Editorial Type:
Article
Article Type:
Research Article

Evaluation of 22 Precipitation and 23 Soil Moisture Products over a Semiarid Area in Southeastern Arizona

Susan Stillman Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Susan Stillman in
Current site
Google Scholar
PubMed Close
,
Xubin Zeng Department of Atmospheric Sciences, The University of Arizona, Tucson, Arizona

Search for other papers by Xubin Zeng in
Current site
Google Scholar
PubMed Close
, and
Michael G. Bosilovich Global Modeling and Assimilation Office, NASA Goddard Space Flight Center, Greenbelt, Maryland

Search for other papers by Michael G. Bosilovich in
Current site
Google Scholar
PubMed Close
Print Publication:
01 Feb 2016
Collections:
Peter J. Lamb Special Collection: Climate Variability and Its Impacts
DOI:
https://doi.org/10.1175/JHM-D-15-0007.1
Page(s):
211–230
Restricted access

Abstract

Precipitation and soil moisture are rigorously measured or estimated from a variety of sources. Here, 22 precipitation and 23 soil moisture products are evaluated against long-term daily observed precipitation (Pobs) and July–September daily observationally constrained soil moisture (SM) datasets over a densely monitored 150 km2 watershed in southeastern Arizona, United States. Gauge–radar precipitation products perform best, followed by reanalysis and satellite products, and the median correlations of annual precipitation from these three categories with Pobs are 0.83, 0.68, and 0.46, respectively. Precipitation results from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are the worst, including an overestimate of cold season precipitation and a lack of significant correlation of annual precipitation with Pobs from all (except one) models. Satellite soil moisture products perform best, followed by land data assimilation systems and reanalyses, and the CMIP5 results are the worst. For instance, the median unbiased root-mean-square difference (RMSD) values of July–September soil moisture compared with SM are 0.0070, 0.011, 0.014, and 0.029 m3 m−3 for these four product categories, respectively. All 17 (except 3) precipitation [17 (except 2) soil moisture] products with at least 20 years of data agree with Pobs (SM) without significant trends. The uncertainties associated with the scale mismatch between Pobs and coarser-resolution products are addressed using two 4-km gauge–radar precipitation products, and their impact on the results presented in this study is overall small. These results identify strengths and weaknesses of each product for future improvement; they also emphasize the importance of using multiple gauge–radar and satellite products along with their uncertainties in evaluating reanalyses and models.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-15-0007.s1.

Corresponding author address: Susan Stillman, Department of Atmospheric Sciences, The University of Arizona, Physics–Atmospheric Sciences Bldg., Rm. 542, 1118 E. 4th St., P.O. Box 210081, Tucson, AZ 85721-0081. E-mail: sstill88@email.arizona.edu

This article is included in the In Honor of Peter J. Lamb special collection.

Abstract

Precipitation and soil moisture are rigorously measured or estimated from a variety of sources. Here, 22 precipitation and 23 soil moisture products are evaluated against long-term daily observed precipitation (Pobs) and July–September daily observationally constrained soil moisture (SM) datasets over a densely monitored 150 km2 watershed in southeastern Arizona, United States. Gauge–radar precipitation products perform best, followed by reanalysis and satellite products, and the median correlations of annual precipitation from these three categories with Pobs are 0.83, 0.68, and 0.46, respectively. Precipitation results from phase 5 of the Coupled Model Intercomparison Project (CMIP5) are the worst, including an overestimate of cold season precipitation and a lack of significant correlation of annual precipitation with Pobs from all (except one) models. Satellite soil moisture products perform best, followed by land data assimilation systems and reanalyses, and the CMIP5 results are the worst. For instance, the median unbiased root-mean-square difference (RMSD) values of July–September soil moisture compared with SM are 0.0070, 0.011, 0.014, and 0.029 m3 m−3 for these four product categories, respectively. All 17 (except 3) precipitation [17 (except 2) soil moisture] products with at least 20 years of data agree with Pobs (SM) without significant trends. The uncertainties associated with the scale mismatch between Pobs and coarser-resolution products are addressed using two 4-km gauge–radar precipitation products, and their impact on the results presented in this study is overall small. These results identify strengths and weaknesses of each product for future improvement; they also emphasize the importance of using multiple gauge–radar and satellite products along with their uncertainties in evaluating reanalyses and models.

