A Description of the Impact of Changes to NOGAPS Convection Parameterization and the Increase in Resolution to T239L30

2002 ◽  
Author(s):  
Timothy F. Hogan ◽  
Melinda S. Peng ◽  
James A. Ridout ◽  
William M. Clune
Keyword(s):  
Convection Parameterization ◽  
The Impact

scholarly journals Evaluation of the Plant–Craig stochastic convection scheme in an ensemble forecasting system

2015 ◽  
Vol 8 (12) ◽  
pp. 10199-10236
Author(s):  
R. J. Keane ◽  
R. S. Plant ◽  
W. J. Tennant
Keyword(s):  
Random Parameter ◽  
Convective Precipitation ◽  
Ensemble Prediction ◽  
Convection Scheme ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Version 2.0 ◽  
Convection Parameterization ◽  
The Impact

Abstract. The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic element only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

scholarly journals Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

2020 ◽  
Vol 27 (2) ◽  
pp. 187-207
Author(s):  
Michiel Van Ginderachter ◽  
Daan Degrauwe ◽  
Stéphane Vannitsem ◽  
Piet Termonia
Keyword(s):  
Reference Model ◽  
Deep Convection ◽  
Model Error ◽  
Statistical Properties ◽  
Forecast Skill ◽  
Small Scale ◽  
Model Errors ◽  
Target Model ◽  
Convection Parameterization ◽  
The Impact

Abstract. Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model with a parameterization of deep convection. This configuration is also run with the deep-convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep-convection parameterization and the target model without deep-convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that the total mass, heat and momentum are conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep-convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread, leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded.

scholarly journals Evaluation of the Plant–Craig stochastic convection scheme (v2.0) in the ensemble forecasting system MOGREPS-R (24 km) based on the Unified Model (v7.3)

2016 ◽  
Vol 9 (5) ◽  
pp. 1921-1935 ◽  
Author(s):  
Richard J. Keane ◽  
Robert S. Plant ◽  
Warren J. Tennant
Keyword(s):  
Random Parameter ◽  
Convective Precipitation ◽  
Ensemble Prediction ◽  
Convection Scheme ◽  
Ensemble Forecasts ◽  
Ensemble Prediction System ◽  
Forecasting System ◽  
Version 2.0 ◽  
Convection Parameterization ◽  
The Impact

Abstract. The Plant–Craig stochastic convection parameterization (version 2.0) is implemented in the Met Office Regional Ensemble Prediction System (MOGREPS-R) and is assessed in comparison with the standard convection scheme with a simple stochastic scheme only, from random parameter variation. A set of 34 ensemble forecasts, each with 24 members, is considered, over the month of July 2009. Deterministic and probabilistic measures of the precipitation forecasts are assessed. The Plant–Craig parameterization is found to improve probabilistic forecast measures, particularly the results for lower precipitation thresholds. The impact on deterministic forecasts at the grid scale is neutral, although the Plant–Craig scheme does deliver improvements when forecasts are made over larger areas. The improvements found are greater in conditions of relatively weak synoptic forcing, for which convective precipitation is likely to be less predictable.

scholarly journals A Pan-African Convection-Permitting Regional Climate Simulation with the Met Office Unified Model: CP4-Africa

2018 ◽  
Vol 31 (9) ◽  
pp. 3485-3508 ◽  
Author(s):  
Rachel A. Stratton ◽  
Catherine A. Senior ◽  
Simon B. Vosper ◽  
Sonja S. Folwell ◽  
Ian A. Boutle ◽  
...  
Keyword(s):  
Land Surface ◽  
Regional Climate ◽  
Atmospheric Model ◽  
Unified Model ◽  
Global Model ◽  
Climate Simulation ◽  
Regional Climate Simulation ◽  
Pan African ◽  
Convection Parameterization ◽  
The Impact

Abstract A convection-permitting multiyear regional climate simulation using the Met Office Unified Model has been run for the first time on an Africa-wide domain. The model has been run as part of the Future Climate for Africa (FCFA) Improving Model Processes for African Climate (IMPALA) project, and its configuration, domain, and forcing data are described here in detail. The model [Pan-African Convection-Permitting Regional Climate Simulation with the Met Office UM (CP4-Africa)] uses a 4.5-km horizontal grid spacing at the equator and is run without a convection parameterization, nested within a global atmospheric model driven by observations at the sea surface, which does include a convection scheme. An additional regional simulation, with identical resolution and physical parameterizations to the global model, but with the domain, land surface, and aerosol climatologies of CP4-Africa, has been run to aid in the understanding of the differences between the CP4-Africa and global model, in particular to isolate the impact of the convection parameterization and resolution. The effect of enforcing moisture conservation in CP4-Africa is described and its impact on reducing extreme precipitation values is assessed. Preliminary results from the first five years of the CP4-Africa simulation show substantial improvements in JJA average rainfall compared to the parameterized convection models, with most notably a reduction in the persistent dry bias in West Africa, giving an indication of the benefits to be gained from running a convection-permitting simulation over the whole African continent.

scholarly journals Simulating model uncertainty of subgrid-scale processes by sampling model errors at convective scales

2019 ◽  
Author(s):  
Michiel Van Ginderachter ◽  
Daan Degrauwe ◽  
Stéphane Vannitsem ◽  
Piet Termonia
Keyword(s):  
Reference Model ◽  
Deep Convection ◽  
Model Error ◽  
Statistical Properties ◽  
Forecast Skill ◽  
Small Scale ◽  
Model Errors ◽  
Target Model ◽  
Convection Parameterization ◽  
The Impact

