LAMEPS Limited area ensemble forecasting in Norway, using targeted EPS

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1 Limited re ensemle forecsting in Norwy, using trgeted Mrit H. Jensen, Inger-Lise Frogner* nd Ole Vignes, Norwegin Meteorologicl Institute, (*held the presenttion) At the Norwegin Meteorologicl Institute we hve run qusi-opertionlly since mid-ferury 25. is run with the Norwegin version of the HIRLAM model nd it is driven y memers of the ECMWF which re trgeted to produce mximum spred mongst ensemle memers fter 48 hours in northern Europe nd djcent se res. This system is revited to trgeted, or simply. We hve mde comprison etween the 5 memer nd the 2 memer for n re covering much of the trget re. A multi-model ensemle system (NOR) is lso used which simply comines nd y using ll ensemle memers from oth systems simultneously. This comintion gives lrger ensemle without extr model runs. Even though the comined system is to some extent n uto-dupliction, the ensemle spred is lrger ecuse for two resons: there re un-correlted differences etween fields from the different models, nd the control forecst with HIRLAM cn devite considerly from the control with the ECMWF Integrted Forecst System (IFS). Here we descrie the model setup for, nd NOR, nd show some verifiction results for the summer nd spring of 25. Model setup is n ensemle of runs with the Norwegin version of the limited re model HIRLAM (horizontl resolution of.2º with 4 levels). It uses ensemle memers from to pertur oth the initil nd the lterl oundry conditions. uses the sme model version nd the sme set-up s used for the opertionl. Only singulr vectors re clculted s opposed to 25 in the. These singulr vectors re trgeted to mximize the totl energy t finl optimiztion time (48 h) in Northern Europe nd djcent se res (see Figure ). Singulr vectors t initil time nd 48 hours evolved singulr vectors vlid t the sme time re comined to form initil stte perturtions. These re dded to nd sutrcted from the initil stte nlysis (the control ) with mplitudes sed on nlysis error estimtes. thus contins 2 ensemle memers in ddition to the control forecst. The forecst length is 96 hours nd is run t ECMWF once per dy t 2 UTC. Ech ensemle memer is constructed y running HIRLAM from 2 lterntive initil sttes otined y dding the 2 ensemle perturtions (the difference etween ech ensemle memer nd the control) to the HIRLAM 8 UTC nlysis. At the open lterl oundries the time-developed ensemle memers, corresponding to those used for initil perturtions, re imposed. Thus we otin 2 different forecsts in ddition to the HIRLAM control run. Since HIRLAM strts with n 8 UTC nlysis nd with 2 UTC nlysis, the forecsts from re 6 hours shorter thn the forecsts from nd. is run t 8 UTC every dy nd the forecst length is 6 hours. NOR comines the forecsts from nd to provide single sttistic for events, even though they re not entirely independent of ech other. Without extr cost, the totl numer of ensemle memers is then 4 in ddition to the HIRLAM control forecst. In this wy NOR is supposed to prtly ccount for uncorrelted forecsts errors cused y model imperfections. The differences etween the initil fields in nd re prtly cused y these model differences. Verifiction methodology Verifiction of precipittion forecsts ginst SYNOP oservtions is not strightforwrd ecuse of the very different scles of oservtions nd the forecsts. Furthermore, nd / lso hve different resolutions. Hence comprison of the different systems using either of the nlysed fields from the HIRLAM or the ECMWF IFS model s the truth would fvour one of the systems. We use the pproch proposed y Ghelli & Llurette (2) nd construct so-clled super-oservtions which re representtive of precipittion gridsqures. Here ll precipittion sttions in Norwy (severl hundreds) inside the verifiction re (Figure ) re ggregted to regulr grids. Precipittion super-oservtions representtive of our.2º x.2º HIRLAM grid re thus clculted. All the verifiction of precipittion descried here uses such super-oservtions. Totl precipittion from,, nd NOR re compred to the super-oservtions using Rnk Histogrms, Reliility Digrms, Brier Skill Scores, ROC curves, nd cost/loss nlysis. The diurnlly ccumulted precipittion oservtions re tken t 6 UTC. Since the forecsts strts t 2 UTC nd 8 UTC nd re 66/6 hours long, this leves only two possile time-intervls in the forecst rnge for verifiction: 22

