scholarly journals Impact of Data Assimilation on Forecasting Convection over the United Kingdom Using a High-Resolution Version of the Met Office Unified Model

2009 ◽  
Vol 137 (5) ◽  
pp. 1562-1584 ◽  
Author(s):  
Mark Dixon ◽  
Zhihong Li ◽  
Humphrey Lean ◽  
Nigel Roberts ◽  
Sue Ballard
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Unified Model ◽  
High Resolution Data ◽  
Surface Precipitation ◽  
Analysis Scheme ◽  
Satellite Radar ◽  
The Individual ◽  
Grid Length

Abstract A high-resolution data assimilation system has been implemented and tested within a 4-km grid length version of the Met Office Unified Model (UM). A variational analysis scheme is used to correct larger scales using conventional observation types. The system uses two nudging procedures to assimilate high-resolution information: radar-derived surface precipitation rates are assimilated via latent heat nudging (LHN), while cloud nudging (CN) is used to assimilate moisture fields derived from satellite, radar, and surface observations. The data assimilation scheme was tested on five convection-dominated case studies from the Convective Storm Initiation Project (CSIP). Model skill was assessed statistically using radar-derived surface-precipitation hourly accumulations via a scale-dependent verification scheme. Data assimilation is shown to have a dramatic impact on skill during both the assimilation and subsequent forecast periods on nowcasting time scales. The resulting forecasts are also shown to be much more skillful than those produced using either a 12-km grid length version of the UM with data assimilation in place, or a 4-km grid-length UM version run using a 12-km state as initial conditions. The individual contribution to overall skill attributable to each data-assimilation component is investigated. Up until T + 3 h, LHN has the greatest impact on skill. For later times, VAR, LHN, and CN contribute equally to the skill in predicting the spatial distribution of the heaviest rainfall; while VAR alone accounts for the skill in depicting distributions corresponding to lower accumulation thresholds. The 4-km forecasts tend to overpredict both the intensity and areal coverage of storms. While it is likely that the intensity bias is partially attributable to model error, both VAR and LHN clearly contribute to the overestimation of the areal extent. Future developments that may mitigate this problem are suggested.

scholarly journals Recent Progress of the NHM-4DVAR towards a Super-High Resolution Data Assimilation

SOLA ◽  
2014 ◽  
Vol 10 (0) ◽  
pp. 145-149 ◽  
Author(s):  
Takuya Kawabata ◽  
Kosuke Ito ◽  
Kazuo Saito
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Recent Progress ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals Estimating cardiac contraction through high resolution data assimilation of a personalized mechanical model

2018 ◽  
Vol 24 ◽  
pp. 85-90 ◽  
Author(s):  
Henrik Finsberg ◽  
Gabriel Balaban ◽  
Stian Ross ◽  
Trine F. Håland ◽  
Hans Henrik Odland ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Mechanical Model ◽  
Cardiac Contraction ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals A Potential Density Gradient Dependent Analysis Scheme for Ocean Multiscale Data Assimilation

2017 ◽  
Vol 2017 ◽  
pp. 1-13
Author(s):  
Hongli Fu ◽  
Jinkun Yang ◽  
Wei Li ◽  
Xinrong Wu ◽  
Guijun Han ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Density Gradient ◽  
Initial Conditions ◽  
Ocean Model ◽  
Potential Density ◽  
3 Dimensional ◽  
Ocean Data Assimilation ◽  
Variational Framework ◽  
Analysis Scheme ◽  
Density Surface

This study addresses how to maintain oceanic mixing along potential density surface in ocean data assimilation (ODA). It is well known that the oceanic mixing across the potential density surface is much weaker than that along the potential density surface. However, traditional ODA schemes allow the mixing across the potential density surface and thus may result in extra assimilation errors. Here, a new ODA scheme that uses potential density gradient information of the model background to rescale observational adjustment is designed to improve the quality of assimilation. The new scheme has been tested using a regional ocean model within a multiscale 3-dimensional variational framework. Results show that the new scheme effectively prevents the excessive unphysical projection of observational information in the direction across potential density surface and thus improves assimilation quality greatly. Forecast experiments also show that the new scheme significantly improves the model forecast skills through providing more dynamically consistent initial conditions

scholarly journals Towards high-resolution climate reconstruction using an off-line data assimilation and COSMO-CLM 5.00 model

2018 ◽  
Vol 14 (9) ◽  
pp. 1345-1360
Author(s):  
Bijan Fallah ◽  
Emmanuele Russo ◽  
Walter Acevedo ◽  
Achille Mauri ◽  
Nico Becker ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Climate Models ◽  
Climate Model ◽  
Chaotic Behavior ◽  
Initial Conditions ◽  
Estimation Error ◽  
Time Step ◽  
Proxy Records ◽  
Geographical Regions

