Towards a better ice sheet model initialisation and basal knowledge using data assimilation

Abstract. Ice flow models are now routinely used to forecast the ice sheets' contribution to 21st century sea-level rise. For such short term simulations, the model response is greatly affected by the initial conditions. Data assimilation algorithms have been developed to invert for the friction of the ice on its bedrock using observed surface velocities. A drawback of these methods is that remaining uncertainties, especially in the bedrock elevation, lead to non-physical ice flux divergence anomalies resulting in undesirable transient effects. In this study, we compare two different assimilation algorithms based on adjoints and nudging to constrain both bedrock friction and elevation. Using synthetic twin experiments with realistic observation errors, we show that the two algorithms lead to similar performances in reconstructing both variables and allow the flux divergence anomalies to be significantly reduced.

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Implementation of a 3-D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-6-3581-2013 ◽

2013 ◽

Vol 6 (2) ◽

pp. 3581-3610

Author(s):

S. Federico

Keyword(s):

Relative Humidity ◽

Data Assimilation ◽

Initial Conditions ◽

Weather Prediction ◽

Wind Profiler ◽

Short Term ◽

Recursive Filters ◽

Data Assimilation System ◽

Rams Model ◽

Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system. The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost-function, and the use of an analysis space represented by recursive filters and eigenmodes of the vertical background error matrix. This matrix and the length-scale of the recursive filters are estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analysis solutions because they are closer to the observations (lower RMSE) compared to the background (higher RMSE), and the differences of the RMSEs are consistent with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of a three-hours forecast of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3-D-Var scheme as initial conditions, then is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at a-synoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range (1–2 h). The results are in agreement with the set-up of the numerical experiment.

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Evaluation of a new middle-lower tropospheric CO<sub>2</sub> product using data assimilation

Atmospheric Chemistry and Physics ◽

10.5194/acp-13-4487-2013 ◽

2013 ◽

Vol 13 (9) ◽

pp. 4487-4500 ◽

Cited By ~ 7

Author(s):

A. Tangborn ◽

L. L. Strow ◽

B. Imbiriba ◽

L. Ott ◽

S. Pawson

Keyword(s):

Data Assimilation ◽

Land Surface ◽

Initial Conditions ◽

Model Simulation ◽

Lower Troposphere ◽

Earth Observing System ◽

Peak Sensitivity ◽

The Difference ◽

Using Data ◽

Mean Differences

Abstract. Atmospheric CO2 retrievals with peak sensitivity in the mid- to lower troposphere from the Atmospheric Infrared Sounder (AIRS) have been assimilated into the GEOS-5 (Goddard Earth Observing System Model, Version 5) constituent assimilation system for the period 1 January 2005 to 31 December 2006. A corresponding model simulation, using identical initial conditions, circulation, and CO2 boundary fluxes was also completed. The analyzed and simulated CO2 fields are compared with surface measurements globally and aircraft measurements over North America. Surface level monthly mean CO2 values show a marked improvement due to the assimilation in the Southern Hemisphere, while less consistent improvements are seen in the Northern Hemisphere. Mean differences with aircraft observations are reduced at all levels, with the largest decrease occurring in the mid-troposphere. The difference standard deviations are reduced slightly at all levels over the ocean, and all levels except the surface layer over land. These initial experiments indicate that the used channels contain useful information on CO2 in the middle to lower troposphere. However, the benefits of assimilating these data are reduced over the land surface, where concentrations are dominated by uncertain local fluxes and where the observation density is quite low. Away from these regions, the study demonstrates the power of the data assimilation technique for evaluating data that are not co-located, in that the improvements in mid-tropospheric CO2 by the sparsely distributed partial-column retrievals are transported by the model to the fixed in situ surface observation locations in more remote areas.

