Póster presentado en: 1st ACCORD All Staff Workshop celebrado del 12 al 16 de abril de 2021 de forma online

Calvo Sánchez, Francisco Javier

Díez Muyo, María Victoria

Escribá, Pau

Jiménez de Mingo, Alberto

Martín Pérez, Daniel

Morales Martín, Gema

Navascués, Beatriz

Peral-García, María C.

Sánchez Arriola, Jana

Viana Jiménez, Samuel

English

Escribà, Pau

Peral García, M. Candelas

Agencia Estatal de Meteorología

1st ACCORD All Staff Workshop, 12-16 April 2021AEMET has developed a prototype to produce a regional reanalysis forthe Iberian Peninsula and Balearic Islands, called IBERA.The goal is to test its added value compared to other existing reanalysisat global scale (ERA5, 33km) or European scale (UERRA, 11km; UERRA-MESCAN, 5.5km). Apart from the higher resolution (2.5km) there arepotential advantages associated to a regional reanalysis. Among them, abetter tuning of the HARMONIE-AROME system: to reduce the modelbiases, to better represent regional effects in error statistics used by theanalysis systems and to increase the number of assimilated data. In thiswork the methods used and some preliminary results are briefly shown.A similar reanalysis was performed for Ireland with very good results(Gleeson et al., 2017)Introduction/Motivation1Reanalysis setupCANARI and SPAN error statisticsAssessmentObservations usedSPAN analysis for precipitation and daily extremetemperatures assimilates observations from theAEMET high resolution climate stations network(red points) and observations from other externalnetworks operated by different Spanish institutionsare used for validation (blue, black and greenpoints). These obs. may be assimilated as well inthe future.The system is based on the HARMONIE configuration developed for theCopernicus Artic Reanalysis CARRA, carra.beta.2 version which is basedon HARMONIE-AROME cy40h1.1. The main modifications introduced forIberian Peninsula reanalysis are:1. Linear grid at 2.5 km resolution and 65 vertical levels2. SURFEX with 1 patch and Stable Boundary Layer disabled3. Orographic parameterization OROTUR included4. Large Scale Mixing of humidity from the host model in the Fg5. 3DVAR background error covariances have been calculated6. Canari structure functions have been tuned and 10m wind analysis hasbeen activated in this surface analysisIBERA is driven by ERA5 analysis fields as boundaries every hour. TheHARMONIE system is complemented by an offline modified HIRLAM SPANsurface analysis to analyse tmax/tmin and 24-hr precipitation.Analysis of Extreme  Temperatures and  24-hr precipitationCANARI error statisticsSPAN analysis Obs fit to Fg Obs fit to analysisParam Resol kmFg BiasomfSTD omf R (ob,fg)Bias oma STD omaR(ob,an)6-6h ppt 5.5 clim -0.1 mm 3.5 mm 0.846-6h ppt 5.5 MESCAN rean 0.0 mm 5.5 mm 0.71 0.0 mm 3.1 mm 0.916-6h ppt 2.5 HARM fo 0.3 mm 5.9 mm 0.69 0.0 mm 3.2 mm 0.91Tmax 5.5 clim 3.2K 3.1K 0.7 0.0K 1.5K 0.94Tmax 2.5 HARM fo 0.9K 1.7K 0.92 0.0K 1.2K 0.96Tmin 5.5 clim 0.5K 3.4K 0.62 0.0K 1.9K 0.89Tmin 2.5 HARM fo -0.3K 2.1K 0.86 0.0K 1.5K 0.93Assessment of the SPAN analyses using 1200/500 rain/temperature independent observations from AEMET external networksSPAN analysis Validation against independent obsParam Resol km Fg R (ob,an) STD Bias6-6h ppt 5.5 clim 0.85 3.6mm 0.3mm6-6h ppt 5.5 MESCAN rean 0.87 3.2mm 0.3mm6-6h ppt 2.5 HARM fo 0.88 3.2mm 0.2mmTmax 5.5 clim 0.94 1.4K 0.1KTmax 2.5 HARM fo 0.97 1.1K 0.1KTmin 5.5 clim 0.89 2.1K 0.9KTmin 2.5 HARM fo 0.92 1.8K 0.8KMaps above show the accumulated precipitation over January 2016 produced by the SPANanalysis assimilating the same high resolution set of observations and using the differentfirst guesses. The original UERRA-MESCAN reanalysis archived at the Copernicus ClimateData Store is also shown (MESCAN-CDS). Greater detail structures appear in the productbased on HARMONIE IBERA. Note that the main differences occur at mountain areas and atNorthwestern Spain, the most rainy area this month.REF IBERAMESCAN-CDS