Optimal control data assimilation with an atmospheric model

1997 ◽  
Vol 18 (7-8) ◽  
pp. 691-722
Author(s):  
Ch.-H. Bruneau ◽  
P. Fabrie ◽  
F. Veersé
Keyword(s):  
Optimal Control ◽  
Data Assimilation ◽  
Atmospheric Model ◽  
Control Data

Integral Control Data Assimilation in Wave Predictions

1997 ◽  
Author(s):  
L.H. Holthuijsen ◽  
N. Booij ◽  
M. van Endt ◽  
S. Caires ◽  
C. Guedes Soares
Keyword(s):  
Data Assimilation ◽  
Control Data ◽  
Integral Control

Development of a Coupled Subseasonal-to-Seasonal Prediction Model Using Community-based Unified Forecast System for NCEP Operations

2021 ◽  
Author(s):  
Yan Xue ◽  
Dorothy Koch ◽  
Vijay Tallapragada ◽  
Avichal Mehra ◽  
Fanglin Yang ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Online Survey ◽  
Ocean Waves ◽  
Atmospheric Model ◽  
Seasonal Forecasts ◽  
Forecast System ◽  
Community Based ◽  
Medium Range Weather Forecast ◽  
Model Components

<p>The Unified Forecast System (UFS) is a community-based coupled Earth modeling system, designed to support the Weather Enterprise and also be the source system for NOAA’s operations. NOAA’s Unified Forecast System Research to Operations Project (UFS-R2O) aims to develop the next generation coupled Global Forecast System (GFS v17)/Global Ensemble Forecast System (GEFS v13) targeting operational implementation in FY24. The Project is part of the larger UFS community and includes scientists from NOAA Labs and Centers, NCAR, UCAR, NRL and several U.S. universities.</p><p>The UFS is targeted to be a six-way coupled Earth prediction system, consisting of the FV3 dynamical core with the Common Community Physics Package (CCPP) for the atmosphere,  MOM6 for the ocean, CICE6 for the sea ice, WW3 for ocean waves, Noah-MP for the land surface and GOCART for aerosols.  Currently, four of the six model components have been coupled using the Community Mediator for Earth Prediction Systems (CMEPS). All the components of the coupled system will be initialized with a weakly coupled data assimilation system based on the Joint Effort for Data Assimilation Integration (JEDI) framework. A 30-year coupled reanalysis and reforecast will be conducted for model calibration and post-processing forecast products. The UFS is the basis for the future updates of the deterministic GFS medium-range weather forecast up to 16 days, the ensemble GEFS subseasonal forecast up to 45 days, and the seasonal forecasts up to one year using the new Seasonal Forecast System (SFS) planned to replace the operational Climate Forecast System (CFSv2).</p><p>Several prototypes of a four-way coupled atmosphere-ocean-ice-wave model have been built and tested with a C384 horizontal grid (~25km) and 64 vertical levels for the atmospheric model, and a ¼ degree tripolar grid for the ocean and ice model components. The presentation will highlight the results of these prototype runs. The UFS-R2O Project has made the latest UFS prototype (S2Sp5) output available on Amazon Web Services (AWS). Researchers interested in the S2S prediction and model development are invited to evaluate the UFS S2Sp5 data. Analysis of the data may include process-based evaluations, diagnostic measures that reveal coupled feedback processes, model biases and S2S forecast skill estimations. To identify and prioritize key metrics in evaluating the UFS applications, the UFS-R2O Project is soliciting community inputs through a online survey and UFS Evaluation Metric Workshop in Feb 2021. The metrics will be incorporated into the METplus verification tools for both research and operation. </p><p>A few more prototypes are planned beyond S2Sp5 which include increasing the vertical resolution of the atmospheric model to 127 vertical levels, the transition of land model from Noah to Noah-MP, inclusion of aerosol component, advanced physics suites as well as stochastic physics parameterizations to account for uncertainties in each model component. Coarser and higher resolution configurations along with coupled ensemble prototypes are also being built in order to evaluate the resolution-dependence of forecast biases and to assess the benefit vs cost of higher resolution. The development code is available on Github, and the UFS community contributes to the development through a R2O process.</p>

Analysis of Soil Moisture Changes in Europe during a Single Growing Season in a New ECMWF Soil Moisture Assimilation System

2008 ◽  
Vol 9 (1) ◽  
pp. 116-131 ◽  
Author(s):  
Bart van den Hurk ◽  
Janneke Ettema ◽  
Pedro Viterbo
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Land Surface Model ◽  
Growing Season ◽  
Atmospheric Model ◽  
Surface Model ◽  
Soil Moisture Data ◽  
The Impact ◽  
Assimilation System

