Multiple satellite observations of cloud cover in extratropical cyclones
Using cloud observations from NASA Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and CloudSat‐CALIPSO, composites of cloud fraction in southern and northern hemisphere extratropical cyclones are obtained for cold and warm seasons between 2006 and 2010, to asses...
| Published in: | Journal of Geophysical Research: Atmospheres |
|---|---|
| Main Authors: | , , , |
| Format: | Article in Journal/Newspaper |
| Language: | unknown |
| Published: |
Wiley Periodicals, Inc.
2013
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| Subjects: | |
| Online Access: | https://hdl.handle.net/2027.42/100326 https://doi.org/10.1002/jgrd.50718 |
| _version_ | 1835020882783764480 |
|---|---|
| author | Naud, Catherine M. Booth, James F. Posselt, Derek J. Heever, Susan C. |
| author_facet | Naud, Catherine M. Booth, James F. Posselt, Derek J. Heever, Susan C. |
| author_sort | Naud, Catherine M. |
| collection | Unknown |
| container_issue | 17 |
| container_start_page | 9982 |
| container_title | Journal of Geophysical Research: Atmospheres |
| container_volume | 118 |
| description | Using cloud observations from NASA Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and CloudSat‐CALIPSO, composites of cloud fraction in southern and northern hemisphere extratropical cyclones are obtained for cold and warm seasons between 2006 and 2010, to assess differences between these three data sets, and between summer and winter cyclones. In both hemispheres and seasons, over the open ocean, the cyclone‐centered cloud fraction composites agree within 5% across the three data sets, but behind the cold fronts, or over sea ice and land, the differences are much larger. To supplement the data set comparison and learn more about the cyclones, we also examine the differences in cloud fraction between cold and warm season for each data set. The difference in cloud fraction between cold and warm season southern hemisphere cyclones is small for all three data sets, but of the same order of magnitude as the differences between the data sets. The cold‐warm season contrast in northern hemisphere cyclone cloud fractions is similar for all three data sets: in the warm sector, the cold season cloud fractions are lower close to the low, but larger on the equator edge than their warm season counterparts. This seasonal contrast in cloud fraction within the cyclones warm sector seems to be related to the seasonal differences in moisture flux within the cyclones. Our analysis suggests that the three different data sets can all be used confidently when studying the warm sector and warm frontal zone of extratropical cyclones but caution should be exerted when studying clouds in the cold sector. Key points Cyclone‐centered cloud fraction composites from three data sets are compared Seasonal contrast in cyclone cloudiness is more pronounced in NH than SH The three data sets together better constrain models than individually Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/100326/1/jgrd50718.pdf |
| format | Article in Journal/Newspaper |
| genre | Sea ice |
| genre_facet | Sea ice |
| id | ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/100326 |
| institution | Open Polar |
| language | unknown |
| op_collection_id | ftumdeepblue |
| op_container_end_page | 9996 |