Supplemental information related to this paper is available at the Journals Online website: http://dx.doi.org/10.1175/JHM-D-15-0007.s1.

Corresponding author address: Susan Stillman, Department of Atmospheric Sciences, The University of Arizona, Physics–Atmospheric Sciences Bldg., Rm. 542, 1118 E. 4th St., P.O. Box 210081, Tucson, AZ 85721-0081. E-mail: sstill88@email.arizona.edu

This article is included in the In Honor of Peter J. Lamb special collection.

Supplementary Materials

    • Supplemental Materials (DOCX 329.89 KB)
Save
Cover Journal of Hydrometeorology
Journal of Hydrometeorology

  • Amitai, E., Unkrich C. L. , Goodrich D. C. , Habib E. , and Thill B. , 2012: Assessing satellite-based rainfall estimates in semiarid watersheds using the USDA-ARS Walnut Gulch gauge network and TRMM PR. J. Hydrometeor., 13, 1579–1588, doi:10.1175/JHM-D-12-016.1.

    • Search Google Scholar
    • Export Citation

  • Balsamo, G., and Coauthors, 2015: ERA-Interim/Land: A global land surface reanalysis data set. Hydrol. Earth Syst. Sci., 19, 389–407, doi:10.5194/hess-19-389-2015.

    • Search Google Scholar
    • Export Citation

  • Bartalis, Z., Wagner W. , Naeimi V. , Hasenauer S. , Scipal K. , Bonekamp H. , Figa J. , and Anderson C. , 2007: Initial soil moisture retrievals from the MetOp-A Advanced Scatterometer (ASCAT). Geophys. Res. Lett., 34, L20401, doi:10.1029/2007GL031088.

    • Search Google Scholar
    • Export Citation

  • Becker, A., Finger P. , Meyer-Christoffer A. , Rudolf B. , Schamm K. , Schneider U. , and Ziese M. , 2013: A description of the global land-surface precipitation data products of the Global Precipitation Climatology Centre with sampling applications including centennial trend analysis from 1901–present. Earth Syst. Sci. Data, 5, 71–99, doi:10.5194/essd-5-71-2013.

    • Search Google Scholar
    • Export Citation

  • Bitew, M. M., Gebremichael M. , Ghebremichael L. T. , and Bayissa Y. A. , 2012: Evaluation of high-resolution satellite rainfall products through streamflow simulation in a hydrological modeling of a small mountainous watershed in Ethiopia. J. Hydrometeor., 13, 338–350, doi:10.1175/2011JHM1292.1.

    • Search Google Scholar
    • Export Citation

  • Bosilovich, M. G., 2013: Regional climate and variability of NASA MERRA and recent reanalyses: U.S. summertime precipitation and temperature. J. Appl. Meteor. Climatol., 52, 1939–1951, doi:10.1175/JAMC-D-12-0291.1.

    • Search Google Scholar
    • Export Citation

  • Bosilovich, M. G., Chen J. , Robertson F. R. , and Adler R. F. , 2008: Evaluation of global precipitation in reanalyses. J. Appl. Meteor. Climatol., 47, 2279–2299, doi:10.1175/2008JAMC1921.1.

    • Search Google Scholar
    • Export Citation

  • Chang, M., and Flannery L. A. , 1998: Evaluating the accuracy of rainfall catch by three different gauges. J. Amer. Water Resour. Assoc., 34, 559–564, doi:10.1111/j.1752-1688.1998.tb00954.x.

    • Search Google Scholar
    • Export Citation

  • Chen, M., Dickinson R. E. , Zeng X. , and Hahmann A. , 1996: Comparison of precipitation observed over the continental United States to that simulated by a climate model. J. Climate, 9, 2233–2249, doi:10.1175/1520-0442(1996)009<2233:COPOOT>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Chen, M., Xie P. , Janowiak J. E. , and Arkin P. A. , 2002: Global land precipitation: A 50-yr monthly analysis based on gauge observations. J. Hydrometeor., 3, 249–266, doi:10.1175/1525-7541(2002)003<0249:GLPAYM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Corbosiero, K. L., Dickinson M. J. , and Bosart L. F. , 2009: The contribution of eastern North Pacific tropical cyclones to the rainfall climatology of the southwest United States. Mon. Wea. Rev., 137, 2415–2435, doi:10.1175/2009MWR2768.1.

