Influence of Surface Observations in Mesoscale Data Assimilation Using an Ensemble Kalman Filter

Abstract The assimilation of surface observations using an ensemble Kalman filter (EnKF) approach was successfully performed in the Advanced Research version of the Weather Research and Forecasting Model (WRF) coupled with the Data Assimilation Research Testbed (DART) system. The mesoscale cycling experiment for the continuous ensemble data assimilation was verified against independent surface mesonet observations and demonstrated the positive impact on short-range forecasts over the contiguous U.S. (CONUS) domain throughout the month-long period of June 2008. The EnKF assimilation of surface observations was found useful for systematically improving the simulation of the depth and the structure of the planetary boundary layer (PBL) and the reduction of surface bias errors. These benefits were extended above PBL and resulted in a better precipitation forecast for up to 12 h. With the careful specification of observation errors, not only the reliability of the ensemble system but also the quality of the following forecast was improved, especially in moisture. In this retrospective case study of a squall line, assimilation of surface observations produced analysis increments consistent with the structure and dynamics of the boundary layer. As a result, it enhanced the horizontal gradient of temperature and moisture across the frontal system to provide a favorable condition for the convective initiation and the following heavy rainfall prediction in the Oklahoma Panhandle. Even with the assimilation of upper-level observations, the analysis without the assimilation of surface observations simulated a surface cold front that was much weaker and slower than observed.

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

Monthly Weather Review ◽

10.1175/mwr-d-16-0298.1 ◽

2017 ◽

Vol 145 (5) ◽

pp. 1897-1918 ◽

Cited By ~ 20

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.

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Ensemble-based data assimilation of atmospheric boundary layer observations improves the soil moisture analysis

10.5194/hess-2020-672 ◽

2021 ◽

Author(s):

Tobias Sebastian Finn ◽

Gernot Geppert ◽

Felix Ament

Keyword(s):

Boundary Layer ◽

Soil Moisture ◽

Kalman Filter ◽

Data Assimilation ◽

Atmospheric Boundary Layer ◽

Ensemble Kalman Filter ◽

Land Surface ◽

Model Systems ◽

Surface Component ◽

Moisture Analysis

Abstract. We revise the potential of assimilating atmospheric boundary layer observations into the soil moisture. Previous studies often stated a negative assimilation impact of boundary layer observations on the soil moisture analysis, but recent developments in physically-consistent hydrological model systems and ensemble-based data assimilation lead to an emerging potential of boundary layer observations for land surface data assimilation. To explore this potential, we perform idealized twin experiments for a seven-day period in Summer 2015 with a coupled atmosphere-land modelling platform. We use TerrSysMP for these limited-area simulations with a horizontal resolution 1.0 km in the land surface component. We assimilate sparse synthetic 2-metre-temperature observations into the land surface component and update the soil moisture with a localized Ensemble Kalman filter. We show a positive assimilation impact of these observations on the soil moisture analysis during day-time and a neutral impact during night. Furthermore, we find that hourly-filtering with a three-dimensional Ensemble Kalman filter results in smaller errors than daily-smoothing with a one-dimensional Simplified Extended Kalman filter, whereas the Ensemble Kalman filter additionally allows us to directly assimilate boundary layer observations without an intermediate optimal interpolation step. We increase the physical consistency in the analysis for the land surface and boundary by updating the atmospheric temperature together with the soil moisture, which as a consequence further reduces errors in the soil moisture analysis. Based on these results, we conclude that we can merge the decoupled data assimilation cycles for the land surface and the atmosphere into one single cycle with hourly-like update steps.

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Influence of Assimilating Surface Observations on Numerical Prediction of Landfalls of Hurricane Katrina (2005) with an Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr-d-14-00014.1 ◽

2014 ◽

Vol 142 (8) ◽

pp. 2915-2934 ◽

Cited By ~ 12

Author(s):

Hailing Zhang ◽

Zhaoxia Pu

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Hurricane Katrina ◽

Ensemble Kalman Filter ◽

Surface Wind ◽

Initial Time ◽

Filter Method ◽

Low Level ◽

The Impact ◽

Surface Observations

Abstract A series of numerical experiments are conducted to examine the impact of surface observations on the prediction of landfalls of Hurricane Katrina (2005), one of the deadliest disasters in U.S. history. A specific initial time (0000 UTC 25 August 2005), which led to poor prediction of Hurricane Katrina in several previous studies, is selected to begin data assimilation experiments. Quick Scatterometer (QuikSCAT) ocean surface wind vectors and surface mesonet observations are assimilated with the minimum central sea level pressure and conventional observations from NCEP into an Advanced Research version of the Weather Research and Forecasting Model (WRF) using an ensemble Kalman filter method. Impacts of data assimilation on the analyses and forecasts of Katrina’s track, landfalling time and location, intensity, structure, and rainfall are evaluated. It is found that the assimilation of QuikSCAT and mesonet surface observations can improve prediction of the hurricane track and structure through modifying low-level thermal and dynamical fields such as wind, humidity, and temperature and enhancing low-level convergence and vorticity. However, assimilation of single-level surface observations alone does not ensure reasonable intensity forecasts because of the lack of constraint on the mid- to upper troposphere. When surface observations are assimilated with other conventional data, obvious enhancements are found in the forecasts of track and intensity, realistic convection, and surface wind structures. More importantly, surface data assimilation results in significant improvements in quantitative precipitation forecasts (QPFs) during landfalls.

