Precipitation Type Research Articles

A Statistical Forecast Model for Extratropical Cyclones Including Intensity and Precipitation Type

Abstract Winter extratropical cyclones (ETCs) are dominant features of winter weather on the east coast of North America. These storms are characterized by high winds and heavy precipitation (rain, snow, and ice). ETCs are well predicted by numerical weather prediction models (NWPs) at short- to midrange forecast lead times, but prediction on seasonal time scales is lacking. We develop a set of multiple linear regression models, using stepwise regression and cross validation, to predict the number of storms expected to affect a specific location throughout the winter storm season. Each model in the set predicts a specific storm type (e.g., snow, rain, or bomb storms). This set of models is applied in a probabilistic forecast framework that uses the probability density function of the prediction in combination with climatological mean storm activity. The resulting forecast makes statements about the likelihood of below-average, average, or above-average activity for all storms and for each of the type-specific subsets of storms. Though this forecast framework could in theory be applied anywhere, we demonstrate its skill in forecasting the characteristics of the winter storm season experienced in Halifax, Nova Scotia, Canada. Significance Statement Winter storms are a disruptive but inevitable part of life on the eastern coast of North America all the way from the Carolinas to Labrador. Knowing each fall what to expect for the upcoming winter storm season is not only a matter of public interest, but also of great public safety and financial importance. Here we develop a model that uses the state of the atmosphere over the month of September to forecast the upcoming winter storm characteristics for a specified region of interest. Our model uses a multiple linear regression approach to make skilled forecasts including probability statements about the level and type of storm activity. Forecasts can be used to inform planning for the winter ahead.

The effect of nickel on the carbide precipitation behavior in Cr–Mo–V hot-working die steel

The addition of small amount of nickel to hot-working die steel has been found to be beneficial for hardenability and toughness. In this paper, the addition of nickel to Cr–Mo–V hot-working die steel was studied to improve hardenability, and the inherent mechanism of enhancing toughness was further explored. The research results indicate that nickel as a non-carbide-forming element does not alter the type and total amount of carbide precipitation at equilibrium, but it can dissolve into the matrix and form substitutional lattice distortions with Fe atoms, promoting the dispersion of secondary carbides during the tempering process. This mechanism can reduce intergranular precipitation and inhibit grain boundary embrittlement, thereby improving toughness. However, it can also accelerate the precipitation, aggregation, and growth of secondary carbides during prolonged tempering, which deteriorates the high-temperature anti-softening properties of die steels. The role of Ni has been revealed through thermodynamic and kinetic calculations. The results show that Ni significantly enhances the diffusion coefficient of Cr atoms in the face centered cubic (fcc) Fe matrix during tempering and also improves the phase transition driving force of various precipitates. This work provides new insights into the mechanism of Ni in hot-working die steels, and can provide guidance for designing novel hot-working die steel.

Comparisons of Rainfall Microphysical Characteristics between the Southeastern Tibetan Plateau and Low-Altitude Areas

Abstract The low air pressure and density over the Tibetan Plateau may have an impact on the microphysical features of rainfall. Using a two-dimensional video disdrometer (2DVD), a Micro Rain Radar (MRR), and a microwave radiometer (MWR), the features of the raindrop size distribution (DSD) on the southeastern Tibetan Plateau (SETP) are explored and compared with those in low-altitude regions. The falling speed of raindrops on the SETP is higher than that in low-altitude areas. Under different rainfall-rate categories, the number concentration and the maximum diameter of raindrops on the SETP are smaller than those in low-altitude regions. The convective rainfall on the SETP is more maritime-like because the South Asian summer monsoon brings water vapor from the ocean here. For stratiform and convective rainfall, the SETP has more small-sized raindrops than low-altitude locations. The mass-weighted mean diameters (Dm) on the SETP are the smallest among six sites. The generalized intercept parameter (Nw) of stratiform rainfall is balanced at a low rainfall rate, while that of convective rainfall is balanced at a high rainfall rate. Furthermore, for a given μ (the shape parameter of gamma distribution) value, the λ (the slope parameter of gamma distribution) value on the SETP is the highest of the six sites. Significance Statement For the occurrence and progression of rainfall, microphysical processes (for instance, collision, fragmentation, coalescence, and evaporation) that take place when rainfall particles descend are crucial. A key factor in the microphysical features of rainfall that varies with rainfall rates and types is the raindrop size distribution (DSD). The southeastern Tibetan Plateau (SETP)’s unique terrain ensures that there is enough moisture for rain to fall there, but little is known about the microphysical aspects of this type of precipitation. There has not been enough research done on how the high altitude affects the microphysical features of rainfall. The microphysical features of rainfall in this area must thus be studied.