    • Search Google Scholar
    • Export Citation

  • Cosgrove, B. A., and Coauthors, 2003: Real-time and retrospective forcing in the North American Land Data Assimilation System (NLDAS) project. J. Geophys. Res., 108, 8842, doi:10.1029/2002JD003118.

    • Search Google Scholar
    • Export Citation

  • Crow, W. T., and Coauthors, 2012: Upscaling sparse ground-based soil moisture observations for the validation of coarse-resolution satellite soil moisture products. Rev. Geophys., 50, RG2002, doi:10.1029/2011RG000372.

    • Search Google Scholar
    • Export Citation

  • Daly, C., Neilson R. P. , and Phillips D. L. , 1994: A statistical–topographic model for mapping climatological precipitation over mountainous terrain. J. Appl. Meteor., 33, 140–158, doi:10.1175/1520-0450(1994)033<0140:ASTMFM>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Decker, M., Brunke M. A. , Wang Z. , Sakaguchi K. , Zeng X. , and Bosilovich M. G. , 2012: Evaluation of the reanalysis products from GSFC, NCEP, and ECMWF using flux tower observations. J. Climate, 25, 1916–1944, doi:10.1175/JCLI-D-11-00004.1.

    • Search Google Scholar
    • Export Citation

  • Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828.

    • Search Google Scholar
    • Export Citation

  • de Jeu, R. A. M., Wagner W. W. , Holmes T. R. H. , Dolman A. J. , van de Giesen N. C. , and Friesen J. , 2008: Global soil moisture patterns observed by space borne microwave radiometers and scatterometers. Surv. Geophys., 29, 399–420, doi:10.1007/s10712-008-9044-0.

    • Search Google Scholar
    • Export Citation

  • Devine, K., and Mekis E. , 2008: Field accuracy of Canadian rain measurements. Atmos.–Ocean, 46, 213–227, doi:10.3137/ao.460202.

  • Donner, L. J., and Coauthors, 2011: The dynamical core, physical parameterizations, and basic simulation characteristics of the atmospheric component AM3 of the GFDL Global Coupled Model CM3. J. Climate, 24, 3484–3519, doi:10.1175/2011JCLI3955.1.

    • Search Google Scholar
    • Export Citation

  • Ebita, A., and Coauthors, 2011: The Japanese 55-year Reanalysis “JRA-55”: An interim report. SOLA, 7, 149–152, doi:10.2151/sola.2011-038.

    • Search Google Scholar
    • Export Citation

  • Entekhabi, D., and Coauthors, 2010: The Soil Moisture Active and Passive (SMAP) Mission. Proc. IEEE, 98, 704–716, doi:10.1109/JPROC.2010.2043918.

    • Search Google Scholar
    • Export Citation

  • Gao, Y. C., and Liu M. F. , 2013: Evaluation of high-resolution satellite precipitation products using rain gauge observations over the Tibetan Plateau. Hydrol. Earth Syst. Sci., 17, 837–849, doi:10.5194/hess-17-837-2013.

    • Search Google Scholar
    • Export Citation

  • Geil, K. L., Serra Y. L. , and Zeng X. , 2013: Assessment of CMIP5 model simulations of the North American monsoon system. J. Climate, 26, 8787–8801, doi:10.1175/JCLI-D-13-00044.1.

    • Search Google Scholar
    • Export Citation

  • Gent, P. R., and Coauthors, 2011: The Community Climate System Model version 4. J. Climate, 24, 4973–4991, doi:10.1175/2011JCLI4083.1.

    • Search Google Scholar
    • Export Citation

  • Goodrich, D. C., Keefer T. O. , Unkrich C. L. , Nichols M. H. , Osborn H. B. , Stone J. J. , and Smith J. R. , 2008: Long-term precipitation database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res., 44, W05S04, doi:10.1029/2006WR005782.

