METinfo Verification of Operational Weather Prediction Models September to November 2016 Mariken Homleid and Frank Thomas Tveter

Size: px
Start display at page:

Download "METinfo Verification of Operational Weather Prediction Models September to November 2016 Mariken Homleid and Frank Thomas Tveter"

Transcription

1 METinfo No. /6 ISSN 89-79X Meteorology Verification of Operational Weather Prediction Models September to November 6 Mariken Homleid and Frank Thomas Tveter Photo: Jan Erik Haugen

2 Contents Introduction Models Postprocessedforecasts TheHARMONIE system Verification measures Observations Mean Sea Level Pressure figures Statisticsbylead time Monthlysummarystatisticsthelastthreeyears Temperature m figures Statisticsbylead time Monthlymeanerrorthe lastthreeyears Monthlystandarddeviation oferrorthe lastthreeyears Monthlymeanabsoluteerrorthe lastthreeyears Mapsfor eachregion Timeseriesfor selectedstations Post processed temperature m figures Statisticsbylead time Long term ECMWF temperature m figures Statisticsbylead time Wind speed m figures Statisticssummarizedover Norwegianstations Statisticssummarizedover Svalbardstations Statisticssummarizedover northnowegian stations Monthlymeanerrorthe lastthreeyears Monthlystandarddeviation oferrorthe lastthreeyears Monthlymeanabsoluteerrorthe lastthreeyears Mapsfor eachregion Timeseriesfor selectedstations Post processed maximum mean wind speed m figures Statisticsbylead time Statisticsforcategorisedevents

3 Wind gust figures Statisticsbylead time Statisticsforcategorisedevents Long term ECMWF wind speed m figures Statisticsbylead time Precipitation figures Statisticsbylead time(rr) Statisticsforcategorisedevents (RR) Monthlymeanerrorthe lastthreeyears (RR) Monthlystandarddeviation oferrorthe lastthreeyears (RR) Monthlymeanabsoluteerrorthe lastthreeyears (RR) Mapsfor eachregion(rr) Timeseriesfor selectedstations(rr) More information... Verification results are also available on internal web pages AROME-MetCoOp verification- 7 and 3 last days - timeseries and windroses- on Google map 3

4 Models The following models are verified in this report. ECMWF AROME-Norway AROME-MetCoOp (AM) AROME-Arctic (AA) MetCoOp ensemble system (MEPSmbr) Global model (IFS) at the European Centre for Medium-Range Weather Forecasts. From 6 January horizontal resolution approximately 6 6 km. From 8 March 6 cycle r with horizontal resolution about 9km. ECMWF is available about hours laterthan models runat MET. HARMONIE cycle 37h. with AROME physics and nonhydrostatic dynamics, horizontal resolution defined by a.. km grid. Experimental since October, replacing Harmonie. from 6 February 3, on Yr from October 3. HARMONIE with AROME physics, horizontal resolution defined by a.. km grid on same domain as AROME- Norway. Run in cooperation with Swedish Meteorological and Hydrological Institute (SMHI). Experimental with cycle 38h. since 9 December 3, operational since March, cycle 38h. since 8 December. HARMONIE with AROME physics, horizontal resolution defined by a.. km grid, same domain size as AROME- MetCoOp. Experimental with cycle 38h. since October. MEPS has ensemble members, only member, the control, is verified in this report. MEPS is based on HARMONIE cycle h. with AROME physics and non-hydrostatic dynamics, horizontal resolution defined by a.. km grid. Experimental since May 6. Operational status since 8 November 6. Analysis and lead times of forecasts are denoted by e.g. +3 UTC which indicates forecast generated atutcand valid3hourslater. Post processed forecasts Mostof therawmodeldataarepostprocessedbeforebeingpublished on Yr. For m temperature, the raw data is first adjusted to a fine scale ( m) topography applying a vertical temperature gradient of -.6 degrees pr. m. The height corrected m temperature forecasts (AMHC) are in a next step adapted to observations by a Kalman filter at all observing stations. The Kalman filter corrections are interpolated horizontally to the m grid by kriging, using weights which decrease with increasing distance from the observing stations, both horizontally and vertically. The interpolated Kalman filter corrections are finally added to the heigth corrected field, giving AMKF. m wind speed is statistically post processed to represent maximum wind speed m last hour, and called AMPP.

5 The precipitation forecasts are post processed by a neighbourhood method giving a median field, AMmedian. This field is used to determine the precipitation symbol on Yr. ECMWF provides probabilistic forecasts based on a member ensemble system at 3 km resolution. Consensus forecasts(ecens) are calibrated (ECensPP) before being presented on Yr. The HARMONIE system HARMONIE is the acronym for HIRLAM s meso-scale forecast system(hirlam Aladin Regional/Mesoscale Operational NWP In Europe). The HARMONIE system includes several configuration options. This section presents some of the main components and setups that are used at MET. More documentation is available on and A change log for AROME- MetCoOp is found on ALARO- physics ALARO- has physical parameterizations targeted for grey scale resolutions( - km). It is a spin-off of the Météo-France physical parameterizations used in the globale ARPEGE, but with a separate radiation scheme, 3MT micro-physical frame work, and the Toucans turbulence scheme. Much of the development has been done by the RC LACE (Regional Cooperation for Limited Area modeling in Central Europe) community. AROME physics AROME(Applications of Research to Operations at MEsoscale) is targeted for horizontal resolution. km or finer. It uses physical parameterizations based on the French academia model Meso-NH and the external surface model SURFEX. AROME has been operational at Météo-France since 8 December 8, with a horizontal resolution of. km. SURFEX as surface model SURFEX (Surface externalisée) is developed at Météo-France and academia for offline experiments and introduced in NWP models to ensure consistent treatment of processes related to surface. Météo- France is already using SURFEX for some of their configurations and is planning to use it for all their configurations. Surface modelling and assimilation will benefit from the possibility of running offline experiments. SURFEX is also used for offline applications in e.g. hydrology, vegetation monitoring and snow avalanche forecasts. SURFEX includes routines to simulate the exchange of energy and water between the atmosphere and surface types (tiles); land, sea (ocean), lake (inland water) and town. The land or nature tile can be divided further into vegetation types (patches). ISBA (Interaction between Soil Biosphere and Atmosphere) is used for modelling the land surface processes. There are 3 ISBA options; - and 3-layer force restore and a diffusive approach, where the first one is used in HIRLAM. Towns may be treated by a separate TEB (Town Energy Balance) module. Seas and lakes are also treated separately. The lake model, FLAKE (Freshwater LAKE), has recently been introduced in SURFEX. A global ECOCLIMAP database which combines land cover maps and satellite information gives information about surface properties. The orography is taken from gtopo3.

6 SURFEX Scientific Documentation and User s Guide are available on Data assimilation NWP models are updated regularly using observations received in real-time from the global observing system. AROME-MetCoOpis updatedeach thirdhour;at,3,6,9,,,8andutc. Surface analysis Surface analysis is performed by CANARI (Code d Analyse Nécessaire à ARPEGE pour ses Rejets et son Initialisation) (Taillefer, ). The analysis method is Optimal Interpolation and only conventional synoptic observations are used. meter temperature and relative humidity observations are used to update the surface and soil temperature and moisture. The snow analysis is also performed with CANARI in analogy with the HIRLAM snow analysis. Snow depth observations are used to update Snow Water Equivalent. The snow fields are analysed only at 6 and8utc asthereareveryfewsnowdepth observationsat,3,9,, and. The Sea Surface Temperature (SST) and Sea Ice Concentration (SIC) is not analysed, but taken from the boundaries. ECMWF uses the OSTIA (Operational Sea Surface Temperature and Sea Ice Analysis) product, including SST from UK Met Office and SIC from MET. SST and SIC for the Baltic Sea have since6november been takenfromthe oceanmodel HIROMBrunat SMHI. The surface temperature over sea ice was taken from the boundary model and remained unchanged through the forecast. A simple thermodynamical sea ice scheme (SICE) giving prognostic sea ice temperatures in fixed layers was introduced 6 November. Upper air analysis AROME-MetCoOp runs three dimensional variational (3D VAR) data assimilation using conventional observations from synop stations, ships, radiosondes and aircrafts and AMSU-A and AMSU-B/MHS data from polar orbiting NOAA and METOP satellites. GNSS were introduced 7 February, radar reflectivities 6 June and IASI data 6 November. Boundary fields AROME-MetCoOp gets its boundary values (-hourly) from the ECMWF model at approximately 6 km resolution, and has currently 6 vertical levels. None of the HARMONIE configurations at MET have applied digital filter initialization (DFI). 6

