| The application of ensemble Kalman filter in adaptive observation and information content estimation studies |
| Junjie Liu and Eugenia Kalnay | |
| July 13th, 2007 | |
| Question to address in adaptive observation study |
| Adaptive observation: temporarily adjust observation locations | |
| Common question: how to allocate the limited observation resources to maximize effectiveness of observations (improve the analysis and forecast as much as possible)? | |
| Question in hand: how to allocate 10% Doppler Wind Lidar (DWL) scanning range? (Future DWL will operate in adaptive targeting mode (NPOESS P3I science team) (observation locations change with time) | |
| LETKF-based ensemble spread adaptive observation strategy |
| It is the square root difference between ensemble members and ensemble mean state. | |
| Ensemble spread estimated from ensemble Kalman filter (EnKF) reflects the dynamical uncertainties related with background dynamic flow.. | |
| In EnKF the ensemble spread strategy is very simple: we add the adaptive observations where the ensemble spread is large. |
| Rawinsonde observation locations and simulated satellite winds scanning range |
| Sampling strategies |
| Ensemble spread strategy (from Local Ensemble Transform Kalman Filter) | ||
| Adaptive observations are at locations with large ensemble wind spread at 500hPa. | ||
| 3D-Var and LETKF have the same adaptive observation points | ||
| Random picking | ||
| Randomly pick locations from potential locations. | ||
| Uniform distribution | ||
| Uniformly distributed. | ||
| Climatology ensemble spread | ||
| Adaptive observations are at locations with large climatological average ensemble wind spread from rawinsonde assimilation. | ||
| Constant with time, and same for 3D-Var and LETKF. | ||
| ÒIdealÓ sampling | ||
| Adaptive observations are at locations with large background error obtained from the ÒtruthÓ. | ||
| 500hPa zonal wind RMS error |
| Slide 7 |
| Analysis sensitivity study within LETKF |
| Control experiment: rawinsonde only |
| Exp_uv (winds are observed in both rawinsonde and dense network, 30%) |
| Slide 11 |
| Possible applications to Carbon problem |
| Observation system design: ensemble spread method; Using the posterior uncertainty estimation. | |
| Evaluate the significance of the carbon observations: based on the sensitivity study (impact of the carbon concentration data on the flux estimation) | |
| Will address the essential problem: uncertainty estimation | |