2 (+2/8 h to +36/42 h) nd (+36/42 h to +6/66 h). Note tht since is strted 6 hours lter thn nd, the forecst from is 6 hours shorter thn the other two forecsts. The distriution of precipittion in Norwy is dominted y shrp grdients, cused y predominnt westerly winds, long costline, nd complex topogrphy. The grdient cross the divide etween the western nd estern wtersheds in Southern Norwy is prticulrly lrge. The western wtersheds receive lrge mounts of precipittion, while the estern ones re frequently sheltered y the mountins. Typiclly the nnul difference mounts to fctor of 2 to 3, ut in severl cses the differences re even much lrger. It ws noted tht gglomertions of smples spnning loctions nd times with different climtologicl frequencies cn led to spurious skill mesures. To circumvent this prolem we verify seprtely su-regions with grossly different precipittion climtologies (divided Norwy into three - est, west nd north). The precipittion frequencies lso vry over the yer, nd we split the verifiction results into spring (Ferury-April) nd summer (My-July). Averges re clculted using weights reflecting the re of the su-regions nd y the numer of dys in the two periods. The ensemle spred We hve chosen to define the spred s the rms-difference etween the ensemle memers nd the ensemle men s: I N 2 s = eind mid I N D i= D ( ) n= d= where I is the numer of grid-points inside the verifiction re, N is the numer of ensemle memers, D is the numer of cses, e ind is the ensemle memer vlue for memer n in the cse d nd in the specific point i, nd m id is the ensemle men for the sme cse nd in the sme point. NOR +36/ / Tle Spred round ensemle men for totl precipittion for the four ensemle systems. Tle shows the spred for the systems for totl precipittion forecsts. The spred increses with forecst time, which is in line with expected ehviour of unstle systems strting from smll perturtions. The hs the smllest spred for oth forecst times. The min reson for trgeting is to constrin the perturtions to predefined re of prticulr interest over certin forecst rnge. It is expected tht trgeted system will hve lrger spred etween ensemle memers in this trget re thn system tht is not trgeted, given the sme numer of singulr vector sed perturtions. Thus 2-memer ensemle hs considerly lrger spred etween the memers thn with 5 memers. Hence the ensemle includes wider selection of fstgrowing disturnces over the time-rnge nd re of interest thn the ensemle. As consequence forecsts mde from over the selected time-rnge cn quntify risks of more extreme cses etter thn those sed on. The relism of the incresed risk needs to e investigted. The ensemle with 5 memers hs considerly smller spred thn oth ensemles from nd ech of which hs only 2 memers. The ensemle hs slightly lrger spred thn the originl ensemle, even though the initil nd oundry perturtions re entirely sed on. NOR, which is simple comintion of the nd, hs the lrgest ensemle spred of ll the tested systems for forecst rnge 36/42 h. Hence, triggers slightly different unstle structures in HIRLAM thn the glol model used for generting. This difference cn prtly originte from the higher resolution of nd prtly from the fct tht two different models re used to compute the ensemle memers in NOR. The spred in the NOR ensemle nd the ssocited risks of potentilly extreme wether developments should therefore e tken s due to comintion of chos nd model uncertinty. It is impossile to tell to wht extent the dditionl spred from model uncertinty stems from uncorrelted model errors or if it is due to eqully relistic ut different time developments. Proilistic scores All four ensemle systems re evluted for totl precipittion ccumulted over the verifiction times. The oservtions of precipittion re minly t 6 UTC nd therefore we use this time for the verifiction. The verifiction time for differ from tht for nd ecuse of the forecst strts 6 hour for. The verifiction times 23

