Remote gas detection and quantitative analysis from infrared emission spectra obtained by Fourier transform infrared spectroscopy

Techniques for obtaining quantitative values of the temperatures and concentrations of remote hot gaseous effluents from their measured passive emission spectra have been examined in laboratory experiments. The high sensitivity of the spectrometer in the vicinity of the 2397 cm-1 band head region of CO2 has allowed the gas temperature to be calculated from the relative intensity of the observed rotational lines. The spatial distribution of the CO2 in a methane flame has been reconstructed tomographically using a matrix inversion technique. The spectrometer has been calibrated against a black body source at different temperatures and a self absorption correction has been applied to the data avoiding the need to measure the transmission directly. Reconstruction artifacts have been reduced by applying a smoothing routine to the inversion matrix.

Estimating discharge from inundation imagery

Remote sensing from space-borne platforms is often seen as an appealing method of monitoring components of the hydrological cycle, including river discharge, due to its spatial coverage. However, data from these platforms is often less than ideal because the geophysical properties of interest are rarely measured directly and the measurements that are taken can be subject to significant errors. This study assimilated water levels derived from a TerraSAR-X synthetic aperture radar image and digital aerial photography with simulations from a two dimensional hydraulic model to estimate discharge, inundation extent, depths and velocities at the confluence of the rivers Severn and Avon, UK. An ensemble Kalman filter was used to assimilate spot heights water levels derived by intersecting shorelines from the imagery with a digital elevation model. Discharge was estimated from the ensemble of simulations using state augmentation and then compared with gauge data. Assimilating the real data reduced the error between analyzed mean water levels and levels from three gauging stations to less than 0.3 m, which is less than typically found in post event water marks data from the field at these scales. Measurement bias was evident, but the method still provided a means of improving estimates of discharge for high flows where gauge data are unavailable or of poor quality. Posterior estimates of discharge had standard deviations between 63.3 m3s-1 and 52.7 m3s-1, which were below 15% of the gauged flows along the reach. Therefore, assuming a roughness uncertainty of 0.03-0.05 and no model structural errors discharge could be estimated by the EnKF with accuracy similar to that arguably expected from gauging stations during flood events. Quality control prior to assimilation, where measurements were rejected for being in areas of high topographic slope or close to tall vegetation and trees, was found to be essential. The study demonstrates the potential, but also the significant limitations of currently available imagery to reduce discharge uncertainty in un-gauged or poorly gauged basins when combined with model simulations in a data assimilation framework.

Ice formation and development in aged, wintertime cumulus over the UK: observations and modelling

In situ high resolution aircraft measurements of cloud microphysical properties were made in coordination with ground based remote sensing observations of a line of small cumulus clouds, using Radar and Lidar, as part of the Aerosol Properties, PRocesses And InfluenceS on the Earth's climate (APPRAISE) project. A narrow but extensive line (~100 km long) of shallow convective clouds over the southern UK was studied. Cloud top temperatures were observed to be higher than −8 °C, but the clouds were seen to consist of supercooled droplets and varying concentrations of ice particles. No ice particles were observed to be falling into the cloud tops from above. Current parameterisations of ice nuclei (IN) numbers predict too few particles will be active as ice nuclei to account for ice particle concentrations at the observed, near cloud top, temperatures (−7.5 °C). The role of mineral dust particles, consistent with concentrations observed near the surface, acting as high temperature IN is considered important in this case. It was found that very high concentrations of ice particles (up to 100 L−1) could be produced by secondary ice particle production providing the observed small amount of primary ice (about 0.01 L−1) was present to initiate it. This emphasises the need to understand primary ice formation in slightly supercooled clouds. It is shown using simple calculations that the Hallett-Mossop process (HM) is the likely source of the secondary ice. Model simulations of the case study were performed with the Aerosol Cloud and Precipitation Interactions Model (ACPIM). These parcel model investigations confirmed the HM process to be a very important mechanism for producing the observed high ice concentrations. A key step in generating the high concentrations was the process of collision and coalescence of rain drops, which once formed fell rapidly through the cloud, collecting ice particles which caused them to freeze and form instant large riming particles. The broadening of the droplet size-distribution by collision-coalescence was, therefore, a vital step in this process as this was required to generate the large number of ice crystals observed in the time available. Simulations were also performed with the WRF (Weather, Research and Forecasting) model. The results showed that while HM does act to increase the mass and number concentration of ice particles in these model simulations it was not found to be critical for the formation of precipitation. However, the WRF simulations produced a cloud top that was too cold and this, combined with the assumption of continual replenishing of ice nuclei removed by ice crystal formation, resulted in too many ice crystals forming by primary nucleation compared to the observations and parcel modelling.