Precipitation forecasting: from geophysical aspects to machine learning applications

Intense precipitation events pose a significant threat to human life. Mathematical and computational models have been developed to simulate atmospheric dynamics to predict and understand these climates and weather events. However, recent advancements in artificial intelligence (AI) algorithms, particularly in machine learning (ML) techniques, coupled with increasing computer processing power and meteorological data availability, have enabled the development of more cost-effective and robust computational models that are capable of predicting precipitation types and aiding decision-making to mitigate damage. In this paper, we provide a comprehensive overview of the state-of-the-art in predicting precipitation events, addressing issues and foundations, physical origins of rainfall, potential use of AI as a predictive tool for forecasting, and computational challenges in this area of research. Through this review, we aim to contribute to a deeper understanding of precipitation formation and forecasting aided by ML algorithms.

Are there any Differences in the Raindrop Size Distributions between Summer Typhoons and Autumn Typhoons？

ABSTRACT To improve typhoon forecasting in southern China, which is one of the most susceptible regions in China by typhoons, it is essential to investigate the microphysical characteristics of various types of typhoon precipitation. In this study, the dual-frequency precipitation radar (DPR) data from the Global Precipitation Measurement (GPM) satellite is adopted to reveal the raindrop size distribution (RSD) characteristics of 18 typhoons that made landfall in southern China during 2014 – 2022. The RSD characteristics of precipitation in the horizontal and vertical directions of summer typhoons (STs) and autumn typhoons (ATs) are examined. There is no obvious difference between the parameters corresponding to STs andATs without considering the precipitation types. The mass-weighted mean diameter (D m ) both STs and ATs are primarily composed of small-sized (D m <1.4 mm) and medium-sized (1.4 mm ≤ D m <2.4 mm) precipitation particles, while STs contain a higher proportion of large-sized (D m ≥2.4 mm) precipitation particles. Convective precipitation during STs contributes most to the large-sized precipitation particles. The STs and the ATs are both dominated by stratiform precipitation, the proportion of convective precipitation is higher in the STs than that in the ATs. While the strong precipitation centres of ATs are primarily made up of small-sized and medium-to-small-sized (1.4 mm ≤ D m <1.7 mm) precipitation particles, the strong precipitation centres of STs are primarily made up of medium-to-large-sized (1.7 mm ≤ D m <2.4 mm) precipitation particles at various vertical profiles. Compared to ATs, the storm height and freezing level height of STs are typically higher, and these variables may contribute to divert D m to relatively larger droplet diameters. An examination of the RSD characteristics of STs and ATs in southern China may assist in advancing our comprehension of the microphysical characteristics of typhoon precipitation.

Understanding the Snow Cover Climatology over Turkey from ERA5-Land Reanalysis Data and MODIS Snow Cover Frequency Product

Understanding the distribution, patterns, and characteristics of snowfall and snow cover within a given region over extended periods is important. Snow climatology provides valuable insights into the seasonal and long-term variations in snowfall, helping researchers and meteorologists understand the impacts of climate change on snow accumulation, melt rates, and snowmelt runoff. In this study, in order to understand the spatial and temporal variation in snow cover in Turkey, the temporal and spatial dynamics of snow cover in the country were analyzed during the latest and longest period from 1970 to 2022 using ERA5-Land reanalysis product. It is aimed (1) to show snow-covered area (SCA), snow duration, and snow depth trends over the country; (2) to examine the altitudinal difference of snow phenology response to climate change; and (3) to evaluate the Snow Cover Frequency Maps from MODIS Snow Cover Products with the reanalysis snow depth data. It is found that the “false snow” mapping problem still exists in the MOD10C1_CGF Snow Cover Frequency maps over Turkey, especially in the melting period. We found that an increasing trend of 0.4 °C/decade and snow duration have a decreasing trend due to the early melting between 1970 and 2022. This trend is even more noticeable at elevations below 2000 m. Another important finding is the decreasing trend in snow duration at altitudes below 500 m, indicating a shift from snow to rain for precipitation types.

