Abstract:
Access to reliable weather data is a pre-requisite for ecosystem modeling. The availability of weather observations has been a key obstacle in the development of real-time EF systems. Historically, weather data was made available on tapes or CDs months after it was collected and corrected for errors. This time lag precluded real-time simulations, a precursor to developing forecasting capability. ... Through the World Wide Web, however, there are now thousands of on-line weather stations providing real-time weather data. These real-time data include ground-based observations of max/min/dew temperature and wind speed, satellite-based solar radiation, and spatially continuous rainfall fields produced by weather agencies.
Another important advancement for EF is the ability to grid point observations onto the landscape at various spatial resolutions, as observations are rarely sufficient to represent the spatial variability. Models such as PRISM, DAYMET, and SOGS (Daly et al.1994; Thornton et al. 1997; Jolly et al. 2004) ingest point surface observations, and use topography and other ancillary information to compute spatially continuous meteorological fields (temperature, humidity, solar radiation, and rainfall) that can be directly used in ecosystem modeling.
To produce gridded climate fields, the user specifies a geographic area of interest and the spatial resolution for the gridded fields. The ImageBot planner uses these specifications to create a data processing plan comprised of a series of requirements and corresponding actions. For example, ImageBot will identify the acquisition of topographic data as a requirement, evaluate the possible sources for this data from the data library, identify the required resolution, and create the set of actions required to obtain the data at the appropriate resolution. These actions are then passed to JDAF, which fetches the data from the source, and reformats and reprojects the data to meet the user-specified requirements. Similarly, for meteorological data, ImageBot produces a list of weather networks available for the region, a list of variables available from each network, and the frequency of observations available from the network. From this information and the user-defined set of constraints, ImageBot again formulates a series of actions specifying which networks and what variables need to be retrieved and input to the database. After receiving these instructions, JDAF fetches the necessary data, checks for consistency against historical averages, fills-in missing values from additional sources, flags missing values, and finally interfaces these observations with the Surface Observation and Gridding System (SOGS, Jolly et al. 2004), a component layer within TOPS.
SOGS is an operational climate-gridding system, and an improvement upon DAYMET (Thornton et al. 1997), that uses maximum, minimum, and dewpoint temperatures, in addition to rainfall, to create spatially continuous surfaces for air temperatures, vapor pressure deficits, and incident radiation. The cross-validation statistics returned from SOGS allow ImageBot to decide if the user-specified requirements for accuracy have been achieved, or if alternative gridding methods need to be found.
[Summary provided by NASA.]
Service Citation
Originators:
NASA Ames Research Center, University of Montana
Title:
Surface Observation Gridding System (SOGS)
Provider:
NASA Ames Research Center
URL:
http://ecocast.arc.nasa.gov/
Name:
DR.
RAMAKRISHNA R.
NEMANI
Email:
rama.nemani at nasa.gov
Contact Address:
Biospheric Sciences Branch
NASA Ames Research Center
MS 242-4 City:
Moffett Field
Province or State:
CA
Postal Code:
94087
Country:
USA
Personnel
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
Thornton et al. 2000. Simultaneous estimation of daily solar radiation and humidity from observed temperature and precipitation: an application over complex terrain in Austria. Agricultural and Forest Meteorology, 104 (2000):255–271.
Thorton & Running. 1999. An improved algorithm for estimating incident daily solar radiation from measurements of temperature, humidity, and precipitation. Agricultural and Forest Meteorology, 93 (1999):211-228.
Thornton et al. 1997. Generating surfaces of daily meteorological variables over large regions of complex terrain. Journal of Hydrology, 190(1997):214-251.
Creation and Review Dates
SERF Creation Date:
2008-09-15
SERF Last Revision Date:
2009-05-06
Links
