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R. G. Pinnick, S. G. Jennings, and G. Fernandez

Abstract

Volatile properties of aerosols at an isolated rural site in south-central New Mexico were measured with a light-scattering particle counter equipped with a temperature-controlled heated inlet. Intermittent measurements throughout a one-year period show that submicron particles am highly volatile and display temperature-fractionation characteristics of ammonium sulfate or bisulfate. It is estimated that 60–98% of the submicron aerosol fraction (by mass) is composed of these sulfates. Larger supermicron particles with radii r > 0.4 μm, which are composed mostly of quartz and clay minerals of soil origin, are relatively involatile.

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R. G. Pinnick, D. L. Hoihjelle, G. Fernandez, E. B. Stenmark, J. D. Lindberg, G. B. Hoidale, and S. G. Jennings

Abstract

Vertical structure of the size distribution and number concentration of particulates in atmospheric fog and haze near Grafenwöhr, West Germany, were measured with a balloonborne light-scattering aerosol counter for periods spanning parts of eight days in February 1976. For haze (∼5 km visibility) conditions, little vertical variation is seen; but for low visibility (<1 km) fog conditions, significant vertical increases in concentration of droplets with radii larger than 4 μm are seen over the first 150 m altitude. For haze, the particle size distribution is approximated by a log-normal with geometric mean radius rg≈0.2 μm and geometric standard deviation σg≈1.9. For fog, a bimodal distribution is found with a relative maximum for the larger particle mode at radii of 4 to 6 μm and corresponding values rg≈5 μm and σg≈1.6; the smaller particle mode has values of rg≈0.3 μm to rg≈0.6 μm and σg≈1.8 to σg≈2.5. Liquid water content values for haze and fog range from 10−4 to 0.45 g m−3. Extinction calculated from the particle size distributions shows an approximate 1/λ wavelength dependence for haze conditions, but nearly neutral (wavelength independent) extinction for heavy fog. A correlation exists between calculated particulate extinction and calculated liquid water content, independent of particle size distribution, for the fogs and hues studied.

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Alok K. Shrestha, Seiji Kato, Takmeng Wong, David A. Rutan, Walter F. Miller, Fred G. Rose, G. Louis Smith, Kristopher M. Bedka, Patrick Minnis, and Jose R. Fernandez

Abstract

The NOAA-9 Earth Radiation Budget Experiment (ERBE) scanner measured broadband shortwave, longwave, and total radiances from February 1985 through January 1987. These scanner radiances are reprocessed using the more recent Clouds and the Earth’s Radiant Energy System (CERES) unfiltering algorithm. The scene information, including cloud properties, required for reprocessing is derived using Advanced Very High Resolution Radiometer (AVHRR) data on board NOAA-9, while no imager data were used in the original ERBE unfiltering. The reprocessing increases the NOAA-9 ERBE scanner unfiltered longwave radiances by 1.4%–2.0% during daytime and 0.2%–0.3% during nighttime relative to those derived from the ERBE unfiltering algorithm. Similarly, the scanner unfiltered shortwave radiances increase by ~1% for clear ocean and land and decrease for all-sky ocean, land, and snow/ice by ~1%. The resulting NOAA-9 ERBE scanner unfiltered radiances are then compared with NOAA-9 nonscanner irradiances by integrating the ERBE scanner radiance over the nonscanner field of view. The comparison indicates that the integrated scanner radiances are larger by 0.9% for shortwave and 0.7% smaller for longwave. A sensitivity study shows that the one-standard-deviation uncertainties in the agreement are ±2.5%, ±1.2%, and ±1.8% for the shortwave, nighttime longwave, and daytime longwave irradiances, respectively. The NOAA-9 and ERBS nonscanner irradiances are also compared using 2 years of data. The comparison indicates that the NOAA-9 nonscanner shortwave, nighttime longwave, and daytime longwave irradiances are 0.3% larger, 0.6% smaller, and 0.4% larger, respectively. The longer observational record provided by the ERBS nonscanner plays a critical role in tying the CERES-like NOAA-9 ERBE scanner dataset from the mid-1980s to the present-day CERES scanner data record.

