Cansado, Alberto

Navascués, Beatriz

English

Agencia Estatal de Meteorología

58Optimum Interpolation Analysis Method for Snow DepthA.Cansado, B.NavascuésInstituto Nacional de Meteorología. Madrid (Spain)1.- Introduction: Analysis MethodHIRLAM surface analysis has been modified to use the optimum interpolation methodinstead of the succesive corrections for the snow depth analysis. Preliminary runs havebeen carried out to check the impact of changes in the analized snow cover.Qualitative primary information have been learned from this.The current surface analysis in HIRLAM makes use of the succesive correction (SC)method to analize snow depth. For a detailed description of the HIRLAM snow depthanalysis see, for instance, [Rodriguez et al, 2003]. In order to improve the quality ofthe snow depth field, the optimum interpolation (OI) method have been essayed. Thisallows us to have an additional quality control of the observations.The horizontal structure function has been modelled with a Gaussian function. Thehorizontal scale length LH is 85 km, corresponding with an e-folding distance ofapproximately 120 km. The same e-folding value is also used by the snow depth globalanalysis at the Canadian Meteorological Centre, eventhough they use a second orderautoregressive function to model the horizontal structure function [Brasnett, 1999]The anisotropy induced by orography has been introduced in the O.I. structurefunction by means of a Gaussian function depending on the difference between modelorography and observation height, ∆z, with a vertical scale length LV of 565 m, similarlyto Canadian snow depth global analysis [Brasnett, 1999] and the HIRLAM 2-metertemperature and relative humidity analysis [Navascués, 1997]So, the total structure function used in the optimum interpolation analysis is( )	 ∆+−=∆ 22225.0exp,VH LzLrzrρ2.- Preliminary testsPreliminary parallel runs have been carried out for February 1996 to test the impact ofthe snow depth analysis method.HIRLAM version 6.1.0 has been utilized, where surface temperature is relaxed toclimatological values. The saturation water vapour pressure over ice for soil processesover snow covered fractions is not yet available in this release.The results obtained by the objective verification against surface observations showvery similar scores (not shown here) in most of the areas analized (all, ewglam,France, Scandinavia and Spain) for 2m temperature which is supposed to be the mostsensitive variable to snow cover changes, but care must be taken when data areinterpreted. The most important changes in snow cover are located over France andItaly where snow is more unusual. The pattern is very similar in Northern Europe inboth experiments. So changes in the scores are expected to be greater for those areas(France), and in fact a slight improvement can be observed in France scores.59Moreover, differences in snow cover field can be observed between both analysismethods. The main divergences hold in the limit of snow cover, that is, over SouthernEurope.Figure 1. Analized snow cover field using succesive correction method on 1996 February 5th,7th, 9th and 11th at 6 UTC. Contour intervals: 1 , 2 , 5 , 10 , 20 , 50 , 100 and 500 cm.60Optimum interpolation analysis seems to agree better to SSM/I derived snow coverproduced routinely at the NOAA National Climatic Data Center.[http://lwf.ndcd.noaa.gov/servlets/SSMIBrowser].Figure 2. Analized snow cover field using optimum interpolation method on 1996 February 5th,7th, 9th and 11th at 6 UTC. Contour intervals: 1 , 2 , 5 , 10 , 20 , 50 , 100 and 500 cm.61Figures 1 and 2 show the time evolution of analized snow depth field on February 1996from 5th to 11th using succesive corrections (SC) and optimum interpolation (OI),respectively.In Figure 3, SSM/I derived snow cover data are presented. Data are not directlycomparable because SSM/I product shows percentage of time with snow coverobserved during the week from 5th to 11th February 1996. However the SSM/I producttends to suggest a better fitting of the OI experiment, specially over Southern Europe,whereas in the rest of Europe differences remain quite small.See, for instance, that O.I. is able to “see” the Rhône Valley free of snow in SouthEastern France, how O.I. detects snow in North Western Italy (Piemonte, EmiliaRomagna and Toscana areas) and that snow cover distribution in Greece and Albany ismore accurate in O.I experiment according to SSM/I data.In fact, it seems as if O.I. method seems to catch better the cases of transient snow,whereas differences are negligible over permanent covered areas.Figure 3. Snow Cover data from SSM/I. Percentage of time with observed snowcover in Europe region.Other analysis diagnostic tools support O.I. against S.C. Figures 4 and 5 showinnovations (observation minus first-guess) and residuals (analysis minus first-guess)histograms for the month of February 1996.62Figure 4. Innovations histogram for both experiments: SC and OI.Figure 5. Residuals histogram for both experiments: SC and OI.63From Figure 4, it can be observed that the first guess is biased. Observed snow depthdeparture from first guess is mostly positive. However, Figure 5 shows that theanalysis process is able to correct the first guess and to remove almost all the bias. So,it seems that the model physics might be the responsible for the degradation of snowdepth field in the forecast.It should be taken into account that the new tables of saturation water vapourpressure over ice were not used at any of the experiments here presented. The usageof the new tables reduces substantially the latent heat flux over snow covered surfacesand possibly may reduce the observed bias.Both analysis and first guess distance to observations appears to be slightly smaller inOI than in SC experiment.Histograms of rejected observations are very different for both experiments. Optimuminterpolation has a different Quality Control System for the observations. Figure 6shows innovations and residuals for the rejected observations in SC an OI experiments,respectively.Figure 6. Histograms of innovations (left) and residuals (right) of rejected observationsdistribution in Succesive Corrections (top) and Optimum Interpolation (bottom) experiments.64We can see that observations near the first guess are always accepted in the SCanalysis. OI analysis has an additional quality control of which observations arechecked against neighbouring observations. This way, OI analysis is able to rejectobservations which are very different from others situated in the vicinity, not allowingthe analysis to use observations which probably are incorrect.3.- Conclusions and future directionsUp to now, we have seen that -even if verification scores are not very different for SCand OI experiments-, it seems that OI gives a better description of the snow coverfield, especially over Southern Europe.This effect appears to be linked with the zone where snow cover is not permanentduring the period considered. So, it is possible that this effect will shift northwards assnow starts to thaw in Spring.The work will go on, trying to build a snow depth first guess instead of using monthlyconstant homogeneous snow density [Brasnett, 1999] and tuning the structurefunctions parameters for snow cover in the same way as it has been done for 2-metertemperature and relative humidity.4.- AcknowledgementsThe authors would like to thank Ernesto Rodriguez for helpful suggestions regardingthe interpretation of the results obtained and for reviewing the manuscript.BibliographyBrasnett, Bruce. 1999. A global analysis of snow depth for numerical weather prediction.Journal of Applied Meteorology. Vol. 38, pp. 726-740.Navascués B. 1997. Analysis of 2-meter temperature and relative humidity. HIRLAM TechnicalReport No. 28.Rodriguez E.,Navascués B., Ayuso J.J., Järvenoja S. 2003. Analysis of surface variables andparameterization of surface processes in HIRLAM. Part I: Approach and verification by parallelruns. HIRLAM Technical Report No. 58.View publication stats

Optimum interpolation analysis method for snow depth

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Optimum interpolation analysis method for snow depth

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