scholarly journals Assimilating near-real-time mass balance stake readings into a model ensemble using a particle filter

2021 ◽  
Vol 15 (11) ◽  
pp. 5017-5040
Author(s):  
Johannes Marian Landmann ◽  
Hans Rudolf Künsch ◽  
Matthias Huss ◽  
Christophe Ogier ◽  
Markus Kalisch ◽  
...  
Keyword(s):  
Energy Balance ◽  
Particle Filter ◽  
Mass Balance ◽  
Real Time ◽  
Model Performance ◽  
Point Mass ◽  
Solar Irradiation ◽  
Balance Model ◽  
Model Parameters ◽  
Mass Balances

Abstract. Short-term glacier variations can be important for water supplies or hydropower production, and glaciers are important indicators of climate change. This is why the interest in near-real-time mass balance nowcasting is considerable. Here, we address this interest and provide an evaluation of continuous observations of point mass balance based on online cameras transmitting images every 20 min. The cameras were installed on three Swiss glaciers during summer 2019, provided 352 near-real-time point mass balances in total, and revealed melt rates of up to 0.12 m water equivalent per day (mw.e.d-1) and of more than 5 mw.e. in 81 d. By means of a particle filter, these observations are assimilated into an ensemble of three TI (temperature index) and one simplified energy-balance mass balance models. State augmentation with model parameters is used to assign temporally varying weights to individual models. We analyze model performance over the observation period and find that the probability for a given model to be preferred by our procedure is 39 % for an enhanced TI model, 24 % for a simple TI model, 23 %, for a simplified energy balance model, and 14 % for a model employing both air temperature and potential solar irradiation. When compared to reference forecasts produced with both mean model parameters and parameters tuned on single mass balance observations, the particle filter performs about equally well on the daily scale but outperforms predictions of cumulative mass balance by 95 %–96 %. A leave-one-out cross-validation on the individual glaciers shows that the particle filter is also able to reproduce point observations at locations not used for model calibration. Indeed, the predicted mass balances is always within 9 % of the observations. A comparison with glacier-wide annual mass balances involving additional measurements distributed over the entire glacier mostly shows very good agreement, with deviations of 0.02, 0.07, and 0.24 mw.e.

scholarly journals Assimilating near real-time mass balance observations into a model ensemble using a particle filter

2020 ◽  
Author(s):  
Johannes M. Landmann ◽  
Hans R. Künsch ◽  
Matthias Huss ◽  
Christophe Ogier ◽  
Markus Kalisch ◽  
...  
Keyword(s):  
Particle Filter ◽  
Mass Balance ◽  
Real Time ◽  
Model Performance ◽  
Point Mass ◽  
Model Parameters ◽  
Mass Balances ◽  
Additional Energy ◽  
Mass Balance Models ◽  
The Individual

Abstract. Glaciers fulfil important short-term functions like drinking water supply and they are important indicators of climate change. This is why the interest in near real-time mass balance nowcasting is high. Here, we address this interest and provide an evaluation of seven continuous observations of point mass balance based on on-line cameras transmitting images every 20 minutes on three Swiss glaciers during summer 2019. Like this, we read 352 near real-time daily point mass balances in total from the camera images, revealing melt rates of up to 0.12 meter water equivalent per day (m w.e. d−1) and the biggest total melt on the tongue of Findelgletscher with more than 5 m w.e. in 81 days. These observations are assimilated into an ensemble of three temperature index (TI) and one simplified energy balance mass balance models using an augmented particle filter with a custom resampling method. The state augmentation allows estimating model parameters over time. The custom resampling ensures that temporarily poorly performing models are kept in the ensemble instead of being removed during the resampling step of the particle filter. We analyse model performance over the observation period, and find that the model probability within the ensemble is highest on average with 58 % for an enhanced TI model, a simple TI model reaches about 19 %, while models incorporating additional energy fluxes have probabilities between 8 % and 15 %. When compared to reference forecasts produced with both mean model parameters and parameters tuned on single mass balance observations, the mass balances produced with the particle filter performs about equally well on the daily scale, but outperforms predictions of cumulative mass balance. The particle filter improves the performance scores of the reference forecasts by 91–97 % in these cases. A leave-one-out cross-validation on the individual glaciers shows that the particle filter is able to reproduce point observations at locations on the glacier where it was not calibrated, as the filtered mass balances do not deviate more than 8 % from the cumulative observations at the test locations. A comparison with glacier-wide annual mass balance by Glacier Monitoring Switzerland (GLAMOS) involving additional measurements distributed over the entire glacier, mostly show good agreement, but also deviations of up to 0.41 m w.e. for one instance.

