Probability Of Precipitation Research Articles

Non-stationary large-scale statistics of precipitation extremes in central Europe

Extreme precipitation shows non-stationarity, meaning that its distribution can change with time or other large-scale variables. For a classical frequency-intensity analysis this effect is often neglected. Here, we propose a model including the influence of North Atlantic Oscillation, time, surface temperature and a blocking index. The model features flexibility to use annual maxima as well as seasonal maxima to be fitted in a generalized extreme value setting. To further increase the efficiency of data usage, maxima from different accumulation durations are aggregated so that information for extremes on different time scales can be provided. Our model is trained to individual station data with temporal resolutions ranging from one minute to one day across Germany. Models are chosen with a stepwise BIC model selection and verified with a cross-validated quantile skill index. The verification shows that the new model performs better than a reference model without large-scale information. Also, the new model enables insights into the effect of large-scale variables on extreme precipitation. Results suggest that the probability of extreme precipitation increases with time since 1950 in all seasons. High probabilities of extremes are positively correlated with blocking situations in summer and with temperature in winter. However, they are negatively correlated with blocking situations in winter and temperature in summer.

Concurrent influence of geological parameters on the integrated nano-pore structure and discretized pore families of the petroliferous Cambay shale assessed through multivariate dependence measure

Heterogeneous nanopore structure and distribution regulate the gas trapping, desorption kinetics, and diffusion in shale matrices. In shale, pores range from continuous micro- and mesopore size distributions, varying with organic (total organic matter-TOC) and inorganic constituents (clay content, Fe-bearing minerals, quartz, etc.). Previous research only showed a linear relationship of pore parameters with these intrinsic properties of shale, which limits our understanding of the concurrent influence of multiple intrinsic rock properties. As a result, in this work, we established multivariate dependency of nanopore structure, distribution, and complexity (from low-pressure N2 and CO2 sorption and small-angle scattering; SAXS/MSANS) in the previously little-studied Cambay shales and provided a better tool (partial least square regression) for analyzing the simultaneous effect of intrinsic shale properties on multiply connected pore-parameters. Furthermore, we discretized continuous pore-size distribution into individual pore families using deconvolution to understand the pore space better. Additionally, predicted shale formation environment in terms of deposition probability (P+) and dissolution probability (P−) using a dynamic model of the fractal interface by precipitation and dissolution. Our findings indicate that the Cambay shales have a high potential for future hydrocarbon exploration (S2: 2.42–12.04 mg HC/g rock), “very good” (2-4 wt.%) to “excellent” (>4 wt.%) TOC content, and thermally mature type II–III admixed and type III kerogen. Deconvolution of the micro- and mesopore size distributions reveals that pore width (w) ranges ∼15.30–35 nm occupies greater than 50% of the total pore volume, and its pore volume increases with the presence of quartz, Fe-bearing minerals, and clay content. However, pores with w∼ 3.60–15.30 nm increase exclusively with TOC. In the micro- and early mesopore region, pore volume decreases with TOC from w∼ 0.30–0.75 nm and increases with TOC from w∼ 0.75–3.60 nm. Furthermore, TOC in shale increases the specific surface area and pore volume (micro-, meso-, and total pores), enhancing both sorption and free gas storage capacities. Cambay shales were likely deposited in three distinct environments, with precipitation probability (P+) values of 1, 0.7–0.8, and 0.5, as revealed by a fractal dimension (Ds) analysis of multiple samples.