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.

Statistical evaluation of different surface precipitation-type algorithms and its implications for NWP prediction and operational decision making

Abstract Several new precipitation-type algorithms have been developed to improve NWP predictions of surface precipitation type during winter storms. In this study, we evaluate whether it is possible to objectively declare one algorithm as superior to another through comparison of three precipitation-type algorithms when validated using different techniques. The apparent skill of the algorithms is dependent on the choice of performance metric – algorithms can have high scores for some metrics and poor scores for others. It is also possible for an algorithm to have high skill at diagnosing some precipitation types and poor skill with others. Algorithm skill is also highly dependent on the choice of verification data/methodology. Just by changing what data is considered “truth,” we were able to substantially change the apparent skill of all algorithms evaluated herein. These findings suggest an objective declaration of algorithm “goodness” is not possible. Moreover, they indicate the unambiguous declaration of superiority is difficult, if not impossible. A contributing factor to algorithm performance is uncertainty of the microphysical processes that lead to phase changes of falling hydrometeors, which are treated differently by each algorithm thus resulting in different biases in near-0°C environments. These biases are evident even when algorithms are applied to ensemble forecasts. Hence, a multi-algorithm approach is advocated to account for this source of uncertainty. Though the apparent performance of this approach is still dependent on the choice of performance metric and precipitation type, a case-study analysis shows it has the potential to provide better decision support than the single-algorithm approach.

Warmer and wetter: Outlining climate services for snow-dependent tourism in Norway – The case of Lofoten

Human-induced climate change potentially impacts nature-based activities in Lofoten and may limit the attractiveness of the destination for tourists seeking recreation and adventure in the mountains. As a climate service, we calculated climate indicators relevant to the tourism sector based on the representative concentration pathways RCP4.5 and RCP8.5 until 2060. We used high-resolution gridded climate data and projections to calculate indicators such as changes in the frequency and intensity of consecutive wet days, changes in precipitation type (snow, sleet, rain), changes in the number of skiing days on ungroomed, natural snow, and changes of the monthly 0 °C-isoline. We found a minor, but non-robust increase in the number of consecutive wet days with a precipitation intensity > 8 mm/day, and a clear change in the precipitation regime depending on altitude that leads to more precipitation falling as rain instead of snow. Also, a strong decrease in the number of skiing days is projected by the climate models as the monthly near-surface 0 °C-isoline increases. These are important findings for long-term planning and investments in the tourism sector in Lofoten, especially as tourism growth is considered an important tool for regional economic development. The analytical methods used in this study are transferable to analyses on a regional to national scale. National maps and data material for 11 regions were recently published on https://klimaservicesenter.no/kss/framskr/sno-sludd-regn.

Drought Exerted a Stronger Controlling Effect on Soil Carbon Release than Moisturizing in a Global Meta-Analysis

The carbon cycle within a terrestrial ecosystem is a pivotal functional process that drives ecosystem evolution, and the precipitation pattern variations exert a profound influence on it. To comprehensively assess the response of carbon release in the global terrestrial ecosystem to water variation, we performed a global meta-analysis by extracting data from 144 publications. Additionally, we incorporated various moderators to elucidate the heterogeneity observed in the data. The results showed that soil carbon release was highly sensitive to water variation, with drying and moisturizing treatments responding differently to water variability. Specifically, drought inhibited the soil carbon release of terrestrial ecosystems (24% reduction in effect size), but precipitation promoted it (11% increase in effect size). Moreover, this sensitivity could be affected by other ambient factors, depending on water manipulation (drying or moisturizing treatment). In moisturizing treatment cases, ambient precipitation, altitude, and vegetation type more or less affected the sensitivity of soil carbon release to a water increase. However, in drying treatment cases, these factors had no significant influence on the water sensitivity of soil carbon release. Unlike the above ambient factors, a temperature increase strengthened this sensitivity in both of the treatments. In addition, our study also showed that the response of carbon release to water variation did not depend on the substrate type or the carbon–nitrogen ratio (C/N) of the substrates, revealing that these effect factors on carbon release on the local scale could be overshadowed by water conditions. Overall, water variation positively affected soil carbon release on the global scale. Particularly, drought had a strong controlling effect on carbon release over the other environmental factors. Therefore, the impact of soil water loss on carbon release should be of great concern for the management of ecosystems and the prediction of carbon release models, especially when high temperatures and drought have been occurring more and more frequently on the planet in recent years.

