Validation of remotely sensed profiles of atmospheric state variables: strategies and terminology

This paper summarizes and classifies the various approaches to validation of remote measurements of atmospheric state variables, and tries to recommend a clear and unambiguous terminology. The following approaches have been identified: Intercomparison of individual profiles for accuracy validation; statistical comparison of matched pairs of measurements with respect to bias determination and precision validation; statistical intercomparison of randomly sampled measurements by two instruments, and comparison of a single measurement to an ensemble of measurements. Applicable statistics are shortly reviewed, and recipes for evaluation of the co-incidence error due to less than perfect coincidences are presented. An approach is suggested to quantitatively validate profile measurements when full covariance matrices are unavailable

Validation of remotely sensed profiles of atmospheric state variables: strategies and terminology

This paper summarizes and classifies the various approaches to validation of remote measurements of atmospheric state variables, and tries to recommend a clear and unambiguous terminology. The following approaches have been identified: Intercomparison of individual profiles for accuracy validation; statistical comparison of matched pairs of measurements with respect to bias determination and precision validation; statistical intercomparison of randomly sampled measurements by two instruments, and comparison of a single measurement to an ensemble of measurements. Applicable statistics are shortly reviewed, and recipes for evaluation of the co-incidence error due to less than perfect co-incidences are presented. An approach is suggested to quantitatively validate profile measurements when full covariance matrices are unavailable

Climatologies from satellite measurements: the impact of orbital sampling on the standard error of the mean

Climatologies of atmospheric observations are often produced by binning measurements according to latitude, and calculating zonal means. The uncertainty in these climatological means is characterized by the standard error of the mean (SEM). However, the usual estimator of the SEM, i.e. the sample standard deviation divided by the square root of the sample size, holds only for uncorrelated randomly sampled measurements. Measurements of the atmospheric state along a satellite orbit cannot always be considered as independent because (a) the time-space interval between two nearest observations is often smaller than the typical scale of variations in the atmospheric state, and (b) the regular time-space sampling pattern of a satellite instrument strongly deviates from random sampling. We have developed an experiment where global chemical fields from a chemistry climate model are sampled according to real sampling patterns of satellite-borne instruments. As case studies, sampling patterns of the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS) and Atmospheric Chemistry Experiment Fourier-Transform Spectrometer (ACE-FTS) satellite instruments are used to iteratively subsample the model O3 fields and produce empirical estimates of the standard error of monthly mean zonal mean model O3 in 5° latitude bins. We find that generally the classic SEM estimator is a conservative estimate of the SEM, i.e. the empirical SEM is often less than the classic estimate. Exceptions occur in instances where the zonal sampling distribution shows non-uniformity with a similar zonal structure as variations in the sampled field, leading to maximum sensitivity to arbitrary phase shifts between the sample distribution and sampled field. The occurrence of such instances is thus very sensitive to slight changes in the sampling distribution, and to the variations in the measured field. This study highlights the need for caution in the interpretation of the oft-used classically computed SEM, and outlines a relatively simple methodology that can be used to assess one component of the uncertainty in monthly mean zonal mean climatologies produced from measurements from satellite-borne instrument

SPARC Data Initiative: climatology uncertainty assessment

The SPARC Data Initiative aims to produce trace gas climatologies for a number of species from a number of instruments. In order to properly compare these climatologies, and interpret differences between them, it is necessary to know the uncertainty in each calculated climatological mean field. The inhomogeneous and finite temporal-spatial sampling pattern of each instrument can lead to biases and uncertainties in the mean climatologies. Sampling which is unevenly weighted in time and space leads to biases between a data set's climatology and the truth. Furthermore, the systematic sampling patterns of some instruments may mean that uncertainties in mean fields calculated through traditional methods that assume random sampling may be inappropriate. We aim to address these issues through an exercise wherein high resolution chemical fields from a coupled Chemistry Climate Model are sub-sampled based on the sampling pattern of each instrument. Climatologies based on the sub-sampled data can be compared to those calculated with the full data set, in order to assess sampling biases. Furthermore, investigating the ensemble variability of climatologies based on subsampled fields will allow us to assess the proper methodology for estimating the uncertainty in climatological mean fields

How to average logarithmic retrievals?