Abstract This study aims at stimulating the development of soil moisture data assimilation systems in a direction where they can provide both the necessary control of slow drift in operational NWP applications and support the physical insight in the performance of the land surface component. It addresses four topics concerning the systematic nature of soil moisture data assimilation experiments over Europe during the growing season of 2000 involving the European Centre for Medium-Range Weather Forecasts (ECMWF) model infrastructure. In the first topic the effect of the (spinup related) bias in 40-yr ECMWF Re-Analysis (ERA-40) precipitation on the data assimilation is analyzed. From results averaged over 36 European locations, it appears that about half of the soil moisture increments in the 2000 growing season are attributable to the precipitation bias. A second topic considers a new soil moisture data assimilation system, demonstrated in a coupled single-column model (SCM) setup, where precipitation and radiation are derived from observations instead of from atmospheric model fields. For many of the considered locations in this new system, the accumulated soil moisture increments still exceed the interannual variability estimated from a multiyear offline land surface model run. A third topic examines the soil water budget in response to these systematic increments. For a number of Mediterranean locations the increments successfully increase the surface evaporation, as is expected from the fact that atmospheric moisture deficit information is the key driver of soil moisture adjustment. In many other locations, however, evaporation is constrained by the experimental SCM setup and is hardly affected by the data assimilation. Instead, a major portion of the increments eventually leave the soil as runoff. In the fourth topic observed evaporation is used to evaluate the impact of the data assimilation on the forecast quality. In most cases, the difference between the control and data assimilation runs is considerably smaller than the (positive) difference between any of the simulations and the observations.

scholarly journals Land-atmosphere interactions in an high resolution atmospheric simulation coupled with a surface data assimilation scheme

2009 ◽  
Vol 9 (5) ◽  
pp. 1613-1624 ◽  
Author(s):  
L. Campo ◽  
F. Castelli ◽  
D. Entekhabi ◽  
F. Caparrini
Keyword(s):  
Energy Balance ◽  
Data Assimilation ◽  
Surface Energy ◽  
Energy Budget ◽  
Land Surface ◽  
Surface States ◽  
Atmospheric Model ◽  
Turbulent Fluxes ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

Abstract. A valid tool for the retrieving of the turbulent fluxes that characterize the surface energy budget is constituted by the remote sensing of land surface states. In this study sequences of satellite-derived observations (from SEVIRI sensors aboard the Meteosat Second Generation) of Land Surface Temperature have been used as input in a data assimilation scheme in order to retrieve parameters that describe energy balance at the ground surface in the Tuscany region, in central Italy, during summer 2005. A parsimonious 1-D multiscale variational assimilation procedure has been followed, that requires also near surface meteorological observations. A simplified model of the surface energy balance that includes such assimilation scheme has been coupled with the limited area atmospheric model RAMS, in order to improve in the latter the accuracy of the energy budget at the surface. The coupling has been realized replacing the assimilation scheme products, in terms of surface turbulent fluxes and temperature and humidity states during the meteorological simulation. Comparisons between meteorological model results with and without coupling with the assimilation scheme are discussed, both in terms of reconstruction of surface variables and of vertical characterization of the lower atmosphere. In particular, the effects of the coupling on the moisture feedback between surface and atmosphere are considered and estimates of the precipitation recycling ratio are provided. The results of the coupling experiment showed improvements in the reconstruction of the surface states by the atmospheric model and considerable influence on the atmospheric dynamics.

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 A Fuzzy-Logic-Based Covariance Localization Method in Data Assimilation

Atmosphere ◽  
2020 ◽  
Vol 11 (10) ◽  
pp. 1055
Author(s):  
Yulong Bai ◽  
Xiaoyan Ma ◽  
Lin Ding
Keyword(s):  
Fuzzy Logic ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Atmospheric Model ◽  
Localization Method ◽  
Error Covariance ◽  
Logic Control ◽  
Power Spectral ◽  
Performance Evaluation Index ◽  
Lorenz 96

In ensemble data assimilation systems, the impracticalities of full sampling and systematic error often lead to spurious correlations between two variables with low actual correlations. To solve these problems, researchers have previously proposed a covariance localization (CL) method, which mainly involves the Schur product between a state error covariance matrix and a distance-based correlation matrix. Although this CL method can reduce spurious correlations to a certain extent, observational data remain difficult to be used effectively, which results in unreasonable assimilation. In this study, we develop a new CL method coupled with a fuzzy logic control algorithm, which we call the covariance fuzzy (CF) method. The proposed CF method is a distance-based localization method with “fuzzy” vanishing correlations in data assimilation (DA) systems. To verify the effectiveness of the new algorithm, we conducted a set of experiments using an ensemble Kalman filter (EnKF) that combines the nonlinear Lorenz-96 model or the quasi-geostrophic (QG) models. First, the performances of the CL and CF methods are discussed with respect to different strength forcings, ensemble sizes, and covariance inflation factors. The experimental results show that the proposed CF method can obtain a more effective observation weight than the CL method and can reduce the errors caused by spurious correlations. Additionally, using power spectral density (PSD) as a performance evaluation index, the robustness of the proposed fuzzy logic localization method is demonstrated. However, the application of the fuzzy logic-based localization methodology to a real atmospheric model remains to be tested.

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