scholarly journals Assimilation of Pseudo-GLM Data Using the Ensemble Kalman Filter

2016 ◽  
Vol 144 (9) ◽  
pp. 3465-3486 ◽  
Author(s):  
Blake J. Allen ◽  
Edward R. Mansell ◽  
David C. Dowell ◽  
Wiebke Deierling
Keyword(s):  
Kalman Filter ◽  
Space Charge ◽  
Ensemble Kalman Filter ◽  
Charge Separation ◽  
Real Data ◽  
Charging Rate ◽  
Convective Scale ◽  
Mapping Array ◽  
Total Lightning ◽  
Resolution Data

Total lightning observations that will be available from the GOES-R Geostationary Lightning Mapper (GLM) have the potential to be useful in the initialization of convection-resolving numerical weather models, particularly in areas where other types of convective-scale observations are sparse or nonexistent. This study used the ensemble Kalman filter (EnKF) to assimilate real-data pseudo-GLM flash extent density (FED) observations at convection-resolving scale for a nonsevere multicell storm case (6 June 2000) and a tornadic supercell case (8 May 2003). For each case, pseudo-GLM FED observations were generated from ground-based lightning mapping array data with a spacing approximately equal to the nadir pixel width of the GLM, and tests were done to examine different FED observation operators and the utility of temporally averaging observations to smooth rapid variations in flash rates. The best results were obtained when assimilating 1-min temporal resolution data using any of three observation operators that utilized graupel mass or graupel volume. Each of these three observation operators performed well for both the weak, disorganized convection of the multicell case and the much more intense convection of the supercell case. An observation operator using the noninductive charging rate performed poorly compared to the graupel mass and graupel volume operators, a result that appears likely to be due to the inability of the noninductive charging rate to account for advection of space charge after charge separation occurs.

scholarly journals Convective-Scale Data Assimilation for the Weather Research and Forecasting Model Using the Local Particle Filter

2017 ◽  
Vol 145 (5) ◽  
pp. 1897-1918 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Ryan A. Sobash ◽  
Jeffrey L. Anderson
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Weather Prediction ◽  
Squall Line ◽  
Monte Carlo Data ◽  
Mixing Ratios ◽  
Convective Scale ◽  
Non Gaussian ◽  
Mean Square Errors

Abstract Particle filters (PFs) are Monte Carlo data assimilation techniques that operate with no parametric assumptions for prior and posterior errors. A data assimilation method introduced recently, called the local PF, approximates the PF solution within neighborhoods of observations, thus allowing for its use in high-dimensional systems. The current study explores the potential of the local PF for atmospheric data assimilation through cloud-permitting numerical experiments performed for an idealized squall line. Using only 100 ensemble members, experiments using the local PF to assimilate simulated radar measurements demonstrate that the method provides accurate analyses at a cost comparable to ensemble filters currently used in weather models. Comparisons between the local PF and an ensemble Kalman filter demonstrate benefits of the local PF for producing probabilistic analyses of non-Gaussian variables, such as hydrometeor mixing ratios. The local PF also provides more accurate forecasts than the ensemble Kalman filter, despite yielding higher posterior root-mean-square errors. A major advantage of the local PF comes from its ability to produce more physically consistent posterior members than the ensemble Kalman filter, which leads to fewer spurious model adjustments during forecasts. This manuscript presents the first successful application of the local PF in a weather prediction model and discusses implications for real applications where nonlinear measurement operators and nonlinear model processes limit the effectiveness of current Gaussian data assimilation techniques.

Ensemble Kalman Filter Assimilation of HIWRAP Observations of Hurricane Karl (2010) from the Unmanned Global Hawk Aircraft

2014 ◽  
Vol 142 (12) ◽  
pp. 4559-4580 ◽  
Author(s):  
Jason A. Sippel ◽  
Fuqing Zhang ◽  
Yonghui Weng ◽  
Lin Tian ◽  
Gerald M. Heymsfield ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Positive Impact ◽  
Simulated Data ◽  
Real Data ◽  
Horizontal Wind ◽  
Doppler Velocity ◽  
Flight Duration ◽  
The Impact ◽  
Global Hawk

