Assessment of precipitation type discrimination methods on glacier of Qilian Mountains

Solid precipitation is not only the main supply for glacier mass,but also exerts an important influence on surface albedo and intensifies glacier melting.However,precipitation type observation is very scarce in the high alpine glaciers,which limits the precise simulation of glacier mass balance.This study assessed three discrimination methods of precipitation types including Ding method,Dai method and Froidurot method based on surface albedo observation data on the Laohugou Glacier No.12(LHG Glacier)in western Qilian Mountains.The results showed that Ding method had a best applicability on the LHG Glacier,the other two need to calibrate parameters when they are used in the high elevation glacier region.Then we fitted the relationship between snowfall probability and fresh snow albedo,and put forward a revised formula to simulate fresh snow albedo based on Ding method,which is expected to reduce the uncertainty in glacier mass and energy balance model.Finally,we found a best air temperature threshold of 4℃for discriminating monthly precipitation types.In order to accurately simulate the glacier melt,it is necessary to obtain the threshold temperature appropriately in different glacier region with different elevation and humidity.

Precipitation Type Estimation and Validation in China

The results from three methods aimed at improving precipitation type （e.g., rain, sleet, and snow） estimation are presented and compared in this paper. The methods include the threshold air temperature （AT）, threshold wet bulb temperature （WBT） and Koistinen and Saltikoff （KSS） methods. Dot graphs are plotted to acquire the threshold air temperature or the threshold wet bulb temperature using daily averaged air temperature, wet bulb temperature and precipitation data at 643 stations from 1961 to 1979 （precipitation types are not labeled in the database from 1980 to present） in China. The results indicate that the threshold AT or WBT methods are not able to differentiate rain, sleet and snow in the most regions in China; sleet is difficult to differentiate from other precipitation types based on the two threshold methods. Therefore, one threshold AT and WBT method was used in this study to differentiate rain and snow. Based on Gaussian- Kriging interpolation of threshold air temperature （To） and wet bulb temperature （Tw）, the To and Tw contour lines and contour surfaces are calculated for China. Finally, a comparison between the KSS, AT and WBT methods are provided in which the KSS method is calculated based on air temperature and relative humidity. The results suggest that the KSS method is more appropriate for water phase estimation than are the other methods; the maximum precision for rain and snow is 99% and 94%, respectively. The AT method performs better than the WBT method when the critical air temperature is 2℃.

Determination of Surface Precipitation Type Based on the Data Fusion Approach

Hazardous events related to atmospheric precipitation depend not only on the intensity of surface precipitation,but also on its type.Uncertainty related to determination of the precipitation type(PT)leads to financial losses in many areas of human activity,such as the power industry,agriculture,transportation,and many more.In this study,we use machine learning(ML)algorithms with the data fusion approach to more accurately determine surface PT.Based on surface synoptic observations,ERA5 reanalysis,and radar data,we distinguish between liquid,mixed,and solid precipitation types.The study domain considers the entire area of Poland and a period from 2015 to 2017.The purpose of this work is to address the question:“How can ML techniques applied in observational and NWP data help to improve the recognition of the surface PT?”Despite testing 33 parameters,it was found that a combination of the near-surface air temperature and the depth of the warm layer in the 0-1000 m above ground level(AGL)layer contains most of the signal needed to determine surface PT.The accrued probability of detection for liquid,solid,and mixed PTs according to the developed Random Forest model is 98.0%,98.8%,and 67.3%,respectively.The application of the ML technique and data fusion approach allows to significantly improve the robustness of PT prediction compared to commonly used baseline models and provides promising results for operational forecasters.

An observational study of precipitation types in the Alaskan Arctic

The effects of various precipitation types,such as snow,rain,sleet,hail and freezing rain,on regional hydrology,ecology,snow and ice surfaces differ significantly.Due to limited observations,however,few studies into precipitation types have been conducted in the Arctic.Based on the high-resolution precipitation records from an OTT Parsivel^(2) disdrometer in Utqiaġvik,Alaska,this study analysed variations in precipitation types in the Alaskan Arctic from 15 May to 16 October,2019.Results show that rain and snow were the dominant precipitation types during the measurement period,accounting for 92%of the total precipitation.In addition,freezing rain,sleet,and hail were also observed(2,4 and 11 times,respectively),accounting for the rest part of the total precipitation.The records from a neighbouring U.S.Climate Reference Network(USCRN)station equipped with T-200B rain gauges support the results of disdrometer.Further analysis revealed that Global Precipitation Measurement(GPM)satellite data could well characterise the observed precipitation changes in Utqiaġvik.Combined with satellite data and station observations,the spatiotemporal variations in precipitation were verified in various reanalysis datasets,and the results indicated that ECMWF Reanalysis v5(ERA5)could better describe the observed precipitation time series in Utqiaġvik and the spatial distribution of data in the Alaskan Arctic.Modern-Era Retrospective analysis for Research and Applications,Version 2(MERRA-2)overestimated the amount and frequency of precipitation.Japanese 55-year Reanalysis(JRA-55)could better simulate heavy precipitation events and the spatial distribution of the precipitation phase,but it overestimated summer snowfall.

Analysis of a Beijing Heavy Snowfall Related to an Inverted Trough in November 2009

This paper studies a heavy snowfall in Beijing that took place on 1 November 2009. The date of the snowfall was about one month earlier than the average. The National Centers for Environmental Prediction (NCEP) reanalysis data, conventional data, and Automatic Weather Station (AWS) data are utilized to explore the reasons for the snowfall and the influencing systems. The main conclusions are as follows: (1) It is revealed from the average geopotential height and average temperature fields at 500 hPa that the large scale circulation in November 2009 was favorable to snowfall. The cold-dry air from West Siberia and the warm-moist air from the Bay of Bengal converged in North China. In addition, it was found from the average moisture flux field at 700 hPa that the main water vapor source was in the Bay of Bengal. (2) Not only the "return current", as usually accepted, but also the inverted trough on the current had an important contribution to the snowfall. The inverted trough could produce the obvious upward motion that is an important environmental condition of snowfalls. (3) More attention should be paid to mesoscale systems such as mesolows during the cold season because of their importance, though they do not occur as frequently as in the warm season. It should be pointed out that AWS data are very useful in mesoscale system analysis during both warm and cold seasons.