Improvement of RAMS precipitation forecast at the short-range through lightning data assimilation
This study shows the application of a total lightning data assimilation technique to the RAMS (Regional Atmospheric Modeling System) forecast. The method, which can be used at high horizontal resolution, helps to initiate convection whenever flashes are observed by adding water vapour to the model g...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2017-01-01
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| Series: | Natural Hazards and Earth System Sciences |
| Online Access: | http://www.nat-hazards-earth-syst-sci.net/17/61/2017/nhess-17-61-2017.pdf |
| Summary: | This study shows the application of a total lightning data assimilation
technique to the RAMS (Regional Atmospheric Modeling System) forecast. The
method, which can be used at high horizontal resolution, helps to initiate
convection whenever flashes are observed by adding water vapour to the model
grid column. The water vapour is added as a function of the flash rate, local
temperature, and graupel mixing ratio. The methodology is set up to improve
the short-term (3 h) precipitation forecast and can be used in real-time
forecasting applications. However, results are also presented for the daily
precipitation for comparison with other studies.
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The methodology is applied to 20 cases that occurred in fall 2012, which
were characterized by widespread convection and lightning activity. For
these cases a detailed dataset of hourly precipitation containing thousands
of rain gauges over Italy, which is the target area of this study, is
available through the HyMeX (HYdrological cycle in the Mediterranean
Experiment) initiative. This dataset gives the unique opportunity to verify
the precipitation forecast at the short range (3 h) and over a wide area (Italy).
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Results for the 27 October case study show how the methodology works and its
positive impact on the 3 h precipitation forecast. In particular, the model
represents better convection over the sea using the lightning data
assimilation and, when convection is advected over the land, the
precipitation forecast improves over the land. It is also shown that the
precise location of convection by lightning data assimilation improves the
precipitation forecast at fine scales (meso-<i>β</i>).
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The application of the methodology to 20 cases gives a statistically
robust evaluation of the impact of the total lightning data assimilation on
the model performance. Results show an improvement of all statistical
scores, with the exception of the bias. The probability of detection (POD)
increases by 3–5 % for the 3 h forecast and by more than 5 % for daily
precipitation, depending on the precipitation threshold considered.
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Score differences between simulations with or without data assimilation are
significant at 95 % level for most scores and thresholds considered,
showing the positive and statistically robust impact of the lightning data
assimilation on the precipitation forecast. |
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| ISSN: | 1561-8633 1684-9981 |