| op_relation | https://hdl.handle.net/2027.42/100326 doi:10.1002/jgrd.50718 Journal of Geophysical Research: Atmospheres Moroney, C., R. Davis, and J.‐P. Muller ( 2002 ), Operational retrieval of cloud‐top heights using MISR data, IEEE Trans. Geosci. Remote Sens., 40, 1532 – 1540. Field, P. R., A. Bodas‐Salcedo, and M. E. Brooks ( 2011 ), Using model analysis and satellite data to assess cloud and precipitation in midlatitude cyclones, Q. J. R. Meteorol. Soc., 137, 1501 – 1515, doi:10.1002/qj.858. Govekar, P. D., C. Jakob, M. J. Reeder, and J. Haynes ( 2011 ), The three‐dimensional distribution of clouds around Southern Hemisphere extratropical cyclones, Geophys. Res. Lett., 38, L21805, doi:10.1029/2011GL049091. Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown ( 2011 ), Characteristics of southern ocean cloud regimes and their effects on the energy budget, J. Clim., 24, 5061 – 5080. Lau, N.‐C., and M. W. Crane ( 1995 ), A satellite view of the synoptic‐scale organization of cloud properties in midlatitude and tropical circulation systems, Mon. Weather Rev., 123, 1984 – 2006. Mace, G. G., Q. Zhang, M. Vaughan, R. Marchand, G. L. Stephens, C. Trepte, and D. Winker ( 2009 ), A description of hydrometeor layer occurrence statistics derived from the first year of merged CloudSat and CALIPSO data, J. Geophys. Res., 114, D00A26, doi:10.1029/2007JD009755. Mace, G. G., S. Houser, S. Benson, S. A. Klein, and Q. Min ( 2011 ), Critical evaluation of the ISCCP simulator using ground‐based remote sensing data, J. Clim., 24, 1598 – 1612. Marchand, R., G. G. Mace, T. Ackerman, and G. Stephen ( 2008 ), Hydrometeor detection using CloudSat – An Earth orbiting 94‐GHz cloud radar, J. Atmos. Oceanic Technol., 25, 519 – 533. Marchand, R., T. Ackerman, M. Smyth, and W. B. Rossow ( 2010 ), A review of cloud top height and optical depth histograms from MISR, ISCCP and MODIS, J. Geophys. Res., 115, D16206, doi:10.1029/2009JD013422. Naud, C. M., A. D. Del Genio, M. Bauer, and W. Kovari ( 2010 ), Cloud vertical distribution across warm and cold fronts in CloudSat‐CALIPSO data and a general circulation model, J. Clim., 23, 3397 – 3415, doi:10.1175/2010JCLI3282.1. Naud, C. M., D. J. Posselt, and S. C. van den Heever ( 2012 ), Observational analysis of cloud and precipitation in midlatitude cyclones: Northern versus southern hemisphere warm fronts, J. Clim., 25, 5135 – 5151. Pincus, R., S. Platnick, S. A. Ackerman, R. S. Hemler, and R. J. P. Hofmann ( 2012 ), Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators, J. Clim., 25, 4699 – 4720. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey ( 2003 ), The MODIS cloud products: Algorithms and examples from Terra, IEEE Trans. Geosci. Remote Sens., 41, 459 – 473. Rienecker, M. M., et al. ( 2011 ), MERRA: NASA's Modern Era Retrospective analysis for Research and Applications, J. Clim., 24, 3624 – 3648. Salomonson, V. V., W. L. Barnes, P. W. Maymon, H. E. Montgomery, and H. Ostrow ( 1989 ), MODIS: Advanced facility instrument for studies of the earth as a system, IEEE Trans. Geosci. Remote Sens., 27, 145 – 153. Stephens, G. L., et al. ( 2002 ), The CloudSat mission and the A‐TRAIN: A new dimension of space‐based observations of clouds and precipitation, Bull. Am. Meteorol. Soc., 83, 1771 – 1790. Trenberth, K. E., and J. Fasullo ( 2010 ), Simulation of present day and 21 st century energy budgets of the southern oceans, J. Clim., 23, 440 – 454. Tselioudis, G., and C. Jakob ( 2002 ), Evaluation of midlatitude cloud properties in a weather and a climate model: Dependence on dynamic regime