On instabilities in data assimilation algorithms

Data assimilation algorithms are a crucial part of operational systems in numerical weather prediction, hydrology and climate science, but are also important for dynamical reconstruction in medical applications and quality control for manufacturing processes. Usually, a variety of diverse measurement data are employed to determine the state of the atmosphere or to a wider system including land and oceans. Modern data assimilation systems use more and more remote sensing data, in particular radiances measured by satellites, radar data and integrated water vapor measurements via GPS/GNSS signals. The inversion of some of these measurements are ill-posed in the classical sense, i.e. the inverse of the operator H which maps the state onto the data is unbounded. In this case, the use of such data can lead to significant instabilities of data assimilation algorithms. The goal of this work is to provide a rigorous mathematical analysis of the instability of well-known data assimilation methods. Here, we will restrict our attention to particular linear systems, in which the instability can be explicitly analyzed. We investigate the three-dimensional variational assimilation and four-dimensional variational assimilation. A theory for the instability is developed using the classical theory of ill-posed problems in a Banach space framework. Further, we demonstrate by numerical examples that instabilities can and will occur, including an example from dynamic magnetic tomography.

A change detection approach to flood mapping in urban areas using TerraSAR-X

Very high-resolution Synthetic Aperture Radar sensors represent an alternative to aerial photography for delineating floods in built-up environments where flood risk is highest. However, even with currently available SAR image resolutions of 3 m and higher, signal returns from man-made structures hamper the accurate mapping of flooded areas. Enhanced image processing algorithms and a better exploitation of image archives are required to facilitate the use of microwave remote sensing data for monitoring flood dynamics in urban areas. In this study a hybrid methodology combining radiometric thresholding, region growing and change detection is introduced as an approach enabling the automated, objective and reliable flood extent extraction from very high-resolution urban SAR images. The method is based on the calibration of a statistical distribution of “open water” backscatter values inferred from SAR images of floods. SAR images acquired during dry conditions enable the identification of areas i) that are not “visible” to the sensor (i.e. regions affected by ‘layover’ and ‘shadow’) and ii) that systematically behave as specular reflectors (e.g. smooth tarmac, permanent water bodies). Change detection with respect to a pre- or post flood reference image thereby reduces over-detection of inundated areas. A case study of the July 2007 Severn River flood (UK) observed by the very high-resolution SAR sensor on board TerraSAR-X as well as airborne photography highlights advantages and limitations of the proposed method. We conclude that even though the fully automated SAR-based flood mapping technique overcomes some limitations of previous methods, further technological and methodological improvements are necessary for SAR-based flood detection in urban areas to match the flood mapping capability of high quality aerial photography.

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.

Opportunities for magnetospheric research using EISCAT/ESR and CLUSTER

The four Cluster spacecraft offer a unique opportunity to study structure and dynamics in the magnetosphere and we discuss four general ways in which ground-based remote-sensing observations of the ionosphere can be used to support the in-situ measurements. The ionosphere over the Svalbard islands will be studied in particular detail, not only by the ESR and EISCAT incoherent scatter radars, but also by optical instruments, magnetometers, imaging riometers and the CUTLASS bistatic HF radar. We present an on-line procedure to plan coordinated measurements by the Cluster spacecraft with these combined ground-based systems. We illustrate the philosophy of the method, using two important examples of the many possible configurations between the Cluster satellites and the ground-based instruments.