MESCANfgIn this IBERA reanalysis prototype, error statistics used by the two surface analysis packagesemployed (CANARI for 3 hourly t2m and rh2m, and SPAN for daily tmax, tmin andprecipitation) have been tuned. Hollingsworth and Lönnberg method is used to calculatestructure functions from time series of innovations (ob-fg). The first guess errorsautocorrelation model in CANARI is a Gaussian function. In case of SPAN, for daily extremetemperatures and precipitation, a combination of two different scale Third OrderAutoregressive functions (TOAR) is used to represent the error’s dependence on the horizontaldistance. SPAN structure functions are also anisotropic to take into account topographic effects.Impact of warm-up and of different background error statisticsCloser fit of IBERA to observations compared with other reanalysisIBERA ERA5MESCANUERRAUERRAERA5MESCANIBERAReferences• Bojarova, J. and  Gustafsson, N. (2019) Relevance of climatological background error statistics for mesoscale data assimilation, Tellus A: Dynamic Meteorology and Oceanography, 71:1• Gleeson, E., Whelan, E., and Hanley, J. (2017): Met Éireann high resolution reanalysis for Ireland, Adv. Sci. Res., 14, 49–61, https://doi.org/10.5194/asr-14-49-2017• Copernicus Climate Reanalysis ERA5: https://climate.copernicus.eu/climate-reanalysis• Copernicus Artic Regional Reanalysis (CARRA):  https://climate.copernicus.eu/copernicus-arctic-regional-reanalysis-service• UERRA Reanalysis over Europe (UERRA): http://www.uerra.eu/• Peral, C., Navascues, B. and Ramos, P. (2017): Serie de precipitación diaria en rejilla con fines climáticos. AEMET Technical Note, No. 24. Xiaohua Yang (DMI) has given support to different tasks of this work. Rain gauge and dailyextreme temperatures observations used for the assessment of this prototype come from threeexternal Spanish institutions: D.G. Water and River Basin Authorities, D.G. forRural Development, Innovation and Agrifood Training (Ministry forAgriculture, Fisheries and Food), and Xunta de Galicia.BG error statistics for 3DVarB-BRAND: higher background error std values. larger amplitude of spectral densities at themesoscale. less balanced background errors of differentvariables.Both B-Matrices are calculated using an ensemble of short-rangeHARMONIE-AROME model forecasts. The main differences are:ERA5ENDA method uses a four member ensemble obtained witha dynamical downscaling of 4 ERA5 global ensemble dataassimilation members.BRAND method uses a 10 member ensemble whose controlmember is a high resolution 3DVAR and the rest of members areconstructed by stochastic perturbation of the analysis controlvariables in state space. Boundary conditions for this ensemblecome also from ERA5 global ensemble.Figures to the left compare background error covariances obtained withboth methods. A previously calculated B obtained through dynamicaldownscaling of the operational IFS ensemble data assimilation over adifferent period is also presented (labelled as “warm”).Obsfit to the analysis using different B matricesPrototype of a high resolution regional reanalysis for Iberian Peninsula an Balearic Islands (IBERA)J. Calvo, M. Díez, P. Escribà, A. Jiménez, D. Martín, G. Morales, B. Navascués, M. Peral, J. Sánchez-Arriola and S. VianaB matrix representing background error covariances for3DVar, has been calculated in two steps. The first oneobtains a B matrix based in a “downscaled B” method(ERA5ENDA). ERA5ENDA is then used to create B matrixfinally used by IBERA following the BRAND approach(Bojarova and Gustafsson, 2019).In this IBERA prototype only conventionalobservations are assimilated by the 3DVaranalysis from radiosondes, aircraft, andsea and land surface reports. Large scaleinformation from ERA5 analysis isintroduced every 3h with the Large ScaleMixing with ERA5 (LSMIXBC activated).10m windT2mForecast impactDistribution of upper observations usedHowever the number of SYNOPs (left)has been substantially increased