Reducing Forecast Errors Due to Model Imperfections Using Ensemble Kalman Filtering

2010 ◽  
Vol 138 (8) ◽  
pp. 3316-3332 ◽  
Author(s):  
Hiroshi Koyama ◽  
Masahiro Watanabe
Keyword(s):  
Parameter Estimation ◽  
Time Scale ◽  
Lead Time ◽  
General Circulation ◽  
Forecast Error ◽  
Small Scale ◽  
Model Parameters ◽  
Forecast Errors ◽  
Model Ensemble ◽  
Forecast Lead Time

Abstract A method is introduced for reducing forecast errors in an extended-range to one-month forecast based on an ensemble Kalman filter (EnKF). The prediction skill in such a forecast is typically affected not only by the accuracy of initial conditions but also by the model imperfections. Hence, to improve the forecast in imperfect models, the framework of EnKF is modified by using a state augmentation method. The method includes an adaptive parameter estimation that optimizes mismatched model parameters and a model ensemble initialized with the perturbed model parameter. The main features are the combined ensemble forecast of the initial condition and the parameter, and the assimilation for time-varying parameters with a theoretical basis. First, the method is validated in the imperfect Lorenz ’96 model constructed by parameterizing the small-scale variable of the perfect model. The results indicate a reduction in the ensemble-mean forecast error and the optimization of the ensemble spread. It is found that the time-dependent parameter estimation contributes to reduce the forecast error with a lead time shorter than one week, whereas the model ensemble is effective for improving a forecast with a longer lead time. Moreover, the parameter assimilation is useful when model imperfections have a longer time scale than the forecast lead time, and the model ensemble appears to be relevant in any time scale. Preliminary results using a low-resolution atmospheric general circulation model that implements this method support some of the above findings.

Integrating Parameter Estimation Solutions From the Time and Time-Scale Domains

2009 ◽  
Author(s):  
James R. McCusker ◽  
Kourosh Danai
Keyword(s):  
Parameter Estimation ◽  
Least Squares ◽  
Time Scale ◽  
Prediction Error ◽  
Nonlinear Least Squares ◽  
Integrated Approach ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Single Model ◽  
Iterative Estimation

A method of parameter estimation was recently introduced that separately estimates each parameter of the dynamic model [1]. In this method, regions coined as parameter signatures, are identified in the time-scale domain wherein the prediction error can be attributed to the error of a single model parameter. Based on these single-parameter associations, individual model parameters can then be estimated for iterative estimation. Relative to nonlinear least squares, the proposed Parameter Signature Isolation Method (PARSIM) has two distinct attributes. One attribute of PARSIM is to leave the estimation of a parameter dormant when a parameter signature cannot be extracted for it. Another attribute is independence from the contour of the prediction error. The first attribute could cause erroneous parameter estimates, when the parameters are not adapted continually. The second attribute, on the other hand, can provide a safeguard against local minima entrapments. These attributes motivate integrating PARSIM with a method, like nonlinear least-squares, that is less prone to dormancy of parameter estimates. The paper demonstrates the merit of the proposed integrated approach in application to a difficult estimation problem.

Impact of Forecast Uncertainty on Wind Farm Profitability

2020 ◽  
Vol 142 (4) ◽  
Author(s):  
Guido Francesco Frate ◽  
Lorenzo Ferrari ◽  
Umberto Desideri
Keyword(s):  
Power Plants ◽  
Wind Farm ◽  
Forecast Error ◽  
Market Competition ◽  
Renewable Energy Sources ◽  
Power Level ◽  
Point Of View ◽  
Energy Market ◽  
Forecast Errors ◽  
Forecast Lead Time