and spatial resolution, J. Geophys. Res., 107 ( D24 ), 4781, doi:10.1029/2002JD002259. Wentz, F., and T. Meissner ( 2004 ), AMSR‐E/Aqua L2B Global swath ocean products derived from Wentz Algorithm V002, 2006‐2009, updated daily, National Snow and Ice Data Center, Boulder, Colo., Digital media. Williams, K. D., A. Bodas‐Salcedo, M. Deque, S. Fermepin, B. Medeiros, M. Watanabe, C. Jakob, S. A. Klein, C. A. Senior, and D. L. Williamson ( 2013 ), The Transpose‐AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models, J. Clim., 26, 3258 – 3274, doi:10.1175/JCLI‐D‐12‐00429.1. Winker, D. M., M. A. Vaughan, A. H. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young ( 2009 ), Overview of the CALIPSO mission and CALIOP data processing algorithms, J. Atmos. Oceanic Technol., 26, 2310 – 2323. Zhao, G., and L. Di Girolamo ( 2004 ), A cloud fraction versus view angle technique for automatic in‐scene evaluation of the MISR cloud mask, J. Appl. Meteorol., 43, 860 – 869. Zhao, G., and L. Di Girolamo ( 2006 ), Cloud fraction errors for trade wind cumuli from EOS‐Terra instruments, Geophys. Res. Lett., 33, L20802, doi:10.1029/2006GL027088. Holz, R. E., S. A. Ackerman, F. W. Nagle, R. Frey, S. Dutcher, R. E. Kuehn, M. A. Vaughan, and B. Baum ( 2008 ), Global Moderate resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP, J. Geophys. Res., 113, D00A19, doi:10.1029/2008JD009837. Huang, Y., S. T. Siems, M. J. Manton, L. B. Hande, and J. M. Haynes ( 2012 ), The structure of low‐altitude clouds over the Southern Ocean as seen by CloudSat, J. Clim., 25, 2535 – 2546. Igel, A. L., S. C. van den Heever, C. M. Naud, S. M. Saleeby, and D. J. Posselt ( 2013 ), Sensitivity of warm frontal processes to cloud‐nucleating aerosol concentrations, J. Atmos. Sci., 70, 1768 – 1783. Kawanishi, T., T. Sezai, Y. Ito, K. Imaoka, T. Takeshima, Y. Ishido, A. Shibata, M. Miura, H. Inahata, and R. W. Spencer ( 2003 ), The Adanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E), NASDA's contribution to the EOS for global energy and water cycle studies, IEEE Trans. Geosci. Remote Sens., 41, 184 – 194. Klein, S. A., and C. Jakob ( 1999 ), Validation and sensitivities of frontal clouds simulated by the ECMWF model, Mon. Weather Rev., 127, 2514 – 2531. Ackerman, S. A., R. E. Holz, R. Frey, E. W. Eloranta, B. C. Maddux, and M. McGill ( 2008 ), Cloud detection with MODIS. Part II: Validation, J. Atmos. Oceanic Technol., 25, 1073 – 1086. Bauer, M., and A. D. Del Genio ( 2006 ), Composite analysis of winter cyclones in a GCM: Influence on climatological humidity, J. Clim., 19, 1652 – 1672. Bodas‐Salcedo, A., et al. ( 2011 ), COSP Satellite simulation software for model assessment, Bull. Am. Meteorol. Soc., 92, 1023 – 1043. Bodas‐Salcedo, A., K. D. Williams, P. R. Field, and A. P. Lock ( 2012 ), The surface downwelling solar radiation surplus over the southern ocean in the Met Office model: The role of midlatitude cyclone clouds, J. Clim., 25, 7467 – 7486. Booth, J. F., C. M. Naud, and A. D. Del Genio ( 2013 ), Diagnosing warm frontal cloud formation in a GCM: A novel approach using conditional subsetting, J. Clim., 26, 5827 – 5845, doi:10.1175/JCLI‐D‐12‐00637.1. Carlson, T. N. ( 1980 ), Airflow through midlatitude cyclons and the comma cloud pattern, Mon. Weather Rev., 108, 1498 – 1509. Catto, J. L., L. C. Shaffrey, and K. I. Hodges ( 2010 ), Can climate models capture the structure of extratropical cyclones?, J. Clim., 23, 1621 – 1635. Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally ( 1996 ), Sea ice concentrations from Nimbus‐7 SMMR and DMSP SSM/I‐SSMIS Passive Microwave Data, [Final data], updated yearly, National Snow and Ice Data Center, Boulder, Colo. Dee, D. P., et al. ( 2011 ), The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553 – 597. Di Girolamo, L., and M. J. Wilson ( 2003 ), A first look at band‐differenced angular signatures for cloud detection from MISR, IEEE Trans. Geosci. Remote Sens., 41, 1730 – 1734. Di Girolamo, L., A. Menzies, G. Zhao, K. Mueller, C. Moroney, and D. J. Diner ( 2010 ), Level 3 cloud fraction by altitude algorithm theoretical basis, JPL D‐62358. Diner, D. J., et al. ( 1998 ), Multi‐Angle Imaging Spectro‐radiometer (MISR) description and experiment overview, IEEE Trans. Geosci. Remote Sens., 36, 1072 – 1087. Diner, D. J., R. Davies, L. Di Girolamo, A. Horvath, C. Moroney, J.‐P. Muller, S. R. Paradise, D. Wenkert, and J. Zong ( 1999 ), Level 2 cloud detection and classification algorithm theoretical basis, JPL D‐11399, Rev. D. Field, P. R., and R. Wood ( 2007 ), Precipitation and cloud structure in midlatitude cyclones, J. Clim., 20, 233 – 254. Field, P. R., A. Gettelman, R. B. Neale, R. Wood, P. J. Rasch, and H. Morrison ( 2008 ), Midlatitude cyclone compositing to constrain climate model behavior using satellite observations, J. Clim., 21, 5887 – 5903. |
| op_rights | IndexNoFollow |
| publishDate | 2013 |
| publisher | Wiley Periodicals, Inc. |
| record_format | openpolar |
| spelling | ftumdeepblue:oai:deepblue.lib.umich.edu:2027.42/100326 2025-06-15T14:49:03+00:00 Multiple satellite observations of cloud cover in extratropical cyclones Naud, Catherine M. Booth, James F. Posselt, Derek J. Heever, Susan C. 2013-09-16 application/pdf https://hdl.handle.net/2027.42/100326 https://doi.org/10.1002/jgrd.50718 unknown Wiley Periodicals, Inc. https://hdl.handle.net/2027.42/100326 doi:10.1002/jgrd.50718 Journal of Geophysical Research: Atmospheres Moroney, C., R. Davis, and J.‐P. Muller ( 2002 ), Operational retrieval of cloud‐top heights using MISR data, IEEE Trans. Geosci. Remote Sens., 40, 1532 – 1540. Field, P. R., A. Bodas‐Salcedo, and M. E. Brooks ( 2011 ), Using model analysis and satellite data to assess cloud and precipitation in midlatitude cyclones, Q. J. R. Meteorol. Soc., 137, 1501 – 1515, doi:10.1002/qj.858. Govekar, P. D., C. Jakob, M. J. Reeder, and J. Haynes ( 2011 ), The three‐dimensional distribution of clouds around Southern Hemisphere extratropical cyclones, Geophys. Res. Lett., 38, L21805, doi:10.1029/2011GL049091. Haynes, J. M., C. Jakob, W. B. Rossow, G. Tselioudis, and J. Brown ( 2011 ), Characteristics of southern ocean cloud regimes and their effects on the energy budget, J. Clim., 24, 5061 – 5080. Lau, N.‐C., and M. W. Crane ( 1995 ), A satellite view of the synoptic‐scale organization of cloud properties in midlatitude and tropical circulation systems, Mon. Weather Rev., 123, 1984 – 2006. Mace, G. G., Q. Zhang, M. Vaughan, R. Marchand, G. L. Stephens, C. Trepte, and D. Winker ( 2009 ), A description of hydrometeor layer occurrence statistics derived from the first year of merged CloudSat and CALIPSO data, J. Geophys. Res., 114, D00A26, doi:10.1029/2007JD009755. Mace, G. G., S. Houser, S. Benson, S. A. Klein, and Q. Min ( 2011 ), Critical evaluation of the ISCCP simulator using ground‐based remote sensing data, J. Clim., 24, 1598 – 1612. Marchand, R., G. G. Mace, T. Ackerman, and