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NASA NTRS2020

Subseasonal-to-Seasonal Hindcast Skill Assessment of Ridging Events Related to Drought Over the Western United States

Persistent atmospheric ridging events centered near the western United States are associated with widespread precipitation deficits and meteorological drought. Due to the relatively low skill of- dynamical models in forecasting precipitation on subseasonal‐to‐seasonal (S2S) time scales across this region, forecasts of ridging are explored in this study as a potential bridge for early warning drought prediction. To assess skill, we evaluate deterministic and probabilistic S2S hindcasts (out to 6 week lead time) of ridging events and geopotential height anomalies over the western United States in five ensemble hindcast systems. Prediction skill for ridging events is shown to be highly variable across models. For some models, longer‐time‐averaged patterns of geopotential height anomalies across the first 6 weeks can be skillfully simulated, when evaluated probabilistically against climatology. The most skillful models show modest skill in forecasting above normal ridging occurrences at lead times of Weeks 3–4 and Weeks 5–6,with some sensitivity to the specific ridge location and method for determining skill. Using the European Centre for Medium‐Range Weather Forecasts (ECMWF) model as a case study, longer lead time forecast busts are shown to often occur under extended periods of highly active La Nina‐like tropical convection. Model errors at simulating these tropical convection features may have a disproportionately large impact and degrade downstream forecasts over the western United States. Despite the documented model shortcomings, our results highlight an opportunity to target skillful ridging forecasts from dynamical models for improving early warning drought forecasting at S2S lead times.

S2S↗
NASA NTRS1962

Application of Statistical Filter Theory to the Optimal Estimation of Position and Velocity on Board a Circumlunar Vehicle

Concepts from statistical filter theory are applied to the problem of in-flight determination of the position and velocity of a space vehicle for the purposes of midcourse guidance. The source of information is assumed to be an arbitrary sequence of measurements of any desired set of "observables" (e.g., space angles), the measurements being corrupted by additive errors so that the position and velocity are never known perfectly. A state transition approach is employed which leads naturally to a computational scheme that is readily implemented by a digital computer. The scheme can be regarded as a dynamical time-varying filter which weights the incoming observations in an optimal sense for use in producing an up-to-date optimal estimate of position and velocity. The advantages of the scheme are (1) it provides the best possible estimate (minimum error) based upon ensemble statistics of injection conditions and measurement errors; (2) it is extremely versatile, not requiring adherence to a predetermined observation schedule or reference trajectory; and (3) the computations are sufficiently simple to be practical in an on-board computer. A digital computer simulation of the proposed system is employed to demonstrate the feasibility of an all on-board system and to illustrate the performance attainable in a hypothetical physical situation.

SPACE VEHICLE↗
NASA NTRS1992

Measurements of the interaction of wave groups with shorter wind-generated waves

Fields of statistically steady wind-generated waves produced in a wind wave facility were perturbed by the injection of groups of longer, mechanically generated waves with various slopes. The time histories of the surface displacements were measured at four fetches in ensembles consisting of 100 realizations of each set of experimental conditions; the data were stored and analyzed digitally. Four distinct stages in the overall interaction are identified and characterized. The properties of the wave energy front are documented, and a preliminary discussion is given of the dynamic processes involved in its formation.

Chu, Jacob S.↗
NASA NTRS2011

Effect of Cross-Correlation on Geomagnetic Forecast Accuracies

Surface geomagnetic observation can determine up to degree L = 14 time-varying spherical harmonic coefficients of the poloidal magnetic field. Assimilation of these coefficients to numerical dynamo simulation could help us understand better the dynamical processes in the Earth's outer core, and to provide more accurate forecast of geomagnetic secular variations (SV). In our previous assimilation studies, only the poloidal magnetic field in the core is corrected by the observations in the analysis. Unobservable core state variables (the toroidal magnetic field and the core velocity field) are corrected via the dynamical equations of the geodynamo. Our assimilation experiments show that the assimilated core state converges near the CMB, implying that the dynamo state is strongly constrained by surface geomagnetic observations, and is pulled closer to the truth by the data. We are now carrying out an ensemble of assimilation runs with 1000 years of geomagnetic and archeo/paleo magnetic record. In these runs the cross correlation between the toroidal and the poloidal magnetic fields is incorporated into the analysis. This correlation is derived from the physical boundary conditions of the toroidal field at the core-mantle boundary (CMB). The assimilation results are then compared with those of the ensemble runs without the cross-correlation, aiming at understanding two fundamental issues: the effect of the crosscorrelation on (1) the convergence of the core state, and (2) the SV prediction accuracies. The constrained dynamo solutions will provide valuable insights on interpreting the observed SV, e.g. the near-equator magnetic flux patches, the core-mantle interactions, and possibly other geodynamic observables.

