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

2011 ◽  
Vol 8 (4) ◽  
pp. 7207-7235 ◽  
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.

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

2011 ◽  
Vol 15 (11) ◽  
pp. 3399-3410 ◽  
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.

scholarly journals On the sources of hydrological prediction uncertainty in the Amazon

2012 ◽  
Vol 16 (9) ◽  
pp. 3127-3137 ◽  
Author(s):  
R. C. D. Paiva ◽  
W. Collischonn ◽  
M. P. Bonnet ◽  
L. G. G. de Gonçalves
Keyword(s):  
Data Assimilation ◽  
River Basin ◽  
Hydrological Model ◽  
Initial Conditions ◽  
State Variables ◽  
Streamflow Prediction ◽  
Amazon River Basin ◽  
Forecast Uncertainty ◽  
Ensemble Streamflow Prediction ◽  
Hydrological Forecast

Abstract. Recent extreme events in the Amazon River basin and the vulnerability of local population motivate the development of hydrological forecast systems using process based models for this region. In this direction, the knowledge of the source of errors in hydrological forecast systems may guide the choice on improving model structure, model forcings or developing data assimilation systems for estimation of initial model states. We evaluate the relative importance of hydrologic initial conditions and model meteorological forcings errors (precipitation) as sources of stream flow forecast uncertainty in the Amazon River basin. We used a hindcast approach that compares Ensemble Streamflow Prediction (ESP) and a reverse Ensemble Streamflow Prediction (reverse-ESP). Simulations were performed using the physically-based and distributed hydrological model MGB-IPH, comprising surface energy and water balance, soil water, river and floodplain hydrodynamics processes. The model was forced using TRMM 3B42 precipitation estimates. Results show that uncertainty on initial conditions plays an important role for discharge predictability, even for large lead times (∼1 to 3 months) on main Amazonian Rivers. Initial conditions of surface waters state variables are the major source of hydrological forecast uncertainty, mainly in rivers with low slope and large floodplains. Initial conditions of groundwater state variables are important, mostly during low flow period and in the southeast part of the Amazon where lithology and the strong rainfall seasonality with a marked dry season may be the explaining factors. Analyses indicate that hydrological forecasts based on a hydrological model forced with historical meteorological data and optimal initial conditions may be feasible. Also, development of data assimilation methods is encouraged for this region.

On the initial conditions of the ICON-D2-EPS ensemble: An analysis in terms of spread and skill.

2020 ◽  
Author(s):  
Chiara Marsigli
Keyword(s):  
Data Assimilation ◽  
Initial Conditions ◽  
Weather Forecast ◽  
Point Of View ◽  
Ensemble Forecasting ◽  
Limited Area ◽  
Initial Condition ◽  
Weather Situation ◽  
Operational Phase ◽  
Data Assimilation System

<p><span>The COSMO-D2-EPS ensemble is running operationally at DWD at a resolution of 2.2 km. In the framework of the transition from the COSMO to the ICON model for the limited-area applications, the ICON-D2-EPS ensemble is starting its pre-operational phase. Therefore, the perturbation strategy developed for COSMO-D2-EPS is adapted to the new ensemble.</span><br><span>In this work, the focus is on the initial conditions, which are provided by the first 20 analyses generated by a LETKF ensemble data assimilation system (KENDA).</span><br><span>The KENDA analyses present the advantage of providing perturbed initial conditions to the convection-permitting ensemble, where the perturbations contain also the information on the convection-permitting scale uncertainties. On the other hand, the KENDA analyses are optimised for </span><span>the purpose of data assimilation. The ensemble of analyses which is the most suitable for initialising the next data assimilation cycle may not be the same which is the most suitable for initialising the weather forecast ensemble, e.g. in terms of spread.</span></p><p><span>The analyses generated by the KENDA cycle are evaluated from the point of view of their usage for ensemble forecasting initialisation. Their spread is computed for different variables, assessing also how it varies with the spatial scale and with the weather situation. Furthermore, the spread is compared to the error of the analyses and of the forecasts, in order to assess the ability of the analyses to describe the initial condition uncertainty. </span><br><span>The growth of the differences between the members during the first hours of the forecasts is studied as well, in dependence on the weather situation.</span></p><p><span>The final aim of this work is to identify possible improvements for deriving the ensemble initial conditions from the KENDA analyses.</span></p>

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

2013 ◽  
Vol 13 (9) ◽  
pp. 4487-4500 ◽  
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.

scholarly journals An integrated uncertainty and ensemble-based data assimilation approach for improved operational streamflow predictions

