Optimization of Snow-Related Parameters in Noah Land Surface Model (v3.4.1) Using Micro-Genetic Algorithm (v1.7a)

The snowfall prediction is important in winter and early spring because snowy conditions generate enormous economic damages. However, there is a lack of previous studies dealing with snow prediction, especially using land surface models (LSMs). Numerical weather prediction models directly interpret the snowfall events, whereas the LSMs evaluate the snow cover fraction, snow albedo, and snow depth through interaction with atmospheric conditions. When the initially-developed empirical parameters are local or inadequate, we need to optimize the parameter sets for a certain region. In this study, we seek for the optimal parameter values in the snow-related processes – snow cover fraction, snow albedo, and snow depth – of the Noah LSM, for South Korea, using the micro-genetic algorithm and the in-situ surface observations and remotely-sensed satellite data. Snow data from surface observation stations representing five land cover types – deciduous broadleaf forest, mixed forest, woody savanna, cropland, and urban and built-up lands – are used to optimize five snow-related parameters that calculate the snow cover fraction, maximum snow albedo of fresh snow, and the fresh snow density associated with the snow depth. Another parameter, reflecting the dependence of snow cover fraction on the land cover types, is also optimized. Optimization of these six snow-related parameters has led to improvement in the root-mean squared errors by 17.0 %, 8.2 %, and 5.6 % on snow depth, snow cover fraction, and snow albedo, respectively. In terms of the mean bias, the underestimation problems of snow depth and overestimation problems of snow albedo have been alleviated through optimization of parameters calculating the fresh snow by about 45.1 % and 32.6 %, respectively.

Light-Absorbing Impurities on Urumqi Glacier No.1 in Eastern Tien Shan: Concentrations and Implications for Radiative Forcing Estimates During the Ablation Period

Light-absorbing impurities (LAIs) in surface snow and snow pits together with LAIs’ concentrations and their impacts on albedo reduction and sequent radiative forcing (RF) have been investigated in the past. Here, we focused on temporal–spatial distributions of LAIs, especially on the albedo reduction and radiative forcing caused by the LAIs in Urumqi Glacier No.1. Various snow samples, including fresh snow, aged snow, and granular ice were collected between 3,770 and 4,105 m a.s.l of Urumqi Glacier No.1 during the snowmelt season of 2015. For the surface snow samples, BC and OC concentrations were 582 and 1,590 ng g−1, respectively. Mineral dust (MD) concentrations were 110 μg g−1. Due to the different ablation status of the glacier surface, LAIs accumulate at the lower altitude of the glacier. The estimation by the Snow, Ice, and Aerosol Radiative (SNICAR) model indicated that BC and MD could reduce the albedo by 12.8 and 10.3% in fresh snow, aged snow by 23.3 and 5.9%, and granular ice by 22.4 and 26.7%, respectively. The RF of MD was higher than that of BC in fresh snow and granular ice, whereas the RF of BC exceeded MD in aged snow. These findings suggested that BC was the main forcing factor in snow melting and dust was the main forcing factor in accelerating glacier melt.

A Study on the Prediction of Daily Snowmelt Depth using Multiple Linear Regression

In this study, daily snowmelt was predicted using observed meteorological data and multiple regression analysis. Five observation stations (located in Daegwallyeong, Gwangju, Seosan, Mokpo, and Jeonju) were selected to analyze fresh snow depth from 2000 to 2010. The dependent variable used in the multiple regression analysis was daily snowmelt depth, and the independent variables were fresh snow depth, diurnal temperature range, temperature interception, diurnal humidity range, humidity intercept, and solar radiation. Seventy percent of the total observed data was used to develop a multiple regression model and the regression model was verified using the 30% of remaining data. The adjusted R-squared and Root Mean Square Deviation (RMSE) were used to examine the developed regression model. As a result, the adjusted R-squared was higher than 0.769 (except Daegwallyeong); thus the developed model represented well the daily snowmelt depth. Even Jeonju had an adjusted R-squared of 0.869. Also, the RMSE in all of the five stations was lower than 2.5 cm. The lowest value in Seosan was 1.7 cm. From the two types of verification, the developed multiple regression model was judged to be suitable to predict the daily snowmelt depth. However, multicollinearity should be explained, as rapid increases in temperature and sustained high temperature could not be reflected in the model. Therefore, if the limitations were resolved in further research, the model could be used to predict the amount of daily snowmelt depth more reliably.