7 Verification measures All model forecasts in this report are verified against observations by interpolating(linear) the grid based forecasts to the observational sites. As a consequence, it should be noted that it is the models abilities to forecast the observations that is being quantifed and assessed. Thus, there is no attempt in this report to verify area averaged precipitation for example. Verification is carried out both for raw and categorized forecasts. In the following, let f,..., f n denote theforecastsando,...,o n thecorrespondingobservations. Forecasts of continuous variables The verification statistics applied to continuous variables are defined in the table below Statistic Acronym Formula Range Optimal score Mean Error Mean Absolute Error ME MAE Standard Deviation of Error SDE Root Mean Square Error Correlation RMSE COR ( n n n i= (f i o i ) to n n i= f i o i to n i= ( n (f i o i ME) ) / n i= In the formula for COR the following definitions are used (f i o i ) ) / n n i= (f i f)(o i ō) SD(f)SD(o) to to to f = n n i= f i, ō = n n o i i= SD(f) = ( n n i= (f i f) ) / (, SD(o) = n for the means and standard deviations of the forecasts and observations. n i= (o i ō) ) / 7

8 Forecasts of categorical variables All variables in this report are continuous in raw form, but it is possible to categorize them and verify these. For example, wind speed above a given threshold could be of interest which would result in two possible outcomes (yes and no). The verification is then completely summarized by a contingency table as theone shownbelow event observed yes no event forecasted yes a b no c d Verification statistics for such forecasts are listed in the following table Statistic Acronym Formula Range Optimal score Hit rate HR a a+c to False alarm rate F b b+d to False alarm ratio FAR b a+b to Equitable threat score ETS a ar a+b+c ar /3to (=noskill) Hanssen-Kuipers skill score KSS HR - F to ( = no skill) Heidke skill score HSS (a+d)/n ssf ssf to (=noskill) Intheformulafor ETSar=(a+b)(a+c)/n. Intheformulafor HSSthescoreforthestandardforecastssf =[(a+b)(a+c)+(b+d)(c+d)]/n. Observations All observations come from Klimadatavarehuset at MET. Only synop stations are used. The model wind speed is verified against mean wind observations, FF. The post processed wind speed is intended to represent maximum wind speed m last hour, and is verified against FX. 8

9 Sep to Nov 6 Mean Sea Level Pressure. Mean Error 6 Nor. stations +3,+6,...,+66 AM ECMWF 3. Standard Deviation of Error 6 Nor. stations +3,+6,..., Mean Absolute Error 6 Nor. stations +3,+6,..., Mean Error Svalbard stations +3,+6,...,+66 AA ECMWF 3. Standard Deviation of Error Svalbard stations +3,+6,..., Mean Absolute Error Svalbard stations +3,+6,..., Mean Error 3 North Scand. stations +3,+6,...,+66 AA AM ECMWF 3. Standard Deviation of Error 3 North Scand. stations +3,+6,..., Mean Absolute Error 3 North Scand. stations +3,+6,...,

10 Sep to Nov 6 Mean Sea Level Pressure Mean Absolute Error Norwegian stations +,+3,+36,+ UTC. ECMWF AROME_Norway AM MEPSmbr..... Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Standard Deviation of Error Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6. Mean Error Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6

11 Sep to Nov 6 Temperature m Mean Error Nor. stations +3,+6,...,+66 AM ECMWF 3. Standard Deviation of Error Nor. stations +3,+6,..., Mean Absolute Error Nor. stations +3,+6,..., Mean Error Svalbard stations +3,+6,...,+66 AA ECMWF 3. Standard Deviation of Error Svalbard stations +3,+6,..., Mean Absolute Error Svalbard stations +3,+6,..., Mean Error 7 North Scand. stations +3,+6,...,+66 AA AM ECMWF 3. Standard Deviation of Error 7 North Scand. stations +3,+6,..., Mean Absolute Error 7 North Scand. stations +3,+6,...,

12 Sep to Nov 6 Temperature m Mean Error 6 Norwegian stations +,+3,+36,+ UTC ECMWF AROME_Norway AM MEPSmbr 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian coastal stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian inland stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6

13 Sep to Nov 6 Temperature m Standard Deviation of Error 6 Norwegian stations ECMWF AROME_Norway AM MEPSmbr +,+3,+36,+ UTC 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian coastal stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian inland stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 3

14 Sep to Nov 6 Temperature m Mean Absolute Error 6 Norwegian stations +,+3,+36,+ UTC ECMWF AROME_Norway AM MEPSmbr 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian coastal stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian inland stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6

15 Sep to Nov 6 Temperature m AM + ME at observing sites (numbers in black) forecast means

16 Sep to Nov 6 Temperature m AM + ME at observing sites (numbers in black) forecast means

17 Sep to Nov 6 Temperature m AM + SDE at observing sites (numbers in black) forecast means

18 Sep to Nov 6 Temperature m AM + SDE at observing sites (numbers in black) forecast means

19 Sep to Nov 6 Temperature m AM + ME at observing sites (numbers in black) forecast means

20 Sep to Nov 6 Temperature m AM + ME at observing sites (numbers in black) forecast means

21 Sep to Nov 6 Temperature m AM + SDE at observing sites (numbers in black) forecast means

22 Sep to Nov 6 Temperature m AM + SDE at observing sites (numbers in black) forecast means

23 Sep to Nov 6 Temperature m AM + ME at observing sites (numbers in black) forecast means

24 Sep to Nov 6 Temperature m AM + ME at observing sites (numbers in black) forecast means

25 Sep to Nov 6 Temperature m AM + SDE at observing sites (numbers in black) forecast means

26 Sep to Nov 6 Temperature m AM + SDE at observing sites (numbers in black) forecast means

27 Sep to Nov 6 Temperature m SVALBARD LUFTHAVN Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AA synop ECMWF synop

28 Sep to Nov 6 Temperature m BJØRNØYA Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AA synop ECMWF synop

29 Sep to Nov 6 Temperature m TROMSØ Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop AA synop ECMWF synop

30 Sep to Nov 6 Temperature m KAUTOKEINO Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop AA synop ECMWF synop

31 Sep to Nov 6 Temperature m ØRLAND III Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

32 Sep to Nov 6 Temperature m BERGEN FLORIDA Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

33 Sep to Nov 6 Temperature m FINSEVATN Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

34 Sep to Nov 6 Temperature m NESBYEN TODOKK Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

35 Sep to Nov 6 Temperature m EKOFISK Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

36 Sep to Nov 6 Temperature m SOLA Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

37 Sep to Nov 6 Temperature m FÆRDER FYR Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

38 Sep to Nov 6 Temperature m OSLO BLINDERN Celsius Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct Celsius Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct Celsius Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

39 Sep to Nov 6 Post processed temperature m (AMHC and AMKF). Mean Error 6 Nor. stations +3,+6,...,+66 AM AMHC AMKF 3. Standard Deviation of Error 6 Nor. stations +3,+6,..., Mean Absolute Error 6 Nor. stations +3,+6,..., Mean Error Nor. coastal stations +3,+6,...,+66 AM AMHC AMKF 3. Standard Deviation of Error Nor. coastal stations +3,+6,..., Mean Absolute Error Nor. coastal stations +3,+6,..., Mean Error 8 Nor. inland stations +3,+6,...,+66 AM AMHC AMKF 3. Standard Deviation of Error 8 Nor. inland stations +3,+6,..., Mean Absolute Error 8 Nor. inland stations +3,+6,...,

