Efficient computation of matrix-vector products with full observation weighting matrices in data assimilation

Recent studies have demonstrated improved skill in numerical weather prediction via the use of spatially correlated observation error covariance information in data assimilation systems. In this case, the observation weighting matrices (inverse error covariance matrices) used in the assimilation may be full matrices rather than diagonal. Thus, the computation of matrix-vector products in the variational minimization problem may be very time-consuming, particularly if the parallel computation of the matrix-vector product requires a high degree of communication between processing elements. Hence, we introduce a well-known numerical approximation method, called the fast multipole method (FMM), to speed up the matrix-vector multiplications in data assimilation. We explore a particular type of FMM that uses a singular value decomposition (SVD-FMM) and adjust it to suit our new application in data assimilation. By approximating a large part of the computation of the matrix-vector product, the SVD-FMM technique greatly reduces the computational complexity compared with the standard approach. We develop a novel possible parallelization scheme of the SVD-FMM for our application, which can reduce the communication costs. We investigate the accuracy of the SVD-FMM technique in several numerical experiments: we first assess the accuracy using covariance matrices that are created using different correlation functions and lengthscales; then investigate the impact of reconditioning the covariance matrices on the accuracy; and finally examine the feasibility of the technique in the presence of missing observations. We also provide theoretical explanations for some numerical results. Our results show that the SVD-FMM technique can compute the matrix-vector product with good accuracy in a wide variety of circumstances, and hence, it has potential as an efficient technique for assimilation of a large volume of observational data within a short time interval

Tobacco advertising, promotion, and sponsorship ban adoption:A pilot study of the reporting challenges faced by low- and middle-income nations.

INTRODUCTION: The WHO Framework Convention for Tobacco Control (FCTC) Secretariat has identified issues with Article 13 (Tobacco Advertising, Promotion and Sponsorship) Party policy progress reporting, whilst some researchers remain skeptical of the completeness and accuracy of the data collected as part of the required reporting questionnaire. Gaining a deeper understanding of the challenges encountered when completing these questionnaires could provide insights to improve WHO FCTC progress reporting. METHODS: Qualitative semi-structured interviews were conducted between January and June 2021, with nine national tobacco control focal point (NFP) individuals (designates who report on WHO FCTC implementation on the Party’s behalf) from low- and middle-income countries. The study analysis used a thematic framework approach involving data familiarization, thematic framework construction, indexing and refining, mapping and interpretation of the results. RESULTS: The analysis generated four themes: 1) use of different resources, 2) presence of compounding complexities, 3) use of supporting mechanisms employed for tackling the challenges, and 4) recommendations for refinements within the questionnaire and for those completing it. CONCLUSIONS: The WHO FCTC reporting questionnaire needs improvements that could be piloted and discussed between the Convention Secretariat and the Parties prior to wide scale implementation

Scheduling satellite-based SAR acquisition for sequential assimilation of water level observations into flood modelling

Satellite-based Synthetic Aperture Radar (SAR) has proved useful for obtaining information on flood extent, which, when intersected with a Digital Elevation Model (DEM) of the floodplain, provides water level observations that can be assimilated into a hydrodynamic model to decrease forecast uncertainty. With an increasing number of operational satellites with SAR capability, information on the relationship between satellite first visit and revisit times and forecast performance is required to optimise the operational scheduling of satellite imagery. By using an Ensemble Transform Kalman Filter (ETKF) and a synthetic analysis with the 2D hydrodynamic model LISFLOOD-FP based on a real flooding case affecting an urban area (summer 2007,Tewkesbury, Southwest UK), we evaluate the sensitivity of the forecast performance to visit parameters. We emulate a generic hydrologic-hydrodynamic modelling cascade by imposing a bias and spatiotemporal correlations to the inflow error ensemble into the hydrodynamic domain. First, in agreement with previous research, estimation and correction for this bias leads to a clear improvement in keeping the forecast on track. Second, imagery obtained early in the flood is shown to have a large influence on forecast statistics. Revisit interval is most influential for early observations. The results are promising for the future of remote sensing-based water level observations for real-time flood forecasting in complex scenarios

Robust algorithm for detecting floodwater in urban areas using Synthetic Aperture Radar images

Flooding is a major hazard in both rural and urban areas worldwide, but it is in urban areas that the impacts are most severe. High resolution Synthetic Aperture Radar (SAR) sensors are able to detect flood extents in urban areas during both day- and night-time. If obtained in near real-time, these flood extents can be used for emergency flood relief management or as observations for assimilation into flood forecasting models. In this paper a method for detecting flooding in urban areas using near real-time SAR data is developed and extensively tested under a variety of scenarios involving different flood events and different images. The method uses a SAR simulator in conjunction with LiDAR data of the urban area to predict areas of radar shadow and layover in the image caused by buildings and taller vegetation. Of the urban water pixels visible to the SAR, the flood detection accuracy averaged over the test examples was 83%, with a false alarm rate of 9%. The results indicate that flooding can be detected in the urban area to reasonable accuracy, but that this accuracy is limited partly by the SAR’s poor visibility of the urban ground surface due to shadow and layover

Estimating correlated observation error statistics using an ensemble transform Kalman filter

For certain observing types, such as those that are remotely sensed, the observation errors are correlated and these correlations are state- and time-dependent. In this work, we develop a method for diagnosing and incorporating spatially correlated and time-dependent observation error in an ensemble data assimilation system. The method combines an ensemble transform Kalman filter with a method that uses statistical averages of background and analysis innovations to provide an estimate of the observation error covariance matrix. To evaluate the performance of the method, we perform identical twin experiments using the Lorenz ’96 and Kuramoto-Sivashinsky models. Using our approach, a good approximation to the true observation error covariance can be recovered in cases where the initial estimate of the error covariance is incorrect. Spatial observation error covariances where the length scale of the true covariance changes slowly in time can also be captured. We find that using the estimated correlated observation error in the assimilation improves the analysis

Assimilating high resolution remotely sensed soil moisture into a distributed hydrologic model to improve runoff prediction

The susceptibility of a catchment to flooding during an extreme rainfall event is affected by its soil moisture condition prior to the event. A study to improve the state vector of a distributed hydrologic model by assimilating high resolution remotely sensed soil moisture is described. The launch of Sentinel-1 has stimulated interest in measuring soil moisture at high resolution suitable for hydrological studies using Synthetic Aperture Radars (SARs). The advantages of using SAR soil moisture in conjunction with land cover data are considered. These include the ability to reduce contamination of the surface soil signal due to vegetation, radar artefacts, mixed pixels and land cover classes not providing meaningful soil moistures. Results for 2008 using ASAR data showed that the assimilation of ASAR soil moisture values improved the predicted flows for all images. The improvement was less marked for 2007, probably because the antecedent soil moisture conditions were of reduced importance during the extreme flooding that occurred then. Particularly for 2008, the higher resolution of ASAR data improved predicted flows compared to low resolution ASCAT data that were not disaggregated and limited to the temporal frequency of ASAR. The method is likely to give better results with Sentinel-1 rather than ASAR data due to its higher temporal resolution