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Christopher W. O’Dell, Peter Bauer, and Ralf Bennartz

Abstract

The assimilation of cloud- and rain-affected radiances in numerical weather prediction systems requires fast and accurate radiative transfer models. One of the largest sources of modeling errors originates from the assumptions regarding the vertical and horizontal subgrid-scale variability of model clouds and precipitation. In this work, cloud overlap assumptions are examined in the context of microwave radiative transfer and used to develop an accurate reference model. A fast cloud overlap algorithm is presented that allows for the accurate simulation of microwave radiances with a small number of radiative transfer calculations. In particular, the errors for a typical two-column approach currently used operationally are found to be relatively large for many cases of cloudy fields containing precipitation, even those with an overall cloud fraction of unity; these errors are largely eliminated by using the new approach presented here, at the cost of a slight increase in computation time. Radiative transfer cloud overlap errors are also evident in simulations when compared to actual satellite observations, in that the biases are somewhat reduced when applying a more accurate treatment of cloud overlap.

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Christopher W. O’Dell, Frank J. Wentz, and Ralf Bennartz

Abstract

This work describes a new climatology of cloud liquid water path (LWP), termed the University of Wisconsin (UWisc) climatology, derived from 18 yr of satellite-based passive microwave observations over the global oceans. The climatology is based on a modern retrieval methodology applied consistently to the Special Sensor Microwave Imager (SSM/I), the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI), and the Advanced Microwave Scanning Radiometer (AMSR) for Earth Observing System (EOS) (AMSR-E) microwave sensors on eight different satellite platforms, beginning in 1988 and continuing through 2005. It goes beyond previously published climatologies by explicitly solving for the diurnal cycle of cloud liquid water by providing statistical error estimates, and includes a detailed discussion of possible systematic errors.

A novel methodology for constructing the climatology is used in which a mean monthly diurnal cycle as well as monthly means of the liquid water path are derived simultaneously from the data on a 1° grid; the methodology also produces statistical errors for these quantities, which decrease toward the end of the time record as the number of observations increases. The derived diurnal cycles are consistent with previous findings in the tropics, but are also derived for higher latitudes and contain more information than in previous studies. The new climatology exhibits differences with previous observationally based climatologies and is found to be more consistent with the 40-yr ECMWF Re-Analysis (ERA-40) than are the previous climatologies.

Potential systematic errors of the order of 15%–30% or higher exist in the LWP climatology. A previously unexplored source of systematic error is caused by the assumption that all microwave-based retrievals of LWP must make regarding the partitioning of cloud water and rainwater, which cannot be determined using microwave observations alone. The potentially large systematic errors that result may hamper the usefulness of microwave-based climatologies of both cloud liquid water and especially rain rate, particularly in certain regions of the tropics and midlatitudes where the separation of rain from liquid cloud water is most critical.

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Andrew Manaster, Christopher W. O’Dell, and Gregory Elsaesser

Abstract

In this study, observed cloud liquid water path (LWP) trends from the Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP) dataset (1988–2014) are compared to trends computed from the temporally coincident records of 16 global climate models (GCMs) participating in phase 5 of the Coupled Model Intercomparison Project (CMIP5). For many regions, observed trend magnitudes are several times larger than the corresponding model mean trend magnitudes. Muted model mean trends are thought to be the result of cancellation effects arising from differing interannual variability characteristics and differences in model physics–dynamics. In most regions, the majority of modeled trends were statistically consistent with the observed trends. This was thought to be because of large estimated errors in both the observations and the models due to interannual variability. Over the southern oceans (south of 40°S latitude), general agreement between the observed trend and virtually all GCM trends is also found (about 1–2 g m−2 decade−1). Observed trends are also compared to those from the Atmospheric Model Intercomparison Project (AMIP). Like the CMIP5 models, the majority of modeled AMIP trends were statistically consistent with the observed trends. It was also found that, in regions where the AMIP model mean time series better captures observed interannual variability, it tends to better capture the magnitude of the observed trends.

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Alan J. Geer, Peter Bauer, and Christopher W. O’Dell

Abstract

The assimilation of cloud- and precipitation-affected observations into weather forecasting systems requires very fast calculations of radiative transfer in the presence of multiple scattering. At the European Centre for Medium-Range Weather Forecasts (ECMWF), performance limitations mean that only a single cloudy calculation (including any precipitation) can be made, and the simulated radiance is a weighted combination of cloudy- and clear-sky radiances. Originally, the weight given to the cloudy part was the maximum cloud fraction in the atmospheric profile. However, this weighting was excessive, and because of nonlinear radiative transfer (the “beamfilling effect”) there were biases in areas of cloud and precipitation. A new approach instead uses the profile average cloud fraction, and decreases RMS errors by 40% in areas of rain or heavy clouds when “truth” comes from multiple independent column simulations. There is improvement all the way from low (e.g., 19 GHz) to high (e.g., 183 GHz) microwave frequencies. There is also improvement when truth comes from microwave imager observations. One minor problem is that biases increase slightly in mid- and upper-tropospheric sounding channels in light-cloud situations, which shows that future improvements will require the cloud fraction to vary according to the optical properties at different frequencies.

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Christopher W. O’Dell, Andrew K. Heidinger, Thomas Greenwald, Peter Bauer, and Ralf Bennartz

Abstract

Radiative transfer models for scattering atmospheres that are accurate yet computationally efficient are required for many applications, such as data assimilation in numerical weather prediction. The successive-order-of-interaction (SOI) model is shown to satisfy these demands under a wide range of conditions. In particular, the model has an accuracy typically much better than 1 K for most microwave and submillimeter cases in precipitating atmospheres. Its speed is found to be comparable to or faster than the commonly used though less accurate Eddington model. An adjoint has been written for the model, and so Jacobian sensitivities can be quickly calculated. In addition to a conventional error assessment, the correlation between errors in different microwave channels is also characterized. These factors combine to make the SOI model an appealing candidate for many demanding applications, including data assimilation and optimal estimation, from microwave to thermal infrared wavelengths.