40 Sep to Nov 6 Long term ECMWF temperature m Mean Error 66 Norwegian stations ECMWF ECens ECensPP coastal stations inland stations

41 Sep to Nov 6 Long term ECMWF temperature m Standard Deviation of Error ECMWF ECens ECensPP 66 Norwegian stations coastal stations inland stations

42 Sep to Nov 6 Long term ECMWF temperature m Mean Absolute Error ECMWF ECens ECensPP 66 Norwegian stations coastal stations inland stations

43 Sep to Nov 6 Wind speed m. Mean Error 9 Nor. stations +3,+6,...,+66 AM ECMWF 3. Standard Deviation of Error 9 Nor. stations +3,+6,..., Mean Absolute Error 9 Nor. stations +3,+6,..., Hit Rate 3 Nor. stations +8,+,+3,+36 AM ECMWF. False Alarm Rate +8,+,+3,+36. False Alarm Ratio +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s). Equitable Threat Score 3 Nor. stations +8,+,+3,+36 AM ECMWF. Hansen Kuiper Skill Score +8,+,+3,+36. Heidke Skill Score +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s) 3

44 Sep to Nov 6 Wind speed m. Mean Error Svalbard stations +3,+6,...,+66 AA ECMWF 3. Standard Deviation of Error Svalbard stations +3,+6,..., Mean Absolute Error Svalbard stations +3,+6,..., Hit Rate Svalbard stations +8,+,+3,+36 AA ECMWF. False Alarm Rate +8,+,+3,+36. False Alarm Ratio +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s). Equitable Threat Score Svalbard stations +8,+,+3,+36 AA ECMWF. Hansen Kuiper Skill Score +8,+,+3,+36. Heidke Skill Score +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s)

45 Sep to Nov 6 Wind speed m. Mean Error 63 North Scand. stations +3,+6,...,+66 AA AM ECMWF 3. Standard Deviation of Error 63 North Scand. stations +3,+6,..., Mean Absolute Error 63 North Scand. stations +3,+6,..., Hit Rate 9 North Scand. stations +8,+,+3,+36 AA AM ECMWF. False Alarm Rate +8,+,+3,+36. False Alarm Ratio +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s). Equitable Threat Score 9 North Scand. stations +8,+,+3,+36 AA AM ECMWF. Hansen Kuiper Skill Score +8,+,+3,+36. Heidke Skill Score +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s)

46 Sep to Nov 6 Wind speed m Mean Error 9 Norwegian stations ECMWF AROME_Norway AM MEPSmbr +,+3,+36,+ UTC 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian coastal stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian mountainous stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 6

47 Sep to Nov 6 Wind speed m Standard Deviation of Error 9 Norwegian stations +,+3,+36,+ UTC ECMWF AROME_Norway AM MEPSmbr Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian coastal stations Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian mountainous stations Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 7

48 Sep to Nov 6 Wind speed m Mean Absolute Error 9 Norwegian stations +,+3,+36,+ UTC ECMWF AROME_Norway AM MEPSmbr 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian coastal stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 Norwegian mountainous stations 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 8

49 Sep to Nov 6 Wind speed m AM + ME at observing sites (numbers in black) forecast means

50 Sep to Nov 6 Wind speed m AM + SDE at observing sites (numbers in black) forecast means

51 Sep to Nov 6 Wind speed m AM + ME at observing sites (numbers in black) forecast means

52 Sep to Nov 6 Wind speed m AM + SDE at observing sites (numbers in black) forecast means

53 Sep to Nov 6 Wind speed m AM + ME at observing sites (numbers in black) forecast means

54 Sep to Nov 6 Wind speed m AM + SDE at observing sites (numbers in black) forecast means

55 Sep to Nov 6 Wind speed m SVALBARD LUFTHAVN m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AA synop ECMWF synop

56 Sep to Nov 6 Wind speed m BJØRNØYA m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AA synop ECMWF synop

57 Sep to Nov 6 Wind speed m TROMSØ m/s 8 6 Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s 8 6 Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s 8 6 Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop AA synop ECMWF synop

58 Sep to Nov 6 Wind speed m SLETTNES FYR m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, AA: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop AA synop ECMWF synop

59 Sep to Nov 6 Wind speed m ØRLAND III m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

60 Sep to Nov 6 Wind speed m YTTERØYANE FYR m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

61 Sep to Nov 6 Wind speed m FINSEVATN m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

62 Sep to Nov 6 Wind speed m NESBYEN TODOKK m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

63 Sep to Nov 6 Wind speed m EKOFISK m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

64 Sep to Nov 6 Wind speed m SOLA m/s 8 6 Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s 8 6 Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s 8 6 Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

65 Sep to Nov 6 Wind speed m FÆRDER FYR m/s Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct m/s Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct m/s Oct 3 Nov Nov Nov Nov Nov Nov Min Mean Max Std N synop:,6,, AM: +8,+,+3, ECMWF: +8,+,+3, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

66 Sep to Nov 6 Post processed maximum mean wind speed m (AMPP). Mean Error Nor. stations +3,+6,...,+66 AM AMPP 3. Standard Deviation of Error Nor. stations +3,+6,..., Mean Absolute Error Nor. stations +3,+6,..., Hit Rate 8 Nor. stations +8,+,+3,+36 AM AMPP. False Alarm Rate 8 Nor. stations +8,+,+3,+36. False Alarm Ratio 8 Nor. stations +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s). Equitable Threat Score 8 Nor. stations +8,+,+3,+36 AM AMPP. Hansen Kuiper Skill Score 8 Nor. stations +8,+,+3,+36. Heidke Skill Score 8 Nor. stations +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s) 66

67 Sep to Nov 6 Wind gust (AM FG) and AM 9 Mean Error 3 Nor. stations +3,+6,...,+66 AM_FG AM_9 Standard Deviation of Error 3 Nor. stations +3,+6,...,+66 Mean Absolute Error 3 Nor. stations +3,+6,..., Hit Rate Nor. stations +8,+,+3,+36 AMFG AM9. False Alarm Rate Nor. stations +8,+,+3,+36. False Alarm Ratio Nor. stations +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s). Equitable Threat Score Nor. stations +8,+,+3,+36 AMFG AM9. Hansen Kuiper Skill Score Nor. stations +8,+,+3,+36. Heidke Skill Score Nor. stations +8,+,+3, Exceedance threshold (m/s) Exceedance threshold (m/s) Exceedance threshold (m/s) 67

68 Sep to Nov 6 Long term ECMWF wind speed m Mean Error ECMWF ECens ECensPP 8 Norwegian stations coastal stations mountainous stations

69 Sep to Nov 6 Long term ECMWF wind speed m Standard Deviation of Error ECMWF ECens ECensPP 8 Norwegian stations coastal stations mountainous stations

70 Sep to Nov 6 hour precipitation Mean Absolute Error. ECMWF ECens ECensPP 8 Norwegian stations coastal stations mountainous stations

71 Sep to Nov 6 hour precipitation. Mean Error 6 Nor. stations +8,+3,...,+66 AM ECMWF. Standard Deviation of Error 6 Nor. stations +8,+3,...,+66. Mean Absolute Error 6 Nor. stations +8,+3,..., Hit Rate 9 Nor. stations +8,+3,+,+,+66 AM ECMWF. False Alarm Rate 9 Nor. stations +8,+3,+,+,+66. False Alarm Ratio 9 Nor. stations +8,+3,+,+, Exceedance threshold (mm) Exceedance threshold (mm) Exceedance threshold (mm). Equitable Threat Score 9 Nor. stations +8,+3,+,+,+66 AM ECMWF. Hansen Kuiper Skill Score 9 Nor. stations +8,+3,+,+,+66. Heidke Skill Score 9 Nor. stations +8,+3,+,+, Exceedance threshold (mm) Exceedance threshold (mm) Exceedance threshold (mm) 7

72 Sep to Nov 6 Daily precipitation Mean Error 8 Norwegian stations ECMWF AROME_Norway AM MEPSmbr +3 UTC Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 coastal stations Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 37 stations more than m above sea level Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 stations with daily mean precipitation > mm Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 7