    • Search Google Scholar
    • Export Citation

  • Habib, E., Krajewski W. F. , Nespor V. , and Kruger A. , 1999: Numerical simulation studies of rain gauge data correction due to wind effect. J. Geophys. Res., 104, 19 723–19 733, doi:10.1029/1999JD900228.

    • Search Google Scholar
    • Export Citation

  • Higgins, R. W., Shi W. , Yarosh E. , and Joyce R. , 2000: Improved United States precipitation quality control system and analysis. NCEP/CPC Atlas 7, 40 pp.

  • Huffman, G. J., Adler R. F. , Morrissey M. , Bolvin D. T. , Curtis S. , Joyce R. , McGavock B. , and Susskind J. , 2001: Global precipitation at one-degree daily resolution from multisatellite observations. J. Hydrometeor., 2, 36–50, doi:10.1175/1525-7541(2001)002<0036:GPAODD>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Huffman, G. J., and Coauthors, 2007: The TRMM Multisatellite Precipitation Analysis (TMPA): Quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J. Hydrometeor., 8, 38–55, doi:10.1175/JHM560.1.

    • Search Google Scholar
    • Export Citation

  • Jackson, T., Le Vine D. , Griffis A. , Goodrich D. , Schmugge T. , Swift C. , and O’Neill P. , 1993: Soil moisture and rainfall estimation over a semiarid environment with the ESTAR microwave radiometer. IEEE Trans. Geosci. Remote Sens., 31, 836–841, doi:10.1109/36.239906.

    • Search Google Scholar
    • Export Citation

  • Jiang, S., Ren L. , Hong Y. , Yong B. , Yang X. , Yuan F. , and Ma M. , 2012: Comprehensive evaluation of multi-satellite precipitation products with a dense rain gauge network and optimally merging their simulated hydrological flows using the Bayesian model averaging method. J. Hydrol., 452–453, 213–225, doi:10.1016/j.jhydrol.2012.05.055.

    • Search Google Scholar
    • Export Citation

  • Johns, T. C., and Coauthors, 2003: Anthropogenic climate change for 1860 to 2100 simulated with the HadCM3 model under updated emissions scenarios. Climate Dyn., 20, 583–612, doi:10.1007/s00382-002-0296-y.

    • Search Google Scholar
    • Export Citation

  • Jones, C. D., and Coauthors, 2011: The HadGEM2-ES implementation of CMIP5 centennial simulations. Geosci. Model Dev., 4, 543–570, doi:10.5194/gmd-4-543-2011.

    • Search Google Scholar
    • Export Citation

  • Joyce, R. J., Janowiak J. E. , Arkin P. A. , and Xie P. , 2004: CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution. J. Hydrometeor., 5, 487–503, doi:10.1175/1525-7541(2004)005<0487:CAMTPG>2.0.CO;2.

    • Search Google Scholar
    • Export Citation

  • Keefer, T. O., Moran M. S. , and Paige G. B. , 2008: Long-term meteorological and soil hydrology database, Walnut Gulch Experimental Watershed, Arizona, United States. Water Resour. Res., 44, W05S07, doi:10.1029/2006WR005702.

    • Search Google Scholar
    • Export Citation

  • Kerr, Y. H., Waldteufel P. , Wigneron J.-P. , Martinuzzi J.-M. , Font J. , and Berger M. , 2001: Soil moisture retrieval from space: The Soil Moisture and Ocean Salinity (SMOS) mission. IEEE Trans. Geosci. Remote Sens., 39, 1729–1735, doi:10.1109/36.942551.

    • Search Google Scholar
    • Export Citation

  • Lamarque, J.-F., and Coauthors, 2012: CAM-chem: Description and evaluation of interactive atmospheric chemistry in the Community Earth System Model. Geosci. Model Dev., 5, 369–411, doi:10.5194/gmd-5-369-2012.

    • Search Google Scholar
    • Export Citation

  • Legates, D. R., and Willmott C. J. , 1990: Mean seasonal and spatial variability in gauge-corrected, global precipitation. J. Climate, 10, 111–127, doi:10.1002/joc.3370100202.

    • Search Google Scholar
    • Export Citation

  • Le Vine, D. M., Lagerloef G. S. E. , Colomb R. , Yueh S. , and Pellerano F. , 2007: Aquarius: An instrument to monitor sea surface salinity from space. IEEE Trans. Geosci. Remote Sens., 45, 2040–2050, doi:10.1109/TGRS.2007.898092.