Calculation of mean trace gas contributions from profiles obtained by retrievals of the logarithm of the abundance rather than retrievals of the abundance itself are prone to biases. By means of a system simulator, biases of linear versus logarithmic averaging were evaluated for both maximum likelihood and maximum a priori retrievals, for various signal to noise ratios and atmospheric variabilities. These biases can easily reach ten percent or more. As a rule of thumb we found for maximum likelihood retrievals that linear averaging better represents the true mean value in cases of large local natural variability and high signal to noise ratios, while for small local natural variability logarithmic averaging often is superior. In the case of maximum a posteriori retrievals, the mean is dominated by the a priori information used in the retrievals and the method of averaging is of minor concern. For larger natural variabilities, the appropriateness of the one or the other method of averaging depends on the particular case because the various biasing mechanisms partly compensate in an unpredictable manner. This complication arises mainly because of the fact that in logarithmic retrievals the weight of the prior information depends on abundance of the gas itself. No simple rule was found on which kind of averaging is superior, and instead of suggesting simple recipes we cannot do much more than to create awareness of the traps related with averaging of mixing ratios obtained from logarithmic retrievals.The authors like to thank the Toronto SPARC office and the International Space Science Institute (ISSI) in Berne for hosting team meetings where the issue of this paper became evident and for providing generous travel support. The authors would also like to thank the team members of the SPARC Data Initiative led by Michaela Hegglin and Susann Tegtmeier for triggering our interest in this interesting problem, and Charles Jackman for kindly providing WACCM model results. BF was supported by by the Spanish MICINN under project AYA2008-03498/ESP and project 200950I081 of CSIC.Peer Reviewe

Technical Note: Trend estimation from irregularly sampled, correlated data

Estimation of a trend of an atmospheric state variable is often performed by fitting a linear regression line to a set of data of this variable sampled at different times. Often these data are irregularly sampled in space and time and clustered in a sense that 5 error correlations among data points cause a similar error of data points sampled at similar times. Since this can affect the estimated trend, we suggest to take the full error covariance matrix of the data into account. Superimposed periodic variations can be jointly fitted in a straight forward manner, even if the shape of the periodic function is not known. Global data sets, particularly satellite data, can form the basis to estimate 10 the error correlations

The impact of mixing across the polar vortex edge on Match ozone loss estimates

The Match method for quantification of polar chemical ozone loss is investigated mainly with respect to the impact of mixing across the vortex edge onto this estimate. We show for the winter 2002/03 that significant mixing across the vortex edge occurred and was accurately modeled by the Chemical Lagrangian Model of the Stratosphere. Observations of inert tracers and ozone in-situ from HAGAR on the Geophysica aircraft and sondes and also remote from MIPAS on ENVISAT were reproduced well. The model even reproduced a small vortex remnant that was isolated until June 2003 and was observed in-situ by a balloon-borne whole air sampler. We use this CLaMS simulation to quantify the impact of cross vortex edge mixing on the results of the Match method. It is shown that a time integration of the determined vortex average ozone loss rates as performed in Match results in larger ozone loss than the polar vortex average ozone loss in CLaMS. Also, the determination of the Match ozone loss rates can be influenced by mixing. This is especially important below 430 K, where ozone outside the vortex is lower than inside and the vortex boundary is not a strong transport barrier. This effect and further sampling effects cause an offset between vortex average ozone loss rates derived from Match and deduced from CLaMS with an even sampling for the entire vortex. Both, the time-integration of ozone loss and the determination of ozone loss rates for Match are evaluated using the winter 2002/03 CLaMS simulation. These impacts can explain the differences between CLaMS and Match column ozone loss. While the investigated effects somewhat reduce the apparent discrepancy in January ozone loss rates, a discrepancy between simulations and Match remains. However, its contribution to the accumulated ozone loss over the winter is not large

