Rain And Snow Research Articles

Simulated particle evolution within a winter storm: contributions of riming to radar moments and precipitation fallout

Abstract. Remote sensing radars from airborne and spaceborne platforms provide critical observations of clouds to estimate precipitation rates across the globe. The ability of these radars to detect changes in precipitation properties is advanced by Doppler measurements of particle fall speed. Within mixed-phase clouds, precipitation mass and its fall characteristics are especially sensitive to the effects of riming. In this study, we quantified these effects and investigated the distinction of riming from aggregation in Doppler radar vertical profiles using quasi-idealized particle-based model simulations. Observational constraints of a control simulation were determined from airborne in situ and remote sensing measurements collected during the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) for a wintry–mixed precipitation event over the northeastern United States on 4 February 2022. From the upper boundary of a one-dimensional column, particle evolution was simulated through vapor deposition, aggregation, and riming processes, producing realistic Doppler radar profiles. Despite a modest observed amount of supercooled liquid water (0.05 g m−3), riming accounted for 55 % of the ice-phase precipitation mass, cumulatively increasing reflectivity by 44 % and Doppler velocity by 68 %. Independent evaluation of process-based sensitivities showed that, while radar reflectivity is comparably sensitive to either riming- or aggregation-based particle morphology, the Doppler velocity profile is uniquely sensitive to particle density changes during riming. Thus, Doppler velocity profiles advance the diagnosis of riming as a dominant microphysical process in stratiform clouds from single-wavelength radars, which has implications for quantitative constraints of particle properties in remote sensing applications.

Bias Adjustment of Long‐Term (1961–2020) Daily Precipitation for China

AbstractThe observation errors in precipitation gauges contribute to diminished precision in precipitation data sets. To reduce the impact of these errors, the World Meteorological Organization Solid Precipitation Intercomparison Experiments recommended the Double Fence Intercomparison Reference as a reference standard for precipitation measurements. This study proposed a new rain, snow, and mixed precipitation adjustment method for national standard precipitation gauges, using DFIR‐measured precipitation as the standard values. This method was used to adjust for systematic errors, including wind‐induced errors, wetting loss, and trace precipitation, in precipitation data collected by 785 stations in China from 1961 to 2020. After bias adjustment, the annual precipitation increased by 6.1–177.9 mm (with an average of 52.7 mm), accounting for 3.3%–52.1% (8.9%) of the total precipitation. The average annual error‐adjustment amounts for wind‐induced error, wetting loss, and trace precipitation were 21.9 (3.6% of total precipitation), 26.6 (4.7%), and 4.2 mm (1.3%), respectively. The adjustment percentage in winter was higher than that in summer, with the high‐adjusted‐percentage regions predominantly located in areas with drought, high proportion of snowfall, and strong wind speeds. Additionally, the annual average error‐adjustment amounts for rain, snow, and mixed precipitation respectively accounted for 5.2%, 38.2%, and 17.1% of their corresponding total amounts, indicating the significance of bias adjustment, particularly for snow and mixed precipitation, in the northern and Qinghai‐Tibet Plateau regions. Therefore, bias adjustment is necessary to enhance the accuracy of the precipitation data set in China.

Freshwater microbial mud: Punctuated diagenesis during marine transgression in the Florida Everglades, USA