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Antarctic blue ice areas - towards extracting palaeoclimate information

Antarctic Science ◽

10.1017/s0954102009990691 ◽

2010 ◽

Vol 22 (2) ◽

pp. 99-115 ◽

Cited By ~ 32

Author(s):

Anna Sinisalo ◽

John C. Moore

Keyword(s):

Ice Sheets ◽

Ice Sheet ◽

Thermal Transition ◽

Gas Composition ◽

Accumulation Rates ◽

Ice Flow ◽

Flow Models ◽

Flow Modelling ◽

Substantial Progress ◽

The Last Glacial

AbstractWe review the current scientific knowledge about Antarctic Blue Ice Areas (BIAs) with emphasis on their application for palaeoclimate studies. Substantial progress has been made since the review by Bintanja (1999), in particular dating the archive of ancient ice found on the surface of BIAs has progressed with advances in 14C measurements, tephrachronology, and geomorphological evidence giving better constraints to more sophisticated ice flow models. Flow modelling also provides information about past changes in ice flow velocities, accumulation rates and ice sheet elevation. The availability of gas composition in vertical cores from BIAs allows matching to well-dated global records of greenhouse gas variability over the last glacial-interglacial cycle and longer. It is clear from the limited number of studies to date that BIAs from different regions have quite different histories of formation and preservation, and that they are intimately linked to the response of their surrounding ice sheets to climate variability on glacial-interglacial time-scales. Looking to the future, climate records from BIAs are expected to provide information on variations in Southern Ocean processes as well as ice sheet evolution within the East Antarctic ice sheet at the thermal transition from cold based to warm based ice.

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Glacier hydromechanics: early insights and the lasting legacy of three works by Iken and colleagues

Journal of Glaciology ◽

10.3189/002214311796406103 ◽

2010 ◽

Vol 56 (200) ◽

pp. 1069-1078 ◽

Cited By ~ 3

Author(s):

Gwenn E. Flowers

Keyword(s):

Shear Stress ◽

Vertical Velocity ◽

Water Pressure ◽

Ice Sheets ◽

Strong Correlations ◽

Velocity Measurements ◽

Short Term ◽

Ice Flow ◽

Alpine Glaciers ◽

Basal Shear

AbstractThe association between basal hydrology and glacier sliding has become nearly synonymous with the early work of Almut Iken and colleagues. Their research published in theJournal of Glaciologyfrom 1981 to 1986 made an indelible impact on the study of glacier hydromechanics by documenting strong correlations between basal water pressure and short-term ice-flow variations. With a passion for elucidating the physics of glacier-bed processes, Iken herself made fundamental contributions to our theoretical and empirical understanding of the sliding process. From the theoretical bound on basal shear stress, to the inferences drawn from detailed horizontal and vertical velocity measurements, the work of Iken and colleagues continues to inform the interpretation of data from alpine glaciers and has found increasing relevance to observations from the ice sheets.

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Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

Hydrology and Earth System Sciences ◽

10.5194/hess-15-3399-2011 ◽

2011 ◽

Vol 15 (11) ◽

pp. 3399-3410 ◽

Cited By ~ 69

Author(s):

C. M. DeChant ◽

H. Moradkhani

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Water Storage ◽

Initial Condition ◽

Streamflow Prediction ◽

Western Us ◽

Operational Forecasting ◽

Snow Water ◽

Ensemble Streamflow Prediction ◽

Using Data

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

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Implementation of a 3D-Var system for atmospheric profiling data assimilation into the RAMS model: initial results

Atmospheric Measurement Techniques ◽

10.5194/amt-6-3563-2013 ◽

2013 ◽

Vol 6 (12) ◽

pp. 3563-3576 ◽

Cited By ~ 8

Author(s):

S. Federico

Keyword(s):

Relative Humidity ◽

Data Assimilation ◽

Initial Conditions ◽

Weather Prediction ◽

Wind Profiler ◽

Short Term ◽

Background Error ◽

Data Assimilation System ◽

Rams Model ◽

Assimilation System

Abstract. This paper presents the current status of development of a three-dimensional variational data assimilation system (3D-Var). The system can be used with different numerical weather prediction models, but it is mainly designed to be coupled with the Regional Atmospheric Modelling System (RAMS). Analyses are given for the following parameters: zonal and meridional wind components, temperature, relative humidity, and geopotential height. Important features of the data assimilation system are the use of incremental formulation of the cost function, and the representation of the background error by recursive filters and the eigenmodes of the vertical component of the background error covariance matrix. This matrix is estimated by the National Meteorological Center (NMC) method. The data assimilation and forecasting system is applied to the real context of atmospheric profiling data assimilation, and in particular to the short-term wind prediction. The analyses are produced at 20 km horizontal resolution over central Europe and extend over the whole troposphere. Assimilated data are vertical soundings of wind, temperature, and relative humidity from radiosondes, and wind measurements of the European wind profiler network. Results show the validity of the analyses because they are closer to the observations (lower root mean square error (RMSE)) compared to the background (higher RMSE), and the differences of the RMSEs are in agreement with the data assimilation settings. To quantify the impact of improved initial conditions on the short-term forecast, the analyses are used as initial conditions of three-hours forecasts of the RAMS model. In particular two sets of forecasts are produced: (a) the first uses the ECMWF analysis/forecast cycle as initial and boundary conditions; (b) the second uses the analyses produced by the 3D-Var as initial conditions, then it is driven by the ECMWF forecast. The improvement is quantified by considering the horizontal components of the wind, which are measured at asynoptic times by the European wind profiler network. The results show that the RMSE is effectively reduced at the short range. The results are in agreement with the set-up of the numerical experiment.

