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

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

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.

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

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, 2006 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.

Leading Weather Center Newsletters

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.

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

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

Climate (This was slide 43) Image from NOAA.

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

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 +/-0.045 %/5 yrs (+/-0.06K/5 yrs), which is mainly caused by sampling errors.

Comparability of CHAMP to COSMIC:Long-term stability Global COSMIC-CHAMP Comparison from 200607-200707 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

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

↑ 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

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 (0.02-0.05 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

The GPS Observation Equation

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

IGS Clock Estimation Network Data from (parts of) this network are presently used to estimate GPS satellite clocks in real-time (@UCAR) or for double differencing GPS clocks (@JPL). 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-13 - 1e-12 or 0.03 - 0.3 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

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

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

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