Non-Maxwellian ion velocity distributions observed using EISCAT

Recent observations from the EISCAT incoherent scatter radar have revealed bursts of poleward ion flow in the dayside auroral ionosphere which are consistent with the ionospheric signature of flux transfer events at the magnetopause. These bursts frequently contain ion drifts which exceed the neutral thermal speed and, because the neutral thermospheric wind is incapable of responding sufficiently rapidly, toroidal, non-Maxwellian ion velocity distributions are expected. The EISCAT observations are made with high time resolution (15 seconds) and at a large angle to the geomagnetic field (73.5°), allowing the non-Maxwellian nature of the distribution to be observed remotely for the first time. The observed features are also strongly suggestive of a toroidal distribution: characteristic spectral shape, increased scattered power (both consistent with reduced Landau damping and enhanced electric field fluctuations) and excessively high line-of-sight ion temperatures deduced if a Maxwellian distribution is assumed. These remote sensing observations allow the evolution of the distributions to be observed. They are found to be non-Maxwellian whenever the ion drift exceeds the neutral thermal speed, indicating that such distributions can exist over the time scale of the flow burst events (several minutes).

Evidence of a topographic signal in surface soil moisture derived from ENVISAT ASAR wide swath data

The susceptibility of a catchment to flooding is affected by its soil moisture prior to an extreme rainfall event. While soil moisture is routinely observed by satellite instruments, results from previous work on the assimilation of remotely sensed soil moisture into hydrologic models have been mixed. This may have been due in part to the low spatial resolution of the observations used. In this study, the remote sensing aspects of a project attempting to improve flow predictions from a distributed hydrologic model by assimilating soil moisture measurements are described. Advanced Synthetic Aperture Radar (ASAR) Wide Swath data were used to measure soil moisture as, unlike low resolution microwave data, they have sufficient resolution to allow soil moisture variations due to local topography to be detected, which may help to take into account the spatial heterogeneity of hydrological processes. Surface soil moisture content (SSMC) was measured over the catchments of the Severn and Avon rivers in the South West UK. To reduce the influence of vegetation, measurements were made only over homogeneous pixels of improved grassland determined from a land cover map. Radar backscatter was corrected for terrain variations and normalized to a common incidence angle. SSMC was calculated using change detection. To search for evidence of a topographic signal, the mean SSMC from improved grassland pixels on low slopes near rivers was compared to that on higher slopes. When the mean SSMC on low slopes was 30–90%, the higher slopes were slightly drier than the low slopes. The effect was reversed for lower SSMC values. It was also more pronounced during a drying event. These findings contribute to the scant information in the literature on the use of high resolution SAR soil moisture measurement to improve hydrologic models.

Spatial variability of liquid cloud and rain: observations and microphysical effects

Spatial variability of liquid cloud water content and rainwater content is analysed from three different observational platforms: in situ measurements from research aircraft, land-based remote sensing techniques using radar and lidar, and spaceborne remote sensing from CloudSat. The variance is found to increase with spatial scale, but also depends strongly on the cloud or rain fraction regime, with overcast regions containing less variability than broken cloud fields. This variability is shown to lead to large biases, up to a factor of 4, in both the autoconversion and accretion rates estimated at a model grid scale of ≈40 km by a typical microphysical parametrization using in-cloud mean values. A parametrization for the subgrid variability of liquid cloud and rainwater content is developed, based on the observations, which varies with both the grid scale and cloud or rain fraction, and is applicable for all model grid scales. It is then shown that if this parametrization of the variability is analytically incorporated into the autoconversion and accretion rate calculations, the bias is significantly reduced.