G. Stephen ( 2008 ), Hydrometeor detection using CloudSat – An Earth orbiting 94‐GHz cloud radar, J. Atmos. Oceanic Technol., 25, 519 – 533. Marchand, R., T. Ackerman, M. Smyth, and W. B. Rossow ( 2010 ), A review of cloud top height and optical depth histograms from MISR, ISCCP and MODIS, J. Geophys. Res., 115, D16206, doi:10.1029/2009JD013422. Naud, C. M., A. D. Del Genio, M. Bauer, and W. Kovari ( 2010 ), Cloud vertical distribution across warm and cold fronts in CloudSat‐CALIPSO data and a general circulation model, J. Clim., 23, 3397 – 3415, doi:10.1175/2010JCLI3282.1. Naud, C. M., D. J. Posselt, and S. C. van den Heever ( 2012 ), Observational analysis of cloud and precipitation in midlatitude cyclones: Northern versus southern hemisphere warm fronts, J. Clim., 25, 5135 – 5151. Pincus, R., S. Platnick, S. A. Ackerman, R. S. Hemler, and R. J. P. Hofmann ( 2012 ), Reconciling simulated and observed views of clouds: MODIS, ISCCP, and the limits of instrument simulators, J. Clim., 25, 4699 – 4720. Platnick, S., M. D. King, S. A. Ackerman, W. P. Menzel, B. A. Baum, J. C. Riedi, and R. A. Frey ( 2003 ), The MODIS cloud products: Algorithms and examples from Terra, IEEE Trans. Geosci. Remote Sens., 41, 459 – 473. Rienecker, M. M., et al. ( 2011 ), MERRA: NASA's Modern Era Retrospective analysis for Research and Applications, J. Clim., 24, 3624 – 3648. Salomonson, V. V., W. L. Barnes, P. W. Maymon, H. E. Montgomery, and H. Ostrow ( 1989 ), MODIS: Advanced facility instrument for studies of the earth as a system, IEEE Trans. Geosci. Remote Sens., 27, 145 – 153. Stephens, G. L., et al. ( 2002 ), The CloudSat mission and the A‐TRAIN: A new dimension of space‐based observations of clouds and precipitation, Bull. Am. Meteorol. Soc., 83, 1771 – 1790. Trenberth, K. E., and J. Fasullo ( 2010 ), Simulation of present day and 21 st century energy budgets of the southern oceans, J. Clim., 23, 440 – 454. Tselioudis, G., and C. Jakob ( 2002 ), Evaluation of midlatitude cloud properties in a weather and a climate model: Dependence on dynamic regime and spatial resolution, J. Geophys. Res., 107 ( D24 ), 4781, doi:10.1029/2002JD002259. Wentz, F., and T. Meissner ( 2004 ), AMSR‐E/Aqua L2B Global swath ocean products derived from Wentz Algorithm V002, 2006‐2009, updated daily, National Snow and Ice Data Center, Boulder, Colo., Digital media. Williams, K. D., A. Bodas‐Salcedo, M. Deque, S. Fermepin, B. Medeiros, M. Watanabe, C. Jakob, S. A. Klein, C. A. Senior, and D. L. Williamson ( 2013 ), The Transpose‐AMIP II experiment and its application to the understanding of Southern Ocean cloud biases in climate models, J. Clim., 26, 3258 – 3274, doi:10.1175/JCLI‐D‐12‐00429.1. Winker, D. M., M. A. Vaughan, A. H. Omar, Y. Hu, K. A. Powell, Z. Liu, W. H. Hunt, and S. A. Young ( 2009 ), Overview of the CALIPSO mission and CALIOP data processing algorithms, J. Atmos. Oceanic Technol., 26, 2310 – 2323. Zhao, G., and L. Di Girolamo ( 2004 ), A cloud fraction versus view angle technique for automatic in‐scene evaluation of the MISR cloud mask, J. Appl. Meteorol., 43, 860 – 869. Zhao, G., and L. Di Girolamo ( 2006 ), Cloud fraction errors for trade wind cumuli from EOS‐Terra instruments, Geophys. Res. Lett., 33, L20802, doi:10.1029/2006GL027088. Holz, R. E., S. A. Ackerman, F. W. Nagle, R. Frey, S. Dutcher, R. E. Kuehn, M. A. Vaughan, and B. Baum ( 2008 ), Global Moderate resolution Imaging Spectroradiometer (MODIS) cloud detection and height