Ratio Of Snow Research Articles

Integrating physics-based WRF atmospheric variables and machine learning algorithms to predict snowfall accumulation in Northeast United States

Accurate snowfall prediction is crucial for enhancing preparedness and resilience in the Northeast United States during winter weather events. This study introduces a novel approach that integrates Machine Learning (ML) models with atmospheric variables from the Weather Research and Forecasting (WRF) model to improve snowfall forecasts in the region. The significance lies in bridging the gap between physics-based Numerical Weather Prediction (NWP) models and the versatility of ML models, offering a promising advancement in winter storm predictions. Atmospheric variables related to 32 winter storms simulated by WRF, were used to feed Random Forest (RF) and Extreme Gradient Boosting (XGBoost) algorithms to predict 24 h accumulations of snowfall using the National Snowfall Analysis (NSA) product as reference. The comprehensive results revealed that the integrated approach provided more accurate snowfall prediction than the WRF and Air Force Weather Agency (AFWA) diagnostic, but faced challenges in the precision estimated by under-dispersion of the results. The WRF/XGBoost reduced the RMSE by 10.34 %, whereas WRF/RF reduced RMSE by 9.72 %, compared to WRF/AFWA. Similarly, WRF/XGBoost and WRF/RF increased the correlation coefficient by 12 % and 11 %. The most important variables in both ML algorithms were liquid water equivalent precipitation (LWE), snow ratio, wet-bulb temperature, temperature, and humidity at different heights, pressure tendency and wind speed, validating their ability to discern crucial factors influencing snowfall. Specific cases highlight the integrated approach’s effectiveness in addressing challenges faced by traditional diagnostics, including LWE overestimation and warm temperature bias. However, limitations emerge in capturing storms characterized by anomalous atmospheric conditions and high snowfall values, most likely attributed to underrepresentation in the training data. By highlighting strengths and acknowledging limitations, this integrated approach holds the potential for improved snowfall prediction for future storms in the region compared to the traditional approach.

Dust effects on mixed-phase clouds and precipitation during a super dust storm over northern China

Dust aerosols have been generally regarded as efficient ice nuclei (IN), but their influences on mixed-phase clouds and precipitation are poorly quantified. In this study, combining satellite observations, reanalysis data and the Weather Research and Forecasting (WRF) model, we investigate the impacts of dust aerosols on mixed-phase clouds and precipitation during a super dust storm over northern China. The Suomi National Polar-Orbiting Partnership (S-NPP) satellite observed a super dust storm from March 15–19, 2021 in northern China, followed by heavy precipitation between March 17 and 19. The results showed that the occurrence of super dust storms may play an important role in cloud formation and precipitation processes. Furthermore, numerical modeling revealed that dust aerosols can increase the ice crystal number concentration (QNi) and decrease the cloud droplet number concentration (QNc) in mixed-phase clouds through the Wegener–Bergeron–Findeisen (WBF) process, in which the maximum increase in QNi can reach 21%. Simultaneously, the mass mixing ratios of rain, graupel and snow increased due to dust aerosols. Consequently, precipitation could increase by up to 9.8% in northern China. This study could provide evidence for understanding the mechanisms of dust effects on mixed-phase clouds over northern China.

Spatial and temporal trends in δ66Zn and 206Pb/207Pb isotope ratios along a rural transect downwind from the Upper Silesian industrial area: Role of legacy vs. present-day pollution

