scholarly journals Performance evaluation of throughput-aware framework for ensemble data assimilation: The case of NICAM-LETKF

2016 ◽  
Author(s):  
H. Yashiro ◽  
K. Terasaki ◽  
T. Miyoshi ◽  
H. Tomita
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Weather Prediction ◽  
File Systems ◽  
Atmospheric Model ◽  
Distributed File Systems ◽  
Ensemble Data Assimilation ◽  
Computational Performance ◽  
Ensemble Data ◽  
Data Throughput

Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a Local Ensemble Transform Kalman Filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

scholarly journals Performance evaluation of a throughput-aware framework for ensemble data assimilation: the case of NICAM-LETKF

2016 ◽  
Vol 9 (7) ◽  
pp. 2293-2300 ◽  
Author(s):  
Hisashi Yashiro ◽  
Koji Terasaki ◽  
Takemasa Miyoshi ◽  
Hirofumi Tomita
Keyword(s):  
Data Assimilation ◽  
High Performance ◽  
Weather Prediction ◽  
File Systems ◽  
Atmospheric Model ◽  
Distributed File Systems ◽  
Ensemble Data Assimilation ◽  
Computational Performance ◽  
Ensemble Data ◽  
Data Throughput

Abstract. In this paper, we propose the design and implementation of an ensemble data assimilation (DA) framework for weather prediction at a high resolution and with a large ensemble size. We consider the deployment of this framework on the data throughput of file input/output (I/O) and multi-node communication. As an instance of the application of the proposed framework, a local ensemble transform Kalman filter (LETKF) was used with a Non-hydrostatic Icosahedral Atmospheric Model (NICAM) for the DA system. Benchmark tests were performed using the K computer, a massive parallel supercomputer with distributed file systems. The results showed an improvement in total time required for the workflow as well as satisfactory scalability of up to 10 K nodes (80 K cores). With regard to high-performance computing systems, where data throughput performance increases at a slower rate than computational performance, our new framework for ensemble DA systems promises drastic reduction of total execution time.

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

Ensemble Data Assimilation to Characterize Surface-Layer Errors in Numerical Weather Prediction Models

2013 ◽  
Vol 141 (6) ◽  
pp. 1804-1821 ◽  
Author(s):  
J. P. Hacker ◽  
W. M. Angevine
Keyword(s):  
Surface Layer ◽  
Data Assimilation ◽  
Atmospheric Surface Layer ◽  
Weather Prediction ◽  
Model Error ◽  
Time Of Day ◽  
Turbulent Heat ◽  
Model Errors ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

Abstract Experiments with the single-column implementation of the Weather Research and Forecasting Model provide a basis for deducing land–atmosphere coupling errors in the model. Coupling occurs both through heat and moisture fluxes through the land–atmosphere interface and roughness sublayer, and turbulent heat, moisture, and momentum fluxes through the atmospheric surface layer. This work primarily addresses the turbulent fluxes, which are parameterized following the Monin–Obukhov similarity theory applied to the atmospheric surface layer. By combining ensemble data assimilation and parameter estimation, the model error can be characterized. Ensemble data assimilation of 2-m temperature and water vapor mixing ratio, and 10-m wind components, forces the model to follow observations during a month-long simulation for a column over the well-instrumented Atmospheric Radiation Measurement (ARM) Central Facility near Lamont, Oklahoma. One-hour errors in predicted observations are systematically small but nonzero, and the systematic errors measure bias as a function of local time of day. Analysis increments for state elements nearby (15 m AGL) can be too small or have the wrong sign, indicating systematically biased covariances and model error. Experiments using the ensemble filter to objectively estimate a parameter controlling the thermal land–atmosphere coupling show that the parameter adapts to offset the model errors, but that the errors cannot be eliminated. Results suggest either structural errors or further parametric errors that may be difficult to estimate. Experiments omitting atypical observations such as soil and flux measurements lead to qualitatively similar deductions, showing the potential for assimilating common in situ observations as an inexpensive framework for deducing and isolating model errors.

