Predictability of U.S. Regional Extreme Precipitation Occurrence Based on Large-Scale Meteorological Patterns (LSMPs)

2021 ◽  
pp. 1-61
Author(s):  
Xiang Gao ◽  
Shray Mathur
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Warm Season ◽  
Prediction Skill ◽  
Large Set ◽  
Synoptic Scale ◽  
Potential Predictability ◽  
Precipitation Occurrence ◽  
Skill Scores ◽  
Analogue Method

AbstractIn this study, we use analogue method and Convolutional Neural Networks (CNNs) to assess the potential predictability of extreme precipitation occurrence based on Large-Scale Meteorological Patterns (LSMPs) for the winter (DJF) of Pacific Coast California (PCCA) and the summer (JJA) of Midwestern United States (MWST). We evaluate the LSMPs constructed with a large set of variables at multiple atmospheric levels and quantify the prediction skill with a variety of complementary performance measures. Our results suggest that LSMPs provide useful predictability of daily extreme precipitation occurrence and its interannual variability over both regions. The 14-year (2006-2019) independent forecast shows Gilbert Skill Scores (GSS) in PCCA range from 0.06 to 0.32 across 24 CNN schemes and from 0.16 to 0.26 across 4 analogue schemes, in contrast to those from 0.1 to 0.24 and from 0.1 to 0.14 in MWST. Overall, CNN is shown to be more powerful in extracting the relevant features associated with extreme precipitation from the LSMPs than analogue method, with several single-variate CNN schemes achieving more skillful prediction than the best multi-variate analogue scheme in PCCA and more than half of CNN schemes in MWST. Nevertheless, both methods highlight the Integrated Vapor Transport (IVT, or its zonal and meridional components) enables higher skills than other atmospheric variables over both regions. Warm-season extreme precipitation in MWST presents a forecast challenge with overall lower prediction skill than in PCCA, attributed to the weak synoptic-scale forcing in summer.

scholarly journals A Dynamical and Statistical Characterization of U.S. Extreme Precipitation Events and Their Associated Large-Scale Meteorological Patterns

2017 ◽  
Vol 30 (4) ◽  
pp. 1307-1326 ◽  
Author(s):  
Siyu Zhao ◽  
Yi Deng ◽  
Robert X. Black
Keyword(s):  
United States ◽  
Extreme Precipitation ◽  
Large Scale ◽  
Warm Season ◽  
Base Function ◽  
Regional Patterns ◽  
Statistical Characterization ◽  
Climate System Model ◽  
Extreme Precipitation Events ◽  
Precipitation Events

Abstract Regional patterns of extreme precipitation events occurring over the continental United States are identified via hierarchical cluster analysis of observed daily precipitation for the period 1950–2005. Six canonical extreme precipitation patterns (EPPs) are isolated for the boreal warm season and five for the cool season. The large-scale meteorological pattern (LMP) inducing each EPP is identified and used to create a “base function” for evaluating a climate model’s potential for accurately representing the different patterns of precipitation extremes. A parallel analysis of the Community Climate System Model, version 4 (CCSM4), reveals that the CCSM4 successfully captures the main U.S. EPPs for both the warm and cool seasons, albeit with varying degrees of accuracy. The model’s skill in simulating each EPP tends to be positively correlated with its capability in representing the associated LMP. Model bias in the occurrence frequency of a governing LMP is directly related to the frequency bias in the corresponding EPP. In addition, however, discrepancies are found between the CCSM4’s representation of LMPs and EPPs over regions such as the western United States and Midwest, where topographic precipitation influences and organized convection are prominent, respectively. In these cases, the model representation of finer-scale physical processes appears to be at least equally important compared to the LMPs in driving the occurrence of extreme precipitation.

scholarly journals Climatology of Daily Precipitation and Extreme Precipitation Events in the Northeast United States

2015 ◽  
Vol 16 (6) ◽  
pp. 2537-2557 ◽  
Author(s):  
Laurie Agel ◽  
Mathew Barlow ◽  
Jian-Hua Qian ◽  
Frank Colby ◽  
Ellen Douglas ◽  
...  
Keyword(s):  
Extreme Precipitation ◽  
Seasonal Cycle ◽  
Large Scale ◽  
Daily Precipitation ◽  
Warm Season ◽  
Extreme Precipitation Events ◽  
Tail Distribution ◽  
Hourly Data ◽  
Early Fall ◽  
Precipitation Events