Kuang, Weijia↗
NASA NTRS1975

Satellite disintegration dynamics

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.

Dasenbrock, R. R.↗
NASA NTRS2019

Global Evolution of Solar Magnetic Fields and Prediction of Solar Activity Cycles

Prediction of solar activity cycles is challenging because the physical processes inside the Sun involve a broad range of multiscale dynamics that no model can reproduce, and the available observations are highly limited and cover mostly surface layers. Helioseismology makes it possible to probe solar dynamics in the convective zone, but variations in the differential rotation and meridional circulation are currently available for only two solar activity cycles. It has been demonstrated that sunspot observations, which cover over 400 years, can be used to calibrate the Parker-Kleeorin-Ruzmaikin model and that the Ensemble Kalman Filter (EnKF) method can be used to link the model magnetic fields to sunspot observations to make reliable predictions of a following cycle. However, for more accurate predictions, it is necessary to use actual observations of the solar magnetic fields, which are available for only four solar cycles. This raises the question of how limitations in observational data and model uncertainties affect predictive capabilities and implies the need for the development of new forecast methodologies and validation criteria. In this presentation, I will discuss the influence of the limited number of available observations on the accuracy of EnKF estimates of solar cycle parameters.

Kitiashvili, Irina N.↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST toreduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRS2007

Dual-Beam Atom Laser Driven by Spinor Dynamics

An atom laser now undergoing development simultaneously generates two pulsed beams of correlated Rb-87 atoms. (An atom laser is a source of atoms in beams characterized by coherent matter waves, analogous to a conventional laser, which is a source of coherent light waves.) The pumping mechanism of this atom laser is based on spinor dynamics in a Bose-Einstein condensate. By virtue of the angular-momentum conserving collisions that generate the two beams, the number of atoms in one beam is correlated with the number of atoms in the other beam. Such correlations are intimately linked to entanglement and squeezing in atomic ensembles, and atom lasers like this one could be used in exploring related aspects of Bose-Einstein condensates, and as components of future sensors relying on atom interferometry. In this atom-laser apparatus, a Bose-Einstein condensate of about 2 x 10(exp 6) Rb-87 atoms at a temperature of about 120 micro-K is first formed through all-optical means in a relatively weak singlebeam running-wave dipole trap that has been formed by focusing of a CO2-laser beam. By a technique that is established in the art, the trap is loaded from an ultrahigh-vacuum magnetooptical trap that is, itself, loaded via a cold atomic beam from an upstream two-dimensional magneto-optical trap that resides in a rubidium-vapor cell that is differentially pumped from an adjoining vacuum chamber, wherein are performed scientific observations of the beams ultimately generated by the atom laser.

Thompson, Robert↗
NASA NTRS2019

Solar Activity Modeling: From Subgranular Dynamical Scales to the Solar Cycles

Dynamical effects of solar magnetoconvection span a wide range spatial and temporal scales that extends from the interior to the corona and from fast turbulent motions to the global-Sun magnetic activity. To study the solar activity on short temporal scales (from minutes to hours), we use 3D radiative MHD simulations that allow us to investigate complex turbulent interactions that drive various phenomena, such as plasma eruptions, spontaneous formation of magnetic structures, funnel-like structures and magnetic loops in the corona, and others. In particular, we focus on multi-scale processes of energy exchange across the different layers, which contribute to the corona heating and eruptive dynamics, as well as interlinks between different layers of the solar interior and atmosphere. For modeling the global-scale activity we use the data assimilation approach that has demonstrated great potential for building reliable long-term forecasts of solar activity. In particular, it has been shown that the Ensemble Kalman Filter (EnKF) method applied to the Parker-Kleeorin-Ruzmakin dynamo model is capable of predicting solar activity up to one sunspot cycle ahead in time, as well as estimating the properties of the next cycle a few years before it begins. In this presentation, using the available magnetogram data, we discuss development of the methodology and forecast quality criteria (including forecast uncertainties and sources of errors). We demonstrate the influence of observational limitation on the prediction accuracy. We present the EnKF predictions of the upcoming Solar Cycle 25 based on both the sunspot number series and observed magnetic fields, and discuss the uncertainties and potential of the data assimilation approach for modeling and forecasting the solar activity.