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Jérôme Vidot, Ralf Bennartz, Christopher W. O’Dell, René Preusker, Rasmus Lindstrot, and Andrew K. Heidinger

Abstract

Spectral characteristics of the future Orbiting Carbon Observatory (OCO) sensor, which will be launched in January 2009, were used to infer the carbon dioxide column-averaged mixing ratio over liquid water clouds over ocean by means of radiative transfer simulations and an inversion process based on optimal estimation theory. Before retrieving the carbon dioxide column-averaged mixing ratio over clouds, cloud properties such as cloud optical depth, cloud effective radius, and cloud-top pressure must be known. Cloud properties were not included in the prior in the inversion but are retrieved within the algorithm. The high spectral resolution of the OCO bands in the oxygen absorption spectral region around 0.76 μm, the weak CO2 absorption band around 1.61 μm, and the strong CO2 absorption band around 2.06 μm were used. The retrieval of all parameters relied on an optimal estimation technique that allows an objective selection of the channels needed to reach OCO’s requirement accuracy. The errors due to the radiometric noise, uncertainties in temperature profile, surface pressure, spectral shift, and presence of cirrus above the liquid water clouds were quantified. Cirrus clouds and spectral shifts are the major sources of errors in the retrieval. An accurate spectral characterization of the OCO bands and an effective mask for pixels contaminated by cirrus would mostly eliminate these errors.

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Gregory S. Elsaesser, Christopher W. O’Dell, Matthew D. Lebsock, Ralf Bennartz, Thomas J. Greenwald, and Frank J. Wentz

Abstract

The Multisensor Advanced Climatology of Liquid Water Path (MAC-LWP), an updated and enhanced version of the University of Wisconsin (UWisc) cloud liquid water path (CLWP) climatology, currently provides 29 years (1988–2016) of monthly gridded (1°) oceanic CLWP information constructed using Remote Sensing Systems (RSS) intercalibrated 0.25°-resolution retrievals. Satellite sources include SSM/I, TMI, AMSR-E, WindSat, SSMIS, AMSR-2, and GMI. To mitigate spurious CLWP trends, the climatology is corrected for drifting satellite overpass times by simultaneously solving for the monthly average CLWP and the monthly mean diurnal cycle. In addition to a longer record and six additional satellite products, major enhancements relative to the UWisc climatology include updating the input to version 7 RSS retrievals, correcting for a CLWP bias (based on matchups to clear-sky MODIS scenes), and constructing a total (cloud + rain) liquid water path (TLWP) record for use in analyses of columnar liquid water in raining clouds. Because the microwave emission signal from cloud water is similar to that of precipitation-sized hydrometeors, greater uncertainty in the CLWP record is expected in regions of substantial precipitation. Therefore, the TLWP field can also be used as a quality-control screen, where uncertainty increases as the ratio of CLWP to TLWP decreases. For regions where confidence in CLWP is highest (i.e., CLWP:TLWP > 0.8), systematic differences in MAC CLWP relative to UWisc CLWP range from −15% (e.g., global oceanic stratocumulus decks) to +5%–10% (e.g., portions of the higher latitudes, storm tracks, and shallower convection regions straddling the ITCZ). The dataset is currently hosted at the Goddard Earth Sciences Data and Information Services Center.

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Kenneth J. Davis, Edward V. Browell, Sha Feng, Thomas Lauvaux, Michael D. Obland, Sandip Pal, Bianca C. Baier, David F. Baker, Ian T. Baker, Zachary R. Barkley, Kevin W. Bowman, Yu Yan Cui, A. Scott Denning, Joshua P. DiGangi, Jeremy T. Dobler, Alan Fried, Tobias Gerken, Klaus Keller, Bing Lin, Amin R. Nehrir, Caroline P. Normile, Christopher W. O’Dell, Lesley E. Ott, Anke Roiger, Andrew E. Schuh, Colm Sweeney, Yaxing Wei, Brad Weir, Ming Xue, and Christopher A. Williams

Abstract

The Atmospheric Carbon and Transport (ACT) – America NASA Earth Venture Suborbital Mission set out to improve regional atmospheric greenhouse gas (GHG) inversions by exploring the intersection of the strong GHG fluxes and vigorous atmospheric transport that occurs within the midlatitudes. Two research aircraft instrumented with remote and in situ sensors to measure GHG mole fractions, associated trace gases, and atmospheric state variables collected 1140.7 flight hours of research data, distributed across 305 individual aircraft sorties, coordinated within 121 research flight days, and spanning five, six-week seasonal flight campaigns in the central and eastern United States. Flights sampled 31 synoptic sequences, including fair weather and frontal conditions, at altitudes ranging from the atmospheric boundary layer to the upper free troposphere. The observations were complemented with global and regional GHG flux and transport model ensembles. We found that midlatitude weather systems contain large spatial gradients in GHG mole fractions, in patterns that were consistent as a function of season and altitude. We attribute these patterns to a combination of regional terrestrial fluxes and inflow from the continental boundaries. These observations, when segregated according to altitude and air mass, provide a variety of quantitative insights into the realism of regional CO2 and CH4 fluxes and atmospheric GHG transport realizations. The ACT-America data set and ensemble modeling methods provide benchmarks for the development of atmospheric inversion systems. As global and regional atmospheric inversions incorporate ACT-America’s findings and methods, we anticipate these systems will produce increasingly accurate and precise sub-continental GHG flux estimates.

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