scholarly journals Variance-Based Sensitivity Analysis: Preliminary Results in COAMPS

2014 ◽  
Vol 142 (5) ◽  
pp. 2028-2042 ◽  
Author(s):  
Caren Marzban ◽  
Scott Sandgathe ◽  
James D. Doyle ◽  
Nicholas C. Lederer
Keyword(s):  
Sensitivity Analysis ◽  
Prediction Models ◽  
Theoretical Calculations ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Cumulus Parameterization ◽  
Convective Precipitation ◽  
Primary Concern ◽  
Total Precipitation ◽  
Parameter Values

Abstract Numerical weather prediction models have a number of parameters whose values are either estimated from empirical data or theoretical calculations. These values are usually then optimized according to some criterion (e.g., minimizing a cost function) in order to obtain superior prediction. To that end, it is useful to know which parameters have an effect on a given forecast quantity, and which do not. Here the authors demonstrate a variance-based sensitivity analysis involving 11 parameters in the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS). Several forecast quantities are examined: 24-h accumulated 1) convective precipitation, 2) stable precipitation, 3) total precipitation, and 4) snow. The analysis is based on 36 days of 24-h forecasts between 1 January and 4 July 2009. Regarding convective precipitation, not surprisingly, the most influential parameter is found to be the fraction of available precipitation in the Kain–Fritsch cumulus parameterization fed back to the grid scale. Stable and total precipitation are most affected by a linear factor that multiplies the surface fluxes; and the parameter that most affects accumulated snow is the microphysics slope intercept parameter for snow. Furthermore, all of the interactions between the parameters are found to be either exceedingly small or have too much variability (across days and/or parameter values) to be of primary concern.

scholarly journals Forecasting of flash floods by Algorithm of Storm Prediction

2018 ◽  
Vol 210 ◽  
pp. 04033 ◽  
Author(s):  
David Šaur ◽  
Kateřina Víchová
Keyword(s):  
Prediction Models ◽  
Preventive Measures ◽  
Weather Prediction ◽  
Flash Floods ◽  
Convective Precipitation ◽  
Result Section ◽  
Rescue Service ◽  
Weather Events ◽  
Storm Prediction ◽  
Numerical Weather Prediction Models

This article focuses on the forecasting of flash floods using the Algorithm of Storm Prediction as a new tool to predict convective precipitation, severe phenomena and the risk of flash floods. The first part of the article contains information on methods for predicting dangerous severe phenomena. This algorithm uses mainly data from numerical weather prediction models (NWP models), database of historic weather events and relief characteristics describing the influence of orography on the initiation of atmospheric convection. The result section includes verification of predicted algorithm outputs, selected NWP models and warnings of CHMI and ESTOFEX on three events related to the floods that hit the Zlín Region between years of 2015 - 2017. The main result is a report with prediction outputs of the algorithm visualized in maps for the territory of municipalities with extended competence and their regions. The outputs of the algorithm will be used primarily to increase the effectiveness of preventive measures against flash floods not only by the Fire Rescue Service of Czech Republic but also by the flood and crisis management authorities.

scholarly journals Parameter variations in prediction skill optimization at ECMWF

2013 ◽  
Vol 20 (6) ◽  
pp. 1001-1010 ◽  
Author(s):  
P. Ollinaho ◽  
P. Bechtold ◽  
M. Leutbecher ◽  
M. Laine ◽  
A. Solonen ◽  
...  
Keyword(s):  
Parameter Estimation ◽  
Weather Prediction ◽  
Prediction Skill ◽  
Convective Precipitation ◽  
Ensemble Prediction ◽  
Model Parameters ◽  
Ongoing Research ◽  
Medium Range ◽  
Parameter Values ◽  
Very High