Kitiashvili, I. N.↗
NASA NTRS2014

Reply to Comment by Laprise on 'the Added Value to Global Model Projections of Climate Change by Dynamical Downscaling: a Case Study over the Continental U.S. Using the GISS-ModelE2 and WRF Models'

In his comment, Laprise raises several points that we agree merit consideration. His primary critique is that our study [Racherla et al., 2012] tested the ability of the WRF regional climate model to reproduce historical temperature and precipitation change relative to the driving global climate model (GCM) using only a single simulation rather than an ensemble. He asserts that the observed changes are smaller than the internal variability in the climate system (i.e., not statistically significant) and that thus a single simulation should not necessarily be able to capture the observations. Laprise points out that the statistical signal is reduced for a multi-decadal trend such as the one we analyzed in comparison with mean climatology and cites two studies showing that for particular climate parameters it can take any years for a signal to be discerned over internal variability. He states that The results of theexperiment as designed were strongly influenced by the presence of internal variability and sampling errors,which masked the rather small climate changes that may have occurred as a consequence of changes inforcing during the period considered. While Laprise discusses statistics in general terms at some length, for the actual climate trends examined in our study, he offers no evidence that the forced signal was smallcompared with internal variability. The two studies he cites [de Ela et al., 2013; Maraun, 2013] do not provide convincing evidence as they concern climate variables averaged over different times and areas. One in fact examines extreme precipitation events, which by definition are rare and thus have a lower significance level. We accept the general point that it is important to consider internal variability, and as noted in our paper we agree that an ensemble of simulations is in principle an optimal, though computationally expensive, approach. While we did not present the statistical significance of the observations in our original paper, we have now evaluated those for the regional temperature trends used in our study to evaluate the added value of WRF and thus can analyze data as to the magnitude of the trends with respect to internal variability.

climate↗
NASA NTRS2004

ENSO Bred Vectors in Coupled Ocean-Atmosphere General Circulation Models

The breeding method has been implemented in the NASA Seasonal-to-Interannual Prediction Project (NSIPP) Coupled General Circulation Model (CGCM) with the goal of improving operational seasonal to interannual climate predictions through ensemble forecasting and data assimilation. The coupled instability as cap'tured by the breeding method is the first attempt to isolate the evolving ENSO instability and its corresponding global atmospheric response in a fully coupled ocean-atmosphere GCM. Our results show that the growth rate of the coupled bred vectors (BV) peaks at about 3 months before a background ENSO event. The dominant growing BV modes are reminiscent of the background ENSO anomalies and show a strong tropical response with wind/SST/thermocline interrelated in a manner similar to the background ENSO mode. They exhibit larger amplitudes in the eastern tropical Pacific, reflecting the natural dynamical sensitivity associated with the presence of the shallow thermocline. Moreover, the extratropical perturbations associated with these coupled BV modes reveal the variations related to the atmospheric teleconnection patterns associated with background ENSO variability, e.g. over the North Pacific and North America. A similar experiment was carried out with the NCEP/CFS03 CGCM. Comparisons between bred vectors from the NSIPP CGCM and NCEP/CFS03 CGCM demonstrate the robustness of the results. Our results strongly suggest that the breeding method can serve as a natural filter to identify the slowly varying, coupled instabilities in a coupled GCM, which can be used to construct ensemble perturbations for ensemble forecasts and to estimate the coupled background error covariance for coupled data assimilation.

