Presentación realizada en la 3rd European Nowcasting Conference, celebrada en la sede central de AEMET en Madrid del 24 al 26 de abril de 2019

Alastrué, Javier

Buisán, Samuel

Collado, José Luis

Earle, Michael E.

Kochendorfer, John

Laine, Timo

Nitu, Rodica

Rasmussen, Roy

Roulet, Yves-Alain

Smith, Craig D.

Wolff, Mareile

English

Buisán Sanz, Samuel Tomás

Collado Aceituno, José Luis

Alastrué Tierra, José Javier

Agencia Estatal de Meteorología

Applications of the WMO Solid Precipitation Intercomparison Experiment (WMO-SPICE) results for nowcastingactivities Samuel T. Buisán1, Craig D. Smith2, John Kochendorfer3, Mareile Wolff 4, Yves-Alain Roulet5 , José Luís Collado1, Javier Alastrué1, Timo Laine6, Roy Rasmussen7, Michael E.Earle8, Rodica Nitu9Correspondence to: Samuel T. Buisan (sbuisans@aemet.es)(Based on a true story) - Mosqueruela (Spain)2ObservationGauge: 16 mmSnow depth: 60 cmSomething is wrong !Reference: Buisán, S. T., Earle, M. E., Collado, J. L., Kochendorfer, J., Alastrué, J., Wolff, M., Smith, C. D., and López-Moreno, J. I.: Assessment of snowfall accumulation underestimation by tipping bucket gauges in the Spanish operational network, Atmos. Meas. Tech., 10, 1079-1091, https://doi.org/10.5194/amt-10-1079-2017, 2017. Measured16 mmTransfer function(adjustment of precipitation in real-time) using windand temperatura Adjusted50.4 mmPrecip (mm) Temp (°C) Wind (km/h) Catch Adjusted (mm)19/01/2017 15:00 0.0 -4.08 28.32 0.17 0.0019/01/2017 16:00 0.4 -4.22 28.56 0.18 2.2719/01/2017 17:00 0.4 -4.40 27.18 0.19 2.1519/01/2017 18:00 0.4 -4.60 23.82 0.22 1.8519/01/2017 19:00 0.4 -4.18 24.24 0.22 1.8319/01/2017 20:00 0.4 -3.87 26.10 0.20 1.9719/01/2017 21:00 0.6 -3.63 26.28 0.21 2.8819/01/2017 22:00 1.0 -3.62 20.34 0.29 3.4819/01/2017 23:00 1.4 -3.53 16.50 0.36 3.8920/01/2017 0:00 0.6 -3.45 14.76 0.37 1.6220/01/2017 1:00 0.4 -3.37 19.68 0.29 1.3920/01/2017 2:00 0.2 -3.15 20.34 0.28 0.7220/01/2017 3:00 0.2 -3.08 18.84 0.30 0.6620/01/2017 4:00 0.2 -2.97 18.30 0.31 0.6420/01/2017 5:00 0.2 -2.85 13.08 0.41 0.4920/01/2017 6:00 0.2 -2.67 14.22 0.39 0.5120/01/2017 7:00 0.2 -2.63 15.78 0.36 0.5520/01/2017 8:00 0.2 -2.40 15.48 0.37 0.5420/01/2017 9:00 0.2 -2.07 18.93 0.32 0.6220/01/2017 10:00 1.4 -1.33 14.04 0.47 2.9620/01/2017 11:00 0.4 -1.10 19.74 0.34 1.1920/01/2017 12:00 1.6 -0.97 17.64 0.41 3.8720/01/2017 13:00 1.4 -0.90 16.50 0.43 3.2420/01/2017 14:00 1.2 -1.08 17.82 0.39 3.0520/01/2017 15:00 0.6 -1.60 17.28 0.37 1.6120/01/2017 16:00 0.4 -2.02 20.28 0.31 1.3020/01/2017 17:00 0.4 -2.20 21.48 0.29 1.4020/01/2017 18:00 0.2 -2.02 26.46 0.22 0.8920/01/2017 19:00 0.2 -2.00 24.00 0.25 0.7920/01/2017 20:00 0.0 -1.88 21.24 0.29 0.0020/01/2017 21:00 0.4 -1.70 22.26 0.29 1.4020/01/2017 22:00 0.2 -1.80 19.38 0.32 0.6220/01/2017 23:00 0.0 -1.65 23.67 0.26 0.00(yellow warning)(red warning)Undercatch > 50%High wind snowfallepisodeThe accurate prediction and verification of snowfall is encumbered by the large potential undercatch of solid precipitationCombined effect of gauge and wind perturb air flow around the gauge. Particles are deviated from their original trajectoriesA wind shield can be used to reduce the perturbation above the gauge orifice.WIND SPEEDUNDERCATCH OF SOLID PRECIPITATION (SNOW)World Meteorological Organization(WMO) SPICE (Solid Precipitation Intercomparison Experiment)ONE OF THE MAIN OBJECTIVES OF THIS PROJECT  ANALYZE PERFOMANCE OF AUTOMATIC INSTRUMENTS  FOR MEASURING SOLID PRECIPITATION (snowfall)“ONLY 1000 pages”IT IS ALSO A REPOSITORY  OF HIGH QUALITY DATA FOR VALIDATION AND TESTING OF SATELLITE AND RADAR PRODUCTSPUBLISHED IN JANUARY 2019TRUE PRECIPITATION (REFERENCE)DFAR (FORMIGAL-SPAIN)Haukeliseter, Norway, 990 m DFAR (REFERENCE)Weighing gauge single-Alter (SA)OPERATIONALFORMIGAL – SARRIOS 1800 m