1. Radio Occultation Observations for Weather, Climate and Ionosphere
Overview and Infrastructure Needs
C. Rocken

2. Overview Radio Occultation (RO) introduction
Societal and scientific impact of RO
Weather
Climate
Ionosphere
Future missions - and infrastructure needs

3. The LEO tracks the GPS phase
while the signal is occulted to determine the Doppler
LEO
vleo
vGPS
Tangent point
The velocity of GPS relative to LEO must be estimated to ~0.1 mm/sec (10 ppb) to determine precise temperature profiles
… so once the orbits have been determined well it is possible to determine atmospheric profiles.
The lower right panel on this slide shows a birds-eye view of the CHAMP satellite in LEO. The white line indicates the ray to the GPS which is not shown and the while dot shows the location of the tangent point introduced in the top left panel.
As the tangent point descends through the atmosphere the temperature profile is shown in the little panel to the right of the globe.
The temperature profiles is obtained in post processing.

4. Evolving COSMIC Constellation
Temperature [C] at 100 mb (16km)

5. Comparison of collocated Profiles
Fig. 7a: Vertical profiles of “dry” temperature (blue and red lines) from two independent receivers on separate COSMIC satellites (FM-3 and FM-4) on 3 August, The satellites were approximately 5 seconds apart, which corresponds to a distance separation at the ray tangent point of about 1 km. The latitude and longitude of the soundings are 21.8°S and 32.9°W. The black line (GFS) is the NCEP analysis of temperature interpolated to the time and location of the COSMIC soundings.

6. Leading Weather Center Newsletters

7. ECMWF SH T Forecast Improvements from COSMIC Assimilation of bending angles above 4 km
Sean Healy, ECMWF
Fractional reduction of 100 mb RMS temperature errors in the Southern Hemisphere, verified against radiosonde observations, as a result of assimilation of COSMIC soundings. Positive values represent improvement in terms of RMS errors. The vertical bars represent the 95% confidence interval. Based on these results ECMWF started using COSMIC data operationally in December 2006.

8. Impact of COSMIC on Hurricane Ernesto (2006) Forecast
With COSMIC
Without COSMIC
Effect of assimilation of COSMIC data on Hurricane prediction. Left with COSMIC, right w/o COSMIC. Integrated cloud liquid from model output is shown in grey shades.
Results from Hui Liu, NCAR

9. Impact of COSMIC on Hurricane Ernesto (2006) Forecast
With COSMIC
GOES Image
.. . And now compreed to the GOES image of the same storm
GOES Image from Tim Schmitt, SSEC

10. Climate
(This was slide 43)
Image from NOAA.

11. Upper Air Temperature Trends
Pressure (hPa)
Figure from S. Leroy

12. Estimates of the Uncertainty in RO Data for
Climate Monitoring: Inter-Comparisons of Refractivity
Derived from Four Independent Centers
To estimate the uncertainty for using Global Positioning System (GPS) radio occultation (RO) data for climate monitoring, here we plot the time series of monthly mean fractional refractivity (N) anomalies for UCAR, JPL, Weg (Wegner Center from University of Graz, Austria) and GFZ here. The Challenging Mini-satellite Payload (CHAMP) refractivity profiles from 2002 to 2005 are used here. The monthly mean time series of fractional refractivity anomalies are computed from the average of the N values from 8 km to 30 km for 90°N-90°S, 90°N-60°N, 60°N-20°N, 20°N-20°S, 20°S-60°S, 60°S-90°S zones.
The fractional refractivity anomalies for UCAR, WEG, JPL and GFZ are computed from removing the mean seasonal cycle for the corresponding time series for each dataset, then divided by the monthly mean N values for all four datasets.
The time series of the fractional refractivity anomalies for UCAR, Weg, JPL, and GFZ are in black, green, red and pink lines, respectively. The linear fit for UCAR time series is in an orange line. Results show that even different initial conditions, assumptions, approximations, and inversion algorithms and even different quality control criteria are used by different centers, their time series of the fractional refractivity anomalies are highly consistent to each other. The uncertainty of the trend for refractivity fractional anomalies is within ± 0.045%/5 yrs, which is about ± 0.06 K/5 yrs. The small trend difference is mainly caused by the different sampling numbers used by different centers, which results in different quality control criteria is applied in the data binning process.
The uncertainty of the trend of fractional refractivity anomalies is within +/ %/5 yrs (+/-0.06K/5 yrs), which is mainly caused by sampling errors.

