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 On the Impact of Unmanned Aerial System Observations on Numerical Weather Prediction in the Coastal Zone

2018 ◽  
Vol 146 (2) ◽  
pp. 599-622 ◽  
Author(s):  
David D. Flagg ◽  
James D. Doyle ◽  
Teddy R. Holt ◽  
Daniel P. Tyndall ◽  
Clark M. Amerault ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Research Laboratory ◽  
Weather Prediction ◽  
Ocean Model ◽  
Naval Research ◽  
Unmanned Aerial System ◽  
Numerical Weather ◽  
The Impact

Abstract The Trident Warrior observational field campaign conducted off the U.S. mid-Atlantic coast in July 2013 included the deployment of an unmanned aerial system (UAS) with several payloads on board for atmospheric and oceanic observation. These UAS observations, spanning seven flights over 5 days in the lowest 1550 m above mean sea level, were assimilated into a three-dimensional variational data assimilation (DA) system [the Naval Research Laboratory Atmospheric Variational Data Assimilation System (NAVDAS)] used to generate analyses for a numerical weather prediction model [the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS)] with a coupled ocean model [the Naval Research Laboratory Navy Coastal Ocean Model (NCOM)]. The impact of the assimilated UAS observations on short-term atmospheric prediction performance is evaluated and quantified. Observations collected from 50 radiosonde launches during the campaign adjacent to the UAS flight paths serve as model forecast verification. Experiments reveal a substantial reduction of model bias in forecast temperature and moisture profiles consistently throughout the campaign period due to the assimilation of UAS observations. The model error reduction is most substantial in the vicinity of the inversion at the top of the model-estimated boundary layer. Investigations reveal a consistent improvement to prediction of the vertical position, strength, and depth of the boundary layer inversion. The relative impact of UAS observations is explored further with experiments of systematic denial of data streams from the NAVDAS DA system and removal of individual measurement sources on the UAS platform.

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.

scholarly journals Background Error in WRF Model

2020 ◽  
Vol 16 ◽  
Keyword(s):  
Data Assimilation ◽  
Physical Properties ◽  
Covariance Matrix ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Wrf Model ◽  
Background Error ◽  
Numerical Weather ◽  
Error Covariance ◽  
First Time

WRF model have been tuned and tested over Georgia’s territory for years. First time in Georgia theprocess of data assimilation in Numerical weather prediction is developing. This work presents how forecasterror statistics appear in the data assimilation problem through the background error covariance matrix – B, wherethe variances and correlations associated with model forecasts are estimated. Results of modeling of backgrounderror covariance matrix for control variables using WRF model over Georgia with desired domain configurationare discussed and presented. The modeling was implemented in two different 3DVAR systems (WRFDA andGSI) and results were checked by pseudo observation benchmark cases using also default global and regional BEmatrixes. The mathematical and physical properties of the covariances are also reviewed.

scholarly journals Progress toward the Application of a Localized Particle Filter for Numerical Weather Prediction

2019 ◽  
Vol 147 (4) ◽  
pp. 1107-1126 ◽  
Author(s):  
Jonathan Poterjoy ◽  
Louis Wicker ◽  
Mark Buehner
Keyword(s):  
Data Assimilation ◽  
Particle Filter ◽  
Numerical Weather Prediction ◽  
Large Scale ◽  
Sequential Monte Carlo ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Systems ◽  
State Variables ◽  
Numerical Weather

Abstract A series of papers published recently by the first author introduce a nonlinear filter that operates effectively as a data assimilation method for large-scale geophysical applications. The method uses sequential Monte Carlo techniques adopted by particle filters, which make no parametric assumptions for the underlying prior and posterior error distributions. The filter also treats the underlying dynamical system as a set of loosely coupled systems to effectively localize the effect observations have on posterior state estimates. This property greatly reduces the number of particles—or ensemble members—required for its implementation. For these reasons, the method is called the local particle filter. The current manuscript summarizes algorithmic advances made to the local particle filter following recent tests performed over a hierarchy of dynamical systems. The revised filter uses modified vector weight calculations and probability mapping techniques from earlier studies, and new strategies for improving filter stability in situations where state variables are observed infrequently with very accurate measurements. Numerical experiments performed on low-dimensional data assimilation problems provide evidence that supports the theoretical benefits of the new improvements. As a proof of concept, the revised particle filter is also tested on a high-dimensional application from a real-time weather forecasting system at the NOAA/National Severe Storms Laboratory (NSSL). The proposed changes have large implications for researchers applying the local particle filter for real applications, such as data assimilation in numerical weather prediction models.