2012 ◽  
Vol 16 (3) ◽  
pp. 815-831 ◽  
Author(s):  
M. He ◽  
T. S. Hogue ◽  
S. A. Margulis ◽  
K. J. Franz
Keyword(s):  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Model Parameters ◽  
Streamflow Prediction ◽  
Forecasting Method ◽  
Uncertainty Structure ◽  
The Us ◽  
Ensemble Mean ◽  
Snow Water ◽  
Parameter Uncertainty Analysis

Abstract. The current study proposes an integrated uncertainty and ensemble-based data assimilation framework (ICEA) and evaluates its viability in providing operational streamflow predictions via assimilating snow water equivalent (SWE) data. This step-wise framework applies a parameter uncertainty analysis algorithm (ISURF) to identify the uncertainty structure of sensitive model parameters, which is subsequently formulated into an Ensemble Kalman Filter (EnKF) to generate updated snow states for streamflow prediction. The framework is coupled to the US National Weather Service (NWS) snow and rainfall-runoff models. Its applicability is demonstrated for an operational basin of a western River Forecast Center (RFC) of the NWS. Performance of the framework is evaluated against existing operational baseline (RFC predictions), the stand-alone ISURF and the stand-alone EnKF. Results indicate that the ensemble-mean prediction of ICEA considerably outperforms predictions from the other three scenarios investigated, particularly in the context of predicting high flows (top 5th percentile). The ICEA streamflow ensemble predictions capture the variability of the observed streamflow well, however the ensemble is not wide enough to consistently contain the range of streamflow observations in the study basin. Our findings indicate that the ICEA has the potential to supplement the current operational (deterministic) forecasting method in terms of providing improved single-valued (e.g., ensemble mean) streamflow predictions as well as meaningful ensemble predictions.

scholarly journals Data Assimilation of Terrestrial Water Storage Observations to Estimate Precipitation Fluxes: A Synthetic Experiment

2021 ◽  
Vol 13 (6) ◽  
pp. 1223
Author(s):  
Manuela Girotto ◽  
Rolf Reichle ◽  
Matthew Rodell ◽  
Viviana Maggioni
Keyword(s):  
Data Assimilation ◽  
Snow Water Equivalent ◽  
Water Storage ◽  
Synthetic Data ◽  
Terrestrial Water Storage ◽  
Grace Data ◽  
Precipitation Estimates ◽  
Terrestrial Water ◽  
Snow Water ◽  
Gravity Recovery

The Gravity Recovery and Climate Experiment (GRACE) mission and its Follow-On (GRACE-FO) mission provide unprecedented observations of terrestrial water storage (TWS) dynamics at basin to continental scales. Established GRACE data assimilation techniques directly adjust the simulated water storage components to improve the estimation of groundwater, streamflow, and snow water equivalent. Such techniques artificially add/subtract water to/from prognostic variables, thus upsetting the simulated water balance. To overcome this limitation, we propose and test an alternative assimilation scheme in which precipitation fluxes are adjusted to achieve the desired changes in simulated TWS. Using a synthetic data assimilation experiment, we show that the scheme improves performance skill in precipitation estimates in general, but that it is more robust for snowfall than for rainfall, and it fails in certain regions with strong horizontal gradients in precipitation. The results demonstrate that assimilation of TWS observations can help correct (adjust) the model’s precipitation forcing and, in turn, enhance model estimates of TWS, snow mass, soil moisture, runoff, and evaporation. A key limitation of the approach is the assumption that all errors in TWS originate from errors in precipitation. Nevertheless, the proposed approach produces more consistent improvements in simulated runoff than the established GRACE data assimilation techniques.

For How Long Should What Data Be Assimilated for the Mesoscale Forecasting of Convection and Why? Part I: On the Propagation of Initial Condition Errors and Their Implications for Data Assimilation

2010 ◽  
Vol 138 (1) ◽  
pp. 242-255 ◽  
Author(s):  
Frédéric Fabry ◽  
Juanzhen Sun
Keyword(s):  
Data Assimilation ◽  
Great Plains ◽  
Incomplete Data ◽  
Weather Forecasting ◽  
Time Window ◽  
Initial Conditions ◽  
Time Interval ◽  
Initial Condition ◽  
Convective Storms ◽  
Assimilation Time