Transect sampling is an under-exploited tool in isotope studies of atmospheric pollution. Few studies have combined Zn and Pb isotope ratios to investigate whether atmospheric pollution at a receptor site is dominated by a different anthropogenic source of each of these toxic elements. It has been also unclear whether pollution abatement strategies in Central Europe have already resulted in regionally well-mixed background isotope signature of atmospheric Zn and Pb. Zinc and lead isotope ratios were determined in snow collected along a rural transect downwind from the Upper Silesian industrial area (southern Poland). Spatial and temporal gradients in δ66Zn and 206Pb/207Pb ratios at four sites were compared with those of ore and coal collected in eight Czech and Polish mining districts situated at distances of up to 500 km. Snow pollution was extremely high 8 km from Olkusz in 2011 (1670 μg Zn L−1; 240 μg Pb L−1), sharply decreased between 2011 and 2018, and remained low in 2019–2021. Snow pollution was lower at sites situated 28–68 km from Olkusz. Across study sites, mean δ66Zn and 206Pb/207Pb ratios of snow were −0.13‰ and 1.155, respectively. With an increasing distance from Olkusz, the δ66Zn values first increased and then decreased, while the 206Pb/207Pb ratios first decreased and then increased. The δ66Zn values in snow plotted closer to those of Upper Silesian ores (−0.20‰) than to the δ66Zn values of Upper Silesian stone coal (0.52‰), showing predominance of smelter-derived over power-plant derived Zn pollution. The 206Pb/207Pb ratios of Upper Silesian coal (1.171) and Upper Silesian ores (1.180) were higher compared to those of snow. A206Pb/207Pb vs.208Pb/207Pb plot identified legacy pollution from leaded gasoline as the low-radiogenic mixing end-member. Across the transect sites, only the last sampling campaign exhibited a high degree of isotope homogenization for both Zn and Pb.

A stochastic approach to simulate realistic continuous snow depth time series

We propose a stochastic approach for simulation of realistic continuous snow depth time series using a snow depth estimation model and a stochastic weather generation model. The snow depth estimation model consists of three steps: defining the precipitation type, estimating the snow ratio, and estimating the decreased snow depth. First, air temperature and relative humidity are used as indices to determine the type of precipitation when precipitation occurs. When the type is identified as snow, the snow ratio is estimated, converting the precipitation depth into depth of fresh snow. Here, the snow ratio is estimated through sigmoidal relationship with the air temperature. Lastly, the amount of decreased snow depth was estimated using a novel temperature index snowmelt equation that can consider depth-dependent melting of snowpack. The model was applied to four meteorological stations of Korea and yielded high Nash Sutcliffe efficiency values which ranged between 0.745 and 0.875 for calibration, and ranged between 0.432 and 0.753 for validation. This calibrated snow depth estimation model was then applied to synthetic weather data (precipitation, temperature, and relative humidity) that was generated by stochastic weather generation model to simulate continuous snow depth time series. The simulated snow depth data accurately reproduced standard and extreme value statistics of the observed data, the latter of which were consistent with the current Korean Building Code. This model can be widely used not only in the field of snow related risk analysis, but also in data simulation for ungauged areas and future trend study using the climate projection.

Attribution Analysis of Runoff Change in the Upper Reaches of the Kaidu River Basin Based on a Modified Budyko Framework

The Kaidu River plays an important role in the water development and utilization in the Tarim River basin in northwestern China. In this study, we used a modified Budyko framework, which considered the snowmelt to analyze and attribute the runoff change in the upper Kaidu River basin based on the observations during the period of 1960–2010. The time series was divided into two periods: 1960–1995 and 1996–2010. The contribution rate of runoff change between these two periods and the elasticity coefficient of runoff were estimated to quantify the effect of climatic variables and landscape changes on runoff alteration. The results show that the increase in precipitation was the major cause of increase in runoff, whose contribution accounted for 81.42%. The contribution rate of the landscape change was lower than that of the precipitation change, accounting for 9.07%. The elasticity coefficient of runoff to precipitation was 1.24, and the elasticity coefficient of runoff to the landscape was −0.74. Compared with the original Budyko framework, without considering the snowmelt, the contribution rates of precipitation and potential evaporation to runoff change would decrease after considering the snowmelt in the modified Budyko framework, while the contribution rate of landscape would increase. The increased snow ratio would cause more fluctuations in the runoff. This study provides a valuable reference for the water resources management in the upper Kaidu River basin and deepens our understanding of the response of runoff to climate change in snowmelt-affected regions.

