Forudsigelse og måling af nedbør ved DMI med særligt fokus på kortidsforudsigelse

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1 Forudsigelse og måling af nedbør ved DMI med særligt fokus på kortidsforudsigelse

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3 Sammensat og præsenteret af Henrik Vedel Center for meteorologiske modeller Forsknings- og udviklingsafdelingen (F&U), DMI Væsentlige bidrag fra Thomas Bøvith og Flemming Vejen (F&U) Georg Bergeton Larsen, Service- og informationsafdelingen (SI)

4 Menu Radarbaseret nowcasting, very short time forecasting NWP-baseret nowcasting, short term forecasting (NWP= numerical weather prediction). NWP ensemblebaseret forecasting, medium and long range forecasting. Radarbaseret nedbørsmåling. Sammenfatning.

5 DMI s radar network

6 Radar-based nowcasting? OBSERVATION 16:30 OBSERVATION 16:40 NOWCAST 16:50

7 DMI radar-based nowcasting Lightweight model => fast computation Run when new weather radar images arrive (at 5-10 minute intervals) Only data input: weather radar data Motion field of precipitation derived from image analysis (optical flow) Simple advection model for prediction Typically, higher prediction skill than NWP models at very short leadtimes Very high resolution precipitation forecast (500 m pixel size)

8 Weather radar-based nowcasting of precipitation at DMI

22 Point-based observation (green) and nowcast (blue) Max. and median statistics computed in a area within 5 km radius from point.

23 Challenges to radar-based nowcasting Nowcasting model assumes no growth and decay of precipitation Does not hold, especially not for convective precipitation (cloudbursts) and precipitation due to orographic forcing. Nowcasting model assumes stationary motion field: Does not hold for rotational precipitation systems. Not well determined if only few precipitation systems are visible. Radar clutter (radar echoes from non-precipitation targets) Echoes from the ground, the sea, buildings and electronic interference from radio local-area networks can cause erroneous nowcasted precipitation The radar observes precipitation at heights up to several km in atmosphere. Radar observes the reflectivity of precipitation which does not directly relate to precipitation intensity

24 One of the big challenges: the quality of radar data Where do radar signals come from? We are not certain!

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60 The DMI radar nowcasting is now being tested in the OMOVAST project (next presentation) Version 1 product will be available from summer. It will be further developed in the Water Smart Cities project.

61 NWP nowcasting NWP (numerical weather prediction) models solve the equations for the evolution of the weather on a computer. Based on knowledge (observations and older NWP runs) about the current weather, an initial state is estimated, and the future weather subsequently predicted. In NWP nowcasting this is done with special focus on short term prediction of phenomena with low predictability. This is done by adding extra observations and running more frequently the NWP model (rapid update cycling = RUC). DMI has developed a RUC NWP model with special focus on precipitation.

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63 Ingredients of a standard numerical weather prediction system Observations Boundary values from external model Computer generated forecasts (fx byvejr) Data assimilation system provide Analysis (=initial conditions) Old model state Numerical weather model Forecasts by forecasters (fx landsudsigt og regionaludsigter) Combined and done on a very powerfull computer

64 DMI nudging scheme Basic idea: increase/decrease low level convergence in correspondence with the offset between the 2D precipitation field determined from radar observations and the NWP model precipitation. Done by nudging of the divergence term in the continuity equation. Removal of cloud in areas with no radar precipitation. A benefit of using nudging is that ALL radar data can be used.

65 1: 3DVAR, done hourly, with cut-off time of approx. 1.5 h. 2: Nudging, done hourly with no cut-off time. SYNOPs, TEMPs, Aircraft, Satellite radiances etc. Ground-based GNSS data 3D-VAR Satellite data on clouds/ humidity Nowcasting SAF + DMI cloud product Quality control 2D composite, specifically for NWP data assimilation DMI radars Quality control by intercomparison (to be developed) Nudging algorithm for radar rain rates and cloud data (modified divergence field) New forecast Forecast model, including nudging terms 3 km resolution New forecasts minimum once an hour, possible several times per hour, each time including new observations in the nudging, and made available shortly after the valid time of the observations. The forecast takes about 5 min.

66 CASE 1 2/7-2011

67 CASE 1 2/ Fractions skill score Uniform

68 CASE 2 14/ Copenhagen Subjective verification

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70 CASE 2 14/ Fractions skill score

71 By inclusion of radar data via nudging, DMI has significantly improved the skill of the NWP model to forecast the location and amount of precipitation on the short term. The model output is tested in the project OMOVAST. It will be further developed in the project Water Smart Cities. Compared to radar nowcasting its relative skill increases with time. Its spatial resolution is much less (gridbox = 3 km, means effective resolution i km) To improve it is planned to include more and better observations Water vapuor from gruond-based GPS, Better filtering of radar data (when DMI new radars arrive) Time interpolated radar data Foreign radar data Additional aircraft data of wind and temperature Increase resolution and change to better NWP model.

72 Ensemble forecasting The NWP models are not perfect The initial conditions are heavily under-determined by the observations. The atmosphere is a chaotic system By making variations of the initial conditions and the unknowns of the NWP model, in a systematic and realistic way, we can assess the quality and robustness of the forecasts, enabling estimation of the uncertainty of the forecasts, as well as identify situations where several outcomes (forecasts) are possible, given our lack of perfect models and lack of a perfect observing system. The new DMI ensemble prediction system with 25 members run every 6 h with 25 members at 5 km resolution. Among other things it provides: Forecast probabilities /uncertainties Ensemble means

73 Example of ensemble prediction, rainfall 14 Aug 2010

74 Ensemble mean and forecast probabilities

75 Ensembles can be used to estimate forecast uncertainty. Estimation of the longer term risk of local, convective precipitation is only realistically possible with ensembles. Current DMI ensemble runs 4 times a day, has 5 km gridbox size, and cover 0 48 h. DMI can deliver also ensemble predictions from ECMWF, which goes out to 15 days. The future DMI ensemble will create new members every hour and have higher spatial resolution. Due to the low predictability of convective precipitation, ensemble forecasting is a vital tool to assess the risk of it.

