SEARCH

SEARCH BY CITATION

References

  • Adler, R. F., A. J. Negri, P. R. Keehn, and I. M. Hakkarinen (1993), Estimation of monthly rainfall over Japan and surrounding water from a combination of low-orbit microwave and geosynchronous IR data, J. Appl. Meteorol., 32, 335357.
  • Burgers, G., P. J. van Leeuwen, and G. Evensen (1998), Analysis scheme in the Ensemble Kalman filter, Mon. Weather Rev., 126, 17191724.
  • Chronis, T., E. N. Anagnostou, and T. Dinku (2004), High-frequency estimation of thunderstorms via satellite infrared and a long-range lightning network in Europe, Q. J. R. Meteorol. Soc., Part B, 130, 15551575.
  • Crow, W. T. (2003), Correcting land surface model predictions for the impact of temporally sparse rainfall rate measurements using an ensemble Kalman filter and surface brightness temperature observations, J. Hydrometeorol., 4, 960973.
  • Crow, W. T., and E. F. Wood (2003), The assimilation of remotely sensed soil brightness temperature imagery into a land surface model using Ensemble Kalman filtering: A case study based on ESTAR measurements during SGP97, Adv. Water Resour., 26, 137149.
  • Crow, W. T., and E. van Loon (2006), Impact of incorrect model error assumptions on the sequential assimilation of remotely sensed surface soil moisture, J. Hydrometeorol., 7, 421432.
  • Darnell, W. L., W. F. Staylor, S. K. Gupta, and F. M. Denn (1988), Estimation of surface insolation using sun-synchronous satellite data, J. Clim., 1, 820835.
  • Dedieu, G., P. Y. Deschamps, and Y. H. Kerr (1987), Satellite estimation of solar irradiance at the surface of the earth and of surface albedo using a physical model applied to meteosat data, J. Clim. Appl. Meteorol., 26, 7987.
  • Diak, G. R., and C. Gautier (1983), Improvements to a simple physical model for estimating insolation from GOES data, J. Clim. Appl. Meteorol., 22, 505508.
  • Diak, G. R., C. Gautier, and S. Masse (1982), An operational system for mapping insolation from GOES satellite data, Sol. Energy, 28, 371376.
  • Diak, G. R., J. R. Mecikalski, M. C. Anderson, J. M. Norman, W. P. Kustas, R. D. Torn, and R. L. DeWolf (2004), Estimating land surface energy budgets from space, Bull. Am. Meteorol. Soc., 85(1), 6578, doi:10.1175/BAMS-85-1-65.
  • Dong, X., P. Minnis, G. G. Mace, W. L. Smith Jr., M. Poellot, R. T. Marchand, and A. D. Rapp (2002), Comparison of stratus cloud properties deduced from surface, GOES, and aircraft data during the March 2000 ARM Cloud IOP, J. Atmos. Sci., 59(23), 32653284.
  • Durand, M. T., and S. A. Margulis (2006), Feasibility test of multifrequency radiometric data assimilation to estimate snow water equivalent, J. Hydromteorol., 7, 443457.
  • Gautier, C. (1982), Mesoscale insolation variability derived from satellite data, J. Appl. Meteorol., 21, 5158.
  • Gautier, C., and M. Landsfeld (1997), Surface solar radiation flux and cloud radiative forcing for the atmospheric radiation measurement (ARM) Southern Great Plains (SGP): A satellite, surface observations, and radiative transfer model study, J. Atmos. Sci., 54, 12891307.
  • Gautier, C., G. Diak, and S. Masse (1980), A simple physical model to estimate incident solar radiation at the surface from GOES satellite data, J. Appl. Meteorol., 19, 10051012.
  • Gautier, C., G. Diak, and S. Masse (1984), An investigation of the effects of spatially averaging satellite brightness measurements on the calculation of insolation, J. Clim. Appl. Meteorol., 23, 13801386.
  • Hong, Y., K.-L. Hsu, S. Sorooshian, and X. Gao (2005), Improved representation of diurnal variability of rainfall retrieved from the Tropical Rainfall Measurement Mission microwave imager adjusted Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN) system, J. Geophys. Res., 110, D06102, doi:10.1029/2004JD005301.
  • Houtekamer, P. L., and H. L. Mitchell (1998), Data assimilation using an Ensemble Kalman filter technique, Mon. Weather Rev., 126, 796811.
  • Houtekamer, P. L., and H. L. Mitchell (2001), A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123137.
  • Hsu, K., X. Gao, S. Sorooshian, and H. V. Gupta (1997), Precipitation estimation from remotely sensed information using artificial neural networks, J. Appl. Meteorol., 36, 11761190.
  • Huffman, G. J., R. F. Adler, P. Arkin, A. Chang, R. Ferraro, A. Gruber, J. Janowiak, A. McNab, B. Rudolf, and U. Schneider (1997), The global precipitation climatology project (GPCP) combined precipitation dataset, Bull. Am. Meteorol. Soc., 78, 520.
  • Huffman, G. J., R. F. Adler, M. M. Morrissey, D. T. Bolvin, S. Curtis, R. Joyce, B. McGavock, and J. Susskind (2001), Global precipitation at one-degree daily resolution from multisatellite observations, J. Hydrometeorol., 2, 3650.