The Role of the Snow Ratio in Mass Balance Change under a Warming Climate for the Dongkemadi Glacier, Tibetan Plateau

Abstract Understanding the effects of the snow ratio on glacier mass balance under variable climatic conditions is crucial for predicting how glaciers will respond to climate change, and for forecasting water supplies to surrounding lowland areas. Due to recent climate change, the historical annual snow ratio of the Dongkemadi (DKMD) Glacier showed a significant increasing trend (0.0538% a−1, p < 0.05), and an abrupt upward change in 1977 due to decreasing precipitation concentration. Snow ratios with fixed precipitation concentration and nonwarming climate scenarios were calculated to isolate the impact of the snow ratio on glacier mass balance. Under nonwarming conditions, the snow ratio showed little variability, ranging from 88.4% to 99.9%. Glacier modeling results comparing five snow ratio scenarios from 1961 to 2009 showed three main features as follows. (i) Glacier mass balance was low and more sensitive to a warming climate for lower snow ratio scenarios. (ii) The difference in mass balance between the scenarios fluctuated, but generally increased with time. Spatially, the ablation area change was larger (0.4 km2), and the equilibrium line altitude was higher (5.9 m) in scenarios with lower snow ratios. (iii) The change in net shortwave radiation was the main reason for changes in glacial melt, and the albedo played a key role in controlling the difference of glacier energy balance between snow ratio scenarios. Rain increment only accounted for about 20%–33% of meltwater increment. Overall, this study provides valuable information to evaluate how snow ratios impact the mass balance of glaciers with ongoing climate change.

The role of temperature and the NAO index in the changing snow-related variables in European regions in the period 1900-2010

Snow-related variables are analysed in the present paper in the period 1901‒2010 on the basis of the ERA-20C dataset. Relationships between different snow characteristics, temperature and the NAO index are investigated on monthly, yearly and decadal scales for eight regions within Europe representing different climatic types (i.e. oceanic, continental, polar) to analyse the differences and similarities between them depending on the climatic conditions. According to our results, the ratio of snow (i.e. snowfall compared to total precipitation) can reach 1 in winter in the colder, northern regions, whereas it is about 0.6 in the continental areas of Central Europe, even in the coldest months. During a strong positive phase of NAO more snow falls in the northern regions of Europe due to the large-scale circulation characteristics. When a negative NAO phase occurs, the temperature and snowfall anomalies are the opposite in northern Europe. The highest temperature values generally occurred after 2000, and the snowfall amount was smaller in the first decades of the 21st century compared to the previous decades. The relationship between temperature and snowfall is the strongest in autumn in the colder regions; in spring in the continental areas and in winter in the oceanic climate.

Parsimonious Models of Precipitation Phase Derived from Random Forest Knowledge: Intercomparing Logistic Models, Neural Networks, and Random Forest Models

The precipitation phase (PP) affects the hydrologic cycle which in turn affects the climate system. A lower ratio of snow to rain due to climate change affects timing and duration of the stream flow. Thus, more knowledge about the PP occurrence and drivers is necessary and especially important in cities dependent on water coming from glaciers, such as Quito, the capital of Ecuador (2.5 million inhabitants), depending in part on the Antisana glacier. The logistic models (LM) of PP rely only on air temperature and relative humidity to predict PP. However, the processes related to PP are far more complex. The aims of this study were threefold: (i) to compare the performance of random forest (RF) and artificial neural networks (ANN) to derive PP in relation to LM; (ii) to identify the main drivers of PP occurrence using RF; and (iii) to develop LM using meteorological drivers derived from RF. The results show that RF and ANN outperformed LM in predicting PP in 8 out of 10 metrics. RF indicated that temperature, dew point temperature, and specific humidity are more important than wind or radiation for PP occurrence. With these predictors, parsimonious and efficient models were developed showing that data mining may help in understanding complex processes and complements expert knowledge.

AUTOMATED CALCULATION OF THE QUALITY CHARACTERISTICS OF THE LENDERKA RIVER FLOW THROUGH THE GAUGING STATION AT LENDERKA VILLAGE

The quality characteristics of the transboundary flow of the Lenderka River across the border to Finland were calculated using an automated software complex (APC) with the input of data obtained during regime observations of the North-Western Administration for Hydrometeorology and Environmental Monitoring for the period from 2003 to 2012. The main characteristics of the river flow quality were, firstly, volume shares of the water flow contaminated with individual chemical components and, secondly, volume shares of the water flow differing in total pollution (Vpoll%). In the first case, the average values of Vpoll% in the series Fetotal, pH, Cu2+, O2 %, BOD5, and oil products decreased over the study period from 100 to 2.4 %. In the second case, the calculations showed that the river flow averaged over the entire period in accordance with the Guideline 52.24.643-2002 consists of 73.91 % “relatively clean” 1st class water and 26.09 % “slightly polluted”2nd class water. Factor analysis showed that factor 1, which accounted for more than 48 % of the total variance, was of primary significance for the interannual variation of the water flow quality. It depends on interannual fluctuations in the water discharge. Factor 2 (contributing more than 21 %) depends on the water temperature in the cold period, and factor 3 (explaining more than 14 %) depends on the water temperature in the warm period. The mechanism of the water temperature influence on the flow quality is not yet clear; it is possible that its values depend on the ratio of snow and rain input to the river.

