scholarly journals Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

2016 ◽  
Author(s):  
Emlyn M. Jones ◽  
Mark E. Baird ◽  
Mathieu Mongin ◽  
John Parslow ◽  
Jenny Skerratt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Great Barrier Reef ◽  
Forecast Error ◽  
Weather Prediction ◽  
Biogeochemical Model ◽  
Forecast Errors ◽  
Barrier Reef ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Assimilation System

Abstract. Skilful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically-derived relationships between IOPs and variables such as Chlorophyll-a concentration (Chl-a), Total Suspended Solids (TSS) and Color Dissolved Organic Matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due the additional signal from bottom reflectance. Rather than assimilate quantities calculated using error-prone IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance. The assimilation of a directly-observed quantity, in this case remote-sensing reflectance, is analogous to the assimilation of temperature brightness in Numerical Weather Prediction (NWP), or along-track sea-surface height in hydrodynamic models. To assimilate the observed reflectance, we use an in-water optical model to produce an equivalent simulated remote-sensing reflectance, and calculate the mis-match between the observed and simulated quantities to constrain the BGC model with a Deterministic Ensemble Kalman Filter (DEnKF). Using the assumption that simulated surface Chl-a is equivalent to remotely-sensed OC3M estimate of Chl-a resulted in a forecast error of approximately 75 %. Alternatively, assimilation of remote-sensing reflectance resulted in a forecast error of less than 40 %. Thus, in the coastal waters of the GBR, assimilating remote-sensing reflectance halved the forecast errors. When the analysis and forecast fields from the assimilation system are compared with the non-assimilating model, an independent comparison to in-situ observations of Chl-a, TSS, and dissolved inorganic nutrients (NO3, NH4 and DIP) show that errors are reduced by up to 90 %. In all cases, the assimilation system improves the result compared to the non-assimilating model. This approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially-derived TSS and CDOM, or the lack of a calibrated regional IOP algorithm.

scholarly journals Use of remote-sensing reflectance to constrain a data assimilating marine biogeochemical model of the Great Barrier Reef

2016 ◽  
Vol 13 (23) ◽  
pp. 6441-6469 ◽  
Author(s):  
Emlyn M. Jones ◽  
Mark E. Baird ◽  
Mathieu Mongin ◽  
John Parslow ◽  
Jenny Skerratt ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Great Barrier Reef ◽  
Forecast Error ◽  
Biogeochemical Model ◽  
Barrier Reef ◽  
Chl A ◽  
Carbon Cycles ◽  
Additional Signal ◽  
Remote Sensing Reflectance ◽  
Assimilation System

Abstract. Skillful marine biogeochemical (BGC) models are required to understand a range of coastal and global phenomena such as changes in nitrogen and carbon cycles. The refinement of BGC models through the assimilation of variables calculated from observed in-water inherent optical properties (IOPs), such as phytoplankton absorption, is problematic. Empirically derived relationships between IOPs and variables such as chlorophyll-a concentration (Chl a), total suspended solids (TSS) and coloured dissolved organic matter (CDOM) have been shown to have errors that can exceed 100 % of the observed quantity. These errors are greatest in shallow coastal regions, such as the Great Barrier Reef (GBR), due to the additional signal from bottom reflectance. Rather than assimilate quantities calculated using IOP algorithms, this study demonstrates the advantages of assimilating quantities calculated directly from the less error-prone satellite remote-sensing reflectance (RSR). To assimilate the observed RSR, we use an in-water optical model to produce an equivalent simulated RSR and calculate the mismatch between the observed and simulated quantities to constrain the BGC model with a deterministic ensemble Kalman filter (DEnKF). The traditional assumption that simulated surface Chl a is equivalent to the remotely sensed OC3M estimate of Chl a resulted in a forecast error of approximately 75 %. We show this error can be halved by instead using simulated RSR to constrain the model via the assimilation system. When the analysis and forecast fields from the RSR-based assimilation system are compared with the non-assimilating model, a comparison against independent in situ observations of Chl a, TSS and dissolved inorganic nutrients (NO3, NH4 and DIP) showed that errors are reduced by up to 90 %. In all cases, the assimilation system improves the simulation compared to the non-assimilating model. Our approach allows for the incorporation of vast quantities of remote-sensing observations that have in the past been discarded due to shallow water and/or artefacts introduced by terrestrially derived TSS and CDOM or the lack of a calibrated regional IOP algorithm.

scholarly journals Southern High-Latitude Ensemble Data Assimilation in the Antarctic Mesoscale Prediction System