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Using Precipitation Observations in a Mesoscale Short-Range Ensemble Analysis and Forecasting System

Weather and Forecasting ◽

10.1175/2007waf2006108.1 ◽

2008 ◽

Vol 23 (3) ◽

pp. 357-372 ◽

Cited By ~ 5

Author(s):

Tadashi Fujita ◽

David J. Stensrud ◽

David C. Dowell

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Grid Point ◽

Precipitation Data ◽

Simple Method ◽

Forecasting System ◽

Filter Approach ◽

Water Vapor Mixing Ratio ◽

Surface Observations

Abstract A simple method to assimilate precipitation data from a synthesis of radar and gauge data is developed to operate alongside an ensemble Kalman filter that assimilates hourly surface observations. The mesoscale ensemble forecast system consists of 25 members with 30-km grid spacing and incorporates variability in both initial and boundary conditions and model physical process schemes. The precipitation assimilation method only incorporates information on when and where rainfall is observed. Model temperature and water vapor mixing ratio profiles at each grid point are modified if rainfall is observed but not predicted, or if rainfall is predicted but not observed. These modifications act to either increase or decrease, respectively, the likelihood that precipitation develops at that grid point. Two cases are examined in which this technique is applied to assimilate precipitation data every 15 min from 1200 to 1800 UTC, while hourly surface observations are also assimilated at the same time using the more sophisticated ensemble Kalman filter approach. Results show that the simple method for assimilating precipitation data helps the model develop precipitation where it is observed, resulting in the precipitation area being reproduced more accurately than in the run without precipitation-data assimilation, while not negatively influencing the positive results from the surface data assimilation. Improvement is also seen in the reliability of precipitation probabilities for a 1 mm h−1 threshold after the assimilation period, indicating that assimilating precipitation data may provide improved forecasts of the mesoscale environment for a few hours.

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Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. Part I: Perfect Model Experiments

Monthly Weather Review ◽

10.1175/mwr3101.1 ◽

2006 ◽

Vol 134 (2) ◽

pp. 722-736 ◽

Cited By ~ 100

Author(s):

Fuqing Zhang ◽

Zhiyong Meng ◽

Altug Aksoy

Keyword(s):

Kalman Filter ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Mesoscale Model ◽

Regional Scale ◽

Model Assumption ◽

Filter Performance ◽

Perfect Model ◽

Scale Data ◽

Surface Observations

Abstract Through observing system simulation experiments, this two-part study exploits the potential of using the ensemble Kalman filter (EnKF) for mesoscale and regional-scale data assimilation. Part I focuses on the performance of the EnKF under the perfect model assumption in which the truth simulation is produced with the same model and same initial uncertainties as those of the ensemble, while Part II explores the impacts of model error and ensemble initiation on the filter performance. In this first part, the EnKF is implemented in a nonhydrostatic mesoscale model [the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5)] to assimilate simulated sounding and surface observations derived from simulations of the “surprise” snowstorm of January 2000. This is an explosive East Coast cyclogenesis event with strong error growth at all scales as a result of interactions between convective-, meso-, and subsynoptic-scale dynamics. It is found that the EnKF is very effective in keeping the analysis close to the truth simulation under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the control experiment, in which the truth simulation was produced with the same model and same initial uncertainties as those of the ensemble, a 24-h continuous EnKF assimilation of sounding and surface observations of typical temporal and spatial resolutions is found to reduce the error by as much as 80% (compared to a 24-h forecast without data assimilation) for both observed and unobserved variables including zonal and meridional winds, temperature, and pressure. However, it is observed to be relatively less efficient in correcting errors in the vertical velocity and moisture fields, which have stronger smaller-scale components. The analysis domain-averaged root-mean-square error after 24-h assimilation is ∼1.0–1.5 m s−1 for winds and ∼1.0 K for temperature, which is comparable to or less than typical observational errors. Various sensitivity experiments demonstrated that the EnKF is quite successful in all realistic observational scenarios tested. However, as will be presented in Part II, the EnKF performance may be significantly degraded if an imperfect forecast model is used, as is likely the case when real observations are assimilated.

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Storm-Relative Observations in Tropical Cyclone Data Assimilation with an Ensemble Kalman Filter

Monthly Weather Review ◽

10.1175/mwr-d-12-00094.1 ◽

2013 ◽

Vol 141 (2) ◽

pp. 506-522 ◽

Cited By ~ 17

Author(s):

Altuğ Aksoy

Keyword(s):

Spatial Distribution ◽

Kalman Filter ◽

Tropical Cyclone ◽

Data Assimilation ◽

Ensemble Kalman Filter ◽

Doppler Radar ◽

Positive Impact ◽

Research Division ◽

Wind Structure ◽

Relative Version

Abstract A storm-relative data assimilation method for tropical cyclones is introduced for the ensemble Kalman filter, using the Hurricane Weather Research and Forecasting (HWRF) Ensemble Data Assimilation System (HEDAS) developed at the Hurricane Research Division of the Atlantic Oceanographic and Meteorological Laboratory at the National Oceanic and Atmospheric Administration. The method entails translating tropical cyclone observations to storm-relative coordinates and requires the assumption of simultaneity of all observations. The observations are then randomly redistributed to assimilation cycles to achieve a more homogeneous spatial distribution. A proof-of-concept study is carried out in an observing system simulation experiment in which airborne Doppler radar radial wind observations are simulated from a higher-resolution (4.5/1.5 km) version of the same model. The results here are compared to the earth-relative version of HEDAS. When storm-relative observations are assimilated using the original HEDAS configuration, improvements are observed in the kinematic representation of the tropical cyclone vortex in analyses. The use of the storm-relative observations with a more homogeneous spatial distribution also reveals that a reduction of the covariance localization horizontal length scale by ½ to ~120 km provides the greatest incremental improvements. Potential positive impact is also seen in the slower cycle-to-cycle error growth. Spatially smoother analyses are obtained in the horizontal, and the evolution of the azimuthally averaged wind structure during short-range forecasts demonstrates better consistency with the nature run.

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