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On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations

Atmospheric Environment ◽

10.1016/j.atmosenv.2011.09.066 ◽

2012 ◽

Vol 46 ◽

pp. 318-328 ◽

Cited By ~ 22

Author(s):

J. Vira ◽

M. Sofiev

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Variational Data Assimilation ◽

Short Term ◽

Emission Fluxes ◽

Short Term Forecasting

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Improving the characterization of initial condition for ensemble streamflow prediction using data assimilation

Hydrology and Earth System Sciences Discussions ◽

10.5194/hessd-8-7207-2011 ◽

2011 ◽

Vol 8 (4) ◽

pp. 7207-7235 ◽

Cited By ~ 1

Author(s):

C. M. DeChant ◽

H. Moradkhani

Keyword(s):

Data Assimilation ◽

Initial Conditions ◽

Water Storage ◽

Initial Condition ◽

Streamflow Prediction ◽

Western Us ◽

Operational Forecasting ◽

Snow Water ◽

Ensemble Streamflow Prediction ◽

Using Data

Abstract. Within the National Weather Service River Forecast System, water supply forecasting is performed through Ensemble Streamflow Prediction (ESP). ESP relies both on the estimation of initial conditions and historically resampled forcing data to produce seasonal volumetric forecasts. In the western US, the accuracy of initial condition estimation is particularly important due to the large quantities of water stored in mountain snowpack. In order to improve the estimation of snow quantities, this study explores the use of ensemble data assimilation. Rather than relying entirely on the model to create single deterministic initial snow water storage, as currently implemented in operational forecasting, this study incorporates SNOTEL data along with model predictions to create an ensemble based probabilistic estimation of snow water storage. This creates a framework to account for initial condition uncertainty in addition to forcing uncertainty. The results presented in this study suggest that data assimilation has the potential to improve ESP for probabilistic volumetric forecasts but is limited by the available observations.

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Deep learning speeds up ice flow modelling by several orders of magnitude

Journal of Glaciology ◽

10.1017/jog.2021.120 ◽

2021 ◽

pp. 1-14

Author(s):

Guillaume Jouvet ◽

Guillaume Cordonnier ◽

Byungsoo Kim ◽

Martin Lüthi ◽

Andreas Vieli ◽

...

Keyword(s):

High Spatial Resolution ◽

State Of The Art ◽

Ice Sheets ◽

Ice Dynamics ◽

Ice Flow ◽

Flow Models ◽

Mountain Glaciers ◽

Central Processing ◽

Model Component ◽

User Friendly

Abstract This paper introduces the Instructed Glacier Model (IGM) – a model that simulates ice dynamics, mass balance and its coupling to predict the evolution of glaciers, icefields or ice sheets. The novelty of IGM is that it models the ice flow by a Convolutional Neural Network, which is trained from data generated with hybrid SIA + SSA or Stokes ice flow models. By doing so, the most computationally demanding model component is substituted by a cheap emulator. Once trained with representative data, we demonstrate that IGM permits to model mountain glaciers up to 1000 × faster than Stokes ones on Central Processing Units (CPU) with fidelity levels above 90% in terms of ice flow solutions leading to nearly identical transient thickness evolution. Switching to the GPU often permits additional significant speed-ups, especially when emulating Stokes dynamics or/and modelling at high spatial resolution. IGM is an open-source Python code which deals with two-dimensional (2-D) gridded input and output data. Together with a companion library of trained ice flow emulators, IGM permits user-friendly, highly efficient and mechanically state-of-the-art glacier and icefields simulations.

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