Measurement of Snow Physical Properties and Stable Isotope Variations in the Canadian Sub-Arctic and Arctic Snowpack

ABSTRACT In northern Canada, the annual peak in river discharge is dominated by the seasonal input of snowmelt. As such, climatic changes that alter snowmelt properties and timing will have cascading impacts on the hydrological system as the Arctic warms. Geochemical tracers provide a tool to characterize the various processes governing the seasonal evolution of the snowpack; however, a lack of snow observations from a variety of Arctic landscapes limits the broad applicability of such tracers and further impedes our understanding of the various processes governing snowpack evolution and its ultimate contribution to the spring discharge peak. This study aims to gain a better understanding of the spatial distribution and the temporal evolution of the natural stable isotope signatures of snow from two distinct ecoregions: open tundra and taiga. More specifically, we describe the geophysical and stable isotope properties of the snow cover at Wekweètì (Northwest Territories), a high sub-Arctic taiga site, and within the Greiner Lake Watershed, near Cambridge Bay (Nunavut), an open Arctic tundra site. Results illustrate a link between snowpack formation and stable isotope distributions at both study sites. Stable oxygen isotope ratios of snow (δ 18O-H2O) show a wide range from −41‰ to −17‰ across all snow depth classes; however, heavy isotope enrichment is clearly visible in the bottom snow layers at both sites. Vapour flux from the ground under a strong temperature gradient is considered to be the main driver for this enrichment due to kinetic metamorphism, which is more prominent at the open tundra site. The stable isotope signatures of the bottom hoar layers during winter were found to be similar to river water values sampled during spring and summer, highlighting the need for more in-depth hydrological cycle assessment.

Present-day radiative effect from radiation-absorbing aerosols in snow

Abstract. Black carbon (BC), brown carbon (BrC), and soil dust are the most important radiation-absorbing aerosols (RAAs). When RAAs are deposited on the snowpack, they lower the snow albedo, causing an increase in the solar radiation absorption. The climatic impact associated with the snow darkening induced by RAAs is highly uncertain. The Intergovernmental Panel on Climate Change (IPCC) Special Report on the Ocean and Cryosphere in a Changing Climate (SROCC) attributes low and medium confidence to radiative forcing (RF) from BrC and dust in snow, respectively. Therefore, the contribution of anthropogenic sources and carbonaceous aerosols to RAA RF in snow is not clear. Moreover, the snow albedo perturbation induced by a single RAA species depends on the presence of other light-absorbing impurities contained in the snowpack. In this work, we calculated the present-day RF of RAAs in snow starting from the deposition fields from a 5-year simulation with the GEOS-Chem global chemistry and transport model. RF was estimated taking into account the presence of BC, BrC, and mineral soil dust in snow, simultaneously. Modeled BC and black carbon equivalent (BCE) mixing ratios in snow and the fraction of light absorption due to non-BC compounds (fnon-BC) were compared with worldwide observations. We showed that BC, BCE, and fnon-BC, obtained from deposition and precipitation fluxes, reproduce the regional variability and order of magnitude of the observations. Global-average all-sky total RAA-, BC-, BrC-, and dust-snow RF were 0.068, 0.033, 0.0066, and 0.012 W m−2, respectively. At a global scale, non-BC compounds accounted for 40 % of RAA-snow RF, while anthropogenic RAAs contributed to the forcing for 56 %. With regard to non-BC compounds, the largest impact of BrC has been found during summer in the Arctic (+0.13 W m−2). In the middle latitudes of Asia, the forcing from dust in spring accounted for 50 % (+0.24 W m−2) of the total RAA RF. Uncertainties in absorbing optical properties, RAA mixing ratio in snow, snow grain dimension, and snow cover fraction resulted in an overall uncertainty of −50 %/+61 %, −57 %/+183 %, −63 %/+112 %, and −49 %/+77 % in BC-, BrC-, dust-, and total RAA-snow RF, respectively. Uncertainty upper bounds of BrC and dust were about 2 and 3 times larger than the upper bounds associated with BC. Higher BrC and dust uncertainties were mainly due to the presence of multiple absorbing impurities in the snow. Our results highlight that an improvement of the representation of RAAs in snow is desirable, given the potential high efficacy of this forcing.