AbstractThis manuscript documents the diagenetic progression from deposition to early burial of freshwater low‐Mg calcite mud within a Holocene sequence of the Everglades and Florida Bay (Florida, USA). During initial, in situ burial, the texturally‐complex, organic‐rich mud, exhibits high susceptibility to alteration, within a primarily dissolution‐driven realm. Seaward, a brackish‐water transitional burial environment currently displays evidence of mixed precipitation and dissolution. This brackish stage burial alteration may be due to fluid exchange (a relatively high fluid‐to‐rock ratio) in a submerged estuary setting. Lastly, the burial environment in Florida Bay records a transgression from freshwater to near‐normal marine conditions. This island setting currently exhibits a diagenetic trend towards low‐Mg calcite precipitation in intervals of both the marine and freshwater mud. Burial of freshwater mud beneath nearly 3 m of marine mud during rising sea level appears to have preserved a considerable amount of original textures, as compared with younger freshwater deposits now within the transitional brackish‐water setting. Geochemical and textural data within the freshwater mud sections suggest that complex early diagenesis begins shortly after burial at the site of formation, and perhaps even during deposition. The findings herein document three major implications for coastal zone paludal carbonates: (i) that incipient textural diagenesis in low‐Mg calcite crystals can be extremely early and the stability and petrographic fidelity of these deposits are vulnerable due to the open, trellis‐like structure of the crystals; (ii) relatedly, the stable isotope signature of the original low‐Mg calcite crystals is also vulnerable to alteration by secondary precipitation within the open crystal structure; and (iii) during marine transgression, the paludal calcite crystals likely experience a shift from interstitial fluids that are dissolution‐dominated (freshwater) to more precipitation‐dominated as marine fluids overtake the pore fluid regime. Because palustrine deposits are used as palaeoenvironmental archives, it is important to understand their diagenetic and preservation potential. This study has implications for those concerned with the deposition, reactivity and preservation potential of continental and microbial carbonates.

Elevational control of isotopic composition and application in understanding hydrologic processes in the mid Merced River catchment, Sierra Nevada, California, USA

Abstract. Mountain snowpack has been declining and more precipitation has fallen as rainfall than snowfall, particularly in the western US. Isotopic composition in stream water, springs, groundwater, and precipitation was examined to understand the impact of declining snowpack on hydrologic processes in the mid Merced River catchment (1873 km2), Sierra Nevada, California. Mean isotopic values in small tributaries (catchment area < 122 km2), rock glacier outflows, and groundwater from 2005 to 2008 were strongly correlated with mean catchment elevation (R2= 0.96 for δ2H, n= 16, p < 0.001), with an average isotopic lapse rate of −1.9 ‰ per 100 m for δ2H and −0.22 ‰ per 100 m for δ18O in meteoric water. The lapse rate did not change much over the seasons and was not strongly affected by isotopic fractionation. A catchment-characteristic isotopic value, representing the catchment arithmetic mean isotopic signature in meteoric water, was thus established for each sub-catchment based on the lapse rate to elucidate hydrometeorologic and hydrologic processes such as the duration and the magnitude of snowmelt events and elevational water sources of streamflow and groundwater for ungauged catchments. Compared to Tenaya Creek without water falls, the flow and flow duration of Yosemite Creek appear to be much more sensitive to seasonal temperature increases during the baseflow period due to a strong evaporation effect caused by waterfalls, suggesting a possible prolonged dry-up period of Yosemite Falls in the future. Groundwater in Yosemite Valley (∼ 900–1200 m) was recharged primarily from the upper snow–rain transition zone (2000–2500 m), suggesting its strong vulnerability to shifts in the snow–rain ratio. The information gained from this study helps advance our understanding of hydrologic responses to climate change in snowmelt-fed river systems.

Assessing the Impact of Climate Change on Snowfall Conditions in Poland Based on the Snow Fraction Sensitivity Index

This study focuses on temperature and snowfall conditions in Poland, both of which were analyzed from 1981 to 2020. A 40-year record of daily snow fraction time series values was reconstructed using a unique and global multi-source weighted-ensemble precipitation (MSWEP) product, which provided a spatially and temporally consistent reference for the assessment of meteorological conditions. The average states and trends in snow fraction and temperature were analyzed across several years, focusing on the 6-month cold season (November–April). The impact of temperature on the snow fraction pattern was assessed by introducing a snow fraction sensitivity index. To predict short-term changes in snow conditions, a proxy model was established; it incorporated historical trends in the snow fraction as well as its mean state. This study provides clear evidence that the snow fraction is principally controlled by increases in temperature. A warming climate will thus cause a decline in the snow fraction, as we observed in vast lowland areas. Given the ongoing global warming, by the 2050s, snow-dominated areas may go from covering 86% to only 30% of the country’s surface; they will be converted into transient rain–snow areas. Our results demonstrate that a decline in snow water resources has already occurred, and these resources are expected to diminish further in the near future. New insights into the sensitivity of the snow fraction to climate warming will expand our collective knowledge of the magnitude and spatial extent of snow degradation. Such widespread changes have implications for the timing and availability of soil and groundwater resources as well as the timing and likelihood of floods and droughts. Thus, these findings will provide valuable information that can inform environmental managers of the importance of changing snowfall conditions, guiding them to include this aspect in future climate adaptation strategies.