73 Sep to Nov 6 Daily precipitation Standard Deviation of Error 8 8 Norwegian stations ECMWF AROME_Norway AM MEPSmbr +3 UTC 6 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 coastal stations 8 6 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 37 stations more than m above sea level 8 6 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 stations with daily mean precipitation > mm 8 6 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 73

74 Sep to Nov 6 Daily precipitation Mean Absolute Error Norwegian stations ECMWF AROME_Norway AM MEPSmbr +3 UTC Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 7 coastal stations 6 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep stations more than m above sea level 6 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 7 stations with daily mean precipitation > mm 6 3 Sep3 Dec3 Mar Jun Sep Dec Mar Jun Sep Dec Mar6 Jun6 Sep6 7

75 Sep to Nov 6 Daily precipitation AM +3 ME at observing sites (numbers in black) forecast means

76 Sep to Nov 6 Daily precipitation AM +3 SDE at observing sites (numbers in black) forecast means

77 Sep to Nov 6 Daily precipitation AM +3 ME at observing sites (numbers in black) forecast means

78 Sep to Nov 6 Daily precipitation AM +3 SDE at observing sites (numbers in black) forecast means

79 Sep to Nov 6 Daily precipitation AM +3 ME at observing sites (numbers in black) forecast means

80 Sep to Nov 6 Daily precipitation AM +3 SDE at observing sites (numbers in black) forecast means

81 Sep to Nov 6 hour precipitation SVALBARD LUFTHAVN mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AA: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AA synop ECMWF synop

82 Sep to Nov 6 hour precipitation BJØRNØYA mm 6 Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm 6 Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm 6 Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AA: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AA synop ECMWF synop

83 Sep to Nov 6 hour precipitation TROMSØ mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AM: +8, AA: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AM synop AA synop ECMWF synop

84 Sep to Nov 6 hour precipitation BODØ VI mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AM: +8, AA: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AM synop AA synop ECMWF synop

85 Sep to Nov 6 hour precipitation ØRLAND III mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AM: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

86 Sep to Nov 6 hour precipitation BERGEN FLORIDA mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AM: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

87 Sep to Nov 6 hour precipitation LÆRDAL IV mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AM: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

88 Sep to Nov 6 hour precipitation GARDERMOEN mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AM: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

89 Sep to Nov 6 hour precipitation NELAUG mm Sep Sep 6 Sep Sep 6 Sep Sep 6 Oct mm Oct Oct 6 Oct Oct 6 Oct Oct 6 Oct mm Nov Nov Nov Nov Nov Nov 3 Min Mean Max Std N synop: 6, AM: +8, ECMWF: +8, ME SDE RMSE MAE Max.abs.err. N AM synop ECMWF synop

METinfo Verification of Operational Weather Prediction Models December 2017 to February 2018 Mariken Homleid and Frank Thomas Tveter

METinfo Verification of Operational Weather Prediction Models December 2017 to February 2018 Mariken Homleid and Frank Thomas Tveter METinfo No. /8 ISSN 89-79X Meteorology Verification of Operational Weather Prediction Models December 7 to February 8 Mariken Homleid and Frank Thomas Tveter Photo: Jan Erik Haugen Contents Introduction

More information

METinfo Verification of Operational Weather Prediction Models June to August 2017 Mariken Homleid and Frank Thomas Tveter

METinfo Verification of Operational Weather Prediction Models June to August 2017 Mariken Homleid and Frank Thomas Tveter METinfo No. /7 ISSN 89-79X Meteorology Verification of Operational Weather Prediction Models June to August 7 Mariken Homleid and Frank Thomas Tveter Contents Introduction Models..............................................

More information

Application and verification of ECMWF products in Norway 2008

Application and verification of ECMWF products in Norway 2008 Application and verification of ECMWF products in Norway 2008 The Norwegian Meteorological Institute 1. Summary of major highlights The ECMWF products are widely used by forecasters to make forecasts for

More information

SMHI activities on Data Assimilation for Numerical Weather Prediction

SMHI activities on Data Assimilation for Numerical Weather Prediction SMHI activities on Data Assimilation for Numerical Weather Prediction ECMWF visit to SMHI, 4-5 December, 2017 Magnus Lindskog and colleagues Structure Introduction Observation usage Monitoring Methodologies

More information

Snow analysis in HIRLAM and HARMONIE. Kuopio, 24 March 2010 Mariken Homleid

Snow analysis in HIRLAM and HARMONIE. Kuopio, 24 March 2010 Mariken Homleid Snow analysis in HIRLAM and HARMONIE Kuopio, 24 March 2010 Mariken Homleid Outline Motivation: the surface temperature depends on snow cover, in reality and in NWP advanced snow schemes show larger sensitivity

More information

Application and verification of ECMWF products 2016

Application and verification of ECMWF products 2016 Application and verification of ECMWF products 2016 Icelandic Meteorological Office (www.vedur.is) Bolli Pálmason and Guðrún Nína Petersen 1. Summary of major highlights Medium range weather forecasts

More information

Application and verification of ECMWF products 2009

Application and verification of ECMWF products 2009 Application and verification of ECMWF products 2009 Hungarian Meteorological Service 1. Summary of major highlights The objective verification of ECMWF forecasts have been continued on all the time ranges

More information

Meteo-France operational land surface analysis for NWP: current status and perspectives

Meteo-France operational land surface analysis for NWP: current status and perspectives Meteo-France operational land surface analysis for NWP: current status and perspectives Camille Birman, Jean-François Mahfouf, Yann Seity, Eric Bazile, Françoise Taillefer, Zied Sassi, Nadia Fourrié, Vincent

More information

STATISTICAL MODELS and VERIFICATION

STATISTICAL MODELS and VERIFICATION STATISTICAL MODELS and VERIFICATION Mihaela NEACSU & Otilia DIACONU ANM/LMN/AMASC COSMO-September 2013 Mihaela NEACSU & Otilia DIACONU (ANM/LMN/AMASC) MOS & VERIF COSMO-September 2013 1 / 48 SUMMARY 1

More information

HIRLAM/HARMONIE work on surface data assimilation and modelling

HIRLAM/HARMONIE work on surface data assimilation and modelling HIRLAM/HARMONIE work on surface data assimilation and modelling Ekaterina Kourzeneva *, Laura Rontu, Trygve Aspelien, Maria Diez, Mariken Homleid, Kalle Eerola, Suleiman Mostamandi, Patrick Samuelsson,

More information

MET report. The IASI moisture channel impact study in HARMONIE for August-September 2011

MET report. The IASI moisture channel impact study in HARMONIE for August-September 2011 MET report no. 19/2013 Numerical Weather Prediction The IASI moisture channel impact study in HARMONIE for August-September 2011 Trygve Aspelien, Roger Randriamampianina, Harald Schyberg, Frank Thomas

More information

Scatterometer Wind Assimilation at the Met Office

Scatterometer Wind Assimilation at the Met Office Scatterometer Wind Assimilation at the Met Office James Cotton International Ocean Vector Winds Science Team (IOVWST) meeting, Brest, June 2014 Outline Assimilation status Global updates: Metop-B and spatial

More information

Application and verification of ECMWF products 2008

Application and verification of ECMWF products 2008 Application and verification of ECMWF products 2008 RHMS of Serbia 1. Summary of major highlights ECMWF products are operationally used in Hydrometeorological Service of Serbia from the beginning of 2003.