Abstract. Algorithmic numerical weather prediction (NWP) skill optimization has been tested using the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). We report the results of initial experimentation using importance sampling based on model parameter estimation methodology targeted for ensemble prediction systems, called the ensemble prediction and parameter estimation system (EPPES). The same methodology was earlier proven to be a viable concept in low-order ordinary differential equation systems, and in large-scale atmospheric general circulation models (ECHAM5). Here we show that prediction skill optimization is possible even in the context of a system that is (i) of very high dimensionality, and (ii) carefully tuned to very high skill. We concentrate on four closure parameters related to the parameterizations of sub-grid scale physical processes of convection and formation of convective precipitation. We launch standard ensembles of medium-range predictions such that each member uses different values of the four parameters, and make sequential statistical inferences about the parameter values. Our target criterion is the squared forecast error of the 500 hPa geopotential height at day three and day ten. The EPPES methodology is able to converge towards closure parameter values that optimize the target criterion. Therefore, we conclude that estimation and cost function-based tuning of low-dimensional static model parameters is possible despite the very high dimensional state space, as well as the presence of stochastic noise due to initial state and physical tendency perturbations. The remaining question before EPPES can be considered as a generally applicable tool in model development is the correct formulation of the target criterion. The one used here is, in our view, very selective. Considering the multi-faceted question of improving forecast model performance, a more general target criterion should be developed. This is a topic of ongoing research.

scholarly journals Towards improving the simulation of meteorological fields in urban areas through updated/advanced surface fluxes description

2008 ◽  
Vol 8 (3) ◽  
pp. 523-543 ◽  
Author(s):  
A. Baklanov ◽  
P. G. Mestayer ◽  
A. Clappier ◽  
S. Zilitinkevich ◽  
S. Joffre ◽  
...  
Keyword(s):  
Air Pollution ◽  
Urban Areas ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Surface Exchange ◽  
Urban Meteorology ◽  
Recent Developments ◽  
Urban Roughness ◽  
Meteorology And Air Pollution

Abstract. The increased resolution of numerical weather prediction models allows nowadays addressing more realistically urban meteorology and air pollution processes. This has triggered new interest in modelling and describing experimentally the specific features and processes of urban areas. Recent developments and results performed within the EU-funded project FUMAPEX on integrated systems for forecasting urban meteorology and air pollution are reported here. Sensitivity studies with respect to optimum resolution, parametrisation of urban roughness and surface exchange fluxes and the role of urban soil layers are carried out with advanced meso- or sub-meso meteorological models. They show that sensible improvements can be achieved by higher model resolution that is accompanied with better description of urban surface features. Recommendations, especially with respect to advanced urban air quality forecasting and information systems, are given together with an assessment of the needed further research and data.

scholarly journals The 1-way on-line coupled atmospheric chemistry model system MECO(n) – Part 3: Meteorological evaluation of the on-line coupled system

2012 ◽  
Vol 5 (1) ◽  
pp. 129-147 ◽  
Author(s):  
C. Hofmann ◽  
A. Kerkweg ◽  
H. Wernli ◽  
P. Jöckel
Keyword(s):  
Atmospheric Chemistry ◽  
Lead Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Coupled Model ◽  
Coupled System ◽  
Regional Model ◽  
Model System ◽  
Convective Precipitation ◽  
On Line

Abstract. Three detailed meteorological case studies are conducted with the global and regional atmospheric chemistry model system ECHAM5/MESSy(→COSMO/MESSy)n, shortly named MECO(n). The aim of this article is to assess the general performance of the on-line coupling of the regional model COSMO to the global model ECHAM5. The cases are characterised by intense weather systems in Central Europe: a cold front passage in March 2010, a convective frontal event in July 2007, and the high impact winter storm "Kyrill" in January 2007. Simulations are performed with the new on-line-coupled model system and compared to classical, off-line COSMO hindcast simulations driven by ECMWF analyses. Precipitation observations from rain gauges and ECMWF analysis fields are used as reference, and both qualitative and quantitative measures are used to characterise the quality of the various simulations. It is shown that, not surprisingly, simulations with a shorter lead time generally produce more accurate simulations. Irrespective of lead time, the accuracy of the on-line and off-line COSMO simulations are comparable for the three cases. This result indicates that the new global and regional model system MECO(n) is able to simulate key mid-latitude weather systems, including cyclones, fronts, and convective precipitation, as accurately as present-day state-of-the-art regional weather prediction models in standard off-line configuration. Therefore, MECO(n) will be applied to simulate atmospheric chemistry exploring the model's full capabilities during meteorologically challenging conditions.

scholarly journals Impact of Flow-Dependent Horizontal Diffusion on Resolved Convection in AROME

