Not exact matches
We employed 20 individual GCMs from the CMIP5 project for the Montana Climate Assessment ensemble, chosen because they provide
daily outputs and a range of important climate variables.9 For this first Montana Climate Assessment, we are only using climate variables of temperature and
precipitation (later assessment may evaluate other important variables such
as wind and relative humidity).
To solve this problem I looked at three patterns of the 6558 day period, overlaid them at the
daily weather data level, and plotted the resultant combined signal for
Precipitation, and temperature patterns for the USA, extended that cyclic interpenetration for a six year period, and plotted out maps to show the repeating reoccurring patterns in the global circulation,
as a (6 year long stretch, we are now ~ 40 months into the posted 6 years long) forecast for part of the current repeat of the 6558 day long cycle.
The extremes considered are for weather elements that are monitored
daily, such
as temperature and
precipitation.
Daily mean NCEP / NCAR reanalysis data are used
as atmospheric forcing, i.e., 10 - m surface winds, 2 - m surface air temperature (SAT), specific humidity,
precipitation, evaporation, downwelling longwave radiation, sea level pressure, and cloud fraction.
Specifically, we used a variety of linear and nonlinear methods such
as artificial neural networks (ANN), decision trees and ensembles, multiple linear regression, and k - nearest neighbors to generate present and future
daily precipitation occurrences and amounts.
In extreme seasons — when
precipitation falls infrequently — July and August
daily high temperatures could average between 100 and 110 degrees Fahrenheit in cities such
as Chicago, Washington, and Atlanta.
Specifically, this analysis is of the average of minimum and maximum
daily temperature
as well
as precipitation totals.
The indicators are based on
daily maximum and minimum temperature series,
as well
as daily totals of
precipitation, and represent changes in all seasons of the year.
We blended surface meteorological observations, remotely sensed (TRMM and NDVI) data, physiographic indices, and regression techniques to produce gridded maps of annual mean
precipitation and temperature,
as well
as parameters for site - specific,
daily weather generation for any location in Yemen.
UKCIP08 will provide a statistical «weather generator,» which will allow users to see what
daily (or even hourly) sequences of weather could look like at specified locations, given changes in basic aspects of climate such
as average temperature, frequency of dry days, and average
precipitation on wet days.
In a study of maize irrigation in Illinois under profit - maximising conditions, it was found that a 25 % decrease of annual
precipitation had the same effect on irrigation profitability
as a 15 % decrease combined with a doubling of the standard deviation of
daily precipitation (Eheart and Tornil, 1999).
Analysis of extreme
precipitation simulated by climate models has included the
daily variability of anomalous
precipitation (Zwiers and Kharin, 1998; McGuffie et al., 1999; Kharin and Zwiers, 2000), patterns of heavy rainfall (Bhaskran and Mitchell, 1998; Zhao et al., 2000b),
as well
as wet and dry spells (Thorncroft and Rowell, 1998; McGuffie et al., 1999).