Weather Generator Technology (GEM)
Introducing the GEM Weather Simulation Model (ARS
Weather and climate data are often the most critical pieces of
information needed for strategic environmental planning.
Agricultural, natural resource and engineering management decisions
require a variety of climatic information, dependent on the specific
application. Unfortunately, very often only rough estimates of
weather are used because historical records for the location of
interest are either unavailable or of insufficient duration. To
address the need for readily-available climate data for any
location, a new stochastic simulation model is being developed which
delivers accurate time series of daily or sub-daily weather
The model is known as GEM (Generation of weather Elements
for Multiple applications), and is being developed by researchers
within the USDA-Natural Resources Conservation Service (NRCS),
Agricultural Research Service (ARS), and collaborating universities.
Overall project leadership is being undertaken by the NRCS National
Water and Climate Center in Portland, Oregon. Building upon the
strength of stochastic weather modeling work by several ARS
researchers over the past 20 years, GEM retains the basic internal
structure of the USCLIMATE (Hanson et al. 1994) and WGEN (Richardson
and Wright 1984) models but includes several significant
What GEM Provides
GEM provides easy access to simulated daily weather data for as
many months or years as needed, for any location within the
contiguous United States. The time series which is produced is
statistically representative of the weather that can be expected at
that location over a period of time. A recent study has shown that
data generated by GEM closely mimics nearly all aspects of the true
climate of a location (Johnson et al. 1996). At present, GEM
delivers a daily time series of maximum and minimum temperature,
precipitation amount and solar radiation. Planned enhancements to
the model will result in a more complete suite of products from GEM,
including additional elements such as dewpoint temperature and wind
speed, as well as higher time resolution data (such as hourly
precipitation), and a spatial version of the model for realistic
weather simulation over a small region, such as a watershed or small
Current Enhancements and Improvements to GEM
Distribution of GEM Parameters for Spatial Modeling
A method of spatially distributing the necessary parameters for
GEM using the PRISM modeling system (Daly et al. 1998) at Oregon
State University has been developed. This means representative
weather scenarios can be developed for any location, even in regions
where no long-term climatic data exist. Presently, this methodology
has been used and tested in a region of significant climatic
diversity over portions of Idaho and Oregon. Time series of daily
precipitation and maximum and minimum temperature can be generated
for any 4 km grid point in the region using a point-and-click,
map-oriented user interface. It is anticipated that this technology
will be available for the entire United States in the coming few
years. Lead Scientists: Dr. Greg Johnson, USDA-NRCS, WNTSC, Portland, Oregon, (503) 273-2424, email: Greg.Johnson@por.usda.gov;
and Dr. Chris Daly, Oregon State University, Corvallis, Oregon,
(503)754-5705, email: firstname.lastname@example.org.
Methods of Generating Sub-Daily Time Steps
Methods of generating weather at sub-daily time steps (hour,
minute) are being developed and will be incorporated into GEM.
Included in this is a method of generating within-storm
precipitation intensities, with a resolution of the order of
minutes. Storm-occurrence and within-storm statistical
characteristics for any given location will be maintained. The
short-time interval precipitation outputs will enable hydrologic and
natural resource modelers to utilize more advanced water-movement
process algorithms, where previously a lack of appropriate
precipitation data limited their utility. Better synthesis of time
series of sediment yields, peak flows, runoff volumes and chemical
loads will result from these improvements to GEM. Lead Scientist:
Dr. Jim Bonta, USDA-ARS, Coshocton, Ohio (740) 545-6349; email:
Applications of GEM
GEM is being used by numerous agencies, research institutions and
private companies. The model has been linked to other computer
models, including those used to manage crops, predict yields, and
determine runoff and erosion, as well as to investigate the impact
of potential climate change on climate variability and shifts in
agricultural production. It is in use in several countries in
addition to the United States.
For Information about GEM
For more information please contact ARS-NRCS Weather Simulation
Dr. Greg Johnson, USDA-NRCS, WNTSC, Portland, Oregon, (503) 273-2424, email: Greg.Johnson@por.usda.gov.
Daly, C., W.P. Gibson, G.H. Taylor, G.L. Johnson and P.A.
Pasteris. 1998. New methods for mapping climate in complex regions. J.
Appl. Meteor. (In Review)
Hanson, C.L., K.A. Cumming, D.A. Woolhiser and C.W. Richardson.
1994. Microcomputer program for daily weather simulation. U.S. Dept.
Agric., Agric. Res. Svc. Pub. No. ARS-114, 38 pp.
Johnson, G.L., C. Daly, G.H. Taylor and C.L. Hanson. 2000.
Spatial variability and interpolation of stochastic weather
simulation model parameters. J. Appl. Meteor., 39, 778-796.
Johnson, G.L., C.L. Hanson, S.P. Hardegree and E.B. Ballard.
1996. Stochastic weather simulation: Overview and analysis of two
commonly used models. J. Appl. Meteor., 35, 1878-1896.
Richardson, C.W. and D.A. Wright. 1984. WGEN: A model for
generating daily weather variables. U.S. Dept. Agric., Agric. Res.
Svc. Pub. No. ARS-8, 83 pp.
Woolhiser, D.A., T.O. Keefer and K.T. Redmond. 1993. Southern
oscillation effects on daily precipitation in the southwestern U.S. Water
Resources Research, 29, 1287-1295.
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