Abstract Data assimilation is used among other things to constrain the initial conditions of weather forecasting models by fitting the model fields to observations made over a certain time interval. In particular, it tries to tie incomplete data with model constraints to detect and correct for initial condition errors. This is possible only if initial condition errors leave their signature on the data assimilated and if the model is capable of faithfully reproducing such signatures. Using simulations of the evolution of convective storms in the Great Plains over an active 6-day period, the propagation of initial condition errors to other variables as well as their effect on the accuracy of the forecasts were investigated. Increasing the assimilation time window boosts the ability of assimilation systems to detect a variety of initial condition errors; however, limits to the predictability of convective events impose a maximum assimilation period that is a function of the type of measurements assimilated as well as of the type of errors one tries to correct for. These findings are then used to suggest changes in assimilation approaches to take into account the different predictability times of the model fields constrained by assimilation.

scholarly journals Impact of Resolution Degradation of the Initial Condition on Typhoon Track Forecasts

2010 ◽  
Vol 25 (5) ◽  
pp. 1568-1573 ◽  
Author(s):  
Takemasa Miyoshi ◽  
Takuya Komori ◽  
Hitoshi Yonehara ◽  
Ryota Sakai ◽  
Munenhiko Yamaguchi
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Initial Conditions ◽  
Global Analysis ◽  
Weather Prediction ◽  
Japan Meteorological Agency ◽  
Ensemble Prediction ◽  
Initial Condition ◽  
Ensemble Prediction System ◽  
Typhoon Track

Abstract The operational numerical weather prediction (NWP) systems at the Japan Meteorological Agency (JMA) indicated that the typhoon track forecasts made by the control member of the ensemble prediction system (EPS) tended to be worse than those made by the high-resolution global NWP. The control forecast of the EPS with horizontal triangular truncation at 319 wavenumbers and 60 vertical levels (T319/L60 resolution) was initialized by eliminating the higher-wavenumber components of the global analysis at T959/L60 resolution. When the data assimilation cycle was performed at the lower T319/L60 resolution, the forecast gave typhoon track forecasts closer to the high-resolution global NWP. Therefore, it stands to reason that the resolution transform of the initial condition must be responsible for the degradation of the typhoon track forecasts at least to considerable extent. To improve the low-resolution forecast, two approaches are tested in this study: 1) applying a smoother spectral truncation for the resolution transform and 2) performing noncycled lower-resolution data assimilation during preprocessing. Results from the single case study of Typhoon Nuri (2008) indicate almost no impact from the former approach, but a significant positive impact when using the latter approach. The results of this study illuminate the importance of considering a model’s resolving capability during data assimilation. Namely, if the initial conditions contain features caused by unresolved scales, degraded forecasts may result.

scholarly journals Seasonal hydrologic prediction in the United States: understanding the role of initial hydrologic conditions and seasonal climate forecast skill

2011 ◽  
Vol 15 (11) ◽  
pp. 3529-3538 ◽  
Author(s):  
S. Shukla ◽  
D. P. Lettenmaier
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Forecast Skill ◽  
Climate Forecast ◽  
Streamflow Prediction ◽  
Seasonal Climate ◽  
Western Us ◽  
Ensemble Streamflow Prediction ◽  
Hydrologic Forecast ◽  
Hydrologic Conditions

Abstract. Seasonal hydrologic forecasts derive their skill from knowledge of initial hydrologic conditions and climate forecast skill associated with seasonal climate outlooks. Depending on the type of hydrological regime and the season, the relative contributions of initial hydrologic conditions and climate forecast skill to seasonal hydrologic forecast skill vary. We seek to quantify these contributions on a relative basis across the Conterminous United States. We constructed two experiments – Ensemble Streamflow Prediction and reverse-Ensemble Streamflow Prediction – to partition the contributions of the initial hydrologic conditions and climate forecast skill to overall forecast skill. In ensemble streamflow prediction (first experiment) hydrologic forecast skill is derived solely from knowledge of initial hydrologic conditions, whereas in reverse-ensemble streamflow prediction (second experiment), it is derived solely from atmospheric forcings (i.e. perfect climate forecast skill). Using the ratios of root mean square error in predicting cumulative runoff and mean monthly soil moisture of each experiment, we identify the variability of the relative contributions of the initial hydrologic conditions and climate forecast skill spatially throughout the year. We conclude that the initial hydrologic conditions generally have the strongest influence on the prediction of cumulative runoff and soil moisture at lead-1 (first month of the forecast period), beyond which climate forecast skill starts to have greater influence. Improvement in climate forecast skill alone will lead to better seasonal hydrologic forecast skill in most parts of the Northeastern and Southeastern US throughout the year and in the Western US mainly during fall and winter months; whereas improvement in knowledge of the initial hydrologic conditions can potentially improve skill most in the Western US during spring and summer months. We also observed that at a short lead time (i.e. lead-1) contribution of the initial hydrologic conditions in soil moisture forecasts is more extensive than in cumulative runoff forecasts across the Conterminous US.

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