Effect of the Ensemble System Size on the Precipitation Forecast Accuracy
Numerical weather prediction (NWP) models are not completely accurate and error free, and there is always some uncertainty. The errors in weather forecasting stem from the limitations of human theoretical understanding of the atmosphere and the operational capacity to produce forecasts. It is necess...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | fas |
| Published: |
I.R. of Iran Meteorological Organization
2022-09-01
|
| Series: | Nīvār |
| Subjects: | |
| Online Access: | https://nivar.irimo.ir/article_164096_72ac1ce86da076c6bf1febc9be794f01.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1841559915911446528 |
|---|---|
| author | Atefeh Mohamadi Majid Azadi |
| author_facet | Atefeh Mohamadi Majid Azadi |
| author_sort | Atefeh Mohamadi |
| collection | DOAJ |
| description | Numerical weather prediction (NWP) models are not completely accurate and error free, and there is always some uncertainty. The errors in weather forecasting stem from the limitations of human theoretical understanding of the atmosphere and the operational capacity to produce forecasts. It is necessary to make a forecast, along with an estimate of its uncertainty. This is accomplished by creating ensemble systems of weather forecasts differing in the initial conditions or physical formulation of NWP models. There are several methods for post-processing of ensemble forecasting, including Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) that they are more popular because of higher efficiency and accuracy. In this research, first, an 18-member ensemble system is formed, which each member is an independent run of the WRF model with different physical configurations. BMA method was used to estimate the density function of predicting 24-hour cumulative precipitation. Due to some hardware limitations and access to an ensemble system with fewer number and more efficient members, the size of the ensemble system has been reduced to 7 members. Using the BMA method, a weight is assigned to each ensemble member. The size of the ensemble system is reduced by removing the members who had less weight. The probabilistic prediction verification obtained from the 7-member ensemble system in a test period from 15 January 2020 to 15 May 2020 has been checked using reliability diagram. The results show that the probabilistic predictions are sufficiently skilled for 24-hour cumulative precipitation. |
| format | Article |
| id | doaj-art-d087e5d2357a4450add7b31a954f2bba |
| institution | Kabale University |
| issn | 1735-0565 2645-3347 |
| language | fas |
| publishDate | 2022-09-01 |
| publisher | I.R. of Iran Meteorological Organization |
| record_format | Article |
| series | Nīvār |
| spelling | doaj-art-d087e5d2357a4450add7b31a954f2bba2025-01-05T11:56:52ZfasI.R. of Iran Meteorological OrganizationNīvār1735-05652645-33472022-09-0146118-119738410.30467/nivar.2023.375268.1233164096Effect of the Ensemble System Size on the Precipitation Forecast AccuracyAtefeh Mohamadi0Majid Azadi1Phd, Atmospheric Science and Meteorological Research Center (ASMERC), Tehran, IranAssociate Professor, Atmospheric Science and Meteorological Research Center (ASMERC) ), Tehran, IranNumerical weather prediction (NWP) models are not completely accurate and error free, and there is always some uncertainty. The errors in weather forecasting stem from the limitations of human theoretical understanding of the atmosphere and the operational capacity to produce forecasts. It is necessary to make a forecast, along with an estimate of its uncertainty. This is accomplished by creating ensemble systems of weather forecasts differing in the initial conditions or physical formulation of NWP models. There are several methods for post-processing of ensemble forecasting, including Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) that they are more popular because of higher efficiency and accuracy. In this research, first, an 18-member ensemble system is formed, which each member is an independent run of the WRF model with different physical configurations. BMA method was used to estimate the density function of predicting 24-hour cumulative precipitation. Due to some hardware limitations and access to an ensemble system with fewer number and more efficient members, the size of the ensemble system has been reduced to 7 members. Using the BMA method, a weight is assigned to each ensemble member. The size of the ensemble system is reduced by removing the members who had less weight. The probabilistic prediction verification obtained from the 7-member ensemble system in a test period from 15 January 2020 to 15 May 2020 has been checked using reliability diagram. The results show that the probabilistic predictions are sufficiently skilled for 24-hour cumulative precipitation.https://nivar.irimo.ir/article_164096_72ac1ce86da076c6bf1febc9be794f01.pdfnumerical weather predictionprobabilistic forecastensemble systemwrf modelbma ensemble post-processing method |
| spellingShingle | Atefeh Mohamadi Majid Azadi Effect of the Ensemble System Size on the Precipitation Forecast Accuracy Nīvār numerical weather prediction probabilistic forecast ensemble system wrf model bma ensemble post-processing method |
| title | Effect of the Ensemble System Size on the Precipitation Forecast Accuracy |
| title_full | Effect of the Ensemble System Size on the Precipitation Forecast Accuracy |
| title_fullStr | Effect of the Ensemble System Size on the Precipitation Forecast Accuracy |
| title_full_unstemmed | Effect of the Ensemble System Size on the Precipitation Forecast Accuracy |
| title_short | Effect of the Ensemble System Size on the Precipitation Forecast Accuracy |
| title_sort | effect of the ensemble system size on the precipitation forecast accuracy |
| topic | numerical weather prediction probabilistic forecast ensemble system wrf model bma ensemble post-processing method |
| url | https://nivar.irimo.ir/article_164096_72ac1ce86da076c6bf1febc9be794f01.pdf |
| work_keys_str_mv | AT atefehmohamadi effectoftheensemblesystemsizeontheprecipitationforecastaccuracy AT majidazadi effectoftheensemblesystemsizeontheprecipitationforecastaccuracy |