More information

IMPACT OF IASI DATA ON FORECASTING POLAR LOWS

IMPACT OF IASI DATA ON FORECASTING POLAR LOWS IMPACT OF IASI DATA ON FORECASTING POLAR LOWS Roger Randriamampianina rwegian Meteorological Institute, Pb. 43 Blindern, N-0313 Oslo, rway rogerr@met.no Abstract The rwegian THORPEX-IPY aims to significantly

More information

Application and verification of ECMWF products 2015

Application and verification of ECMWF products 2015 Application and verification of ECMWF products 2015 Turkish State Meteorological Service Unal TOKA,Yelis CENGIZ 1. Summary of major highlights The verification of ECMWF products has continued as in previous

More information

Data impact studies in the global NWP model at Meteo-France

Data impact studies in the global NWP model at Meteo-France Data impact studies in the global NWP model at Meteo-France Patrick Moll, Fatima Karbou, Claudia Faccani, Nathalie Saint- Ramond, Florence Rabier Centre National de Recherches Météorologiques CNRS/GAME,

More information

Application and verification of ECMWF products 2016

Application and verification of ECMWF products 2016 Application and verification of ECMWF products 2016 Turkish State Meteorological Service Ünal TOKA, Mustafa BAŞARAN, Yelis CENGİZ 1. Summary of major highlights The verification of ECMWF products has continued

More information

Validation of 2-meters temperature forecast at cold observed conditions by different NWP models

Validation of 2-meters temperature forecast at cold observed conditions by different NWP models Validation of 2-meters temperature forecast at cold observed conditions by different NWP models Evgeny Atlaskin Finnish Meteorological Institute / Russian State Hydrometeorological University OUTLINE Background

More information

S e a s o n a l F o r e c a s t i n g f o r t h e E u r o p e a n e n e r g y s e c t o r

S e a s o n a l F o r e c a s t i n g f o r t h e E u r o p e a n e n e r g y s e c t o r S e a s o n a l F o r e c a s t i n g f o r t h e E u r o p e a n e n e r g y s e c t o r C3S European Climatic Energy Mixes (ECEM) Webinar 18 th Oct 2017 Philip Bett, Met Office Hadley Centre S e a s

More information

Application and verification of ECMWF products 2010

Application and verification of ECMWF products 2010 Application and verification of ECMWF products 2010 Icelandic Meteorological Office (www.vedur.is) Guðrún Nína Petersen 1. Summary of major highlights Medium range weather forecasts issued at IMO are mainly

More information

Application and verification of the ECMWF products Report 2007

Application and verification of the ECMWF products Report 2007 Application and verification of the ECMWF products Report 2007 National Meteorological Administration Romania 1. Summary of major highlights The medium range forecast activity within the National Meteorological

More information

Application and verification of ECMWF products in Croatia

Application and verification of ECMWF products in Croatia Application and verification of ECMWF products in Croatia August 2008 1. Summary of major highlights At Croatian Met Service, ECMWF products are the major source of data used in the operational weather

More information

Re-analysis activities at SMHI

Re-analysis activities at SMHI Re-analysis activities at SMHI by: Anna Jansson and Per Kållberg Contents Variational data assimilation at SMHI Operational MESAN Applications of MESAN MESAN used for a long time period Variational data

More information

Surface issues for High Resolution NWP

Surface issues for High Resolution NWP Surface issues for High Resolution NWP François Bouyssel - Jean-François Mahfouf with many contributors High Resolution NWP Workshop, 17-20 May 2010, Brac, Croatia Outline Introduction (specificities of

More information

CERA-SAT: A coupled reanalysis at higher resolution (WP1)

CERA-SAT: A coupled reanalysis at higher resolution (WP1) CERA-SAT: A coupled reanalysis at higher resolution (WP1) ERA-CLIM2 General assembly Dinand Schepers 16 Jan 2017 Contributors: Eric de Boisseson, Per Dahlgren, Patrick Lalolyaux, Iain Miller and many others

More information

Swedish Meteorological and Hydrological Institute

Swedish Meteorological and Hydrological Institute Swedish Meteorological and Hydrological Institute Norrköping, Sweden 1. Summary of highlights HIRLAM at SMHI is run on a CRAY T3E with 272 PEs at the National Supercomputer Centre (NSC) organised together

More information

What s new for ARPEGE/ALADIN since Utrecht 2009?

What s new for ARPEGE/ALADIN since Utrecht 2009? What s new for ARPEGE/ALADIN since Utrecht 2009? E. Bazile with contributions from GMAP/OBS and GMAP/PROC ASM HIRLAM ALADIN 12-16 April, 2010 Krakow Outline QPF performance of ARPEGE/ALADIN with the new

More information

Application and verification of ECMWF products in Croatia - July 2007

Application and verification of ECMWF products in Croatia - July 2007 Application and verification of ECMWF products in Croatia - July 2007 By Lovro Kalin, Zoran Vakula and Josip Juras (Hydrological and Meteorological Service) 1. Summary of major highlights At Croatian Met

More information

Application and verification of ECMWF products 2012

Application and verification of ECMWF products 2012 Application and verification of ECMWF products 2012 Instituto Português do Mar e da Atmosfera, I.P. (IPMA) 1. Summary of major highlights ECMWF products are used as the main source of data for operational

More information

Application and verification of ECMWF products 2016

Application and verification of ECMWF products 2016 Application and verification of ECMWF products 2016 Meteorological and Hydrological Service of Croatia Lovro Kalin and Zoran Vakula 1. Summary of major highlights At Meteorological and Hydrological Service

More information

Application and verification of ECMWF products 2017

Application and verification of ECMWF products 2017 Application and verification of ECMWF products 2017 Finnish Meteorological Institute compiled by Weather and Safety Centre with help of several experts 1. Summary of major highlights FMI s forecasts are

More information

Application and verification of ECMWF products 2010

Application and verification of ECMWF products 2010 Application and verification of ECMWF products Hydrological and meteorological service of Croatia (DHMZ) Lovro Kalin. Summary of major highlights At DHMZ, ECMWF products are regarded as the major source

More information

Surface data assimilation activities in the HIRLAM consortium

Surface data assimilation activities in the HIRLAM consortium Surface data assimilation activities in the HIRLAM consortium LACE DA Working Days 016 September, 016 Magnus Lindskog and Patrick Samuelsson Outline Introduction Surface data assimilation for nature tile

More information

No. 8/2018 ISSN Meteorology. METreport. StrålInn. Evaluation of Predicted Shortwave Radiation Åsmund Bakketun, Jørn Kristiansen

No. 8/2018 ISSN Meteorology. METreport. StrålInn. Evaluation of Predicted Shortwave Radiation Åsmund Bakketun, Jørn Kristiansen METreport No. 8/2018 ISSN 2387-4201 Meteorology StrålInn Evaluation of Predicted Shortwave Radiation Åsmund Bakketun, Jørn Kristiansen METreport Title Date StrålInn August 27, 2018 Section Report no. Senter

More information

Application and verification of ECMWF products 2015

Application and verification of ECMWF products 2015 Application and verification of ECMWF products 2015 Hungarian Meteorological Service 1. Summary of major highlights The objective verification of ECMWF forecasts have been continued on all the time ranges

More information

Current Limited Area Applications

Current Limited Area Applications Current Limited Area Applications Nils Gustafsson SMHI Norrköping, Sweden nils.gustafsson@smhi.se Outline of talk (contributions from many HIRLAM staff members) Specific problems of Limited Area Model

More information

State of the art of wind forecasting and planned improvements for NWP Helmut Frank (DWD), Malte Mülller (met.no), Clive Wilson (UKMO)

State of the art of wind forecasting and planned improvements for NWP Helmut Frank (DWD), Malte Mülller (met.no), Clive Wilson (UKMO) State of the art of wind forecasting and planned improvements for NWP Helmut Frank (DWD), Malte Mülller (met.no), Clive Wilson (UKMO) thanks to S. Bauernschubert, U. Blahak, S. Declair, A. Röpnack, C.

More information

Presentation of met.no s experience and expertise related to high resolution reanalysis

Presentation of met.no s experience and expertise related to high resolution reanalysis Presentation of met.no s experience and expertise related to high resolution reanalysis Oyvind Saetra, Ole Einar Tveito, Harald Schyberg and Lars Anders Breivik Norwegian Meteorological Institute Daily

More information

Application and verification of ECMWF products 2009

Application and verification of ECMWF products 2009 Application and verification of ECMWF products 2009 RHMS of Serbia 1. Summary of major highlights ECMWF products are operationally used in Hydrometeorological Service of Serbia from the beginning of 2003.