Abstract The great amount of support schemes that initially fueled the fast and often uncontrollable, renewable energy sources (RESs) growth have been strongly reduced or revoked in many countries. Currently, the general trend is to try to equate RESs to traditional power plants. From the energy market point of view, this entails exposing RESs to market competition and mechanics. For example, it could be requested that RESs submit a production schedule in advance and are financially responsible for any deviation from it. This could push the wind farm (WF) operators to make accurate forecasts, thus fostering the electric system resiliency and an efficient use of balancing resources. From the forecasting point of view, this is not a trivial problem since the schedule submission is often due 10–12 h before the actual delivery. Since forecast errors are unavoidable, the submitted schedule could turn out to be infeasible, thus forcing the WF to adopt correcting actions, which are generally costly. This study estimates the revenue reduction that would affect a WF operating in the energy market due to forecast errors. To do this in a realistic way, a case study is selected, and realistic forecast scenarios are generated by using a copula approach. Relevant forecast error features, like autocorrelation and dependency on forecasted power level and forecast lead time, are modeled. The revenue reduction due to balancing actions is calculated on an annual basis, by using typical days. These were derived through a clustering procedure based on production data. Losses ranging from 5% to 35% have been found, depending on the days and market prices. A sensitivity analysis to the costs of balancing actions is performed. The effect of different market architectures and different RESs penetration level is considered in the analysis. Finally, the effectiveness of two techniques (i.e., curtailment and batteries) to reduce forecast error impact in highly penalizing market environments is assessed.

A Study of Impacts of Coupled Model Initial Shocks and State–Parameter Optimization on Climate Predictions Using a Simple Pycnocline Prediction Model

2011 ◽  
Vol 24 (23) ◽  
pp. 6210-6226 ◽  
Author(s):  
S. Zhang
Keyword(s):  
Time Scales ◽  
Parameter Optimization ◽  
Time Scale ◽  
Model Simulation ◽  
Coupled Model ◽  
State Parameter ◽  
Model Parameters ◽  
Forecast Errors ◽  
Long Time ◽  
The Impact

Abstract A skillful decadal prediction that foretells varying regional climate conditions over seasonal–interannual to multidecadal time scales is of societal significance. However, predictions initialized from the climate-observing system tend to drift away from observed states toward the imperfect model climate because of the model biases arising from imperfect model equations, numeric schemes, and physical parameterizations, as well as the errors in the values of model parameters. Here, a simple coupled model that simulates the fundamental features of the real climate system and a “twin” experiment framework are designed to study the impact of initialization and parameter optimization on decadal predictions. One model simulation is treated as “truth” and sampled to produce “observations” that are assimilated into other simulations to produce observation-estimated states and parameters. The degree to which the model forecasts based on different estimates recover the truth is an assessment of the impact of coupled initial shocks and parameter optimization on climate predictions of interests. The results show that the coupled model initialization through coupled data assimilation in which all coupled model components are coherently adjusted by observations minimizes the initial coupling shocks that reduce the forecast errors on seasonal–interannual time scales. Model parameter optimization with observations effectively mitigates the model bias, thus constraining the model drift in long time-scale predictions. The coupled model state–parameter optimization greatly enhances the model predictability. While valid “atmospheric” forecasts are extended 5 times, the decadal predictability of the “deep ocean” is almost doubled. The coherence of optimized model parameters and states is critical to improve the long time-scale predictions.

scholarly journals Hydrological response to climate change of the Brahmaputra basin using CMIP5 general circulation model ensemble

2017 ◽  
Vol 9 (3) ◽  
pp. 434-448 ◽  
Author(s):  
A. K. M. Saiful Islam ◽  
Supria Paul ◽  
Khaled Mohammed ◽  
Mutasim Billah ◽  
Md. Golam Rabbani Fahad ◽  
...  
Keyword(s):  
Climate Change ◽  
Fresh Water ◽  
General Circulation ◽  
Water Discharge ◽  
Model Parameters ◽  
Projection Data ◽  
Hydrological Response ◽  
Model Ensemble ◽  
Brahmaputra Basin ◽  
Monsoon Flow

