scholarly journals A Localized Adaptive Particle Filter within an Operational NWP Framework

2019 ◽  
Vol 147 (1) ◽  
pp. 345-362 ◽  
Author(s):  
Roland Potthast ◽  
Anne Walter ◽  
Andreas Rhodin
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Degrees Of Freedom ◽  
Grid Point ◽  
Forecast Model ◽  
Horizontal Resolution ◽  
Classical Particle ◽  
Mcmc Methods ◽  
Ensemble Transform Kalman Filter ◽  
Filter Divergence

Particle filters are well known in statistics. They have a long tradition in the framework of ensemble data assimilation (EDA) as well as Markov chain Monte Carlo (MCMC) methods. A key challenge today is to employ such methods in a high-dimensional environment, since the naïve application of the classical particle filter usually leads to filter divergence or filter collapse when applied within the very high dimension of many practical assimilation problems (known as the curse of dimensionality). The goal of this work is to develop a localized adaptive particle filter (LAPF), which follows closely the idea of the classical MCMC or bootstrap-type particle filter, but overcomes the problems of collapse and divergence based on localization in the spirit of the local ensemble transform Kalman filter (LETKF) and adaptivity with an adaptive Gaussian resampling or rejuvenation scheme in ensemble space. The particle filter has been implemented in the data assimilation system for the global forecast model ICON at Deutscher Wetterdienst (DWD). We carry out simulations over a period of 1 month with a global horizontal resolution of 52 km and 90 layers. With four variables analyzed per grid point, this leads to 6.6 × 106 degrees of freedom. The LAPF can be run stably and shows a reasonable performance. We compare its scores to the operational setup of the ICON LETKF.

scholarly journals Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

2017 ◽  
Author(s):  
Yun Liu ◽  
Eugenia Kalnay ◽  
Ning Zeng ◽  
Ghassem Asrar ◽  
Zhaohui Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Grid Point ◽  
Carbon Fluxes ◽  
Transfer Model ◽  
Observation Window ◽  
Surface Carbon ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform ◽  
Assimilation System

Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. [2011, 2012], who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as variable localization, and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the Running-in-Place (RIP) method used to accelerate the spin-up of EnKF data assimilation [Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014]. Like RIP, the new assimilation system uses the no-cost smoothing algorithm for the LETKF [Kalnay et al., 2007b], which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long observation window (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems.

scholarly journals Diagnosing Summertime Mesoscale Vertical Motion: Implications for Atmospheric Data Assimilation

2007 ◽  
Vol 135 (6) ◽  
pp. 2076-2094 ◽  
Author(s):  
Christian Pagé ◽  
Luc Fillion ◽  
Peter Zwack
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Control Variable ◽  
Vertical Motion ◽  
Forecast Model ◽  
Horizontal Resolution ◽  
Limited Area ◽  
Significant Information ◽  
Special Procedure ◽  
Atmospheric Data

Abstract Balance omega equations have recently been used to try to improve the characterization of balance in variational data assimilation schemes for numerical weather prediction (NWP). Results from Fisher and Fillion et al. indicate that a quasigeostrophic omega equation can be used adequately in the definition of the control variable to represent synoptic-scale balanced vertical motion. For high-resolution limited-area data assimilation and forecasting (1–10-km horizontal resolution), such a diagnostic equation for vertical motion needs to be revisited. Using a state-of-the-art NWP forecast model at 2.5-km horizontal resolution, these issues are examined. Starting from a complete diagnostic partial differential equation for omega, the rhs forcing terms were computed from model-generated fields. These include the streamfunction, temperature, and physical time tendencies of temperature in gridpoint space. To accurately compute one term of second-order importance (i.e., the ageostrophic vorticity tendency forcing term), a special procedure was used. With this procedure it is shown that Charney’s balance equation brings significant information in order to deduce the geostrophic time tendency term. Under these conditions, results show that for phenomena of length scales of 15–100 km over convective regions, a diagnostic equation can capture the major part of the model-generated vertical motion. The limitations of the digital filter initialization approach when used as in Fillion et al. with a cutoff period reduced to 1 h are also illustrated. The potential usefulness of this study for mesoscale atmospheric data assimilation is briefly discussed.

scholarly journals Evaluation of a Strategy for the Assimilation of Satellite Radiance Observations with the Local Ensemble Transform Kalman Filter

2011 ◽  
Vol 139 (6) ◽  
pp. 1932-1951 ◽  
Author(s):  
José A. Aravéquia ◽  
Istvan Szunyogh ◽  
Elana J. Fertig ◽  
Eugenia Kalnay ◽  
David Kuhl ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Grid Point ◽  
Earth Observing System ◽  
Wind Analysis ◽  
Correction Technique ◽  
Ensemble Transform Kalman Filter ◽  
Environmental Prediction ◽  
Observation Bias ◽  
Ensemble Transform

