Ionospheric Models at the NOAA Space Weather Prediction Center - - PowerPoint PPT Presentation

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Ionospheric Models at the NOAA Space Weather Prediction Center - - PowerPoint PPT Presentation

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Ionospheric Models at the NOAA Space Weather Prediction Center Rodney Viereck Rashid Akmaev, David Anderson 1 , Mihail Codrescu, Tzu-Wei Fang 1 , Mariangle Fedrizzi 1 , Tim Fuller-Rowell 1 , Chih-Ting Hsu 1 , Paul Lotoaniu 1 , Naomi Maruyama 1

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SLIDE 1

Ionospheric Models at the NOAA Space Weather Prediction Center

Rodney Viereck

Rashid Akmaev, David Anderson1, Mihail Codrescu, Tzu-Wei Fang1, Mariangle Fedrizzi1, Tim Fuller-Rowell1, Chih-Ting Hsu1, Paul Loto’aniu1, Naomi Maruyama1, Tomoko Matsuo1, Houjun Wang1, Valery Yudin1

Space Weather Prediction Testbed

  • 1. And Univ. Colorado CIRES

Outline: Specification Forecasts Ensemble Modeling and Data Assimilation

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SLIDE 2

Space Weather Prediction Center

12 May, 2015 2

Operations – Space Weather Forecast Office R & D – Space Weather Prediction Testbed

Improving Products and Services

Putting out daily forecast since 1965. Specifications; Current conditions Forecast; Conditions tomorrow Watches; Conditions are favorable for storm Warnings; Storm is imminent with high probability Alerts; observed conditions meeting or exceeding storm thresholds

Research-to-Operations

  • Applied Research
  • Model Development
  • Model Test/Evaluation
  • Model Transition
  • Operations Support

Operations-to-Research

  • Customer Requirements
  • Observation Requirements
  • Research Requirements

IES 2015

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SLIDE 3

SWPC Models

  • Solar

– Wang Sheeley Arge (USAF)

  • Heliosphere

– Enlil (George Masson U.)

  • Magnetosphere

– Space Weather Modeling Framework (U. Mich.) – OVATION Prime 2013 (JHU APL)

  • Ionosphere

– D-RAP: D-Region Absorption Product – US-TEC: US Total Electron Content – CTIPe: Coupled Thermosphere Ionosphere Plasmasphere with electrodynamics – GIP: CTIPe ionosphere only – IPE: Ionosphere-Plasmasphere-Electrodynamics Model (3D-FLIP)

  • Thermosphere/Atmosphere

– Whole Atmosphere Model (WAM)

16 April, 2015 Space Weather Workshop 3

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SLIDE 4

Ionospheric Models and Products

US-TEC HF Com Absorption Aurora Forecast Model

  • 30 Minute Forecast

Solar EUV Irradiance Model Aurora Forecast Model

  • 3-Day Forecast

Global TEC ROTI GPS Product Whole Atmosphere Model Electric Field Model Ionosphere/Plasm asphere/ Electrodynamics Model

Development Prototype Operations

12 May, 2015 IES 2015

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SLIDE 5

D-Region Absorption Product (D-RAP) and HF Com

  • Global D-Region Specification for

HF Com.

  • Inputs: Solar X-ray, Energetic

Protons, Kp

  • Customers: Airlines, Maritime, DHS,

DOD, Ham

12 May, 2015 IES 2015 5

NOAA Radio Absorption Plot

D-RAP San Francisco Air Traffic Control Center

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SLIDE 6

D-Rap: Current HF

Minor proton event 5/12/2015 http://www.swpc.noaa.gov/products/d-region- absorption-predictions-d-rap

12 May, 2015 IES 2015 6

1 dB Absorption Absorption at 5 MHz

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SLIDE 7

US – Total Electron Content

  • Assimilative product creating TEC maps from ground GPS receivers
  • Customers: GPS/GNSS Users (Airlines, FAA, Transportation, etc…)
  • Future:

– Expand to all of North America – Input from COSMIC II Radio Occultation – ROTI product for precision GPS customers

12 May, 2015 IES 2015 7

USTEC captures the TEC enhancement during a moderate storm(Kp<6) Storm produced day-side ionospheric structures that impacted the FAA WAAS

ROTI product by Propagation Research

  • Ass. And JPL
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SLIDE 8

Comparing Empirical and Physics-Based Models

Real-time Global TEC Specification

12 May, 2015 8 GAIM: Air Force Data Assimilation Model SWACI: German DLR Data Assimilation Model CTIPe: A NOAA Physics-Based Model

SWPC CTIPe

– NOAA is testing global TEC specification models:

– Air Force GAIM – DLR SWACI – NOAA CTIPe (physics-based)

– Each model has its strengths and weaknesses.

