H41L-02
Multivariate assimilation of satellite-derived land remote sensing datasets: Advances, gaps and challenges
Thursday, 17 December 2015: 08:15
3022 (Moscone West)
Sujay Kumar1, Christa D Peters-Lidard1, David M Mocko1, Michael F Jasinski1, Rolf H Reichle2, Benjamin F Zaitchik3, Augusto Getirana1, Matthew Rodell4, Youlong Xia5, Michael B Ek6 and Sujay V. Kumar, Christa D. Peters-Lidard, David Mocko, Rolf Reichle, Ben Zaitchik, Mattew Rodell, Michael Jasinski, (1)NASA Goddard Space Flight Center, Greenbelt, MD, United States, (2)NASA GSFC, Greenbelt, MD, United States, (3)Johns Hopkins University, Baltimore, MD, United States, (4)NASA/GSFC, Greenbelt, MD, United States, (5)Environmental Modeling Center, College Park, MD, United States, (6)NOAA NWS NCEP/EMC, College Park, MD, United States
Abstract:
Remote sensing advancements in recent years have enabled monitoring of the Earth's land surface with unprecedented scale and frequency. In the past decade, remote sensing observations of the land surface have become available from a number of satellite instruments and platforms including soil moisture (AMSR-E, ASCAT, AMSR2, SMOS, SMAP), snow depth (AMSR-E, AMSR2), snow cover (MODIS, VIIRS), terrestrial water storage (GRACE) and land surface temperature (MODIS, VIIRS, GOES). To support the effective exploitation of the information content of the remote sensing observations, computational tools such as data assimilation are necessary. In this presentation, I will describe the efforts towards the concurrent use of all available remote sensing observations in a multivariate data assimilation configuration in the North American Land Data Assimilation System (NLDAS). Though NLDAS has produced over 34 years (Jan 1979 to present) of hourly land-surface meteorology and surface states using the best-available observations and reanalyses for “off-line” land surface model (LSM) simulations, to-date it has not included the assimilation of relevant hydrological remote sensing datasets. The new phase of NLDAS attempts to bridge this gap by assimilating all land relevant datasets in the NLDAS configuration using the NASA Land Information System (LIS). The results from individually assimilating the soil moisture, snow and terrestrial water storage datasets indicate that improvements can be obtained not only in soil moisture and snow states, but also on evapotranspiration and streamflow estimates. The results from the multivariate, multisensor assimilation of the above-mentioned remote sensing datasets in NLDAS and an evaluation of the resulting improvements and trends in soil moisture, snowpack, evapotranspiration and streamflow will also be presented. Through this talk, I will describe the advances made towards the effective utilization of remote sensing data for hydrologic prediction and some of the challenges and gaps that remain.