Technical Note: Trend estimation from irregularly sampled, correlated data

Estimation of a trend of an atmospheric state variable is usually performed by fitting a linear regression line to a set of data of this variable sampled at different times. Often these data are irregularly sampled in space and time and clustered in a sense that error correlations among data points cause a similar error of data points sampled at similar times. Since this can affect the estimated trend, we suggest to take the full error covariance matrix of the data into account. Superimposed periodic variations can be jointly fitted in a straightforward manner, even if the shape of the periodic function is not known. Global data sets, particularly satellite data, can form the basis to estimate the error correlations. State-dependent amplitudes of superimposed periodic corrections result in a non-linear optimization problem which is solved iteratively

Local impact of solar variation on NO2 in the lower mesosphere and upper stratosphere from 2007 to 2012

MIPAS/ENVISAT data of nighttime NO2 volume mixing ratios (VMR) from 2007 until 2012 between 40 km and 62 km altitude are compared with the geomagnetic Ap index and solar Lyman-α radiation. The local impact of variations in geomagnetic activity and solar radiation on the VMR of NO 2 in the lower mesosphere and upper stratosphere in the Northern Hemisphere is investigated by means of superposed epoch analysis. Observations in the Northern Hemisphere show a clear 27-day period of the NO2 VMR. This is positively correlated with the geomagnetic Ap index at 60-70° N geomagnetic latitude but also partially correlated with the solar Lyman-α radiation. However, the dependency of NO2 VMR on geomagnetic activity can be distinguished from the impact of solar radiation. This indicates a direct response of NOx (NO + NO2) to geomagnetic activity, probably due to precipitating particles. The response is detected in the range between 46 km and 52 km altitude. The NO2 VMR epoch maxima due to geomagnetic activity is altitude-dependent and can reach up to 0.4 ppb, leading to mean production rates of 0.029 ppb (Ap d)-1. Observations in the Southern Hemisphere do not have the same significance due to a worse sampling of geomagnetic storm occurances. Variabilities due to solar variation occur at the same altitudes at 60-70° S geomagnetic latitude but cannot be analyzed as in the Northern Hemisphere. This is the first study showing the direct impact of electron precipitation on NOx at those altitudes in the spring/summer/autumn hemisphere. © 2014 Author(s).F. Friederich and M. Sinnhuber gratefully acknowledge funding by the Helmholtz Association of German Research Centres (HGF), grant VH-NG-624Peer Reviewe

A method for merging nadir-sounding climate records, with an application to the global-mean stratospheric temperature data sets from SSU and AMSU

A method is proposed for merging different nadir-sounding climate data records using measurements from high-resolution limb sounders to provide a transfer function between the different nadir measurements. The two nadir-sounding records need not be overlapping so long as the limb-sounding record bridges between them. The method is applied to global-mean stratospheric temperatures from the NOAA Climate Data Records based on the Stratospheric Sounding Unit (SSU) and the Advanced Microwave Sounding Unit-A (AMSU), extending the SSU record forward in time to yield a continuous data set from 1979 to present, and providing a simple framework for extending the SSU record into the future using AMSU. SSU and AMSU are bridged using temperature measurements from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is of high enough vertical resolution to accurately represent the weighting functions of both SSU and AMSU. For this application, a purely statistical approach is not viable since the different nadir channels are not sufficiently linearly independent, statistically speaking. The near-global-mean linear temperature trends for extended SSU for 1980–2012 are −0.63 ± 0.13, −0.71 ± 0.15 and −0.80 ± 0.17 K decade−1 (95 % confidence) for channels 1, 2 and 3, respectively. The extended SSU temperature changes are in good agreement with those from the Microwave Limb Sounder (MLS) on the Aura satellite, with both exhibiting a cooling trend of ~ 0.6 ± 0.3 K decade−1 in the upper stratosphere from 2004 to 2012. The extended SSU record is found to be in agreement with high-top coupled atmosphere–ocean models over the 1980–2012 period, including the continued cooling over the first decade of the 21st century