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

Observation operators for assimilation of satellite observations in fluvial inundation forecasting

Images from satellite-based synthetic aperture radar (SAR) instruments contain large amounts of information about the position of flood water during a river flood event. This observational information typically covers a large spatial area, but is only relevant for a short time if water levels are changing rapidly. Data assimilation allows us to combine valuable SAR derived observed information with continuous predictions from a computational hydrodynamic model and thus to produce a better forecast than using the model alone. In order to use observations in this way a suitable observation operator is required. In this paper we show that different types of observation operator can produce very different corrections to predicted water levels; this impacts on the quality of the forecast produced.We discuss the physical mechanisms by which different observation operators update modelled water levels and introduce a novel observation operator for inundation forecasting. The performance of the new operator is compared in synthetic experiments with that of two more conventional approaches. The conventional approaches both use observations of water levels derived from SAR to correct model predictions. Our new operator is instead 10 designed to use backscatter values from SAR instruments as observations; such an approach has not been used before in an ensemble Kalman filtering framework. Direct use of backscatter observations opens up the possibility of using more information from each SAR image and could potentially speed up the time taken to produce observations needed to update model predictions. We compare the strengths and weaknesses of the three different approaches with reference to the physical mechanisms by which each of the observation operators allow data assimilation to update water levels in synthetic twin experiments in an idealised domain

Floodwater detection in urban areas using Sentinel-1 and WorldDEM data

Remote sensing using Synthetic Aperture Radar (SAR) is an important tool for emergency flood incident management. At present operational services are mainly aimed at flood mapping in rural areas, as mapping in urban areas is hampered by the complicated backscattering mechanisms occurring there. A method for detecting flooding at high resolution in urban areas that may contain dense housing is presented. This largely uses remotely sensed data sets that are readily available on a global basis, including open-access Sentinel-1 SAR data, the WorldDEM Digital Surface Model (DSM), and open-access World Settlement Footprint data to identify urban areas. The method is a change detection technique that estimates flood levels in urban areas locally. It searches for increased SAR backscatter in the post-flood image due to double scattering between water (rather than unflooded ground) and adjacent buildings, and reduced SAR backscatter in areas away from high slopes. Areas of urban flooding are detected by comparing an interpolated flood level surface to the DSM. The method was tested on two flood events that occurred in the UK during the storms of Winter 2019-20. High urban flood detection accuracies were achieved for the event in moderate density housing. The accuracy reduced for the event in dense housing, when street widths became comparable to the DSM resolution, though would still be useful for incident management. The method has potential for operational use for detecting urban flooding in near real-time on a global basis

Observation error statistics for Doppler radar radial wind superobservations assimilated into the DWD COSMO-KENDA system

Currently in operational numerical weather prediction (NWP) the density of high-resolution observations, such as Doppler radar radial winds (DRWs), is severely reduced in part to avoid violating the assumption of uncorrelated observation errors. To improve the quantity of observations used and the impact that they have on the forecast requires an accurate specification of the observation uncertainties. Observation uncertainties can be estimated using a simple diagnostic that utilises the statistical averages of observation-minus-background and observation-minus-analysis residuals. We are the first to use a modified form of the diagnostic to estimate spatial correlations for observations used in an operational ensemble data assimilation system. The uncertainties for DRW superobservations assimilated into the Deutscher Wetterdienst convection-permitting NWP model are estimated and compared to previous uncertainty estimates for DRWs. The new results show that most diagnosed standard deviations are smaller than those used in the assimilation, hence it may be feasible assimilate DRWs using reduced error standard deviations. However, some of the estimated standard deviations are considerably larger than those used in the assimilation; these large errors highlight areas where the observation processing system may be improved. The error correlation length scales are larger than the observation separation distance and influenced by both the superobbing procedure and observation operator. This is supported by comparing these results to our previous study using Met Office data. Our results suggest that DRW error correlations may be reduced by improving the superobbing procedure and observation operator; however, any remaining correlations should be accounted for in the assimilation

Observation impact, domain length and parameter estimation in data assimilation for flood forecasting

Accurate inundation forecasting provides vital information about the behaviour of fluvial flood water. Using data assimilation with an Ensemble Transform Kalman Filter we combine forecasts from a numerical hydrodynamic model with synthetic observations of water levels. We show that reinitialising the model with corrected water levels can cause an initialisation shock and demonstrate a simple novel solution. In agreement with others, we find that although assimilation can accurately correct water levels at observation times, the corrected forecast quickly relaxes to the open loop forecast. Our new work shows that the time taken for the forecast to relax to the open loop case depends on domain length; observation impact is longer-lived in a longer domain. We demonstrate that jointly correcting the channel friction parameter as well as water levels greatly improves the forecast. We also show that updating the value of the channel friction parameter can compensate for bias in inflow

Accounting for observation uncertainty and bias due to unresolved scales with the Schmidt-Kalman filter

Data assimilation combines observations with numerical model data, to provide a best estimate of a real system. Errors due to unresolved scales arise when there is a spatiotemporal scale mismatch between the processes resolved by the observations and model. We present theory on error, uncertainty and bias due to unresolved scales for situations where observations contain information on smaller scales than can be represented by the numerical model. The Schmidt-Kalman filter, which accounts for the uncertainties in the unrepresented processes, is investigated and compared with an optimal Kalman filter that treats all scales, and a suboptimal Kalman filter that accounts for the largescales only. The equation governing true analysis uncertainty is reformulated to include representation uncertainty for each filter. We apply the filters to a random walk model with one variable for large-scale processes and one variable for small-scale processes. Our new results show that the Schmidt-Kalman filter has the largest benefit over a suboptimal filter in regimes of high representation uncertainty and low instrument uncertainty but performs worse than the optimal filter. Furthermore, we review existing theory showing that errors due to unresolved scales often result in representation error bias. We derive a novel bias-correcting form of the Schmidt-Kalman filter and apply it to the random walk model with biased observations. We show that the bias-correcting Schmidt-Kalman filter successfully compensates for representation error biases. Indeed, it is more important to treat an observation bias than an unbiased error due to unresolved scales

Evaluating errors due to unresolved scales in convection permitting numerical weather prediction

In numerical weather prediction (NWP), observations and models are quantitatively compared for the purposes of data assimilation and forecast verification. The spatial and temporal scales represented by the observation and model may differ and this results in a scale mis‐match error which may be biased and correlated. The aim of this paper is to investigate the structure of representation error in convection‐permitting NWP models for four meteorological variables: temperature, specific humidity, zonal and meridional wind. We use high resolution data from the experimental Met Office London Model (approximately 300 m grid‐length) to simulate perfect observations and lower resolution model data. The scale mis‐match error and its bias, variance and correlation are calculated from the perfect observation and low‐resolution model equivalents. Our new results show that the scale mis‐match bias is significant in the boundary layer for temperature and specific humidity, whereas the variance is significant in the boundary layer for all analysed variables. Furthermore, they are shown to be related to the mismatch in the high‐ and low‐resolution orography. Contrary to previous studies using low‐resolution, (km‐scale) data, horizontal correlations are shown to be insignificant. However, all variables exhibit considerable vertical representation error correlation throughout the boundary layer; for temperature a significant positive vertical correlation persists for all model levels in the troposphere. Our results suggest that significant biases and vertical correlations exist that should be accounted for to give maximum observation impact in data assimilation and for fairness in model verification and validation