Large-scale circulation dominated precipitation variation and its effect on potential water availability across the Tibetan Plateau

The large-scale circulation, Indian summer monsoon (ISM), has a strong influence on the Tibetan Plateau (TP) since its onset and intensity have profound impacts on regional precipitation, which then can supply water for glaciers, lakes, rivers and social demands. Weakening monsoon intensity and longer monsoon duration seem contradictory, as a weaker monsoon tends to produce less precipitation, while a longer duration increases the probability of precipitation. Past research has focused on how ISM’s intensity affects precipitation, with little consideration of the impacts of ISM duration. Here, we investigate the long-term (1979–2100) variability in the ISM’s duration and intensity. We find a prolonged ISM from 1979 to 2018, accompanied by monsoon weakening. Different combinations of duration and intensity have resulted in different spatial patterns of precipitation across the southeastern TP. Weakening and prolonged ISM is helpful to produce more precipitation around the southeastern TP, with intensity acting as a dominant control. Afterwards, an obvious impact can be found on potential water availability. Climate projections suggest that the ISM will weaken and lengthen until 2100, thereby increasing precipitation and potential water availability across the southeastern TP. This long-term trend should receive more attentions as increased regional extreme precipitation will increase the probability of flood risks until the end of this century.

Precipitation Probability Prediction through NWP Bias Correction for South Korea Using Random Forest

This study presents the results of an effort to improve the forecast of precipitation (> 0.1 mm/hr or > 0.1 mm/3hr) in the Local Data Assimilation and Prediction System (LDAPS) and the Global Data Assimilation and Prediction System (GDAPS) by applying the Random Forest (RF) model in South Korea. LDAPS and GDAPS are Numerical Weather Prediction (NWP) models operated by the Korea Meteorological Administration (KMA) for weather forecasting. GDAPS operates the Unified Model (UM) and the Korean Integrated Model (KIM). This study used weather forecast data from LDAPS, GDAPS/KIM, and GDAPS/UM. Precipitation forecasts from LDAPS and GDAPS were corrected by RF training with rain gauge observations from about 685 stations. Approximately 35 selected NWP model output variables were used as inputs to the RF training. To reflect recent trends in biases between observations and NWP, the precipitation probability prediction model was designed for real-time learning using a sliding window technique. In addition, the precipitation data had a data imbalance problem with more precipitation cases than non-precipitation cases, so an under-sampling method was applied to solve this problem. Comparing the performance of the proposed method with NWP in predicting precipitation, the CSI was improved by 14.7-23.1% (LDAPS), 33.9% (GDAPS/KIM), and 6.7%-38% (GDAPS/UM) over NWP, and the accuracy was also better. In future research, automating the sampling rate selection to reflect recent weather trends when under-sampling is likely to improve forecast performance.

Improving National Blend of Models Probabilistic Precipitation Forecasts Using Long Time Series of Reforecasts and Precipitation Reanalyses. Part I: Methods

Abstract This article describes proposed revised methods for the statistical postprocessing of precipitation amount intended for the NOAA’s National Blend of Models using the Global Ensemble Forecast System version 12 data (GEFSv12). The procedure updates the previously established procedure of quantile mapping, weighting of sorted members, and dressing of the ensemble. The revised method leverages the long reforecast training dataset that has become available to improve quantile mapping of GEFSv12 data by eliminating the use of supplemental locations, that is, training data from other grid points. It establishes improved definitions of cumulative distributions through a spline-fitting approach. It provides updated algorithms for the weighting of sorted members based on closest-member histogram statistics, and it establishes an objective method for the dressing of the quantile-mapped, weighted ensemble. Verification statistics and case studies are provided in the accompanying article (Part II).

Improving National Blend of Models Probabilistic Precipitation Forecasts Using Long Time Series of Reforecasts and Precipitation Reanalyses. Part II: Results

Abstract This second part of the series presents results from verifying a precipitation forecast calibration method discussed in the first part, based on quantile mapping (QM), weighting of sorted members, and dressing of the ensemble. NOAA’s Global Ensemble Forecast System, version 12 (GEFSv12), reforecasts were used in this study. The method was validated with preoperational GEFSv12 forecasts from December 2017 to November 2019. The method is proposed as an enhancement for GEFSv12 precipitation postprocessing in NOAA’s National Blend of Models. The first part described adaptations to the methodology to leverage the ∼20-yr GEFSv12 reforecast data. As shown here in this part, when compared with probabilistic quantitative precipitation forecasts from the raw ensemble, the adapted method produced downscaled, high-resolution forecasts that were significantly more reliable and skillful than raw ensemble-derived probabilities, especially at shorter lead times (i.e., <5 days) and for forecasts of events from light precipitation to >10 mm (6 h)−1. Cool-season events in the western United States were especially improved when the QM algorithm was applied, providing a statistical downscaling with realistic smaller-scale detail related to terrain features. The method provided less value added for forecasts of longer lead times and for the heaviest precipitation.