2005 ◽  
Vol 133 (12) ◽  
pp. 3431-3449 ◽  
Author(s):  
D. M. Barker
Keyword(s):  
Data Assimilation ◽  
Sampling Error ◽  
Forecast Error ◽  
Weather Prediction ◽  
Forecast Errors ◽  
Prediction System ◽  
Spatial Covariance ◽  
Ensemble Data Assimilation ◽  
Ensemble Data ◽  
The Antarctic

Abstract Ensemble data assimilation systems incorporate observations into numerical models via solution of the Kalman filter update equations, and estimates of forecast error covariances derived from ensembles of model integrations. In this paper, a particular algorithm, the ensemble square root filter (EnSRF), is tested in a limited-area, polar numerical weather prediction (NWP) model: the Antarctic Mesoscale Prediction System (AMPS). For application in the real-time AMPS, the number of model integrations that can be run to provide forecast error covariances is limited, resulting in an ensemble sampling error that degrades the analysis fit to observations. In this work, multivariate, climatologically plausible forecast error covariances are specified via averaged forecast difference statistics. Ensemble representations of the “true” forecast errors, created using randomized control variables of the fifth-generation Pennsylvania State University–National Center for Atmospheric Research (PSU–NCAR) Mesoscale Model (MM5) three-dimensional variational (3DVAR) data assimilation system, are then used to assess the dependence of sampling error on ensemble size, data density, and localization of covariances using simulated observation networks. Results highlight the detrimental impact of ensemble sampling error on the analysis increment structure of correlated, but unobserved fields—an issue not addressed by the spatial covariance localization techniques used to date. A 12-hourly cycling EnSRF/AMPS assimilation/forecast system is tested for a two-week period in December 2002 using real, conventional (surface, rawinsonde, satellite retrieval) observations. The dependence of forecast scores on methods used to maintain ensemble spread and the inclusion of perturbations to lateral boundary conditions are studied.

scholarly journals Remote Sensing of Mangroves and Estuarine Communities in Central Queensland, Australia

2020 ◽  
Vol 12 (1) ◽  
pp. 197
Author(s):  
Debbie Chamberlain ◽  
Stuart Phinn ◽  
Hugh Possingham
Keyword(s):  
Remote Sensing ◽  
Time Series ◽  
Land Cover ◽  
Great Barrier Reef ◽  
National Park ◽  
Landsat 8 ◽  
Barrier Reef ◽  
Change Analysis ◽  
Estuarine Wetland ◽  
Open Forest

Great Barrier Reef catchments are under pressure from the effects of climate change, landscape modifications, and hydrology alterations. With the use of remote sensing datasets covering large areas, conventional methods of change detection can expose broad transitions, whereas workflows that excerpt data for time-series trends divulge more subtle transformations of land cover modification. Here, we combine both these approaches to investigate change and trends in a large estuarine region of Central Queensland, Australia, that encompasses a national park and is adjacent to the Great Barrier Reef World Heritage site. Nine information classes were compiled in a maximum likelihood post classification change analysis in 2004–2017. Mangroves decreased (1146 hectares), as was the case with estuarine wetland (1495 hectares), and saltmarsh grass (1546 hectares). The overall classification accuracies and Kappa coefficient for 2004, 2006, 2009, 2013, 2015, and 2017 land cover maps were 85%, 88%, 88%, 89%, 81%, and 92%, respectively. The cumulative area of open forest, estuarine wetland, and saltmarsh grass (1628 hectares) was converted to pasture in a thematic change analysis showing the “from–to” change. We generated linear regression relationships to examine trends in pixel values across the time series. Our findings from a trend analysis showed a decreasing trend (p value range = 0.001–0.099) in the vegetation extent of open forest, fringing mangroves, estuarine wetlands, saltmarsh grass, and grazing areas, but this was inconsistent across the study site. Similar to reports from tropical regions elsewhere, saltmarsh grass is poorly represented in the national park. A severe tropical cyclone preceding the capture of the 2017 Landsat 8 Operational Land Imager (OLI) image was likely the main driver for reduced areas of shoreline and stream vegetation. Our research contributes to the body of knowledge on coastal ecosystem dynamics to enable planning to achieve more effective conservation outcomes.

scholarly journals Spatio-Temporal Variability in Bio-Optical Properties of the Southern Caspian Sea: A Historic Analysis of Ocean Color Data