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G. Reverdin, S. Morisset, J. Boutin, N. Martin, M. Sena-Martins, F. Gaillard, P. Blouch, J. Rolland, J. Font, J. Salvador, P. Fernández, and D. Stammer

Abstract

Salinity measurements from 119 surface drifters in 2007–12 were assessed; 80% [Surface Velocity Program with a barometer with a salinity sensor (SVP-BS)] and 75% [SVP with salinity (SVP-S)] of the salinity data were found to be usable, after editing out some spikes. Sudden salinity jumps are found in drifter salinity records that are not always associated with temperature jumps, in particular in the wet tropics. A method is proposed to decide whether and how to correct those jumps, and the uncertainty in the correction applied. Northeast of South America, in a region influenced by the Amazon plume and fresh coastal water, drifter salinity is very variable, but a comparison with data from the Soil Moisture and Ocean Salinity satellite suggests that this variability is usually reasonable. The drifter salinity accuracy is then explored based on comparisons with data from Argo floats and from thermosalinographs (TSGs) of ships of opportunity. SVP-S/SVP-BS drifter records do not usually present significant biases within the first 6 months, but afterward biases sometimes need to be corrected (altogether, 16% of the SVP-BS records). Biases start earlier after 3 months for drifters not protected by antifouling paint. For the few drifters for which large corrections were applied to portions of the record, the accuracy cannot be proven to be better than 0.1 psu, and it cannot be proven to be better than 0.5 psu for data in the largest variability area off northeast South America. Elsewhere, after excluding portions of the records with suspicious salinity jumps or when large corrections were applied, the comparisons rule out average biases in individual drifter salinity record larger than 0.02 psu (midlatitudes) and 0.05 psu (tropics).

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Fan Chen, Wade T. Crow, Michael H. Cosh, Andreas Colliander, Jun Asanuma, Aaron Berg, David D. Bosch, Todd G. Caldwell, Chandra Holifield Collins, Karsten Høgh Jensen, Jose Martínez-Fernández, Heather McNairn, Patrick J. Starks, Zhongbo Su, and Jeffrey P. Walker

Abstract

Despite extensive efforts to maximize ground coverage and improve upscaling functions within core validation sites (CVS) of the NASA Soil Moisture Active Passive (SMAP) mission, spatial averages of point-scale soil moisture observations often fail to accurately capture the true average of the reference pixels. Therefore, some level of pixel-scale sampling error from in situ observations must be considered during the validation of SMAP soil moisture retrievals. Here, uncertainties in the SMAP core site average soil moisture (CSASM) due to spatial sampling errors are examined and their impact on CSASM-based SMAP calibration and validation metrics is discussed. The estimated uncertainty (due to spatial sampling limitations) of mean CSASM over time is found to be large, translating into relatively large sampling uncertainty levels for SMAP retrieval bias when calculated against CSASM. As a result, CSASM-based SMAP bias estimates are statistically insignificant at nearly all SMAP CVS. In addition, observations from temporary networks suggest that these (already large) bias uncertainties may be underestimated due to undersampled spatial variability. The unbiased root-mean-square error (ubRMSE) of CSASM is estimated via two approaches: classical sampling theory and triple collocation, both of which suggest that CSASM ubRMSE is generally within the range of 0.01–0.02 m3 m−3. Although limitations in both methods likely lead to underestimation of ubRMSE, the results suggest that CSASM captures the temporal dynamics of the footprint-scale soil moisture relatively well and is thus a reliable reference for SMAP ubRMSE calculations. Therefore, spatial sampling errors are revealed to have very different impacts on efforts to estimate SMAP bias and ubRMSE metrics using CVS data.

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J. C. Doran, S. Abbott, J. Archuleta, X. Bian, J. Chow, R. L. Coulter, S. F. J. de Wekker, S. Edgerton, S. Elliott, A. Fernandez, J. D. Fast, J. M. Hubbe, C. King, D. Langley, J. Leach, J. T. Lee, T. J. Martin, D. Martinez, J. L. Martinez, G. Mercado, V. Mora, M. Mulhearn, J. L. Pena, R. Petty, W. Porch, C. Russell, R. Salas, J. D. Shannon, W. J. Shaw, G. Sosa, L. Tellier, B. Templeman, J. G. Watson, R. White, C. D. Whiteman, and D. Wolfe

A boundary layer field experiment in the Mexico City basin during the period 24 February–22 March 1997 is described. A total of six sites were instrumented. At four of the sites, 915-MHz radar wind profilers were deployed and radiosondes were released five times per day. Two of these sites also had sodars collocated with the profilers. Radiosondes were released twice per day at a fifth site to the south of the basin, and rawinsondes were flown from another location to the northeast of the city three times per day. Mixed layers grew to depths of 2500–3500 m, with a rapid period of growth beginning shortly before noon and lasting for several hours. Significant differences between the mixed-layer temperatures in the basin and outside the basin were observed. Three thermally and topographically driven flow patterns were observed that are consistent with previously hypothesized topographical and thermal forcing mechanisms. Despite these features, the circulation patterns in the basin important for the transport and diffusion of air pollutants show less day-to-day regularity than had been anticipated on the basis of Mexico City's tropical location, high altitude and strong insolation, and topographical setting.