Determining optimal probability distributions for gridded precipitation data based on L-moments

Understanding the probability distributions of precipitation is crucial for predicting climatic events and constructing hydraulic facilities. To overcome the inadequacy of precipitation data, regional frequency analysis was commonly used by “trading space for time”. However, with the increasing availability of gridded precipitation datasets with high spatial and temporal resolutions, the probability distributions of precipitation for these datasets have been less explored. We used L-moments and goodness-of-fit criteria to identify the probability distributions of annual, seasonal, and monthly precipitation for a 0.5° × 0.5° dataset across the Loess Plateau (LP). We examined five 3-parameter distributions, namely General Extreme Value (GEV), Generalized Logistic (GLO), Generalized Pareto (GPA), Generalized Normal (GNO), and Pearson type III (PE3), and evaluated the accuracy of estimated rainfall using the leave-one-out method. We also presented pixel-wise fit-parameters and quantiles of precipitation as supplements. Our findings indicated that precipitation probability distributions vary by location and time scale, and the fitted probability distribution functions are reliable for estimating precipitation under various return periods. Specifically, for annual precipitation, GLO was prevalent in humid and semi-humid areas, GEV in semi-arid and arid areas, and PE3 in cold-arid areas. For seasonal precipitation, spring precipitation mainly conforms to GLO distribution, summer precipitation around the 400 mm isohyet prevalently follows GEV distribution, autumn precipitation primarily meets GPA and PE3 distributions, and winter precipitation in the northwest, south, and east of the LP mainly conforms to GPA, PE3 and GEV distributions, respectively. Regarding monthly precipitation, the common distribution functions are PE3 and GPA for the less-precipitation months, whereas the distribution functions of precipitation for more-precipitation months vary substantially across different regions of the LP. Our study contributes to a better understanding of precipitation probability distributions in the LP and provides insights for future studies on gridded precipitation datasets using robust statistical methods.

Regionally high risk increase for precipitation extreme events under global warming

Daily precipitation extremes are projected to intensify with increasing moisture under global warming following the Clausius-Clapeyron (CC) relationship at about 7% /^circ {text{C}} . However, this increase is not spatially homogeneous. Projections in individual models exhibit regions with substantially larger increases than expected from the CC scaling. Here, we leverage theory and observations of the form of the precipitation probability distribution to substantially improve intermodel agreement in the medium to high precipitation intensity regime, and to interpret projected changes in frequency in the Coupled Model Intercomparison Project Phase 6. Besides particular regions where models consistently display super-CC behavior, we find substantial occurrence of super-CC behavior within a given latitude band when the multi-model average does not require that the models agree point-wise on location within that band. About 13% of the globe and almost 25% of the tropics (30% for tropical land) display increases exceeding 2CC. Over 40% of tropical land points exceed 1.5CC. Risk-ratio analysis shows that even small increases above CC scaling can have disproportionately large effects in the frequency of the most extreme events. Risk due to regional enhancement of precipitation scale increase by dynamical effects must thus be included in vulnerability assessment even if locations are imprecise.

In-situ observation and evaluation of silver precipitation behavior in borosilicate glass

Applying voltage to alkali-containing glass, doped with silver ions by the solid-state ion-exchange method, yields silver precipitates inside the glass. We attempted in-situ observation of the precipitation phenomena to elucidate the silver precipitation mechanisms, where the direction of the voltage application was rotated 90 deg. to increase the precipitation area compared to that of our previous method. The results show that the shape of the precipitate changed from clumpy to dendritic as the applied voltage increased. The shape of the precipitate, predicted by the dielectric breakdown model, implied that the higher the precipitation probability (i.e., the higher the frequency of a silver ion to encounter with the precipitate), the more branches the precipitate formed. In addition, the un-doped, silver-doped, and silver-precipitated areas inside the glass were modeled as electrical resistances to predict the growth behavior of the precipitated areas.