  • Jackson, R. D. (1984), Total reflected solar radiation calculated from multiband sensor data, Agric. For. Meteorol., 33, 163175.
  • Joyce, R. J., J. E. Janowiak, P. A. Arkin, and P. Xie (2004), CMORPH: A method that produces global precipitation estimates from passive microwave and infrared data at high spatial and temporal resolution, J. Hydrometeorol., 5, 487503.
  • Lee, S., and S. A. Margulis (2007), High-resolution ensemble surface insolation estimates through assimilation of coarse-scale retrievals into a simple physical model: 2. Physical model development and accuracy tests, J. Geophys. Res., 112, D08212, doi:10.1029/2006JD007872.
  • Marani, M., G. Grossi, F. Napolitano, M. Wallace, and D. Entekhabi (1997), Forcing, intermittency, and land surface hydrologic partitioning, Water Resour. Res., 33, 167175.
  • Margulis, S., and D. Entekhabi (2003), Variational assimilation of radiometric surface temperature and reference-level micrometeorology into a model of the atmospheric boundary layer and land surface, Mon. Weather Rev., 131(7), 12721288.
  • Margulis, S., and D. Entekhabi (2004), Boundary layer entrainment estimation through assimilation of radiosonde and micrometeorology data into a mixed-layer model, Boundary Layer Meteorol., 110, 405433.
  • Margulis, S. A., D. McLaughlin, and D. Entekahbi (2002), Land data assimilation and estimation of soil moisture using measurements from the Southern Great Plains 1997 Field Experiment, Water Resour. Res., 38(12), 1299, doi:10.1029/2001WR001114.
  • Margulis, S. A., E. F. Wood, and P. A. Troch (2006a), The terrestrial water cycle: Modeling and data assimilation across catchment scales, J. Hydrometeorol., 7, 309311.
  • Margulis, S. A., D. Entekhabi, and D. McLaughlin (2006b), Spatio-temporal disaggregation of remotely sensed precipitation for ensemble hydrologic modeling and data assimilation, J. Hydrometeorol., 7, 511533.
  • Marzano, F. S., M. Palmacci, D. Cimini, G. Giuliani, and F. J. Turk (2004), Multivariate statistical integration of satellite infrared and microwave radiometric measurements for rainfall retrieval at the geostationary scale, IEEE Trans. Geosci. Remote Sens., 42(5), 10181032.
  • Morales, C., and E. N. Anagnostou (2003), Extending the capabilities of high-frequency rainfall estimation from geostationary-based satellite infrared via a network of long-range lightning observations, J. Hydrometeorol., 4, 141159.
  • Otkin, J. A., M. C. Anderson, J. R. Mecikalski, and G. R. Diak (2005), Validation of GOES-based insolation estimates using data from the U.S. climate reference network, J. Hydrometeorol., 6, 460475.
  • Pan, M., and E. F. Wood (2006), Data assimilation for estimating terrestrial water budget using a constrained ensemble Kalman filter, J. Hydrometeorol., 7, 548565.
  • Pinker, R. T., and I. Laszlo (1990), Improved prospects for estimating insolation for calculating regional evapotranspiration from remotely sensed data, Agric. For. Meteorol., 52, 227251.
  • Pinker, R. T., and I. Laszlo (1992), Modeling surface solar irradiance for satellite applications on a global scale, J. Appl. Meteorol., 31, 194211.
  • Pinker, R. T., R. Frouin, and Z. Li (1995), A review of satellite methods to derive surface shortwave irradiance, Remote Sens. Environ., 51, 108124.
  • Pinker, R. T., I. Laszlo, J. D. Tarpley, and K. Mitchell (2002), Geostationary satellite products for surface energy balance models, Adv. Space Res., 30, 24272432.
  • Pinker, R. T., et al. (2003), Surface radiation budgets in support of the GEWEX Continental-Scale International Project (GCIP) and the GEWEX Americas Prediction Project (GAPP), including the North American Land Data Assimilation System (NLDAS) project, J. Geophys. Res., 108(D22), 8844, doi:10.1029/2002JD003301.
  • Reichle, R., D. McLaughlin, and D. Entekhabi (2002), Data assimilation using an ensemble Kalman Filter technique, Mon. Weather Rev., 130(1), 103114.
  • Tapiador, F. J., C. Kidd, V. Levizzani, and F. S. Marzano (2004), A neural networks-based fusion technique to estimate half-hourly rainfall estimates at 0.1° resolution from satellite passive microwave and infrared data, J. Appl. Meteorol., 43(4), 576594.
  • Vonder Haar, T. H., J. M. Forsythe, D. McKague, D. L. Randel, B. C. Ruston, and S. Woo (2003), Continuation of the NVAP global water vapor data sets for pathfinder science analysis, STC Tech. Rep. 3333, 36 pp., NASA, Washington, D. C. (Available at http://eosweb.larc.nasa.gov/PRODOCS/nvap/sci_tech_report_3333.pdf).