Abstract This paper evaluates a strategy for the assimilation of satellite radiance observations with the local ensemble transform Kalman filter (LETKF) data assimilation scheme. The assimilation strategy includes a mechanism to select the radiance observations that are assimilated at a given grid point and an ensemble-based observation bias-correction technique. Numerical experiments are carried out with a reduced (T62L28) resolution version of the model component of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS). The observations used for the evaluation of the assimilation strategy are AMSU-A level 1B brightness temperature data from the Earth Observing System (EOS) Aqua spacecraft. The assimilation of these observations, in addition to all operationally assimilated nonradiance observations, leads to a statistically significant improvement of both the temperature and wind analysis in the Southern Hemisphere. This result suggests that the LETKF, combined with the proposed data assimilation strategy for the assimilation of satellite radiance observations, can efficiently extract information from radiance observations.

scholarly journals A mechanism for catastrophic filter divergence in data assimilation for sparse observation networks

2013 ◽  
Vol 20 (5) ◽  
pp. 705-712 ◽  
Author(s):  
G. A. Gottwald ◽  
A. J. Majda
Keyword(s):  
Data Assimilation ◽  
Blow Up ◽  
Forecast Model ◽  
Numerical Instability ◽  
Dimensional Model ◽  
Numerical Instabilities ◽  
Five Dimensional Model ◽  
Observational Noise ◽  
Filter Divergence ◽  
Filtering Procedure

Abstract. We study catastrophic filter divergence in data assimilation procedures whereby the forecast model develops severe numerical instabilities leading to a blow-up of the solution. Catastrophic filter divergence can occur in sparse observational grids with small observational noise for intermediate observation intervals and finite ensemble sizes. Using a minimal five-dimensional model, we establish that catastrophic filter divergence is a numerical instability of the underlying forecast model caused by the filtering procedure producing analyses which are not consistent with the true dynamics, and stiffness caused by the fast attraction of the inconsistent analyses towards the attractor during the forecast step.

scholarly journals A local particle filter for high-dimensional geophysical systems

2016 ◽  
Vol 23 (6) ◽  
pp. 391-405 ◽  
Author(s):  
Stephen G. Penny ◽  
Takemasa Miyoshi
Keyword(s):  
Particle Filter ◽  
Grid Point ◽  
Computational Cost ◽  
Local Analysis ◽  
Observation Error ◽  
Space Localization ◽  
Highly Nonlinear ◽  
Importance Resampling ◽  
Non Gaussian ◽  
Filter Divergence

Abstract. A local particle filter (LPF) is introduced that outperforms traditional ensemble Kalman filters in highly nonlinear/non-Gaussian scenarios, both in accuracy and computational cost. The standard sampling importance resampling (SIR) particle filter is augmented with an observation-space localization approach, for which an independent analysis is computed locally at each grid point. The deterministic resampling approach of Kitagawa is adapted for application locally and combined with interpolation of the analysis weights to smooth the transition between neighboring points. Gaussian noise is applied with magnitude equal to the local analysis spread to prevent particle degeneracy while maintaining the estimate of the growing dynamical instabilities. The approach is validated against the local ensemble transform Kalman filter (LETKF) using the 40-variable Lorenz-96 (L96) model. The results show that (1) the accuracy of LPF surpasses LETKF as the forecast length increases (thus increasing the degree of nonlinearity), (2) the cost of LPF is significantly lower than LETKF as the ensemble size increases, and (3) LPF prevents filter divergence experienced by LETKF in cases with non-Gaussian observation error distributions.

scholarly journals Estimating Surface Carbon Fluxes Based on a Local Ensemble Transform Kalman Filter with a Short Assimilation Window and a Long Observation Window

2017 ◽  
Author(s):  
Yun Liu ◽  
Eugenia Kalnay ◽  
Ning Zeng ◽  
Ghassem Asrar ◽  
Zhaohui Chen ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Grid Point ◽  
Carbon Fluxes ◽  
Transfer Model ◽  
Observation Window ◽  
Surface Carbon ◽  
Ensemble Transform Kalman Filter ◽  
Ensemble Transform ◽  
Assimilation System