2012 ◽  
Vol 51 (1) ◽  
pp. 54-67 ◽  
Author(s):  
Lisa Bengtsson ◽  
Sander Tijm ◽  
Filip Váňa ◽  
Gunilla Svensson
Keyword(s):  
Prediction Models ◽  
Mesoscale Model ◽  
Deep Convection ◽  
Weather Prediction ◽  
Nonlinear Flow ◽  
Dynamical Model ◽  
Convective Precipitation ◽  
Physical Parameters ◽  
Rain Intensity ◽  
Horizontal Diffusion

AbstractHorizontal diffusion in numerical weather prediction models is, in general, applied to reduce numerical noise at the smallest atmospheric scales. In convection-permitting models, with horizontal grid spacing on the order of 1–3 km, horizontal diffusion can improve the model skill of physical parameters such as convective precipitation. For instance, studies using the convection-permitting Applications of Research to Operations at Mesoscale model (AROME) have shown an improvement in forecasts of large precipitation amounts when horizontal diffusion is applied to falling hydrometeors. The nonphysical nature of such a procedure is undesirable, however. Within the current AROME, horizontal diffusion is imposed using linear spectral horizontal diffusion on dynamical model fields. This spectral diffusion is complemented by nonlinear, flow-dependent, horizontal diffusion applied on turbulent kinetic energy, cloud water, cloud ice, rain, snow, and graupel. In this study, nonlinear flow-dependent diffusion is applied to the dynamical model fields rather than diffusing the already predicted falling hydrometeors. In particular, the characteristics of deep convection are investigated. Results indicate that, for the same amount of diffusive damping, the maximum convective updrafts remain strong for both the current and proposed methods of horizontal diffusion. Diffusing the falling hydrometeors is necessary to see a reduction in rain intensity, but a more physically justified solution can be obtained by increasing the amount of damping on the smallest atmospheric scales using the nonlinear, flow-dependent, diffusion scheme. In doing so, a reduction in vertical velocity was found, resulting in a reduction in maximum rain intensity.

scholarly journals The HIRLAM fast radiation scheme for mesoscale numerical weather prediction models

2017 ◽  
Vol 14 ◽  
pp. 195-215 ◽  
Author(s):  
Laura Rontu ◽  
Emily Gleeson ◽  
Petri Räisänen ◽  
Kristian Pagh Nielsen ◽  
Hannu Savijärvi ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Surface Fluxes ◽  
Surface Radiation ◽  
Numerical Weather ◽  
Clear Sky ◽  
Radiation Output ◽  
Dry Conditions ◽  
Nwp Model

Abstract. This paper provides an overview of the HLRADIA shortwave (SW) and longwave (LW) broadband radiation schemes used in the HIRLAM numerical weather prediction (NWP) model and available in the HARMONIE-AROME mesoscale NWP model. The advantage of broadband, over spectral, schemes is that they can be called more frequently within the model, without compromising on computational efficiency. In mesoscale models fast interactions between clouds and radiation and the surface and radiation can be of greater importance than accounting for the spectral details of clear-sky radiation; thus calling the routines more frequently can be of greater benefit than the deterioration due to loss of spectral details. Fast but physically based radiation parametrizations are expected to be valuable for high-resolution ensemble forecasting, because as well as the speed of their execution, they may provide realistic physical perturbations. Results from single-column diagnostic experiments based on CIRC benchmark cases and an evaluation of 10 years of radiation output from the FMI operational archive of HIRLAM forecasts indicate that HLRADIA performs sufficiently well with respect to the clear-sky downwelling SW and longwave LW fluxes at the surface. In general, HLRADIA tends to overestimate surface fluxes, with the exception of LW fluxes under cold and dry conditions. The most obvious overestimation of the surface SW flux was seen in the cloudy cases in the 10-year comparison; this bias may be related to using a cloud inhomogeneity correction, which was too large. According to the CIRC comparisons, the outgoing LW and SW fluxes at the top of atmosphere are mostly overestimated by HLRADIA and the net LW flux is underestimated above clouds. The absorption of SW radiation by the atmosphere seems to be underestimated and LW absorption seems to be overestimated. Despite these issues, the overall results are satisfying and work on the improvement of HLRADIA for the use in HARMONIE-AROME NWP system is ongoing. In a HARMONIE-AROME 3-D forecast experiment we have shown that the frequency of the call for the radiation parametrization and choice of the parametrization scheme makes a difference to the surface radiation fluxes and changes the spatial distribution of the vertically integrated cloud cover and precipitation.