More information

Ninth Workshop on Meteorological Operational Systems. Timeliness and Impact of Observations in the CMC Global NWP system

Ninth Workshop on Meteorological Operational Systems. Timeliness and Impact of Observations in the CMC Global NWP system Ninth Workshop on Meteorological Operational Systems ECMWF, Reading, United Kingdom 10 14 November 2003 Timeliness and Impact of Observations in the CMC Global NWP system Réal Sarrazin, Yulia Zaitseva

More information

Application and verification of ECMWF products 2011

Application and verification of ECMWF products 2011 Application and verification of ECMWF products 2011 Icelandic Meteorological Office (www.vedur.is) Guðrún Nína Petersen 1. Summary of major highlights Medium range weather forecasts issued at IMO are mainly

More information

JOINT WMO TECHNICAL PROGRESS REPORT ON THE GLOBAL DATA PROCESSING AND FORECASTING SYSTEM AND NUMERICAL WEATHER PREDICTION RESEARCH ACTIVITIES FOR 2007

JOINT WMO TECHNICAL PROGRESS REPORT ON THE GLOBAL DATA PROCESSING AND FORECASTING SYSTEM AND NUMERICAL WEATHER PREDICTION RESEARCH ACTIVITIES FOR 2007 JOINT WMO TECHNICAL PROGRESS REPORT ON THE GLOBAL DATA PROCESSING AND FORECASTING SYSTEM AND NUMERICAL WEATHER PREDICTION RESEARCH ACTIVITIES FOR 2007 SMHI Swedish Meteorological and Hydrological Institute

More information

Application and verification of ECMWF products 2015

Application and verification of ECMWF products 2015 Application and verification of ECMWF products 2015 Instituto Português do Mar e da Atmosfera, I.P. 1. Summary of major highlights At Instituto Português do Mar e da Atmosfera (IPMA) ECMWF products are

More information

1. Current atmospheric DA systems 2. Coupling surface/atmospheric DA 3. Trends & ideas

1. Current atmospheric DA systems 2. Coupling surface/atmospheric DA 3. Trends & ideas 1 Current issues in atmospheric data assimilation and its relationship with surfaces François Bouttier GAME/CNRM Météo-France 2nd workshop on remote sensing and modeling of surface properties, Toulouse,

More information

EWGLAM/SRNWP National presentation from DMI

EWGLAM/SRNWP National presentation from DMI EWGLAM/SRNWP 2013 National presentation from DMI Development of operational Harmonie at DMI Since Jan 2013 DMI updated HARMONIE-Denmark suite to CY37h1 with a 3h-RUC cycling and 57h forecast, 8 times a

More information

Revisiting predictability of the strongest storms that have hit France over the past 32 years.

Revisiting predictability of the strongest storms that have hit France over the past 32 years. Revisiting predictability of the strongest storms that have hit France over the past 32 years. Marie Boisserie L. Descamps, P. Arbogast GMAP/RECYF 20 August 2014 Introduction Improving early detection

More information

Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS)

Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS) Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS) Marco L. Carrera, Vincent Fortin and Stéphane Bélair Meteorological Research Division Environment

More information

AVIATION APPLICATIONS OF A NEW GENERATION OF MESOSCALE NUMERICAL WEATHER PREDICTION SYSTEM OF THE HONG KONG OBSERVATORY

AVIATION APPLICATIONS OF A NEW GENERATION OF MESOSCALE NUMERICAL WEATHER PREDICTION SYSTEM OF THE HONG KONG OBSERVATORY P452 AVIATION APPLICATIONS OF A NEW GENERATION OF MESOSCALE NUMERICAL WEATHER PREDICTION SYSTEM OF THE HONG KONG OBSERVATORY Wai-Kin WONG *1, P.W. Chan 1 and Ivan C.K. Ng 2 1 Hong Kong Observatory, Hong

More information

Verification of ECMWF products at the Finnish Meteorological Institute

Verification of ECMWF products at the Finnish Meteorological Institute Verification of ECMWF products at the Finnish Meteorological Institute by Juha Kilpinen, Pertti Nurmi, Petra Roiha and Martti Heikinheimo 1. Summary of major highlights A new verification system became

More information

Advances in weather modelling

Advances in weather modelling Advances in weather modelling www.cawcr.gov.au Robert Fawcett - speaking on behalf of CAWCR Earth-System Modelling and CAWCR Weather and Environmental Prediction May 2013 The Centre for Australian Weather

More information

Changes in the Arpège 4D-VAR and LAM 3D-VAR. C. Fischer With contributions by P. Brousseau, G. Kerdraon, J.-F. Mahfouf, T.

Changes in the Arpège 4D-VAR and LAM 3D-VAR. C. Fischer With contributions by P. Brousseau, G. Kerdraon, J.-F. Mahfouf, T. Changes in the Arpège 4D-VAR and LAM 3D-VAR C. Fischer With contributions by P. Brousseau, G. Kerdraon, J.-F. Mahfouf, T. Montmerle Content Arpège 4D-VAR Arome-France Other applications: Aladin Overseas,

More information

HIRLAM Regional 3D-VAR and MESAN downscaling re-analysis for the recent. 20 year period in the FP7 EURO4M project SMHI

HIRLAM Regional 3D-VAR and MESAN downscaling re-analysis for the recent. 20 year period in the FP7 EURO4M project SMHI HIRLAM Regional 3D-VAR and MESAN downscaling re-analysis for the recent 20 year period in the FP7 EURO4M project Per Undén, Tomas Landelius Per Dahlgren, Per Kållberg Stefan Gollvik, Sébastien Villaume

More information

Weather Forecasting Models in Met Éireann. Eoin Whelan UCD Seminar 3 rd April 2012

Weather Forecasting Models in Met Éireann. Eoin Whelan UCD Seminar 3 rd April 2012 Weather Forecasting Models in Met Éireann Eoin Whelan UCD Seminar 3 rd April 2012 Overview Background HIRLAM Models Local Implementation Verification Development work Background Met Éireann Dept of the

More information

Snow-atmosphere interactions at Dome C, Antarctica

Snow-atmosphere interactions at Dome C, Antarctica Snow-atmosphere interactions at Dome C, Antarctica Éric Brun, Vincent Vionnet CNRM/GAME (Météo-France and CNRS) Christophe Genthon, Delphine Six, Ghislain Picard LGGE (CNRS and UJF)... and many colleagues

More information

High resolution regional reanalysis over Ireland using the HARMONIE NWP model

High resolution regional reanalysis over Ireland using the HARMONIE NWP model High resolution regional reanalysis over Ireland using the HARMONIE NWP model Emily Gleeson, Eoin Whelan With thanks to John Hanley, Bing Li, Ray McGrath, Séamus Walsh, Motivation/Inspiration KNMI 5 year

More information

The ECMWF Extended range forecasts

The ECMWF Extended range forecasts The ECMWF Extended range forecasts Laura.Ferranti@ecmwf.int ECMWF, Reading, U.K. Slide 1 TC January 2014 Slide 1 The operational forecasting system l High resolution forecast: twice per day 16 km 91-level,

More information

Summary of activities with SURFEX data assimilation at Météo-France. Jean-François MAHFOUF CNRM/GMAP/OBS

Summary of activities with SURFEX data assimilation at Météo-France. Jean-François MAHFOUF CNRM/GMAP/OBS Summary of activities with SURFEX data assimilation at Météo-France Jean-François MAHFOUF CNRM/GMAP/OBS Outline Status at Météo-France in 2008 Developments undertaken during 2009-2011 : Extended Kalman

More information

Assimilating AMSU-A over Sea Ice in HIRLAM 3D-Var

Assimilating AMSU-A over Sea Ice in HIRLAM 3D-Var Abstract Assimilating AMSU-A over Sea Ice in HIRLAM 3D-Var Vibeke W. Thyness 1, Leif Toudal Pedersen 2, Harald Schyberg 1, Frank T. Tveter 1 1 Norwegian Meteorological Institute (met.no) Box 43 Blindern,