76 ARNE - ARealNEdbør vha. radardata + ARNE

77 The main challenges: Uncertainty/errors of precipitation data have large impact on QPE Uncertainty of precipitation data comes from two aspects: bias and noise Errors of radar data Improper radar-gauge adjustment due to relatively low radar sampling interval during fast movement/evolution of storms Summary of radar errors Example of advection error Clutter, AP VPR bright-band blocking

78 Højde (km) log(g/r) Fejlkilder på radardata Punktnedbør (200 cm 2 ) sammenlignes med volumendata (>>>punktet) Radarstrålens højde øges med afstand fra radar Pulsvolumen øges med afstand fra radar VPR problemer (Vertical Profile of Reflectivity): i hvilken højde måler radaren? 12 Radarstrålens bredde for alle elevationer 2,5 Bias som funktion af afstand fra radar , , , afstand [km] Afstand (km) Der kan til dels korrigeres empirisk for systematisk afstandsbias, men det radaren ikke ser, kan ikke bringes til live.

79 Fejlkilder på radardata Bright-band effekt Blokering af radarstrålen

80 Fejlkilder på nedbørdata, eksempler Pålejring af sne Snefygning Turbulens omkring måleråbning Isslag pålejring isslag Turbulens Nedbør flyver forbi eller op af måleren

81 Develop and evaluate suitable techniques for resolving the impact of radar echo advection on the radar-gauge comparison

82 Skybruddet over København 31. august 2014

83 Radarberegnet nedbørsum Nedbørmålingerne er uafhængige af radarberegningerne Hvid tekst: radarsum, rød tekst: nedbørmåling, grøn tekst: nedbørmåling ved Lynetten

84 Radarbaserede tidsserier til hydrologisk modellering

85 Realtids radarbaseret timenedbørsum

86 Jackknifing test of Rømø radar adjusted QPE at different ranges

87 Radar derived precipitation with rain gauge adjustments (ARNE) has a very high spatial resolution 500x500 m. Much better than purely rain gauge based 2D estimates of precipitation. Version 1 of ARNE data will become available in summer. Filtering out of non precipitating echoes is vital. DMI new radars will help when installed. Calibration and maintenance of both radars and rain gauges is vital, as well as quality control of the data they provide. Precipitation clouds can sneak below the radar, especially at winter time and at large distances from the radar.

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89 Måling Resumé: DMI måler og forudsiger nedbør for hele Danmark Punktmålinger med nedbørsmålere på DMI s vejrstationer. Anvendes desuden til kontruktion af et 2D nedbørsfelt, men fanger ikke den store rumlige variabilitet i konvektiv nedbør. Begge dele tilgængeligt gennem mange år. Radermålinger justeret vha. nedbørsmålere (ARNE). 2D nedbørsfelt med høj opløsning i tid og rum. Version 1 tilgængelig til sommer. Fordel: Høj opløsning, 500 x500 m. Bagdel: Varierende kvalitet, bl.a. afh. af afstand til radar og nedbørstype. Forbedres gradvis de kommende år, bl.a. qua udskiftningen af DMIs ældste radarer. Nedbørsmålinger er behæftet med flere fejl end meteorologiske målinger generelt, både regnmålere og radarer. Forudsigelse: Radar nowcasting Radarbaseret forudsigelse af nedbør. Fordel: Høj opløsning (500 x 500 m) og høj troværdighed tæt på nu. Anvendes korttidsforudsigelse. Bagdel: Ingen vejrforståelse, kan fx kun forudsige regn på baggrund af observationer af regn. Version 1 af radar nowcasting vil blive operationel til sommer. Testes pt i bl. a. OMOVAST-projektet. Senere forbedringer, bl.a. i forbindelse med projekt Water Smart Cities.

90 Forudsigelse: NWP nowcasting Baseret på NWP model som køres i rapid update cycle (hver time) og med ekstra observationer i forhold til alm. NWP, bl.a. fra radar og satelliter. Fordel: Stor vejrforståelse samt anvendelse af alle typer vejrobservationer. God lokation af nedbør sammenlignet med andre NWP-modeller. Bedre lokation af nedbør end andre NWP-modeller. Bagdel: Ringe opløsning i forhold til radar, gridbox 3 km = reel opløsning km. Anvendes h. Version 1 tilgængelig nu. Testes bl.a. i OMOVAST-projektet. Vil blive videreudviklet, bl.a. i Water Smart Cities, ved inklusion af flere observationer og forbedringer af modelsystemet, både mht. opløsning og NWP-model. Forudsigelse: Ensemble forecasting Undersøger på systematisk vis usikkerheden i vejrforudsigelser. DMIs nuværende ensemble giver den bedste tidlige indikation of risiko for kraftig, lokal nedbør, ud til 48 h. Fordel: Tidligt varsel. Bagdel: Større usikkerhed i lokation. Tilgængeligt i dag. Nyt ensemble 4 gange i døgnet. Vil blive forbedret fremover, med nye ensemblemedlemmer hver time, højere opløsning og bedre NWP-model. Forudsigelse: Seamless forecasting. De forskellige forudsigelsesmetoder har deres styrker og svagheder. Man er nødt til at kombinere dem. Pt må man selv gøre det for bedste resultat. Det håber vi at kunne lave om på..

91 Gå hjem besked Husk at vælge med usikkerhed hvis du kikker på nedbør i DMIs byvejr.