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
2019-08-19 00:55 57.7 66.8 46.8 5.8 320 6.6 341 24.870 0.0 0.00
2019-08-19 00:50 55.8 72.6 47.1 5.4 340 6.6 338 24.871 0.0 0.00
2019-08-19 00:45 56.2 79.1 49.8 3.5 327 4.8 331 24.873 0.0 0.00
2019-08-19 00:40 58.1 72.3 49.2 3.0 340 3.7 331 24.873 0.1 0.00
2019-08-19 00:35 59.2 70.7 49.7 2.8 351 3.6 324 24.873 0.0 0.00
2019-08-19 00:30 59.7 68.4 49.3 3.4 324 4.1 311 24.872 0.1 0.00
2019-08-19 00:25 58.9 70.2 49.2 4.0 305 4.7 304 24.872 0.1 0.00
2019-08-19 00:20 57.7 71.7 48.6 4.1 322 5.1 329 24.872 0.1 0.00
2019-08-19 00:15 57.2 74.2 49.1 2.6 316 3.8 328 24.872 0.1 0.00
2019-08-19 00:10 57.3 75.5 49.7 2.6 322 3.7 285 24.872 0.2 0.00
2019-08-19 00:05 58.4 74.3 50.2 3.8 292 4.4 291 24.872 0.0 0.00
2019-08-19 00:00 60.3 67.8 49.6 3.8 327 4.8 342 24.872 0.0 0.00
2019-08-18 23:55 60.6 67.8 49.9 3.4 342 4.3 346 24.870 0.1 0.00
2019-08-18 23:50 59.1 71.6 50.0 2.8 357 4.0 21 24.870 0.0 0.00
2019-08-18 23:45 59.1 75.5 51.3 3.6 21 4.2 21 24.870 0.0 0.00
2019-08-18 23:40 59.6 77.7 52.6 4.3 14 5.1 16 24.872 0.0 0.00
2019-08-18 23:35 61.4 75.7 53.7 1.9 28 4.6 28 24.872 0.0 0.00
2019-08-18 23:30 60.4 78.8 53.8 0.7 275 1.7 275 24.872 0.1 0.00
2019-08-18 23:25 61.0 73.9 52.6 1.6 287 2.0 293 24.872 0.1 0.00
2019-08-18 23:20 61.2 74.3 52.9 1.8 293 2.1 315 24.873 0.1 0.00
2019-08-18 23:15 60.5 79.3 54.1 0.8 308 1.8 308 24.876 0.1 0.00
2019-08-18 23:10 61.0 78.9 54.4 0.9 328 1.7 343 24.875 0.1 0.00
2019-08-18 23:05 61.2 79.1 54.6 0.1 325 0.6 320 24.875 0.2 0.00
2019-08-18 23:00 61.2 79.3 54.8 0.2 320 1.1 340 24.876 0.1 0.00
2019-08-18 22:55 61.4 78.4 54.6 1.6 340 2.5 351 24.874 0.1 0.00
2019-08-18 22:50 62.5 71.3 53.1 2.3 358 3.3 353 24.873 0.2 0.00
2019-08-18 22:45 61.9 75.6 54.1 2.8 342 3.8 8 24.872 0.2 0.00
2019-08-18 22:40 61.0 78.9 54.4 2.3 354 3.6 353 24.873 0.2 0.00
2019-08-18 22:35 61.8 76.0 54.2 3.3 311 4.4 317 24.871 0.2 0.00
2019-08-18 22:30 62.9 69.9 53.0 2.8 307 3.4 331 24.870 0.1 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.