Comunicación presentada en: 4th SRNWP/HIRLAM workshop on surface processes and assimilation of surface variables celebrado en Norrköping, Suecia, del 15 al 17 de septiembre de 2004.New Optimum Interpolation Snow Depth Analysis has been recently developed for HIRLAM. Now, SSMI derived NESDIS snow cover weekly product and MODIS TERRA daily 0.05º snow cover data have been tested as information supply for this analysis. Some conclusions are drawn from them. Additionally, we tested
the differences found in the snow cover field depending on the spatial resolution of the experiments

Cansado, Alberto

Navascués, Beatriz

English

Agencia Estatal de Meteorología

104Usage of Satellite Data in the HIRLAM New Snow Depth AnalysisAlberto Cansado and Beatriz NavascuésInstituto Nacional de Meteorología (INM)AbstractA New Optimum Interpolation Snow Depth Analysis has been recently developed for HIRLAM. Now, SSMIderived NESDIS snow cover weekly product and MODIS TERRA daily 0.05º snow cover data have beentested as information supply for this analysis. Some conclusions are drawn from them. Additionally, we testedthe differences found in the snow cover field depending on the spatial resolution of the experiments.1.- IntroductionVariability in snow cover causes dramatic changes in surface albedo and, as a consequence, in the landsurface energy budget [Drusch et al, 2004]. So, a correct description of snow cover extent and snow waterequivalent is important to produce accurate forecasts of screen level and surface variablesRecently, a New Optimum Interpolation Snow Depth Analysis has been developed based on the CanadianGlobal Snow Depth Analysis [Brasnett, 1999]. Major changes, including modifications on the background fieldand new autoregressive structure functions, and the results of some experiments have been described yet[Cansado et al, 2004]. Here, we are focusing on the usage of satellite data as a source of information toanalyse snow depth.Two different satellite derived snow cover information sources have been tested:First of all, SSMI derived NESDIS snow cover product has been used besides conventional real-time data.This product is global, has a spatial resolution of 1/3 degree and is provided once a week.Next thing we did is using MODIS TERRA global snow cover information. The spatial resolution in this case is0.05 degrees, so much bigger than SSMI, and is produced daily. To build up the MODIS TERRA 0.05 degreesproduct, 500 m resolution MODIS data were used.Both satellite products were not available in real time. Consequently, the purpose of our work is only to test ifthe assimilation of satellite derived snow cover information in HIRLAM is able to produce a more realisticsnow cover analysis and to have a positive impact in analysed and forecasted surface of screen levelvariables such as 2 meters temperature and relative humidity or skin temperature.Finally, a test compares qualitatively two different experiments in which the only difference was the spatialresolution. Both experiments included modified horizontal structure functions, new computation of the firstguess and a bias correction scheme for mountain stations. The first one was run at 0.5 degrees resolution andthe second one at 0.2 degrees.2.- SSMISSMI derived NESDIS snow cover product has been used to try to improve the snow edge in our analysis.The product has a spatial resolution of 1/3 degree and a time resolution of 7 days. The information thatsupplies is the percentage of time during the week in which snow have been observed over a land pixel. Wecreate 0 cm pseudoobservations that we included in our analysis. Wherever snow has been observed 0 percent of time during a week, a 0 cm snow cover pseudobservation is introduced in the analysis every day of theconsidered week.No other information was considered. We introduced SSMI satellite derived pseudobservations only at 6 UTCanalysis where the amount of conventional data is big enough to ensure that a 0 cm pseudoobservation willnot spread very much in the analysis, eliminating real snow from our background field.An experiment (SND) was carried out to test how the usage of these satellite derived pseudobservationsaffects our analysis. The period considered was February 2003. The results were compared to SNA105experiment (reference in this case) and SNC. SNA includes Optimum Interpolation Snow Depth Analysis, withchanges in the first guess computation and a new autoregressive horizontal structure function. SNC includesa simple bias correction scheme and SND introduce SSMI derived pseudobservations. More informationabout SNA and SNC experiments in [Cansado et al, 2004].Fig. 1. Innovation (left) and residual (right) histograms of experiments SNA, SNC and SNDHistograms of innovations (left) shows how the usage of SSMI pseudobsevations makes the first guess to getcloser to the observations. The same can be seen in the residuals histograms (right). The analysis get closerto the observations when SSMI pseudobservations are used.3. MODISUp to now, we have not used daily data. MODIS (Moderate-Resolution Imaging Spectroradiometer) providesdaily satellite snow cover data which can be ingested in HIRLAM in order to get a better snow depth analysis.MODIS data are available in different products at different spatial resolutions. We have chosenMODIS/TERRA MOD10C1 product. Its