Abstract. We developed a Carbon data assimilation system to estimate the surface carbon fluxes using the Local Ensemble Transform Kalman Filter and atmospheric transfer model of GEOS-Chem driven by the MERRA-1 reanalysis of the meteorological fields based on the Goddard Earth Observing System Model, Version 5 (GEOS-5). This assimilation system is inspired by the method of Kang et al. (2011, 2012), who estimated the surface carbon fluxes in an Observing System Simulation Experiment (OSSE) mode, as evolving parameters in the assimilation of the atmospheric CO2, using a short assimilation window of 6 hours. They included the assimilation of the standard meteorological variables, so that the ensemble provided a measure of the uncertainty in the CO2 transport. After introducing new techniques such as “variable localization”, and increased observation weights near the surface, they obtained accurate carbon fluxes at grid point resolution. We developed a new version of the LETKF related to the “Running-in-Place” (RIP) method used to accelerate the spin-up of EnKF data assimilation (Kalnay and Yang, 2010; Wang et al., 2013, Yang et al., 2014). Like RIP, the new assimilation system uses the “no-cost smoothing” algorithm for the LETKF (Kalnay et al., 2007b), which allows shifting at no cost the Kalman Filter solution forward or backward within an assimilation window. In the new scheme a long “observation window” (e.g., 7-days or longer) is used to create an LETKF ensemble at 7-days. Then, the RIP smoother is used to obtain an accurate final analysis at 1-day. This analysis has the advantage of being based on a short assimilation window, which makes it more accurate, and of having been exposed to the future 7-days observations, which accelerates the spin up. The assimilation and observation windows are then shifted forward by one day, and the process is repeated. This reduces significantly the analysis error, suggesting that this method could be used in other data assimilation problems. The newly developed assimilation method can be used with other Earth system models, especially for greater use of observations in conjunction with models.

scholarly journals Ensemble Dispersion Simulation of a Point-Source Radioactive Aerosol Using Perturbed Meteorological Fields over Eastern Japan

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 662
Author(s):  
Tsuyoshi Thomas Sekiyama ◽  
Mizuo Kajino ◽  
Masaru Kunii
Keyword(s):  
Point Source ◽  
Nuclear Power ◽  
Weather Forecast ◽  
Forecast Model ◽  
Horizontal Resolution ◽  
Dispersion Modeling ◽  
Radioactive Aerosol ◽  
Ensemble Simulations ◽  
Ensemble Transform Kalman Filter ◽  
Aerosol Dispersion

We conducted single-model initial-perturbed ensemble simulations to quantify uncertainty in aerosol dispersion modeling, focusing on a point-source radioactive aerosol emitted from the Fukushima Daiichi Nuclear Power Plant (FDNPP) in March 2011. The ensembles of the meteorological variables were prepared using a data assimilation system that consisted of a non-hydrostatic weather-forecast model with a 3-km horizontal resolution and a four-dimensional local ensemble transform Kalman filter (4D-LETKF) with 20 ensemble members. The emission of radioactive aerosol was not perturbed. The weather and aerosol simulations were validated with in-situ measurements at Hitachi and Tokai, respectively, approximately 100 km south of the FDNPP. The ensemble simulations provided probabilistic information and multiple case scenarios for the radioactive aerosol plumes. Some of the ensemble members successfully reproduced the arrival time and intensity of the radioactive aerosol plumes, even when the deterministic simulation failed to reproduce them. We found that a small ensemble spread of wind speed produced large uncertainties in aerosol concentrations.

scholarly journals A Localized Particle Filter for High-Dimensional Nonlinear Systems

2015 ◽  
Vol 144 (1) ◽  
pp. 59-76 ◽  
Author(s):  
Jonathan Poterjoy
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Remotely Sensed Data ◽  
Ensemble Kalman Filters ◽  
New Approach ◽  
Monte Carlo Approximation ◽  
Non Gaussian ◽  
Filter Divergence ◽  
Number Of Particles ◽  
Observation Networks

Abstract This paper presents a new data assimilation approach based on the particle filter (PF) that has potential for nonlinear/non-Gaussian applications in geoscience. Particle filters provide a Monte Carlo approximation of a system’s probability density, while making no assumptions regarding the underlying error distribution. The proposed method is similar to the PF in that particles—also referred to as ensemble members—are weighted based on the likelihood of observations in order to approximate posterior probabilities of the system state. The new approach, denoted the local PF, extends the particle weights into vector quantities to reduce the influence of distant observations on the weight calculations via a localization function. While the number of particles required for standard PFs scales exponentially with the dimension of the system, the local PF provides accurate results using relatively few particles. In sensitivity experiments performed with a 40-variable dynamical system, the local PF requires only five particles to prevent filter divergence for both dense and sparse observation networks. Comparisons of the local PF and ensemble Kalman filters (EnKFs) reveal advantages of the new method in situations resembling geophysical data assimilation applications. In particular, the new filter demonstrates substantial benefits over EnKFs when observation networks consist of densely spaced measurements that relate nonlinearly to the model state—analogous to remotely sensed data used frequently in weather analyses.

scholarly journals Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

2021 ◽  
Vol 13 (11) ◽  
pp. 2103
Author(s):  
Yuchen Liu ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu ◽  
Wei Wang
Keyword(s):  
Data Assimilation ◽  
Northern China ◽  
Horizontal Resolution ◽  
Radar Reflectivity ◽  
Small Scale ◽  
Time Interval ◽  
Outer Domain ◽  
Storm Prediction ◽  
The Impact ◽  
Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

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