Abstract. Ideally, perturbation schemes in ensemble forecasts should be based on the statistical properties of the model errors. Often, however, the statistical properties of these model errors are unknown. In practice, the perturbations are pragmatically modelled and tuned to maximize the skill of the ensemble forecast. In this paper a general methodology is developed to diagnose the model error, linked to a specific physical process, based on a comparison between a target and a reference model. Here, the reference model is a configuration of the ALADIN (Aire Limitée Adaptation Dynamique Développement International) model with a parameterization of deep convection. This configuration is also run with the deep convection parameterization scheme switched off, degrading the forecast skill. The model error is then defined as the difference of the energy and mass fluxes between the reference model with scale-aware deep convection parameterization and the target model without deep convection parameterization. In the second part of the paper, the diagnosed model-error characteristics are used to stochastically perturb the fluxes of the target model by sampling the model errors from a training period in such a way that the distribution and the vertical and multivariate correlation within a grid column are preserved. By perturbing the fluxes it is guaranteed that that the total mass, heat and momentum remain conserved. The tests, performed over the period 11–20 April 2009, show that the ensemble system with the stochastic flux perturbations combined with the initial condition perturbations, not only outperforms the target ensemble, where deep convection is not parameterized, but for many variables it even performs better than the reference ensemble (with scale-aware deep convection scheme). The introduction of the stochastic flux perturbations reduces the small-scale erroneous spread while increasing the overall spread leading to a more skillful ensemble. The impact is largest in the upper troposphere with substantial improvements compared to other state-of-the-art stochastic perturbation schemes. At lower levels the improvements are smaller or neutral, except for temperature where the forecast skill is degraded.

scholarly journals Explicit Convection and Scale-Aware Cumulus Parameterizations: High-Resolution Simulations over Areas of Different Topography in Germany

2018 ◽  
Vol 146 (6) ◽  
pp. 1925-1944 ◽  
Author(s):  
Andreas Wagner ◽  
Dominikus Heinzeller ◽  
Sven Wagner ◽  
Thomas Rummler ◽  
Harald Kunstmann
Keyword(s):  
High Resolution ◽  
Land Surface ◽  
Climate Model ◽  
Regional Climate ◽  
Surface Characteristics ◽  
Radar Data ◽  
Explicit Calculation ◽  
Climate Model Simulations ◽  
Convection Parameterization ◽  
The Impact

An increase in the spatial resolution of regional climate model simulations improves the representation of land surface characteristics and may allow the explicit calculation of important physical processes such as convection. The present study investigates further potential benefits with respect to precipitation, based on a small ensemble of high-resolution simulations with WRF with grid spacings up to 1 km. The skill of each experiment is evaluated regarding the temporal and spatial performance of the simulation of precipitation of one year over both a mountainous region in southwestern Germany and a mainly flat region in northern Germany. This study allows us to differentiate between the impact of grid spacing, topography, and convection parameterization. Furthermore, the performance of a state-of-the-art convection parameterization scheme in the gray zone of convection is evaluated against an explicit calculation of convection only. Our evaluation demonstrates the following: high-resolution simulations (5 and 1 km) are generally able to represent the diurnal cycle, structure, and intensity distribution of precipitation, when compared to observational datasets such as radar data and interpolated station data. The influence of the improved representation of the topography at higher resolution (1 km) becomes apparent in complex terrain, where the localization of precipitation maxima is more accurate, although these maxima are overestimated. In flat areas, differences in spatial evaluations arise between simulations with parameterized and explicitly calculated convection, whereas smaller grid spacings (1 km vs 5 km) show hardly any impact on precipitation results.

Formation of Lunar Craters and Bright Rays as a Result of Meteorite Impacts

1962 ◽  
Vol 14 ◽  
pp. 415-418
Author(s):  
K. P. Stanyukovich ◽  
V. A. Bronshten
Keyword(s):  
Explosive Charge ◽  
The Moon ◽  
Meteorite Impacts ◽  
The Earth ◽  
Lunar Craters ◽  
The Impact

The phenomena accompanying the impact of large meteorites on the surface of the Moon or of the Earth can be examined on the basis of the theory of explosive phenomena if we assume that, instead of an exploding meteorite moving inside the rock, we have an explosive charge (equivalent in energy), situated at a certain distance under the surface.

The Geosciences Applied to Lunar Exploration

1962 ◽  
Vol 14 ◽  
pp. 169-257 ◽  
Author(s):  
J. Green
Keyword(s):  
Lunar Surface ◽  
Probable Mechanism ◽  
Point Of View ◽  
Lunar Exploration ◽  
Geological Model ◽  
Apply Geophysics ◽  
Surface Features ◽  
The Impact

The term geo-sciences has been used here to include the disciplines geology, geophysics and geochemistry. However, in order to apply geophysics and geochemistry effectively one must begin with a geological model. Therefore, the science of geology should be used as the basis for lunar exploration. From an astronomical point of view, a lunar terrain heavily impacted with meteors appears the more reasonable; although from a geological standpoint, volcanism seems the more probable mechanism. A surface liberally marked with volcanic features has been advocated by such geologists as Bülow, Dana, Suess, von Wolff, Shaler, Spurr, and Kuno. In this paper, both the impact and volcanic hypotheses are considered in the application of the geo-sciences to manned lunar exploration. However, more emphasis is placed on the volcanic, or more correctly the defluidization, hypothesis to account for lunar surface features.

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