts would be of great benefit for society. However, one key variable that has yet to be fully exploited within coupled forecast systems is accurate estimation of near-surface ocean density. Satellite sea surface salinity (SSS), combined with temperature, help to identify ocean density changes and associated mixing near the ocean surface. We assess the impact of satellite SSS observations for improving near-surface dynamics within ocean analyses and how these impact dynamical ENSO forecasts using the NASA GMAO (Global Modeling and Assimilation Office) Sub-seasonal to Seasonal (S2S_v2.1) coupled forecast system (Molod et al. 2018 - i.e. NASA's contribution to the NMME (North American Multi-Model Ensemble) project). For all initialization experiments, all available along-track absolute dynamic topography and in situ observations are assimilated using the LETKF (Local Ensemble Transform Kalman Filter) scheme similar to Penny et al., 2013. A separate reanalysis additionally assimilates Aquarius V5 (September 2011 to June 2015) and SMAP (Soil Moisture Active Passive satellite) V4.1 (March 2015 to present) along-track data.We highlight the impact of satellite SSS on ocean reanalyses by comparing validation statistics of experiments that assimilate SSS versus our current prediction system that withholds SSS. We find that near-surface validation versus observed statistics for salinity are slightly degraded when assimilating SSS. This is an expected result due to known biases between SSS (measured by satellite at approximately 1-centimeter depth) and in situ measurements (typically measured by Argo floats at 3-meters depth). On the other hand, a very encouraging result is that both temperature, absolute dynamic topography, and mixed layer statistics are improved with SSS assimilation. Previous work has shown that correcting near-surface density structure via gridded SSS assimilation can improve coupled forecasts. Here we present results of coupled forecasts that are initialized from GMAO S2S spring reanalyses that assimilate/withhold along-track (L2) SSS. In particular, we contrast forecasts of the big 2015 El Nino, the 2017 La Nina and the 2018 weak El Nino. For each of these ENSO scenarios, assimilation of satellite SSS improves the forecast validation. Improved SSS and density upgrade the mixed layer depth leading to more accurate coupled air/sea interaction. From March to June 2015, the availability of two overlapping satellite SSS instruments, Aquarius and SMAP, allows a unique opportunity to compare and contrast forecasts initialized with the benefit of these two satellite SSS observation types. We assess the impact of gridded satellite sea surface salinity observations on dynamical ENSO forecasts for the big 2015 El Nino.
Monte Carlo simulation experiments were performed in order to study the velocity diffusion of charged particles in a static turbulent magnetic field. By following orbits of particles moving in a large ensemble of random magnetic field realizations with suitable chosen statistical properties, a pitch-angle diffusion coefficient is derived. Results are presented for a variety of particle rigidities and rms random field strengths and compared with the predictions of standard quasi-linear theory and the nonlinear partially averaged field theory.
Monte Carlo simulation experiments have been performed in order to study the velocity diffusion of charged particles in a static turbulent magnetic field. By following orbits of particles moving in a large ensemble of random magnetic field realizations with suitably chosen statistical properties, a pitch-angle diffusion coefficient is derived. Results are presented for a variety of particle rigidities and rms random field strengths and compared with the predictions of standard quasi-linear theory and the nonlinear partially averaged field theory.