2020 ◽  
Vol 12 (23) ◽  
pp. 3975
Author(s):  
Bonyad Ahmadi ◽  
Mehdi Gholamalifard ◽  
Tiit Kutser ◽  
Stefano Vignudelli ◽  
Andrey Kostianoy
Keyword(s):  
Remote Sensing ◽  
Optical Properties ◽  
Attenuation Coefficient ◽  
Temporal Variability ◽  
Caspian Sea ◽  
Ocean Color ◽  
Time Lags ◽  
Chl A ◽  
Remote Sensing Reflectance ◽  
Southern Caspian Sea

Currently, satellite ocean color imageries play an important role in monitoring of water properties in various oceanic, coastal, and inland ecosystems. Although there is a long-time and global archive of such valuable data, no study has comprehensively used these data to assess the changes in the Caspian Sea. Hence, this study assessed the variability of bio-optical properties of the upper-water column in the Southern Caspian Sea (SCS) using the archive of the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS) and the Moderate Resolution Imaging Spectroradiometer (MODIS). The images acquired from SeaWiFS (January 1998 to December 2002) and MODIS Aqua (January 2003 to December 2015) satellites were used to investigate the spatial–temporal variability of bio-optical properties including Chlorophyll-a (Chl-a), attenuation coefficient, and remote sensing reflectance, and environmental parameters such as sea surface temperature (SST), wind stress and the El Nino-southern oscillation (ENSO) phenomena at different time lags in the study area. The trend analysis demonstrated an overall increase of 0.3358 mg m−3 in the Chl-a concentration during 1998–2015 (annual increase rate of 0.018 mg m−3 year−1) and four algal blooms and in turn an abnormal increase in Chl-a concentration were occurred in August 2001, September 2005, 2009, and August 2010. The linear model revealed that Chl-a in the northern and middle part of the study area had been influenced by the attenuation coefficient after a one-month lag time. The analysis revealed a sharp decline in Chl-a concentration during 2011–2015 and showed a high correlation with the turbidity and attenuation coefficient in the southern region, while Kd_490nm and remote sensing reflectance did a low one. Generally, Chl-a concentration exhibited a positive correlation with the attenuation coefficient (r = 0.63) and with remote sensing reflectance at the 555 nm (r = 0.111). This study can be used as the basis for predictive modeling to evaluate the changes of water quality and bio-optical indices in the Southern Caspian Sea (SCS).

Contemporary Cartography: Remote sensing of the Great Barrier Reef—an effective management tool

Cartography ◽  
1985 ◽  
Vol 14 (2) ◽  
pp. 116-123 ◽  
Author(s):  
Gerard Wedderburn-Bisshop ◽  
Peter Cuerin ◽  
Peter Holland
Keyword(s):  
Remote Sensing ◽  
Great Barrier Reef ◽  
Management Tool ◽  
Barrier Reef ◽  
Effective Management

scholarly journals The Impact of MetOp and Other Satellite Data within the Met Office Global NWP System Using an Adjoint-Based Sensitivity Method

2013 ◽  
Vol 141 (10) ◽  
pp. 3331-3342 ◽  
Author(s):  
Sangwon Joo ◽  
John Eyre ◽  
Richard Marriott
Keyword(s):  
Satellite Data ◽  
Forecast Error ◽  
Weather Prediction ◽  
Satellite System ◽  
Error Reduction ◽  
Forecast Errors ◽  
Data Types ◽  
Satellite Platform ◽  
Atmospheric Sounding ◽  
The Impact

Abstract The role of observations in reducing 24-h forecast errors is evaluated using the adjoint-based forecast sensitivity to observations (FSO) method developed within the Met Office global numerical weather prediction (NWP) system. The impacts of various subsets of observations are compared, with emphasis on space-based observations, particularly those from instruments on board the European Organisation for the Exploitation of Meteorological Satellites Meteorological Operational-A (MetOp-A) platform. Satellite data are found to account for 64% of the short-range global forecast error reduction, with the remaining 36% coming from the assimilation of surface-based observation types. MetOp-A data are measured as having the largest impact of any individual satellite platform (about 25% of the total impact on global forecast error reduction). Their large impact, compared to that of NOAA satellites, is mainly due to MetOp's additional sensors [Infrared Atmospheric Sounding Interferometer (IASI), Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS), and the Advanced Scatterometer (ASCAT)]. Microwave and hyperspectral infrared sounding techniques are found to give the largest total impacts. However, the GPS radio occultation technique is measured as having the largest mean impact per profile of observations among satellite types. This study demonstrates how the FSO technique can be used to assess the impact of individual satellite data types in NWP. The calculated impacts can be used to guide improvements in the use of currently available data and to contribute to discussions on the evolution of future observing systems.

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