Yang, S. C.↗
NASA NTRS2012

A Robust Multi-Scale Modeling System for the Study of Cloud and Precipitation Processes

During the past decade, numerical weather and global non-hydrostatic models have started using more complex microphysical schemes originally developed for high resolution cloud resolving models (CRMs) with 1-2 km or less horizontal resolutions. These microphysical schemes affect the dynamic through the release of latent heat (buoyancy loading and pressure gradient) the radiation through the cloud coverage (vertical distribution of cloud species), and surface processes through rainfall (both amount and intensity). Recently, several major improvements of ice microphysical processes (or schemes) have been developed for cloud-resolving model (Goddard Cumulus Ensemble, GCE, model) and regional scale (Weather Research and Forecast, WRF) model. These improvements include an improved 3-ICE (cloud ice, snow and graupel) scheme (Lang et al. 2010); a 4-ICE (cloud ice, snow, graupel and hail) scheme and a spectral bin microphysics scheme and two different two-moment microphysics schemes. The performance of these schemes has been evaluated by using observational data from TRMM and other major field campaigns. In this talk, we will present the high-resolution (1 km) GeE and WRF model simulations and compared the simulated model results with observation from recent field campaigns [i.e., midlatitude continental spring season (MC3E; 2010), high latitude cold-season (C3VP, 2007; GCPEx, 2012), and tropical oceanic (TWP-ICE, 2006)].

Tao, Wei-Kuo↗
NASA NTRS2019

Mid-21st Century Ozone Air Quality and Health Burden in China Under Emissions Scenarios and Climate Change

Despite modest emissions reductions of air pollutants in recent years, China still suffers from poor air quality, and the outlook for future air quality in China is uncertain. We explore the impact of two disparate 2050 emissions scenarios relative to 2015 in the context of a changing climate with the Geophysical Fluid Dynamics Laboratory Atmospheric Model version 3 (GFDL-AM3) chemistry-climate model. We impose the same near-term climate change for both emission scenarios by setting global sea surface temperature (SST) and sea ice cover (SIC) to the average over 2010–2019 and 2046–2055, respectively, from a three-member ensemble of GFDL coupled climate model simulations under the RCP8.5 (Representative Concentration Pathway) scenario. By the 2050s, annual mean surface ozone increases throughout China by up to 8 ppbv from climate change alone (estimated by holding air pollutants at 2015 levels while setting SIC and SST to 2050 conditions in the model) and by 8–12 ppbv in a scenario in which emissions of ozone precursors nitrogen oxides (NO (sub x) ) and anthropogenic volatile organic compounds (VOCs) increase by ~10%. In a scenario in which NO (sub x) and anthropogenic VOC emissions decline by 60%, annual mean surface ozone over China decreases by 16–20 ppbv in the 2050s relative to the 2010s. The ozone increase from climate change alone results in an additional 62 000 premature deaths in China as compared to 330 000 fewer premature deaths by the 2050s under a strong emissions mitigation scenario. In springtime over Southwestern China in the 2050s, the model projects 9–12 ppbv enhancements to surface ozone from the stratosphere (diagnosed with a model tracer) and from international anthropogenic emissions (diagnosed by differencing AM3 simulations with the same emissions within China but higher versus lower emissions in the rest of the world). Our findings highlight the effectiveness of emissions controls in reducing the health burden in China due to air pollution, and also the potential for climate change and rising global emissions to offset, at least partially, some of the ozone decreases attained with regional emission reductions in China.

Westervelt, D. M.↗
NASA NTRS1974

Statistical mechanics of 'negative temperature' states

Consideration of the dynamics of a two-dimensional guiding center plasma, recently shown by Taylor and McNamara (1971) to be identical to the dynamics of the discrete vortex model of Onsager (1949). A semirigorous application of the methods of equilibrium statistical mechanics to the guiding center plasma (or equivalently, the line vortex system) is presented. An adaptation of the apparatus of the theory of probability is attempted, in the form given by Khinchin (1949) to obtain ensemble-average predictions for the states of the guiding center plasma. Interest focuses primarily on the regime in which the interaction energy is high enough to be above the Onsager 'negative temperature' threshold.