Simulations of humidity from 28 general circulation models for the period 1979-88 from the Atmospheric Model Intercomparison Project are compared with observations from radiosondes over North America and the globe and with satellite microwave observations over the Pacific basin. The simulations of decadal mean values of precipitable water (W) integrated over each of these regions tend to be less moist than the real atmosphere in all three cases; the median model values are approximately 5% less than the observed values. The spread among the simulations is larger over regions of high terrain, which suggests that differences in methods of resolving topographic features are important. The mean elevation of the North American continent is substantially higher in the models than is observed, which may contribute to the overall dry bias of the models over that area. The authors do not find a clear association between the mean topography of a model and its mean W simulation, however, which suggests that the bias over land is not purely a matter of orography. The seasonal cycle of W is reasonably well simulated by the models, although over North America they have a tendency to become moister more quickly in the spring than is observed. The interannual component of the variability of W is not well captured by the models over North America. Globally, the simulated W values show a signal correlated with the Southern Oscillation index but the observations do not. This discrepancy may be related to deficiencies in the radiosonde network, which does not sample the tropical ocean regions well. Overall, the interannual variability of W, as well as its climatology and mean seasonal cycle, are better described by the median of the 28 simulations than by individual members of the ensemble. Tests to learn whether simulated precipitable water, evaporation, and precipitation values may be related to aspects of model formulation yield few clear signals, although the authors find, for example, a tendency for the few models that predict boundary layer depth to have large values of evaporation and precipitation. Controlled experiments, in which aspects of model architecture are systematically varied within individual models, may be necessary to elucidate whether and how model characteristics influence simulations.
The high-accuracy Earth-reflected solar spectral radiances from CLARREO Pathfinder (CPF) will serve as in-orbit benchmark measurements for intercalibrating other satellite imagers. The CPF team is developing a state-of-the-art intercalibration method to calibrate the shortwave channel (300-5000 nm) of the CERES instrument against the CPF reference with an uncertainty of 0.3%. Because the CPF instrument only covers spectral range from 350 nm to 2300 nm, the team has developed a spectral extension method to fill in the gaps between the spectral domains of CPF and the CERES shortwave channel. The method solely relies on using the hyperspectral information from the CPF wavelengths to predict the radiances below 350 nm and above 2300 nm. To quantify the relationship between the CPF spectrum and the non-overlapping portions of the spectrum between CPF and CERES, the team has simulated a large ensemble of spectra that cover the spectral range for both CPF and CERES. A hyperspectral bidirectional reflectance model for land surface based on thousands of measured land surface reflectance has been developed. This land surface model as well as the Cox-Munk model for ocean surface are used in the spectral gap filling. Various water/ice clouds, aerosols, atmosphere profiles and surfaces were used to simulate Earth-reflected radiances from 200 nm to 15 um under different solar and sensor geometries. The targeted uncertainty for the CPF-extended broadband CERES radiances is less than 0.1%. The previously derived training spectra were updated to replace the Kurucz solar irradiance model with the TSIS-1 Hybrid Solar Reference Spectrum. The influence of the scalar assumption in radiative transfer model equations on the uncertainty in estimating the broadband radiances was also investigated.
Detecting clouds within a satellite image is essential for retrieving surface geophysical parameters, such as albedo and temperature, from optical and thermal imagery because the retrieval methods tend to be valid for clear skies only. Thus, routine satellite data processing requires reliable automated cloud detection algorithms that are applicable to many surface types. Unfortunately, cloud detection over snow and ice is difficult due to the lack of spectral contrast between clouds and snow. Snow and clouds are both highly reflective in the visible wavelen,ats and often show little contrast in the thermal Infrared. However, at 1.6 microns, the spectral signatures of snow and clouds differ enough to allow improved snow/ice/cloud discrimination. The recent Terra and Aqua Moderate Resolution Imaging Spectro-Radiometer (MODIS) sensors have a channel (channel 6) at 1.6 microns. Presently the most comprehensive, long-term information on surface albedo and temperature over snow- and ice-covered surfaces comes from the Advanced Very High Resolution Radiometer ( AVHRR) sensor that has been providing imagery since July 1981. The earlier AVHRR sensors (e.g. AVHRR/2) did not however have a channel designed for discriminating clouds from snow, such as the 1.6 micron channel available on the more recent AVHRR/3 or the MODIS sensors. In the absence of the 1.6 micron channel, the AVHRR Polar Pathfinder (APP) product performs cloud detection using a combination of time-series analysis and multispectral threshold tests based on the satellite's measuring channels to produce a cloud mask. The method has been found to work reasonably well over sea ice, but not so well over the ice sheets. Thus, improving the cloud mask in the APP dataset would be extremely helpful toward increasing the accuracy of the albedo and temperature retrievals, as well as extending the time-series of albedo and temperature retrievals from the more recent sensors to the historical ones. In this work, we use data mining methods to construct a model of MODIS channel 6 as a function of other channels that are common to both MODIS and AVHRR. The idea is to use the model to generate the equivalent of MODIS channel 6 for AVHRR as a function of the AVHRR equivalents to MODIS channels. We call this a Virtual Sensor because it predicts unmeasured spectra. The goal is to use this virtual channel 6. to yield a cloud mask superior to what is currently used in APP . Our results show that several data mining methods such as multilayer perceptrons (MLPs), ensemble methods (e.g., bagging), and kernel methods (e.g., support vector machines) generate channel 6 for unseen MODIS images with high accuracy. Because the true channel 6 is not available for AVHRR images, we qualitatively assess the virtual channel 6 for several AVHRR images.