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G. Janssens-Maenhout, B. Pinty, M. Dowell, H. Zunker, E. Andersson, G. Balsamo, J.-L. Bézy, T. Brunhes, H. Bösch, B. Bojkov, D. Brunner, M. Buchwitz, D. Crisp, P. Ciais, P. Counet, D. Dee, H. Denier van der Gon, H. Dolman, M. R. Drinkwater, O. Dubovik, R. Engelen, T. Fehr, V. Fernandez, M. Heimann, K. Holmlund, S. Houweling, R. Husband, O. Juvyns, A. Kentarchos, J. Landgraf, R. Lang, A. Löscher, J. Marshall, Y. Meijer, M. Nakajima, P. I. Palmer, P. Peylin, P. Rayner, M. Scholze, B. Sierk, J. Tamminen, and P. Veefkind

Abstract

Under the Paris Agreement (PA), progress of emission reduction efforts is tracked on the basis of regular updates to national greenhouse gas (GHG) inventories, referred to as bottom-up estimates. However, only top-down atmospheric measurements can provide observation-based evidence of emission trends. Today, there is no internationally agreed, operational capacity to monitor anthropogenic GHG emission trends using atmospheric measurements to complement national bottom-up inventories. The European Commission (EC), the European Space Agency, the European Centre for Medium-Range Weather Forecasts, the European Organisation for the Exploitation of Meteorological Satellites, and international experts are joining forces to develop such an operational capacity for monitoring anthropogenic CO2 emissions as a new CO2 service under the EC’s Copernicus program. Design studies have been used to translate identified needs into defined requirements and functionalities of this anthropogenic CO2 emissions Monitoring and Verification Support (CO2MVS) capacity. It adopts a holistic view and includes components such as atmospheric spaceborne and in situ measurements, bottom-up CO2 emission maps, improved modeling of the carbon cycle, an operational data-assimilation system integrating top-down and bottom-up information, and a policy-relevant decision support tool. The CO2MVS capacity with operational capabilities by 2026 is expected to visualize regular updates of global CO2 emissions, likely at 0.05° x 0.05°. This will complement the PA’s enhanced transparency framework, providing actionable information on anthropogenic CO2 emissions that are the main driver of climate change. This information will be available to all stakeholders, including governments and citizens, allowing them to reflect on trends and effectiveness of reduction measures. The new EC gave the green light to pass the CO2MVS from exploratory to implementing phase.

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Rolf H. Reichle, Gabrielle J. M. De Lannoy, Qing Liu, Joseph V. Ardizzone, Andreas Colliander, Austin Conaty, Wade Crow, Thomas J. Jackson, Lucas A. Jones, John S. Kimball, Randal D. Koster, Sarith P. Mahanama, Edmond B. Smith, Aaron Berg, Simone Bircher, David Bosch, Todd G. Caldwell, Michael Cosh, Ángel González-Zamora, Chandra D. Holifield Collins, Karsten H. Jensen, Stan Livingston, Ernesto Lopez-Baeza, José Martínez-Fernández, Heather McNairn, Mahta Moghaddam, Anna Pacheco, Thierry Pellarin, John Prueger, Tracy Rowlandson, Mark Seyfried, Patrick Starks, Zhongbo Su, Marc Thibeault, Rogier van der Velde, Jeffrey Walker, Xiaoling Wu, and Yijian Zeng

Abstract

The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone Soil Moisture (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. The L4_SM product is available from 31 March 2015 to present (within 3 days from real time) and provides 3-hourly, global, 9-km resolution estimates of surface (0–5 cm) and root-zone (0–100 cm) soil moisture and land surface conditions. This study presents an overview of the L4_SM algorithm, validation approach, and product assessment versus in situ measurements. Core validation sites provide spatially averaged surface (root zone) soil moisture measurements for 43 (17) “reference pixels” at 9- and 36-km gridcell scales located in 17 (7) distinct watersheds. Sparse networks provide point-scale measurements of surface (root zone) soil moisture at 406 (311) locations. Core validation site results indicate that the L4_SM product meets its soil moisture accuracy requirement, specified as an unbiased RMSE (ubRMSE, or standard deviation of the error) of 0.04 m3 m−3 or better. The ubRMSE for L4_SM surface (root zone) soil moisture is 0.038 m3 m−3 (0.030 m3 m−3) at the 9-km scale and 0.035 m3 m−3 (0.026 m3 m−3) at the 36-km scale. The L4_SM estimates improve (significantly at the 5% level for surface soil moisture) over model-only estimates, which do not benefit from the assimilation of SMAP brightness temperature observations and have a 9-km surface (root zone) ubRMSE of 0.042 m3 m−3 (0.032 m3 m−3). Time series correlations exhibit similar relative performance. The sparse network results corroborate these findings over a greater variety of climate and land cover conditions.