scholarly journals Climatic controls and climate proxy potential of Lewis Glacier, Mt. Kenya

2016 ◽  
Vol 10 (1) ◽  
pp. 133-148 ◽  
Author(s):  
R. Prinz ◽  
L. I. Nicholson ◽  
T. Mölg ◽  
W. Gurgiser ◽  
G. Kaser
Keyword(s):  
Energy Balance ◽  
Mass Balance ◽  
Radiative Energy ◽  
Balance Model ◽  
Model Parameters ◽  
Glacier Mass Balance ◽  
Mass Balances ◽  
Positive Mass ◽  
Current Sensitivity ◽  
Maximum Extent

Abstract. The Lewis Glacier on Mt. Kenya is one of the best studied tropical glaciers and has experienced considerable retreat since a maximum extent in the late 19th century (L19). From distributed mass and energy balance modelling, this study evaluates the current sensitivity of the surface mass and energy balance to climatic drivers, explores climate conditions under which the L19 maximum extent might have been sustained, and discusses the potential for using the glacier retreat to quantify climate change. Multi-year meteorological measurements at 4828 m provide data for input, optimization, and evaluation of a spatially distributed glacier mass balance model to quantify the exchanges of energy and mass at the glacier–atmosphere interface. Currently the glacier loses mass due to the imbalance between insufficient accumulation and enhanced melt, because radiative energy gains cannot be compensated by turbulent energy sinks. Exchanging model input data with synthetic climate scenarios, which were sampled from the meteorological measurements and account for coupled climatic variable perturbations, reveals that the current mass balance is most sensitive to changes in atmospheric moisture (via its impact on solid precipitation, cloudiness, and surface albedo). Positive mass balances result from scenarios with an increase of annual (seasonal) accumulation of 30 % (100 %), compared to values observed today, without significant changes in air temperature required. Scenarios with lower air temperatures are drier and associated with lower accumulation and increased net radiation due to reduced cloudiness and albedo. If the scenarios currently producing positive mass balances are applied to the L19 extent, negative mass balances are the result, meaning that the conditions required to sustain the glacier in its L19 extent are not reflected in today's meteorological observations using model parameters optimized for the present-day glacier. Alternatively, a balanced mass budget for the L19 extent can be achieved by changing both climate and optimized gradients (used to extrapolate the meteorological measurements over the glacier) in a manner that implies a distinctly different coupling between the glacier's local surface-air layer and its surrounding boundary layer. This result underlines the difficulty of deriving palaeoclimates for larger glacier extents on the basis of modern measurements of small glaciers.

scholarly journals Climatic controls and climate proxy potential of Lewis Glacier, Mt Kenya

2015 ◽  
Vol 9 (4) ◽  
pp. 3887-3924
Author(s):  
R. Prinz ◽  
L. I. Nicholson ◽  
T. Mölg ◽  
W. Gurgiser ◽  
G. Kaser
Keyword(s):  
Energy Balance ◽  
Mass Balance ◽  
Radiative Energy ◽  
Balance Model ◽  
Model Parameters ◽  
Glacier Mass Balance ◽  
Mass Balances ◽  
Positive Mass ◽  
Current Sensitivity ◽  
Maximum Extent