Spatial and temporal variations of fractionation of stable isotopes in East-Antarctic snow

AbstractStable isotope ratios (δ18O and δD) in Antarctic snow and ice are basic proxy indices of climate in ice core studies. The relation between the ratios has important indicative significance for moisture sources. In general, the fractionation characteristics of the two isotopes vary with different meteorological and topographical conditions. This paper presents the spatial and temporal distribution of meteoric water line (MWL) slopes along a traverse from the Zhongshan Station (ZSS) to Dome A in East Antarctica. It is found that the slopes decrease with the increasing distance inland from the coast and the lowest slope occurred at Dome A, where the long-range transported moisture dominates and clear sky snowing have an influence. The slopes in different layers of the snowpack showed a decreasing trend with depth and this is attributed to the fractionation during the interstitial sublimation and re-condensation processes of the water vapor. Frost flower development on the interior plateau surface can greatly alter the depth evolution of the MWL slope. The coastal snow pits also go through the post-depositional smoothing effect, but their influences are not so significant as the inland regions.

Assessment of Airborne Lead Provenance in Northern Ontario, Canada, Using Isotopic Ratios in Snow and Cladonia rangiferina Lichens

Fresh snow and lichens (Cladonia rangiferina) were collected along two ~300-km transects in a regional survey around two smelter areas in Northern Ontario, i.e., the Sudbury and Timmins areas, in 2009–2010, as a part of a monitoring program. The samples were analyzed for Pb isotope ratios in snow from 47 sites, and in lichens from 28 sites, for background airborne lead and to determine the influence of distance versus local sources of Pb on a regional scale. Moreover, the lichen samples were split into two portions, the top portion, corresponding to recent growth (2–4 years), and the lower portion (up to 10+ years old) to determine the regime of recent versus old deposition. The study also investigated whether the isotopic signature in fresh snow (~1–2 weeks old) could correlate to the lichens’ recent growth. The Pb isotope signatures, reported as Pb isotope ratios 208Pb/206Pb and 207Pb/206Pb, in both snow and lichen recent growth were uniform over the region, except for localized influences near Sudbury and Timmins, suggesting that the background Pb represented remote sources for most of the region. Three-isotope plots 208Pb/206Pb versus 207Pb/206Pb of snow and lichens followed a linear model typical of mixing lines between two sources. As there are no statistical differences of Pb isotope ratios between fresh snow and recent lichen growth at neighboring stations, either type of sample may be used to describe the short-term record of airborne inputs. Finally, we found a strong relationship between the Pb isotope ratios of the recent growth (upper part) and old growth (lower part) of the lichens, with a slope of 0.9 between the lichen parts. This observation suggests either an isotopic segregation or the older parts of the lichens developed under different Pb source inputs in earlier times.

Isotopic analysis of nickel, copper, and zinc in various freshwater samples for source identification

Nickel (Ni), copper (Cu), and zinc (Zn) are commonly used in human activities and pollute aquatic environments including rivers and oceans. Recently, Ni, Cu, and Zn isotope ratios have been measured to identify their sources and cycles in environments. We precisely determined the Ni, Cu, and Zn isotope ratios in rain, snow, and rime collected from Uji City and Mt. Kajigamori in Japan, and investigated the potential of isotopic ratios as tracers of anthropogenic materials. The isotope and elemental ratios suggested that road dust is the main source of Cu in most rain, snow, and rime samples and that some of the Cu may originate from fossil fuel combustion. Zinc in the rain, snow, and rime samples may be partially attributed to Zn in road dust. Zinc isotope ratios in the Uji rain samples are lower than those in the road dust, which would be emitted via high temperature processes. Nickel isotope ratios are correlated with V/Ni ratios in the rain, snow, and rime samples, suggesting that their main source is heavy oil combustion. Furthermore, we analyzed water samples from the Uji and Tawara Rivers and the Kakita River spring in Japan. Nickel and Cu isotope ratios in the river water samples were significantly heavier than those in rain, snow, and rime samples, while Zn isotope ratios were similar. This is attributed to isotopic fractionation of Cu and Ni between particulate-dissolved phases in river water or soil.

