scholarly journals Coupled parameter perturbations in the SPP scheme of the HarmonEPS

2021 ◽  
Author(s):  
Aristofanis Tsiringakis ◽  
Wim de Rooy ◽  
Sibbo van der Veen ◽  
Jan Barkmeijer
Keyword(s):  
Boundary Layer ◽  
Boundary Conditions ◽  
Model Performance ◽  
Ensemble Prediction ◽  
Model Bias ◽  
Ensemble Spread ◽  
Physical Processes ◽  
Parameter Perturbations ◽  
Atmospheric State ◽  
Parameter Values

<p>In an ensemble prediction system (EPS) the uncertainty in the initial atmospheric conditions is usually represented via perturbation of the initial atmospheric state and different boundary conditions at the beginning and throughout the duration of the forecast. These approaches exclude the uncertainty due to the representation of physical processes within the parameterization schemes of a numerical weather prediction model (NWP). Much of the uncertainty in the presentation of physical process arises from uncertain parameter values regulating key physical processes in the boundary-layer and microphysics schemes. This uncertainty can be represented with a Stochastically Perturbed Parameterization (SPP) scheme, where parameter values for the different model grid points are randomly selected from a defined probability density function. The SPP scheme can improve model performance and increase ensemble spread, but may lead to unrealistic parameter values, which can introduce additional model bias. A potential solution is to use coupled/correlated perturbations for relevant SPP parameters to increase the model performance and ensemble spread, while maintaining physically realistic ranges for the parameters. In this study, we investigate the impact of coupled perturbations in key parameters within the boundary-layer and microphysics schemes of the HarmonEPS model using the new SPP scheme. The performance of the coupled perturbations experiment is evaluated against HarmonEPS experiments using independent parameter perturbations, and perturbations in the initial atmospheric state and boundary conditions for both a winter and a summer period.  We find that coupled perturbations in the SPP scheme can decrease model bias and increase the ensemble spread for the 2m temperature and relative humidity, 10m-wind speed and total cloud cover.</p>

scholarly journals Ensemble Typhoon Quantitative Precipitation Forecasts Model in Taiwan

2015 ◽  
Vol 30 (1) ◽  
pp. 217-237 ◽  
Author(s):  
Jing-Shan Hong ◽  
Chin-Tzu Fong ◽  
Ling-Feng Hsiao ◽  
Yi-Chiang Yu ◽  
Chian-You Tzeng
Keyword(s):  
Model Performance ◽  
Precipitation Forecast ◽  
Ensemble Prediction ◽  
Model Bias ◽  
Ensemble Prediction System ◽  
Rainfall Forecast ◽  
Quantitative Precipitation Forecast ◽  
Quantitative Precipitation Forecasts ◽  
Typhoon Rainfall ◽  
Typhoon Tracks

Abstract In this study, an ensemble typhoon quantitative precipitation forecast (ETQPF) model was developed to provide typhoon rainfall forecasts for Taiwan. The ETQPF rainfall forecast is obtained by averaging the pick-out cases, which are screened using certain criterion based on given typhoon tracks from an ensemble prediction system (EPS). Therefore, the ETQPF model resembles a climatology model. However, the ETQPF model uses the quantitative precipitation forecasts (QPFs) from an EPS instead of historical rainfall observations. Two typhoon cases, Fanapi (2010) and Megi (2010), are used to evaluate the ETQPF model performance. The results show that the rainfall forecast from the ETQPF model, which is qualitatively compared and quantitatively verified, provides reasonable typhoon rainfall forecasts and is valuable for real-time operational applications. By applying the forecast track to the ETQPF model, better track forecasts lead to better ETQPF rainfall forecasts. Moreover, the ETQPF model provides the “scenario” of the typhoon QPFs according to the uncertainty of the forecast tracks. Such a scenario analysis can provide valuable information for risk assessment and decision making in disaster prevention and reduction. Deficiencies of the ETQPF model are also presented, including that the average over the pick-out case usually offsets the extremes and reduces the maximum ETQPF rainfall, the underprediction is especially noticeable for weak phase-locked rainfall systems, and the ETQPF rainfall error is related to the model bias. Therefore, reducing model bias is an important issue in further improving the ETQPF model performance.

scholarly journals Sensitivity of Ensemble Forecast Verification to Model Bias

2018 ◽  
Vol 146 (3) ◽  
pp. 781-796 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Jun Du ◽  
Yutao Zhang ◽  
Yu Xia ◽  
...  
Keyword(s):  
Bias Correction ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Bias ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Forecast Bias ◽  
Probabilistic Forecasts ◽  
Before And After

This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent “strong” and “weak” bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread’s spatial structure is much less; the spread–skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.