Abstract. The Lewis Glacier on Mt Kenya is one of the best studied tropical glaciers and has experienced considerable retreat since a maximum extent in the late 19th century (L19). From distributed mass and energy balance modelling, this study evaluates the current sensitivity of the surface mass and energy balance to climatic drivers, explores climate conditions under which the L19 maximum extent might have sustained, and discusses the potential for using the glacier retreat to quantify climate change. Multiyear meteorological measurements at 4828 m provide data for input, optimization and evaluation of a spatially distributed glacier mass balance model to quantify the exchanges of energy and mass at the glacier–atmosphere interface. Currently the glacier loses mass due to the imbalance between insufficient accumulation and enhanced melt, because radiative energy gains cannot be compensated by turbulent energy sinks. Exchanging model input data with synthetic climate scenarios, which were sampled from the meteorological measurements and account for coupled climatic variable perturbations, reveal that the current mass balance is most sensitive to changes in atmospheric moisture (via its impact on solid precipitation, cloudiness and surface albedo). Positive mass balances result from scenarios with an increase of annual (seasonal) accumulation of 30 % (100 %), compared to values observed today, without significant changes in air temperature required. Scenarios with lower air temperatures are drier and associated with lower accumulation and increased net radiation due to reduced cloudiness and albedo. If the scenarios currently producing positive mass balances are applied to the L19 extent, negative mass balances are the result, meaning that the conditions required to sustain the glacier in its L19 extent are not reflected in today's observations. Alternatively, a balanced mass budget for the L19 extent can be explained by changing model parameters that imply a distinctly different coupling between the glacier's local surface-air layer and its surrounding boundary-layer. This result underlines the difficulty of deriving paleoclimates for larger glacier extents on the basis of modern measurements of small glaciers.

CRAMPON: a model- and observation-based near real-time platform for glacier mass balance monitoring in Switzerland

2021 ◽  
Author(s):  
Johannes Marian Landmann ◽  
Matthias Huss ◽  
Hans Rudolf Künsch ◽  
Christophe Ogier ◽  
Lea Geibel ◽  
...  
Keyword(s):  
Particle Filter ◽  
Mass Balance ◽  
Real Time ◽  
Resource Planning ◽  
Glacier Mass Balance ◽  
Sequential Data ◽  
Mass Balances ◽  
Individual Parameter ◽  
High Interest ◽  
The Past

<p>As glaciers shrink, high interest in their near real-time mass balance arises. This is mainly for two reasons: first, there are concerns about water availability and short-term water resource planning, and second, glaciers are one of the most prominent indicators of climate change, resulting in a high interest of the broader public.</p><p>To satisfy both interests regarding information on near real-time mass balance, we are running the project CRAMPON – “Cryospheric Monitoring and Prediction Online”. Within this project, we set up an operational assimilation platform where it is possible to query daily mass balance estimates in near real-time, i.e. updated with a lag of max. 24 hours. During the operational alpha phase, we increase the amount of modelled glaciers and assimilated observations steadily. We start with about 15 glaciers from the Glacier Monitoring Switzerland (GLAMOS) program, for which time series of seasonal mass balances from the glaciological method are available. After that, we expand our set of modelled glaciers to about 50 glaciers that have frequent geodetic mass balances in the past, and finally to all glaciers in Switzerland. The assimilated observations reach from the operational GLAMOS seasonal mass balance observations via daily point mass balances from nine in situ cameras providing instantaneous ablation rates to satellite-derived albedo and snow distribution on the glacier.<br>As basis for the platform, we run an ensemble of three temperature index and one simplified energy balance melt models. This ensemble takes gridded temperature, precipitation and radiation as input and aims at quantifying uncertainties of the produced daily mass balances. To determine uncertainties in the model prediction of a current mass budget year correctly, we run the models with parameter distributions we have fitted on individual parameter sets calibrated in the past. Since a purely model-based prediction can reveal high uncertainties though, we choose a sequential data assimilation approach in the form of a Particle Filter to constrain this uncertainty with observations, whenever available. We have customized the standard Particle Filter to (1) use a resampling method that is able to keep models in the ensemble despite a temporary bad performance, and (2) allow parameter and model probability evolution over time.</p><p>In this contribution, we focus on giving a holistic overview over the already existing platform features and discuss the future developments. We plan to make the calculated mass balances publicly available in summer 2021, and to extend this platform to the global scale at a later stage.</p>