Coupling Downscaling and Calibrating Methods for Generating High-Quality Precipitation Data with Multisource Satellite Data in the Yellow River Basin

Remote sensing precipitation data have the characteristics of wide coverage and revealing spatiotemporal information, but their spatial resolution is low. The accuracy of the data is obviously different in different study areas and hydrometeorological conditions. This study evaluated four precipitation products in the Yellow River basin from 2001 to 2019, constructed the optimal combined product, conducted downscaling with various machine algorithms, and performed corrections using meteorological station precipitation data to analyze the spatiotemporal trends of precipitation. The results showed that (1) GPM and MSWEP had the best four evaluation indicators, with R2 values of 0.93 and 0.90, respectively, and the smallest FSE and RMSE, with a BIAS close to 0. A high-precision mixed precipitation dataset, GPM-MSWEP, was constructed. (2) Among the three methods, the downscaling results of DFNN showed higher accuracy. (3) The results, after correction with GWR, could more effectively enhance the accuracy of the data. (4) Precipitation in the Yellow River Basin showed a decreasing trend in January, September, and December, while it exhibited an increasing trend in other months and seasons, with 2002 and 2016 being points of abrupt change. This study provides a reference for the production of high-precision satellite precipitation products in the Yellow River basin.

Improved Diagnosis of Precipitation Type with LightGBM Machine Learning

Abstract Existing precipitation-type algorithms have difficulty discerning the occurrence of freezing rain and ice pellets. These inherent biases are not only problematic in operational forecasting but also complicate the development of model-based precipitation-type climatologies. To address these issues, this paper introduces a novel light gradient-boosting machine (LightGBM)-based machine learning precipitation-type algorithm that utilizes reanalysis and surface observations. By comparing it with the Bourgouin precipitation-type algorithm as a baseline, we demonstrate that our algorithm improves the critical success index (CSI) for all examined precipitation types. Moreover, when compared with the precipitation-type diagnosis in reanalysis, our algorithm exhibits increased F1 scores for snow, freezing rain, and ice pellets. Subsequently, we utilize the algorithm to compute a freezing-rain climatology over the eastern United States. The resulting climatology pattern aligns well with observations; however, a significant mean bias is observed. We interpret this bias to be influenced by both the algorithm itself and assumptions regarding precipitation processes, which include biases associated with freezing drizzle, precipitation occurrence, and regional synoptic weather patterns. To mitigate the overall bias, we propose increasing the precipitation cutoff from 0.04 to 0.25 mm h−1, as it better reflects the precision of precipitation observations. This adjustment yields a substantial reduction in the overall bias. Finally, given the strong performance of LightGBM in predicting mixed precipitation episodes, we anticipate that the algorithm can be effectively utilized in operational settings and for diagnosing precipitation types in climate model outputs. Significance Statement Freezing rain can have significant impacts on transportation and infrastructure, making accurate prediction of precipitation types crucial. In this study, we use a machine learning method known as LightGBM to predict precipitation types. We show that the new algorithm performs better than the existing methods for all precipitation types examined. Additionally, we compute a freezing-rain climatology over the eastern United States. Although the resulting climatology pattern corresponds well to observations, the algorithm overpredicts freezing-rain occurrence. We argue that this bias can be substantially reduced by increasing the precipitation cutoff from 0.04 to 0.25 mm h−1. Overall, this work highlights the potential of the LightGBM algorithm for both weather forecasting and diagnosing precipitation types in climate models.