spatial resolution is 0.05 degrees and its coverage is global. To buildup every single MOD10C1 pixel, 500 metres resolution MOD10A1 data are used.HIRLAM resolution is much lower (0.5 degrees). Therefore, the amount of information coming from MODIS ishuge. MOD10C1 MODIS product have 4 SDSs (Scientific Data Sets): snow cover percentage, confidenceindex, cloud obscured information and a spatial quality assessment [Riggs et al, 2003].We have considered February 2003 as a study period. With this MOD10C1 product, we build upsuperobservations by using all the satellite pixels within a square centered in the model gridpoint with a sideequal to 0.5 degrees. So, we have a potential superobservation in every single gridpoint. From those potentialsuperobservations we only use snow free land pixels with a confidence index equal to 100% and a qualityassessment flag which indicates good quality of datum. The confidence index indicates how confident thealgorithm is that the snow percentage in a cell is correct based on 500 m resolution data (snow, snow-freeland, cloud, unknown) binned into the grid cell [Riggs et al, 2003]. What we found is that very fewsuperobservations were created in high and mid latitudes (in some case, no superobservation were createdabove 32.6 N)We think this could be probably due to the high difficulty of finding 0.05 degrees (∼ 5 km) absolutely cloud freepixels in those areas in February.The result is that the usage of MODIS 0 cm superobservations seems not to be satisfactory, at least usingsuch a restrictive conditions, because very few superobservations are created and all of them are located atvery low latitudes where they are not really useful.A relaxation in the conditions imposed to allow superobservations to be built might be necessary. On the otherside, it might be worth to test the impact in the analysis of introducing appropriate non-0 cmsuperobservations [Drusch et al, 2004], when no snow is present in the first guess while MODIS data seesnow on the ground.1064. Impact of Spatial ResolutionFinally, a test was carried out comparing the snow cover fields produced by different experiments in which theonly difference was the spatial resolution. The aim is to investigate if a better spatial resolution of theexperiments can produce a better analysis of the field. We ran two experiments SNC and SHN. Both includedthe new autoregressive horizontal structure function, the new calculation of the snow depth first guess and theobservation bias correction scheme to prevent mountain observations from being rejected in the qualitycontrol.As we mentioned before, the only difference between both experiment was spatial resolution: SNC ran at 0.5degrees and SHN ran at 0.2 degrees and both use HIRLAM 6.3.2 release.Figure 2 shows the differences between the snow cover field in SHN and SNC experiments. Qualitatively, wesee that higher spatial resolutions help to produce, as one might expect, more realistic snow cover patterns inSouthern Europe (Northwestern Iberia or Italy for instance) and North Africa (Atlas)5. ConclusionsTwo different satellite snow cover products have been tried to derive 0 cm snow depth observations.The usage of satellite SSMI 0 cm pseudobservations is positive since, according to innovations and residualshistograms, this make the first guess and the analysis to get closer to observations.The usage of daily MODIS 0 cm superobservations has not shown any impact because very few or nonesuperobservation were created at high or mid latitudes due to the important restrictions we imposed to itscreation. A relaxation in the conditions to build up superobservations might be necessary. However some careseems to be needed to ensure the quality of the derived snow depth observations.Usage of appropriate non-0 cm superobservations [Drusch et al, 2004], wherever the first guess has no snowand MODIS satellite data see snow on the ground, should be essayed.Other sources of information available in near real time (the daily NOAA/NESDIS Northern Hemisphere snowcover product or the LAND SAF snow cover product) will be tested in the future to try to improve the HIRLAMSnow Cover in an operational context.Increasing the resolution helps to produce a more realistic analysed snow cover field.Fig. 2. Percentage of time with snow on the ground during a week (19-25 Feb 2004) inSHN (left, 0.2 degrees resolution) and SNC (right, 0.5 degrees resolution)1076. BibliographyBrasnett B. 1999. A Global Analysis of Snow Depth for Numerical Weather Prediction. Journal of AppliedMeteorology. Vol. 38, No. 6, pp. 726-740.Cansado A, Martín C and Navascués B. Optimum interpolation new snow depth analysis in HIRLAM. HIRLAMNewsletter 45, pp. 248-252Drusch M, Vasiljevic D and Viterbo P. 2004. ECMWF’s Global Snow Analysis: Assessment and RevisionBased on Satellite Observations. Journal of Applied Meteorology. Vol. 43, No. 9, pp. 1282-1294.Riggs G.A, Hall D.K, Salomonson V.V. 2003. MODIS Snow Products User Guide for Collection 4 DataProducts. Revised January 2003. Available at http://modis-snow-ice.gsfc.nasa.gov/sug_main.htmlView publication stats

Usage of satellite data in the HIRLAM new snow depth analysis

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Usage of satellite data in the HIRLAM new snow depth analysis

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