Mar.  2020

Article Contents

# Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

Funds:

National Key Technology Research and Development Program of the Ministry of Science and Technology of China (Grant No. 2015BAC03B01)

• To represent model uncertainties more comprehensively, a stochastically perturbed parameterization (SPP) scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes, as well as the stochastically perturbed parameterization tendencies (SPPT) scheme, and the stochastic kinetic energy backscatter (SKEB) scheme, is applied in the Global and Regional Assimilation and Prediction Enhanced System—Regional Ensemble Prediction System (GRAPES-REPS) to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes. Six experiments are performed for a summer month (1–30 June 2015) over China and multiple verification metrics are used. The results show that: (1) All stochastic experiments outperform the control (CTL) experiment, and all combinations of stochastic parameterization schemes perform better than the single SPP scheme, indicating that stochastic methods can effectively improve the forecast skill, and combinations of multiple stochastic parameterization schemes can better represent model uncertainties; (2) The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill; (3) Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed. SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields, and it contributes most to the improvement in spread and outliers for wind; (4) The introduction of SPP has a positive added value, and does not lead to large changes in the evolution of the kinetic energy (KE) spectrum at any wavelength; (5) The introduction of SPPT and SKEB would cause a 5%–10% and 30%–80% change in the KE of mesoscale systems, and all three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the KE of mesoscale systems. This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.
• Figure FIG. 196..

Figure 1.  Domain and topography for the model simulation.

Figure 2.  The perturbation pattern $\exp \left( {{\psi _j}} \right)$ of SPP from a randomly chosen ensemble member and model timestep.

Figure 3.  Probability of precipitation exceeding (a, c, e, g, i, k) 25-mm and (b, d, f, h, j, l) 50-mm thresholds for monthly mean 24-h accumulated precipitation for the (a, b) CTL experiment, (c, d) SPP minus CTL experiment, (e, f) SPP_SPPT minus CTL experiment, (g, h) SPP_SKEB minus CTL experiment, (i, j) SPPT_SKEB minus CTL experiment, and (k, l) SPP_SPPT_SKEB minus CTL experiment. The results are the monthly averages for the 0000 UTC cycle during June 2015.

Figure 4.  The domain-averaged AROC scores of 24-h accumulated precipitation for four thresholds [(a) 0.1, (b) 10, (c) 25, (d) 50 mm] for the six experiments, varying with forecast hour. The results are the monthly average for the 0000 UTC cycle during June 2015.

Figure 5.  The ensemble mean frequency bias of 24-h accumulated precipitation for the six experiments for four thresholds [(a) 0.1, (b) 10, (c) 25, (d) 50 mm] varying with forecast hour. The results are the monthly average for the 0000 UTC cycle during June 2015.

Figure 6.  The horizontal distribution of (a, c, e, g, i, k) ensemble spread and (b, d, f, h, j, l) RMSE of the ensemble mean for (a, b) CTL, (c, d) SPP, (e, f) SPP_SPPT, (g, h) SPP_SKEB, (i, j) SPPT_SKEB, and (k, l) SPP_SPPT_SKEB. The variable is 850-hPa zonal wind speed. The results are the monthly average for the 0000 UTC cycle during June 2015.

Figure 7.  Percentage change of (a, d, g, j, m) ensemble mean RMSE, (b, e, h, k, n) ensemble spread and (c, f, i, l,o) consistency of the five stochastic experiments relative to the CTL experiment for (a, b, c) 250-hPa zonal wind speed, (d, e, f) 500-hPa temperature, (g, h, i) 850-hPa zonal wind speed, (j, k, l) 10-m zonal wind speed, and (m, n, o) 2-m temperature, varying with forecast hour. The results are the monthly average for the 0000 UTC cycle during June 2015.

Figure 9.  Rank histograms and outlier scores at the 48-h forecast lead time for (a, b) 250-hPa zonal wind speed, (c, d) 500-hPa temperature, (e, f) 850-hPa temperature, (g, h) 850-hPa zonal wind speed, (i, j) 10-m zonal wind speed, and (k, l) 2-m temperature, varying with forecast hour. The results are the monthly average for the 0000 UTC cycle during June 2015.

