Improving solar wind forecasts using data assimilation

2020 ◽  
Author(s):  
Matthew Lang ◽  
Mathew Owens ◽  
Amos Lawless
Keyword(s):  
Solar Wind ◽  
Data Assimilation ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Solar Wind Speed ◽  
Great Success ◽  
Near Earth Space ◽  
Data Assimilation Scheme ◽  
Assimilation Scheme

<p>Data assimilation has been used in Numerical Weather Prediction models with great success, and it can be seen that the improvement of data assimilation methods has gone hand-in-hand with improvements in weather forecasting skill. The implementation of data assimilation for solar wind forecasting is still in its infancy and is still underused in the field. Hence, it is important to investigate the optimal implementation of these methods to improve our understanding of the solar wind.</p><p>To do this, we have generated a variational data assimilation scheme for use with a steady-state solar wind speed model based upon the Burger equation. This relatively simple scheme has the advantage of updating the inner-boundary conditions of the solar wind model allowing the updates to persist and improve the solar wind estimates throughout the whole domain.</p><p>To this effect, we present numerical experiments using our data assimilation scheme with STEREO and ACE data to improve estimates and forecasts of the solar wind in near-Earth space. Particular focus will be applied to assimilating data when the satellites are 60 degrees apart, such that they simulate Earth-L5 forecasting scenarios.</p>

scholarly journals Improving solar wind forecasting using Data Assimilation

2021 ◽  
Author(s):  
Matthew Lang ◽  
Jake Witherington ◽  
Harriet Turner ◽  
Mathew Owens ◽  
Pete Riley
Keyword(s):  
Solar Wind ◽  
Data Assimilation ◽  
Space Weather ◽  
Initial Conditions ◽  
Solar Rotation ◽  
Weather Prediction ◽  
Model Parameters ◽  
Wind Model ◽  
Near Earth Space ◽  
Solar Wind Model

<div> <p>In terrestrial weather prediction, Data Assimilation (DA) has enabled huge improvements in operational forecasting capabilities. It does this by producing more accurate initial conditions and/or model parameters for forecasting; reducing the impacts of the “butterfly effect”. However, data assimilation is still in its infancy in space weather applications and it is not quantitatively understood how DA can improve space weather forecasts.</p> <p>To this effect, we have used a solar wind DA scheme to assimilate observations from STEREO A, STEREO B and ACE over the operational lifetime of STEREO-B (2007-2014). This scheme allows observational information at 1AU to update and improve the inner boundary of the solar wind model (at 30 solar radii). These improved inner boundary conditions are then input into the efficient solar wind model, HUXt, to produce forecasts of the solar wind over the next solar rotation.</p> <p>In this talk, I will be showing that data assimilation is capable of improving solar wind predictions not only in near-Earth space, but in the whole model domain, and compare these forecasts to corotation of observations from STEREO-B at Earth. I will also show that the DA forecasts are capable of reducing systematic errors that occur to latitudinal offset in STEREO-B’s corotation forecast.</p> </div>

Assessing Predictability with a Local Ensemble Kalman Filter

2007 ◽  
Vol 64 (4) ◽  
pp. 1116-1140 ◽  
Author(s):  
David Kuhl ◽  
Istvan Szunyogh ◽  
Eric J. Kostelich ◽  
Gyorgyi Gyarmati ◽  
D. J. Patil ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Data Assimilation ◽  
Ensemble Kalman Filter ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Data Assimilation Scheme ◽  
Ensemble Mean ◽  
Assimilation Scheme