13. Comparability of CHAMP to COSMIC:Long-term stability
Global COSMIC-CHAMP Comparison from
Comparison of
measurements between old
and new instrument
CHAMP launched in 2001
COSMIC launched 2006
Challenges:
a. Different inclination angle
b. Different atmospheric
paths
c. Temporal/spatial mismatch
d. Reasonable sample number
Within 90 Mins
and 100 Km

14. Space Weather
Image from NOAA. Are thay actually going to say anything about this. If not, then not needed.
(This was slide 56)

15. ↑ 3-D structure of the feature during daytime (constant LT) 12:00 LT
Weaker EIA
Stronger EIA
Weaker EIA
Stronger EIA
Weaker EIA
12:00 LT ↑
EIA is Equatorial Ionospheric Anomaly. Pink arrow in left-most vertical section shows examples of the “caves.” This slide shows the rich detail of the ionosphere that is revealed by COSMIC observations. Tiger Liu, NCU Taiwan
Tiger Liu, NCU

16. Characteristics of RO Data
Limb sounding geometry complementary to ground and space nadir viewing instruments
Global 3-D coverage 40 km to surface
High accuracy (equivalent to <1 K; average accuracy <0.1 K)
High precision ( K)
High vertical resolution (0.1 km surface – 1 km tropopause)
Only system from space to resolve atmospheric boundary layer
All weather-minimally affected by aerosols, clouds or precipitation
Independent height and pressure
Requires no first guess sounding
Independent of radiosonde calibration
Independent of processing center
No instrument drift
No satellite-to-satellite bias
Compact sensor, low power, low cost

17. The GPS Observation Equation

18. From Phase to Excess Phase
RO phase
observation
RO excess phase
must be know to <0.1 mm/sec

19. IGS Clock Estimation Network
Data from (parts of) this network are presently used to estimate GPS satellite clocks in real-time or for double differencing GPS clocks
Global distribution of high-performance clocks in the IGS network. Hydrogen masers (57) are indicated in brown, cesiums (32) in green, and rubidiums (30) in blue. Stars indicate clocks at BIPM timing laboratories.
This network takes measurements of the GPS satellites every 30 seconds and determinates the offsets of the GPS clocks at those epochs relative to the very stable ground based clocks. In this way the GPS satellites are all synchronized every 30 secs.
These 30-sec GPS satellite clocks are then interpolated and used to synchronize the GPS receiver clock every second - based on 1-sec observations of satellites in view from the LEO satellite.
Radio occultation observations for COSMIC are taken every
Satellite clocks can still drift during the 30-sec between ground observations on the ground (at a rate of ~ 1-e e-12 or mm / sec). And receiver clocks (for COSMIC) can still drift at a rate of 1e-7 (30000 mm/sec) during the 1-sec synchronization intervals.
These errors are eliminated by differencing as shown on the last slide.
Hydrogen masers (57) are indicated in brown, cesiums (32) in green, and rubidiums (30) in blue. Stars indicate clocks at BIPM timing laboratories (includesstandard labs at NIST, Paris, Braunschweig, ….)
Figure courtesy J. Ray

20. Coverage of possible future LEO constellations 12 sat constellation-24 Hours
28 GPS Add 24 GLONASS Add 30 Galileo
Future constellations will track Galileo + possibly Glonass

21. GNSS Infrastructure Needs for RO
Weather
Near-Real-Time (<15 min) global ground fiducial data (1-sec from ~50 sites GPS + Galileo + Glonass)
High quality GNSS orbits (real-time / predicted) and clocks (5 sec)
Climate
Stable long-term (decades) reference frame
Reliable fiducial data & meta-data
Space Weather
Real-Time reference data
Satellite + tracking station DCBs
All will need PCVs for transmitters and receivers

22. Summary
Atmospheric sensing with RO has important operational and scientific applications
All applications depend in GNSS infrastructure provided by the non-atmospheric community
Future RO missions are planned and the atmospheric community (climate community) will need many decades of infrastructure support