The formation of atmospheric air pollution emissions from separate explosions as a result of military operations

As is well known, military operations in urbanized areas lead to deterioration of atmospheric air quality. The article examines the model of the formation of atmospheric polluted air from individual explosions as a result of military operations. A technique based on the theory of the convective jet is proposed for estimating the concentrations of hazardous substances formed above the burning area as a result of the explosion. The proposed approach makes it possible to obtain the concentration of pollutants in the conditions of emission into the atmosphere open to solar radiation at a height of three or more diameters of the explosion funnel (or the conventional diameter of the warm surface), the temperature and speed of hot air jets over the warm surface, the consumption of the desired substance. The obtained data make it possible to calculate the emission of hazardous substances into the atmosphere according to the formulas for a high unshaded source of pollution depending on the meteorological conditions of the area, to determine the concentration of the main acid-forming substances when assessing the probability of acid precipitation, and to serve as a basis for modern innovative atmospheric air monitoring systems.An assessment of the existing approach to determining atmospheric air pollution due to the impact of X-101 missile fragments in Kyiv on December 16, 2022 was made, which is a confirmation of the validity of this approach when calculating the impact of a separate explosion on atmospheric air quality. Calculated emissions of pollutants and their concentrations in the air in the upper part of the convective polluted jet in the case of separate explosions of the Kalibr cruise missile, the Kh-101 missile and the BUK-M1 projectile. The error from the comparison of these calculation results with the atmospheric air monitoring system depends on the nearest active observation points. This approach to assessing and forecasting concentrations of pollutants in atmospheric air can be used under neutral atmospheric conditions that determine the emission of pollutants and transformations of chemical substances in the air.

Variations of extreme precipitation events with sub-daily data: a case study in the Ganjiang River basin

Abstract. Climate warming increases the intensity of extreme precipitation. Studying extreme precipitation patterns and changes is vital to reducing risk. This paper investigates thresholds, changes and timescales for extreme precipitation using sub-daily records from meteorological stations in the Ganjiang River basin. We use the gamma distribution and select the L-moment method to estimate the parameters α and β. Results show that (1) continuous precipitation events of 36 h contributed the most precipitation to the total but with lower frequency, which would be key events for flood monitoring; (2) the intensity and the occasional probability of extreme precipitation will increase in spring in the future in stations like Yifeng, Zhangshu and Ningdu, which will in turn increase the risk of storm floods; and (3) spatial distribution of extreme precipitation risk shows that the risk increases as elevation increases in the northern lowland and the Jitai Basin in the midstream region, while the risk in the southern mountainous region decreases as elevation increases. These findings will facilitate emergency preparedness, including risk management and disaster assistance, in the study areas.

Postprocessing of NWP Precipitation Forecasts Using Deep Learning

Abstract This study analyzes the potential of deep learning using probabilistic artificial neural networks (ANNs) for postprocessing ensemble precipitation forecasts at four observation locations. We split the precipitation forecast problem into two tasks: estimating the probability of precipitation and predicting the hourly precipitation. We then compare the performance with classical statistical postprocessing (logistical regression and GLM). ANNs show a higher performance at three of the four stations for estimating the probability of precipitation and at all stations for predicting the hourly precipitation. Further, two more general ANN models are trained using the merged data from all four stations. These general ANNs exhibit an increase in performance compared to the station-specific ANNs at most stations. However, they show a significant decay in performance at one of the stations at estimating the hourly precipitation. The general models seem capable of learning meaningful interactions in the data and generalizing these to improve the performance at other sites, which also causes the loss of local information at one station. Thus, this study indicates the potential of deep learning in weather forecasting workflows.

