A New A-Train Collocated Product : MODIS and OMI cloud data on the OMI footprint Brad Fisher 1, Joanna Joiner 2, Alexander Vasilkov 1, Pepijn Veefkind 3, Johan de Haan 3, Maarten Sneep 3, Steve Platnick 2, Galina Wind 1 and Paul Menzel 4 1 Science Systems Applications, Inc. (SSAI) 2 NASA Goddard Space Flight Center, 3 Royal Netherlands Meteorological Institute (KNMI), 4 University of Wisconsin NASA A-TRAIN METHODOLOGY INTRODUCTION Clouds cover about 60% of the earth’s surface at any given time and when present satellite’s field of view (FOV), complicate the retrieval of ozone, trace gases and aerosols by the earth observing satellites. Cloud properties associated with optical thickness, cloud pressure, water phase, drop size distribution (DSD), cloud fraction, vertical and areal extent can vary significantly over short spatio-temporal scales and should be taken into account in radiative transfer models used to retrieve column estimates of ozone, trace gases and aerosols. The OMI science team is currently working on a new data product (OMMYDCLD) that combines the cloud information from sensors on board two earth observing satellites in the NASA A-Train: Aura/OMI and Aqua/MODIS. The product collocates high resolution cloud and radiance fields from MODIS with the much larger OMI pixel and combines this cloud information with parameters derived from the three OMI cloud products: OMCLDRR, OMCLDO2 and OMTO3. Histograms of the MODIS cloud information are provided for each OMI pixel along with the mean and standard deviation associated with each distribution. Some MODIS data fields (e.g., optical depth, effective particle radius, cloud water path and cirrus reflectivity) are further separated into ice, liquid and combined ice-liquid distributions using the optical cloud phase information from MODIS. Product applications product include: OMI calibration work, multi-year studies of cloud vertical structure, and development of other products such as multi-layer cloud detection X Sample Space boundary as defined by OMI pixel corners Test Point OMI pixel boundary MODIS point A MODIS point B INSTRUMENT CHARACTERISTICS Instruments Ozone Monitoring Instrument (OMI) o measures 3 solar bands → UV1: 270 to 314 nm → UV2: 306 to 380 nm → VIS: 350 to 500 nm (co-location window OMMYDCLD) MODerate-resolution Imaging Spectroradiometer (MODIS) o 36 spectral bands ranging from 0.4 to 14.4 um Spatial Coverage OMI swath width: 2600 km MODIS swath width: 2300 km MODIS overlap of OMI covers rows 4 to 60 along Field of View OMI VIS: 13 x 24 km 2 for row 30, 28 x 150 km 2 for row 60 MODIS: 1 x 1 km 2 and 5 x 5 km 2 (depending on the SDS) MODIS Sampling of OMI pixel At 1 km: ~300 for row 30, ~700 for row 60 At 5 km: ~12 for row 30, ~ 30 for row 60 MODIS Cloud Top Pressure Thermal IR and CO2 Cloud Slicing UV/VIS OMI Optical Centroid Pressure Raman Scattering/ O2-O2 Absorption Photon Trapping Deep Convection MODIS CTP < < OCP MODIS Cloud Top Pressure July OMI OCP - MODIS CTP July 2006 MODIS Cloud Top Pressure July 2006 Aqua L1b July 20, :15 UTZ 1600 km 800 km Above Cloud Absorbing Aerosols OCP < MODIS CTP Aqua L1b July 4, :50 UTZ 2000 km 1000 km Aqua L1b July 04, :50 UTZ A B C OMI MODIS Clear Liq Ice Clear Liq Ice g/m^ 2 The OMI-MODIS cloud product co-locates the data from MODIS and OMI in space but a small temporal separation still exists between the satellites. The above schematic shows the NASA A-Train with Aqua (MODIS) leading Aura (OMI). From 2004 to 2007, the separation between the two satellites was about 15 minutes. This time difference was reduced to about 8 minutes in REFERENCES Joiner, J., Vasilkov, A. P., Gupta, P., Bhartia, P. K., Veefkind, P., Sneep, M., de Haan, J., Polonsky, I., and Spurr, R., 2012: Fast simulators for satellite cloud optical centroid pressure retrievals; evaluation of OMI cloud retrievals, Atmos. Meas. Tech., 5, , doi: /amt Joiner, J., Vasilkov, A. P., Bhartia, P. K., Wind, G., Platnick, S. and W. P. Menzel, 2010: Detection of multi-layer and vertically-extended clouds using A-train sensors, Atmos. Meas. Tech., 3, Sneep, M., J. F. de Haan, P. Stammes, P. Wang, C. Vanbauce, J. Joiner, A. P. Vasilkov, and P. F. Levelt, 2008: Three-way comparison between OMI and PARASOL cloud pressure products, J. Geophys. Res., 113, D15S23, doi: /2007JD Platnick, S, M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Riedi and R. A. Frey, 2003: The MODIS cloud products: algoritms and examples from Terra, IEEE, 41, Issue 2. pp. 459 – 473. Vasilkov, A. P., J. Joiner, R. Spurr, P. K. Bhartia, P. F. Levelt, and G. Stephens, 2008: Evaluation of the OMI cloud pressures derived from rotational Raman scattering by comparisons with satellite data and radiative transfer simulations, J. Geophys. Res., 113, D15S19, doi: /2007JD APPLICATIONS Improve radiative transfer associated with the scattering and absorption properties of clouds at the scale of the OMI pixel (e.g., OMI calibration work) Investigate scientific questions related to cloud structure, dynamics and climatology Improve trace gas retrievals in presence of clouds Combine multi-satellite measurements from different instruments on the NASA A-Train into a single science product MODIS AND OMI CLOUD PRESSURES OMI and MODIS use different observational techniques to probe the vertical structure of clouds. Cloud pressures are needed for the accurate retrieval of ozone and other trace gases from satellite observations. The MODIS cloud top algorithm applies two methods to estimate cloud top pressure (CTP): 1)CO 2 slicing technique using IR radiances associated with CO2 absorption bands (ch. 33, 34, 35, 36) 2)Comparison of the 11  m thermal IR band (ch. 31) to a gridded profile of brightness temperatures. In the case of OMI, solar backscattered radiances observed at the top of the atmosphere (TOA) penetrate deeper into the cloud resulting in a mean cloud pressure that represents the average pressure reached by backscattered photons. This cloud pressure is called the Optical Centroid Pressure (OCP). OMI provides two estimates of OCP using: 1)Rotational Raman Scattering (RRS) Method 2)O 2 -O 2 Absorption Method. The RRS Method utilizes the high frequency structure of the TOA reflectance in the UV (~354 nm) due to RRS. RRS produces a filling-in of Fraunhofer lines in the TOA spectrum, where the magnitude of the filling-in is related to cloud pressure [Vasilkov et al., 2008]. The O2-O2 Absorption Method determines the OCP from the oxygen dimer absorption in the visible part of the solar spectrum [Sneep et al., 2008]. As can be seen in the figure to the left, the OCP depends on the vertical extent and optical thickness of the cloud and can differ markedly from the CTP. MODIS and OMI cloud pressures can therefore be used to detect multi-layer clouds, often associated with the overhanging anvils and cirrus associated with deep convective systems [Joiner et al., 2010].