fromthe ERA5 observations archived at theECMWF MARS (right). These data areassimilated by the 3DVAR system (ps,10m wind) and by the CANARI surfaceanalysis (t2m, rh2m).The Blue and green experiments that use B-BRAND arecloser to observations than the red experiment that isbased on B-ERA5ENDA. This is consistent with the fact thatthe background error is higher for B-BRAND than for B-ERA5ENDA.Tuned structure functions for CANARI are sharper than the default ones. Thefirst guess to observation errors ratio has been also modified.Upper figure shows the fit of the two autocorrelation modelsto the empirical correlation found between first guess errorsof maximum temperature for stations pairs at differentclasses of horizontal distanceUpper figure displays the empirical correlationsfound for first guess errors of 24h accumulatedprecipitation for stations pairs horizontal distances atdifferent vertical displacement intervalst2m​ Default settings Tuned parametersL (km) 80 60Eb(K) 1,6 1,6E0(K) 1,4 1,2rh2m Default settings Tuned parametersL (km) 85 60Eb(%) 18 9E0(%) 10 6Background: The SPAN analysis runs regularly at AEMET to produce 24h precipitation and daily extreme temperatures gridded data at 5.5km resolution assimilating allobservations from its climate stations network (Peral et al., 2017). Time series of this SPAN analysis starts in 1951 and lasts until the current year.SPAN 24-hr precipitation analysis applied to differentfirst guess fields (test period January 2016):• Climatological information (REF)• HARMONIE IBERA forecasts (IBERA)• UERRA-MESCAN reanalysis (MESCANfg). Note that this reanalysis  does not assimilate the high resolution rain gauge data over SpainFirst guess error statistics were tuned separately for the different background fields.Fit to the First Guess/Analysis by 3250/1600 rain/temperature obs. assimilated Test period: January 2016Reanalysis experiments:• Red: Standard Jb without a warmup period• Green: Standard Jb with a warmup period of 4 months• Blue: Retuned Jb for the reanalysis and warmup period of 4 monthsObsfit to the analysisSpin-up. Evolution of the T2m and RH2m errors (STDV and Bias) for a period of5 months: Comparison of ERA5 with IBERA errors. The period is started at thebeginning of the hydrological season. The plots suggest that a longer warm-upperiod is needed. In a similar reanalysis for Ireland, Gleeson et al (2017) need awarm-up period of 1 yearV 10m (m/s) T 2m (K) RH 2m (%)BIAS STDV BIAS STDV BIAS STDVERA5 0,37 1,00 0,04 1,75 -0,18 9,48UERRA 0,09 1,97 -0,35 1,58 -0,47 9,94IBERA -0,16 0,91 -0,01 0,70 -0,56 3,82Overall performance compared to other Reanalysis.Test period: January 2026. HARMONIE-AROME IBERAseems to have a clear added value compared to otherreanalysis. Note that here we are not using anindependent set of obs in the verification: the obs.entered in all the reanalysis.AcknowledgementsConclusions• AEMET has developed a prototype to produce a regional reanalysisfor the Iberian Regional Reanalysis at 2.5 km resolution (IBERA).• It is based on HARMONIE-AROME cycle40h1 in a similar configurationto the CARRA reanalysis but only using conventional observations.• The system benefits from the possibility to reduce model biases forthe region, an increased number of surface observations and a betterrepresentation of regional effects in error statistics used by theanalysis.• The system is enhanced with an off-line surface analysis, SPAN,specially tuned to analyse precipitation and extreme temperatures.• The preliminary tests performed show a clear added value comparedto other available reanalysis over the region.

Prototype of a high resolution regional reanalysis for Iberian Peninsula an Balearic Islands (IBERA)

https://repositorio.aemet.es/bitstream/20.500.11765/12840/1/2021-asw-iberianreanalisis_aemet.pdf

Prototype of a high resolution regional reanalysis for Iberian Peninsula an Balearic Islands (IBERA)

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