Abstract. Data assimilation (DA) methods have been used recently to constrain the climate model forecasts by paleo-proxy records. Both DA and climate models are computationally very expensive. Moreover, in paleo-DA, the time step of consequence for observations is usually too long for a dynamical model to follow the previous analysis state and the chaotic behavior of the model becomes dominant. The majority of recent paleoclimate studies using DA have performed low- or intermediate-resolution global simulations along with an “off-line” DA approach. In an off-line DA, the re-initialization cycle is completely removed after the assimilation step. In this paper, we design a computationally affordable DA to assimilate yearly pseudo-observations and real observations into an ensemble of COSMO-CLM high-resolution regional climate model (RCM) simulations over Europe, for which the ensemble members slightly differ in boundary and initial conditions. Within a perfect model experiment, the performance of the applied DA scheme is evaluated with respect to its sensitivity to the noise levels of pseudo-observations. It was observed that the injected bias in the pseudo-observations linearly impacts the DA skill. Such experiments can serve as a tool for the selection of proxy records, which can potentially reduce the state estimation error when they are assimilated. Additionally, the sensitivity of COSMO-CLM to the boundary conditions is addressed. The geographical regions where the model exhibits high internal variability are identified. Two sets of experiments are conducted by averaging the observations over summer and winter. Furthermore, the effect of the spurious correlations within the observation space is studied and a optimal correlation radius, within which the observations are assumed to be correlated, is detected. Finally, the pollen-based reconstructed quantities at the mid-Holocene are assimilated into the RCM and the performance is evaluated against a test dataset. We conclude that the DA approach is a promising tool for creating high-resolution yearly analysis quantities. The affordable DA method can be applied to efficiently improve climate field reconstruction efforts by combining high-resolution paleoclimate simulations and the available proxy records.

scholarly journals High-resolution data assimilation of cardiac mechanics applied to a dyssynchronous ventricle

2017 ◽  
Vol 33 (11) ◽  
pp. e2863 ◽  
Author(s):  
Gabriel Balaban ◽  
Henrik Finsberg ◽  
Hans Henrik  Odland ◽  
Marie E. Rognes ◽  
Stian Ross ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Cardiac Mechanics ◽  
High Resolution Data ◽  
Resolution Data

scholarly journals Assimilation of GOES-13 Imager Clear-Sky Water Vapor (6.5 μm) Radiances into a Warn-on-Forecast System

2018 ◽  
Vol 146 (4) ◽  
pp. 1077-1107 ◽  
Author(s):  
Thomas A. Jones ◽  
Xuguang Wang ◽  
Patrick Skinner ◽  
Aaron Johnson ◽  
Yongming Wang
Keyword(s):  
Water Vapor ◽  
High Resolution ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Positive Impact ◽  
Horizontal Resolution ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Tropospheric Temperature ◽  
Clear Sky

A prototype convection-allowing system using the Advanced Research version of the Weather Research and Forecasting (WRF-ARW) Model and employing an ensemble Kalman filter (EnKF) data assimilation technique has been developed and used during the spring 2016 and 2017 Hazardous Weather Testbeds. This system assimilates WSR-88D reflectivity and radial velocity, geostationary satellite cloud water path (CWP) retrievals, and available surface observations over a regional domain with a 3-km horizontal resolution at 15-min intervals, with 3-km initial conditions provided by an experimental High-Resolution Rapid Refresh ensemble (HRRR-e). However, no information on upper-level thermodynamic conditions in cloud-free regions is currently assimilated, as few timely observations exist. One potential solution is to also assimilate clear-sky satellite radiances, which provide information on mid- and upper-tropospheric temperature and moisture conditions. This research assimilates GOES-13 imager water vapor band (6.5 μm) radiances using the GSI-EnKF system to take advantage of the Community Radiative Transfer Model (CRTM) integration. Results using four cases from May 2016 showed that assimilating radiances generally had a neutral-to-positive impact on the model analysis, reducing humidity bias and/or errors at the appropriate model levels where verification observations were present. The effects on high-impact weather forecasts, as verified against forecast reflectivity and updraft helicity, were mixed. Three cases (9, 22, and 24 May) showed some improvement in skill, while the other (25 May) performed worse, despite the improved environment. This research represents the first step in designing a high-resolution ensemble data assimilation system to use GOES-16 Advanced Baseline Imager data, which provides additional water vapor bands and increased spatial and temporal resolution.

scholarly journals The Impact of Data Assimilation Length Scales on Analysis and Prediction of Convective Storms

2014 ◽  
Vol 142 (10) ◽  
pp. 3781-3808 ◽  
Author(s):  
Heiner Lange ◽  
George C. Craig
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Length Scales ◽  
Localization Radius ◽  
Filter Convergence ◽  
Analysis Scheme ◽  
High Resolution Analysis ◽  
Resolution Analysis ◽  
Set Up ◽  
The Impact

Abstract An idealized convective test bed for the local ensemble transform Kalman filter (LETKF) is set up to perform storm-scale data assimilation of simulated Doppler radar observations. Convective systems with lifetimes exceeding 6 h are triggered in a doubly periodic domain. Perfect-model experiments are used to investigate the limited predictability in precipitation forecasts by comparing analysis schemes that resolve different length scales. Starting from a high-resolution reference scheme with 8-km covariance localization and observations with 2-km resolution on a 5-min cycle, an experimental hierarchy is set up by successively choosing a larger covariance localization radius of 32 km, observations that are horizontally averaged by a factor of 4, a coarser resolution in the calculation of the analysis weights, and a cycling interval of 20 min. After 3 h of assimilation, the high-resolution analysis scheme is clearly superior to the configurations with coarser scales in terms of RMS error and field-oriented measures. The difference is associated with the observation resolution and a larger localization radius required for filter convergence with coarse observations. The high-resolution analysis leads to better forecasts for the first hour, but after 3 hours, the forecast quality of the schemes is indistinguishable. The more rapid error growth in forecasts from the high-resolution analysis appears to be associated with a limited predictability of the small scales, but also with gravity wave noise and spurious convective cells. The latter suggests that the field is in some sense less balanced, or less consistent with the model dynamics, than in the coarser-resolution analysis.

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