Figure 8.  Percentage change of CRPS of the five stochastic experiments relative to the CTL experiment for (a) 250-hPa zonal wind speed, (b) 500-hPa temperature, (c) 850-hPa zonal wind speed, and (d) 10-m zonal wind speed, varying with forecast hour. The results are the monthly average for the 0000 UTC cycle during June 2015.

Figure 10.  Percentage change in the KE spectrum of (a) SPP_SPPT_SKEB over SPPT_SKEB, (b) SPP_SPPT over SPP, and (c) SPP_SKEB over SPP for the 500-hPa winds at a 48-h lead time. The results are the monthly mean for the 0000 UTC cycle during June 2015.

Figure 11.  Percentage change in the KE spectrum of (a) SPP_SPPT_SKEB over SPPT_SKEB, (b) SPP_SPPT over SPP, and (c) SPP_SKEB over SPP for five typical and representative wavelengths varying with forecast hour. The results are the monthly mean for the 0000 UTC cycle during June 2015.

Figure 12.  Percentage change of the SPP_SPPT_SKEB scheme over the SPPT_SKEB scheme in (a) spread, (b) RMSE, (c) consistency, (d) outliers, and (e) CRPS, for 250-hPa zonal wind speed (blue), 500-hPa temperature (green), 850-hPa temperature (gray), 850-hPa zonal wind speed (pink), and 10-m zonal wind speed (red), as well as (f) AROC scores for precipitation verification, varying with forecast hour.

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## Manuscript History

Manuscript revised: 27 December 2019
Manuscript accepted: 19 January 2020
###### 通讯作者: 陈斌, bchen63@163.com
• 1.

沈阳化工大学材料科学与工程学院 沈阳 110142

## Representing Model Uncertainty by Multi-Stochastic Physics Approaches in the GRAPES Ensemble

###### Corresponding author: Jing CHEN, chenj@cma.gov.cn;
• 1. Department of Atmospheric and Oceanic Sciences and Institute of Atmospheric Sciences, Fudan University, Shanghai 200438, China
• 2. Chinese Academy of Meteorological Sciences, China Meteorological Administration, Beijing 100081, China
• 3. Numerical Weather Prediction Center, China Meteorological Administration, Beijing 100081, China
• 4. Key Laboratory of Land Surface Pattern and Simulation, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China

Abstract: To represent model uncertainties more comprehensively, a stochastically perturbed parameterization (SPP) scheme consisting of temporally and spatially varying perturbations of 18 parameters in the microphysics, convection, boundary layer, and surface layer parameterization schemes, as well as the stochastically perturbed parameterization tendencies (SPPT) scheme, and the stochastic kinetic energy backscatter (SKEB) scheme, is applied in the Global and Regional Assimilation and Prediction Enhanced System—Regional Ensemble Prediction System (GRAPES-REPS) to evaluate and compare the general performance of various combinations of multiple stochastic physics schemes. Six experiments are performed for a summer month (1–30 June 2015) over China and multiple verification metrics are used. The results show that: (1) All stochastic experiments outperform the control (CTL) experiment, and all combinations of stochastic parameterization schemes perform better than the single SPP scheme, indicating that stochastic methods can effectively improve the forecast skill, and combinations of multiple stochastic parameterization schemes can better represent model uncertainties; (2) The combination of all three stochastic physics schemes (SPP, SPPT, and SKEB) outperforms any other combination of two schemes in precipitation forecasting and surface and upper-air verification to better represent the model uncertainties and improve the forecast skill; (3) Combining SKEB with SPP and/or SPPT results in a notable increase in the spread and reduction in outliers for the upper-air wind speed. SKEB directly perturbs the wind field and therefore its addition will greatly impact the upper-air wind-speed fields, and it contributes most to the improvement in spread and outliers for wind; (4) The introduction of SPP has a positive added value, and does not lead to large changes in the evolution of the kinetic energy (KE) spectrum at any wavelength; (5) The introduction of SPPT and SKEB would cause a 5%–10% and 30%–80% change in the KE of mesoscale systems, and all three stochastic schemes (SPP, SPPT, and SKEB) mainly affect the KE of mesoscale systems. This study indicates the potential of combining multiple stochastic physics schemes and lays a foundation for the future development and design of regional and global ensembles.

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