Precipitation and tensile behaviors of Allvac 718Plus superalloy during long-term thermal exposure

Allvac 718Plus alloy is a promising Ni-based superalloy with excellent thermal stability and mechanical properties up to 704 °C. The γ′ coarsening, σ precipitation and the resulting degraded tensile properties at room and elevated (704 °C) temperatures during long-term thermal exposure at 704 and 760 °C are systematically investigated in this work. The results showed that a higher exposure temperature and a longer exposure time significantly accelerate the γ′ coarsening and σ precipitation. The σ particles were detrimental to the alloy by acting as the preferential nucleation sites for cracks, and the reduction in tensile strengths was mainly caused by the increasing size of γ′ precipitates during thermal exposure. Furthermore, there were significant differences between the deformation mechanisms of room-temperature and elevated-temperature (760 °C) tensile tests. Concerning the deformation mechanism of γ-matrix, planar slipping dominates during tensile testing at room temperature, while the formation of stacking faults and microtwinning are primary during tensile testing at elevated temperature (760 °C). The dominant precipitation strengthening mechanism depended on the γ′ characteristics of different thermal exposure conditions and the tensile temperature. The 704 °C/100 h exposure sample shows a dislocation shearing mechanism when tested at room or elevated (760 °C) temperature. Notably, the 760 °C/1000 h exposure sample exhibits a mixed precipitation strengthening mechanism of Orowan bypassing and superlattice stacking faults shearing when tensile tested at room temperature while showing a dislocation and superlattice stacking faults shearing mechanism when tensile tested at elevated (760 °C) temperature.

GEMS v1.0: Generalizable Empirical Model of Snow Accumulation and Melt, based on daily snow mass changes in response to climate and topographic drivers

Abstract. Snow modelling is often hampered by the availability of input and calibration data, which can affect the choice of models, their complexity, and transferability. To address the trade-off between model parsimony and transferability, we present the Generalizable Empirical Model of Snow Accumulation and Melt (GEMS), a machine-learning-based model, which requires only daily precipitation, temperature or its daily diurnal cycle, and basic topographic features to simulate snow water equivalent (SWE). The model embeds a support vector regression pretrained on a large dataset of daily observations from a diverse set of the SNOwpack TELemetry Network (SNOTEL) stations in the United States. GEMS does not require any user calibration, except for the option to adjust the temperature threshold for rain–snow partitioning, though the model achieves robust simulation results with the default value. We validated the model with long-term daily observations from numerous independent SNOTEL stations not included in the training and with data from reference stations of the Earth System Model–Snow Model Intercomparison Project. We demonstrate how the model advances large-scale SWE modelling in regions with complex terrain that lack in situ snow mass observations for calibration, such as the Pamir and Andes mountains, by assessing the model's ability to reproduce daily snow cover dynamics. Future model improvements should consider the effects of vegetation, improve simulation accuracy for shallow snow in warm locations at lower elevations, and possibly address wind-induced snow redistribution. Overall, GEMS provides a new approach for snow modelling that can be useful for hydroclimatic research and operational monitoring in regions where in situ snow observations are scarce.

Spatiotemporal snow water storage uncertainty in the midlatitude American Cordillera

Abstract. This work quantifies the uncertainty of accumulation-season peak snow water storage in the portions of the midlatitude American Cordillera where snow is a dominant driver of hydrology. This is accomplished through intercomparison of commonly used global and regional products over the Western United States (WUS) and Andes domains, which have similar hydrometeorology but are disparate with respect to the amount of available in situ information. The recently developed WUS Snow Reanalysis (WUS-SR) and Andes Snow Reanalysis (Andes-SR) datasets, which have been extensively verified against in situ measurements, are used as baseline reference datasets in the intercomparison. Relative to WUS-SR climatological peak snow water equivalent (SWE) storage (269 km3), high- and moderate-resolution products (i.e., those with resolutions less than ∼10 km) are in much better agreement (284±14 km3; overestimated by 6 %) compared to low-resolution products (127±54 km3; underestimated by 53 %). In comparison to the Andes-SR peak snow storage (29 km3), all other products show large uncertainty and bias (19±16 km3; underestimated by 34 %). Examination of spatial patterns related to orographic effects showed that only the high- to moderate-resolution Snow Data Assimilation System (SNODAS) and University of Arizona (UA) products show comparable estimates of windward–leeward SWE patterns over a subdomain (Sierra Nevada) of the WUS. Coarser products distribute too much snow on the leeward side in both the Sierra Nevada and Andes, missing orographic and rain shadow patterns that have important hydrological implications. The uncertainty of peak seasonal snow storage is primarily explained by precipitation uncertainty in both the WUS (R2=0.55) and Andes (R2=0.84). Despite using similar forcing inputs, snow storage diverges significantly within the ECMWF Reanalysis v5 (ERA5) (i.e., ERA5 vs. ERA5-Land) products and the Global Land Data Assimilation System (GLDAS) (modeled with Noah, Variable Infiltration Capacity (VIC), and Catchment model) products due to resolution-induced elevation differences and/or differing model process representation related to rain–snow partitioning and accumulation-season snowmelt generation. The availability and use of in situ precipitation and snow measurements (i.e., in WUS) in some products adds value by reducing snow storage uncertainty; however, where such data are limited, i.e., in the Andes, significant biases and uncertainty exist.