More information

The benefits and developments in ensemble wind forecasting

The benefits and developments in ensemble wind forecasting The benefits and developments in ensemble wind forecasting Erik Andersson Slide 1 ECMWF European Centre for Medium-Range Weather Forecasts Slide 1 ECMWF s global forecasting system High resolution forecast

More information

Comparing the SEKF with the DEnKF on a land surface model

Comparing the SEKF with the DEnKF on a land surface model Comparing the SEKF with the DEnKF on a land surface model David Fairbairn, Alina Barbu, Emiliano Gelati, Jean-Francois Mahfouf and Jean-Christophe Caret CNRM - Meteo France Partly funded by European Union

More information

Methodology for the creation of meteorological datasets for Local Air Quality modelling at airports

Methodology for the creation of meteorological datasets for Local Air Quality modelling at airports Methodology for the creation of meteorological datasets for Local Air Quality modelling at airports Nicolas DUCHENE, James SMITH (ENVISA) Ian FULLER (EUROCONTROL Experimental Centre) About ENVISA Noise

More information

GPC Exeter forecast for winter Crown copyright Met Office

GPC Exeter forecast for winter Crown copyright Met Office GPC Exeter forecast for winter 2015-2016 Global Seasonal Forecast System version 5 (GloSea5) ensemble prediction system the source for Met Office monthly and seasonal forecasts uses a coupled model (atmosphere

More information

Snow Analysis for Numerical Weather prediction at ECMWF

Snow Analysis for Numerical Weather prediction at ECMWF Snow Analysis for Numerical Weather prediction at ECMWF Patricia de Rosnay, Gianpaolo Balsamo, Lars Isaksen European Centre for Medium-Range Weather Forecasts IGARSS 2011, Vancouver, 25-29 July 2011 Slide

More information

Investigating the urban climate characteristics of two Hungarian cities with SURFEX/TEB land surface model

Investigating the urban climate characteristics of two Hungarian cities with SURFEX/TEB land surface model Investigating the urban climate characteristics of two Hungarian cities with SURFEX/TEB land surface model Gabriella Zsebeházi Gabriella Zsebeházi and Gabriella Szépszó Hungarian Meteorological Service,

More information

ALASKA REGION CLIMATE OUTLOOK BRIEFING. November 17, 2017 Rick Thoman National Weather Service Alaska Region

ALASKA REGION CLIMATE OUTLOOK BRIEFING. November 17, 2017 Rick Thoman National Weather Service Alaska Region ALASKA REGION CLIMATE OUTLOOK BRIEFING November 17, 2017 Rick Thoman National Weather Service Alaska Region Today Feature of the month: More climate models! Climate Forecast Basics Climate System Review

More information

Short range weather forecasts in the European Arctic; recent work and experiences with AROME Arctic

Short range weather forecasts in the European Arctic; recent work and experiences with AROME Arctic Short range weather forecasts in the European Arctic; recent work and experiences with AROME Arctic Morten Køltzow, Malte Müller, Teresa Valkonen, Yurii Batrak, Eivind Støylen, Jørn Kristiansen MET Norway

More information

Dijana Klarić RC LACE Program Manager

Dijana Klarić RC LACE Program Manager Dijana Klarić RC LACE Program Manager www.rclace.eu 10-13 October 2011 33rd EWGLAM, Tallinn, Estonia 1 2011 report LACE Projects () 3y summary 2011 R&D highlights ~ 2012 plans LACE future ALARO phy 5km

More information

Recent ECMWF Developments

Recent ECMWF Developments Recent ECMWF Developments Tim Hewson (with contributions from many ECMWF colleagues!) tim.hewson@ecmwf.int ECMWF November 2, 2017 Outline Last Year IFS upgrade highlights 43r1 and 43r3 Standard web Chart

More information

Report on stay at ZAMG

Report on stay at ZAMG Report on stay at ZAMG Viena, Austria 13.05.2013 05.07.2013 Simona Tascu NMA, Romania Supervised by: Yong Wang and Theresa Gorgas Introduction The goal of the present stay was to develop and optimize the

More information

Introduction : Scientific context : SRNWP Expert Team on Surface Processes (model and data assimilation) Draft of a workplan for

Introduction : Scientific context : SRNWP Expert Team on Surface Processes (model and data assimilation) Draft of a workplan for SRNWP Expert Team on Surface Processes (model and data assimilation) Draft of a workplan for 2008-2009 Revised version (05 September 2008) Introduction : The Expert Team on Surface Processes will focus

More information

Application and verification of ECMWF products 2009

Application and verification of ECMWF products 2009 Application and verification of ECMWF products 2009 Icelandic Meteorological Office (www.vedur.is) Gu rún Nína Petersen 1. Summary of major highlights Medium range weather forecasts issued at IMO are mainly

More information

Current research issues. Philippe Bougeault, Météo France

Current research issues. Philippe Bougeault, Météo France Current research issues for short range NWP Philippe Bougeault, Météo France The expectations from government and society as seen from Météo France Improve the accuracy of short range forecasts for security

More information

Precipitation verification. Thanks to CMC, CPTEC, DWD, ECMWF, JMA, MF, NCEP, NRL, RHMC, UKMO

Precipitation verification. Thanks to CMC, CPTEC, DWD, ECMWF, JMA, MF, NCEP, NRL, RHMC, UKMO Precipitation verification Thanks to CMC, CPTEC, DWD, ECMWF, JMA, MF, NCEP, NRL, RHMC, UKMO Outline 1) Status of WGNE QPF intercomparisons 2) Overview of the use of recommended methods for the verification

More information

Gridded observation data for Climate Services

Gridded observation data for Climate Services Gridded observation data for Climate Services Ole Einar Tveito, Inger Hanssen Bauer, Eirik J. Førland and Cristian Lussana Norwegian Meteorological Institute Norwegian annual temperatures Norwegian annual

More information

Application and verification of ECMWF products 2016

Application and verification of ECMWF products 2016 Application and verification of ECMWF products 2016 RHMS of Serbia 1 Summary of major highlights ECMWF forecast products became the backbone in operational work during last several years. Starting from

More information

Application and verification of ECMWF products 2015

Application and verification of ECMWF products 2015 Application and verification of ECMWF products 2015 METEO- J. Stein, L. Aouf, N. Girardot, S. Guidotti, O. Mestre, M. Plu, F. Pouponneau and I. Sanchez 1. Summary of major highlights The major event is

More information

Towards a Definitive High- Resolution Climate Dataset for Ireland Promoting Climate Research in Ireland

Towards a Definitive High- Resolution Climate Dataset for Ireland Promoting Climate Research in Ireland Towards a Definitive High- Resolution Climate Dataset for Ireland Promoting Climate Research in Ireland Jason Flanagan, Paul Nolan, Christopher Werner & Ray McGrath Outline Project Background and Objectives

More information

Impact of GPS and TMI Precipitable Water Data on Mesoscale Numerical Weather Prediction Model Forecasts

Impact of GPS and TMI Precipitable Water Data on Mesoscale Numerical Weather Prediction Model Forecasts Journal of the Meteorological Society of Japan, Vol. 82, No. 1B, pp. 453--457, 2004 453 Impact of GPS and TMI Precipitable Water Data on Mesoscale Numerical Weather Prediction Model Forecasts Ko KOIZUMI

More information

Assimilation of the IASI data in the HARMONIE data assimilation system

Assimilation of the IASI data in the HARMONIE data assimilation system Assimilation of the IASI data in the HARMONIE data assimilation system Roger Randriamampianina Acknowledgement: Andrea Storto (met.no), Andrew Collard (ECMWF), Fiona Hilton (MetOffice) and Vincent Guidard

More information

Application and verification of ECMWF products 2009

Application and verification of ECMWF products 2009 Application and verification of ECMWF products 2009 Danish Meteorological Institute Author: Søren E. Olufsen, Deputy Director of Forecasting Services Department and Erik Hansen, forecaster M.Sc. 1. Summary