aslWeighing gauge single-Alter (SA)DFAR (reference) 𝐶𝐸 = 𝑒−𝑎 𝑈 1 − tan−1 𝑏 𝑇𝑎𝑖𝑟 +𝑐 (3)𝐶𝐸 = (𝑎)𝑒−𝑏 𝑈 +𝑐 (4)• CE is catch efficiency, U is wind speed, Tair is air temperature, and a, b, and c are coefficients • Eq. 4 is defined separately for liquid, mixed, and solid precipitationUniversal transfer functions (and determining how universal they are)Reference:Kochendorfer and other SPICE authors.: Analysis of single-Alter-shielded and unshielded measurements of mixed and solid precipitationfrom WMO-SPICE, Hydrol. Earth Syst. Sci., 21, 3525-3542, https://doi.org/10.5194/hess-21-3525-2017, 2017.REFERENCE (DFAR)AccumulationAccumulationat sensor whichisoperational in nationalnetworksWind, TemperatureDerive a transfer function(30 minutes data)OBJECTIVES OF THIS PRESENTATION1) To demonstrate the utility of transfer functions for the adjustment of precipitation measurements for nowcastingactivities in operational networks. 2) Highlight a number of challenges for solid precipitation measurements that were identified and/or characterized in WMO-SPICE and must be considered for nowcastingactivitiesIMPACTS- Radar precipitation validation products- Issue suitable warnings of snowfall- Assimilation of precipitation- Verification of near-real time products- Aviation- Mountain environments (security)- …Examples of aplication of transfer functionsSwitzerlandStation: Marsens, 714 m a.s.l.Low wind snowfall episode < 2m/sUndercatch of 10%Temperature near 0°CSwitzerlandStation:Marsens, 714 m a.s.l.AdjustmentNorwayStation:TromsØ, 100 m a.s.l.Moderate wind speed snowfallepisode≈ 4 m/sUndercatch of 50%Temperature < -2°CNorwayStation:TromsØ, 100 m a.s.l.AdjustmentSpainRoncesvalles 1000 m aslBrigth band located between1000 – 1400 m aslRoncesvalles station, Spain1000 m aslSpainRoncesvalles 1000 m aslLow wind snowfall episode < 2m/sUndercatch of 10%Temperature near 0°COther issues affecting nowcastingactivitiesPartial cappingHaukeliseter, NorwayCanfranc, SpainAccretion of snow around the orifice that reduces the opening area, without capping it completely, thus allowing new snow to be measured, but producing real time erroneous data Col de Porte, FranceComplete cappingSwitzerlandBritish Columbia, CanadaFactors favoring capping: absence of heating, wet snow, calm conditions (no wind) Snow depth sensor, Sodankylӓ, Finland  Delays in melting snow for tipping buckets rain gaugesTime required for melting can result in delays in the reporting of precipitation relative to the referenceconfigurations. These delays increase the potential for missed and false reports overoperational time scales. Heating can also cause evaporative and wetting losses, which reducereported precipitation totals.Non-catchment technologies(more often used in nowcasting) Present Weather Detectors, Disdrometers, etc1) Complex technologies2) A lot of data in short periods of time3) In windy conditions, errors suchas:i) WRONG METAR AUTO CODEReference: SNSensor under test : +SNii) Wrong accumulationintensitiesNEXT WMO-CIMO IntercomparisonDiscussions (and conclusions)• Snowfall measurements remain a great challenge,especially under windy environments  Impact innowcasting activities:Some other examples: Verification of short range NWP models integrating in-situobservations (blending techniques) Another source of error for validation of radar precipitationproducts … (all that you can imagine)Thank you for your attention

Applications of the WMO Solid Precipitation Intercomparison Experiment (WMO-SPICE) results for nowcasting activities

https://repositorio.aemet.es/bitstream/20.500.11765/10584/3/OBN5_Buisan_3ENC2019.pdf

Applications of the WMO Solid Precipitation Intercomparison Experiment (WMO-SPICE) results for nowcasting activities

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