Modeling the interannual variability in Maipo and Rapel river plumes off central Chile

Abstract. River plumes have a direct influence on coastal environments, impacting coastal planktonic and benthic communities, including fishery resources. In general, the main drivers of river plume dynamics are the river discharge and the alongshore wind stress, whereas the tides and topography play a secondary role. In central Chile, rivers flowing into the eastern Pacific have a relatively short path on land, with a high slope and a mixed snow–rain regime. This study aims to understand the interannual variability in the plumes of the Maipo and Rapel rivers in the coastal/shelf area off central Chile and their influence on local ocean dynamics. We used the Coastal and Regional Ocean Community (CROCO) model, with 1 km horizontal resolution and 20 sigma levels, to simulate the ocean dynamics for the period 2003–2011. The results show that the plume's area coverage and coastal ocean salinity are strongly correlated with the river discharges. The predominant northeastward winds control the plumes' orientation toward the northwest. However, episodes of southeastward winds in winter can reverse the plumes' direction, promoting their attachment to the coast and southward transport. Results also show a salification trend linked to the severe droughts hitting central Chile during the studied period. This salification determines a change in local dynamics which could be more frequent in future scenarios of climate change with a significant lack of rain and river discharges along central Chile.

Ensemble-based analysis of heavy rainfall–snowfall associated with mesoscale precipitation bands within an extratropical cyclone over northeastern China

This paper investigates the key synoptic-scale factors that affected the forecasting of mesoscale rainfall and snowfall and their associated uncertainties in a heavy rain–snow event in northeastern China on 18–20 November 2020, using ensemble-based sensitivity analysis based on global ensemble forecasts from the European Centre for Medium-Range Weather Forecasts. The heavy precipitation event was attributed to an extratropical cyclone and experienced two stages, with the snowfall stage having a better precipitation forecast skill than the rainfall stage. The mesoscale rainfall and snowfall were caused by a mesoscale rainband over Liaoning Province and two mesoscale snowbands over Heilongjiang Province, respectively, and they showed some differences with respect to their forecast skill and related key synoptic-scale factors contributing to the precipitation centers. The precipitation amount in the two different stages was correlated significantly with the midlevel trough and sensitive to the location and intensity of the low-level vortex (surface cyclone), and particularly the low-level jets and the associated water vapor transport. However, some differences were confirmed in the two different stages: the weaker midlevel trough and accompanying weaker low-level temperature trough in the rainfall stage were related to increased precipitation because the midlevel trough was far away from the control area, while the stronger midlevel trough and accompanying stronger low-level temperature trough were associated with increased precipitation in the snowfall stage. In addition to the synoptic-scale low-level jet (SLLJ), the precipitation in the rainfall stage was also affected by a boundary layer jet (BLJ) over the ocean, while only SLLJs were present in the snowfall stage. The uncertainty of the precipitation forecast was derived mainly from the uncertainty in the strength and location of the SLLJs and BLJ. Notably, the intensity of northeasterly winds west of the low-level vortex may affect the predictability of heavy snowfall.

