Thor Erik Nordeng (P.O. Box 43, N-0313 OSLO, NORWAY)

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1 (Picture: A study of model performance for the February 2007 snowstorm on Sørlandet. Part I: Evaluation against observations Thor Erik Nordeng (P.O. Box 43, N-0313 OSLO, NORWAY)

2 2

3 1. Introduction. Observed precipitation amounts in February 2007 for Norway as a whole were close to normal (110%). But the southern coast of Norway (counties Vest-Agder, Aust-Agder and Telemark) experienced significantly more. Torungen lighthouse got nearly 3 times more precipitation than an average February (140.6 mm i.e. 293%). Landvik got mm (281%) and Mandal got mm (202%) Highest precipitation amounts in a single day was recorded in Mandal with 66.1 mm on the 24. February, which is the second highest amount observed (observations date back to 1861). Along parts of the coastline in Agder and Telemark the combined effect of strong winds, low temperatures and heavy snowfall forced road authorities to close major highways between 23 and 25 February. It is well known that the coastal areas of Sørlandet may experience large amounts of precipitation. The phenomenon is recently described and discussed by Holmebakken and Grønås (2005). They show a figure taken from a work of Bergeron (1949) where he describes a case in March 1927 (replicated here as Figure 1a). Bergeron s figure is remarkably similar to results from the atmospheric model UM4 for February 2007 (Figure 1b). Large precipitation amounts in Agder and Telemark are common for southerly winds due to forced lifting of moist air over land. When the wind direction is north-easterly however, coastal convergence due to frictional effects is the main reason for large precipitation amounts, but the precipitation is then restricted to a relatively narrow strip along the coast. (a) (b) Figure 1. Precipitation, wind, pressure and temperature in southern Norway during the period March 1927 taken from Bergeron (1949) (a) and 24 hr accumulated precipitation up to 22 February UTC from UM4 (b). In (b) we have also plotted surface winds from the model (10m above ground) valid at 22 February UTC. 3

4 2. Observed versus forecasted precipitation a. Verification against meteorological stations Observed precipitation at the two lighthouses, Torungen and Lindesnes, together with modelled precipitation with the large scale models ECMWF, HIRLAM20 and HIRLAM10 are shown in Figure 2. Figure 3 shows the same but with the high-resolution models UM4 and HIRLAM4. We have plotted observed and forecasted precipitation accumulated over 12 hr periods. Torungen had particularly large precipitation amounts on 22 February (observed precipitation over 24 hrs was 31.9 mm) and it is seen from figure 2 that all models performed relatively well. It should be mentioned that the HIRLAM and UM forecasts are 12 hrs shorter than the corresponding ECMWF forecasts. In spite of this penalty the ECMWF model was slightly better than the others this day. The fine mesh models HIRLAM4 and UM4 did a pretty good job as well, but the amounts seem to be slightly underestimated. It should be remarked that using figure 2 and figure 3 to a detailed comparison between the large scale models and the fine mesh models is not possible because of different forecasts lengths and different initial times for the integrations. The day after (23 February) observed precipitation is strongly reduced (down 50%), but the models continue to forecast high precipitation amounts. No models manage to forecast the precipitation incident on 27 February. Statistics for the whole period (6 Feb to 6 March) is presented in the same figures. The ECMWF model is somewhat better than the others. The fine mesh models do not seem to be an improvement over the large-scale models for this station and situation. Lindesnes experienced the heaviest precipitation a couple of days later (24 February). For this date all models do a good job. The UM4-model, however, stands out with good timing and amounts while it at the same time is not overestimating precipitation amounts during 21 and 22 February. Overestimation is characteristic for the large scale models. The precipitation between 28 February and 3 March was well predicted by all models, but HIRLAM4 is somewhat worse than the others. The UM4-model was slightly better than the others on this date as well. 4

5 (a) (b) Figur 2. Observed and modelled precipitation with HIRLAM10, HIRLAM20 and ECMWF (accumulation over 12 hours) for Torungen (a) og Lindesnes (b) 5