Using Ultrasound-Treated Washout from Conifer Needles and Fresh Snow Samples in Air Pollution Monitoring

Snow precipitation and snowpack are commonly used to assess the condition of the aerial environment. Another way to monitor air quality is to study trees and shrubs, which are natural barriers for capturing air pollution, including atmospheric particulate matter. The hypothesis of the current study was that using fresh snow precipitation and washout from vegetation for the monitoring of air pollution can produce comparable results. In this study, we compared the results of laser diffraction analysis of suspended particular matter in melted fresh snow and ultrasound-treated washout from conifer needles. The samples were collected at several sites in Primorsky Krai, Russian Federation, and analyzed according to the same scheme. We observed that the content of particulate matter with a smaller aerodynamic diameter in the ultrasound-treated washout from conifer needles was higher than that in the melted fresh snow. The content of PM10 in the ultrasound-treated washout from conifers was increased by 6–27% depending on the site, showing greater efficacy of this method. This method can be used as an alternative to the sampling of snow for the monitoring of ambient air pollution, taking into account several limitations.

Characterizing Surface Albedo of Shallow Fresh Snow and Its Importance for Snow Ablation on the Interior of the Tibetan Plateau

Snow depth on the interior of Tibetan Plateau (TP) in state-of-the-art reanalysis products is almost an order of magnitude higher than observed. This huge bias stems primarily from excessive snowfall, but inappropriate process representation of shallow snow also causes excessive snow depth and snow cover. This study investigated the issue with respect to the parameterization of fresh snow albedo. The characteristics of TP snowfall were investigated using ground truth data. Snow in the interior of the TP is usually only some centimeters in depth. The albedo of fresh snow depends on snow depth, and is frequently less than 0.4. Such low albedo values contrast with the high values (~0.8) used in the existing snow schemes of land surface models. The SNICAR radiative transfer model can reproduce the observations that fresh shallow snow has a low albedo value, based on which a fresh snow albedo scheme was derived in this study. Finally, the impact of the fresh snow albedo on snow ablation was examined at 45 meteorological stations on TP using the land surface model Noah-MP which incorporated the new scheme. Allowing albedo to change with snow depth can produce quite realistic snow depths compared with observations. In contrast, the typically assumed fresh snow albedo of 0.82 leads to too large snow depths in the snow ablation period averaged across 45 stations. The shallow snow transparency impact on snow ablation is therefore particularly important in the TP interior, where snow is rather thin and radiation is strong.

MOSMIX-SNOW – A Model Output Statistics Product for Fresh Snow Forecasts at Mountain Locations

<p>Many Numerical Weather Prediction (NWP) models provide the parameter total snow depth as a Direct Model Output (DMO) surface variable. In mountain regions, however, the orographic flow modification significantly influences precipitation formation and preferential settling, leading to large model biases if DMO is directly compared to fresh snow point observations. Avalanche risk forecasts in turn require calibrated deterministic and probabilistic fresh snow forecasts, as the amount of fresh snow constitutes a crucial driver of avalanche risk.</p><p>In this study, MOSMIX-SNOW, a Model Output Statistics (MOS) product based on multiple linear regression is developed. Ground-based observations and operational forecast data of the two deterministic global NWP models ICON and ECMWF form the basis of the MOS system. MOSMIX-SNOW offers point forecasts for 20 deterministic as well as probabilistic forecast variables like the amount of fresh snow within 24h, the probability of more than 30cm of fresh snow within 24h and some basic variables like 2m temperature and dew point. The unique characteristic of MOSMIX-SNOW is the large number of observation-based, model-based and empirical predictors, which exceeds 200. Furthermore, a long historical data period of 9 years is applied for training of the MOS system. Thus, local orographic effects and large scale flow patterns are implicitly included in the MOS equations by a location and lead time specific choice of predictors. To avoid unrealistic jumps in the forecast, persistence predictors, which represent the forecast value of the previous forecast hour, are included in the MOS system. All forecasts feature a maximum lead time of +48h, have an hourly forecast resolution as well as update cycle and are available for about 15 mountain locations in the Bavarian Alps between 1100m and 2400m above sea level.</p><p>The verification analysis of the winter season 2018/19 shows that MOSMIX-SNOW forecasts offer a significantly higher forecast reliability than the raw ensemble of the regional NWP model COSMO-D2-EPS. The bias of the deterministic forecast parameters is smaller for MOSMIX-SNOW, especially for heavy snowfall events. MOSMIX-SNOW turned out to be a useful tool to support the avalanche risk forecasts on a daily basis during the snowy winter of 2018/19. Furthermore, the deterministic fresh snow forecast of MOSMIX-SNOW and other meteorological parameters like 2m-temperature serve as input for operational snowpack simulations. Measurement related noise and snow drift in the observations, however, are identified as an important source of uncertainty and the application of noise reduction techniques like a Savitzky-Golay filter are expected to have a beneficial impact on the forecast quality. MOSMIX-SNOW will become operational by end of 2020.</p>