Abstract The Ganges–Brahmaputra–Meghna river system carries the world's third-largest fresh water discharge and Brahmaputra alone carries about 67% of the total annual flow of Bangladesh. Climate change will be expected to alter the hydrological cycles and the flow regime of these basins. Assessment of the fresh water availability of the Brahmaputra Basin in the future under climate change condition is crucial for both society and the ecosystem. SWAT, a semi-distributed physically based hydrological model, has been applied to investigate hydrological response of the basin. However, it is a challenging task to calibrate and validate models over this ungauged and poor data basin. A model derived by using gridded rainfall data from the Tropical Rainfall Measuring Mission (TRMM) satellite and temperature data from reanalysis product ERA-Interim provides acceptable calibration and validation. Using the SWAT-CUP with SUFI-2 algorithm, sensitivity analysis of model parameters was examined. A calibrated model was derived using new climate change projection data from the multi-model ensemble CMIP5 Project over the South Asia CORDEX domain. The uncertainty of predicting monsoon flow is less than that of pre-monsoon flow. Most of the regional climate models (RCMs) show an increasing tendency of the discharge of Brahmaputra River at Bahadurabad station during monsoon, when flood usually occurs in Bangladesh.

scholarly journals Error Propagation of Radar Rainfall Nowcasting Fields through a Fully Distributed Flood Forecasting Model

2007 ◽  
Vol 46 (6) ◽  
pp. 932-940 ◽  
Author(s):  
Enrique R. Vivoni ◽  
Dara Entekhabi ◽  
Ross N. Hoffman
Keyword(s):  
Error Propagation ◽  
Lead Time ◽  
Hydrologic Model ◽  
Forecast Errors ◽  
Flood Forecast ◽  
Spatiotemporal Resolution ◽  
Radar Rainfall ◽  
Forecast Lead Time ◽  
Flood Simulations ◽  
Storm Characteristics

Abstract This study presents a first attempt to address the propagation of radar rainfall nowcasting errors to flood forecasts in the context of distributed hydrological simulations over a range of catchment sizes or scales. Rainfall forecasts with high spatiotemporal resolution generated from observed radar fields are used as forcing to a fully distributed hydrologic model to issue flood forecasts in a set of nested subbasins. Radar nowcasting introduces errors into the rainfall field evolution that result from spatial and temporal changes of storm features that are not captured in the forecast algorithm. The accuracy of radar rainfall and flood forecasts relative to observed radar precipitation fields and calibrated flood simulations is assessed. The study quantifies how increases in nowcasting errors with lead time result in higher flood forecast errors at the basin outlet. For small, interior basins, rainfall forecast errors can be simultaneously amplified or dampened in different flood forecast locations depending on the forecast lead time and storm characteristics. Interior differences in error propagation are shown to be effectively averaged out for larger catchment scales.

scholarly journals Impact of Geographic-Dependent Parameter Optimization on Climate Estimation and Prediction: Simulation with an Intermediate Coupled Model

2012 ◽  
Vol 140 (12) ◽  
pp. 3956-3971 ◽  
Author(s):  
Xinrong Wu ◽  
Shaoqing Zhang ◽  
Zhengyu Liu ◽  
Anthony Rosati ◽  
Thomas L. Delworth ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Surface Temperature ◽  
Parameter Optimization ◽  
General Circulation ◽  
Coupled Model ◽  
Model Parameters ◽  
Estimation And Prediction ◽  
Dependent Parameter ◽  
The Impact ◽  
Mean Square Errors