Effect of forging temperature on the microstructure, subsequent aging precipitation behavior, and mechanical properties of Mg-Gd-Y-Zr-Ag alloy

The deformation mechanism, microstructure evolution, and precipitation behavior of a Mg-8.9Gd-1.8Y-0.5Zr-0.2Ag (wt.%) alloy multi-directionally forged at three different temperatures were investigated. As the forging temperature increases, the particle-stimulated nucleation (PSN) effect diminishes as the number of dynamic precipitates decreases, pyramidal slip is activated, grain boundary migration accelerates, and continuous dynamic recrystallization (CDRX) dominates. The microstructures varied greatly, although fine-grained structures were formed at all different forging temperatures. Competitive precipitation between dynamic precipitate growth, dislocation-induced precipitation, and homogeneous precipitation was observed after aging treatment. Among them, the medium temperature (748 K) forged and aged alloy exhibits the best mechanical performance, with an ultimate tensile strength of 436 MPa, and elongation of 16.3%. The calculation indicates that the mixed precipitation structure containing the β′ precipitate band provides a 35% higher strengthening contribution than the typical homogeneously distributed precipitates. The formation of precipitation-free zones (PFZs) ensures that aging will not cause a dramatic decrease in ductility, which provides a reference for the industrial preparation of high-performance wrought Mg-Gd series alloys.

Performance of precipitation phase partitioning methods and their impact on snowpack evolution in a humid continental climate

AbstractAccurate estimations of the precipitation phase at the surface are critical for hydrological and snowpack modelling in cold regions. Precipitation phase partitioning methods (PPMs) vary in their ability to estimate the precipitation phase at around 0°C and can significantly impact simulations of snowpack accumulation and melt. The goal of this study is to evaluate PPMs of varying complexity using high‐quality observations of precipitation phase and to assess the impact on snowpack simulations. We used meteorological data collected in Edmundston, New Brunswick, Canada, during the 2021 Saint John River Experiment on Cold Season Storms (SAJESS). These data were combined with manual observations of snow depth. Five PPMs commonly used in hydrological models were tested against observations from a laser‐optical disdrometer and a Micro Rain Radar. Most PPMs produced similar accuracy in estimating only rainfall and snowfall. Mixed precipitation was the most difficult phase to predict. The multi‐physics model Crocus was then used to simulate snowpack evolution and to diagnose model sensitivity to snowpack accumulation processes (PPM, snowfall density, and snowpack compaction). Sixteen snowpack accumulation periods, including nine warm accumulation events (average temperatures above −2°C) were observed during the study period. When considering all accumulation events, simulated changes in snow water equivalent (SWE) were more sensitive to the type of PPM used, whereas simulated changes in snow depth were more sensitive to uncertainties in snowfall density. Choice of PPM was the main source of model sensitivity for changes in SWE and snow depth when only considering warm events. Overall, this study highlights the impact of precipitation phase estimations on snowpack accumulation at the surface during near‐0°C conditions.

Which adjustment methods are suitable for the wind-induced errors of Geonor T-200BM3 precipitation weighing gauges in a periglacial site?

Single Alter shielded T-200BM3 weighing precipitation gauges are widely used in the measurement of all precipitation types (rainfall, snow and mixed precipitation) in unattended boreal or alpine regions, but their original datasets must be adjusted for undercatch errors caused by wind in snowy, windy and harsh environments. Therefore, previous researchers have developed many adjustment methods for all precipitation types on different time scales. However, which adjustment method is suitable for T-200BM3 weighing gauge wind-induced error adjustment in harsh alpine regions is unclear. Therefore, precipitation measurement intercomparison experiments were conducted in the Qilian Mountains from July 2018 to July 2021, and eight adjustment methods; were evaluated for wind-induced errors for daily, individual precipitation event, hourly, and half-hourly time scales. Z2004 outperformed the other adjustment methods in regard to the daily measurements of snow and mixed precipitation. Regarding individual snowfall events, M2007 reduced the absolute value of RMSE (bias) from 1.44 to 1.32 mm (0.77–0.24 mm) and could be recommended for snowfall event adjustment. K2017-1 attained a better performance than K2017-2 in regard to half-hourly snowfall and mixed sample adjustment and was more suitable for half-hourly snowfall sample adjustment. K2017-1 reduced the absolute value of bias from 0.07 to 0.00 mm for snowfall. Finally, Z2004, M2007, and K2017-1 yielded better adjustment results for the daily accumulation precipitation amount (>2 mm d−1), individual snowfall events (>2 mm per event), and half-hourly accumulation snowfall or mixed samples (>1 mm 30 min−1), respectively. However, further intercomparison in different climate regions is needed for trace precipitation samples.

