Tropospheric ozone column retrieval from OMI data by means of neural networks: a validation exercise with ozone soundings over Europe

Antonio Di Noia,Vincenzo Rizi,Marco Iarlori,Marco Cervino,Fabio Del Frate,Pasquale Sellitto

doi:10.1186/1687-6180-2013-21

Abstract

The retrieval of the tropospheric ozone column from satellite data is very important for the characterization of tropospheric chemical and physical properties. However, the task of retrieving tropospheric ozone from space has to face with one fundamental difficulty: the contribution of the tropospheric ozone to the measured radiances is overwhelmed by a much stronger stratospheric signal, which has to be reliably filtered. The Tor Vergata University Earth Observation Laboratory has recently addressed this issue by developing a neural network (NN) algorithm for tropospheric ozone retrieval from NASA-Aura Ozone Monitoring Instrument (OMI) data. The performances of this algorithm were proven comparable to those of more consolidated algorithms, such as Tropospheric Ozone Residual and Optimal Estimation. In this article, the results of a validation of this algorithm with measurements performed at six European ozonesonde sites are shown and critically discussed. The results indicate that systematic errors, related to the tropopause pressure, are present in the current version of the algorithm, and that including the tropopause pressure in the NN input vector can compensate for these errors, enhancing the retrieval accuracy significantly.

Highlights

Tropospheric ozone is a key player in a number of atmospheric processes that affect both climate and air quality
If these assumptions hold true, it is possible to say that total ozone column retrievals over high altitude clouds represent stratospheric columns, which can be subtracted from total ozone columns retrieved over neighboring clear-sky pixels to yield an approximated value for the tropospheric ozone column
The Ozone Monitoring Instrument (OMI)-tropospheric ozone column (TOC) neural network (NN) performances were found to be comparable, and in some cases slightly better, with respect to those of the trajectory enhanced tropospheric ozone residual (TTOR) [12] and optimal estimation (OE) [17] algorithms over a set of co-located ozonesonde measurements [26]. These results suggest that the OMI-TOC NN is a valuable alternative method for tropospheric ozone retrievals from OMI data

Summary

Introduction

Tropospheric ozone is a key player in a number of atmospheric processes that affect both climate and air quality. NNs allow to handle heterogeneous data in an easy way This is an important feature when a complex model relating a large number of different quantities (e.g., atmospheric optical thickness, tropopause height and tropospheric ozone column) cannot be explicitly formulated, it is known that a physical correlation between these quantities exists. The VIS channel is used for observations of clouds, aerosols and other atmospheric trace gases (e.g., nitrogen dioxide, formaldehyde) It does not cover the region of the ozone Chappuis absorption bands where the ozone absorption cross section is largest (i.e., about 530–610 nm), and it cannot be directly exploited in ozone retrievals.

Validation set and intercomparison methodology

Conclusions