Data Assimilation: Making Sense of Earth Observation
Climate change, air quality and environmental degradation are important societal challenges for the 21st Century. These challenges require an intelligent response from society, which in turn requires access to information about the Earth System. This information comes from observations and prior kno...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2014-05-01
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| Series: | Frontiers in Environmental Science |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/Journal/10.3389/fenvs.2014.00016/full |
| _version_ | 1828768163731341312 |
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| author | William Albert Lahoz Philipp eSchneider |
| author_facet | William Albert Lahoz Philipp eSchneider |
| author_sort | William Albert Lahoz |
| collection | DOAJ |
| description | Climate change, air quality and environmental degradation are important societal challenges for the 21st Century. These challenges require an intelligent response from society, which in turn requires access to information about the Earth System. This information comes from observations and prior knowledge, the latter typically embodied in a model describing relationships between variables of the Earth System. Data assimilation provides an objective methodology to combine observational and model information to provide an estimate of the most likely state and its uncertainty for the whole Earth System. This approach adds value to the observations – by filling in the spatio-temporal gaps in observations; and to the model – by constraining it with the observations. In this review paper we motivate data assimilation as a methodology to fill in the gaps in observational information; illustrate the data assimilation approach with examples that span a broad range of features of the Earth System (atmosphere, including chemistry; ocean; land surface); and discuss the outlook for data assimilation, including the novel application of data assimilation ideas to observational information obtained using Citizen Science. Ultimately, a strong motivation of data assimilation is the many benefits it provides to users. These include: providing the initial state for weather and air quality forecasts; providing analyses and reanalyses for studying the Earth System; evaluating observations, instruments and models; assessing the relative value of elements of the Global Observing System (GOS); and assessing the added value of future additions to the GOS. |
| first_indexed | 2024-12-11T07:42:01Z |
| format | Article |
| id | doaj.art-82a5999a87224f04a9ac787a9269f03f |
| institution | Directory Open Access Journal |
| issn | 2296-665X |
| language | English |
| last_indexed | 2024-12-11T07:42:01Z |
| publishDate | 2014-05-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Environmental Science |
| spelling | doaj.art-82a5999a87224f04a9ac787a9269f03f2022-12-22T01:15:32ZengFrontiers Media S.A.Frontiers in Environmental Science2296-665X2014-05-01210.3389/fenvs.2014.0001688996Data Assimilation: Making Sense of Earth ObservationWilliam Albert Lahoz0Philipp eSchneider1NILU - Norwegian Institute for Air ResearchNILU - Norwegian Institute for Air ResearchClimate change, air quality and environmental degradation are important societal challenges for the 21st Century. These challenges require an intelligent response from society, which in turn requires access to information about the Earth System. This information comes from observations and prior knowledge, the latter typically embodied in a model describing relationships between variables of the Earth System. Data assimilation provides an objective methodology to combine observational and model information to provide an estimate of the most likely state and its uncertainty for the whole Earth System. This approach adds value to the observations – by filling in the spatio-temporal gaps in observations; and to the model – by constraining it with the observations. In this review paper we motivate data assimilation as a methodology to fill in the gaps in observational information; illustrate the data assimilation approach with examples that span a broad range of features of the Earth System (atmosphere, including chemistry; ocean; land surface); and discuss the outlook for data assimilation, including the novel application of data assimilation ideas to observational information obtained using Citizen Science. Ultimately, a strong motivation of data assimilation is the many benefits it provides to users. These include: providing the initial state for weather and air quality forecasts; providing analyses and reanalyses for studying the Earth System; evaluating observations, instruments and models; assessing the relative value of elements of the Global Observing System (GOS); and assessing the added value of future additions to the GOS.http://journal.frontiersin.org/Journal/10.3389/fenvs.2014.00016/fulluncertaintymodelscitizen scienceEarth Observationdata assimilationObservations |
| spellingShingle | William Albert Lahoz Philipp eSchneider Data Assimilation: Making Sense of Earth Observation Frontiers in Environmental Science uncertainty models citizen science Earth Observation data assimilation Observations |
| title | Data Assimilation: Making Sense of Earth Observation |
| title_full | Data Assimilation: Making Sense of Earth Observation |
| title_fullStr | Data Assimilation: Making Sense of Earth Observation |
| title_full_unstemmed | Data Assimilation: Making Sense of Earth Observation |
| title_short | Data Assimilation: Making Sense of Earth Observation |
| title_sort | data assimilation making sense of earth observation |
| topic | uncertainty models citizen science Earth Observation data assimilation Observations |
| url | http://journal.frontiersin.org/Journal/10.3389/fenvs.2014.00016/full |
| work_keys_str_mv | AT williamalbertlahoz dataassimilationmakingsenseofearthobservation AT philippeschneider dataassimilationmakingsenseofearthobservation |