scholarly journals Bottom Topography Mapping via Nonlinear Data Assimilation

2011 ◽  
Vol 28 (12) ◽  
pp. 1606-1623 ◽  
Author(s):  
Edward D. Zaron ◽  
Marie-Aude Pradal ◽  
Patrick D. Miller ◽  
Alan F. Blumberg ◽  
Nickitas Georgas ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Bottom Topography ◽  
Hudson River ◽  
Water Levels ◽  
Picard Iteration ◽  
In Situ Data ◽  
Continuity Equations ◽  
Shallow Water Models ◽  
Nonlinear Line

Abstract A variational data assimilation method is described for bottom topography mapping in rivers and estuaries using remotely sensed observations of water surface currents. The velocity field and bottom topography are related by the vertically integrated momentum and continuity equations, leading to a nonlinear inverse problem for bottom topography, which is solved using a Picard iteration strategy combined with a nonlinear line search. An illustration of the method is shown for Haverstraw Bay, in the Hudson River, where the known bottom topography is well reconstructed. Once the topography has been estimated, currents and water levels may be forecast. The method makes feasible 1) the estimation of bottom topography in regions where in situ data collection may be impossible, dangerous, or expensive, and 2) the calibration of barotropic shallow-water models via control of the bottom topography.

scholarly journals Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon

2013 ◽  
Vol 10 (3) ◽  
pp. 2879-2925 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M.-P. Bonnet ◽  
L. G. G. de Gonçalves ◽  
S. Calmant ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hydrodynamic Model ◽  
Stream Flow ◽  
Large Scale ◽  
Water Levels ◽  
Large Rivers ◽  
Altimetry Data ◽  
Radar Altimetry ◽  
Prediction Approach

Abstract. In this work we introduce and evaluate a data assimilation framework for gauged and radar altimetry-based discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites and also transferring information to ungauged rivers reaches. Altimetry data assimilation improves results at a daily basis in terms of water levels and discharges with minor degree, even though radar altimetry data has a low temporal resolution. Sensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. The deterioration of model performance at some unmonitored reaches indicates the need for proper characterization of model errors and spatial localization techniques for hydrological applications. Finally, we evaluated stream flow forecasts for the Amazon basin based on initial conditions produced by the data assimilation scheme and using the ensemble stream flow prediction approach where the model is forced by past meteorological forcings. The resulting forecasts agreed well with the observations and maintained meaningful skill at large rivers even for long lead times, e.g. > 90 days at the Solimões/Amazon main stem. Results encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote sensing information.

scholarly journals Impact of hydrographic data assimilation on the Atlantic meridional overturning circulation

2009 ◽  
Vol 6 (3) ◽  
pp. 2667-2715 ◽  
Author(s):  
G. C. Smith ◽  
K. Haines ◽  
T. Kanzow ◽  
S. Cunningham
Keyword(s):  
Data Assimilation ◽  
North Atlantic ◽  
Atlantic Meridional Overturning Circulation ◽  
Meridional Overturning Circulation ◽  
Density Gradients ◽  
Overturning Circulation ◽  
In Situ Data ◽  
Meridional Overturning ◽  
Gyre Circulation

Abstract. The poleward ocean heat transports in the North Atlantic controlled by the Atlantic Meridional Overturning Circulation (AMOC), play a key role in regional climate. If the AMOC can be initialized in numerical models through ocean assimilation this may help improve the predictability of North Atlantic climate variability on timescales out to a few years. Here we make an initial step toward the development of an ocean assimilation system that can determine the AMOC to support climate predictions. A detailed comparison is presented of 1° and 1/4° resolution global model simulations with and without sequential data assimilation to the observations and transport estimates from the RAPID/MOCHA mooring array across 26.5° N in the Atlantic. Comparisons of modelled water properties with the observations from the merged RAPID boundary arrays demonstrate the ability of in situ data assimilation to accurately constrain the east-west density gradient between these mooring arrays. However, the presence of an unconstrained "western boundary wedge" between Abaco Island and the RAPID mooring site WB2 (16 km offshore) leads to the intensification of an erroneous southwards flow in this region when in situ data are assimilated. The result is an overly intense southward upper mid-ocean transport (0–1100 m) as compared to the estimates derived from the RAPID array. Correction of upper layer zonal density gradients is found to compensate mostly for a weak subtropical gyre circulation in the free model run (i.e. with no assimilation). Despite the important changes to the density structure and transports in the upper layer imposed by the assimilation, very little change is found in the amplitude and sub-seasonal variability of the AMOC. This shows that assimilation of upper layer density information projects mainly on the gyre circulation with little effect on the AMOC at 26° N due to the absence of corrections to density gradients below 2000 m (the maximum depth of Argo). The sensitivity to initial conditions was explored through two additional experiments using a climatological initial condition. These experiments showed that the weak bias in gyre intensity in the control simulation (without data assimilation) develops over the period of about 6 months, but does so independently from the overturning, with no change to the AMOC. However, differences in the properties and volume of North Atlantic Deep Water (NADW) persisted throughout the 3 year simulations resulting in a difference of 3 Sv in AMOC intensity. The persistence of these dense water anomalies and their influence on the AMOC is promising for the development of decadal forecasting capabilities. The results suggest that the deeper waters must be accurately reproduced in order to constrain the AMOC.