The COnvective Precipitation Experiment (COPE): investigating the origins of heavy precipitation in the southwestern UK

A recent field campaign in southwest England used numerical modeling integrated with aircraft and radar observations to investigate the dynamic and microphysical interactions that can result in heavy convective precipitation. The COnvective Precipitation Experiment (COPE) was a joint UK-US field campaign held during the summer of 2013 in the southwest peninsula of England, designed to study convective clouds that produce heavy rain leading to flash floods. The clouds form along convergence lines that develop regularly due to the topography. Major flash floods have occurred in the past, most famously at Boscastle in 2004. It has been suggested that much of the rain was produced by warm rain processes, similar to some flash floods that have occurred in the US. The overarching goal of COPE is to improve quantitative convective precipitation forecasting by understanding the interactions of the cloud microphysics and dynamics and thereby to improve NWP model skill for forecasts of flash floods. Two research aircraft, the University of Wyoming King Air and the UK BAe 146, obtained detailed in situ and remote sensing measurements in, around, and below storms on several days. A new fast-scanning X-band dual-polarization Doppler radar made 360-deg volume scans over 10 elevation angles approximately every 5 minutes, and was augmented by two UK Met Office C-band radars and the Chilbolton S-band radar. Detailed aerosol measurements were made on the aircraft and on the ground. This paper: (i) provides an overview of the COPE field campaign and the resulting dataset; (ii) presents examples of heavy convective rainfall in clouds containing ice and also in relatively shallow clouds through the warm rain process alone; and (iii) explains how COPE data will be used to improve high-resolution NWP models for operational use.

NDVI With Artificial Neural Networks For SRTM Elevation Model Improvement – Hydrological Model Application

Digital elevation model (DEM) plays a substantial role in hydrological study, from understanding the catchment characteristics, setting up a hydrological model to mapping the flood risk in the region. Depending on the nature of study and its objectives, high resolution and reliable DEM is often desired to set up a sound hydrological model. However, such source of good DEM is not always available and it is generally high-priced. Obtained through radar based remote sensing, Shuttle Radar Topography Mission (SRTM) is a publicly available DEM with resolution of 92m outside US. It is a great source of DEM where no surveyed DEM is available. However, apart from the coarse resolution, SRTM suffers from inaccuracy especially on area with dense vegetation coverage due to the limitation of radar signals not penetrating through canopy. This will lead to the improper setup of the model as well as the erroneous mapping of flood risk. This paper attempts on improving SRTM dataset, using Normalised Difference Vegetation Index (NDVI), derived from Visible Red and Near Infra-Red band obtained from Landsat with resolution of 30m, and Artificial Neural Networks (ANN). The assessment of the improvement and the applicability of this method in hydrology would be highlighted and discussed.

Performance Evaluation Of SMOS Soil Moisture Retrieval Parameters For Hydrological Application

Microwave remote sensing has high potential for soil moisture retrieval. However, the efficient retrieval of soil moisture depends on optimally choosing the soil moisture retrieval parameters. In this study first the initial evaluation of SMOS L2 product is performed and then four approaches regarding soil moisture retrieval from SMOS brightness temperature are reported. The radiative transfer equation based tau-omega rationale is used in this study for the soil moisture retrievals. The single channel algorithms (SCA) using H polarisation is implemented with modifications, which includes the effective temperatures simulated from ECMWF (downscaled using WRF-NOAH Land Surface Model (LSM)) and MODIS. The retrieved soil moisture is then utilized for soil moisture deficit (SMD) estimation using empirical relationships with Probability Distributed Model based SMD as a benchmark. The square of correlation during the calibration indicates a value of R2 =0.359 for approach 4 (WRF-NOAH LSM based LST with optimized roughness parameters) followed by the approach 2 (optimized roughness parameters and MODIS based LST) (R2 =0.293), approach 3 (WRF-NOAH LSM based LST with no optimization) (R2 =0.267) and approach 1(MODIS based LST with no optimization) (R2 =0.163). Similarly, during the validation a highest performance is reported by approach 4. The other approaches are also following a similar trend as calibration. All the performances are depicted through Taylor diagram which indicates that the H polarisation using ECMWF based LST is giving a better performance for SMD estimation than the original SMOS L2 products at a catchment scale.

