Temporal Statistical Downscaling of Precipitation and Temperature Forecasts Using a Stochastic Weather Generator

Statistical downscaling is based on the fact that the large-scale climatic state and regional/local physiographic features control the regional climate. In the present paper, a stochastic weather generator is applied to seasonal precipitation and temperature forecasts produced by the International Research Institute for Climate and Society （IRI）. In conjunction with the GLM （generalized linear modeling） weather generator, a resampling scheme is used to translate the uncertainty in the seasonal forecasts （the IRI format only specifies probabilities for three categories： below normal, near normal, and above normal） into the corresponding uncertainty for the daily weather statistics. The method is able to generate potentially useful shifts in the probability distributions of seasonally aggregated precipitation and minimum and maximum temperature, as well as more meaningful daily weather statistics for crop yields, such as the number of dry days and the amount of precipitation on wet days. The approach is extended to the case of climate change scenarios, treating a hypothetical return to a previously observed drier regime in the Pampas.

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）.

Identification and Removal of Non-meteorological Echoes in Dual-polarization Radar Data Based on a Fuzzy Logic Algorithm

A major issue in radar quantitative precipitation estimation is the contamination of radar echoes by non-meteorological targets such as ground clutter,chaff,clear air echoes etc.In this study,a fuzzy logic algorithm for the identification of non-meteorological echoes is developed using optimized membership functions and weights for the dual-polarization radar located at Mount Sobaek.For selected precipitation and non-meteorological events,the characteristics of the precipitation and non-meteorological echo are derived by the probability density functions of five fuzzy parameters as functions of reflectivity values.The membership functions and weights are then determined by these density functions.Finally,the nonmeteorological echoes are identified by combining the membership functions and weights.The performance is qualitatively evaluated by long-term rain accumulation.The detection accuracy of the fuzzy logic algorithm is calculated using the probability of detection（POD）,false alarm rate（FAR）,and clutter–signal ratio（CSR）.In addition,the issues in using filtered dual-polarization data are alleviated.

A Case Study of Mesoscale Convective Band(MCB) Development and Evolution along a Quasi-stationary Front

This paper presents a case study of mesoscale convective band （MCB） development along a quasi-stationary front over the Seout metropolitan area.The MCB,which initiated on 1500 UTC 20 September 2010 and ended on 1400 UTC 21 September 2010,produced a total precipitation amount of 259.5 mm.The MCB development occurred during a period of tropopause folding in the upper level and moisture advection with a low-level jet.The analyses show that the evolution of the MCB can be classified into five periods：（1） the cell-forming period,when convection initiated; （2） the frontogenetic period,when the stationary front formed over the Korean peninsula; （3） the quasi-stationary period,when the convective band remained over Seoul for 3 h; （4） the mature period,when the cloud cover was largest and the precipitation rate was greater than 90 mm h-1; and （5） the dissipating period,when the MCB diminished and disappeared.The synoptic,thermodynamic,and dynamic analyses show that the MCB maintained its longevity by a tilted updraft,which headed towards a positive PV anomaly.Precipitation was concentrated under this area,where a tilted ascending southwesterly converged with a tilted ascending northeasterly,at the axis of cyclonic rotation.The formation of the convective cell was attributed in part by tropopause folding,which enhanced the cyclonic vorticity at the surface,and by the low-level convergence of warm moist air and upperlevel divergence.The southwesterly flow ascended in a region with high moisture content and strong relative vorticity that maintained the development of an MCB along the quasi-stationary front.