Application of a SEEK filter to a 1D biogeochemical model of the Ligurian Sea: Twin experiments and real in-situ data assimilation

2007 ◽  
Vol 65 (1-4) ◽  
pp. 561-583 ◽  
Author(s):  
C. Raick ◽  
A. Alvera-Azcarate ◽  
A. Barth ◽  
J.M. Brankart ◽  
K. Soetaert ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Biogeochemical Model ◽  
Ligurian Sea ◽  
In Situ Data

Exploring meso-scale soil water and groundwater storage changes within the USA through a Bayesian combination of GRACE data with monthly 12.5 km model simulations

2020 ◽  
Author(s):  
Nooshin Mehrnegar ◽  
Owen Jones ◽  
Michael B. Singer ◽  
Maike Schumacher ◽  
Thomas Jagdhuber ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Soil Water ◽  
Water Storage ◽  
Balance Model ◽  
Soil Water Storage ◽  
Groundwater Storage ◽  
In Situ Data ◽  
Grace Data ◽  
The Usa

<p>Climatic changes in precipitation intensity across the United States (USA) may also affect the frequency and magnitude of drought and flooding events, with potentially serious consequences for water supply across this country. Reliable estimation of water storage changes in the soil root zone and groundwater aquifers is important for predicting future water availability, drought and flood monitoring and weather prediction. In this study, we assimilate Terrestrial Water Storage (TWS) derived from Gravity Recovery and Climate Experiment (GRACE) satellite observations into a water balance model with 12.5-km spatial resolution. Our goal is to explore meso-scale surface and deep-level soil water storage, as well as groundwater changes within the USA covering the period 2003-2017. A new Bayesian approach is formulated and implemented in this study, which provides a dynamic solution for a state-space equation between hydrological model outputs and TWS observations, while considering their error structures. The unknown state parameters and temporal dependency between them are estimated through a combination of forward/backward Kalman Filtering and Markov Chain Monto Carlo (MCMC) methods.</p><p>The outputs of this methodological approach are evaluated using in situ data from historical USGS groundwater data (over 6600 wells) and the ESA CCI surface soil moisture data. The results indicate that our GRACE data assimilation generally improves the simulation of groundwater and soil moisture across the USA. For example, the long-term linear trend fitted to the Bayesian-derived groundwater and soil water storage are in a same direction as those of in situ data in 63% and 58% of regions studied across the USA, respectively. However, this vale is estimated less than 51% for both water storage estimates derived from the original water balance model, which suggesting that the data assimilation modulates the hydrological models to perform more realistically. The biggest improvements are observed in the southeast USA with considerably large inter-annual variability in precipitation, where modelled groundwater apparently responded too strongly to the changes in atmospheric forcing. The Bayesian data assimilation method also improves the temporal correlation coefficients between the in situ USGS and ESA CCI data and model outputs after merging with GRACE TWS estimates. For instance, the correlation coefficient between groundwater storage and USGS observation increased from -0.52 to 0.48 and from -0.28 to 0.25 in southeast and southwest of USA, respectively. Finally, we will explore changes in Bayesian-derived groundwater and soil water storage within the Florida, California and South of Mississippi regions and interpret their relations with climate-induced factors such as precipitation and ENSO index.</p><p><strong>Keywords:</strong> USA; Data Assimilation; Bayesian Method; Kalman Filtering; MCMC; GRACE; W3RA; groundwater storage; soil water storage; USGS; ESA CCI.</p><p> </p>

scholarly journals An Ensemble Kalman Filter with a complex marine ecosystem model: hindcasting phytoplankton in the Cretan Sea

2003 ◽  
Vol 21 (1) ◽  
pp. 399-411 ◽  
Author(s):  
J. I. Allen ◽  
M. Eknes ◽  
G. Evensen
Keyword(s):  
Data Assimilation ◽  
Marine Ecosystem ◽  
Three Dimensional ◽  
Ecosystem Model ◽  
Sequential Data ◽  
Model Errors ◽  
In Situ Data ◽  
Marine Ecosystem Model ◽  
Cretan Sea

