Neutral Atmosphere Density Interdisciplinary Research

Overview of NADIR

Co-Principal Investigators: Jeff Forbes - Project Manager

Tim Fuller-Rowell - Technical Manager

Objective of NADIR

Significantly advance understanding of drag forces on satellites, including density, winds, factors affecting the drag coefficient. Seek a level of understanding that will enable specification and prediction at the “next level”.

Methodology

• Understand the physical process• Determine which of the processes create the

structure on a scale that is important to satellite drag

• Determine the driver-response relationships - internal and external

• Improve forecasts of the drivers• Determine the most valuable datasets required to

specify the system state and forecast the drivers

Jacchia/Bowman

• Static

• Single indices - F10.7, Ap

NADIR• Improve basic structure

MTMNe/ relationshipCusp heating

• Time-dependentResponse and recovery timescales

• Spectrally-resolved EUV and UV• Magnetospheric sources

Joule heating, momentum forcing, Poynting flux

• Lower atmosphere forcing• Forecasting EUV, geomagnetic, and lower atmosphere forcing

ParticipantsCo-Investigators

• Rashid Akmaev• Brian Argrow• George Born• Gary Bust• Geoff Crowley• David Falconer• Juan Fontenla• Delores Knipp• Tomoko Matsuo• Dusan Odstrcil• Joachim Raeder• Jeff Thayer

Collaborators• Eugene Avrett• Jeff Anderson• Christopher Bass• Bruce Bowman• Mihail Codrescu• Doug Drob• Irene Gonzalez-Hernandez• Cheryl Huang• Charles Lindsey• Chin Lin• Joseph Liu• Frank Marcos• Geoff McHarg• Craig McLaughlin• Cliff Minter• Jah Moriba• Steve Nerem• Andrew Nicholas• Vic Pizzo• Eric Quemaris• Stan Solomon• Mark Storz• Tom Woods

Focus AreasI. Scales of Density Variability, Winds, and Drag

PredictionII. Internal Processes and Thermosphere-Ionosphere

CouplingIII. Energy Partitioning at High Latitudes and Density

ImplicationsIV. Wave Forcing from the Lower AtmosphereV. Forecasting Geomagnetic ActivityVI. Forecasting Solar EUV/UV RadiationVII. Driver-Response RelationshipsVIII.Satellite Drag in the Transition Region

Focus Area I: Scales of Density Variability, Winds, and Drag Prediction

Forbes, Born, McLaughlin, Thayer, Fuller-Rowell

Objectives

• Gain quantitative knowledge and a deeper understanding of how prediction error depends on the various facets of density variability.

• Connect our scientific research activities to the actual prediction of satellite ephemerides.

Methodology: A test bed of satellites will be used to perform satellite orbit predictions, and to evaluate predicted versus actual in-track satellite positions (“in-track errors”) in terms of characteristics of density variability (e.g., scale size).

Anticipated Outcome: Understand what spatial and temporal resolutions that both empirical and first-principles models should seek to achieve, as well as the required temporal resolution of geophysical indices or data that drive the models.

CHAMP Densities

Sample Question: What spatial and temporal scales of drag variability are most relevant to in-track error?

Science questions• What is the source of the SAV variation, the amplitude variation over the solar cycle, and the reason for the phase modulation?• How does the seasonal/latitude, solar cycle, and storm-time variation in radiative cooling impact global neutral density structure?• What is the cause of the phase lag in the neutral density response to solar UV radiation?• What is the temporal response of neutral density to flares, substorms, and storms?• What is the impact of T-I coupling on the neutral density structure?

Goal

•Capture the improved physical understanding in the next generation hybrid empirical/physical models.

Focus Area II: Internal Processes and Thermosphere-Ionosphere Coupling

Fuller-Rowell, Forbes, Thayer, Codrescu, Crowley, Solomon, Richmond

Relationship between and Ne - from CHAMP

Focus Area III: Energy Partitioning at High Latitudes and Density Implications

Thayer, Fuller-Rowell, Codrescu, Crowley, Knipp, Forbes, Richmond

Objectives

• Improve scientific understanding of the high latitudeenergy input, partitioning of this energy into otherforms within the thermosphere and identify the neutraldensity and wind response to these high latitudeenergy inputs.

• Develop driver-response relationships to improve empiricalmodel specifications.

