Parallel Data Assimilation Framework (PDAF)<\/span><\/span><\/span>\u00a0<\/span><\/h4>\nMy <\/span>role <\/span>in the National Centre for Earth Observation (NCEO) and DARC is applying and developing <\/span>a piece of data assimilation software called <\/span><\/span>PDAF<\/span><\/span><\/a> (<\/span>Parallel Data Assimilation Framework<\/span>)<\/span>. <\/span>The software offers efficient implementations with the possibility to a parallel DA system<\/span>.<\/span> This <\/span>mean<\/span>s <\/span>that the DA system can utilise multiple computer processors at the same time. This design is particularly useful when we use ensemble DA where e<\/span>ach <\/span>p<\/span>ossible <\/span>outcome<\/span> of the <\/span>model<\/span> forecast is represented by an ensemble member. As <\/span>ensemble member<\/span>s<\/span> do<\/span> not exchange information <\/span>during the forecast<\/span>, they can be run<\/span> perfectly <\/span>independently on <\/span>different processors<\/span>.<\/span> The<\/span> parallel DA system <\/span>can also utilise<\/span> properties of the physical system<\/span>s<\/span> referred to <\/span>as <\/span>localisation<\/span>. When <\/span>the state <\/span>at<\/span> one location is only influenced by <\/span>adjacent regions<\/span>, the DA algorithms can be broken into small problems <\/span>and <\/span>solved<\/span> in parallel.<\/span>\u00a0<\/span><\/span>\u00a0<\/span><\/p>\nPDAF can supplement a<\/span> wide range of NCEO research <\/span>by<\/span> provid<\/span>ing<\/span> a suite of DA algorithms<\/span> and <\/span>being usable <\/span>flexibly with <\/span>any numerical models and observations.<\/span> For example, a<\/span> global marine biogeochemistry DA system is under development with PDAF in NCEO. <\/span>Meanwhile, <\/span>PDAF<\/span> has been <\/span>used <\/span>with some notable climate models including <\/span>AWI-CM, <\/span>CICE, FES<\/span>OM,<\/span> MI<\/span>Tgc<\/span>m<\/span>, MPI-E<\/span>SM,<\/span> TerrS<\/span>ysM<\/span>P<\/span> and NEMO.<\/span><\/span>\u00a0<\/span> <\/span><\/p>\n![\"\"](\"https:\/\/research.reading.ac.uk\/met-darc\/wp-content\/uploads\/sites\/48\/2024\/04\/PDAF-300x177.png\")
<\/p>\n
Figure 1: The design principle of PDAF where the model and observation feed information into the DA algorithm denoted by Filter\/Core of PDAF (<\/span>source<\/span><\/a>).<\/span>\u00a0<\/span><\/p>\n\u00a0A new python interface to PDAF<\/h4>\n
However, the efficiency and flexibility of PDAF comes with a price. The framework is written in Fortran, an efficient and popular programming language for weather and climate models. However, writing code in Fortran can be laborious. Recently, we released a Python interface to PDAF, <\/span>pyPDAF<\/span><\/a>. This new Python package eases the difficulties in developing a DA system with Python models that are used in NCEO. This new Python package can be particularly useful for machine learning models, usually programmed in Python.<\/span>\u00a0<\/span><\/p>\nCertainly, PDAF is just one example of DA software for complex weather and climate problems.\u00a0\u00a0 On the basis of different purposes and applications, many other DA tools have been developed. For instance, JCSDA (Joint Center for Satellite Data Assimilation) is collaborating with many operational centres, including the UK Met Office, to develop JEDI (Joint Effort for Data assimilation Integration); NCAR (the US National Center for Atmospheric Research) developed and maintained <\/span>DART<\/span><\/a> (The Data Assimilation Research Testbed); The <\/span>LAVENDAR<\/span><\/a> (Land Variational Ensemble Data Assimilation fRamework) has been developed for land surface models. Developed in Norwegian Research Centre, <\/span>DAPPER<\/span><\/a> provides a great tool for DA methodology research.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"
PDAF can supplement a<\/span> wide range of NCEO research <\/span>by<\/span> provid<\/span>ing<\/span> a suite of DA algorithms<\/span> and <\/span>being usable <\/span>flexibly with <\/span>any numerical models and observations.<\/span> For example, a<\/span> global marine biogeochemistry DA system is under development with PDAF in NCEO. <\/span>Meanwhile, <\/span>PDAF<\/span> has been <\/span>used <\/span>with some notable climate models including <\/span>AWI-CM, <\/span>CICE, FES<\/span>OM,<\/span> MI<\/span>Tgc<\/span>m<\/span>, MPI-E<\/span>SM,<\/span> TerrS<\/span>ysM<\/span>P<\/span> and NEMO.<\/span><\/span>\u00a0<\/span> <\/span><\/p>\n Figure 1: The design principle of PDAF where the model and observation feed information into the DA algorithm denoted by Filter\/Core of PDAF (<\/span>source<\/span><\/a>).<\/span>\u00a0<\/span><\/p>\n However, the efficiency and flexibility of PDAF comes with a price. The framework is written in Fortran, an efficient and popular programming language for weather and climate models. However, writing code in Fortran can be laborious. Recently, we released a Python interface to PDAF, <\/span>pyPDAF<\/span><\/a>. This new Python package eases the difficulties in developing a DA system with Python models that are used in NCEO. This new Python package can be particularly useful for machine learning models, usually programmed in Python.<\/span>\u00a0<\/span><\/p>\n Certainly, PDAF is just one example of DA software for complex weather and climate problems.\u00a0\u00a0 On the basis of different purposes and applications, many other DA tools have been developed. For instance, JCSDA (Joint Center for Satellite Data Assimilation) is collaborating with many operational centres, including the UK Met Office, to develop JEDI (Joint Effort for Data assimilation Integration); NCAR (the US National Center for Atmospheric Research) developed and maintained <\/span>DART<\/span><\/a> (The Data Assimilation Research Testbed); The <\/span>LAVENDAR<\/span><\/a> (Land Variational Ensemble Data Assimilation fRamework) has been developed for land surface models. Developed in Norwegian Research Centre, <\/span>DAPPER<\/span><\/a> provides a great tool for DA methodology research.<\/span>\u00a0<\/span><\/p>\n","protected":false},"excerpt":{"rendered":"<\/p>\n\u00a0A new python interface to PDAF<\/h4>\n