Machine Learning of Key Variables Impacting Extreme Precipitation in Various Regions of the Contiguous United States

AbstractAmplification in extreme precipitation intensity and frequency can cause severe flooding and impose significant social and economic consequences. Variations in extreme precipitation intensity, frequencies, and return periods can be attributed to many physical variables across spatial and temporal scales. Here we employ ensemble machine learning (ML) methods, namely random forest (RF), eXtreme Gradient Boosting (XGB), and artificial neural networks (ANN), to explore key contributing variables to monthly extreme precipitation intensity and frequency in six regions over the United States. We further establish emulators for return periods. Results show that the ML models for intensity perform better in regions with obvious seasonality (i.e., Northern Great Plains, Southern Great Plains, and West Coast) than the other three regions (Northeast, Southwest, and Rocky Mountains), while for frequency the models perform well for most regions. The Shapley additive explanation is used to help explain the relationships between extreme precipitation characteristics and identify top variables for RF and XGB. We find that latent heat flux, relative humidity, soil moisture, and large‐scale subsidence are key common variables across the regions for both monthly intensity and frequency, and their compound effects are non‐negligible. The developed ML models capture the probability and return period of extreme precipitation well for all regions and may be used for decision making (e.g., infrastructure planning and design).

Sub-catchment-based urban flood risk assessment with a multi-index fuzzy evaluation approach: a case study of Jinjiang district, China

Urban flood risk assessment requires attention in inland areas with intensifying climate change and an increasing probability of extreme precipitation. This study describes the developments and testing of a sub-catchment-based multi-index fuzzy evaluation approach that can provide adaptation guidance for municipal decision-makers at a local level. We first built a comprehensive flood risk assessment system considering three categories: hazard, urban system, and social environment. The proposed evaluation system includes hybrid uncertain information that involves random indicator sources and hesitant fuzzy judgments from experts. The storm weather management model combined with geographic information system tools was then applied to obtain random indicators. Subsequently, hesitant fuzzy linguistic sets and the Euclidean distance method were adopted to solve the problem of uncertainty and vagueness from subjective hesitant information. Therefore, the aggregation method provides a beneficial way to assess flood risk in a hybrid uncertain environment. In addition, the proposed approach was applied to the Jinjiang district in an inland city in the P. R. of China. This supports efforts to prioritize locally tailored policies and practical measures for higher-risk sub-catchments within large urban systems.

Non-asymptotic Weibull tails explain the statistics of extreme daily precipitation

The exceedance probability of extreme daily precipitation is usually quantified assuming asymptotic behaviours. Non-asymptotic statistics, however, would allow us to describe extremes with reduced uncertainty and to establish relations between physical processes and emerging extremes. These approaches are still mistrusted by part of the community as they rely on assumptions on the tail behaviour of the daily precipitation distribution. This paper addresses this gap. We use global quality-controlled long rain gauge records to show that daily precipitation annual maxima are samples likely emerging from Weibull tails in most of the stations worldwide. These non-asymptotic tails can explain the statistics of observed extremes better than asymptotic approximations from extreme value theory. We call for a renewed consideration of non-asymptotic statistics for the description of extremes.

Water vapour multi-vortex structure under the interactions of typhoons and mid-low latitude systems during extreme precipitation in North China