Terraços da margem leste da Laguna dos Patos, Litoral Médio do Rio Grande do Sul: estratigrafia e evolução holocênica

O presente trabalho discute os terraços lagunares da margem leste da Laguna dos Patos, localizados próximo à cidade de Mostardas. Através desse estudo é apresentado um novo modelo evolutivo para os últimos 5.000 anos no Litoral Médio do Rio Grande do Sul. Novas ferramentas foram utilizadas pela primeira vez no estudo da planície costeira do Rio Grande do Sul. Com respeito a aquisição dos dados de subsuperfície foi utilizado com êxito um GPR (Ground Penetretion Radar). Na análise e interpretação dos dados foi feita uma tentativa de aplicação da estratigrafia de seqüências. Através do uso de um scanner foram elaborados perfis dos testemunhos de sondagem, identificando aspectos distintos na composição e porosidade de diferentes litologias. Outras ferramentas utilizadas constam de sensoriamento remoto, nivelamento altimétrico e batimétrico, vibracore e datação radiométrica. Dessa forma, foram definidas três feições de terraceamento lagunar de idade holocênica na área de estudo, cada uma relacionada a um nível lagunar distinto. Através dos dados obtidos em subsuperfície foi possível traçar um esboço cronoestratigráfico, bem como uma curva de variação relativa do nível lagunar para a região. Também, como resultado dos dados geológicos de superfície, associados a novas interpretações baseadas em fotografias aéreas e imagens de satélite, foi elaborado um mapa geológico atualizado.

Avaliação ambiental de manguezais adjacentes aos campos petrolíferos de Macau e Serra (RN), como subsídio às medidas mitigadoras ao processo erosivo

Among the various effects caused by the climate change and human intervention, the mangrove ecosystem changes through of the years has been worth mentioning, which hasn t known which are the pros and cons for the adjacent coastal and estuarine environments yet. It happens due to the present dynamism in these areas, besides of the difficult understanding of the processes associated with evolution. This study aimed to environmentally evaluate adjacent mangroves from the Macau and Serra oil fields, located on Rio Grande do Norte northern coast, to support the mitigating actions related to the containment of the erosive process, as well as, according to the principles of the Clean Development Mechanism (CDM), to assess the amount of atmospheric carbon sequestered by the studied ecosystem. An inventory was conducted through mangrouve mapping which has supplied this research, especially regarding to the structural characterization of mangrove areas. To understand the local mangrove behavior in a greater level detail, techniques of remote sensing, GIS and GPS were used to make an analogy between the current and past states of the mangrove studied, allowing to make anticipated projections for the future impacts or changes in that region. This study combined data from multispectral LANDSAT 5 TM, Landsat 7 ETM+ with radar microwave data from SAR RADARSAT-1, which increased the interpretation capacity of the data from optical sensor systems. The interpretations have been supported by the data field, representing a better and innovative methodology for the environmental and taxonomic characterization of mangrove forests considered. The results reveal that mangroves of the Ponta do Tubarão Sustainable Development Reserve are biologically representative areas and providing a variety of benefits, especially for local communities, constituting the priority sites for actions development aimed at conservation. They also have been showing the necessity to make mitigating measures in order to recover degraded areas through reforestation or creating new areas of mangrove, as currently 7.1% of the mangrove forests studied are dead or in an advanced state of decomposition. The amount of atmospheric carbon sequestered proved very significant when analyzed for the whole area, which is able to sequester atmospheric 4,294,458 Ton CO2 per year