6 (a) (b) Figur 3. Observed and modelled precipitation with UM4, HIRLAM4 and ECMWF (accumulation over 12 hours) for Torungen (a) and Lindesnes (b) 6

7 b. Spatial distribution Observed precipitation for the last week of February is plotted in figure 4 and should be compared to figure 5. Spatial interpolation is based on a GIS- technique and is described in Janson et al (2007). Only traditional (SYNOP) and climate stations have been used. A characteristic feature for the observed large precipitation amounts on Sørlandet was the spatial distribution as it was connected to the coastline. We have therefore studied how well this feature is captured by the models. Figure 5 shows total precipitation for the last 8 days of February 2007 for HIRLAM20, HIRLAM10, HIRLAM4 and UM4 respectively. Figure 4. Observed precipitation over land for the last week of February 2007 (taken from All models produce excessive precipitation amounts but there is a marked difference between the HIRLAM models and the UM-model. The latter concentrates its large precipitation amounts to a strip along the coast (over land). UM4 has clearly a lateral boundary value problem (see heavy precipitation along the border of the integration area in figure 4d), which may influence the forecast close to the edges of the integration area, but we do not believe that has any effect on the area of interest. Observed distribution is very similar to modelled precipitation with UM4 with a maximum close to the southern tip of Norway. Even local effects caused by topography are evident, e.g., a precipitation minimum in the Setesdalen valley. 7

8 (a) (b) 8

9 (c) (d) Figure 5. Mean daily precipitation over the last 8 days of February with models HIRLAM20 (upper left panel), HIRLAM10 (upper right panel), HIRLAM4 (lower left panel) and UM4 (lower right panel) in units of mm/day. 9

10 We have also studied accumulated precipitation from day to day and have chosen to compare 24 hr. accumulated precipitation from the models (i.e. between +6 and +30 hrs of integration) valid at 06 UTC with 24 hr precipitation estimates from the radar Hægebostad (figure 6) for the last 8 days of February By using radar data we are also able to validate modelled precipitation over sea. Results from all models have been looked at, but here we present plots from UM4 and HIRLAM4 only as HIRLAM10 and HIRLAM20 have very similar results as HIRLAM4 (see also figure 5). a) HIRLAM (+6, +30) b) UM (+6, +30) c) HIRLAM (+6, +30) d) UM (+6, +30) e) HIRLAM (+6, +30) f) UM (+6, +30) 1

11 g) HIRLAM (+6, +30) h) UM (+6, +30) i) HIRLAM (+6, +30) j) UM (+6, +30) k) HIRLAM (+6, +30) l) UM (+6, +30) m) HIRLAM (+6, +30) n) UM (+6, +30) 1

12 o) HIRLAM (+6, +30) p) UM (+6, +30) Figure 6: 24 hr accumulated precipitation between 6 and 30 hrs into the forecast (contour lines) together with observed 24 hr precipitation from radar (shading). Left column HIRLAM4. Right column UM4. The daily plots support the findings of figures 4 and 5; the coastal maximum, which is particularly clear from the radar pictures in the beginning of the period up to 25th February, is not captured particularly well in HIRLAM4 as compared to UM4. The larger scale patterns are however not that different and particularly precipitation over sea. The period from 22nd to 25th of February stands out as a period where UM4 is superior to HIRLAM. Accumulated precipitation during 23 February is particularly well forecasted with UM4. It is worth noticing that the large precipitation amounts over sea are well captured in both models. In figure 7, modelled precipitation between 06 UTC 23 Feb 2007 and 06 UTC 24 Feb 2007 (i.e., from +6 to +30 hours into the forecast) is compared to land based observations. The similarity (when using the UM-model) is striking; even the two maxima with precipitation exceeding 50 mm. This is the day when the city of Mandal got record high amounts (66.1 mm). Mandal is found close to the southern tip of Norway (see figure 7c for a geographical location of cities along the coast) and within the southernmost precipitation maximum. a) b) 1