With diverse atmospheric circulation and complex mechanisms, typhoon remote rainstorms (TRR) are closely related to a variety of mid-low latitude systems. The anomalous signatures in such processes can improve forecasting heavy rainfall associated with typhoons and fill the gaps in current physical conceptual models of TRR. Using the optimal typhoon path dataset, ground observation data, and the National Centres for Environmental Prediction (NCEP) reanalysis dataset, the impact of anomalous water vapour transport on excessive rainfall in North China is investigated. The vector empirical orthogonal function (VEOF) analysis is applied to typhoon precipitation events from 1970 to 2021, and three major modes are extracted to reflect the anomalous water vapour transport for typhoon precipitation. Mode 1 reflects a moisture circulation pattern in North China due to the coexistence of typhoons and remote and direct precipitation, with the highest probability of heavy precipitation. The differences in remote precipitation areas and intensities are attributed to the location of typhoons in the western North Pacific and the shifting of the subtropical high ridge. To characterise the water vapour transport circulation of TRR in North China, we proposed the ‘multi-vortex’ idea. The transmission of the multi-vortex ensures a continuous supply of TRR water vapour, and the enhancement of the multi-vortex is significantly linked to the enhancement of remote precipitation. Three extreme northern rainstorms, 75·8 Henan, 7·21 Beijing, and 7·20 Zhengzhou rainstorms, have anomalous multi-vortex water vapour convergence, similar to Mode 1. The mechanism of water vapour driven by multi-vortex in the three severe rainstorm events is more extreme than in usual TRR events in North China. The stronger Indian low vortex and relatively southerly subtropical highs can intensify the southwest branch of the double water vapour transport branch, whereas the strengthening of typhoons in the western North Pacific facilitates water vapour transport through the channel on the southwestern side of the subtropical highs and then transports it to key rainstorm areas, promoting TRR development. Therefore, the combination of subtropical highs, typhoons, and the anomalous multi-vortex structure may help in the establishment of key indicators of extreme precipitation in North China.

Rising trends of global precipitable water vapor and its correlation with flood frequency

Using 4 global reanalysis data sets, significant upward trends of precipitable water vapor (PWV) were found in the 3 time periods of 1958–2020, 1979–2020, and 2000–2020. During 1958–2020, the global PWV trends obtained using the ERA5 and JRA55 data sets are 0.19 ± 0.01 mm per decade (1.15 ± 0.31%) and 0.23 ± 0.01 mm per decade (1.45 ± 0.32%), respectively. The PWV trends obtained using the ERA5, JRA55, NCEP-NCAR, and NCEP-DOE data sets are 0.22 ± 0.01 mm per decade (1.18 ± 0.54%), 0.21 ± 0.00 mm per decade (1.76 ± 0.56%), 0.27 ± 0.01 mm per decade (2.20 ± 0.70%) and 0.28 ± 0.01 mm per decade (2.19 ± 0.70%) for the period 1979–2020. During 2000–2020, the PWV trends obtained using ERA5, JRA55, NCEP-DOE, and NCEP-NCAR data sets are 0.40 ± 0.25 mm per decade (2.66 ± 1.51%), 0.37 ± 0.24 mm per decade (2.19 ± 1.54%), 0.40 ± 0.26 mm per decade (1.96 ± 1.53%) and 0.36 ± 0.25 mm per decade (2.47 ± 1.72%), respectively. Rising PWV has a positive impact on changes in precipitation, increasing the probability of extreme precipitation and then changing the frequency of flood disasters. Therefore, exploring the relationship between PWV (derived from ERA5 and JRA55) change and flood disaster frequency from 1958 to 2020 revealed a significant positive correlation between them, with correlation coefficients of 0.68 and 0.79, respectively, which explains the effect of climate change on the increase in flood disaster frequency to a certain extent. The study can provide a reference for assessing the evolution of flood disasters and predicting their frequency trends.

Оцінка утворення осаду, сумісності та ставлення до розчинників вбирних олив у аспекті експлуатаційних властивостей.

