Reliable cloud pressure estimates are needed for accurate retrieval of ozone and other trace gases using satellite-borne hyperspectral backscatter ultraviolet (buv) instruments. The cloud pressures should be consistent with the assumptions made in the retrieval algorithms. Cloud pressure can be derived from buv instruments using the properties of rotational-Raman scattering (RRS) and absorption by O2-O2. Here we estimate cloud pressure using the concept of a Lambert-equivalent reflectivity (LER) surface that is also used in many trace gas retrieval algorithms. An LER cloud pressure (PLER) algorithm is being developed for the ozone monitoring instrument (OMI) that will fly on NASA EOS Aura. As a demonstration, we apply the approach to data from the global ozone monitoring experiment (GOME) in the 355--400 nm spectral range. GOME has full spectral coverage in this range at relatively high spectral resolution with a very high signal-to-noise ratio. This allows for more accurate estimates of cloud pressure than were possible with its predecessors SBUV and TOMS. We also demonstrate for the first time the retrieval of oceanic chlorophyll content using oceanic Raman scattering in buv observations. We compare our retrieved PLER with cloud top pressures, Ptop, derived from the infrared ATSR-2 instrument on the same satellite for overcast situations. The findings confirm results from previous studies that showed retrieved PLER from buv observations is systematically higher than IR-derived Ptop. Simulations using Mie-scattering radiative transfer algorithms with O2-O2 absorption show that these differences can be explained by increased absorption within and below the cloud as well as between multiple cloud decks.