Abstract In this paper, the spatiotemporally changing nature of predictability is studied in a reduced-resolution version of the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS), a state-of-the-art numerical weather prediction model. Atmospheric predictability is assessed in the perfect model scenario for which forecast uncertainties are entirely due to uncertainties in the estimates of the initial states. Uncertain initial conditions (analyses) are obtained by assimilating simulated noisy vertical soundings of the “true” atmospheric states with the local ensemble Kalman filter (LEKF) data assimilation scheme. This data assimilation scheme provides an ensemble of initial conditions. The ensemble mean defines the initial condition of 5-day deterministic model forecasts, while the time-evolved members of the ensemble provide an estimate of the evolving forecast uncertainties. The observations are randomly distributed in space to ensure that the geographical distribution of the analysis and forecast errors reflect predictability limits due to the model dynamics and are not affected by inhomogeneities of the observational coverage. Analysis and forecast error statistics are calculated for the deterministic forecasts. It is found that short-term forecast errors tend to grow exponentially in the extratropics and linearly in the Tropics. The behavior of the ensemble is explained by using the ensemble dimension (E dimension), a spatiotemporally evolving measure of the evenness of the distribution of the variance between the principal components of the ensemble-based forecast error covariance matrix. It is shown that in the extratropics the largest forecast errors occur for the smallest E dimensions. Since a low value of the E dimension guarantees that the ensemble can capture a large portion of the forecast error, the larger the forecast error the more certain that the ensemble can fully capture the forecast error. In particular, in regions of low E dimension, ensemble averaging is an efficient error filter and the ensemble spread provides an accurate prediction of the upper bound of the error in the ensemble-mean forecast.

scholarly journals Fengyun Meteorological Satellite Products for Earth System Science Applications

2021 ◽  
Author(s):  
Di Xian ◽  
Peng Zhang ◽  
Ling Gao ◽  
Ruijing Sun ◽  
Haizhen Zhang ◽  
...  
Keyword(s):  
Data Sharing ◽  
Satellite Data ◽  
Prediction Models ◽  
Weather Forecasting ◽  
Numerical Models ◽  
Weather Prediction ◽  
Vegetation Indices ◽  
Open Data ◽  
Earth System ◽  
Inversion Algorithm

AbstractFollowing the progress of satellite data assimilation in the 1990s, the combination of meteorological satellites and numerical models has changed the way scientists understand the earth. With the evolution of numerical weather prediction models and earth system models, meteorological satellites will play a more important role in earth sciences in the future. As part of the space-based infrastructure, the Fengyun (FY) meteorological satellites have contributed to earth science sustainability studies through an open data policy and stable data quality since the first launch of the FY-1A satellite in 1988. The capability of earth system monitoring was greatly enhanced after the second-generation polar orbiting FY-3 satellites and geostationary orbiting FY-4 satellites were developed. Meanwhile, the quality of the products generated from the FY-3 and FY-4 satellites is comparable to the well-known MODIS products. FY satellite data has been utilized broadly in weather forecasting, climate and climate change investigations, environmental disaster monitoring, etc. This article reviews the instruments mounted on the FY satellites. Sensor-dependent level 1 products (radiance data) and inversion algorithm-dependent level 2 products (geophysical parameters) are introduced. As an example, some typical geophysical parameters, such as wildfires, lightning, vegetation indices, aerosol products, soil moisture, and precipitation estimation have been demonstrated and validated by in-situ observations and other well-known satellite products. To help users access the FY products, a set of data sharing systems has been developed and operated. The newly developed data sharing system based on cloud technology has been illustrated to improve the efficiency of data delivery.

scholarly journals Model error in weather forecasting

2001 ◽  
Vol 8 (6) ◽  
pp. 357-371 ◽  
Author(s):  
D. Orrell ◽  
L. Smith ◽  
J. Barkmeijer ◽  
T. N. Palmer
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Initial Conditions ◽  
Forecast Error ◽  
Weather Prediction ◽  
Model Error ◽  
Local Model ◽  
State Dependent ◽  
Rapid Divergence ◽  
Dependent Model

Abstract. Operational forecasting is hampered both by the rapid divergence of nearby initial conditions and by error in the underlying model. Interest in chaos has fuelled much work on the first of these two issues; this paper focuses on the second. A new approach to quantifying state-dependent model error, the local model drift, is derived and deployed both in examples and in operational numerical weather prediction models. A simple law is derived to relate model error to likely shadowing performance (how long the model can stay close to the observations). Imperfect model experiments are used to contrast the performance of truncated models relative to a high resolution run, and the operational model relative to the analysis. In both cases the component of forecast error due to state-dependent model error tends to grow as the square-root of forecast time, and provides a major source of error out to three days. These initial results suggest that model error plays a major role and calls for further research in quantifying both the local model drift and expected shadowing times.

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