Currently, coke plants are experiencing a shortage of absorbent oil for capturing benzene hydrocarbons. This forces the companies to limit the capacity of regeneration units, which leads to an increase in oil density. At the same time, the content of substances insoluble in toluene increases in the oil, which creates a complex multicomponent dispersed system. In the case of replenishment of the circulation cycle with light fresh foreign-made oils, which are artificial mixtures of fractions obtained during the distillation of coal tar, there is a risk of precipitation as a result of flocculation of dispersed particles. There is a need to assess the probability of precipitation of the absorbing oil using additional methods. In this paper, evidence of the colloidal structure of the absorbent oil is reviewed, showing its tendency to flocculate when aliphatic solvents and other reagents are added. The ability to form a heterogeneous blot of a mixture of oils and their solvents was evaluated according to the method available in the literature. This method was applied to mixtures of fresh foreignmade absorbent oil and polymers from the benzene department. The results showed the absence of heterogeneous blot formation using this method. An assessment of the spreading area of equivalent drops by volume showed that the deterioration of the properties of fresh oil occurs already at the introduction of 8% of polymers of the benzene department. A mixture of crude benzene components with working absorbent oil forms a heterogeneous spot when 60 % o-xylene is added. This makes it possible to calculate the flocculation coefficient and quantify the effectiveness of measures to improve the quality of recycled oil. The data obtained are consistent with the list of solvents available in the literature that cause and do not cause flocculation of dispersed particles in tar and its fractions. Keywords: washing oil, precipitation, dispersed substances, solvents, flocculation, coagulation. Corresponding author: Artem L. Bannikov, e-mail: artiksmartik@gmail.com

Using Nonstationary Depth-Frequency Curves to Characterize Local Precipitation Trends

Highlights Design storms should incorporate nonstationarity under changing climate scenarios. Three generalized extreme value distributions were fitted to represent nonstationarity for local precipitation analysis. The nonstationary models proposed in this study perform well at sites with strong precipitation trends. Abstract. As climate change advances, the stationarity assumption that governs traditional precipitation analysis is becoming untenable. Studies that incorporate nonstationarity typically use global circulation model (GCM) projections to determine the magnitude and direction of expected precipitation changes. However, the high computational costs and the coarse spatial resolution of GCMs make this method unsuitable for local precipitation analysis. In this study, nonstationarity is represented by a precipitation probability distribution with time-varying parameters. Three generalized extreme value (GEV) distributions are fitted: (1) the shift model, where the GEV location parameter varies linearly with time, (2) the stretch model, where the GEV location and scale parameters both vary linearly with time, and (3) the stationary model, a time-invariant distribution provided for the purpose of comparison. This procedure is applied to 24-h annual maximum precipitation records for ninety years (1900-1989) at five long-term measuring sites in Pennsylvania. Results varied among the five sites, suggesting that localized climate effects can cause precipitation differences at a small spatial scale. No significant nonstationarity was detected in two of the five locations. In three locations, however, increases in GEV location and scale combined to create a substantial, though not always significant, rise in the frequency of extreme precipitation. These trends were extrapolated forward over 30 years (1990-2019) and compared with an observed distribution for that year. The nonstationary models appeared to perform better at sites with stronger precipitation trends, which suggests a simple procedure for selecting sites where nonstationary analysis is most needed. Keywords: Climate change, Design storm, Generalized extreme value, Nonstationarity.

Obtaining mesoscale singular vectors reflecting synoptic‐scale uncertainty by projection in phase space

AbstractThe choice of initial perturbation technique in regional ensemble prediction systems is important for forecasts of heavy rain events. Although the uncertainty at synoptic scale has a strong impact on the forecast performance of mesoscale phenomena, there is no established method for explicitly providing synoptic‐scale uncertainty when generating mesoscale initial perturbations. In this study, a new method of calculating mesoscale singular vectors (SVs) that reflect synoptic‐scale uncertainty by applying a regional targeting technique to a subspace of the model phase space spanned by global ensemble perturbations was developed. Comparison of the results of this new method with those of the original SVs showed that the new SVs with high growth rate corresponded to the original SVs with high growth rate, whereas the new SVs with relatively lower growth rate had a larger contribution from the global ensemble perturbations. The new SVs mitigated the tendency of the original SVs to localize over the southern sea of the computational domain, and the forecast errors were better captured by the new SVs than by the original SVs. Furthermore, the effect on forecast performance of the new SVs as initial perturbations was confirmed by using the operational regional ensemble prediction system at the Japan Meteorological Agency. It was found that the precipitation probability forecast performance was superior in the first half of the forecast period at all rainfall thresholds.