scholarly journals Comparison of Cloud Microphysics Schemes in a Warn-on-Forecast System Using Synthetic Satellite Objects

2018 ◽  
Vol 33 (6) ◽  
pp. 1681-1708 ◽  
Author(s):  
Thomas A. Jones ◽  
Patrick Skinner ◽  
Kent Knopfmeier ◽  
Edward Mansell ◽  
Patrick Minnis ◽  
...  
Keyword(s):  
Prediction Models ◽  
Negative Impact ◽  
Cloud Condensation Nuclei ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Variable Density ◽  
Cloud Microphysics ◽  
Convective Precipitation ◽  
Microphysics Scheme ◽  
Double Moment

AbstractForecasts of high-impact weather conditions using convection-allowing numerical weather prediction models have been found to be highly sensitive to the selection of cloud microphysics scheme used within the system. The Warn-on-Forecast (WoF) project has developed a rapid-cycling, convection-allowing, data assimilation and forecasting system known as the NSSL Experimental WoF System for ensembles (NEWS-e), which is designed to utilize advanced cloud microphysics schemes. NEWS-e currently (2017–18) uses the double-moment NSSL variable density scheme (NVD), which has been shown to generate realistic representations of convective precipitation within the system. However, very little verification on nonprecipitating cloud features has been performed with this system. During the 2017 Hazardous Weather Testbed (HWT) experiment, an overestimation of the areal coverage of convectively generated cirrus clouds was observed. Changing the cloud microphysics scheme to Thompson generated more accurate cloud fields. This research undertook the task of improving the cloud analysis generated by NVD while maintaining its skill for other variables such as reflectivity. Adjustments to cloud condensation nuclei (CCN), fall speed, and collection efficiencies were made and tested over a set of six severe weather cases occurring during May 2017. This research uses an object-based verification approach in which objects of cold infrared brightness temperatures, high cloud-top pressures, and cloud water path are generated from model output and compared against GOES-13 observations. Results show that the modified NVD scheme generated much more skillful forecasts of cloud objects than the original formulation without having a negative impact on the skill of simulated composite reflectivity forecasts.

scholarly journals Assessing the Impacts of Different WRF Precipitation Physics in Hurricane Simulations

2012 ◽  
Vol 27 (4) ◽  
pp. 1003-1016 ◽  
Author(s):  
Nasrin Nasrollahi ◽  
Amir AghaKouchak ◽  
Jialun Li ◽  
Xiaogang Gao ◽  
Kuolin Hsu ◽  
...  
Keyword(s):  
Prediction Models ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Precipitation Amount ◽  
Cumulus Parameterization ◽  
Microphysics Scheme ◽  
Practical Applications ◽  
Single Moment ◽  
Hurricane Track ◽  
Cumulus Schemes

Abstract Numerical weather prediction models play a major role in weather forecasting, especially in cases of extreme events. The Weather Research and Forecasting Model (WRF), among others, is extensively used for both research and practical applications. Previous studies have highlighted the sensitivity of this model to microphysics and cumulus schemes. This study investigated the performance of the WRF in forecasting precipitation, hurricane track, and landfall time using various microphysics and cumulus schemes. A total of 20 combinations of microphysics and cumulus schemes were used, and the model outputs were validated against ground-based observations. While the choice of microphysics and cumulus schemes can significantly impact model output, it is not the case that any single combination can be considered “ideal” for modeling all characteristics of a hurricane, including precipitation amount, areal extent, hurricane track, and the time of landfall. For example, the model’s ability to simulate precipitation (with the least total bias) is best achieved using Betts–Miller–Janjić (BMJ) cumulus parameterization in combination with the WRF single-moment five-class microphysics scheme (WSM5). It was determined that the WSM5–BMJ, WSM3 (the three-class version of the WSM scheme)–BMJ, and Ferrier microphysics in combination with the Grell–Devenyi cumulus scheme were the best combinations for simulation of the landfall time. However, the hurricane track was best estimated using the Lin et al. and Kessler microphysics options with BMJ cumulus parameterization. Contrary to previous studies, these results indicated that the use of cumulus schemes improves model outputs when the grid size is smaller than 10 km. However, it was found that many of the differences between parameterization schemes may be well within the uncertainty of the measurements.

Sign in / Sign up

Export Citation Format

Share Document