Montgomery, D.↗
NASA NTRS2013

Dynamics and Control of a Disordered System in Space

In this paper, we present some ideas regarding the modeling, dynamics and control aspects of granular spacecraft. Granular spacecraft are complex multibody systems composed of a spatially disordered distribution of a large number of elements, for instance a cloud of N grains in orbit, with N greater than 10(exp 3). These grains can be large (Cubesat-size) or small (mm-size), and can be active, i.e., a fully equipped vehicle capable sensing their own position and attitude, and enabled with propulsion means, or entirely passive. The ultimate objective would be to study the behavior of the single grains and of large ensembles of grains in orbit and to identify ways to guide and control the shape of a cloud composed of these grains so that it can perform a useful function in space, for instance, as an element of an optical imaging system for astrophysical applications. This concept, in which the aperture does not need to be continuous and monolithic, would increase the aperture size several times compared to large NASA observatories such as ATLAST, allowing for a true Terrestrial Planet Imager that would be able to resolve exo-planet details and do meaningful spectroscopy on distant world. In the paper, we address the modeling and autonomous operation of a distributed assembly (the cloud) of large numbers of highly miniaturized space-borne elements (the grains). A multi-scale, multi-physics model is proposed of the dynamics of the cloud in orbit, as well as a control law for cloud shape maintenance, and preliminary simulation studies yield an estimate of the computational effort, indicating a scale factor of approximately N(exp 1.4) as a function of the number of grains. A granular spacecraft can be defined as a collection of a large number of space-borne elements (in the 1000s) designed and controlled such that a desirable collective behavior emerges, either from the interactions among neighboring grains, and/or between the grains and the environment. In this paper, each grain is considered to be a highly miniaturized spacecraft which has limited size and mass, hence it has limited actuation, limited propulsive capability, limited power, limited sensing, limited communication, limited computational resources, limited range of motion, limited lifetime, and may be expendable. The modeling and dynamics of clouds of vehicles is more challenging than with conventional vehicles because we are faced with a probabilistic vehicle composed of a large number of physically disconnected vehicles. First, different scales of motion occur simultaneously in a cloud: translations and rotations of the cloud as a whole (macro-dynamics), relative rotation and translation of one cloud member with respect to another (meso-dynamics), and individual cloud member dynamics (micro-dynamics). Second, the control design needs to be tolerant of the system complexity, of the system architecture (centralized vs. decentralized large scale system control) as well as robust to un-modeled dynamics and noise sources. Figure 1, top left, shows the kinematic parameters of a 1000 element cloud in orbit. The motion of the system is described with respect to a local vertical-local horizontal (LV-LH) orbiting reference frame (x,y,z)=F(sub ORF) of origin O(sub ORF) which rotates with mean motion omega and orbital semi-major axis R(sub 0). The orbital geometry at the initial time is defined in terms of its six orbital elements, and the orbital dynamics equation for point O(sub ORF) is propagated forward in time under the influence of the gravitational field of the primary and other external perturbations, described below. The origin of this frame coincides with the initial position of the center of mass of the system, and the coordinate axes are z along the local vertical, x toward the flight direction, and y in the orbit normal direction. The assumptions we used to model the dynamics are as follows: 1) The inertial frame is fixed at Earth's center. 2) The orbiting Frame ORF follows Keplerian orbit. 3) the cloud system dynamics is referred to ORF. 4) the attitude of each grain uses the principal body frame as body fixed frame. 5) the atmosphere is assumed to be rigidly rotating with the Earth. Regarding the grains forming the cloud: 1) each grain is modeled as a rigid body; 2) a simple attitude estimator provides attitude estimates, 3) a simple guidance logic commands the position and attitude of each grain, 4) a simple local feedback controller based on PD control of local states is used to stabilize the attitude of the vehicle. Regarding the cloud: 1) the cloud as a whole is modeled as an equivalent rigid body in orbit, and 2) an associated graph establishes agent connectivity and enables coupling between modes of motion at the micro and macro scales; 3) a simple guidance and estimation logic is modeled to estimate and command the attitude of this equivalent rigid body; 4) a cloud shape maintenance controller is based on the dynamics of a stable virtual truss in the orbiting frame. Regarding the environmental perturbations acting on the cloud: 1) a non-spherical gravity field including JO (Earth's spherical field) zonal component, J2 (Earth's oblateness) and J3 zonal components is implemented; 2) atmospheric drag is modeled with an exponential model; 3) solar pressure is modeled assuming the Sun is inertially fixed; and 4) the Earth's magnetic field is model using an equivalent dipole model. The equations of motion are written in a referential system with respect to the origin of the orbiting frame and the state is propagated forward in time using an incremental predictor-corrector scheme. A representative cloud with varying number of grains is simulated to identify the limitations in computation time as the number of grains grows. We derive a control law to track a desired surface in the ORF (equivalently to maintain a reference cloud shape) by defining an error from a desired surface shape, and designing a control law that is exponentially stable and reduces the tracking error to zero. Figure 1 (top right) shows a comparison of various requirements for simulation of single spacecraft vs. granular spacecraft, indicating the high degree of complexity that needs to be taken into consideration. The ORF components of control force required by one of the grains is, for this particular case, in the micro-Newton range. However, no attempt has been made yet to reconfigure (or re-orient) the cloud configuration internally, for which forces in the milli-Newton level are expected, depending on the time required to do the reconfiguration. Figure 1, bottom, shows the computation time as a function of the number of grains, indicating an order N(exp 1.43) scaling on a 8 Gb, 1067 MHz RAM MacOSX computer with a 3.06 GHz Intel Core 2 Duo processor. With this metric, the same simulation for a system of N=1000 grains would take 5.4 hours, and 146 hours (i.e., 6 days) for a system with N=10,000 grains. Therefore, efficient ways to simulate this complex system, where not only the time scales of natural system dynamics, but also the sampling times of the Guidance, Navigation, and Control are included, remain to be explored. Additional details on the cloud modeling, dynamics, and control will be described in the paper.