Abstract This study examines U.S. Northeast daily precipitation and extreme precipitation characteristics for the 1979–2008 period, focusing on daily station data. Seasonal and spatial distribution, time scale, and relation to large-scale factors are examined. Both parametric and nonparametric extreme definitions are considered, and the top 1% of wet days is chosen as a balance between sample size and emphasis on tail distribution. The seasonal cycle of daily precipitation exhibits two distinct subregions: inland stations characterized by frequent precipitation that peaks in summer and coastal stations characterized by less frequent but more intense precipitation that peaks in late spring as well as early fall. For both subregions, the frequency of extreme precipitation is greatest in the warm season, while the intensity of extreme precipitation shows no distinct seasonal cycle. The majority of Northeast precipitation occurs as isolated 1-day events, while most extreme precipitation occurs on a single day embedded in 2–5-day precipitation events. On these extreme days, examination of hourly data shows that 3 h or less account for approximately 50% of daily accumulation. Northeast station precipitation extremes are not particularly spatially cohesive: over 50% of extreme events occur at single stations only, and 90% occur at only 1–3 stations concurrently. The majority of extreme days (75%–100%) are related to extratropical storms, except during September, when more than 50% of extremes are related to tropical storms. Storm tracks on extreme days are farther southwest and more clustered than for all storm-related precipitation days.

scholarly journals Mapping Weather-Type Influence on Senegal Precipitation Based on a Spatial–Temporal Statistical Model*

2013 ◽  
Vol 26 (20) ◽  
pp. 8189-8209 ◽  
Author(s):  
Henning W. Rust ◽  
Mathieu Vrac ◽  
Benjamin Sultan ◽  
Matthieu Lengaigne
Keyword(s):  
Atmospheric Circulation ◽  
Large Scale ◽  
Pearson Correlation ◽  
Precipitation Amount ◽  
Correlation Coefficients ◽  
Drought Risk ◽  
Synoptic Scale ◽  
Weather Types ◽  
Easterly Waves ◽  
Precipitation Occurrence

Abstract Senegal is particularly vulnerable to precipitation variability. To investigate the influence of large-scale circulation on local-scale precipitation, a full spatial–statistical description of precipitation occurrence and amount for Senegal is developed. These regression-type models have been built on the basis of daily records at 137 locations and were developed in two stages: (i) a baseline model describing the expected daily occurrence probability and precipitation amount as spatial fields from monsoon onset to offset, and (ii) the inclusion of weather types defined from the NCEP–NCAR reanalysis 850-hPa winds and 925-hPa relative humidity establishing the link to the synoptic-scale atmospheric circulation. During peak phase, the resulting types appear in two main cycles that can be linked to passing African easterly waves. The models allow the investigation of the spatial response of precipitation occurrence and amount to a discrete set of preferred states of the atmospheric circulation. As such, they can be used for drought risk mapping and the downscaling of climate change projections. Necessary choices, such as filtering and scaling of the atmospheric data (as well as the number of weather types to be used), have been made on the basis of the precipitation models' performance instead of relying on external criteria. It could be demonstrated that the inclusion of the synoptic-scale weather types lead to skill on the local and daily scale. On the interannual scale, the models for precipitation occurrence and amount capture 26% and 38% of the interannual spatially averaged variability, corresponding to Pearson correlation coefficients of rO = 0.52 and ri = 0.65, respectively.

scholarly journals Classification of Australian Thunderstorms Using Multivariate Analyses of Large-Scale Atmospheric Variables

2017 ◽  
Vol 56 (7) ◽  
pp. 1921-1937 ◽  
Author(s):  
Bryson C. Bates ◽  
Andrew J. Dowdy ◽  
Richard E. Chandler
Keyword(s):  
Pacific Northwest ◽  
Large Scale ◽  
Prediction Skill ◽  
Lightning Flash ◽  
Atmospheric Conditions ◽  
Skill Scores ◽  
Wide Range ◽  
Surface Representations ◽  
Low Dimensional ◽  
Atmospheric Variables

AbstractLightning accompanied by inconsequential rainfall (i.e., “dry” lightning) is the primary natural ignition source for wildfires globally. This paper presents a machine-learning and statistical-classification analysis of dry and “wet” thunderstorm days in relation to associated atmospheric conditions. The study is based on daily data for lightning-flash count and precipitation from ground-based sensors and gauges and a comprehensive set of atmospheric variables that are based on ERA-Interim for the period from 2004 to 2013 at six locations in Australia. These locations represent a wide range of climatic zones (temperate, subtropical, and tropical). Quadratic surface representations and low-dimensional summary statistics were used to characterize the main features of the atmospheric fields. Four prediction skill scores were considered, and 10-fold cross validation was used to evaluate the performance of each classifier. The results were compared with those obtained by adopting the approach used in an earlier study for the U.S. Pacific Northwest. It was found that both approaches have prediction skill when tested against independent data, that mean atmospheric field quantities proved to be the most influential variables in determining dry-lightning activity, and that no single classifier or set of atmospheric variables proved to be consistently superior to its counterpart for the six sites examined here.