Extinction of high-power laser radiation under adverse weather conditions.

The propagation of laser radiation over long distances can be significantly affected by atmospheric extinction due to precipitation as well as aerosol particles and molecules. The knowledge of the contribution of precipitation is critical to the operation of a variety of laser-based systems. The study of high-power laser transmission around 1µm is of particular interest because several atmospheric transmission windows are located in this region. To investigate the effect of adverse weather conditions on laser transmission, free-space laser transmission experiments are conducted on the DLR test range in Lampoldshausen, Germany. A high-power laser with a wavelength of 1.03µm is used for the transmission measurements in combination with calibrated power monitors. Local weather conditions are continuously monitored by meteorological instruments during the experiments. Extinction coefficients are derived from transmission measurements showing that the extinction for snow is 7 times higher than for rain, and the extinction for drizzle/rain is 4 times higher than for rain at a given precipitation rate of 1mm/h. For a mixture of rain and snow, the extinction is comparable to that of rain, indicating that the water content strongly influences the optical properties and thus the extinction of laser radiation in mixed precipitation. A good relationship is found between the measured extinction coefficient and visibility for drizzle and rain and a slightly larger scatter of the data for snow. Furthermore, measured extinction coefficients are compared to the extinction coefficients based on the experimental size distributions of precipitation particles and geometric optics. A reasonable agreement is obtained for rain, with no improvement taking the forward-scattering into the detector aperture into account, and a much better agreement is obtained for snow when the forward-scattering contribution is included.

Characteristics of Rain-Snow Transitions Over the Canadian Rockies and their Changes in Warmer Climate Conditions

ABSTRACT The southern Canadian Rockies are prone to extreme precipitation that often leads to high streamflow, deep snowpacks, and avalanche risks. Many of these precipitation events are associated with rain–snow transitions, which are highly variable in time and space due to the complex topography. A warming climate will certainly affect these extremes and the associated rain–snow transitions. The goal of this study is to investigate the characteristics and variability of rain–snow transitions aloft and how they will change in the future. Weather Research and Forecasting (WRF) simulations were conducted from 2000 to 2013 and these were repeated in a warmer pseudo-global warming (PGW) future. Rain–snow transitions occurred aloft throughout the year over the southern Canadian Rockies, but their elevations and depths were highly variable, especially across the continental divide. In PGW conditions, with future air temperatures up to 4–5°C higher on average over the Canadian Rockies, rain–snow transitions are projected to occur more often throughout the year, except during summer. The near-0°C conditions associated with rain–snow transitions are expected to increase in elevation by more than 500 m, resulting in more rain reaching the surface. Overall, this study illustrates the variability of rain–snow transitions, which often impact the location of the snowline. This study also demonstrates the non-uniform changes under PGW conditions, due in part to differences in the types of weather patterns that generate rain–snow transitions across the region.