3 re therefore 2-36 h nd 36-6 h for, 8-42 h nd h for / nd comintion of these times for NOR. The scores re clculted seprtely for ech of the three su-domins shown in Figure 2 nd for the two sesons (spring nd summer). The re-weighted verge over the domins nd the two sesons re computed. We hve used different stndrd proilistic scores for the verifiction of the four systems. In Figure 2 the Brier Skill Scores for the 2/8-36/42 h nd 36/42-6/66 h forecst s function over precipittion threshold, is shown. Figure 3 shows the re under the ROC curves nd Figure 4 the ROC curves (top row) nd the cost/loss nlysis (ottom row) for the weighted men over the tree res nd two sesons nd two time periods (2/8-36/42 h to the left nd 36/42-6/66h to the right). The event threshold is 5 mm/dy. Figure 5 shows the ROC curves nd cost/loss nlysis for event threshold 2 mm/dy. In these figures one cn see tht hs considerly lower score for the low precipittion thresholds. For mid to high thresholds NOR hs very good scores, showing tht gives extr nd vlule informtion to the. We hve lso looked t the rnk histogrms nd the Reliility digrms for the four systems (not shown here). The rnk histogrms indicte is in ll four systems where they ll underestimte the vriility. The underestimtion is smll in nd especilly lrge in. The Reliility digrms show good reliility up to out 7% for nd NOR, fter which the two systems over-forecst the higher proilities. nd over-forecst the proilities from out 3-4%. Discussion nd conclusion From the results we hve seen tht is le to produce more spred thn for precipittion over Norwy. For events with smll precipittion mounts the proilistic scores for nd re etter thn for, ut for lrger precipittion mounts scores etter. The comined system NOR gets the lrgest spred nd lso est proilistic scores from mid to high precipittion mounts. The comintion of nd dds vlue to the two individul systems. The improvement y NOR cn prtly e due to the increse in resolution for, nd prtly from the fct tht it comines results from two different model systems. The comprison of the 2 memers nd 5 memers is interesting for the Norwegin verifiction re. The increse in ensemle spred of compred to demonstrtes the dvntge of trgeting. Also for mny of the proilistic scores is etter or comprle to, even though it hs fewer memers. The verifiction hs shown tht gives etter results for the short rnge verifiction period. This my indicte tht the method of perturing gives very good results erly in the forecst rnge, ut tht the wether system then moves out of our trget re. In this cse the ordinry hs n dvntge. One wy of dispensing with this is to comine nd s input to. 8 N 6 W 6 E 4 W 6 N 4 E 2 W 2 E Fig. Ares used in the experiments. The lrge lck re is the HIRLAM integrtion re, the lrge lue re is the trget re for optimiztion of singulr vectors, nd the smll lue re is the verifiction domin 24

4 .4.3 Brier Skill Score.3.2. NOR Brier Skill Score Fig. 2 Brier Skill Scores for precipittion s function of threshold for () 2/8-36/42 h nd () 36/42-6/66 h forecsts. Men over ll verifiction res nd oth sesons.9 Are under ROC curve NOR Are under ROC curve Fig. 3 Are under the ROC curve for precipittion s function of threshold for () 2/8-36/42 h nd () 36/42-6/66 h forecsts. Men over ll verifiction res nd oth sesons 25

5 NOR Log (cost/loss) Log (cost/loss) Fig. 4 ) ROC curves (top) nd cost/loss nlysis (ottom) for totl 24 hour precipittion for 2/8-36/42 h forecsts. ) s ) ut for 36/42 6/66 h forecsts. The threshold is 5 mm/dy. Men over ll verifiction res nd oth sesons. Blue, green, red nd lck NOR-. NTOT is the totl possile times the event cn occur while NOCC is the numer of times the event occurs in the smple. P_CLI is the totl precipittion smple climtology NOR NTOT=2743, NOCC=3375 P_CLI=.6 NTOT=277, NOCC=3546 P_CLI= PRECIP, +36/42h, THR = 2. PRECIP, +6/66h, THR = Log (cost/loss) Log (cost/loss) Fig. 5 As Figure 4, ut event threshold 2 mm/dy 26