# Presentation: 203060

## Validation of the updated Umkehr ozone retrieval algorithm against SAGE II data

- Petropavlovskikh1, E. Weatherhead1, P.K. Bhartia2, and A. Stevermer1
- 1Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, CO
- 2NASA Goddard Space Flight Center, Greenbelt, MD

- Introduction and Motivation:
- Improvements to the Umkehr ozone profile retrieval algorithm have been developed and are now being implemented.  Ones of the uses of the new, updated long-term Umkehr dataset will be to provide high quality information for identifying signs of ozone recovery.  This recovery may be detected earlier in some layers than in others, for instance, at around 40 km altitude where CFC chemistry is the prevailing factor in ozone destruction.
- Both the Umkehr data and SAGE II measurements have very solid information about ozone variability and loss and recovery rates at the mid- and upper (40 km) levels.  The long Umkehr historical record can provide additional information for separating the dynamic and chemical mechanisms of depletion, and can help the community better understand climate change effects.

- Fixed a priori:
- One of the changes in the updated algorithm eliminates the bias due to total ozone trends, which is known to affect the existing Umkehr ozone profile record. This bias was eliminated by switching from seasonal total ozone a priori information, to a fixed a priori. The new a priori is based on the NASA/Goddard ozone profile climatology and has an annual cycle and annual mean which vary with both altitude (approx. 5-km layers) and latitude (10-degree bands), but have no day-to-day or long-term variability. The updated algorithm is able to simulate observations more accurately and provides data output that is easier to analyze.

- The Averaging Kernel Approach:
- Among the new diagnostic capabilities that the updated algorithm provides is the averaging kernel (AK) method.  The AK approach allows us to study how the algorithm responds when a small perturbation is made in a particular layer of the atmosphere (Rodgers 1976, 1990).  For example, the algorithm-retrieved information can be defined as following:
- Xr = X0 + AK * (Xt – X0) + ε,
- where *X**r**, X**0**, X**t* are retrieved, a priori, and true ozone respectively, *AK* is the averaging kernel, and  denotes the measurement and forward model errors.
- In the absence of measurement and modeling errors, the averaging kernel can tell us what the algorithm is capable of retrieving given a truth profile.  It can also tell us what fraction of the retrieved change in layer ozone (Xr) from a priori information (X0) represents a true change in the layer itself, and provides information on which layers are contributing to the remaining changes.
- The ideal AK is a unity matrix, which allows the system to transfer independent layer information to the appropriate layer of the retrieved profile without interference from other layers and without losing information.  Numbers that are less than one indicate that not all the information was transferred to the layer.  Because the system is conserved to preserve total ozone value, the lost information has to be derived elsewhere, and most of the time is delivered to adjoining layers.  If the AKs have a Gaussian shape, the width of the Gaussian at the half maximum point represents the resolution of the technique.

- Comparison with SAGE II profiles:
- The updated Umkehr retrievals are compared with SAGE II (v?) ozone profiles.  Considerable variability of the ozone profile within a 10-degree latitude envelope creates noise in the SAGE II matching dataset and makes trend analysis difficult.  To eliminate this problem, the SAGE II and Umkehr data were de-seasonalized by subtracting the latitudinally and seasonally depend ozone climatology. We also use the averaging kernel method to define precisely what Umkehr should observe given a set of profiles measured by other platforms.  In this study, averaging kernels were applied to the de-seasonalized SAGE II data to produced AK-smoothed SAGE dataset.
- The approach allows us to compare trends more accurately than has been attainable in the past.  We can evaluate whether any differences are due to the measurement techniques or to latitudinal variations.

- Conclusion:
- Differences between the local station sampling and the satellite coverage (both temporal frequency and latitidunal/longitudinal variability) can noticeably affect the derived trends.  If Umkehr observations were at all times precisely aligned with SAGE II, there would be no differences in the derived trends, as long as the instrument errors associated with each system were well understood.

**Figure 1** demonstrates effect of the a priori (AP) on the ozone profile retrieval (RT) of Dobson measurements taken at Tateno (Japan) station during the 1985-2000 time-period. The RT is done using the new Umkehr algorithm that features new a priori and a linear Jacobean. Eight panels compare the layer ozone retrieved using the a priori with (marked as Season) and without (No Season) annual cycle.
- The correlation and linear slope fits are given above each panel to assess the effect of the a priori on the retrieval. All layers have very negligible sensitivity to the AP, except layer 2+3 where slope is 0.9 instead of 1.

- Fig.2. (a) Each line shows AK for 1⁄4 of an Umkehr layer (approximately 1.2 km wide) as function of altitude. The numbers in the graph represent the altitude of the layer where ozone information is sought. The AK is shown for a typical middle latitude ozone profile. AKs are calculated using Rodgers covariance matrix with 0.2 coefficient and 70-degrees SZA normalization.
- (b) The same as (a), but in 8 coarser layers (see Table 1 for details on layer system).

- Fig.3. Comparisons of monthly averaged SAGE and Umkehr ozone data for the 1985-1999 time-period in 8 layers. The Umkehr data (green crosses) are ozone profile retrievals from a Dobson ground station. SAGE II data (dark squares) are a subset of SAGE ozone measurements from 10-degree band centered at latitude of ground station. (a) Tateno, Japan (36 N), (b) Arosa, Switzerland (47 N).
- The SAGE II data smoothed using Umkehr AK (red squares) are also shown for comparisons. The AK-smoothed SAGE data indicate what the idealized Umkehr retrieval would have been if the ground-based Dobson instrument had observed the SAGE II ozone profiles under perfect measurement conditions (no instrumental problems, no aerosol/cloud interference). There is very good agreement between the SAGE and AK-smoothed data in all layers, showing that the Umkehr is capable of capturing the inter-annual variability seen in SAGE data.
- The ground-based Umkehr and SAGE data do not always compare favorably, however.  Tables 2 and 3 provide some statistics for comparisons (the root-mean square deviations of the data as well as the derived trends). The main reason for the disagreement is the problem of matching data due to differences in sampling schedules of ground-based and satellite measurements (including differences in both temporal and latitude coverage). To reduce noise in the data due to spatial and temporal sampling, both datasets are normalized to ozone climatology. All remaining differences can be due to non-idealized measurement conditions (aerosol/clouds) and instrumental problems.

- Table 2 Root-mean square deviations of the data shown in Fig.3. The results are given in each layer and for SAGE, AK-smoothed SAGE, and Umkehr data. To assess effect of SZA normalization on the retrieval the AK-smoothed and Umkehr results are shown in two rows (marked as 60 and 70 SZA). (a) Tateno, (b) Arosa.
- Table 3 is the same as Table 2 but shows results of a trend analysis, where the trend is given as change of ozone in DU in each layer per decade. Note that time period for this trend analysis is different than that for the SPARC results. Moreover, the trend is fitted to the residuals de-seasonalized climatology instead of to the annual cycle derived from data. The QBO, solar-cycle and aerosol signals are not taken out prior to trend calculations.

- Table 2a

- Table 2b

- Table 3a

- Table 3b