Research on a Clustering Forecasting Method for Short-Term Precipitation in Guangdong Based on the CMA-TRAMS Ensemble Model

In light of the 2020–2021 flood season in Guangdong, we conducted a comprehensive assessment of short-term precipitation forecasts generated by the ensemble prediction system (EPS) based on the China Meteorological Administration Tropical Regional Atmosphere Model for the South China Sea (CMA-TRAMS). Furthermore, we applied four distinct strategies to cluster the ensemble forecast data produced by the model for precipitation, aiming to enhance our understanding of their applicability in short-term precipitation forecasting for Guangdong. Our key findings were as follows.: Precipitation during the 2020–2021 flood season in Guangdong exhibited distinct characteristics. The impacting areas of frontal and subtropical high-edge rainfall were relatively scattered, predominantly occurring in the evening and nighttime. In contrast, monsoon precipitation and return-flow precipitation were concentrated, with their impacts lasting from early morning to evening. Notably, the errors using the ensemble maximum and minimum values were large, while the errors for the ensemble mean values and medians were small. This indicated that the model’s short-term precipitation forecasts possessed a high degree of stability. The vertical shear of different types of precipitation exerted a noticeable influence on the model’s performance. The model consistently displayed a tendency to underestimate short-term precipitation in Guangdong; however, this bias decreased with longer lead times. Simultaneously, the model’s dispersion increased with longer lead times. In terms of mean absolute error (MAE) test results, there was little difference in the performance of ensemble primary forecasts under various strategies, while the “ward” strategy performed well in sub-primary cluster forecasts. This was particularly true for areas and types of precipitation where the model’s performance was poor. While the clustering approach lagged behind ensemble mean forecasts in predicting rainy conditions, it exhibited improvement in forecasting short-term heavy rainfall events. The “complete” and “single” strategies consistently delivered the most accurate forecasts for such events. Our study sheds light on the effectiveness of clustering methods in improving short-term precipitation forecasts for Guangdong, particularly in regions and conditions where the model initially struggled. These findings contribute to our understanding of precipitation forecasting during flood seasons and can inform strategies for enhancing forecast accuracy in similar contexts.

Synthesis of MgO powders by precipitant for transparent ceramics

In this experiment, MgO powder was prepared through co-precipitation using NH3·H2O and NH4HCO3 as precipitant. The study aimed to investigate the effects of precipitant content and type on the yield of MgO powders. It was found that the highest MgO yield was obtained using a mixture of NH4HCO3 and NH3·H2O as the precipitant. Furthermore, NH3·H2O as the sole precipitant resulted in the smallest particle size. However, an increase in the precipitant content led to a decrease in the specific surface area of the powders and caused powder agglomeration. Subsequently, MgO transparent ceramics were fabricated using a hybrid sintering method involving hot pressing followed by a subsequent hot isostatic pressing treatment. The best microstructure and the highest infrared transmittance of 81% at 5.81 μm were achieved for the MgO transparent ceramic samples hot-pressed at 1500 °C and then subjected to hot isostatic pressing at 1500 °C. The relative density reached 99.97%.

A Machine-Learning Method to Integrate Arctic Supersite Observations and Diagnose Weather Element Occurrence

ABSTRACT The accurate detection and quantification of light precipitation is problematic, particularly in the Arctic region. Satellite and ground-based observations of light precipitation are frequently underestimated at high latitudes. Remote sensing and in-situ observations from the Iqaluit, NU supersite (64oN, 69oW) were integrated to train, develop, and validate a random forest (RF) model that can diagnose precipitation type and other weather element occurrences. Observations from multiple lidars, optical disdrometers, traditional precipitation gauges and meteorological aerodrome (METAR) reports from 2015–2020 were integrated and used in the RF model development. The model was trained at Iqaluit, validated over different time periods, and applied to another region (Whitehorse, YT; 61oN, 135oW). Results indicate the importance of accurate visibility observations to train the model. Overall, the RF model was capable of distinguishing precipitation types and demonstrated the potential to be used at all sites/networks where similar automated and cost-effective instruments are already deployed (e.g. radar sites, airports with ceilometers, etc.). This would reduce the dependency on METARs while improving weather element occurrence accuracy.

