The influence of spacecraft latitudinal offset on the accuracy of corotation forecasts

Knowledge of the ambient solar wind is important for accurate space-weather forecasting. A simple-but-effective method of forecasting near-Earth solar-wind speed is “corotation”, wherein solar-wind structure is assumed to be fixed in the reference frame rotating with the Sun. Under this approximation, observations at a source spacecraft can be rotated to a target location, such as Earth. Forecast accuracy depends upon the rate of solar-wind evolution, longitudinal and latitudinal separation between the source and target, and latitudinal structure in the solar wind itself. The time-evolution rate and latitudinal structure of the solar wind are both strongly influenced by the solar cycle, though in opposing ways. A latitudinal separation (offset) between source and target spacecraft is typically present, introducing an error to corotation forecasts. In this study, we use observations from the STEREO and near-Earth spacecraft to quantify the latitudinal error. Aliasing between the solar cycle and STEREO orbits means that individual contributions to the forecast error are difficult to isolate. However, by considering an 18-month interval near the end of solar minimum, we find that the latitudinal-offset contribution to corotation-forecast error cannot be directly detected for offsets < 6°, but is increasingly important as offsets increase. This result can be used to improve solar-wind data assimilation, allowing representivity errors in solar-wind observations to be correctly specified. Furthermore, as the maximum latitudinal offset between L5 and Earth is ≈ 5°, corotation forecasts from a future L5 spacecraft should not be greatly affected by latitudinal offset

Solar wind data assimilation in an operational context: use of near‐real‐time data and the forecast value of an L5 monitor

For accurate and timely space weather forecasting, advanced knowledge of the ambient solar wind is required, both for its direct impact on the magnetosphere and for accurately forecasting the propagation of coronal mass ejections to Earth. Data assimilation (DA) combines model output and observations to form an optimum estimation of reality. Initial experiments with assimilation of in situ solar wind speed observations suggest the potential for significant improvement in the forecast skill of near-Earth solar wind conditions. However, these experiments have assimilated science-quality observations, rather than near-real-time (NRT) data that would be available to an operational forecast scheme. Here, we assimilate both NRT and science observations from the Solar Terrestrial Relations Observatory (STEREO) and near-Earth observations from the Advanced Composition Explorer and Deep Space Climate Observatory spacecraft. We show that solar wind speed forecasts using NRT data are comparable to those based on science-level data. This suggests that an operational solar wind DA scheme would provide significant forecast improvement, with reduction in the mean absolute error of solar wind speed around 46% over forecasts without DA. With a proposed space weather monitor planned for the L5 Lagrange point, we also quantify the solar wind forecast gain expected from L5 observations alongside existing observations from L1. This is achieved using configurations of the STEREO and L1 spacecraft. There is a 15% improvement for forecast lead times of less than 5 days when observations from L5 are assimilated alongside those from L1, compared to assimilation of L1 observations alone

Quantifying the response of the ORAC aerosol optical depth retrieval for MSG SEVIRI to aerosol model assumptions

We test the response of the Oxford-RAL Aerosol and Cloud (ORAC) retrieval algorithm for MSG SEVIRI to changes in the aerosol properties used in the dust aerosol model, using data from the Dust Outflow and Deposition to the Ocean (DODO) flight campaign in August 2006. We find that using the observed DODO free tropospheric aerosol size distribution and refractive index increases simulated top of the atmosphere radiance at 0.55 µm assuming a fixed erosol optical depth of 0.5 by 10–15 %, reaching a maximum difference at low solar zenith angles. We test the sensitivity of the retrieval to the vertical distribution f the aerosol and find that this is unimportant in determining simulated radiance at 0.55 µm. We also test the ability of the ORAC retrieval when used to produce the GlobAerosol dataset to correctly identify continental aerosol outflow from the African continent and we find that it poorly constrains aerosol speciation. We develop spatially and temporally resolved prior distributions of aerosols to inform the retrieval which incorporates five aerosol models: desert dust, maritime, biomass burning, urban and continental. We use a Saharan Dust Index and the GEOS-Chem chemistry transport model to describe dust and biomass burning aerosol outflow, and compare AOD using our speciation against the GlobAerosol retrieval during January and July 2006. We find AOD discrepancies of 0.2–1 over regions of intense biomass burning outflow, where AOD from our aerosol speciation and GlobAerosol speciation can differ by as much as 50 - 70 %