Methodology: Numerical experiments to evaluate solar flux production of electron density and the concomitant change in the Joule heating rate. Assess this correlation and its impact on global temperatures and density. Perform similar numerical experiments using empirical relations with kinetic energy flux and Poynting flux. Anticipated Outcome: Understand the correlations amongst the fluxes to develop driver-response relationships that may depend on multiple energy sources .

Sample Question: How are the solar flux, kinetic energy flux and Poynting flux correlated?

00

12

0618

Science questions• What are the observed characteristics of PW-period thermosphere density oscillations and to what extent do they correlate with similar variability in the strato-mesosphere?

• To what extent are PW-period density variations produced by PW modulation of the lunar semidiurnal and solar atmospheric tides?

• What is the role of gravity waves in this modulation?

• How do seasonal variations of tides and other planetary-scale waves manifest in global mean thermospheric density?

• To what extent can PW-period thermosphere density variations be empirically accounted for in models such as J70 and JB2006?

• Can PW periodicities in density (“thermospheric weather”) be reliably predicted with whole-atmosphere models on time scales of ~one to two weeks in advance?

Focus Area IV: Wave Forcing from the Lower Atmosphere

Temperature correction parameter (dTc) to the empirical J70 model from 4 satellite orbits in 2002 compared to solar and geomagnetic indices. Significant spectral peaks near 11, 14, and 19 days are a possible manifestation of PW effects. (Courtesy B. Bowman, 2006)

Akmaev, Forbes, Fuller-Rowell

Focus Area V: Forecasting Geomagnetic ActivityOdstrcil, Pizzo, Falconer, Raeder, Fuller-Rowell

Methodology: Use observations in the photosphere (left-top), corona (left-bottom), at L1 (bottom-center), and numerical models of the heliosphere (top-center) and magnetosphere (right).

Anticipated Outcome: Improved forecasting ability with the lead times: 30-60 min: driving magnetospheric models by L1 observations; 1-3 days: driving heliospheric models by coronal observations; 3-5 days: using probability of solar magnetic eruptions.

Objectives: Improve existing empirical and numerical models to achieve more realistic short-term and probabilistic long-term forecasting.

Courtesy of D. Braun

EARTH

Courtesy of D. Braun

EARTH

EARTH

Focus Area VI: Forecasting Solar EUV/UV Radiation

Fontenla, Woods, Avrett, Quemaris, Lindsey Images of the near-side produce daily masks of features

Using atmospheric models the spectrum is computed for any day

Without refinement the synoptic mask features obsolescence makes it bad

Synoptic masks are refined by applying trends and far-side imaging:

NOAA 10808 (far side)

NOAA 10808 (near side)

NOAA 10808 (far side)

NOAA 10808 (near side)

AR helioseismic image

AR backscattered image

Using previous rotation is poor

Focus Area VIII: Satellite Drag in the Re-Entry Region:Satellite Drag

Brian Argrow and Jeff Forbes (CU)

Motivation

• Accurate CD essential for drag prediction

• DSMC can be applied for transition flow regime

• Gas surface interaction models are the source of most error for current CD

computations

Methodology: Application of the Direct Simulation Monte Carlo (DSMC) for vehicle simulations from free-molecular flow to slip-flow regimes with emphasis on the gas-surface interaction model.

Anticipated Outcome: Data base of altitude-dependent CD values for representative satellite geometries. Simulate aerodynamic forces for trajectory analysis

DSMC Simulations of a Hypersonic Waverider at 100 km and 145 km (density contours)

100 km 145 km

Focus Area VIII: Satellite Drag in the Re-Entry Region:Tidal and Longitude Variations in Density

Jeff Forbes (CU) and Jens Oberheide (Univ. of Wuppertal)

Motivation

• Re-entry prediction an important problem.

• Few density measurements exist at re- entry altitudes (ca. 80-200 km)

• Strong longitude variations in tides known to exist in temperature and wind measurements

Methodology: A fitting scheme using “Hough Mode Extensions” will be applied to TIMED/SABER and TIMED/TIDI measurements of temperatures and winds over 80-120 km and -50o to +50o latitude during 2002-2006.

Anticipated Outcome: global specifications of longitude-dependent tidal variations in density, winds, and temperature over the 80-200 km height region.

Reconstructed Density Diurnal Amplitudes 110 km, September 2005