Lattice Boltzmann modelling of salt precipitation during brine evaporation

Salt precipitation during brine evaporation in porous media is an important phenomenon in a variety of natural and engineering scenarios. This work establishes a multiphase multicomponent lattice Boltzmann (LB) method with phase change for simulating salt precipitation during brine evaporation. In the proposed LB models, the gas–brine multiphase flow, brine evaporation, salt concentration evolution, salt precipitate nucleation and growth are simultaneously considered. Simulations of the Stefan problem are first conducted to verify the proposed numerical models and determine the diffusion coefficient of brine vapour. Once the lattice Boltzmann models have been validated, salt precipitation during brine evaporation is simulated to investigate the competition mechanisms between salt precipitate nucleation and growth reaction. The results show that the typical salt precipitation patterns in existing experimental observation can be successfully reproduced, including the ring-like and pancake-like patterns. The difference in the salt precipitation patterns is explained by the competition mechanism between precipitate growth and nucleation according to the present study. Furthermore, the salt precipitation during gas injection into a microfluidic chip is investigated. The evolution of salt and brine saturation shows similar patterns to existing experimental results, and the effects of the gas injection rate on salt precipitation performance are clarified. The LB models in the present work can simulate salt precipitation with comprehensive consideration of multiphase brine evaporation, salt species mass transport, precipitate nucleation and growth, which have not been realized in previous studies. The numerical showcases demonstrate the excellent performance of the proposed models for the simulation of salt precipitation in porous media, which promise to guide practical engineering applications like CO2 sequestration.

Evaluation of precipitation measurement methods using data from a precision lysimeter network

Abstract. Accurate precipitation data are essential for assessing the water balance of ecosystems. Methods for point precipitation determination are influenced by wind, precipitation type and intensity and/or technical issues. High-precision weighable lysimeters provide precipitation measurements at ground level that are less affected by wind disturbances and are assumed to be relatively close to actual precipitation. The problem in previous studies was that the biases in precipitation data introduced by different precipitation measurement methods were not comprehensively compared with and quantified on the basis of those obtained by lysimeters in different regions in Germany. The aim was to quantify measurement errors in standard precipitation gauges as compared to the lysimeter reference and to analyze the effect of precipitation correction algorithms on the gauge data quality. Both correction methods rely on empirical constants to account for known external influences on the measurements, following a generic and a site-specific approach. Reference precipitation data were obtained from high-precision weighable lysimeters of the TERrestrial ENvironmental Observatories (TERENO)-SOILCan lysimeter network. Gauge types included tipping bucket gauges (TBs), weighable gauges (WGs), acoustic sensors (ASs) and optical laser disdrometers (LDs). From 2015-2018, data were collected at three locations in Germany, and 1 h aggregated values for precipitation above a threshold of 0.1 mm h−1 were compared. The results show that all investigated measurement methods underestimated the precipitation amounts relative to the lysimeter references for long-term precipitation totals with catch ratios (CRs) of between 33 %–92 %. Data from ASs had overall biases of −0.25 to −0.07 mm h−1, while data from WGs and LDs showed the lowest measurement bias (−0.14 to −0.06 mm h−1 and −0.01 to −0.02 mm h−1). Two TBs showed systematic deviations with biases of −0.69 to −0.61 mm h−1, while other TBs were in the previously reported range with biases of −0.2 mm h−1. The site-specific and generic correction schemes reduced the hourly measurement bias by 0.13 and 0.08 mm h−1 for the TBs and by 0.09 and 0.07 mm h−1 for the WGs and increased long-term CRs by 14 % and 9 % and by 10 % and 11 %, respectively. It could be shown that the lysimeter reference operated with minor uncertainties in long-term measurements under different site and weather conditions. The results indicate that considerable precipitation measurement errors can occur even at well-maintained and professionally operated stations equipped with standard precipitation gauges. This generally leads to an underestimation of the actual precipitation amounts. The results suggest that the application of relatively simple correction schemes, manual or automated data quality checks, instrument calibrations, and/or an adequate choice of observation period can help improve the data quality of gauge-based measurements for water balance calculations, ecosystem modeling, water management, assessment of agricultural irrigation needs, or radar-based precipitation analyses.