More information

Seasonal Climate Watch September 2018 to January 2019

Seasonal Climate Watch September 2018 to January 2019 Seasonal Climate Watch September 2018 to January 2019 Date issued: Aug 31, 2018 1. Overview The El Niño-Southern Oscillation (ENSO) is still in a neutral phase and is still expected to rise towards an

More information

USE OF SATELLITE INFORMATION IN THE HUNGARIAN NOWCASTING SYSTEM

USE OF SATELLITE INFORMATION IN THE HUNGARIAN NOWCASTING SYSTEM USE OF SATELLITE INFORMATION IN THE HUNGARIAN NOWCASTING SYSTEM Mária Putsay, Zsófia Kocsis and Ildikó Szenyán Hungarian Meteorological Service, Kitaibel Pál u. 1, H-1024, Budapest, Hungary Abstract The

More information

Soil Moisture Prediction and Assimilation

Soil Moisture Prediction and Assimilation Soil Moisture Prediction and Assimilation Analysis and Prediction in Agricultural Landscapes Saskatoon, June 19-20, 2007 STEPHANE BELAIR Meteorological Research Division Prediction and Assimilation Atmospheric

More information

Mesoscale meteorological models. Claire L. Vincent, Caroline Draxl and Joakim R. Nielsen

Mesoscale meteorological models. Claire L. Vincent, Caroline Draxl and Joakim R. Nielsen Mesoscale meteorological models Claire L. Vincent, Caroline Draxl and Joakim R. Nielsen Outline Mesoscale and synoptic scale meteorology Meteorological models Dynamics Parametrizations and interactions

More information

Global reanalysis: Some lessons learned and future plans

Global reanalysis: Some lessons learned and future plans Global reanalysis: Some lessons learned and future plans Adrian Simmons and Sakari Uppala European Centre for Medium-Range Weather Forecasts With thanks to Per Kållberg and many other colleagues from ECMWF

More information

VALIDATION RESULTS OF THE OPERATIONAL LSA-SAF SNOW COVER MAPPING

VALIDATION RESULTS OF THE OPERATIONAL LSA-SAF SNOW COVER MAPPING VALIDATION RESULTS OF THE OPERATIONAL LSA-SAF SNOW COVER MAPPING Niilo Siljamo, Otto Hyvärinen Finnish Meteorological Institute, Erik Palménin aukio 1, P.O.Box 503, FI-00101 HELSINKI Abstract Hydrological

More information

ECMWF. ECMWF Land Surface Analysis: Current status and developments. P. de Rosnay M. Drusch, K. Scipal, D. Vasiljevic G. Balsamo, J.

ECMWF. ECMWF Land Surface Analysis: Current status and developments. P. de Rosnay M. Drusch, K. Scipal, D. Vasiljevic G. Balsamo, J. Land Surface Analysis: Current status and developments P. de Rosnay M. Drusch, K. Scipal, D. Vasiljevic G. Balsamo, J. Muñoz Sabater 2 nd Workshop on Remote Sensing and Modeling of Surface Properties,

More information

About HARMONIE/AROME in AEMET

About HARMONIE/AROME in AEMET About HARMONIE/AROME in AEMET Javier Calvo With contributions from Imanol Guerrero, Gema Morales, Arancha Amo, Carlos Santos and Daniel Martín Aladin/Hirlam ASM,15-18 Apr 2013, Reykjavic Outline The Harmonie/Arome

More information

The Impact of Observational data on Numerical Weather Prediction. Hirokatsu Onoda Numerical Prediction Division, JMA

The Impact of Observational data on Numerical Weather Prediction. Hirokatsu Onoda Numerical Prediction Division, JMA The Impact of Observational data on Numerical Weather Prediction Hirokatsu Onoda Numerical Prediction Division, JMA Outline Data Analysis system of JMA in Global Spectral Model (GSM) and Meso-Scale Model

More information

Latest developments and performances in ARPEGE and ALADIN-France

Latest developments and performances in ARPEGE and ALADIN-France Latest developments and performances in ARPEGE and ALADIN-France François Bouyssel ( and many contributors ) CNRM-GAME, Météo-France 18th ALADIN / HIRLAM Workshop, Bruxelles, 7-10 April 2008 Plan Recent

More information

Visualising and communicating probabilistic flow forecasts in The Netherlands

Visualising and communicating probabilistic flow forecasts in The Netherlands Visualising and communicating probabilistic flow forecasts in The Netherlands Eric Sprokkereef Centre for Water Management Division Crisis Management & Information Supply 2-2-2009 Content The basins Forecasting

More information

ECMWF snow data assimilation: Use of snow cover products and In situ snow depth data for NWP

ECMWF snow data assimilation: Use of snow cover products and In situ snow depth data for NWP snow data assimilation: Use of snow cover products and In situ snow depth data for NWP Patricia de Rosnay Thanks to: Ioannis Mallas, Gianpaolo Balsamo, Philippe Lopez, Anne Fouilloux, Mohamed Dahoui, Lars

More information

Operations Portugal (second half of 2010)

Operations Portugal (second half of 2010) Operations Portugal (second half of 2010) In the second half of 2010, important changes happened in the operational procedures, the main event being the operationalization of AROME for Madeira archipelago

More information

Recent experience at Météo-France on the assimilation of observations at high temporal frequency Cliquez pour modifier le style du titre

Recent experience at Météo-France on the assimilation of observations at high temporal frequency Cliquez pour modifier le style du titre Recent experience at Météo-France on the assimilation of observations at high temporal frequency Cliquez pour modifier le style du titre J.-F. Mahfouf, P. Brousseau, P. Chambon and G. Desroziers Météo-France/CNRS

More information

Regional climate modelling in the future. Ralf Döscher, SMHI, Sweden

Regional climate modelling in the future. Ralf Döscher, SMHI, Sweden Regional climate modelling in the future Ralf Döscher, SMHI, Sweden The chain Global H E H E C ( m 3/s ) Regional downscaling 120 adam 3 C HAM 4 adam 3 C HAM 4 trl A2 A2 B2 B2 80 40 0 J F M A M J J A S

More information

Convective-scale NWP for Singapore

Convective-scale NWP for Singapore Convective-scale NWP for Singapore Hans Huang and the weather modelling and prediction section MSS, Singapore Dale Barker and the SINGV team Met Office, Exeter, UK ECMWF Symposium on Dynamical Meteorology

More information

Drought forecasting methods Blaz Kurnik DESERT Action JRC

Drought forecasting methods Blaz Kurnik DESERT Action JRC Ljubljana on 24 September 2009 1 st DMCSEE JRC Workshop on Drought Monitoring 1 Drought forecasting methods Blaz Kurnik DESERT Action JRC Motivations for drought forecasting Ljubljana on 24 September 2009

More information

ALASKA REGION CLIMATE OUTLOOK BRIEFING. December 22, 2017 Rick Thoman National Weather Service Alaska Region

ALASKA REGION CLIMATE OUTLOOK BRIEFING. December 22, 2017 Rick Thoman National Weather Service Alaska Region ALASKA REGION CLIMATE OUTLOOK BRIEFING December 22, 2017 Rick Thoman National Weather Service Alaska Region Today s Outline Feature of the month: Autumn sea ice near Alaska Climate Forecast Basics Climate

More information

Météo-France NWP developments and AROME

Météo-France NWP developments and AROME Météo-France NWP developments and AROME F. Bouttier & collaborators - CNRM/GMAP, Météo-France bouttier@meteo.fr - Bratislava, June 2005 General NWP strategy ARPEGE/ALADIN MF progress & plans AROME progress

More information

ICON. Limited-area mode (ICON-LAM) and updated verification results. Günther Zängl, on behalf of the ICON development team

ICON. Limited-area mode (ICON-LAM) and updated verification results. Günther Zängl, on behalf of the ICON development team ICON Limited-area mode (ICON-LAM) and updated verification results Günther Zängl, on behalf of the ICON development team COSMO General Meeting, Offenbach, 07.09.2016 Outline Status of limited-area-mode

More information