Hypohydrostatic Simulation of a Quasi-Steady Baroclinic Cyclone

2020 ◽  
Vol 77 (4) ◽  
pp. 1415-1428 ◽  
Author(s):  
Tsung-Lin Hsieh ◽  
Stephen T. Garner ◽  
Isaac M. Held
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Computational Cost ◽  
Moist Convection ◽  
Synoptic Scale ◽  
Grid Spacing ◽  
Cyclone Intensity ◽  
Convective Scale ◽  
Mean Structure ◽  
Large Scale Flow

Abstract Simulations of baroclinic cyclones often cannot resolve moist convection but resort to convective parameterization. An exception is the hypohydrostatic rescaling, which in principle can be used to better represent convection with no increase in computational cost. The rescaling is studied in the context of a quasi-steady, convectively active, baroclinic cyclone. This is a novel framework with advantages due to the unambiguous time-mean structure. The rescaling is evaluated against high-resolution solutions up to a 5-km grid spacing. A theoretical scaling combining convective-scale dynamics and synoptic-scale energy balance is derived and verified by the simulations. It predicts the insensitivity of the large-scale flow to resolution finer than 40 km and to moderate rescaling, and a weak bias in the cyclone intensity under very large rescaling. The theory yields a threshold for the rescaling factor that avoids large-scale biases. Below the threshold, the rescaling can be used to control resolution errors at the convective scale, such as the distribution of extreme precipitation rates.

scholarly journals A Statistical Downscaling Model for Forecasting Summer Rainfall in China from DEMETER Hindcast Datasets

2012 ◽  
Vol 27 (3) ◽  
pp. 608-628 ◽  
Author(s):  
Huopo Chen ◽  
Jianqi Sun ◽  
Huijun Wang
Keyword(s):  
Statistical Downscaling ◽  
Large Scale ◽  
Temporal Correlation ◽  
Summer Rainfall ◽  
Prediction Skill ◽  
Dynamical Model ◽  
Coupled Models ◽  
Stable Relationship ◽  
Skill Scores ◽  
Ensemble Strategy

Abstract A new statistical downscaling (SD) scheme is proposed to predict summertime multisite rainfall measurements in China. The potential predictors are multiple large-scale variables from operational dynamical model output. A key step in this SD scheme is finding optimal predictors that have the closest and most stable relationship with rainfall at a given station. By doing so, the most robust signals from the large-scale circulation can be statistically projected onto local rainfall, which can significantly improve forecast skill in predicting the summer rainfall at the stations. This downscaling prediction is performed separately for each simulation with a leave-one-out cross-validation approach and an independent sample validation framework. The prediction skill scores exhibited at temporal correlation, anomaly correlation coefficient, and root-mean-square error consistently demonstrate that dynamical model prediction skill is significantly improved under the SD scheme, especially in the multimodel ensemble strategy. Therefore, this SD scheme has the potential to improve the operational skill when forecasting rainfall based on the coupled models.

scholarly journals Potential Predictability of Seasonal Extreme Precipitation Accumulation in China

2017 ◽  
Vol 18 (4) ◽  
pp. 1071-1080 ◽  
Author(s):  
Wenguang Wei ◽  
Zhongwei Yan ◽  
P. D. Jones
Keyword(s):  
Extreme Precipitation ◽  
Yangtze River ◽  
Large Scale ◽  
Southern China ◽  
Yangtze River Valley ◽  
River Valley ◽  
Northwestern China ◽  
Chain Model ◽  
The Yangtze River ◽  
Potential Predictability

Abstract The potential predictability of seasonal extreme precipitation accumulation (SEPA) across mainland China is evaluated, based on daily precipitation observations during 1960–2013 at 675 stations. The potential predictability value (PPV) of SEPA is calculated for each station by decomposing the observed SEPA variance into a part associated with stochastic daily rainfall variability and another part associated with longer-time-scale climate processes. A Markov chain model is constructed for each station and a Monte Carlo simulation is applied to estimate the stochastic part of the variance. The results suggest that there are more potentially predictable regions for summer than for the other seasons, especially over southern China, the Yangtze River valley, the north China plain, and northwestern China. There are also regions of large PPVs in southern China for autumn and winter and in northwestern China for spring. The SEPA series for the regions of large PPVs are deemed not entirely stochastic, either with long-term trends (e.g., increasing trends in inland northwestern China) or significant correlation with well-known large-scale climate processes (e.g., East Asian winter monsoon for southern China in winter and El Niño for the Yangtze River valley in summer). This fact not only verifies the claim that the regions have potential predictability but also facilitates predictive studies of the regional extreme precipitation associated with large-scale climate processes.

scholarly journals Linking large-scale circulation patterns to low-cloud properties