Risk Assessment of Snow Disaster in Guoluo Prefecture of Southern Qinghai Province Based on GIS

We evaluated snow disaster risk in 44 towns of Guoluo Prefecture by combining the methods of Analytic Hierarchy Process (AHP) and cluster analysis. Data were gathered from meteorological stations in Qinghai and Sichuan Provinces with GIS and RS tools. We considered snow depth, the duration of snow days, the area ratio of snow as three indicators for such evaluation, we can say Guoluo Prefecture is a higher hazard area of snow disaster. From the spatial distribution, we can see that the mild grade areas are Huashixia town in Maduo county, Zhalinghu village and Dongqinggou village, Lajia town in Maqin county; the moderate and minor severe grade areas are distributed basically in every county. And the minor severe grade areas cover almost every town in Dari county and Gande county However, the severe grade areas only distribute in Sanrima village, Tehetu village of Dari County and Dangluo village of Maqin county.

Decadal-Scale Changes in the Seasonal Surface Water Balance of the Central United States from 1984 to 2007

AbstractVariations in climate have important influences on the hydrologic cycle. Observations over the continental United States in recent decades show substantial changes in hydrologically significant variables, such as decreases in cloud cover and increases in solar radiation (i.e., solar brightening), as well as increases in air temperature, changes in wind speed, and seasonal shifts in precipitation rate and rain/snow ratio. Impacts of these changes on the regional water cycle from 1984 to 2007 are evaluated using a terrestrial ecosystem/land surface hydrologic model (Agro-IBIS). Results show an acceleration of various components of the surface water balance in the Upper Mississippi, Missouri, Ohio, and Great Lakes basins over the 24-yr period, but with significant seasonal and spatial complexity. Evapotranspiration (ET) has increased across most of our study domain and seasons. The largest increase is found in fall, when solar brightening trends are also particularly significant. Changes in runoff are characterized by distinct spatial and seasonal variations, with the impact of precipitation often being muted by changes in ET and soil-water storage rate. In snow-dominated regions, such as the northern Great Lakes basin, spring runoff has declined significantly due to warmer air temperatures and an associated decreasing ratio of snow in total precipitation during the cold season. In the northern Missouri basin, runoff shows large increases in all seasons, primarily due to increases in precipitation. The responses to these changes in the regional hydrologic cycle depend on the underlying land cover type—maize, soybean, and natural vegetation. Comparisons are also made with other hydroclimatic time series to place the decadal-scale variability in a longer-term context.

Characterization, sources and transport of dissolved organic carbon and nitrogen from a glacier in the Central Asia

Glacier melting represents an important flux of carbon and nitrogen (N) and affects the hydrological cycle. In this study, we presented the features of dissolved organic carbon (DOC) and N concentrations, their potential sources and export from the Muz Taw glacier in Central Asia. The average DOC and total dissolved nitrogen concentrations were 1.12 ± 1.66 and 0.62 ± 0.59 mg L−1 in surface snow and 0.21 ± 0.04 and 0.31 ± 0.10 mg L−1 in snowpit samples, respectively. The values from snowpit of the Muz Taw glacier were comparable to data reported from glaciers in the Tibetan Plateau but were considerably higher than those from polar regions. The C/N ratios in snow ranged from 0.7 to 11.7, indicating the high bioavailability of DOC. Mass absorption cross section of DOC at 365 nm in snow indicated that during the snow melting process, light-absorbing DOC was prone to be attached to particles, especially in the ablation zone of the Muz Taw glacier. Radiative forcing caused by DOC contributed approximately 38 ± 26% and 18 ± 9.8% of that caused by black carbon for surface snow and snowpit samples, respectively. DOC and N deposition on the glacier surface were influenced by the combined sources from anthropogenic input, wild biomass burning emission, and dust input from local regions and long range transport. Export of DOC and N from the Muz Taw glacier was estimated to be 3.47–18.5 t C yr−1 and 5.11–10.23 t N yr−1 respectively, based on their concentrations and current glacier mass balance. These results enhanced our understanding of the sources and cycle of DOC and N released from glaciers in Central Asia, where glacier meltwater can protect the population from drought stress.

