Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland
<p>The High Resolution Limited Area Model (HIRLAM), used for the operational numerical weather prediction in the Finnish Meteorological Institute (FMI), includes prognostic treatment of lake surface state since 2012. Forecast is based on the Freshwater Lake (FLake) model integrated into HIRLAM...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2019-08-01
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| Series: | Geoscientific Model Development |
| Online Access: | https://www.geosci-model-dev.net/12/3707/2019/gmd-12-3707-2019.pdf |
| _version_ | 1819127898938802176 |
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| author | L. Rontu K. Eerola M. Horttanainen |
| author_facet | L. Rontu K. Eerola M. Horttanainen |
| author_sort | L. Rontu |
| collection | DOAJ |
| description | <p>The High Resolution Limited Area Model (HIRLAM), used for the operational
numerical weather prediction in the Finnish Meteorological Institute
(FMI), includes prognostic treatment of lake surface state since
2012. Forecast is based on the Freshwater Lake (FLake) model
integrated into HIRLAM. Additionally, an independent objective
analysis of lake surface water temperature (LSWT) combines the short
forecast of FLake to observations from the Finnish Environment
Institute (SYKE). The resulting description of lake surface state –
forecast FLake variables and analysed LSWT – was compared to SYKE
observations of lake water temperature, freeze-up and break-up dates,
and the ice thickness and snow depth for 2012–2018 over 45
lakes in Finland. During the ice-free period, the predicted LSWT
corresponded to the observations with a slight overestimation, with a
systematic error of <span class="inline-formula">+0.91</span> K. The colder temperatures were
underrepresented and the maximum temperatures were too high. The
objective analysis of LSWT was able to reduce the bias to
<span class="inline-formula">+0.35</span> K. The predicted freeze-up dates corresponded well to the observed
dates, mostly within the accuracy of a week. The forecast break-up
dates were far too early, typically several weeks ahead of the
observed dates. The growth of ice thickness after freeze-up was
generally overestimated. However, practically no predicted snow
appeared on lake ice. The absence of snow, presumably due to an
incorrect security coefficient value, is suggested to be also the main
reason for the inaccurate simulation of the lake ice melting in spring.</p> |
| first_indexed | 2024-12-22T08:19:15Z |
| format | Article |
| id | doaj.art-e73f007855be4386906c296280ee19a4 |
| institution | Directory Open Access Journal |
| issn | 1991-959X 1991-9603 |
| language | English |
| last_indexed | 2024-12-22T08:19:15Z |
| publishDate | 2019-08-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Geoscientific Model Development |
| spelling | doaj.art-e73f007855be4386906c296280ee19a42022-12-21T18:32:48ZengCopernicus PublicationsGeoscientific Model Development1991-959X1991-96032019-08-01123707372310.5194/gmd-12-3707-2019Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in FinlandL. RontuK. EerolaM. Horttanainen<p>The High Resolution Limited Area Model (HIRLAM), used for the operational numerical weather prediction in the Finnish Meteorological Institute (FMI), includes prognostic treatment of lake surface state since 2012. Forecast is based on the Freshwater Lake (FLake) model integrated into HIRLAM. Additionally, an independent objective analysis of lake surface water temperature (LSWT) combines the short forecast of FLake to observations from the Finnish Environment Institute (SYKE). The resulting description of lake surface state – forecast FLake variables and analysed LSWT – was compared to SYKE observations of lake water temperature, freeze-up and break-up dates, and the ice thickness and snow depth for 2012–2018 over 45 lakes in Finland. During the ice-free period, the predicted LSWT corresponded to the observations with a slight overestimation, with a systematic error of <span class="inline-formula">+0.91</span> K. The colder temperatures were underrepresented and the maximum temperatures were too high. The objective analysis of LSWT was able to reduce the bias to <span class="inline-formula">+0.35</span> K. The predicted freeze-up dates corresponded well to the observed dates, mostly within the accuracy of a week. The forecast break-up dates were far too early, typically several weeks ahead of the observed dates. The growth of ice thickness after freeze-up was generally overestimated. However, practically no predicted snow appeared on lake ice. The absence of snow, presumably due to an incorrect security coefficient value, is suggested to be also the main reason for the inaccurate simulation of the lake ice melting in spring.</p>https://www.geosci-model-dev.net/12/3707/2019/gmd-12-3707-2019.pdf |
| spellingShingle | L. Rontu K. Eerola M. Horttanainen Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland Geoscientific Model Development |
| title | Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland |
| title_full | Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland |
| title_fullStr | Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland |
| title_full_unstemmed | Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland |
| title_short | Validation of lake surface state in the HIRLAM v.7.4 numerical weather prediction model against in situ measurements in Finland |
| title_sort | validation of lake surface state in the hirlam v 7 4 numerical weather prediction model against in situ measurements in finland |
| url | https://www.geosci-model-dev.net/12/3707/2019/gmd-12-3707-2019.pdf |
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