Sensitivity of ensemble forecast verification to model bias

2020 ◽  
Author(s):  
Jingzhuo Wang ◽  
Jing Chen ◽  
Jun Du
Keyword(s):  
Bias Correction ◽  
Ensemble Prediction ◽  
Prediction System ◽  
Model Bias ◽  
Ensemble Spread ◽  
Ensemble Prediction System ◽  
Forecast Bias ◽  
Probabilistic Forecasts ◽  
Before And After

<p>        This study demonstrates how model bias can adversely affect the quality assessment of an ensemble prediction system (EPS) by verification metrics. A regional EPS [Global and Regional Assimilation and Prediction Enhanced System-Regional Ensemble Prediction System (GRAPES-REPS)] was verified over a period of one month over China. Three variables (500-hPa and 2-m temperatures, and 250-hPa wind) are selected to represent "strong" and "weak" bias situations. Ensemble spread and probabilistic forecasts are compared before and after a bias correction. The results show that the conclusions drawn from ensemble verification about the EPS are dramatically different with or without model bias. This is true for both ensemble spread and probabilistic forecasts. The GRAPES-REPS is severely underdispersive before the bias correction but becomes calibrated afterward, although the improvement in the spread' spatial structure is much less; the spread-skill relation is also improved. The probabilities become much sharper and almost perfectly reliable after the bias is removed. Therefore, it is necessary to remove forecast biases before an EPS can be accurately evaluated since an EPS deals only with random error but not systematic error. Only when an EPS has no or little forecast bias, can ensemble verification metrics reliably reveal the true quality of an EPS without removing forecast bias first. An implication is that EPS developers should not be expected to introduce methods to dramatically increase ensemble spread (either by perturbation method or statistical calibration) to achieve reliability. Instead, the preferred solution is to reduce model bias through prediction system developments and to focus on the quality of spread (not the quantity of spread). Forecast products should also be produced from the debiased but not the raw ensemble.</p>

scholarly journals Impact of physical parameterizations and initial conditions on simulated atmospheric transport and CO<sub>2</sub> mole fractions in the US Midwest

2018 ◽  
Vol 18 (20) ◽  
pp. 14813-14835 ◽  
Author(s):  
Liza I. Díaz-Isaac ◽  
Thomas Lauvaux ◽  
Kenneth J. Davis
Keyword(s):  
Boundary Layer ◽  
Wind Speed ◽  
Boundary Conditions ◽  
Planetary Boundary Layer ◽  
Initial Conditions ◽  
Atmospheric Transport ◽  
Atmospheric Co2 ◽  
The Us ◽  
Physics Parameterizations ◽  
Physical Parameterizations

Abstract. Atmospheric transport model errors are one of the main contributors to the uncertainty affecting CO2 inverse flux estimates. In this study, we determine the leading causes of transport errors over the US upper Midwest with a large set of simulations generated with the Weather Research and Forecasting (WRF) mesoscale model. The various WRF simulations are performed using different meteorological driver datasets and physical parameterizations including planetary boundary layer (PBL) schemes, land surface models (LSMs), cumulus parameterizations and microphysics parameterizations. All the different model configurations were coupled to CO2 fluxes and lateral boundary conditions from the CarbonTracker inversion system to simulate atmospheric CO2 mole fractions. PBL height, wind speed, wind direction, and atmospheric CO2 mole fractions are compared to observations during a month in the summer of 2008, and statistical analyses were performed to evaluate the impact of both physics parameterizations and meteorological datasets on these variables. All of the physical parameterizations and the meteorological initial and boundary conditions contribute 3 to 4 ppm to the model-to-model variability in daytime PBL CO2 except for the microphysics parameterization which has a smaller contribution. PBL height varies across ensemble members by 300 to 400 m, and this variability is controlled by the same physics parameterizations. Daily PBL CO2 mole fraction errors are correlated with errors in the PBL height. We show that specific model configurations systematically overestimate or underestimate the PBL height averaged across the region with biases closely correlated with the choice of LSM, PBL scheme, and cumulus parameterization (CP). Domain average PBL wind speed is overestimated in nearly every model configuration. Both planetary boundary layer height (PBLH) and PBL wind speed biases show coherent spatial variations across the Midwest, with PBLH overestimated averaged across configurations by 300–400 m in the west, and PBL winds overestimated by about 1 m s−1 on average in the east. We find model configurations with lower biases averaged across the domain, but no single configuration is optimal across the entire region and for all meteorological variables. We conclude that model ensembles that include multiple physics parameterizations and meteorological initial conditions are likely to be necessary to encompass the atmospheric conditions most important to the transport of CO2 in the PBL, but that construction of such an ensemble will be challenging due to ensemble biases that vary across the region.

