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

State-of-the-art stochastic data assimilation methods for high-dimensional non-Gaussian problems

This paper compares several commonly used state-of-the-art ensemble-based data assimilation methods in a coherent mathematical notation. The study encompasses different methods that are applicable to high-dimensional geophysical systems, like ocean and atmosphere, and provide an uncertainty estimate. Most variants of Ensemble Kalman Filters, Particle Filters and second-order exact methods are discussed, including Gaussian Mixture Filters, while methods that require an adjoint model or a tangent linear formulation of the model are excluded. The detailed description of all the methods in a mathematically coherent way provides both novices and experienced researchers with a unique overview and new insight in the workings and relative advantages of each method, theoretically and algorithmically, even leading to new filters. Furthermore, the practical implementation details of all ensemble and particle filter methods are discussed to show similarities and differences in the filters aiding the users in what to use when. Finally, pseudo-codes are provided for all of the methods presented in this paper

Improving urban flood mapping by merging Synthetic Aperture Radar-derived flood footprints with flood hazard maps

Remotely sensed flood extents obtained in near real-time can be used for emergency flood incident management and as observations for assimilation into flood forecasting models. High resolution Synthetic Aperture Radar (SAR) sensors have the potential to detect flood extents in urban areas through cloud during both day- and night-time. This paper considers a method for detecting flooding in urban areas by merging near real-time SAR flood extents with model-derived flood hazard maps. This allows a two-way symbiosis, whereby currently available SAR urban flood extent improves future model flood predictions, while flood hazard maps obtained after the SAR overpass improve the SAR estimate of the urban flood extent. The method estimates urban flooding using SAR backscatter only in rural areas adjacent to the urban ones. It was compared to an existing method using SAR returns in both the rural and urban areas. The method using SAR solely in rural areas gave an average flood detection accuracy of 94% and a false positive rate of 9% in the urban areas, and was more accurate than the existing method

Properties of the ensemble Kalman filter for convective-scale numerical weather forecasting

Atmospheric data assimilation has now started to deal with high model resolution scales of O(lkm) where dynamical properties of the atmosphere exploited in larger scale models may no longer be valid. This leads to a problem in high-resolution data assimilation systems since balances such as the hydrostatic balance are still used to model forecast errors. From scale analysis arguments we recognise that such balances do not necessarily need to be valid at small scales and in this work we use the convective scale Met Office Global and Regional Ensemble Prediction System (MOGREPS) to show that indeed the hydrostatic balance at a horizontal resolution of 1.5 km ceases to be valid in the ensemble perturbations in regions where convection is present while it is valid in regions with no convection. We show that the horizontal threshold at which the hydrostatic balance becomes valid as a vertical average in the ensemble perturbations regardless of the presence of convection is 22 km. We also make use of ensemble methods to establish their applicability (0 convective scale models. In particular we apply (he ensemble square root filter (EnSRF) to a one-dimensional idealised column model wilh a parameterized cloud scheme and a discontinuous rain scheme. We show that the ensemble filter can caprure the true solution within a linear ('No cloud') model regime and non-linear ('Cloud') regime; however, if many good quality observations are used the ensemble fails to capture the true solution within the discontinuous CRain') regime. Interestingly, this can be alleviated if only a portion of the state space is observed. Moreover, having fewer spatial observations also improves the ensemble estimate for the ~mperature in the 'Rain' regime, while the estimate of state variables is slightly degraded in the 'No cloud' and 'Cloud' regimes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Properties of the ensemble Kalman filter for convective-scale numerical weather forecasting

Atmospheric data assimilation has now started to deal with high model resolution scales of O(lkm) where dynamical properties of the atmosphere exploited in larger scale models may no longer be valid. This leads to a problem in high-resolution data assimilation systems since balances such as the hydrostatic balance are still used to model forecast errors. From scale analysis arguments we recognise that such balances do not necessarily need to be valid at small scales and in this work we use the convective scale Met Office Global and Regional Ensemble Prediction System (MOGREPS) to show that indeed the hydrostatic balance at a horizontal resolution of 1.5 km ceases to be valid in the ensemble perturbations in regions where convection is present while it is valid in regions with no convection. We show that the horizontal threshold at which the hydrostatic balance becomes valid as a vertical average in the ensemble perturbations regardless of the presence of convection is 22 km. We also make use of ensemble methods to establish their applicability (0 convective scale models. In particular we apply (he ensemble square root filter (EnSRF) to a one-dimensional idealised column model wilh a parameterized cloud scheme and a discontinuous rain scheme. We show that the ensemble filter can caprure the true solution within a linear ('No cloud') model regime and non-linear ('Cloud') regime; however, if many good quality observations are used the ensemble fails to capture the true solution within the discontinuous CRain') regime. Interestingly, this can be alleviated if only a portion of the state space is observed. Moreover, having fewer spatial observations also improves the ensemble estimate for the ~mperature in the 'Rain' regime, while the estimate of state variables is slightly degraded in the 'No cloud' and 'Cloud' regimes.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Sensing the ionosphere with the Spire radio occultation constellation

Radio occultation (RO) provides a cost-effective component of the overall sensor mix required to characterise the ionosphere over wide areas and in areas where it is not possible to deploy ground sensors. The paper describes the RO constellation that has been developed and deployed by Spire Global. This constellation and its associated ground infrastructure are now producing data that can be used to characterise the bulk ionosphere, lower ionosphere perturbations, and ionospheric scintillation