13 c) Figure 7. Modelled 24 hr precipitation with HIRLAM4 (a), UM4 (b), and land-based observed precipitation (c) between 06 UTC 23 Feb 2007 and 06 UTC 24 Feb (taken from 3. UM4-HIRLAM4 differences In order to understand why HIRLAM4 and UM4 simulate so different precipitation patterns, we have looked in detail into the forecasts starting from 23 February 00 UTC. UM4 is nonhydrostatic while HIRLAM4 is hydrostatic. Ideally we would like to apply one model with a hydrostatic/non-hydrostatic switch, but neither HIRLAM nor UM has this possibility. However, as far as possible we have tried to make the models as similar as possible: both models use (the 1

14 same) Arakawa C-grid, the same lateral boundary conditions and same climatology (SST, topography, roughness ). Vertical resolution is comparable as well. There are however significant differences in terms of physical parameterisation and numerics. Since a likely candidate for the high precipitation amounts is coastal convergence, we have looked at low level convergence (not shown), but there are only small differences between the two models. Both models have a maximum along the coast and the extensions and strengths are not too different. There are however major differences between the models in terms of convective versus frontal precipitation. The UM4-model has almost no precipitation classified as convective in the simulation (i.e. the convective parameterisation scheme is not triggered; not shown) and there is a distinct partition between areas of rain (solely over sea) and snow (mostly over land) (figure 8). In contrast to this, the HIRLAM-model has large areas with convective activity and a significant proportion of the precipitation is from the convective parameterisation scheme (figure 9). Figure hour accumulated precipitation from the UM-model. Black contour lines are snow and red lines with shading are rain. 1

15 Figure hr accumulated precipitation from the HIRLAM-model. Black contour lines are frontal precipitation and red lines with shading are convective precipitation. Since the UM-model is non-hydrostatic, it is capable of explicitly simulating convective motion (albeit crudely due to the coarse horizontal resolution of 4 km). We believe that in general the model equations do a better job than ad hoc parameterisation schemes with a number of assumptions and simplifications as long as the equations are capable of describing the physics involved. Nordeng et al (1989) showed a case where the best simulation of a polar low was obtained with so-called explicit convection even though the model was hydrostatic and the equations therefore in principle not suited for describing convective motion. We therefore speculate that this may be one factor explaining why UM4 is superior in this case. This theory will be studied further in part II of this paper. 4. Summary During the last week of February 2007 precipitation amounts in coastal regions of counties Vest-Agder, Aust-Agder and Telemark were record high. The combined effect of strong winds, low temperatures and heavy snowfall forced road authorities to close major highways between 23 and 25 February and some areas lost their power supply due to trees falling over power lines. We have studied how well the operational models at met.no performed during the event. All models forecast high precipitation amounts but only UM4 manages to limit the extent on land to coastal areas. It is speculated if parameterisation of convection in HIRLAM (which is not necessary in UM4) is the main reason for HIRLAM performing worse than UM on this case. 1

16 Acknowledgement. Mariken Homleid is thanked for calculating data for figure 5 (weekly accumulated precipitation from the models) and Uta Gjertsen for constructing the radar images of figure 6. References. Bergeron, T., 1949: The Problem of Artificial Control of Rainfall on the Globe. Tellus 1, Jansson, A, Tveito, O.E.,, Pirinen, P., and Scharling, M., 2007: NORDGRID, -a preliminary investigation on the potential for creation of a joint Nordic gridded climate dataset. Met.no Report No , Climate. Holmebakken, C., and Grønås, S., 2005: Store nedbørmengder på Sørlandet. Cicerone 3, June Nordeng, T.E., Foss, A., Grønås, S., Lystad M., and Midtbø, K.H., 1989: On the Role of Resolution and Physical Parameterization for Numerical Simulations of Polar Lows. In: Polar and Arctic Lows. Eds. P.F. Twitchell, E.A. Rasmussen and K.L. Davidson. A. Deepak Publishing, Virginia, USA (With A. Foss, S. Grønås, M. Lystad and K.H. Midtbø). 1

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