Impact of weather type variability on winter precipitation, temperature and annual snowpack in the Spanish Pyrenees

The annual frequency of the occurrence of 10 discriminated weather types were summarized using a principal component analysis that revealed 4 different prevailing winter conditions affecting the Spanish Pyrenees. Northeasterly and easterly flows lead to dry and cold winters where snow only accumulates on northern slopes and mainly in the central Pyrenees. North and northwesterly flows favor wet and cold winters and an increase of snow accumulation in the western Pyrenees and on the northern slopes at lower elevations. Cyclonic and westerly flows favor an increase in precipitation and snow accumulation in all the Pyrenees at lower elevations and cold winters. Finally, southerly flows are associated with milder conditions and high precipitation in the central sector of the Pyrenees, where snow only accumulates at high elevations. For most stations, there were no significant trends in precipitation or temperature during the current reference climatic period (1981−2010), which was in agreement with the lack of observed principal component trends during the same period. Focusing on the shorter 1985−2010 period for which snow data were available, snow depth at mid-March demonstrated significant positive trends associated with an increase in westerly, southwesterly and cyclonic weather during this period. The results demonstrate that the changes in precipitation, temperature and snow accumulation are clearly related to changes in circulation patterns, which are the main driver of temporal fluctuations in the considered climatologies.This study was funded by the research project: CGL2014-52599-P "Estudio del manto de nieve en la montaña española, y su respuesta a la variabilidad y cambio climatico" financed by the Spanish Ministry of Economy and Competitiveness

An Improved Trajectory Model to Evaluate the Collection Performance of Snow Gauges

Recent studies have used numerical models to estimate the collection efficiency\ud of solid precipitation gauges when exposed to the wind, in both\ud shielded and unshielded configurations. The models used computational fluid\ud dynamics (CFD) simulations of the airflow pattern generated by the aerodynamic\ud response to the gauge/shield geometry. These are used as initial conditions\ud to perform Lagrangian tracking of solid precipitation particles. Validation\ud of the results against field observations yielded similarities in the overall\ud behavior, but the model output only approximately reproduced the dependence\ud of the experimental collection efficiency on wind speed. This paper\ud presents an improved snowflake trajectory modeling scheme due to the inclusion\ud of a dynamically-determined drag coefficient. The drag coefficient\ud was estimated using the local Reynolds number as derived from CFD simulations\ud within a time-independent Reynolds Averaged Navier-Stokes (RANS)\ud approach. The proposed dynamic model greatly improves the consistency of\ud results with the field observations recently obtained at the Marshall, CO Winter\ud Precipitation Testbed

Assessment of snowfall accumulation underestimation by tipping bucket gauges in the Spanish operational network

Within the framework of the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), the Thies tipping bucket precipitation gauge was assessed against the SPICE reference configuration at the Formigal–Sarrios test site located in the Pyrenees mountain range of Spain. The Thies gauge is the most widely used precipitation gauge by the Spanish Meteorological State Agency (AEMET) for the measurement of all precipitation types including snow. It is therefore critical that its performance is characterized. The first objective of this study is to derive transfer functions based on the relationships between catch ratio and wind speed and temperature. Multiple linear regression was applied to 1 and 3 h accumulation periods, confirming that wind is the most dominant environmental variable affecting the gauge catch efficiency, especially during snowfall events. At wind speeds of 1.5 m s−1 the tipping bucket recorded only 70 % of the reference precipitation. At 3 m s−1, the amount of measured precipitation decreased to 50 % of the reference, was even lower for temperatures colder than −2 °C and decreased to 20 % or less for higher wind speeds

Evaluation of the WMO Solid Precipitation Intercomparison Experiment (SPICE) transfer functions for adjusting the wind bias in solid precipitation measurements