Physics, Resolution and Data Assimilation: Making sense of Greenland climate and ice sheet Surface Mass Balance with HARMONIE Climate

2021 ◽  
Author(s):  
Ruth Mottram ◽  
Oskar Landgren ◽  
Rasmus Anker Pedersen ◽  
Kristian Pagh Nielsen ◽  
Ole Bøssing Christensen ◽  
...  
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Climate Model ◽  
Weather Prediction ◽  
Ice Sheet ◽  
Greenland Ice Sheet ◽  
Surface Mass ◽  
Mass Budget ◽  
Numerical Weather

<p>The development of the HARMONIE model system has led to huge advances in numerical weather prediction, including over Greenland where a numerical weather prediction (NWP) model is used to forecast daily surface mass budget over the Greenland ice sheet as presented on polarportal.dk. The new high resolution Copernicus Arctic Reanalysis further developed the possibilities in HARMONIE with full 3DVar data assimilation and extended use of quality-controlled local observations. Here, we discuss the development and current status of the climate version of the HARMONIE Climate model (HCLIM). The HCLIM system has opened up the possibility for flexible use of the model at a range of spatial scales using different physical schemes including HARMONIE-AROME, ALADIN and ALARO for different spatial and temporal resolutions and assimilating observations, including satellite data on sea ice concentration from ESA CCI+, to improve hindcasts. However, the range of possibilities means that documenting the effects of different physics and parameterisation schemes is important before widespread application. </p><p>Here, we focus on HCLIM performance over the Greenland ice sheet, using observations to verify the different plausible set-ups and investigate biases in climate model outputs that affect the surface mass budget (SMB) of the Greenland ice sheet. </p><p>The recently funded Horizon 2020 project PolarRES will use the HCLIM model for very high resolution regional downscaling, together with other regional climate models in both Arctic and Antarctic regions, and our analysis thus helps to optimise the use of HCLIM in the polar regions for different modelling purposes.</p>

scholarly journals Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 587
Author(s):  
Magnus Lindskog ◽  
Tomas Landelius
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Numerical Weather ◽  
Extreme Precipitation Events ◽  
Surface Data ◽  
Enhanced Surface ◽  
Precipitation Events

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

scholarly journals Driftsonde Observations to Evaluate Numerical Weather Prediction of the Late 2006 African Monsoon

2013 ◽  
Vol 52 (4) ◽  
pp. 974-995 ◽  
Author(s):  
Philippe Drobinski ◽  
Fatima Karbou ◽  
Peter Bauer ◽  
Philippe Cocquerez ◽  
Christophe Lavaysse ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Vertical Structure ◽  
Weather Prediction ◽  
Meridional Wind ◽  
Numerical Weather Prediction Model ◽  
African Monsoon ◽  
Easterly Waves ◽  
Numerical Weather ◽  
Multidisciplinary Analysis

AbstractDuring the international African Monsoon Multidisciplinary Analysis (AMMA) project, stratospheric balloons carrying gondolas called driftsondes capable of dropping meteorological sondes were deployed over West Africa and the tropical Atlantic Ocean. The goals of the deployment were to test the technology and to study the African easterly waves, which are often the forerunners of hurricanes. Between 29 August and 22 September 2006, 124 sondes were dropped over the seven easterly waves that moved across Africa into the Atlantic between about 10° and 20°N, where almost no in situ vertical information exists. Conditions included waves that developed into Tropical Storm Florence and Hurricanes Gordon and Helene. In this study, a selection of numerical weather prediction model outputs has been compared with the dropsondes to assess the effect of some developments in data assimilation on the quality of analyses and forecasts. By comparing two different versions of the Action de Recherche Petite Echelle Grande Echelle (ARPEGE) model of Météo-France with the dropsondes, first the benefits of the last data assimilation updates are quantified. Then comparisons are carried out using the ARPEGE model and the Integrated Forecast System (IFS) model of the European Centre for Medium-Range Weather Forecasts. It is shown that the two models represent very well the vertical structure of temperature and humidity over both land and sea, and particularly within the Saharan air layer, which displays humidity below 5%–10%. Conversely, the models are less able to represent the vertical structure of the meridional wind. This problem seems to be common to ARPEGE and IFS, and its understanding still requires further investigations.

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