IES 2015

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SLIDE 9

Requirement: Multi-Day Forecasts of the Ionosphere

  • Requires multi-day forecasts of all three drivers

– Solar EUV and X-ray irradiance

  • GOES real time EUV irradiance
  • AFRL ADAPT forecast (1-7 days)

– Geomagnetic Storms

  • WSA – Enlil – SWMF – I/T models
  • 1-7 day geomag forecast.
  • 1-3 day storm forecast

– Still missing Bz

– Forcing from the lower atmosphere (tides and waves)

12 May, 2015 IES 2015 9 Broadband Parameterization based on Solomon and Qian (2005) Sun-to-Earth Modeling Framework

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SLIDE 10

Why Couple to the Lower Atmosphere?

Gravity Waves

12 May, 2015 IES 2015 10

Gravity waves

  • Propagate upward
  • Grow in amplitude as

they go up.

  • Often break at some

altitude

  • When the break, they

deposit energy (both thermal and momentum)

Gravity waves in clouds (~10 km) Gravity waves in clouds (~80 km) Gravity waves in airglow (~100 km) Gravity waves in clouds (~10 km) Gravity waves in airglow (~100 km)

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SLIDE 11

Why Couple to the Lower Atmosphere?

Atmospheric Tides

12 May, 2015 IES 2015 11

The four peaks in diurnal temperature amplitude result from superposition of

  • The migrating (to the west) tide (DW1)
  • Non-migrating eastward mode with zonal wavenumber 3 (DE3).

Tilel structures modifies the Ionosphere/Thermosphere system NASA TIMED SABER and TIDI NASA IMAGE (Immel et al)

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SLIDE 12

Thermosphere Mesosphere GFS 0 – 60 km WAM Neutral Atmosphere 0 – 600 km

Forcing the Thermosphere from Below:

The Whole Atmosphere Model (WAM)

WAM Stratosphere Troposphere

  • FY15: Real-time WAM
  • FY17: Real-time WAM driving IPE
  • FY19: Fully Coupled WAM-IPE with data assimilation

12 May, 2015 12 IES 2015

Ionosphere Plasmasphere Electrodynamics Model

Global Forecast Systems (GFS = weather model) Whole Atmosphere Model (WAM = Extended GFS) Ionosphere Plasmasphere Electrodynamics (IPE) Integrated Dynamics in Earth’s Atmosphere (IDEA = WAM+IPE)

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SLIDE 13

Forecasting Equatorial Ionosphere

12 May, 2015 IES 2015 13

  • One of the key

challenges is forecasting equatorial scintillation

  • Can the coupled WAM-

IPE model forecast conditions that lead to equatorial ionospheric structures (ExB)?

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SLIDE 14

ExB Drift Leads to Plasma Bubbles Plasma Bubbles Lead to Dropped GPS Signals

12 May, 2015 IES 2015 14

TIMED GUVI (Paxton) Ground-Based Imager (Taylor) Ionospheric Model (Huba) Plasma Bubble Model (Retterer)

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SLIDE 15

Temperature (K)

WAM reproduces the MTM:

  • WAM is the first comprehensive model to internally generate an MTM of a realistic magnitude.
  • WAM simulation show robust feature of MTM and the associated midnight density maximum (MDM).

MTM is the Result of Tides in the Lower Atmosphere:

  • MTM can be traced down to the lower thermosphere, where it is manifested primarily in the form of an

upward propagating terdiurnal tides.

  • Tides with higher-order zonal wavenumbers and frequencies modify the MTM amplitude.

