Incorporation of Parameter Uncertainty into Spatial Interpolation Using Bayesian Trans-Gaussian Kriging

Quantitative precipitation estimation （QPE） plays an important role in meteorological and hydrological applications.Ground-based telemetered rain gauges are widely used to collect precipitation measurements.Spatial interpolation methods are commonly employed to estimate precipitation fields covering non-observed locations.Kriging is a simple and popular geostatistical interpolation method,but it has two known problems：uncertainty underestimation and violation of assumptions.This paper tackles these problems and seeks an optimal spatial interpolation for QPE in order to enhance spatial interpolation through appropriately assessing prediction uncertainty and fulfilling the required assumptions.To this end,several methods are tested：transformation,detrending,multiple spatial correlation functions,and Bayesian kriging.In particular,we focus on a short-term and time-specific rather than a long-term and event-specific analysis.This paper analyzes a stratiform rain event with an embedded convection linked to the passing monsoon front on the 23 August 2012.Data from a total of 100 automatic weather stations are used,and the rainfall intensities are calculated from the difference of 15 minute accumulated rainfall observed every 1 minute.The one-hour average rainfall intensity is then calculated to minimize the measurement random error.Cross-validation is carried out for evaluating the interpolation methods at regional and local levels.As a result,transformation is found to play an important role in improving spatial interpolation and uncertainty assessment,and Bayesian methods generally outperform traditional ones in terms of the criteria.

Classification of Precipitation Types Using Fall Velocity–Diameter Relationships from 2D-Video Distrometer Measurements

Fall velocity–diameter relationships for four different snowflake types（dendrite,plate,needle,and graupel） were investigated in northeastern South Korea,and a new algorithm for classifying hydrometeors is proposed for distrometric measurements based on the new relationships.Falling ice crystals（approximately 40 000 particles） were measured with a two-dimensional video disdrometer（2DVD） during a winter experiment from 15 January to 9 April 2010.The fall velocity–diameter relationships were derived for the four types of snowflakes based on manual classification by experts using snow photos and 2DVD measurements：the coefficients（exponents） for different snowflake types were 0.82（0.24） for dendrite,0.74（0.35） for plate,1.03（0.71） for needle,and 1.30（0.94） for graupel,respectively.These new relationships established in the present study（PS） were compared with those from two previous studies.Hydrometeor types were classified with the derived fall velocity–diameter relationships,and the classification algorithm was evaluated using 3 × 3 contingency tables for one rain–snow transition event and three snowfall events.The algorithm showed good performance for the transition event：the critical success indices（CSIs） were 0.89,0.61 and 0.71 for snow,wet-snow and rain,respectively.For snow events,the algorithm performance for dendrite and plate（CSIs = 1.0 and 1.0,respectively） was better than for needle and graupel（CSIs = 0.67 and 0.50,respectively）.