Big Ensemble Data Assimilation in Numerical Weather Prediction

Computer ◽  
2015 ◽  
Vol 48 (11) ◽  
pp. 15-21 ◽  
Author(s):  
Takemasa Miyoshi ◽  
Keiichi Kondo ◽  
Koji Terasaki
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Ensemble Data Assimilation ◽  
Ensemble Data

scholarly journals A Case Study on the Impact of Ensemble Data Assimilation with GNSS-Zenith Total Delay and Radar Data on Heavy Rainfall Prediction

2020 ◽  
Vol 148 (3) ◽  
pp. 1075-1098 ◽  
Author(s):  
Shu-Chih Yang ◽  
Zih-Mao Huang ◽  
Ching-Yuang Huang ◽  
Chih-Chien Tsai ◽  
Ta-Kang Yeh
Keyword(s):  
Data Assimilation ◽  
Heavy Rainfall ◽  
Weather Prediction ◽  
Radar Data ◽  
Total Delay ◽  
Ensemble Data Assimilation ◽  
Zenith Total Delay ◽  
Ensemble Data ◽  
Convective Scale ◽  
The Impact

Abstract The performance of a numerical weather prediction model using convective-scale ensemble data assimilation with ground-based global navigation satellite systems-zenith total delay (ZTD) and radar data is investigated on a heavy rainfall event that occurred in Taiwan on 10 June 2012. The assimilation of ZTD and/or radar data is performed using the framework of the WRF local ensemble transform Kalman filter with a model grid spacing of 2 km. Assimilating radar data is beneficial for predicting the rainfall intensity of this local event but produces overprediction in southern Taiwan and underprediction in central Taiwan during the first 3 h. Both errors are largely overcome by assimilating ZTD data to improve mesoconvective-scale moisture analyses. Consequently, assimilating both the ZTD and radar data show advantages in terms of the location and intensity of the heavy rainfall. Sensitivity experiments involving this event indicate that the impact of ZTD data is improved by using a broader horizontal localization scale than the convective scale used for radar data assimilation. This optimization is necessary in order to consider more fully the network density of the ZTD observations and the horizontal scale of the moisture transport by the southwesterly flow in this case.

Assimilation of Precipitation-Affected Radiances in a Cloud-Resolving WRF Ensemble Data Assimilation System

2013 ◽  
Vol 141 (2) ◽  
pp. 754-772 ◽  
Author(s):  
Sara Q. Zhang ◽  
Milija Zupanski ◽  
Arthur Y. Hou ◽  
Xin Lin ◽  
Samson H. Cheung
Keyword(s):  
Data Assimilation ◽  
Prediction Models ◽  
Weather Prediction ◽  
Rain Event ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Cross Covariance ◽  
Data Assimilation System ◽  
Assimilation System

Abstract Assimilation of remotely sensed precipitation observations into numerical weather prediction models can improve precipitation forecasts and extend prediction capabilities in hydrological applications. This paper presents a new regional ensemble data assimilation system that assimilates precipitation-affected microwave radiances into the Weather Research and Forecasting Model (WRF). To meet the challenges in satellite data assimilation involving cloud and precipitation processes, hydrometeors produced by the cloud-resolving model are included as control variables and ensemble forecasts are used to estimate flow-dependent background error covariance. Two assimilation experiments have been conducted using precipitation-affected radiances from passive microwave sensors: one for a tropical storm after landfall and the other for a heavy rain event in the southeastern United States. The experiments examined the propagation of information in observed radiances via flow-dependent background error auto- and cross covariance, as well as the error statistics of observational radiance. The results show that ensemble assimilation of precipitation-affected radiances improves the quality of precipitation analyses in terms of spatial distribution and intensity in accumulated surface rainfall, as verified by independent ground-based precipitation observations.

scholarly journals Model Error Representation Using the Stochastically Perturbed Hybrid Physical–Dynamical Tendencies in Ensemble Data Assimilation System

2020 ◽  
Vol 10 (24) ◽  
pp. 9010
Author(s):  
Sujeong Lim ◽  
Myung-Seo Koo ◽  
In-Hyuk Kwon ◽  
Seon Ki Park
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Stochastic Perturbation ◽  
Numerical Weather Prediction Model ◽  
Natural State ◽  
Model Errors ◽  
Background Error ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
Stochastically Perturbed

Ensemble data assimilation systems generally suffer from underestimated background error covariance that leads to a filter divergence problem—the analysis diverges from the natural state by ignoring the observation influence due to the diminished estimation of model uncertainty. To alleviate this problem, we have developed and implemented the stochastically perturbed hybrid physical–dynamical tendencies to the local ensemble transform Kalman filter in a global numerical weather prediction model—the Korean Integrated Model (KIM). This approach accounts for the model errors associated with computational representations of underlying partial differential equations and the imperfect physical parameterizations. The new stochastic perturbation hybrid tendencies scheme generally improved the background error covariances in regions where the ensemble spread was not sufficiently expressed by the control experiment that used an additive inflation and the relaxation to prior spread method.

Initiation of ensemble data assimilation

2006 ◽  
Author(s):  
M. Zupanski ◽  
S. J. Fletcher ◽  
I. M. Navon ◽  
B. Uzunoglu ◽  
R. P. Heikes ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ensemble Data Assimilation ◽  
Ensemble Data
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