Abstract Because of the geographic dependence of model sensitivities and observing systems, allowing optimized parameter values to vary geographically may significantly enhance the signal in parameter estimation. Using an intermediate atmosphere–ocean–land coupled model, the impact of geographic dependence of model sensitivities on parameter optimization is explored within a twin-experiment framework. The coupled model consists of a 1-layer global barotropic atmosphere model, a 1.5-layer baroclinic ocean including a slab mixed layer with simulated upwelling by a streamfunction equation, and a simple land model. The assimilation model is biased by erroneously setting the values of all model parameters. The four most sensitive parameters identified by sensitivity studies are used to perform traditional single-value parameter estimation and new geographic-dependent parameter optimization. Results show that the new parameter optimization significantly improves the quality of state estimates compared to the traditional scheme, with reductions of root-mean-square errors as 41%, 23%, 62%, and 59% for the atmospheric streamfunction, the oceanic streamfunction, sea surface temperature, and land surface temperature, respectively. Consistently, the new parameter optimization greatly improves the model predictability as a result of the improvement of initial conditions and the enhancement of observational signals in optimized parameters. These results suggest that the proposed geographic-dependent parameter optimization scheme may provide a new perspective when a coupled general circulation model is used for climate estimation and prediction.

scholarly journals A Study of Capturing AMOC Regime Transition through Observation-Constrained Model Parameters

2021 ◽  
Author(s):  
Zhao Liu ◽  
Shaoqing Zhang ◽  
Yang Shen ◽  
Yuping Guan ◽  
Xiong Deng
Keyword(s):  
Parameter Estimation ◽  
General Circulation ◽  
Multiple Equilibria ◽  
Coupled Model ◽  
Circulation Model ◽  
Meridional Overturning Circulation ◽  
Model Parameters ◽  
Model Errors ◽  
Model Study ◽  
Regime Transitions

Abstract. The multiple equilibria are an outstanding characteristic of the Atlantic meridional overturning circulation (AMOC) that has important impacts on the Earth climate system appearing as regime transitions. The AMOC can be simulated in different models but the behavior deviates from the real world due to the existence of model errors. Here, we first combine a general AMOC model with an ensemble Kalman filter to form an ensemble coupled model data assimilation and parameter estimation (CDAPE) system, and derive the general methodology to capture the observed AMOC regime transitions through utilization of observational information. Then we apply this methodology designed within a twin experiment framework with a simple conceptual model that simulates the transition phenomenon of AMOC multiple equilibria, as well as a more physics-based MOC box model to reconstruct the observed AMOC multiple equilibria. The results show that the coupled model parameter estimation with observations can significantly mitigate the model deviations, thus capturing regime transitions of the AMOC. This simple model study serves as a guideline when a coupled general circulation model is used to incorporate observations to reconstruct the AMOC historical states and make multi-decadal climate predictions.

Error Growth Dynamics within Convection-Allowing Ensemble Forecasts over Central U.S. Regions for Days of Active Convection

2021 ◽  
Author(s):  
Xiaoran Zhuang ◽  
Ming Xue ◽  
Jinzhong Min ◽  
Zhiming Kang ◽  
Naigeng Wu ◽  
...  
Keyword(s):  
Diurnal Cycle ◽  
Large Scale ◽  
Response Times ◽  
Forecast Error ◽  
Small Scale ◽  
Forecast Errors ◽  
Error Growth ◽  
Ensemble Forecasts ◽  
Scale Error ◽  
Active Convection

AbstractError growth is investigated based on convection-allowing ensemble forecasts starting from 0000 UTC for 14 active convection events over central to eastern U.S. regions from spring 2018. The analysis domain is divided into the NW, NE, SE and SW quadrants (subregions). Total difference energy and its decompositions are used to measure and analyze error growth at and across scales. Special attention is paid to the dominant types of convection with respect to their forcing mechanisms in the four subregions and the associated difference in precipitation diurnal cycles. The discussions on the average behaviors of error growth in each region are supplemented by 4 representative cases. Results show that the meso-γ-scale error growth is directly linked to precipitation diurnal cycle while meso-α-scale error growth has strong link to large scale forcing. Upscale error growth is evident in all regions/cases but up-amplitude growth within own scale plays different roles in different regions/cases.When large-scale flow is important (as in the NE region), precipitation is strongly modulated by the large-scale forcing and becomes more organized with time, and upscale transfer of forecast error is stronger. On the other hand, when local instability plays more dominant roles (as in the SE region), precipitation is overall least organized and has the weakest diurnal variations. Its associated errors at the γ– and β-scale can reach their peaks sooner and meso-α-scale error tends to rely more on growth of error with its own scale. Small-scale forecast errors are directly impacted by convective activities and have short response time to convection while increasingly larger scale errors have longer response times and delayed phase within the diurnal cycle.