Modelling mass changes of Dokriani (Central Himalaya) and Chhota Shigri (Western Himalaya) glaciers, India using energy balance approach

2021 ◽  
Author(s):  
Smriti Srivastava ◽  
Mohd Farooq Azam
Keyword(s):  
Energy Balance ◽  
Surface Energy ◽  
Mass Balance ◽  
Western Himalaya ◽  
Reanalysis Data ◽  
Heat Fluxes ◽  
Balance Model ◽  
Mass Balances ◽  
Chhota Shigri Glacier ◽  
Energy Balance Approach

<p>Processes controlling the glacier wastage in the Himalaya are still poorly understood. In the present study, a surface energy-mass balance model is applied to reconstruct the long-term mass balances over 1979-2020 on two benchmark glaciers, Dokriani and Chhota Shigri, located in different climatic regimes. The model is forced with ERA5 reanalysis data and calibrated using field-observed point mass balances. The model is validated against available glacier-wide mass balances. Dokriani and Chhota Shigri glaciers show moderate wastage with a mean value of –0.28 and –0.34 m w.e. a<sup>-1</sup>, respectively over 1979-2020. The mean winter and summer glacier-wide mass balances are 0.44 and –0.72 m w.e. a<sup>-1</sup> for Dokriani Glacier and 0.53 and –0.85 m w.e. a<sup>-1</sup> for Chhota Shigri Glacier, respectively, showing a higher mass turn over on Chhota Shigri Glacier. Net radiation flux is the major component of surface energy balance followed by sensible and latent heat fluxes on both the glaciers. The losses through sublimation is around 10% to the total ablation. Surface albedo is one of the most important drivers controlling the annual mass balance of both Dokriani and Chhota Shigri glacier. Summer mass balance (0.76, p<0.05) mainly controls the annual glacier-wide mass balance on Dokriani Glacier whereas the summer (0.91, p<0.05) and winter (0.78, p<0.05) mass balances together control the annual glacier-wide mass balance on Chhota Shigri Glacier.</p>

Predicting glacier mass balance by data assimilation from on-ice cameras

2020 ◽  
Author(s):  
Johannes Landmann ◽  
Christophe Ogier ◽  
Matthias Huss ◽  
Daniel Farinotti
Keyword(s):  
Water Resources ◽  
Data Assimilation ◽  
Mass Balance ◽  
Regional Scale ◽  
Absolute Error ◽  
Balance Model ◽  
Model Parameters ◽  
Glacier Mass Balance ◽  
Mass Balances ◽  
Average Mass

<p>With the widespread retreat of glaciers, concerns emerge for the availability of water resources. These concerns are largest for future dry spells, when runoff from other sources is low. In this context, mass balance estimates for time horizons from days to weeks might help to better manage water resources in alpine regions. Here, we obtain such estimates from a combined modelling and data assimilation approach. Starting with three glaciers with detailed monitoring in Switzerland, we extrapolate our signal to other unmeasured glaciers in the country.</p><p>For the mass balance modeling, an ensemble of four melt models is tuned to match semi-annual in-situ observations from the Glacier Monitoring Switzerland (GLAMOS) program. With this ensemble, we then infer mass balance for the observed glaciers. Three of the glaciers (Rhonegletscher, Findelgletscher and Glacier de la Plaine Morte) were equipped with on-ice cameras between mid-June and early October 2019. The cameras transmitted 352 daily point mass balance observations which we assimilate into our model results by employing a particle filter.</p><p>To transfer the mass balance information of the three well-observed glaciers to other glaciers in Switzerland, we make use of the strong spatial correlation of cumulative melt. In a workflow here termed “percentile extrapolation method”, first, all glaciers without direct mass balance measurements are calibrated based on geodetic mass balances covering the 1980-2010 period. To reduce the large uncertainty in calibration on geodetic mass changes, we first predict average mass balance model parameters for each glacier with a random forest regressor. Then, we tune these parameters to match the geodetic mass balance in a least squares minimization. As soon as a mass balance climatology for the past has been calculated with this calibration, we determine with which percentiles of this climatology the current year’s mass balance ensemble estimate overlaps at the well-observed glaciers. These percentiles are then extrapolated in space using inverse distance weighting and they are applied to the climatology of unmeasured glaciers. The procedure yields a mass balance estimate at every single day of a year for every Swiss glacier taking into account specific glacier characteristics.</p><p>We compare the assimilated camera mass balances with interpolated measurements from the GLAMOS program. First results indicate that for the annual mass balance, the camera data lower the mean absolute error to 0.19 m water equivalent (w.e.), from 0.36 m w.e for a model prediction without data assimilation. The standard deviation of the prediction ensemble is reduced by 0.37 m w.e. on average. A cross-validation using percentile extrapolation between the glaciers equipped with a camera shows that annual mass balance can be predicted within 0.27 m w.e.. The summer (May to September) melt of other glaciers in the GLAMOS program can be predicted with an absolute error of 0.07m w.e. (model: 0.27 m w.e). Our results indicate that the continuous monitoring of a few selected sites has the potential of strongly improving daily near real-time mass balance estimates at the regional scale.</p>