Reconstructing and Nowcasting the Rainfall Field by a CML Network

AbstractCurrently, the opportunistic method to estimate rainfall using commercial microwave links (CMLs) has been shown as an efficient way to complement traditional instruments in terms of spatial‐temporal resolution and coverage. In this paper, we collected data from 26 CMLs in Jiangyin City, Jiangsu Province, and conducted experiments on rainfall field reconstruction and nowcasting. First, the raw CML data were processed to invert the path‐averaged rainfall intensity. Second, the algorithms of inverse distance weighting (IDW) and ordinary kriging (OK) interpolation were employed to reconstruct the rainfall field. Then a 10‐min prediction of the rainfall field was achieved using a nowcasting model based on the long short‐term memory neural network and a setup window was introduced to improve the prediction performance of the first few minutes. The reconstruction results show that the average correlation coefficient (ACC) and the average root mean square error (ARMSE) between the IDW‐based results and daily cumulative rainfall from rain gauges (RGs) are 0.89 and 8.69 mm, respectively, while the ACC and ARMSE between the OK‐based results and RG data are 0.89 and 9.13 mm, respectively. The nowcasting results show that the ACC between the prediction results with a 5‐min setup window and the IDW‐retrieved rainfall fields can reach 0.91 at the first minute and gradually decrease to 0.20 within 10 min. Furthermore, the model has better nowcasting performance for stratiform precipitation and mixed precipitation compared to convective precipitation.

Climate Change and Hydrological Response in the Ranwu Lake Basin of Southeastern Tibet Plateau

It is of great practical significance to accurately distinguish the different water supply sources of rivers and lakes under climate change for regional water resources utilization. This study examines the impact of climate change on the hydrological processes of the Ranwu Lake basin in the southeastern Tibet Plateau. The authors used China Meteorological Forcing Dataset (CMFD) historical data, CanESM5′s future climate predictor, and the SPHY model to analyze trends in temperature, precipitation, and water supply sources in the basin. The study found that warming in the basin was higher than that in the Tibet Plateau, with high-altitude areas and winter showing more significant warming. From 1998 to 2018, precipitation in the basin showed a trend of fluctuation and decline. The study also found that glacial meltwater accounted for the majority of total runoff in the basin (54.13%), while snow meltwater, rainfall, and baseflow accounted for about 22.98%, 11.84%, and 11.06%, respectively, on average in recent years. The total runoff in the Ranwu Lake Basin will continue to decrease due to the accelerating retreat of glaciers, with the hydrological process transforming from being dominated by glacier processes to rain–snow processes. The study also predicts that three-quarters of glaciers in the basin will vanish within the next forty years, and by 2100, only around 20% of glaciers will remain.

Intercomparison measurements from commonly used precipitation gauges in the Qilian Mountains

Precipitation data is vital fundamental data for climate change. However, obtaining precise gauge-measured precipitation in high-altitude mountains is challenging, and the precipitation obtained from various gauge types at the same station may vary. To understand the differences in precipitation observations among the three commonly used gauges in China (Chinese Standard Precipitation Gauges (CSPG), Total Rain weighing Sensor (TRwS), and Geonor T-200B (T200B)) in high-altitude mountains and to recommend a stable and cost-effective weighing gauge, a precipitation intercomparison experiment was conducted at Hulu-1 station in the Qilian Mountains. The wind-induced error in measurement was corrected with the ‘universal’ transfer function recommended by the Word Meteorological Organization. The comparison results, adjusted for systematic errors, showed that the rain, snow and mixed precipitation of CSPG and TRwS equipped with the same octagonal vertical double fence shields (CSPGDF and TRwSDF) and single-Alter shields (CSPGs and TRwSs) were close, while the precipitation of Tretyakov-shielded T200B was notably higher than that of CSPGs and TRwSs. The average differences in annual and daily precipitation between CSPGDF and TRwSDF from 2017 to 2021 were 12.9 mm and 0.10 mm, respectively. The daily precipitation difference between CSPGs and TRwSs from April 2019–December 2021 was 0.10 mm, while the differences between T200Bs and CSPGs and TRwSs was 0.28 mm and 0.38 mm, respectively. The wind shield performance of Alter and Tretyakov was not much different at Hulu-1 site with low wind speed, thus the measurement principle of T200Bs was the primary cause of the high observations. Taking the corrected CSPGDF measurement as the standard, the dynamic loss of CSPGs was 17.6%, while that of CSPGUn was 55.6%, indicating that the single-Alter shield could effectively reduce the impact of wind on precipitation measurement. Considering the comparison results and the price difference of the instruments, it was recommended to use a single-Alter shielded TRwS gauge for precipitation observation in high-altitude mountains.