Abstract This study utilizes an ensemble Kalman filter (EnKF) to assess the impact of assimilating observations of Hurricane Karl from the High-Altitude Imaging Wind and Rain Airborne Profiler (HIWRAP). HIWRAP is a new Doppler radar on board the NASA Global Hawk unmanned airborne system, which has the benefit of a 24–26-h flight duration, or about 2–3 times that of a conventional aircraft. The first HIWRAP observations were taken during NASA’s Genesis and Rapid Intensification Processes (GRIP) experiment in 2010. Observations considered here are Doppler velocity (Vr) and Doppler-derived velocity–azimuth display (VAD) wind profiles (VWPs). Karl is the only hurricane to date for which HIWRAP data are available. Assimilation of either Vr or VWPs has a significant positive impact on the EnKF analyses and forecasts of Hurricane Karl. Analyses are able to accurately estimate Karl’s observed location, maximum intensity, size, precipitation distribution, and vertical structure. In addition, forecasts initialized from the EnKF analyses are much more accurate than a forecast without assimilation. The forecasts initialized from VWP-assimilating analyses perform slightly better than those initialized from Vr-assimilating analyses, and the latter are less accurate than EnKF-initialized forecasts from a recent proof-of-concept study with simulated data. Likely causes for this discrepancy include the quality and coverage of the HIWRAP data collected from Karl and the presence of model error in this real-data situation. The advantages of assimilating VWP data likely include the ability to simultaneously constrain both components of the horizontal wind and to circumvent reliance upon vertical velocity error covariance.

scholarly journals Examination of Situation-Dependent Background Error Covariances at the Convective Scale in the Context of the Ensemble Kalman Filter

2013 ◽  
Vol 141 (10) ◽  
pp. 3369-3387 ◽  
Author(s):  
Kao-Shen Chung ◽  
Weiguang Chang ◽  
Luc Fillion ◽  
Monique Tanguay
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Early Stage ◽  
Forecast Error ◽  
Radar Data ◽  
Physical Processes ◽  
Background Error ◽  
Time To Build ◽  
Convective Scale ◽  
Thermodynamic Variables

Abstract A high-resolution ensemble Kalman filter (HREnKF) system at the convective scale has been developed based on the Canadian Meteorological Center's operational global ensemble Kalman filter (EnKF) system. This study focuses on the very early stage of transition from purely homogeneous isotropic background error correlations to situation-dependent correlations. It has been found that forecast error structures can develop situation-dependent features in as little as 15 min. Furthermore, the dynamic and thermodynamic variables require different periods of time to build up their own forecast error structures. Differences in these structures between regions with and without precipitation are also investigated. An examination of temperature tendencies revealed that physical processes are as important as dynamical forcing in determining the structure of convective-scale errors structures, and that once physical processes become active, these structures change rapidly before the onset of precipitation. This study is intended to be the basis for a systematic exploration in the near future of the usefulness of the HREnKF system in assimilating high-density observations such as radar data.

scholarly journals Assimilation of SEVIRI Water Vapor Channels With an Ensemble Kalman Filter on the Convective Scale

2020 ◽  
Vol 8 ◽  
Author(s):  
Axel Hutt ◽  
C. Schraff ◽  
H. Anlauf ◽  
L. Bach ◽  
M. Baldauf ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Water Vapor ◽  
Ensemble Kalman Filter ◽  
Convective Scale

scholarly journals A Comparison between the 4DVAR and the Ensemble Kalman Filter Techniques for Radar Data Assimilation

2005 ◽  
Vol 133 (11) ◽  
pp. 3081-3094 ◽  
Author(s):  
A. Caya ◽  
J. Sun ◽  
C. Snyder
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Forecast Model ◽  
Radar Data ◽  
Forecast Errors ◽  
Error Covariance ◽  
Convective Scale ◽  
Cloud Resolving Model ◽  
Comparable Accuracy

Abstract A four-dimensional variational data assimilation (4DVAR) algorithm is compared to an ensemble Kalman filter (EnKF) for the assimilation of radar data at the convective scale. Using a cloud-resolving model, simulated, imperfect radar observations of a supercell storm are assimilated under the assumption of a perfect forecast model. Overall, both assimilation schemes perform well and are able to recover the supercell with comparable accuracy, given radial-velocity and reflectivity observations where rain was present. 4DVAR produces generally better analyses than the EnKF given observations limited to a period of 10 min (or three volume scans), particularly for the wind components. In contrast, the EnKF typically produces better analyses than 4DVAR after several assimilation cycles, especially for model variables not functionally related to the observations. The advantages of the EnKF in later cycles arise at least in part from the fact that the 4DVAR scheme implemented here does not use a forecast from a previous cycle as background or evolve its error covariance. Possible reasons for the initial advantage of 4DVAR are deficiencies in the initial ensemble used by the EnKF, the temporal smoothness constraint used in 4DVAR, and nonlinearities in the evolution of forecast errors over the assimilation window.

scholarly journals Storm-Scale Ensemble Kalman Filter Assimilation of Total Lightning Flash-Extent Data

