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A new method to automatically classify solid hydrometeors based on Multi-Angle Snowflake Camera (MASC) images is presented. For each individual image, the method relies on the calculation of a set of geometric and texture-based descriptors to simultaneously identify the hydrometeor type (among six predefined classes), estimate the degree of riming and detect melting snow. The classification tasks are achieved by means of a regularized multinomial logistic regression (MLR) model trained over more than 3000 MASC images manually labeled by visual inspection. In a second step, the probabilistic information provided by the MLR is weighed on the three stereoscopic views of the MASC in order to assign a unique label to each hydrometeor. The accuracy and robustness of the proposed algorithm is evaluated on data collected in the Swiss Alps and in Antarctica. The algorithm achieves high performance, with a hydrometeor-type classification accuracy and Heidke skill score of 95 % and 0.93, respectively. The degree of riming is evaluated by introducing a riming index ranging between zero (no riming) and one (graupel) and characterized by a probable error of 5.5 %. A validation study is conducted through a comparison with an existing classification method based on two-dimensional video disdrometer (2DVD) data and shows that the two methods are consistent.

Accurate snowfall measurements are necessary for meteorology, hydrology, and climate research. Typical uses include creating and calibrating gridded precipitation products, the verification of model simulations, driving hydrologic models, input into aircraft deicing processes, and estimating streamflow runoff in the spring. These applications are significantly impacted by errors in solid precipitation measurements. The recent WMO Solid Precipitation Intercomparison Experiment (SPICE) attempted to characterize and reduce some of the measurement uncertainties through an international effort involving 15 countries utilizing over 20 types and models of precipitation gauges from various manufacturers. Key results from WMO-SPICE are presented herein. Recent work and future research opportunities that build on the results of WMO-SPICE are also highlighted.

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.

Heated tipping-bucket (TB) gauges are used broadly in national weather monitoring networks, but their performance for the measurement of solid precipitation has not been well characterized. Manufacturer-provided TB gauges were evaluated at five test sites during the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), with most gauge types tested at more than one site. The test results were used to develop and evaluate adjustments for the undercatch of solid precipitation by heated TB gauges. New methods were also developed to address challenges specific to measurements from heated TB gauges. Tipping-bucket transfer functions were created specifically to minimize the sum of errors over the course of the adjusted multiseasonal accumulation. This was based on the hypothesis that the best transfer function produces the most accurate long-term precipitation records, rather than accurate catch efficiency measurements or accurate daily or hourly precipitation measurements. Using this new approach, an adjustment function derived from multiple gauges was developed that performed better than traditional gauge-specific and multigauge catch efficiency derived adjustments. Because this new multigauge adjustment was developed using six different types of gauges tested at five different sites, it may be applicable to solid precipitation measurements from unshielded heated TB gauges that were not evaluated in WMO-SPICE. In addition, this new method of optimizing transfer functions may be useful for other types of precipitation gauges, as it has many practical advantages over the traditional catch efficiency methods used to derive undercatch adjustments.

Although precipitation has been measured for many centuries, precipitation measurements are still beset with significant inaccuracies. Solid precipitation is particularly difficult to measure accurately, and wintertime precipitation measurement biases between different observing networks or different regions can exceed 100 %. Using precipitation gauge results from the World Meteorological Organization Solid Precipitation Intercomparison Experiment (WMO-SPICE), errors in precipitation measurement caused by gauge uncertainty, spatial variability in precipitation, hydrometeor type, crystal habit, and wind were quantified. The methods used to calculate gauge catch efficiency and correct known biases are described. Adjustments, in the form of transfer functions that describe catch efficiency as a function of air temperature and wind speed, were derived using measurements from eight separate WMO-SPICE sites for both unshielded and single-Alter-shielded precipitation-weighing gauges. For the unshielded gauges, the average undercatch for all eight sites was 0.50 mm h−1 (34 %), and for the single-Alter-shielded gauges it was 0.35 mm h−1 (24 %). After adjustment, the mean bias for both the unshielded and single-Alter measurements was within 0.03 mm h−1 (2 %) of zero. The use of multiple sites to derive such adjustments makes these results unique and more broadly applicable to other sites with various climatic conditions. In addition, errors associated with the use of a single transfer function to correct gauge undercatch at multiple sites were estimated.