Meriwether et al. (2013)

Midnight Temperature Maximum (MTM)

Fang et al 2014

15

Akmaev et al. (2009)

Comparisons of WAM MTM (blue) with FPI measurements at Brazil (red) from Sep 2009 to Aug 2012. WAM simulation of relative temperature deviation as a function of height and longitude (local time) 12 May, 2015 IES 2015

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SLIDE 16

Vertical Ion Drifts from Models and Observation

Fang et al 2014 (a) C/NOFS IVM climatology in 2010 June solstice (b) WAM-GIP climatology of June and July 2010

(a) (b)

MTM Produces ExB Drift

  • Driving an ionosphere model (GIP) with WAM wave fields reproduces the magnitude

and longitudinal distribution of nighttime upward drift observed by C/NOFS IVM.

  • The nighttime upward drift is more pronounced in June-July season.

16

WAM + GIP produces the conditions necessary for post sunset plasma bubbles to form

12 May, 2015 IES 2015

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SLIDE 17

Replacing GIP with IPE

(Ionosphere Plasmasphere Electrodynamics)

  • GIP is a science code… not well

suited for operations

  • IPE is a 3D version of the FLIP flux

tube model (Richards)

  • Parallelizable
  • Currently in validation and

verification phase

  • Initial results are very encouraging.

16 April, 2015 Space Weather Workshop 17

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SLIDE 18

Improving Forecasts

  • Data Assimilation: Improving the forecast by

providing a better estimate of the initial or intermediate state

  • Ensemble Modeling: Improving the forecast by

varying key parameters to estimate the range of solutions

12 May, 2015 IES 2015 18

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SLIDE 19

Data Assimilation

Initial State 1 Initial State 2 Final Sate 2 Final Sate 1

Initial Conditions

Current State (t0) Forecast State (t0 + Δt) Chaotic System (Weather)

  • Assimilating data reduces errors in the initial state which has a big

impact on the final solution

  • Ensembles based on different starting conditions or different

models (physics) can improve reslts.

True Final State

12 May, 2015 IES 2015 19

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SLIDE 20

Space Weather Data Assimilation

12 May, 2015 IES 2015 20

Initial State 1 Initial State 2 Final State 2 Final State 1

Initial Conditions

Current State (t0) Forecast State (t0 + Δt) Driven System (Space Weather)

  • Changes in the external forcing dominate. Initial state loses importance quickly
  • ver time
  • Ensembles based on variations in external forcing or from different models

(physics) can improve results.

True Final State

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SLIDE 21

Data Assimilation in the Ionsphere

(Hsu, Matsuo, Wang, Liu, 2014) Using TIEGCM

Question:

  • Does Data Assimilation

Work in the ionosphere? Answers:

  • Assimilating only

electron and ion data does not work very well

  • Assimilating winds and

temperatures helps a little

  • Neutral composition

is the most important parameter for improving forecasts

12 May, 2015 IES 2015 21

Electrons Ions Temperature Winds Neutrals All No Ions

Error Error

Without Neutrals With Neutrals Specification Forecast

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SLIDE 22

Global-scale Observations of the Limb and Disk (GOLD)

NASA Instrument of Opportunity

Imaging Spectrograph: Two independent, identical channels Wavelength range: 132 – 160 nm Target Launch: October 2017 Hosted Payload on geostationary commercial satellite

Observations:

  • Hemispheric maps of…
  • Neutral temperature
  • O/N2 ratio (composition)
  • Electron density
  • Limb scans of temperature

Florida Space Institute (FSI) University of Central Florida PI: Richard Eastes Project Coordinator: Andrey Krywonos Laboratory for Atmospheric and Space Physics (LASP) University of Colorado Deputy PI: William McClintock Project Manager: Mark Lankton NOAA SWPC Collaborator: Mihail Codrescu

12 May, 2015 22 Instrument Summary Mass 34 kg Power 61 W Size 42×42×70 cm IES 2015

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SLIDE 23

Summary

  • Current NOAA operational models

– Empirical specification models

  • Near-term prototype models

– Physics based specification models

  • Future models (2-4 years)

– Fully coupled physics based forecast models

  • WAM for thermosphere
  • IPE for ionosphere
  • Data assimilation (where applicable)

12 May, 2015 IES 2015 23

Questions?