Unifying the validation of ambient solar wind models

Progress in space weather research and awareness needs community-wide strategies and procedures to evaluate our modeling assets. Here we present the activities of the Ambient Solar Wind Validation Team embedded in the COSPAR ISWAT initiative. We aim to bridge the gap between model developers and end-users to provide the community with an assessment of the state-of-the-art in solar wind forecasting. To this end, we develop an open online platform for validating solar wind models by comparing their solutions with in situ spacecraft measurements. The online platform will allow the space weather community to test the quality of state-of-the-art solar wind models with unified metrics providing an unbiased assessment of progress over time. In this study, we propose a metadata architecture and recommend community-wide forecasting goals and validation metrics. We conclude with a status update of the online platform and outline future perspectives

Quantifying the effect of ICME removal and observation age for in situ solar wind data assimilation

Accurate space weather forecasting requires advanced knowledge of the solar wind conditions in near-Earth space. Data assimilation (DA) combines model output and observations to find an optimum estimation of reality and has led to large advances in terrestrial weather forecasting. It is now being applied to space weather forecasting. Here, we use solar wind DA with in-situ observations to reconstruct solar wind speed in the ecliptic plane between 30 solar radii and Earth’s orbit. This is used to provide solar wind speed hindcasts. Here, we assimilate observations from the Solar Terrestrial Relations Observatory (STEREO) and the near-Earth dataset, OMNI. Analysis of two periods of time, one in solar minimum and one in solar maximum, reveals that assimilating observations from multiple spacecraft provides a more accurate forecast than using any one spacecraft individually. The age of the observations also has a significant impact on forecast error, whereby the mean absolute error (MAE) sharply increases by up to 23% when the forecast lead time first exceeds the corotation time associated with the longitudinal separation between the observing spacecraft and the forecast location. It was also found that removing coronal mass ejections from the DA input and verification time series reduces the forecast MAE by up to 10% as it removes false streams from the forecast time series. This work highlights the importance of an L5 space weather monitoring mission for near-Earth solar wind forecasting and suggests that an additional mission to L4 would further improve future solar wind DA forecasting capabilities

A computationally efficient, time-dependent model of the solar wind for use as a surrogate to three-dimensional numerical magnetohydrodynamic simulations

Near-Earth solar-wind conditions, including disturbances generated by coronal mass ejections (CMEs), are routinely forecast using three-dimensional, numerical magnetohydrodynamic (MHD) models of the heliosphere. The resulting forecast errors are largely the result of uncertainty in the near-Sun boundary conditions, rather than heliospheric model physics or numerics. Thus ensembles of heliospheric model runs with perturbed initial conditions are used to estimate forecast uncertainty. MHD heliospheric models are relatively cheap in computational terms, requiring tens of minutes to an hour to simulate CME propagation from the Sun to Earth. Thus such ensembles can be run operationally. However, ensemble size is typically limited to 101 to 102 members, which may be inadequate to sample the relevant high-dimensional parameter space. Here, we describe a simplified solar-wind model that can estimate CME arrival time in approximately 0.01 seconds on a modest desktop computer and thus enables significantly larger ensembles. It is a one-dimensional, incompressible, hydrodynamic model, which has previously been used for the steady-state solar wind, but it is here used in time-dependent form. This approach is shown to adequately emulate the MHD solutions to the same boundary conditions for both steady-state solar wind and CME-like disturbances. We suggest it could serve as a “surrogate” model for the full three-dimensional MHD models. For example, ensembles of 105 to 106 members can be used to identify regions of parameter space for more detailed investigation by the MHD models. Similarly, the simplicity of the model means it can be rewritten as an adjoint model, enabling variational data assimilation with MHD models without the need to alter their code. The model code is available as an Open Source download in the Python language