Combined tillage: A management strategy to improve rainfed maize tolerance to extreme events in northwestern China

Climate warming has increased the frequency of droughts and excessive precipitation, adversely affecting crop growth, particularly under traditional intensive tillage. No-till improves crop tolerance to extreme events by reducing soil evaporation and improving soil structural stability to enhance soil water storage capacity and crop resistance, but long-term mono-no-till cakes the soil, reducing crop yield. Combining intensive tillage with no-till can compensate for some deficiencies arising from conventional tillage or single no-till. A three-year field experiment was conducted in wet (2020) and normal (2019 and 2021, where a drought event occurred in 2021) years to study the effect of tillage practices on summer maize productivity under different precipitation types. Treatments included conventional tillage (CT), no-tillage (NT), ridge cultivation with no-tillage (RNT), and conventional tillage of winter wheat combined with no-tillage of summer maize (NC). Compared with NT, NC and RNT significantly reduced soil bulk density and increased soil porosity in the 0–20 cm soil layer. Compared with CT, NC and RNT significantly improved aggregate stability, NC increased available soil water storage by 19.7% in the dry season (P < 0.05), and NC and RNT significantly reduced lodging rate in the rainy season. Over the three years, NC and RNT maintained higher maize yields (NC: 10.3 t ha–1 and RNT: 10.0 t ha–1) than CT (9.2 t ha–1), and NC had significantly higher yield stability than CT. Meanwhile, NC and RNT had higher precipitation use efficiency (PUE; NC: 21.2 kg ha–1 mm–1, RNT: 20.7 kg ha–1 mm–1) than NT (20.1 kg ha–1 mm–1) or CT (19.1 kg ha–1 mm–1). In terms of combined productivity, NC and RNT provide a more suitable soil environment for crop growth and maintain higher yield than NT and CT. NC rotation is recommended as the optimal tillage system for sustainable crop production under semi - arid agricultural conditions. RNT can be extended to areas prone to flooding with abundant rainfall. These results offer a benchmark for future studies on regional maize production under climate change.

The Role of Changbai Mountain in an Extreme Precipitation Event in Liaoning Province, China

Based on the half-hourly Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data product (0.1°), the fifth-generation European Center for Medium-Range Weather Forecasting atmospheric reanalysis dataset (ERA5), sounding data, and the Weather Research and Forecasting Model (WRF-ARW), this study explored the developmental process of a typical extreme precipitation event in Liaoning Province on 2 June 2021. This study focused on the impact of Changbai Mountain on this precipitation process and its corresponding physical mechanisms. The research findings revealed that Changbai Mountain significantly affected the precipitation event in three main aspects: blocking drag, forcing uplift, and leeside convergence. The blocking drag caused by the mountain topography led to an extension in the duration of heavy rainfall. The dynamic lifting and leeside convergence associated with the mountainous terrain also substantially increased the amount of precipitation. Furthermore, the topography hindered the movement of the Bohai Bay cold pool and enhanced the intensity of the cold pool, contributing to the sustained extreme precipitation in Liaoning Province. Lastly, the terrain sensitivity experiment demonstrated that when the height of Changbai Mountain was reduced, the convergence uplift, moisture condensation, and cold pool intensity were weakened, leading to significant changes in precipitation intensity and spatial distribution. These findings further confirm the crucial role of Changbai Mountain in the occurrence and development of local precipitation in Liaoning Province.

Assessment of the Vulnerability of Riverine Cities and Their Coping Capacity against Floods (Case Study: Ahvaz Metropolis)

Based on global statistics, floods rank the first among natural disasters that severely disrupt human life, property, and the environment. Iran, as one of the flood-prone countries, is located in southwestern Asia. The geographical elevation, maritime influences, seasonal winds, and proximity to mountain ranges or deserts play a significant role in its daily and seasonal temperature fluctuations, as well as its precipitation patterns and types. According to official statistics in Iran, among climate-related hazards, some 70% of the annual funds of the Plan for Reducing the Effects of Natural Disasters have been spent on compensation for damages from floods. Yet, what augments the importance of floods and their damages is the fact that human and urban centers have been located in flood-prone areas. Therefore, considering the dense populations and financial capitals in such areas, it is necessary to assess the vulnerability of urban floods, especially in big cities like Ahvaz. This metropolis, as the capital of Khuzestan province, is located in the marshy plains with a population of over one million people. It is traversed by the country’s most water-rich river, the Karun. The aim of this study is to assess the vulnerability of Ahvaz from flood as a natural hazard and its coping capacity. The methodology of the study is descriptive-analytical and is based on its practical purpose. Fuzzy Hierarchy Model (Fuzzy Analytic Hierarchy Process) has been used for the spatial analysis of vulnerability and the capacity to deal with floods. In summary, the findings are as follows: (a) Considering the floods that have occurred in the past six decades and comparing the affected areas with the results of this research in terms of highly vulnerable areas in the central part of the city, the model used has shown high efficiency in determining the vulnerability caused by urban flooding. (b) As far as vulnerability is concerned, location and physical–environmental conditions stimulate and reinforce other factors which are influential in determining the level of vulnerability. (c) Encroachment on river boundaries, especially limiting the river width, leads to a reduction in flood capacity and an increase in vulnerability in those areas. (d) The vulnerability level of Ahvaz to flooding, based on area, falls within five categories of vulnerability spectrum as follows: Very High 4.6%, High 8.7%, Moderate 13.4%, Low 15.2%, and Very Low 58%.