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Robert J. H. Dunn, F. Aldred, Nadine Gobron, John B. Miller, Kate M. Willett, M. Ades, Robert Adler, Richard, P. Allan, Rob Allan, J. Anderson, Anthony Argüez, C. Arosio, John A. Augustine, C. Azorin-Molina, J. Barichivich, H. E. Beck, Andreas Becker, Nicolas Bellouin, Angela Benedetti, David I. Berry, Stephen Blenkinsop, Olivier Bock, X. Bodin, Michael G. Bosilovich, Olivier Boucher, S. A. Buehler, B. Calmettes, Laura Carrea, Laura Castia, Hanne H. Christiansen, John R. Christy, E.-S. Chung, Melanie Coldewey-Egbers, Owen R. Cooper, Richard C. Cornes, Curt Covey, J.-F. Cretaux, M. Crotwell, Sean M. Davis, Richard A. M. de Jeu, Doug Degenstein, R. Delaloye, Larry Di Girolamo, Markus G. Donat, Wouter A. Dorigo, Imke Durre, Geoff S. Dutton, Gregory Duveiller, James W. Elkins, Vitali E. Fioletov, Johannes Flemming, Michael J. Foster, Stacey M. Frith, Lucien Froidevaux, J. Garforth, Matthew Gentry, S. K. Gupta, S. Hahn, Leopold Haimberger, Brad D. Hall, Ian Harris, D. L. Hemming, M. Hirschi, Shu-pen (Ben) Ho, F. Hrbacek, Daan Hubert, Dale F. Hurst, Antje Inness, K. Isaksen, Viju O. John, Philip D. Jones, Robert Junod, J. W. Kaiser, V. Kaufmann, A. Kellerer-Pirklbauer, Elizabeth C. Kent, R. Kidd, Hyungjun Kim, Z. Kipling, A. Koppa, B. M. Kraemer, D. P. Kratz, Xin Lan, Kathleen O. Lantz, D. Lavers, Norman G. Loeb, Diego Loyola, R. Madelon, Michael Mayer, M. F. McCabe, Tim R. McVicar, Carl A. Mears, Christopher J. Merchant, Diego G. Miralles, L. Moesinger, Stephen A. Montzka, Colin Morice, L. Mösinger, Jens Mühle, Julien P. Nicolas, Jeannette Noetzli, Ben Noll, J. O’Keefe, Tim J. Osborn, T. Park, A. J. Pasik, C. Pellet, Maury S. Pelto, S. E. Perkins-Kirkpatrick, G. Petron, Coda Phillips, S. Po-Chedley, L. Polvani, W. Preimesberger, D. G. Rains, W. J. Randel, Nick A. Rayner, Samuel Rémy, L. Ricciardulli, A. D. Richardson, David A. Robinson, Matthew Rodell, N. J. Rodríguez-Fernández, K.H. Rosenlof, C. Roth, A. Rozanov, T. Rutishäuser, Ahira Sánchez-Lugo, P. Sawaengphokhai, T. Scanlon, Verena Schenzinger, R. W. Schlegel, S. Sharma, Lei Shi, Adrian J. Simmons, Carolina Siso, Sharon L. Smith, B. J. Soden, Viktoria Sofieva, T. H. Sparks, Paul W. Stackhouse Jr., Wolfgang Steinbrecht, Martin Stengel, Dimitri A. Streletskiy, Sunny Sun-Mack, P. Tans, S. J. Thackeray, E. Thibert, D. Tokuda, Kleareti Tourpali, Mari R. Tye, Ronald van der A, Robin van der Schalie, Gerard van der Schrier, M. van der Vliet, Guido R. van der Werf, A. Vance, Jean-Paul Vernier, Isaac J. Vimont, Holger Vömel, Russell S. Vose, Ray Wang, Markus Weber, David Wiese, Anne C. Wilber, Jeanette D. Wild, Takmeng Wong, R. Iestyn Woolway, Xinjia Zhou, Xungang Yin, Guangyu Zhao, Lin Zhao, Jerry R. Ziemke, Markus Ziese, and R. M. Zotta
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