Comparability and Reproducibility of RO Data Shu-peng Ho1,2, Ying-Hwa Kuo1,2, UCAR COSMIC team, Jens Wickert, GFZ team, Gottfried Kirchengast, Wegener. C., Chi Ao, Tony Mannucci, JPL teams, Cheng-Zhi Zou3, and Mitch Goldberg3 1. National Center for Atmospheric Research, 2. University Corporation for Atmospheric Research/COSMIC 3. NOAA/NESDIS/Center for Satellite Applications and Research

Acknowledgements J. Wickert from GFZ, Gottfried Kirchengast from Wegener Center, Chi Ao, Tony Mannucci from JPL team are acknowledged for continuous collaboration Long term processing and maintaining CHAMP RO data from GFZ are especially acknowledged CHAMP, our „good old working horse“ in space since 2000 2

What are the uncertainties for using GPS RO data for climate Motivation: What are the uncertainties for using GPS RO data for climate monitoring ? Can we use GPS RO data to inter-calibrate other climate data ? GPS RO data for climate monitoring: Raw observation is SI traceable, high vertical resolution, insensitive to clouds and precipitation Good temporal and spatial coverage Comparability: High precision Long term stability c) Reproducibility: Reasonable uncertainty among data processed from different centers 2. Outlines : Challenges to define/validate a global trend : a) Satellite b) Radiosonde Characteristics of COSMIC GPS RO data for climate monitoring Compare refractivities generated from different centers 3. Conclusions and Future Work Slide 3 Shu-peng Ben Ho, UCAR/COSMIC

Challenges for defining Climate Trend using satellite data Satellites: Comparability and Reproducibility ? 1) Not designed for climate monitoring 2) Changing platforms and instruments (No Comparability) 3) Different processing/merging method lead to different trends Due to the differing methods used to account for errors before merging the time series of eleven AMSU/MSU satellites into a single, homogeneous time series, these derived trends are different from different groups (RSS vs. UAH). (No Reproducibility) Slide 4 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC Shu-peng Ben Ho, UCAR/COSMIC

Challenges for defining Climate Trend using Radiosonde Data We need measurements with high precision, high accuracy, long term stability, reasonably good temporal and spatial coverage as climate benchmark observations. Radiosonde: Comparability and Reproducibility ? 1) Measurements will be affect on instrument type, location, and the environments (No Comparability) 2) changing instruments and observation practices 3) Uneven spatial coverage, limited spatial coverage especially over the oceans; 4) Uneven temporal coverage Slide 5

Comparability of COSMIC data from different receivers Challenges: a. Extreme on-orbit environment b. Same atmospheric path c. Temporal/spatial mismatch d. Reasonable sample number e. Temporal/spatial dependent biases Using FM3-FM4 pairs in early mission Need to quantify all COSMIC-COSMIC pairs Within 25 km (Ho et al. TAO, 2008) Dry temperature difference between FM3-FM4 receivers Slide 6 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

Global mean FM#3-FM#4 dry temperature difference (K) Mean dry temperature difference between FM3-FM4 is < 0.1 K Not location dependent Not time dependent Natural variability and sampling errors dominate MAD Latitude Latitude Latitude Dry Temperature Difference (K) Median Absolute Deviation (K) Sample Numbers Slide 7

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 Need to have stable calibration reference Within 90 Mins and 250 Km Within 90 Mins and 100 Km Within 60 Mins and 50 Km Slide 8

Approach d(tau)/d(lnP) Approaches: 1. Apply CHAMP and COSMIC soundings to AMSU forward model to simulate AMSU TLS 2. Match simulated GPS RO TLS to NOAA AMSU TLS to find calibration coefficients for different NOAA satellites so that we can d(tau)/d(lnP) Slide 9 Shu-peng Ben Ho, UCAR/COSMIC

NOAA 18 AMSU Ch9 Brightness Temperature The mean weighted dry temperature difference between COSMIC and CHAMP from 300 mb to 10 mb is about 0.07 K b c NOAA 18 AMSU Ch9 Brightness Temperature Slide 10

Monthly Mean Climatology Reproducibility of GPS RO data  Similar measurement errors: Thermal noise Ionospheric calibration Different: Orbital errors Initial Integral of Abel Inversion algorithm (from bending angle to refractivity) Monthly Mean Climatology Quality control method Slide 11

Fractional refractivity from JPL, UCAR and GFZ Monthly, 5 deg-lat, 200-meter mean refractivity profiles from 200201-200512 Fractional refractivity from JPL, UCAR and GFZ Bias=-0.05% Std = 0.45% Bias=0.001% Std = 0.45% 100x 100x Bias and std from 30 km to 8 km Slide 12 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

8-30 km Global North pole North H. Mid-lat Tropics South H. Mid-lat South pole Slide 13

20-30 km Global North pole North H. Mid-lat Tropics South H. Mid-lat South pole Slide 13 Slide 14 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

20-30 km Global North pole Tropics North H. Mid-lat South pole South H. Mid-lat Slide 19 Slide 15 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

8-30 km Global North pole Tropics North H. Mid-lat South pole South H. Mid-lat Slide 16 Fig. 1

The uncertainty of the trend of fractional N anomalies is within +/-0.04 %/5 yrs. What is the cause of small trend difference ? Slide 17 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

20-30 km Global North pole Tropics North H. Mid-lat South H. Mid-lat -0.68% -0.70% -0.43% South pole Slide 19 Slide 18 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

20-30 km Global North pole North H. Mid-lat Tropics South H. Mid-lat South pole Slide 13 Slide 19 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

Bias and MAD from 30km to 8 km GFZ and UCAR pixel to pixel refractivity comparison from 200601-12 Mean MAD from 8 km to 30 km = 0.16% Bias and MAD from 30km to 8 km Slide 20

8-30 km Global North pole Tropics North H. Mid-lat South H. Mid-lat South pole Slide 21

8-30 km Global North pole North H. Mid-lat Tropics South H. Mid-lat South pole Slide 22

Conclusions and Future Work It is a great challenge to use current available datasets to construct reliable climate records. The less than 0.1 K precision of COSMIC will be very useful to inter-calibrate AMSU/MSU data. The long term stability of GPS RO data is very useful for climate monitoring. Although different centers using different inversion procedures and initial conditions to derive refractivity, and using the different quality control criteria to bin the datasets, the mean bias for JPL-UCAR pairs is -0.05%, and for GFZ-UCAR pairs is 0.001%. The uncertainty of the trend of the fractional N anomalies is within +/-0.04 %/5 yrs (+/-0.06 K/5 yrs). And the major causes of uncertainties between these trends are from sample profiles used by different centers. GPS RO temperature shall be very useful to calibrate measurements from other satellites. Slide 23 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC

Conclusions and Future Work Can we use the NOAA satellite measurements calibrated by GPS RO data to calibrate multi-year AMSU/MSU data ? (Ho et al. GRL, 2007) http://www.cosmic.ucar.edu/~spho/ Slide 24 Copy right © UCAR, all rights reserved Shu-peng Ben Ho, UCAR/COSMIC