scholarly journals Benchmarking and Process Diagnostics of Land Models

2018 ◽  
Vol 19 (11) ◽  
pp. 1835-1852 ◽  
Author(s):  
Grey S. Nearing ◽  
Benjamin L. Ruddell ◽  
Martyn P. Clark ◽  
Bart Nijssen ◽  
Christa Peters-Lidard
Keyword(s):  
Land Surface ◽  
Model Performance ◽  
Land Surface Model ◽  
Surface Model ◽  
Process Diagnostics ◽  
Surface Energy Fluxes ◽  
Structural Error ◽  
Intercomparison Project ◽  
Parameter Values ◽  
Process Level

Abstract We propose a conceptual and theoretical foundation for information-based model benchmarking and process diagnostics that provides diagnostic insight into model performance and model realism. We benchmark against a bounded estimate of the information contained in model inputs to obtain a bounded estimate of information lost due to model error, and we perform process-level diagnostics by taking differences between modeled versus observed transfer entropy networks. We use this methodology to reanalyze the recent Protocol for the Analysis of Land Surface Models (PALS) Land Surface Model Benchmarking Evaluation Project (PLUMBER) land model intercomparison project that includes the following models: CABLE, CH-TESSEL, COLA-SSiB, ISBA-SURFEX, JULES, Mosaic, Noah, and ORCHIDEE. We report that these models (i) use only roughly half of the information available from meteorological inputs about observed surface energy fluxes, (ii) do not use all information from meteorological inputs about long-term Budyko-type water balances, (iii) do not capture spatial heterogeneities in surface processes, and (iv) all suffer from similar patterns of process-level structural error. Because the PLUMBER intercomparison project did not report model parameter values, it is impossible to know whether process-level error patterns are due to model structural error or parameter error, although our proposed information-theoretic methodology could distinguish between these two issues if parameter values were reported. We conclude that there is room for significant improvement to the current generation of land models and their parameters. We also suggest two simple guidelines to make future community-wide model evaluation and intercomparison experiments more informative.

Ensemble-Based Atmospheric Reanalysis Using a Global Coupled Atmosphere–Ocean GCM

2018 ◽  
Vol 146 (10) ◽  
pp. 3311-3323
Author(s):  
Nobumasa Komori ◽  
Takeshi Enomoto ◽  
Takemasa Miyoshi ◽  
Akira Yamazaki ◽  
Akira Kuwano-Yoshida ◽  
...  
Keyword(s):  
Boundary Layer ◽  
Potential Benefit ◽  
Specific Humidity ◽  
Surface Boundary ◽  
Ensemble Spread ◽  
Surface Boundary Layer ◽  
Atmospheric Data ◽  
The Common ◽  
Atmospheric Variables ◽  
The Tropical Pacific

ABSTRACT Ensemble-based atmospheric data assimilation (DA) systems are sometimes afflicted with an underestimation of the ensemble spread near the surface caused by the use of identical boundary conditions for all ensemble members and the lack of atmosphere–ocean interaction. To overcome these problems, a new DA system has been developed by replacing an atmospheric GCM with a coupled atmosphere–ocean GCM, in which atmospheric observational data are assimilated every 6 h to update the atmospheric variables, whereas the oceanic variables are subject to no direct DA. Although SST suffers from the common biases among many coupled GCMs, two months of a retrospective analysis–forecast cycle reveals that the ensemble spreads of air temperature and specific humidity in the surface boundary layer are slightly increased and the forecast skill in the midtroposphere is rather improved by using the coupled DA system in comparison with the atmospheric DA system. In addition, surface atmospheric variables over the tropical Pacific have the basinwide horizontal correlation in ensemble space in the coupled DA system but not in the atmospheric DA system. This suggests the potential benefit of using a coupled GCM rather than an atmospheric GCM even for atmospheric reanalysis with an ensemble-based DA system.

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