2014 ◽  
Vol 142 (10) ◽  
pp. 3683-3695 ◽  
Author(s):  
Edward R. Mansell
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Deep Convection ◽  
Spatial Location ◽  
Horizontal Resolution ◽  
Lightning Flash ◽  
Simulation Experiments ◽  
Linear Relationships ◽  
Single Moment ◽  
Total Lightning

Abstract A set of observing system simulation experiments (OSSEs) demonstrates the potential benefit from ensemble Kalman filter (EnKF) assimilation of total lightning flash mapping data. Synthetic lightning data were generated to mimic the Geostationary Lightning Mapper (GLM) instrument that is planned for the Geostationary Operational Environmental Satellite-R series (GOES-R) platform. The truth simulation was conducted using multimoment bulk microphysics, explicit electrification mechanisms, and a branched lightning parameterization to produce 2-min-averaged synthetic pseudo-GLM observations at 8-km GLM resolution and at a hypothetical 1-km resolution. The OSSEs use either perfect (two-moment bulk) or imperfect (single-moment, graupel only) microphysics. One OSSE with perfect microphysics included the same electrification physics as the truth simulation to generate lightning flash rates and flash-extent densities (FED). The other OSSEs used linear relationships between flash rate and graupel echo volume as the observation operator. The assimilation of FED at 8-km horizontal resolution can effectively modulate the convection simulated at 1-km horizontal resolution by sharpening the location of reflectivity echoes and the spatial location probability of convective updrafts. Tests with zero flash rates show that the lightning assimilation can help to limit spurious deep convection, as well. Pseudo-GLM observations at 1 km further sharpen the analyses of location (updraft and reflectivity) of the relatively simple storm structure.

scholarly journals Improving the Distributed Hydrological Model Performance in Upper Huai River Basin: Using Streamflow Observations to Update the Basin States via the Ensemble Kalman Filter

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Yongwei Liu ◽  
Wen Wang ◽  
Yiming Hu ◽  
Wei Cui
Keyword(s):  
Kalman Filter ◽  
River Basin ◽  
Ensemble Kalman Filter ◽  
Hydrological Model ◽  
Model Performance ◽  
Real Data ◽  
Distributed Hydrological Model ◽  
Huai River Basin ◽  
Huai River

This study investigates the capability of improving the distributed hydrological model performance by assimilating the streamflow observations. Incorrectly estimated model states will lead to discrepancies between the observed and estimated streamflow. Consequently, streamflow observations can be used to update the model states, and the improved model states will eventually benefit the streamflow predictions. This study tests this concept in upper Huai River basin. We assimilate the streamflow observations sequentially into the Soil and Water Assessment Tool (SWAT) using the ensemble Kalman filter (EnKF) to update the model states. Both synthetic experiments and real data application are used to demonstrate the benefit of this data assimilation scheme. The experiment shows that assimilating the streamflow observations at interior sites significantly improves the streamflow predictions for the whole basin. Assimilating the catchment outlet streamflow improves the streamflow predictions near the catchment outlet. In real data case, the estimated streamflow at the catchment outlet is significantly improved by assimilating the in situ streamflow measurements at interior gauges. Assimilating the in situ catchment outlet streamflow also improves the streamflow prediction of one interior location on the main reach. This may demonstrate that updating model states using streamflow observations can constrain the flux estimates in distributed hydrological modeling.

scholarly journals Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part III: Comparison with 3DVAR in a Real-Data Case Study

2008 ◽  
Vol 136 (2) ◽  
pp. 522-540 ◽  
Author(s):  
Zhiyong Meng ◽  
Fuqing Zhang
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Regional Scale ◽  
Warm Season ◽  
Real Data ◽  
Background Error ◽  
Background Error Covariance ◽  
Error Covariance ◽  
Scale Data

Abstract The feasibility of using an ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation has been demonstrated in the authors’ recent studies via observing system simulation experiments (OSSEs) both under a perfect-model assumption and in the presence of significant model error. The current study extends the EnKF to assimilate real-data observations for a warm-season mesoscale convective vortex (MCV) event on 10–12 June 2003. Direct comparison between the EnKF and a three-dimensional variational data assimilation (3DVAR) system, both implemented in the Weather Research and Forecasting model (WRF), is carried out. It is found that the EnKF consistently performs better than the 3DVAR method by assimilating either individual or multiple data sources (i.e., sounding, surface, and wind profiler) for this MCV event. Background error covariance plays an important role in the performance of both the EnKF and the 3DVAR system. Proper covariance inflation and the use of different combinations of physical parameterization schemes in different ensemble members (the so-called multischeme ensemble) can significantly improve the EnKF performance. The 3DVAR system can benefit substantially from using short-term ensembles to improve the prior estimate (with the ensemble mean). Noticeable improvement is also achieved by including some flow dependence in the background error covariance of 3DVAR.

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