scholarly journals Distributed Energy Balance Flux Modelling of Mass Balances in the Artesonraju Glacier and Discharge in the Basin of Artesoncocha, Cordillera Blanca, Peru

Climate ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 143
Author(s):  
María Fernanda Lozano Gacha ◽  
Manfred Koch
Keyword(s):  
Energy Balance ◽  
Mass Balance ◽  
Energy Balance Model ◽  
Shortwave Radiation ◽  
Radiative Energy ◽  
Balance Model ◽  
Threshold Temperature ◽  
Mass Balances ◽  
Distributed Energy ◽  
Cordillera Blanca

A distributed energy balance model (DEBAM) is applied to estimate the mass balance of the Artesonraju glacier in the Cordillera Blanca (CB), Peru, and to simulate the ensuing discharge into its respective basin, Artesoncocha. The energy balance model calibrations show that, by using seasonal albedos, reasonable results for mass balances and discharge can be obtained, as witnessed by annually aggregated Nash Sutcliffe coefficients (E) of 0.60–0.87 for discharge and of 0.58–0.71 for mass measurements carried out in the period 2004–2007. Mass losses between −1.42 and −0.45 m.w.e. are calculated for that period. The elevation line altitudes (ELAs), which lie between 5009 and 5050 m.a.s.l., are also well simulated, compared to those measured by the Unidad Glaciologica de Recursos Hídricos del Perú (UGRH). It is demonstrated that the net radiation which drives the energy balance and melting processes is mainly affected by the amount of reflected shortwave radiation from the different surfaces. Moreover, the longwave radiation sinks between 63 and 73% of solar radiative energy in the dry season. Further sensitivity studies indicate that the assumed threshold temperature T0 is crucial in mass balance simulations, as it determines the extension of areas with different albedos. An optimal T0 between 2.6 and 3.8 °C is deduced from these simulations.

scholarly journals Enhanced Neural Network Model for Worldwide Estimation of Weighted Mean Temperature

2021 ◽  
Vol 13 (12) ◽  
pp. 2405
Author(s):  
Fengyang Long ◽  
Chengfa Gao ◽  
Yuxiang Yan ◽  
Jinling Wang
Keyword(s):  
Neural Network ◽  
Real Time ◽  
Model Performance ◽  
Global Scale ◽  
Radiosonde Data ◽  
Model Parameters ◽  
Measured Temperature ◽  
Weighted Mean ◽  
Weighted Mean Temperature ◽  
Application Scope

