Abstract:
North American (NLDAS) is being developed that will lead to more accurate reanalysis and forecast simulations by numerical weather prediction (NWP) models. Specifically, this system will reduce the errors in the stores of soil moisture and energy which are often present in NWP models and which degrade the accuracy of forecasts. NLDAS is currently running retrospectively and in near real-time on a ... 1/8th-degree grid resolution. The system is currently forced by terrestrial (NLDAS) precipitation data, space-based radiation data and numerical model output. In order to create an optimal scheme, the projects involve several LSMs, many sources of data, and several institutions. Data from the project can be accessed on the NLDAS forcing pages, the NLDAS model output pages, as well as on the NLDAS Realtime Image Generator page.
The land surface component of the hydrological cycle is fundamental to the overall functioning of the atmospheric and climate processes. Spatially and temporally variable rainfall and available energy, combined with land surface heterogeneity cause complex variations in all processes related to surface hydrology. The characterization of the spatial and temporal variability of water and energy cycles are critical to improve our understanding of land surface-atmosphere interaction and the impact of land surface processes on climate extremes. Because the accurate knowledge of these processes and their variability is important for climate predictions, most Numerical Weather Prediction (NWP) centers have incorporated land surface schemes in their models. However, errors in the NWP forcing accumulate in the surface and energy stores, leading to incorrect surface water and energy partitioning and related processes. This has motivated the NWP community to impose ad hoc corrections to the land surface states to prevent this drift. A methodology under development here is to implement a Land Data Assimilation System (LDAS), which consists of uncoupled models forced with observations, and is therefore not affected by NWP forcing biases. This research is being implemented in near real time using existing Surface Vegetation Atmosphere Transfer Schemes (SVATS) by NCEP, NASA, Princeton University, and the University of Washington at 1/8 degree (about 14 kilometer) resolution across North America and at 1/4 degree resolution globally to evaluate these critical science questions. The LDAS is forced with real time output from numerical prediction models, satellite data, and radar precipitation measurements. Model parameters will be derived from the existing high-resolution vegetation and soil coverages. The model results will be aggregated to various scales to assess water and energy balances and these will be validated with various in-situ observations. Ultimately, observations of LDAS storages (soil moisture, temperature, snow) and fluxes (evaporation, sensible heat flux, runoff) will be used to further validate and constrain the LDAS predictions using data assimilation techniques.
Name:
YOULONG
XIA
Phone:
301-316-5033
Email:
Youlong.Xia at noaa.gov
Contact Address:
Environmental Modeling Center
National Centers for Environmental Prediction
National Oceanic and Atmospheric Administration City:
Camp Springs
Province or State:
MD
Postal Code:
20746-4304
Country:
USA
Service Provider Personnel
Name:
MICHAEL
EK
Phone:
301-316-5030
Fax:
301-763-8545
Email:
Michael.Ek at noaa.gov
Contact Address:
Environmental Modeling Center
National Centers for Environmental Prediction
NOAA Science Center
5200 Auth Rd, Rm 207 City:
Suitland
Province or State:
MD
Postal Code:
20746-4304
Country:
USA
Distribution Media
Distribution_Media:
Online
Fees:
No fees
Personnel
KENNETH
MITCHELL Role:
TECHNICAL CONTACT
Phone:
301-763-8161
Email:
Kenneth.Mitchell at noaa.gov
Contact Address:
Environmental Modeling Center
National Centers for Environmental Prediction
W/NP2, Room 204
4700 Silver Hill Road
Stop 9910 City:
Washington DC
Postal Code:
20233-9910
Country:
USA
YOULONG
XIA Role:
TECHNICAL CONTACT
Phone:
301-316-5033
Email:
Youlong.Xia at noaa.gov
Contact Address:
Environmental Modeling Center
National Centers for Environmental Prediction
National Oceanic and Atmospheric Administration City:
Camp Springs
Province or State:
MD
Postal Code:
20746-4304
Country:
USA
TYLER
B.
STEVENS Role:
SERF AUTHOR
Phone:
(301) 614-6898
Fax:
301-614-5268
Email:
Tyler.B.Stevens at nasa.gov
Contact Address:
NASA Goddard Space Flight Center
Global Change Master Directory City:
Greenbelt
Province or State:
MD
Postal Code:
20771
Country:
USA
Publications/References
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Chu, D. A., K. Strabala, S. Platnick, E. Moody, M. King, S. Mattoo, R. Hucek, and B. Ridgway, 2000: MODIS Atmosphere QA Plan. Version 2.2 , NASA Goddard Space Flight Center, 46 pp.
Chu, D. A., Y. J. Kaufman, L. A. Remer, and B. N. Holben,1998: Remote sensing of smoke from MODIS Airborne Simulator during SCAR-B experiment. Journal of Geophysical Research, 103, 31979-31988.
Gao, B. C., and Y. J. Kaufman.1998: The MODIS Near-infrared Water Vapor Algorithm, Algorithm Theoretical Basis Document,ATBD-MOD-03, NASA Goddard Space Flight Center,25 pp.
Gao, B. C. , and Y. J. Kaufman,1997: MODIS Total Precipitable Water, MTPE EOS Data Products Handbook,93-94.