The World Meteorological Organization (WMO) Solid Precipitation Intercomparison Experiment (SPICE) involved extensive field intercomparisons of automated instruments for measuring snow during the 2013/2014 and 2014/2015 winter seasons. A key outcome of SPICE was the development of transfer functions for the wind bias adjustment of solid precipitation measurements using various precipitation gauge and wind shield configurations. Due to the short intercomparison period, the data set was not sufficiently large to develop and evaluate transfer functions using independent precipitation measurements, although on average the adjustments were effective at reducing the bias in unshielded gauges from −33.4 % to 1.1 %. The present analysis uses data collected at eight SPICE sites over the 2015/2016 and 2016/2017 winter periods, comparing 30 min adjusted and unadjusted measurements from Geonor T-200B3 and OTT Pluvio2 precipitation gauges in different shield configurations to the WMO Double Fence Automated Reference (DFAR) for the evaluation of the transfer function. Performance is assessed in terms of relative total catch (RTC), root mean square error (RMSE), Pearson correlation (r), and percentage of events (PEs) within 0.1 mm of the DFAR. Metrics are reported for combined precipitation types and for snow only. The evaluation shows that the performance varies substantially by site. Adjusted RTC varies from 54 % to 123 %, RMSE from 0.07 to 0.38 mm, r from 0.28 to 0.94, and PEs from 37 % to 84 %, depending on precipitation phase, site, and gauge configuration (gauge and wind screen type). Generally, windier sites, such as Haukeliseter (Norway) and Bratt's Lake (Canada), exhibit a net under-adjustment (RTC of 54 % to 83 %), while the less windy sites, such as Sodankylä (Finland) and Caribou Creek (Canada), exhibit a net over-adjustment (RTC of 102 % to 123 %). Although the application of transfer functions is necessary to mitigate wind bias in solid precipitation measurements, especially at windy sites and for unshielded gauges, the variability in the performance metrics among sites suggests that the functions be applied with caution

Errors and adjustments for WMO-SPICE tipping-bucket precipitation gauges

Presentación realizada en: 19th Symposium on Meteorological Observation and Instrumentation celebrado del 7 al 11 de enero de 2018 en Austin, Texas

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

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

The potential for uncertainty in Numerical Weather Prediction model verification when using solidprecipitation observations

Precipitation forecasts made by Numerical Weather Prediction (NWP) models are typically verified using precipitation gauge observations that are often prone to the wind‐induced undercatch of solid precipitation. Therefore, apparent model biases in solid precipitation forecasts may be due in part to the measurements and not the model. To reduce solid precipitation measurement biases, adjustments in the form of transfer functions were derived within the framework of the World Meteorological Organization Solid Precipitation Inter‐Comparison Experiment (WMO‐SPICE). These transfer functions were applied to single‐Alter shielded gauge measurements at selected SPICE sites during two winter seasons (2015–2016 and 2016–2017). Along with measurements from the WMO automated field reference configuration at each of these SPICE sites, the adjusted and unadjusted gauge observations were used to analyze the bias in a Global NWP model precipitation forecast. The verification of NWP winter precipitation using operational gauges may be subject to verification uncertainty, the magnitude and sign of which varies with the gauge‐shield configuration and the relation between model and site‐specific local climatologies. The application of a transfer function to alter‐shielded gauge measurements increases the amount of solid precipitation reported by the gauge and therefore reduces the NWP precipitation bias at sites where the model tends to overestimate precipitation, and increases the bias at sites where the model underestimates the precipitation. This complicates model verification when only operational (non‐reference) gauge observations are available. Modelers, forecasters, and climatologists must consider this when comparing modeled and observed precipitation

A preliminary assessment of the biases between forecasted by ECMWF Numerical Weather Prediction model precipitation and the adjusted observed snowfall precipitation in different SPICE sites

Comunicación presentada en: TECO-2018 (Technical Conference on Meteorological and Environmental Instruments and Methods of Observation) celebrada en Amsterdam, del 8 al 11 de octubre de 2018

Errors, Biases, and Corrections for Weighing Gauge Precipitation Measurements from the WMO Solid Precipitation Intercomparison Experiment

Comunicación presentada en: TECO-2016 (Technical Conference on Meteorological and Environmental Instruments and Methods of Observation) celebrada en Madrid, del 27 al 30 de septiembre de 2016.Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant biases and errors. Solid precipitation is particularly difficult to measure accurately, and biases between winter-time precipitation measurements from different measurement networks or different regions can exceed 100%. Using precipitation gauge results from the WMO Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, and wind are quantified. The methods used to calculate gauge catch efficiency and correct known biases are described briefly. Transfer functions describing catch efficiency as a function of air temperature and wind speed are also presented. In addition, the biases and errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites are discussed