Winter Precipitation Type from Microwave Radiometers in New York State Mesonet Profiler Network

Abstract Winter precipitation is a major cause of vehicle accidents, aviation delays, school and business closures, injuries through slips and falls, and adverse health impacts such as cardiac arrests and deaths. However, an improved ability to monitor and predict winter precipitation type (p-type) could significantly reduce and mitigate these adverse impacts. This study presents and evaluates a modified parcel thickness method to derive p-type from a microwave radiometer (MWR) every 10 min. The MWR-retrieved p-types from six selected New York State Mesonet (NYSM) profiler network sites are validated against reference observations from the Meteorological Phenomena Idenfication Near the Ground (mPING) and Automated Surface Observing System (ASOS). Between the two reference observations, the mPING reports are biased toward snow (SN) and sleet (SLT) and away from rain (RA) and freezing rain (FZR) compared to the ASOS. The MWR has the best Pierce skill score (PSS) for RA, followed by SN, FZR, and SLT, and consistently overforecasts FZR and underforecasts SLT compared to both mPING and ASOS. The MWR p-type retrievals are generally found to be in better agreement with ASOS than mPING. Continuous thermodynamic profiles and p-type estimates from across all 17 profiler sites are available at http://www.nysmesonet.org/networks/profiler. Having such thermodynamic information from across the state can be a valuable resource, with a significant advantage over twice daily NWS radiosondes, for monitoring and tracking hazardous winter weather in real time, for accurate forecasting, and for issuing timely warnings and alerts. Significance Statement Accurate prediction and monitoring of winter precipitation type (p-type) is important due to the adverse economic and health impacts posed by winter weather. However, complexities in understanding and modeling the processes that govern p-type and inadequate observational data limit accurate monitoring and prediction. To address these issues, a ground-based microwave radiometer (MWR) that provides thermodynamic profiles up to 10 km every 2 min, as deployed at 17 sites in the New York State Mesonet (NYSM) profiler network, can be a valuable tool. This study evaluates the accuracy of p-type estimates based on the parcel thickness method from the MWR data and its implementation to the NYSM real-time operations.

Improving CINRAD Radar QPE Through GPM‐DPR Reflectivity Bias Correction

AbstractReflectivity bias of ground‐based weather radar (GR) is a common error source that can lead to biases in quantitative precipitation estimation (QPE). In this study, the Dual‐frequency Precipitation Radar onboard the Global Precipitation Measurement Mission Core Observatory (GPM‐DPR) is used to correct GR reflectivity bias. The reflectivity bias correction comprehensively considers variations in GR‐DPR reflectivity difference associated with radar frequency difference, data matching approach, precipitation type, GR orographic beam blockage, attenuation, and sample size. The criteria to perform a robust GR‐DPR comparison are presented. Disdrometer validation, based on eight typical rain events from April to August 2019, demonstrates that GPM‐DPR can effectively reduce GR reflectivity bias to ±1 dB. For the period from June to August 2021, the reflectivity correction method is applied to 17 operational GRs. The multiradar QPE shows a significant increase in correlation coefficient (from 0.62 to 0.71), and decreases in relative mean bias (from −0.28 to −0.19) and root mean square error (from 3.96 to 3.54 mm) compared to rain gauges. These results confirm that GPM‐DPR can effectively correct the reflectivity biases of a GR network and improve multiradar QPE accuracy and consistency.