Weighing precipitation gauges are used widely for the measurement of all forms of precipitation, and are typically more accurate than tipping-bucket precipitation gauges. This is especially true for the measurement of solid precipitation; however, weighing precipitation gauge measurements must still be adjusted for undercatch in snowy, windy conditions. In WMO-SPICE (World Meteorological Organization Solid Precipitation InterComparison Experiment), different types of weighing precipitation gauges and shields were compared, and adjustments were determined for the undercatch of solid precipitation caused by wind. For the various combinations of gauges and shields, adjustments using both new and previously existing transfer functions were evaluated. For most of the gauge and shield combinations, previously derived transfer functions were found to perform as well as those more recently derived. This indicates that wind shield type (or lack thereof) is more important in determining the magnitude of wind-induced undercatch than the type of weighing precipitation gauge. It also demonstrates the potential for widespread use of the previously developed transfer functions. Another overarching result was that, in general, the more effective shields, which were associated with smaller unadjusted errors, also produced more accurate measurements after adjustment. This indicates that although transfer functions can effectively reduce measurement biases, effective wind shielding is still required for the most accurate measurement of solid precipitation.

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. The verification of NWP winter precipitation using operational gauge is affected by wind‐induced undercatch. In the absence of a reference (DFAR), the application of a transfer function to (SA) gauge measurements partially corrects the undercatch, but introduces an additional source of uncertainty on verification results. The adjustment increases the amount of observed solid precipitation, and therefore reduces the NWP bias at sites where the model tends to overestimate precipitation, and increases the bias at sites where the model tends to underestimate precipitation. The issue is illustrated at SPICE sites in various climate regimes, which have the highest quality solid precipitation measurements that are available.

A metrological field intercomparison of thermometer radiation shields in the Arctic was conducted with the aim of obtaining information to increase the worldwide comparability of air temperature measurements. Air temperature measurements are performed by different combinations of thermometers and shields. The response of each system (thermometer + shield) to local meteorological conditions depends on the system itself, limiting the comparability of air temperature measurements. Ten different models of radiation shields were included in the intercomparison, involving two campaigns: (1) the laboratory campaign, where all the instrumentation was calibrated just before and just after the field campaign, and (2) the field campaign that lasted 14 months where 41 thermometers were sampled every 2 min. All the delivered data were subjected to quality control to assure the robustness of the conclusions. A reference shield was defined, and the other shields were compared to the reference one for the conditions where maximum divergences were expected, solar irradiance being the highest impact factor. A maximum divergence value of 1.29 °C was derived for one of the shields and, for all the shields, the difference from the reference one decreases with wind speed. Finally, the uncertainties associated with the shields intercomparison were calculated.

The Basel UrBan Boundary Layer Experiment (BUBBLE) was a year-long experimental effort to investigate in detail the boundary layer structure in the City of Basel, Switzerland. At several sites over different surface types (urban, sub-urban and rural reference) towers up to at least twice the main obstacle height provided turbulence observations at many levels. In addition, a Wind Profiler and a Lidar near the city center were profiling the entire lower troposphere. During an intensive observation period (IOP) of one month duration, several sub-studies on street canyon energetics and satellite ground truth, as well as on urban turbulence and profiling (sodar, RASS, tethered balloon) were performed. Also tracer experiments with near-roof-level release and sampling were performed. In parallel to the experimental activities within BUBBLE, a meso-scale numerical atmospheric model, which contains a surface exchange parameterization, especially designed for urban areas was evaluated and further developed. Finally, the area of the full-scale tracer experiment which also contains several sites of other special projects during the IOP (street canyon energetics, satellite ground truth) is modeled using a very detailed physical scale-model in a wind tunnel. In the present paper details of all these activities are presented together with first results. [PUBLICATION ABSTRACT]