In this presentation we present an overview of the GMAO Sub-Seasonal and Seasonal Prediction System with a focus on the computing time and resources and actual time it takes to complete a full set of hindcasts. The goal is to come up with some solutions to allow us to run more ensemble members for the next version of the system which will be higher resolution and take many more resources.
Greenhouse gas (GHG) emissions estimation methods that use atmospheric trace gas observations, including inverse modeling techniques, perform better when carbon dioxide (CO2) fluxes are more accurately transported and dispersed in the atmosphere by a numerical model. In urban areas, transport and dispersion is particularly difficult to simulate using current mesoscale meteorological models due, in part, to added complexity from surface heterogeneity and fine spatial/temporal scales. It is generally assumed that the errors in GHG estimation methods in urban areas are dominated by errors in transport and dispersion. Other significant errors include, but are not limited to, those from assumed emissions magnitude and spatial distribution. To assess the predictability of simulated trace gas mole fractions in urban observing systems using a numerical weather prediction model, we employ an Eulerian model that combines traditional meteorological variables with multiple passive tracers of atmospheric CO2 from anthropogenic inventories and a biospheric model. The predictability of the Eulerian model is assessed by comparing simulated atmospheric CO2 mole fractions to observations from four in situ tower sites (three urban and one rural) in the Washington DC/Baltimore, MD area for February 2016. Four different gridded fossil fuel emissions inventories along with a biospheric flux model are used to create an ensemble of simulated atmospheric CO2 observations within the model. These ensembles help to evaluate whether the modeled observations are impacted more by the underlying emissions or transport. The spread of modeled observations using the four emission fields indicates the model's ability to distinguish between the different inventories under various meteorological conditions. Overall, the Eulerian model performs well; simulated and observed average CO2 mole fractions agree within 1% when averaged at the three urban sites across the month. However, there can be differences greater than 10% at any given hour, which are attributed to complex meteorological conditions rather than differences in the inventories themselves. On average, the mean absolute error of the simulated compared to actual observations is generally twice as large as the standard deviation of the modeled mole fractions across the four emission inventories. This result supports the assumption, in urban domains, that the predicted mole fraction error relative to observations is dominated by errors in model meteorology rather than errors in the underlying fluxes in winter months. As such, minimizing errors associated with atmospheric transport and dispersion may help improve the performance of GHG estimation models more so than improving flux priors in the winter months. We also find that the errors associated with atmospheric transport in urban domains are not restricted to certain times of day. This suggests that atmospheric inversions should use CO2 observations that have been filtered using meteorological observations rather than assuming that meteorological modeling is most accurate at certain times of day (such as using only mid-afternoon observations).