    • Search Google Scholar
    • Export Citation

  • Lin, Y., and Mitchell K. E. , 2005: The NCEP stage II/IV hourly precipitation analyses: Development and applications. Preprints, 19th Conf. on Hydrology, San Diego, CA, Amer. Meteor. Soc., 1.2. [Available online at https://ams.confex.com/ams/Annual2005/techprogram/paper_83847.htm.]

  • Liu, J., Zhu A. , and Duan Z. , 2012: Evaluation of TRMM 3B42 precipitation product using rain gauge data in Meichuan watershed, Poyang Lake basin, China. J. Resour. Ecol., 3, 359–366, doi:10.5814/j.issn.1674-764x.2012.04.009.

    • Search Google Scholar
    • Export Citation

  • Liu, X., and Coauthors, 2012: Toward a minimal representation of aerosols in climate models: description and evaluation in the Community Atmosphere Model CAM5. Geosci. Model Dev., 5, 709–739, doi:10.5194/gmd-5-709-2012.

    • Search Google Scholar
    • Export Citation

  • Matsuura, K., and Willmott C. J. , 2012: Terrestrial precipitation: 1900–2010 Gridded monthly time series (version 3.01). Accessed 20 August 2015. [Available online at http://climate.geog.udel.edu/~climate/html_pages/Global2011/README.GlobalTsP2011.html.]

  • Mesinger, F., and Coauthors, 2006: North American Regional Reanalysis. Bull. Amer. Meteor. Soc., 87, 343–360, doi:10.1175/BAMS-87-3-343.

    • Search Google Scholar
    • Export Citation

  • Moore, J. K., Lindsay K. , Doney S. C. , Long M. C. , and Misumi K. , 2013: Marine Ecosystem Dynamics and Biogeochemical Cycling in the Community Earth System Model [CESM1(BGC)]: Comparison of the 1990s with the 2090s under the RCP4.5 and RCP8.5 scenarios. J. Climate, 26, 9291–9312, doi:10.1175/JCLI-D-12-00566.1.

    • Search Google Scholar
    • Export Citation

  • Njoku, E. G., Jackson T. L. , Lakshm V. , Chan T. , and Nghiem S. V. , 2003: Soil moisture retrieval from AMSR-E. IEEE Trans. Geosci. Remote Sens., 41, 215–229, doi:10.1109/TGRS.2002.808243.

    • Search Google Scholar
    • Export Citation

  • Owe, M., de Jeu R. A. M. , and Holmes T. R. H. , 2008: Multisensor historical climatology of satellite-derived global land surface moisture. J. Geophys. Res., 113, F01002, doi:10.1029/2007JF000769.

    • Search Google Scholar
    • Export Citation

  • Parinussa, R. M., Holmes T. R. H. , Wanders N. , Dorigo W. A. , and de Jeu R. A. M. , 2015: A preliminary study toward consistent soil moisture from AMSR2. J. Hydrometeor., 16, 932–947, doi:10.1175/JHM-D-13-0200.1.

    • Search Google Scholar
    • Export Citation

  • Reichle, R. H., Koster R. D. , De Lannoy G. J. M. , Forman B. A. , Liu Q. , Mahanama S. P. P. , and Touré A. , 2011: Assessment and enhancement of MERRA land surface hydrology estimates. J. Climate, 24, 6322–6338, doi:10.1175/JCLI-D-10-05033.1.

    • Search Google Scholar
    • Export Citation

  • Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s Modern-Era Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1.

    • Search Google Scholar
    • Export Citation

  • Rodell, M., and Coauthors, 2004: The Global Land Data Assimilation System. Bull. Amer. Meteor. Soc., 85, 381–394, doi:10.1175/BAMS-85-3-381.

    • Search Google Scholar
    • Export Citation

  • Ryu, D., Jackson T. J. , Bindlish R. , Le Vine D. M. , and Haken M. , 2010: Soil moisture retrieval using a two-dimensional L-band synthetic aperture radiometer in a semiarid environment. IEEE Trans. Geosci. Remote Sens., 48, 4273–4284, doi:10.1109/TGRS.2010.2051677.