Scale-Dependent Value of QPF for Real-Time Streamflow Forecasting

2021 ◽  
Author(s):  
Ganesh R. Ghimire ◽  
Witold F. Krajewski ◽  
Felipe Quintero
Keyword(s):  
Real Time ◽  
Time Scale ◽  
Lead Time ◽  
Time Horizon ◽  
Streamflow Forecasting ◽  
Forecasting Accuracy ◽  
Trade Off ◽  
Forecast Lead Time ◽  
Medium Range ◽  
Forecasting System

AbstractIncorporating rainfall forecasts into a real-time streamflow forecasting system extends the forecast lead time. Since quantitative precipitation forecasts (QPFs) are subject to substantial uncertainties, questions arise on the trade-off between the time horizon of the QPF and the accuracy of the streamflow forecasts. This study explores the problem systematically, exploring the uncertainties associated with QPFs and their hydrologic predictability. The focus is on scale dependence of the trade-off between the QPF time horizon, basin-scale, space-time scale of the QPF, and streamflow forecasting accuracy. To address this question, the study first performs a comprehensive independent evaluation of the QPFs at 140 U.S. Geological Survey (USGS) monitored basins with a wide range of spatial scales (~10 – 40,000 km2) over the state of Iowa in the Midwestern United States. The study uses High-Resolution Rapid Refresh (HRRR) and Global Forecasting System (GFS) QPFs for short and medium-range forecasts, respectively. Using Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate (QPE) as a reference, the results show that the rainfall-to-rainfall QPF errors are scale-dependent. The results from the hydrologic forecasting experiment show that both QPFs illustrate clear value for real-time streamflow forecasting at longer lead times in the short- to medium-range relative to the no-rain streamflow forecast. The value of QPFs for streamflow forecasting is particularly apparent for basin sizes below 1,000 km2. The space-time scale, or reference time (tr) (ratio of forecast lead time to basin travel time) ~ 1 depicts the largest streamflow forecasting skill with a systematic decrease in forecasting accuracy for tr > 1.

Evaluation of near-surface temperature forecasts against super-site observations

2020 ◽  
Author(s):  
Polly Schmederer ◽  
Irina Sandu ◽  
Thomas Haiden ◽  
Anton Beljaars ◽  
Martin Leutbecher ◽  
...  
Keyword(s):  
Forecast Error ◽  
Small Scale ◽  
Forecast Errors ◽  
Near Surface ◽  
Surface Weather ◽  
Medium Range ◽  
Weather Parameters ◽  
Temperature Errors

<p><span><strong>ECMWF’s medium-range forecasts of near-surface weather parameters, such as 2 m temperature, humidity and 10 m wind speed, have become more skilful over the years, following the trend of improvements in the forecast skill of upper-air fields. However, they are still affected by systematic errors which have proved difficult to eliminate. Systematic forecast errors in temperature and humidity near the surface can be better understood by also examining errors higher up in the atmospheric boundary layer and in the soil. Meteorological observatories, also known as super-sites, provide long-term observational records of such vertical profiles. ECMWF started to use data from super-sites more systematically to evaluate the quality of forecasts in the lowest part of the atmosphere (up to 100m) and in the soil, in an attempt to disentangle sources of forecast error in near-surface weather parameters. Findings for 2-metre temperature errors in ECMWF forecasts at European super-sites suggest that the errors are partly the result of the model exchanging too much energy between the atmosphere and the land. However, the influence of other factors, such as errors resulting from the representation of vegetation in semi-arid areas and from small-scale variations in vegetation and soil type near measurement stations, mean that it is difficult to adjust the energy exchange in a way which leads to an overall error reduction on the European scale. </strong></span></p>

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