Gao, B. -C, A. F. H. Goetz, and W. J. Wiscombe, 1993: Cirrus detection from Airborne Imaging Radiometer using 1.38 micron water vapor band. Geophys. Res. Letter, 4,301-304.
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Kaufman, Y. J., and D. Tanre,1998: Algorithm For Remote Sensing of Tropospheric Aerosol from MODIS, Algorithm Theoretical Basis Document, ATBD-MOD-02, NASA Goddard Space Flight Center,85 pp.
Kaufman, Y. J., and D. Tanre,1997: MODIS Aerosol Product, in MTPE EOS Data Products Handbook,107-108.
Kaufman, Y. J., D. Tanre, L. Remer, E. F.Vermote, A. Chu, & B. N. Holben, 1997: Operational remote sensing of tropospheric aerosol over the land from EOS-MODIS. Journal of Geophysical Research, 102(14), 17051-17068.
Kaufman, Y. J., and B.-C. Gao, Remote sensing of water vapor in the near IR from EOS/MODIS, IEEE Trans. Geosci. Remote Sensing,, 30, 871-884, 1992.
King, M., Y. Kaufman, P. Menzel, D.Tanre, B. Gao, 1999: MODIS Atmosphere Validation Plan, NASA Goddard Space Flight Center, 48 pp.
King, M. D., S. C. Tsay, S. E. Platnick, M. Wang, and K. Liou, 1997: Cloud Retrieval Algorithms for MODIS: Optical Thickness, Effective Particle Radius, and Thermodynamic Phase , Algorithm Theoretical Basis Document, ATBD-MOD-05, NASA Goddard Space Flight Center,
King, M. D., W. P. Menzel, P. S. Grant, J. S. Myers, G. T. Arnold, S. E. Platnick, L. E.Gumley, S. C. Tsay, C. C. Moeller, M. Fitzgerald, K. S. Brown and F. G.Osterwisch, 1996: Airborne scanning spectrometer for remote sensing of cloud, aerosol, water vapor and surface properties. J. Atmos. Oceanic Technol.,13, 777–794.
King, M. D., Y. J. Kaufman, W. P. Menzel and D. Tanre, 1992: Remote sensing of cloud, aerosol and water vapor properties from the Moderate Resolution Imaging Spectrometer (MODIS). IEEE Trans. Geosci. Remote Sens., 30, 2–27.
King, M. D., 1987: Determination of the scaled optical thickness of clouds from re-flected solar radiation measurements. J. Atmos. Sci., 44, 1734–1751.
King, M. D., M. G. Strange, P. Leone and L. R. Blaine, 1986: Multiwavelength scanningradiometer for airborne measurements of scattered radiation within clouds. J. Atmos. Oceanic Technol., 3, 513–522.
King, M. D., 1981: A method for determining the single scattering albedo of clouds through observation of the internal scattered radiation field. J. Atmos.Sci., 38, 2031–204.
Menzel, W. P., and L. E. Gumley, 1998: MODIS Atmospheric Profiles Retrieval Algorithm Theoretical Basis Document. ATBD-MOD-07, NASA Goddard Space Flight Center, pp.
Menzel, W. P.,and L. E. Gumley, 1997:MODIS Atmospheric Profiles ,in MTPE EOS Data Products Handbook, pp 164-166.
Menzel, P., and M. King, 1997:MODIS Cloud Product, in MTPE EOS Data Products Handbook,109-111.
Menzel, P., and K. Strabela. 1997: Cloud Top Properties and Cloud Phase, Algorithm Theoretical Basis Document. ATBD-MOD-04, NASA Goddard Space Flight Center,56 pp.
Nakajima, T., M. D. King , J. D. Spinhirne and L. F. Radke, 1991: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part II: Marine stratocumulus observations. J. Atmos. Sci., 48, 728–750.
Nakajima, T., and M. D. King, 1990: Determination of the optical thickness and effective particle radius of clouds from reflected solar radiation measurements. Part I: Theory. J. Atmos. Sci., 47, 1878–1893.
Remer, L. A., Y. J. Kaufman, and B. N. Holben, 1996: The size distribution of ambient aerosol particles: Smoke vs. urban/industrial aerosol. Global biomass burning. Cambridge MA: MIT Press.
Rossow, W. B., and L.C. Gardner, 1993: Cloud detection using satellite measurements of infrared and visible radiances for ISCCP, J. Climate, 6, 2341-2369.
Strabala, K. I., S. A. Ackerman and W. P. Menzel, 1994: Cloud properties inferred from 8-12 micron data. J. Appl. Meteor, 33, No. 2, 212-229.
Tanre, D., Y. J. Kaufman, M. Herman, and S. Mattoo, 1997: Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances. Journal of Geophysical Research, 102, 16971-16988.
Tanre, D., M. Herman, and Y. J. Kaufman, 1996: Information on aerosol size distribution contained in solar reflected radiances. Journal of Geophysical Research-Atmospheres, 101, 19043-19060.
Creation and Review Dates
SERF Creation Date:
2012-06-05
SERF Last Revision Date:
2012-06-07
Links
