Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study
<p>The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional- and convective-scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2022-09-01
|
| Series: | Geoscientific Model Development |
| Online Access: | https://gmd.copernicus.org/articles/15/6891/2022/gmd-15-6891-2022.pdf |
| _version_ | 1798002926590885888 |
|---|---|
| author | I. H. Banos I. H. Banos W. D. Mayfield W. D. Mayfield G. Ge G. Ge L. F. Sapucci J. R. Carley L. Nance L. Nance |
| author_facet | I. H. Banos I. H. Banos W. D. Mayfield W. D. Mayfield G. Ge G. Ge L. F. Sapucci J. R. Carley L. Nance L. Nance |
| author_sort | I. H. Banos |
| collection | DOAJ |
| description | <p>The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional- and convective-scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the hybrid three-dimensional ensemble–variational data assimilation (3DEnVar) algorithm in the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. A domain of 3 <span class="inline-formula">km</span> horizontal grid spacing is configured, and hourly update cycles are performed using initial and lateral boundary conditions from the 3 <span class="inline-formula">km</span> grid High-Resolution Rapid Refresh (HRRR). Results show that a baseline RRFS run is able to represent the observed convection, although with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and 25 % of the static BEC with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius from 3 layers to 1 layer in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection is greatly improved when using planetary boundary layer pseudo-observations, especially at 4 h forecast, and the bias of the 2 h forecast of temperature is reduced below 800 <span class="inline-formula">hPa</span>. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into the current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.</p> |
| first_indexed | 2024-04-11T12:00:07Z |
| format | Article |
| id | doaj.art-c0a430a648b042cab757287064de7554 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-04-11T12:00:07Z |
| publishDate | 2022-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-c0a430a648b042cab757287064de75542022-12-22T04:24:54ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032022-09-01156891691710.5194/gmd-15-6891-2022Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case studyI. H. Banos0I. H. Banos1W. D. Mayfield2W. D. Mayfield3G. Ge4G. Ge5L. F. Sapucci6J. R. Carley7L. Nance8L. Nance9Postgraduate Division, Coordination of Teaching, Research and Extension, National Institute for Space Research, São José dos Campos, São Paulo, Brazilnow at: NCAR Mesoscale and Microscale Meteorology Laboratory, Boulder, CO, USANCAR Research Applications Laboratory, Boulder, CO, USADevelopmental Testbed Center, Boulder, CO, USANOAA Global Systems Laboratory, Boulder, CO, USACooperative Institute for Research in Environmental Sciences, CU Boulder, Boulder, CO, USACenter for Weather Forecasts and Climate Studies, National Institute for Space Research, Cachoeira Paulista, São Paulo, BrazilModeling and Data Assimilation Branch, NOAA NCEP Environmental Modeling Center, College Park, MD, USANCAR Research Applications Laboratory, Boulder, CO, USADevelopmental Testbed Center, Boulder, CO, USA<p>The Rapid Refresh Forecast System (RRFS) is currently under development and aims to replace the National Centers for Environmental Prediction (NCEP) operational suite of regional- and convective-scale modeling systems in the next upgrade. In order to achieve skillful forecasts comparable to the current operational suite, each component of the RRFS needs to be configured through exhaustive testing and evaluation. The current data assimilation component uses the hybrid three-dimensional ensemble–variational data assimilation (3DEnVar) algorithm in the Gridpoint Statistical Interpolation (GSI) system. In this study, various data assimilation algorithms and configurations in GSI are assessed for their impacts on RRFS analyses and forecasts of a squall line over Oklahoma on 4 May 2020. A domain of 3 <span class="inline-formula">km</span> horizontal grid spacing is configured, and hourly update cycles are performed using initial and lateral boundary conditions from the 3 <span class="inline-formula">km</span> grid High-Resolution Rapid Refresh (HRRR). Results show that a baseline RRFS run is able to represent the observed convection, although with stronger cells and large location errors. With data assimilation, these errors are reduced, especially in the 4 and 6 h forecasts using 75 % of the ensemble background error covariance (BEC) and 25 % of the static BEC with the supersaturation removal function activated in GSI. Decreasing the vertical ensemble localization radius from 3 layers to 1 layer in the first 10 layers of the hybrid analysis results in overall less skillful forecasts. Convection is greatly improved when using planetary boundary layer pseudo-observations, especially at 4 h forecast, and the bias of the 2 h forecast of temperature is reduced below 800 <span class="inline-formula">hPa</span>. Lighter hourly accumulated precipitation is predicted better when using 100 % ensemble BEC in the first 4 h forecast, but heavier hourly accumulated precipitation is better predicted with 75 % ensemble BEC. Our results provide insight into the current capabilities of the RRFS data assimilation system and identify configurations that should be considered as candidates for the first version of RRFS.</p>https://gmd.copernicus.org/articles/15/6891/2022/gmd-15-6891-2022.pdf |
| spellingShingle | I. H. Banos I. H. Banos W. D. Mayfield W. D. Mayfield G. Ge G. Ge L. F. Sapucci J. R. Carley L. Nance L. Nance Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study Geoscientific Model Development |
| title | Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study |
| title_full | Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study |
| title_fullStr | Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study |
| title_full_unstemmed | Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study |
| title_short | Assessment of the data assimilation framework for the Rapid Refresh Forecast System v0.1 and impacts on forecasts of a convective storm case study |
| title_sort | assessment of the data assimilation framework for the rapid refresh forecast system v0 1 and impacts on forecasts of a convective storm case study |
| url | https://gmd.copernicus.org/articles/15/6891/2022/gmd-15-6891-2022.pdf |
| work_keys_str_mv | AT ihbanos assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT ihbanos assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT wdmayfield assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT wdmayfield assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT gge assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT gge assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT lfsapucci assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT jrcarley assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT lnance assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy AT lnance assessmentofthedataassimilationframeworkfortherapidrefreshforecastsystemv01andimpactsonforecastsofaconvectivestormcasestudy |