Impacts of Horizontal and Vertical Resolutions on the Microphysical Structure and Boundary Layer Fluxes of Typhoon Hato (2017)

We set four sets of simulation experiments to explore the impacts of horizontal resolution (HR) and vertical resolution (VR) on the microphysical structure and boundary layer fluxes of tropical cyclone (TC) Hato (2017). The study shows that higher HR tends to strengthen TC. Increasing VR in the upper layers tends to weaken TC, while increasing VR in the lower layers tends to strengthen TC. Simulated amounts of all hydrometeors were larger with higher HR. Increasing VR at the upper level enhanced the mixing ratios of cloud ice and cloud snow, while increasing VR at the lower level elevated the mixing ratios of graupel and rainwater. HR has greater impact on the distributions of hydrometeors. Higher HR has a more complete ring structure of the eyewall and more concentrated hydrometeors along the cloud wall. Increasing VR at the lower level has little impact on the distribution of TC hydrometeors, while increasing VR at the upper level enhances the cloud thickness of the eyewall area. Surface latent heat flux (SLHF) is influenced greatly by resolution. Higher HR leads to larger water vapor fluxes and larger latent heat, which would result in a stronger TC. A large amount of false latent heat was generated when HR was too high, leading to an extremely strong TC, VR has a smaller impact on SLHF than HR. But increasing VR at the upper-level reduces the SLHF and weakens TC, and elevating VR at the lower-level increases the SLHF and strengthens TC. The changes in surface water vapor flux and SLHF were practically identical and the simulation results were improved when HR and VR were more coordinated. The friction velocity was greater with higher VR. Enhancing VR at the lower level increased the friction velocity, while increasing VR at the upper level reduced it.

Spending degrees of freedom in a economy & extreme snow depth: a case study of building a model in Tianshan, China

Serious loss of human life and property by heavy snowfall in many middle and high Xijiang almost every winter. We apply annual maximum snow depth (AMSD) and snowfall data from the Tianshan Snow-cover & Avalanche Research Station (TSAR station in Xinjiang, Northwest China) collected during the last 28 winters (1978 to 2005) to temporal trends of annual maximum snow depth. The goal of the present article is to statistical analysis of extreme values that is applied to snow depth data from Tianshan Snow-cover & Avalanche Research Station. There were no significant differences between socioeconomic of snow depth, under affects socio-economic development from 1961, but permanently snow depth data (1978-2005) showed significantly impact on socio-economic development. Annual maximum snow depth contents necessary to meet return levels for 10, 25, 50, and 100 years of selected quality parameters were estimated. The research shows a decrease in extreme snow depth. This may would need to be increased by the observed decrease in the snow ratio due to increasing air temperatures. Further research is also needed to develop economy systems that socioeconomic and also improve the quality of snow depth data in the study area.

Light-Absorbing Impurities in Snow: A Personal and Historical Account

The ability of light-absorbing impurities (LAI) to darken snow had been known for decades, even inspiring practical applications, but quantification of the radiative forcing awaited radiative-transfer modeling in 1980 and measurement of soot in Arctic snow in 1983-4. Climate-modeling interest in this forcing began in 2004, spurring a modern explosion of research on several topics: methods to measure black carbon (BC) and other LAI, Arctic air pollution, measurement of BC mixing ratio in snow over large areas, and radiative transfer modeling of this forcing and its climatic and hydrological effects. The BC-content of snow in large remote regions of the northern hemisphere is on the order of 20 parts per billion, causing albedo reductions of ~1-2%. This reduction is climatically significant but difficult to detect by remote sensing, so quantification requires fieldwork to collect and analyze snow samples. This review is a personal account of early research at the National Center for Atmospheric Research and the University of Washington, followed by a brief summary of recent work by the author and his colleagues.