Sensitivity of Stratospheric Retrievals from Radio Occultations on (Abel) Upper Boundary Conditions
C. O. Ao, G. A. Hajj, B. A. Iijima, A. J. Mannucci, T. M. Schrøder, M. de la Torre Juárez,
1and S. S. Leroy
21
Jet Propulsion Laboratory,
California Institute of Technology, Pasadena, USA
2
Department of Chemistry and Chemical Biology,
Harvard University, Cambridge, USA
Overview
There is a need for a priori above the stratopause (~ 50 km altitude):
Bending due to neutral atmosphere is small compared to random and systematic measurement errors (thermal noise, ionospheric residuals, local multipaths, orbital errors, …)
Maximum measurement height (km)
Maximum height after ionosphere-correction (km)
Extrapolation
Fitting data in trust region and extrapolate upward.
Usually an exponential function of the bending angle is assumed (true if atmosphere is isothermal above)
Comparison with Reanalyses
Climatology
More popular approach is to replace noisy bending angles with climatology (e.g., MSIS).
A statistically optimal linear combination of climatology and measurements is typically used.
Open Questions
• Should we use extrapolation or climatology?
• For extrapolation:
What is the cutoff height?
How many data points (or what height range) below the cutoff height should be used to perform the extrapolation?
Should we use a better extrapolation model than exponential?
• For climatology:
What is the “cutoff” noise level? (Or, what is the noise characteristics of the “background model”?)
Does use of climatology make stratospheric retrievals climatology-dependent? (Would systematic -- seasonal,
meridional, zonal -- biases from climatology get mapped into the retrievals?)
Simulation Study
Use lidar profiles as input atmospheric states.
• Mauna Loa, 156 profiles from 2001 (NDSC web site), altitudes ~ 20-90 km
• largely independent of climatology
• captures atmospheric variability
Simulation procedure:
1. Compute bending angle profile from the input refractivity N(inp) 2. Add (uncorrelated!) random noise
3. Compute N(ret) and T(ret) using different Abel UBC 4. Retrieval errors: [N(ret)-N(inp)]/N(inp) and T(ret)-T(inp)
Following the success of using RAOB in simulations to assess lower tropospheric retrievals…
Bending Angle Noise
What level of random noise in the iono-corrected bending angle?
Total noise =
Thermal noise from the occulting links (L1 & L2) + Thermal noise from the calibrating links (L1 & L2)
+ The Great Unknown: small-scale, uncorrected ionospheric noise - Noise reduction through smoothing/filtering
• (Systematic biases not considered)
0 2 4 6 8
0 30 60 90
bend. res. 50km ( µ rad)
0 2 4 6 8 0 2 4 6 8 0 2 4 6 8
bend. res. 50km ( µ rad) bend. res. 50km ( µ rad) bend. res. 50km ( µ rad)
Uncertainty in Climatology
Temp. Diff. (Lidar - MSIS) Frac. N Diff. (Lidar - MSIS)
-15 -10 -5 0 5
20 30 40 50 60 70 80
2001-01 to 2001-03
-15 -10 -5 0 5
20 30 40 50 60 70 80
2001-04 to 2001-06
-15 -10 -5 0 5
20 30 40 50 60 70 80
Lidar - MSIS [%]
2001-07 to 2001-09
-15 -10 -5 0 5
20 30 40 50 60 70 80
Lidar - MSIS [%]
2001-10 to 2001-12
-20 -10 0 10
20 30 40 50 60 70 80
2001-01 to 2001-03
-20 -10 0 10
20 30 40 50 60 70 80
2001-04 to 2001-06
-20 -10 0 10
20 30 40 50 60 70 80
Lidar - MSIS [K]
2001-07 to 2001-09
-20 -10 0 10
20 30 40 50 60 70 80
Lidar - MSIS [K]
2001-10 to 2001-12
Uncertainty in Exponential Extrapolation
5 6 7 8 9 10
0 10 20 30 40 50 60
60-70 km
5 6 7 8 9 10
0 10 20 30 40 50 60
50-60 km
5 6 7 8 9 10
0 20 40 60 80
scale height (km) 40-50 km
5 6 7 8 9 10
0 20 40 60 80 100
scale height (km) 35-45 km
Refractivity Retrieval (Noiseless)
Extrapolation Climatology
Temperature Retrieval (Noiseless)
Extrapolation Climatology
-4 -3 -2 -1 0 1
20 25 30 35 40
Mean T error [K]
60 55 50 45
0 0.5 1 1.5
20 25 30 35 40
RMS T error [K]
0 0.5 1 1.5
20 25 30 35 40
Mean T error [K]
60 55 50 45
0 0.2 0.4 0.6 0.8 1
20 25 30 35 40
RMS T error [K]
Refractivity Retrieval (4 µrad)
Extrapolation Climatology
-10 -5 0 5
20 30 40 50 60
Mean N error [%]
60 55 50 45
0 10 20 30 40 50
20 30 40 50 60
RMS N error [%]
0 2 4 6 8 10
20 30 40 50 60 70 80
Mean N error [%]
60 55 50 45
0 5 10 15 20
20 30 40 50 60 70 80
RMS N error [%]
Temperature Retrieval (4 µrad)
Extrapolation Climatology
-4 -3 -2 -1 0 1
20 25 30 35 40
Mean T error [K]
60 55 50 45
0 1 2 3 4 5
20 25 30 35 40
RMS T error [K]
0 0.5 1 1.5 2
20 25 30 35 40
Mean T error [K]
60 55 50 45
0 1 2 3 4 5
20 25 30 35 40
RMS T error [K]
Refractivity at 30 km
Extrapolation Climatology
-10 -8 -6 -4 -20 0 2 4 6 8 10 50
100 150
60 km
0 2 4
-10 -8 -6 -4 -20 0 2 4 6 8 10 20
40 60 80 100 120 140
50 km
-10 -8 -6 -4 -20 0 2 4 6 8 10 20
40 60 80 100
45 km
50 100 150
0 2 4
20 40 60 80 100 120
50 100 150
Temperature at 30 km
Extrapolation Climatology
-10 -8 -6 -4 -20 0 2 4 6 8 10 20
40 60 80 100 120
60 km
0 2 4
-10 -8 -6 -4 -20 0 2 4 6 8 10 20
40 60 80 100
50 km
-10 -8 -6 -4 -20 0 2 4 6 8 10 10
20 30 40 50 60
45 km
-10 -8 -6 -4 -20 0 2 4 6 8 10 20
40 60 80 100 120
T(ret)-T(inp) [K]
0 2 4
-10 -8 -6 -4 -20 0 2 4 6 8 10 20
40 60 80 100
T(ret)-T(inp) [K]
-10 -8 -6 -4 -20 0 2 4 6 8 10 20
40 60 80 100 120
T(ret)-T(inp) [K]
Comparison with RAOB
Summary
• A simple simulation experiment has been performed whereby lidar profiles on a single site are used to estimate sensitivity of stratospheric retrievals on different Abel UBC.
• Preliminary results indicate that the use of MSIS climatology has slight edge over exponential extrapolation in being more robust under noisy conditions.
Mean errors increase as the cutoff height is lowered. For EXT, the mean N-bias is negative, while for MSIS, the mean N-bias is positive.
At 30 km height, for the 4 µrad noise case,
mean dT = 0.66 K, std dT = 2.04 K (MSIS-50 km) mean dT = -1.13 K, std dT = 2.56 K (EXT-50 km)
• Ongoing & future works
More global simulations.
Use real data and validate against other stratospheric temperature measurements.
Quantification of climatological effects on the refractivity and temperature retrievals.
A more robust extrapolation scheme?