2020 ◽  
Vol 20 (12) ◽  
pp. 7125-7138
Author(s):  
Timothy W. Juliano ◽  
Zachary J. Lebo
Keyword(s):  
Large Scale ◽  
Climate Models ◽  
Marine Boundary Layer ◽  
Warm Season ◽  
Cloud Droplet ◽  
Synoptic Scale ◽  
Liquid Water Path ◽  
Cloud Properties ◽  
Circulation Patterns ◽  
Satellite Retrievals

Abstract. The North Pacific High (NPH) is a fundamental meteorological feature present during the boreal warm season. Marine boundary layer (MBL) clouds, which are persistent in this oceanic region, are influenced directly by the NPH. In this study, we combine 11 years of reanalysis and an unsupervised machine learning technique to examine the gamut of 850 hPa synoptic-scale circulation patterns. This approach reveals two distinguishable regimes – a dominant NPH setup and a land-falling cyclone – and in between a spectrum of large-scale patterns. We then use satellite retrievals to elucidate for the first time the explicit dependence of MBL cloud properties (namely cloud droplet number concentration, liquid water path, and shortwave cloud radiative effect – CRESW) on 850 hPa circulation patterns over the northeast Pacific Ocean. We find that CRESW spans from −146.8 to −115.5 W m−2, indicating that the range of observed MBL cloud properties must be accounted for in global and regional climate models. Our results demonstrate the value of combining reanalysis and satellite retrievals to help clarify the relationship between synoptic-scale dynamics and cloud physics.

scholarly journals Linking large-scale circulation patterns to low-cloud properties

2019 ◽  
Author(s):  
Timothy W. Juliano ◽  
Zachary J. Lebo
Keyword(s):  
Radiative Forcing ◽  
Large Scale ◽  
Climate Models ◽  
Marine Boundary Layer ◽  
Warm Season ◽  
Cloud Droplet ◽  
Cloud Microphysics ◽  
Synoptic Scale ◽  
Cloud Properties ◽  
Circulation Patterns

Abstract. The North Pacific High (NPH) is a fundamental meteorological feature present during the boreal warm season. Marine boundary layer (MBL) clouds, which are persistent in this oceanic region, are influenced directly by the NPH. In this study, we combine 11 years of reanalysis and an unsupervised machine learning technique to examine the gamut of 850-hPa synoptic-scale circulation patterns. This approach, which yields the frequency at which these regimes occur, reveals two distinguishable patterns – a dominant NPH setup and a land-falling cyclone – and in between a spectrum of regimes. We then use satellite retrievals to elucidate for the first time the explicit dependence of MBL cloud properties (namely cloud droplet number concentration and cloud droplet effective radius) on 850-hPa circulation patterns over the northeast Pacific Ocean. Moreover, we find that shortwave cloud radiative forcing ranges from − 144.0 to − 117.5 W/m2, indicating that the range of MBL cloud properties must be accounted for in global and regional climate models. Our results demonstrate the value of combining reanalysis and satellite observations to help clarify the relationship between synoptic-scale dynamics and cloud microphysics.

Adsorption Isotherm Predictions for Multiple Molecules in MOFs Using the Same Deep Learning Model

2019 ◽  
Author(s):  
Ryther Anderson ◽  
Achay Biong ◽  
Diego Gómez-Gualdrón
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Molecular Simulation ◽  
Large Scale ◽  
Learning Model ◽  
Operating Conditions ◽  
Small Subset ◽  
Screening Methods ◽  
Large Set ◽  
Metal Organic

<div>Tailoring the structure and chemistry of metal-organic frameworks (MOFs) enables the manipulation of their adsorption properties to suit specific energy and environmental applications. As there are millions of possible MOFs (with tens of thousands already synthesized), molecular simulation, such as grand canonical Monte Carlo (GCMC), has frequently been used to rapidly evaluate the adsorption performance of a large set of MOFs. This allows subsequent experiments to focus only on a small subset of the most promising MOFs. In many instances, however, even molecular simulation becomes prohibitively time consuming, underscoring the need for alternative screening methods, such as machine learning, to precede molecular simulation efforts. In this study, as a proof of concept, we trained a neural network as the first example of a machine learning model capable of predicting full adsorption isotherms of different molecules not included in the training of the model. To achieve this, we trained our neural network only on alchemical species, represented only by their geometry and force field parameters, and used this neural network to predict the loadings of real adsorbates. We focused on predicting room temperature adsorption of small (one- and two-atom) molecules relevant to chemical separations. Namely, argon, krypton, xenon, methane, ethane, and nitrogen. However, we also observed surprisingly promising predictions for more complex molecules, whose properties are outside the range spanned by the alchemical adsorbates. Prediction accuracies suitable for large-scale screening were achieved using simple MOF (e.g. geometric properties and chemical moieties), and adsorbate (e.g. forcefield parameters and geometry) descriptors. Our results illustrate a new philosophy of training that opens the path towards development of machine learning models that can predict the adsorption loading of any new adsorbate at any new operating conditions in any new MOF.</div>

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