This study uses the NASA MERRA reanalysis and GEOS 5 model simulations to examine the causes of Eurasian heat waves including the recent extreme events that occurred in Europe during 2003 and in Russia during 2010. The focus is on the warm season and the part of the Eurasian continent that extends north of about 40oN, or roughly to the north of the mean upper tropospheric jet stream. The results show that such extreme events are an amplification of natural patterns of atmospheric variability that develop over the Eurasian continent as a result of internal atmospheric forcing. The amplification occurs when the wave occasionally becomes locked in place for several weeks to months resulting in extreme heat and drying with the location depending on the phase of the upper atmospheric wave. An ensemble of very long GEOS-S model simulations (spanning the 20th century) forced with observed SST and greenhouse gases show that the model is capable of generating very similar heat waves, and that they have become more intense in the last thirty years as a result of the overall warming of the Asian continent. Sensitivity experiments with perturbed initial conditions indicate that these events have limited predictability.
Since the 1970s, most airlines have incorporated computerized support for managing disruptions during flight schedule execution. However, existing platforms for airline disruption management (ADM) employ monolithic system design methods that rely on the creation of specific rules and requirements through explicit optimization routines, before a system that meets the specifications is designed. Thus, current platforms for ADM are unable to readily accommodate additional system complexities resulting from the introduction of new capabilities, such as the introduction of unmanned aerial systems (UAS), operations and infrastructure, to the system. To this end, we use historical data on airline scheduling and operations recovery to develop a system of artificial neural networks (ANNs), which describe a predictive transfer function model (PTFM) for promptly estimating the recovery impact of disruption resolutions at separate phases of flight schedule execution during ADM. Furthermore, we provide a modular approach for assessing and executing the PTFM by employing a parallel ensemble method to develop generative routines that amalgamate the system of ANNs. Our modular approach ensures that current industry standards for tardiness in flight schedule execution during ADM are satisfied, while accurately estimating appropriate time-based performance metrics for the separate phases of flight schedule execution.
The subject of satellite disintegration is examined in detail. Elements of the orbits of individual fragments, determined by DOD space surveillance systems, are used to accurately predict the time and place of fragmentation. Dual time independent and time dependent analyses are performed for simulated and real breakups. Methods of statistical mechanics are used to study the evolution of the fragment clouds. The fragments are treated as an ensemble of non-interacting particles. A solution of Liouville's equation is obtained which enables the spatial density to be calculated as a function of position, time and initial velocity distribution.
The final Shuttle-derived atmospheric data base is presented. The relational data base is comprised of data from 32 Space Transportation System (STS) descent flights, to include available meteorology data taken in support of each flight. For the most part, the available data are restricted to the middle atmosphere. In situ accelerations, sensed by the tri-redundant Inertial Measurement Unit (IMU) to an accuracy better than 1 mg, are combined with post-flight Best Estimate Trajectory (BET) information and predicted, flight-substantiated Orbiter aerodynamics to provide determinations up to altitudes of 95 km. In some instances, alternate accelerometry data with micro-g resolution were utilized to extend the data base well into the thermosphere. Though somewhat limited, the ensemble of flights permit a reasonable sampling of monthly, seasonal, and latitudinal variations which can be utilized for atmospheric science investigations and model evaluations and upgrades as appropriate. More significantly, the unparallel vertical resolution in the Shuttle-derived results indicate density shears normally associated with internal gravity waves or local atmospheric instabilities. Consequently, these atmospheres can also be used as stress-atmospheres for Guidance, Navigation and Control (GN and C) system development and analysis as part of any advanced space vehicle design activities.
Scattering of electromagnetic radiation near the backward direction is more sensitive to particle shape than scattering near the forward direction. Mie theory is therefore of dubious applicability to predicting backscattering by atmospheric particles known to be irregular or to inverting measurements on such particles. An irregular particle is one with an uncertain shape. In the face of uncertainty one must adopt a statistical approach in which scattering properties of ensembles are determined. To obtain ensemble averages, a basis is needed for averaging over a set of electromagnetic microstates. Ensemble averages based on the Rayleigh theory for small ellipsoids and on the T matrix method for spheroids agree better with measurements than Mie theory does. The coupled-dipole method also provides a basis for ensemble averaging. This method also leads to a simple physical interpretation of why backscattering is so sensitive to particle shape and can be used to calculate scattering by one- and two-dimensional analogs to three-dimensional irregular particles.