evaluation using CALIOP, J. Geophys. Res., 113, D00A19, doi:10.1029/2008JD009837. Huang, Y., S. T. Siems, M. J. Manton, L. B. Hande, and J. M. Haynes ( 2012 ), The structure of low‐altitude clouds over the Southern Ocean as seen by CloudSat, J. Clim., 25, 2535 – 2546. Igel, A. L., S. C. van den Heever, C. M. Naud, S. M. Saleeby, and D. J. Posselt ( 2013 ), Sensitivity of warm frontal processes to cloud‐nucleating aerosol concentrations, J. Atmos. Sci., 70, 1768 – 1783. Kawanishi, T., T. Sezai, Y. Ito, K. Imaoka, T. Takeshima, Y. Ishido, A. Shibata, M. Miura, H. Inahata, and R. W. Spencer ( 2003 ), The Adanced Microwave Scanning Radiometer for the Earth Observing System (AMSR‐E), NASDA's contribution to the EOS for global energy and water cycle studies, IEEE Trans. Geosci. Remote Sens., 41, 184 – 194. Klein, S. A., and C. Jakob ( 1999 ), Validation and sensitivities of frontal clouds simulated by the ECMWF model, Mon. Weather Rev., 127, 2514 – 2531. Ackerman, S. A., R. E. Holz, R. Frey, E. W. Eloranta, B. C. Maddux, and M. McGill ( 2008 ), Cloud detection with MODIS. Part II: Validation, J. Atmos. Oceanic Technol., 25, 1073 – 1086. Bauer, M., and A. D. Del Genio ( 2006 ), Composite analysis of winter cyclones in a GCM: Influence on climatological humidity, J. Clim., 19, 1652 – 1672. Bodas‐Salcedo, A., et al. ( 2011 ), COSP Satellite simulation software for model assessment, Bull. Am. Meteorol. Soc., 92, 1023 – 1043. Bodas‐Salcedo, A., K. D. Williams, P. R. Field, and A. P. Lock ( 2012 ), The surface downwelling solar radiation surplus over the southern ocean in the Met Office model: The role of midlatitude cyclone clouds, J. Clim., 25, 7467 – 7486. Booth, J. F., C. M. Naud, and A. D. Del Genio ( 2013 ), Diagnosing warm frontal cloud formation in a GCM: A novel approach using conditional subsetting, J. Clim., 26, 5827 – 5845, doi:10.1175/JCLI‐D‐12‐00637.1. Carlson, T. N. ( 1980 ), Airflow through midlatitude cyclons and the comma cloud pattern, Mon. Weather Rev., 108, 1498 – 1509. Catto, J. L., L. C. Shaffrey, and K. I. Hodges ( 2010 ), Can climate models capture the structure of extratropical cyclones?, J. Clim., 23, 1621 – 1635. Cavalieri, D., C. Parkinson, P. Gloersen, and H. J. Zwally ( 1996 ), Sea ice concentrations from Nimbus‐7 SMMR and DMSP SSM/I‐SSMIS Passive Microwave Data, [Final data], updated yearly, National Snow and Ice Data Center, Boulder, Colo. Dee, D. P., et al. ( 2011 ), The ERA‐Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. R. Meteorol. Soc., 137, 553 – 597. Di Girolamo, L., and M. J. Wilson ( 2003 ), A first look at band‐differenced angular signatures for cloud detection from MISR, IEEE Trans. Geosci. Remote Sens., 41, 1730 – 1734. Di Girolamo, L., A. Menzies, G. Zhao, K. Mueller, C. Moroney, and D. J. Diner ( 2010 ), Level 3 cloud fraction by altitude algorithm theoretical basis, JPL D‐62358. Diner, D. J., et al. ( 1998 ), Multi‐Angle Imaging Spectro‐radiometer (MISR) description and experiment overview, IEEE Trans. Geosci. Remote Sens., 36, 1072 – 1087. Diner, D. J., R. Davies, L. Di Girolamo, A. Horvath, C. Moroney, J.