Precise modeling of weighted mean temperature (Tm) is critical for realizing real-time conversion from zenith wet delay (ZWD) to precipitation water vapor (PWV) in Global Navigation Satellite System (GNSS) meteorology applications. The empirical Tm models developed by neural network techniques have been proved to have better performances on the global scale; they also have fewer model parameters and are thus easy to operate. This paper aims to further deepen the research of Tm modeling with the neural network, and expand the application scope of Tm models and provide global users with more solutions for the real-time acquisition of Tm. An enhanced neural network Tm model (ENNTm) has been developed with the radiosonde data distributed globally. Compared with other empirical models, the ENNTm has some advanced features in both model design and model performance, Firstly, the data for modeling cover the whole troposphere rather than just near the Earth’s surface; secondly, the ensemble learning was employed to weaken the impact of sample disturbance on model performance and elaborate data preprocessing, including up-sampling and down-sampling, which was adopted to achieve better model performance on the global scale; furthermore, the ENNTm was designed to meet the requirements of three different application conditions by providing three sets of model parameters, i.e., Tm estimating without measured meteorological elements, Tm estimating with only measured temperature and Tm estimating with both measured temperature and water vapor pressure. The validation work is carried out by using the radiosonde data of global distribution, and results show that the ENNTm has better performance compared with other competing models from different perspectives under the same application conditions, the proposed model expanded the application scope of Tm estimation and provided the global users with more choices in the applications of real-time GNSS-PWV retrival.

scholarly journals Estimates of Uncertainty in Predictions of Global Mean Surface Temperature

2007 ◽  
Vol 20 (5) ◽  
pp. 843-855 ◽  
Author(s):  
J. A. Kettleborough ◽  
B. B. B. Booth ◽  
P. A. Stott ◽  
M. R. Allen
Keyword(s):  
Twentieth Century ◽  
Energy Balance ◽  
Temperature Change ◽  
Radiative Forcing ◽  
Balance Model ◽  
Future Climate Change ◽  
Model Parameters ◽  
Global Mean Temperature ◽  
Mean Temperature ◽  
Global Mean

Abstract A method for estimating uncertainty in future climate change is discussed in detail and applied to predictions of global mean temperature change. The method uses optimal fingerprinting to make estimates of uncertainty in model simulations of twentieth-century warming. These estimates are then projected forward in time using a linear, compact relationship between twentieth-century warming and twenty-first-century warming. This relationship is established from a large ensemble of energy balance models. By varying the energy balance model parameters an estimate is made of the error associated with using the linear relationship in forecasts of twentieth-century global mean temperature. Including this error has very little impact on the forecasts. There is a 50% chance that the global mean temperature change between 1995 and 2035 will be greater than 1.5 K for the Special Report on Emissions Scenarios (SRES) A1FI scenario. Under SRES B2 the same threshold is not exceeded until 2055. These results should be relatively robust to model developments for a given radiative forcing history.

scholarly journals Application of a mass-balance model to a Himalayan glacier

1999 ◽  
Vol 45 (151) ◽  
pp. 559-567 ◽  
Author(s):  
Rijan Bhakta Kayastha ◽  
Tetsuo Ohata ◽  
Yutaka Ageta
Keyword(s):  
Mass Balance ◽  
Surface Albedo ◽  
Balance Model ◽  
Mass Balance Model ◽  
Mass Balances ◽  
Climatic Parameters ◽  
Glacier Surface ◽  
Temperature And Precipitation ◽  
Ice Surface ◽  
Good Agreement

AbstractA mass-balance model based on the energy balance at the snow or ice surface is formulated, with particular attention paid to processes affecting absorption of radiation. The model is applied to a small glacier, Glacier AX010 in the Nepalese Himalaya, and tests of its mass-balance sensitivity to input and climatic parameters are carried out. Calculated and observed area-averaged mass balances of the glacier during summer 1978 (June-September) show good agreement, namely -0.44 and -0.46 m w.e., respectively.Results show the mass balance is strongly sensitive to snow or ice albedo, to the effects of screening by surrounding mountain walls, to areal variations in multiple reflection between clouds and the glacier surface, and to thin snow covers which alter the surface albedo. In tests of the sensitivity of the mass balance to seasonal values of climatic parameters, the mass balance is found to be strongly sensitive to summer air temperature and precipitation but only weakly sensitive to relative humidity.

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