Christman Field Latest Observations
Date Time
MST
Temp
°F
RH
%
DewPt
°F
Wind
mph
Dir
°
Gust
mph
Dir
°
Press
in Hg
Solar
W/m^2
Prec
in
2019-06-15 19:45 61.6 62.3 48.6 2.1 125 3.2 125 24.876 6.1 0.00
2019-06-15 19:40 62.1 63.2 49.4 0.1 90 0.7 90 24.873 10.5 0.00
2019-06-15 19:35 62.6 61.5 49.2 0.0 90 0.0 78 24.870 13.1 0.00
2019-06-15 19:30 62.4 59.7 48.2 1.6 78 2.6 78 24.868 18.1 0.00
2019-06-15 19:25 63.2 60.0 49.1 0.2 245 0.8 245 24.868 23.7 0.00
2019-06-15 19:20 63.4 57.4 48.1 1.9 245 2.8 245 24.869 27.2 0.00
2019-06-15 19:15 63.6 57.5 48.4 1.8 225 3.3 241 24.867 36.7 0.00
2019-06-15 19:10 64.0 58.4 49.1 1.6 290 4.0 312 24.864 49.8 0.00
2019-06-15 19:05 64.4 57.9 49.2 5.2 329 7.3 341 24.862 86.4 0.00
2019-06-15 19:00 64.4 54.3 47.6 8.7 336 11.4 341 24.861 84.3 0.00
2019-06-15 18:55 65.1 54.3 48.2 10.9 332 13.7 344 24.865 87.4 0.00
2019-06-15 18:50 65.0 52.1 47.0 11.0 313 14.0 296 24.868 90.2 0.00
2019-06-15 18:45 64.8 52.2 46.9 12.7 286 16.1 312 24.867 54.5 0.00
2019-06-15 18:40 64.9 53.8 47.8 8.3 310 12.3 306 24.865 54.9 0.00
2019-06-15 18:35 65.3 54.6 48.5 8.8 318 12.3 351 24.861 57.1 0.00
2019-06-15 18:30 65.7 52.4 47.8 9.4 344 13.1 352 24.858 80.1 0.00
2019-06-15 18:25 65.9 51.3 47.5 13.4 351 18.8 3 24.858 79.1 0.00
2019-06-15 18:20 66.4 51.4 47.9 9.9 0 13.3 15 24.856 64.6 0.00
2019-06-15 18:15 67.2 48.8 47.3 14.6 348 21.2 351 24.854 64.7 0.00
2019-06-15 18:10 68.0 46.2 46.6 15.1 7 21.1 21 24.852 71.1 0.00
2019-06-15 18:05 68.5 45.3 46.5 15.6 26 22.7 16 24.852 73.5 0.00
2019-06-15 18:00 69.7 43.9 46.8 19.6 25 26.5 3 24.850 78.7 0.00
2019-06-15 17:55 71.8 44.5 49.1 12.9 16 20.8 354 24.845 72.5 0.00
2019-06-15 17:50 73.3 39.9 47.5 15.4 32 21.1 30 24.839 89.3 0.00
2019-06-15 17:45 74.8 36.5 46.5 8.4 30 10.9 49 24.834 139.4 0.00
2019-06-15 17:40 75.4 33.5 44.7 6.4 55 8.4 42 24.829 214.0 0.00
2019-06-15 17:35 75.4 33.7 44.9 5.8 37 8.0 66 24.828 220.6 0.00
2019-06-15 17:30 76.0 32.0 44.0 7.1 78 9.3 67 24.824 266.4 0.00
2019-06-15 17:25 76.2 30.9 43.3 3.8 68 7.3 67 24.821 316.1 0.00
2019-06-15 17:20 75.7 32.2 43.9 4.4 106 8.9 78 24.821 284.2 0.00
CIRA

Cooperative Institute for Research in the Atmosphere

Data Assimilation


The future state of a dynamical system is typically calculated by integrating a numerical model, and depends on parameters such as initial conditions, model errors, empirical parameters of the model, and possibly on lateral boundary conditions. Observations add new information that can be combined with model prediction to produce optimal values of dynamical system parameters and reduce their uncertainty. A mathematical methodology that can accomplish this is called data assimilation.

 

Data assimilation is fundamentally probabilistic since the uncertainty of dynamical system parameters can be described by a probability density function. Since dynamical models add valuable prior information, data assimilation is commonly based on Bayes theorem and thus represents a Bayesian inference. Data assimilation is also nonlinear since dynamical prediction models and observation operators can be highly nonlinear.

 

CIRA data assimilation research has the following goals:

  1. develop new and improved data assimilation methodologies,
  2. apply data assimilation to high-dimensional problems in geosciences and engineering, including carbon cycle, weather, climate, and hydrology.

Since we are primarily interested in geosciences applications to high-dimensional dynamical systems, the high-performance computational component of data assimilation is also of great importance to our research.

 

Typical data assimilation methodologies developed and improved at CIRA are variational, ensemble, and hybrid ensemble-variational methodologies, but we are also exploring other avenues. Our research is encompassing a wide range of applications including carbon cycle, hydrology, climate, and cloud-resolving processes. We are also actively supporting NOAA research and development by conducting data assimilation research using NOAA operational systems and observations.