‐P. Muller, S. R. Paradise, D. Wenkert, and J. Zong ( 1999 ), Level 2 cloud detection and classification algorithm theoretical basis, JPL D‐11399, Rev. D. Field, P. R., and R. Wood ( 2007 ), Precipitation and cloud structure in midlatitude cyclones, J. Clim., 20, 233 – 254. Field, P. R., A. Gettelman, R. B. Neale, R. Wood, P. J. Rasch, and H. Morrison ( 2008 ), Midlatitude cyclone compositing to constrain climate model behavior using satellite observations, J. Clim., 21, 5887 – 5903. IndexNoFollow MODIS MISR CloudSat‒CALIPSO Cloud Fraction Extratropical Cyclones Atmospheric and Oceanic Sciences Science Article 2013 ftumdeepblue 2025-06-04T05:59:21Z Using cloud observations from NASA Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, and CloudSat‐CALIPSO, composites of cloud fraction in southern and northern hemisphere extratropical cyclones are obtained for cold and warm seasons between 2006 and 2010, to assess differences between these three data sets, and between summer and winter cyclones. In both hemispheres and seasons, over the open ocean, the cyclone‐centered cloud fraction composites agree within 5% across the three data sets, but behind the cold fronts, or over sea ice and land, the differences are much larger. To supplement the data set comparison and learn more about the cyclones, we also examine the differences in cloud fraction between cold and warm season for each data set. The difference in cloud fraction between cold and warm season southern hemisphere cyclones is small for all three data sets, but of the same order of magnitude as the differences between the data sets. The cold‐warm season contrast in northern hemisphere cyclone cloud fractions is similar for all three data sets: in the warm sector, the cold season cloud fractions are lower close to the low, but larger on the equator edge than their warm season counterparts. This seasonal contrast in cloud fraction within the cyclones warm sector seems to be related to the seasonal differences in moisture flux within the cyclones. Our analysis suggests that the three different data sets can all be used confidently when studying the warm sector and warm frontal zone of extratropical cyclones but caution should be exerted when studying clouds in the cold sector. Key points Cyclone‐centered cloud fraction composites from three data sets are compared Seasonal contrast in cyclone cloudiness is more pronounced in NH than SH The three data sets together better constrain models than individually Peer Reviewed http://deepblue.lib.umich.edu/bitstream/2027.42/100326/1/jgrd50718.pdf Article in Journal/Newspaper Sea ice Unknown Journal of Geophysical Research: Atmospheres 118 17 9982 9996 |
| spellingShingle | MODIS MISR CloudSat‒CALIPSO Cloud Fraction Extratropical Cyclones Atmospheric and Oceanic Sciences Science Naud, Catherine M. Booth, James F. Posselt, Derek J. Heever, Susan C. Multiple satellite observations of cloud cover in extratropical cyclones |
| title | Multiple satellite observations of cloud cover in extratropical cyclones |
| title_full | Multiple satellite observations of cloud cover in extratropical cyclones |
| title_fullStr | Multiple satellite observations of cloud cover in extratropical cyclones |
| title_full_unstemmed | Multiple satellite observations of cloud cover in extratropical cyclones |
| title_short | Multiple satellite observations of cloud cover in extratropical cyclones |
| title_sort | multiple satellite observations of cloud cover in extratropical cyclones |
| topic | MODIS MISR CloudSat‒CALIPSO Cloud Fraction Extratropical Cyclones Atmospheric and Oceanic Sciences Science |
| topic_facet | MODIS MISR CloudSat‒CALIPSO Cloud Fraction Extratropical Cyclones Atmospheric and Oceanic Sciences Science |
| url | https://hdl.handle.net/2027.42/100326 https://doi.org/10.1002/jgrd.50718 |