Abstract. The purpose of this paper is to examine the use of a complex ecosystem model along with near real-time in situ data and a sequential data assimilation method for state estimation. The ecosystem model used is the European Regional Seas Ecosystem Model (ERSEM; Baretta et al., 1995) and the assimilation method chosen is the Ensemble Kalman Filer (EnKF). Previously, it has been shown that this method captures the nonlinear error evolution in time and is capable of both tracking the observations and providing realistic error estimates for the estimated state. This system has been used to assimilate long time series of in situ chlorophyll taken from a data buoy in the Cretan Sea. The assimilation of this data using the EnKF method results in a marked improvement in the ability of ERSEM to hindcast chlorophyll. The sensitivity of this system to the type of data used for assimilation, the frequency of assimilation, ensemble size and model errors is discussed. The predictability window of the EnKF appears to be at least 2 days. This is an indication that the methodology might be suitable for future operational data assimilation systems using more complex three-dimensional models. Key words. Oceanography: general (numerical modelling; ocean prediction) – Oceanography: biological and chemical (plankton)

scholarly journals A Nowcast/Forecast System for Japan’s Coasts Using Daily Assimilation of Remote Sensing and In Situ Data

2021 ◽  
Vol 13 (13) ◽  
pp. 2431
Author(s):  
Yasumasa Miyazawa ◽  
Sergey M. Varlamov ◽  
Toru Miyama ◽  
Yukio Kurihara ◽  
Hiroshi Murakami ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Ocean Model ◽  
Temperature Data ◽  
Sea Surface ◽  
Forecast System ◽  
In Situ Data

We have developed an ocean state nowcast/forecast system (JCOPE-T DA) that targets the coastal waters around Japan and assimilates daily remote sensing and in situ data. The ocean model component is developed based on the Princeton Ocean Model with a generalized sigma coordinate and calculates oceanic conditions with a 1/36-degree (2–3 km) resolution and an hourly result output interval. To effectively represent oceanic phenomena with a spatial scale smaller than 100 km, we adopted a data assimilation scheme that explicitly separates larger and smaller horizontal scales from satellite sea surface temperature data. Our model is updated daily through data assimilation using the latest available remote-sensing data. Here we validate the data assimilation products of JCOPE-T DA using various kinds of in situ observational data. This validation proves that the JCOPE-T DA model output outperforms those of a previous version of JCOPE-T, which is based on nudging the values of temperature and salinity toward those provided by a different coarse grid data-assimilated model JCOPE2M. Parameter sensitivity experiments show that the selection of horizontal scale separation parameters considerably affects the representation of sea surface temperature. Additional experiments demonstrate that the assimilation of daily-updated satellite sea surface temperature data actually improves the model’s efficiency in representing typhoon-induced disturbances of sea surface temperature on a time scale of a few days. Assimilation of additional in situ data, such as temperature/salinity/ocean current information, further improves the model’s ability to represent the ocean currents near the coast accurately.

scholarly journals Assimilating in situ and radar altimetry data into a large-scale hydrologic-hydrodynamic model for streamflow forecast in the Amazon

2013 ◽  
Vol 17 (7) ◽  
pp. 2929-2946 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M.-P. Bonnet ◽  
L. G. G. de Gonçalves ◽  
S. Calmant ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Hydrodynamic Model ◽  
Stream Flow ◽  
Large Scale ◽  
Water Levels ◽  
Large Rivers ◽  
Altimetry Data ◽  
Radar Altimetry ◽  
Prediction Approach

Abstract. In this work, we introduce and evaluate a data assimilation framework for gauged and radar altimetry-based discharge and water levels applied to a large scale hydrologic-hydrodynamic model for stream flow forecasts over the Amazon River basin. We used the process-based hydrological model called MGB-IPH coupled with a river hydrodynamic module using a storage model for floodplains. The Ensemble Kalman Filter technique was used to assimilate information from hundreds of gauging and altimetry stations based on ENVISAT satellite data. Model state variables errors were generated by corrupting precipitation forcing, considering log-normally distributed, time and spatially correlated errors. The EnKF performed well when assimilating in situ discharge, by improving model estimates at the assimilation sites (change in root-mean-squared error Δrms = −49%) and also transferring information to ungauged rivers reaches (Δrms = −16%). Altimetry data assimilation improves results, in terms of water levels (Δrms = −44%) and discharges (Δrms = −15%) to a minor degree, mostly close to altimetry sites and at a daily basis, even though radar altimetry data has a low temporal resolution. Sensitivity tests highlighted the importance of the magnitude of the precipitation errors and that of their spatial correlation, while temporal correlation showed to be dispensable. The deterioration of model performance at some unmonitored reaches indicates the need for proper characterisation of model errors and spatial localisation techniques for hydrological applications. Finally, we evaluated stream flow forecasts for the Amazon basin based on initial conditions produced by the data assimilation scheme and using the ensemble stream flow prediction approach where the model is forced by past meteorological forcings. The resulting forecasts agreed well with the observations and maintained meaningful skill at large rivers even for long lead times, e.g. >90 days at the Solimões/Amazon main stem. Results encourage the potential of hydrological forecasts at large rivers and/or poorly monitored regions by combining models and remote-sensing information.

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