    • Search Google Scholar
    • Export Citation

  • Saha, S., and Coauthors, 2010: The NCEP Climate Forecast System Reanalysis. Bull. Amer. Meteor. Soc., 91, 1015–1057, doi:10.1175/2010BAMS3001.1.

    • Search Google Scholar
    • Export Citation

  • Schmugge, T. J., and Jackson T. J. , 1994: Mapping soil moisture with microwave radiometers. Meteor. Atmos. Phys., 54, 213–223, doi:10.1007/BF01030061.

    • Search Google Scholar
    • Export Citation

  • Sheffield, J., and Coauthors, 2013: North American climate in CMIP5 experiments. Part II: Evaluation of historical simulations of intraseasonal to decadal variability. J. Climate, 26, 9247–9290, doi:10.1175/JCLI-D-12-00593.1.

    • Search Google Scholar
    • Export Citation

  • Sillmann, J., Kharin V. V. , Zhang X. , Zwiers F. W. , and Bronaugh D. , 2013: Climate extremes indices in the CMIP5 multimodel ensemble: Part 1. Model evaluation in the present climate. J. Geophys. Res. Atmos., 118, 1716–1733, doi:10.1002/jgrd.50203.

    • Search Google Scholar
    • Export Citation

  • Sorooshian, S., Hsu K. , Gao X. , Gupta H. V. , Imam B. , and Braithwaite D. , 2000: Evaluation of PERSIANN system satellite-based estimates of tropical rainfall. Bull. Amer. Meteor. Soc., 81, 2035–2046, doi:10.1175/1520-0477(2000)081<2035:EOPSSE>2.3.CO;2.

    • Search Google Scholar
    • Export Citation

  • Stampoulis, D., and Anagnostou E. N. , 2012: Evaluation of global satellite rainfall products over continental Europe. J. Hydrometeor., 13, 588–603, doi:10.1175/JHM-D-11-086.1.

    • Search Google Scholar
    • Export Citation

  • Stillman, S., Zeng X. , Shuttleworth W. J. , Goodrich D. C. , Unkrich C. L. , and Zreda M. , 2013: Spatiotemporal variability of summer precipitation in southeastern Arizona. J. Hydrometeor., 14, 1944–1951, doi:10.1175/JHM-D-13-017.1.

    • Search Google Scholar
    • Export Citation

  • Stillman, S., Ninneman J. , Zeng X. , Franz T. , Scott R. L. , Shuttleworth W. J. , and Cummins K. , 2014: Summer soil moisture spatiotemporal variability in southeastern Arizona. J. Hydrometeor., 15, 1473–1485, doi:10.1175/JHM-D-13-0173.1.

    • Search Google Scholar
    • Export Citation

  • Torres, R., and Coauthors, 2012: GMES Sentinel-1 mission. Remote Sens. Environ., 120, 9–24, doi:10.1016/j.rse.2011.05.028.

  • Uppala, S. M., and Coauthors, 2005: The ERA-40 Re-Analysis. Quart. J. Roy. Meteor. Soc., 131, 2961–3012, doi:10.1256/qj.04.176.

  • Ventura, F., Facini O. , Piana S. , and Pisa Paola R. , 2010: Soil moisture measurements: Comparison of instrumentation performances. J. Irrig. Drain. Div., 136, 81–89, doi:10.1061/(ASCE)0733-9437(2010)136:2(81).

    • Search Google Scholar
    • Export Citation

  • Watanabe, M., and Coauthors, 2010: Improved climate simulation by MIROC5: Mean states, variability, and climate sensitivity. J. Climate, 23, 6312–6335, doi:10.1175/2010JCLI3679.1.

    • Search Google Scholar
    • Export Citation

  • Zreda, M., Shuttleworth W. J. , Zeng X. , Zweck C. , Desilets D. , Franz T. , and Rosolem R. , 2012: COSMOS: The Cosmic-Ray Soil Moisture Observing System. Hydrol. Earth Syst. Sci., 16, 4079–4099, doi:10.5194/hess-16-4079-2012.

    • Search Google Scholar
    • Export Citation
All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 774 402 21
PDF Downloads 321 84 6