Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members
Abstract Nowadays, several global ensembles (GEs) which consist of several tens of members are being run operationally. In order to locally improve the probabilistic forecasts, various forecasting centers and research institutes utilize the GEs as initial and boundary conditions to drive regional co...
| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-07-01
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| Series: | Meteorological Applications |
| Subjects: | |
| Online Access: | https://doi.org/10.1002/met.2137 |
| _version_ | 1797733151760449536 |
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| author | Pavel Khain Alon Shtivelman Yoav Levi Anat Baharad Eyal Amitai Yizhak Carmona Elyakom Vadislavsky Amit Savir Nir Stav |
| author_facet | Pavel Khain Alon Shtivelman Yoav Levi Anat Baharad Eyal Amitai Yizhak Carmona Elyakom Vadislavsky Amit Savir Nir Stav |
| author_sort | Pavel Khain |
| collection | DOAJ |
| description | Abstract Nowadays, several global ensembles (GEs) which consist of several tens of members are being run operationally. In order to locally improve the probabilistic forecasts, various forecasting centers and research institutes utilize the GEs as initial and boundary conditions to drive regional convection permitting ensembles (RCPEs). RCPEs demand significant computer resources and often a limited number of ensemble members is affordable, which is smaller than the size of the driving GE. Since each RCPE member obtains the initial and boundary conditions from a specific GE member, there are many options to select the GE members. The study uses the European Centre for Medium‐Range Weather Forecasts (ECMWF) GE consisting of 50 members, to drive 20 members of COSMO model RCPE over the Eastern Mediterranean. We compare various approaches for automatic selection of the GE members and propose several optimal methods, including a random selection, which consistently lead to a better performance of the driven RCPE. The comparison includes verification of near surface variables and precipitation using various verification metrics. The results are validated using several methods of model physics perturbation. Besides the selection of the optimal ensemble configurations, we show that at high precipitation intensities spatial up‐scaling is recommended in order to obtain useful probabilistic forecasts. |
| first_indexed | 2024-03-12T12:24:03Z |
| format | Article |
| id | doaj.art-b486e991595d4c3f97aa226b1799cb94 |
| institution | Directory Open Access Journal |
| issn | 1350-4827 1469-8080 |
| language | English |
| last_indexed | 2024-03-12T12:24:03Z |
| publishDate | 2023-07-01 |
| publisher | Wiley |
| record_format | Article |
| series | Meteorological Applications |
| spelling | doaj.art-b486e991595d4c3f97aa226b1799cb942023-08-30T05:11:03ZengWileyMeteorological Applications1350-48271469-80802023-07-01304n/an/a10.1002/met.2137Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving membersPavel Khain0Alon Shtivelman1Yoav Levi2Anat Baharad3Eyal Amitai4Yizhak Carmona5Elyakom Vadislavsky6Amit Savir7Nir Stav8The Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelThe Israel Meteorological Service Bet‐Dagan IsraelAbstract Nowadays, several global ensembles (GEs) which consist of several tens of members are being run operationally. In order to locally improve the probabilistic forecasts, various forecasting centers and research institutes utilize the GEs as initial and boundary conditions to drive regional convection permitting ensembles (RCPEs). RCPEs demand significant computer resources and often a limited number of ensemble members is affordable, which is smaller than the size of the driving GE. Since each RCPE member obtains the initial and boundary conditions from a specific GE member, there are many options to select the GE members. The study uses the European Centre for Medium‐Range Weather Forecasts (ECMWF) GE consisting of 50 members, to drive 20 members of COSMO model RCPE over the Eastern Mediterranean. We compare various approaches for automatic selection of the GE members and propose several optimal methods, including a random selection, which consistently lead to a better performance of the driven RCPE. The comparison includes verification of near surface variables and precipitation using various verification metrics. The results are validated using several methods of model physics perturbation. Besides the selection of the optimal ensemble configurations, we show that at high precipitation intensities spatial up‐scaling is recommended in order to obtain useful probabilistic forecasts.https://doi.org/10.1002/met.2137ensembleNWPprobabilistic forecastspread skill ratio |
| spellingShingle | Pavel Khain Alon Shtivelman Yoav Levi Anat Baharad Eyal Amitai Yizhak Carmona Elyakom Vadislavsky Amit Savir Nir Stav Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members Meteorological Applications ensemble NWP probabilistic forecast spread skill ratio |
| title | Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members |
| title_full | Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members |
| title_fullStr | Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members |
| title_full_unstemmed | Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members |
| title_short | Optimizing convection‐permitting ensemble via selection of the coarse ensemble driving members |
| title_sort | optimizing convection permitting ensemble via selection of the coarse ensemble driving members |
| topic | ensemble NWP probabilistic forecast spread skill ratio |
| url | https://doi.org/10.1002/met.2137 |
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