simulation↗
NASA NTRS1983

Theory of hydromagnetic turbulence

The present state of MHD turbulence theory as a possible solar wind research tool is surveyed. The theory is statistical, and does not make statements about individual events. The ensembles considered typically have individual realizations which differ qualitatively, unlike equilibrium statistical mechanics. Most of the theory deals with highly symmetric situations; most of these symmetries have yet to be tested in the solar wind. The applicability of MHD itself to solar wind parameters is highly questionable; yet it has no competitors, as a potentially comprehensive dynamical description. The purpose of solar wind research require sharper articulation. If they are to understand radial turbulent plasma flows from spheres, laboratory experiments and numerical solution of equations of motion may be cheap alternative to spacecraft. If "real life" information is demanded, multiple spacecraft with variable separation may be necessary to go further. The principal emphasis in the theory so far has been on spectral behavior for spatial covariances in wave number space. There is no respectable theory of these for highly anisotropic situations. A rather slow development of theory acts as a brake on justifiable measurement, at this point.

Montgomery, D.↗
NASA NTRS2003

Radar Reflectivity Simulated by a 2-D Spectra Bin Model: Sensitivity of Cloud-aerosol Interaction

The Goddard Cumulus Ensemble (GCE) model with bin spectra microphysics is used to simulate mesoscale convective systems.The model uses explicit bins to represent size spectra of cloud nuclei, water drops, ice crystals, snow and graupel. Each hydrometeorite category is described by 33 mass bins. The simulations provide a unique data set of simulated raindrop size distribution in a realistic dynamic frame. Calculations of radar parameters using simulated drop size distribution serve as an evaluation of numerical model performance. In addition, the GCE bin spectra modes is a very useful tool to study uncertainties related to radar observations; all the environmental parameters are precisely known. In this presentation, we concentrate on the discussion of Z-R (ZDR-R) relation in the simulated systems. Due to computational limitations, the spectra bin model has been run in two dimensions with 31 stretched vertical layers and 1026 horizontal grid points (1 km resolution). Two different cases, one in midlatitude continent, the other in tropical ocean, have been simulated. The continental case is a strong convection which lasted for two hours. The oceanic case is a persistent system with more than 10 hours' life span. It is shown that the simulated Z-R (ZDR-R) relations generally agree with observations using radar and rain gauge data. The spatial and temporal variations of Z-R relation in different locations are also analyzed. Impact of aerosols on cloud formation and raindrop size distribution was studied. Both clean (low CCN) and dirty (high CCN) cases are simulated. The Z-R relation is shown to vary considerable in the initial CCN concentrations.

Li, Kiaowen↗