Warm Season Lightning Probability Prediction for Canada and the Northern United States

2005 ◽  
Vol 20 (6) ◽  
pp. 971-988 ◽  
Author(s):  
William R. Burrows ◽  
Colin Price ◽  
Laurence J. Wilson
Keyword(s):  
Statistical Models ◽  
Prediction Models ◽  
Grid Point ◽  
Warm Season ◽  
Precipitable Water ◽  
Large Area ◽  
Maximum Probability ◽  
The North ◽  
The Mean ◽  
Structured Regression

Abstract Statistical models valid May–September were developed to predict the probability of lightning in 3-h intervals using observations from the North American Lightning Detection Network and predictors derived from Global Environmental Multiscale (GEM) model output at the Canadian Meteorological Centre. Models were built with pooled data from the years 2000–01 using tree-structured regression. Error reduction by most models was about 0.4–0.7 of initial predictand variance. Many predictors were required to model lightning occurrence for this large area. Highest ranked overall were the Showalter index, mean sea level pressure, and troposphere precipitable water. Three-hour changes of 500-hPa geopotential height, 500–1000-hPa thickness, and MSL pressure were highly ranked in most areas. The 3-h average of most predictors was more important than the mean or maximum (minimum where appropriate). Several predictors outranked CAPE, indicating it must appear with other predictors for successful statistical lightning prediction models. Results presented herein demonstrate that tree-structured regression is a viable method for building statistical models to forecast lightning probability. Real-time forecasts in 3-h intervals to 45–48 h were made in 2003 and 2004. The 2003 verification suggests a hybrid forecast based on a mixture of maximum and mean forecast probabilities in a radius around a grid point and on monthly climatology will improve accuracy. The 2004 verification shows that the hybrid forecasts had positive skill with respect to a reference forecast and performed better than forecasts defined by either the mean or maximum probability at most times. This was achieved even though an increase of resolution and change of convective parameterization scheme were made to the GEM model in May 2004.

scholarly journals Forecast of Hourly Airport Visibility Based on Artificial Intelligence Methods

Atmosphere ◽  
2022 ◽  
Vol 13 (1) ◽  
pp. 75
Author(s):  
Jin Ding ◽  
Guoping Zhang ◽  
Shudong Wang ◽  
Bing Xue ◽  
Jing Yang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Prediction Models ◽  
Warm Season ◽  
Northwest China ◽  
Cold Season ◽  
Central China ◽  
The North ◽  
Hourly Data ◽  
Artificial Intelligence Algorithm ◽  
Good Visibility

Based on the hourly visibility data, visibility and its changes during 2010–2020 at monthly and annual time scales over 47 international airports in China are investigated, and nine artificial-intelligence-based hourly visibility prediction models are trained (hourly data in 2018–2019) and tested (hourly data in 2020) at these airports. The analyses show that the visibility of airports in eastern and central China is at a poor level all year round, and LXA (in Lhasa) has good visibility all year round. Airports in south and the northwest China have better visibility from May to October and poorer visibility from November to April. In all months, the increasing visibility mainly occurs in the central, northeast and coastal areas of China, while decreasing visibility mainly appears in the western and northern parts of China. In spring, summer and autumn, the changes difference between east and west is particularly obvious. This East–West distribution of trends is obviously different from the North–South distribution shown by the mean. For all airports, good visibility mainly occurs from 14:00–18:00 p.m. Beijing Time, while poor visibility mainly concentrates from 22:00 p.m. to 12:00 p.m. the next day, especially between 3:00–9:00 a.m. Our proposed artificial intelligence algorithm models can be reasonably used in airport visibility prediction. In particular, most algorithm models have the best results in the visibility prediction over HFE (in Hefei) and SJW (in Shijiazhuang). On the contrary, the worst forecast results appear at LXA and LHW (in Lanzhou) airports. The prediction results of airport visibility in the cold season (October–December) are better than those in the warm season (May–September). Among the algorithm models, the prediction performance of the RF-based model is the best.

scholarly journals Evaluation and Calibration of MODIS Near-Infrared Precipitable Water Vapor over China Using GNSS Observations and ERA-5 Reanalysis Dataset

2021 ◽  
Vol 13 (14) ◽  
pp. 2761
Author(s):  
Dantong Zhu ◽  
Kefei Zhang ◽  
Liu Yang ◽  
Suqin Wu ◽  
Longjiang Li
Keyword(s):  
Water Vapor ◽  
Near Infrared ◽  
Grid Point ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
Calibration Model ◽  
Systematic Bias ◽  
Reanalysis Dataset ◽  
The Mean

Water vapor is one of the most important parameters in climatic studies. MODerate-resolution Imaging Spectroradiometer (MODIS) is a key instrument and can provide spatially continuous precipitable water vapor (PWV) products. This study was focused on the performance evaluation of the MODIS near-infrared PWV product (MOD-NIR-PWV) over China. For a comprehensive assessment of the performance of MOD-NIR-PWV, PWV retrieved from the measurements at the global navigation satellite systems (GNSS) stations (i.e., GNSS-PWV) and the ERA5 reanalysis dataset (ERA-PWV) from 2013 to 2018 were used as the reference. To investigate the suitability of using ERA-PWV as the reference for the evaluation, ERA-PWV was compared to the high-accuracy GNSS-PWV at 246 GNSS stations and PWV retrieved from radiosonde observations (RS-PWV) at 78 radiosonde stations over China. The results showed that the mean bias and mean root-mean-square (RMS) of the differences between ERA-PWV and GNSS-PWV across all the stations were 0.5 and 1.7 mm, respectively, and the mean correlation coefficient of the two datasets was above 0.96. The values were 0.4 and 1.9 mm and 0.97, respectively, for the differences between ERA-PWV and RS-PWV. This suggests the suitability of ERA-PWV as the reference for the evaluation of MOD-NIR-PWV. In addition, MOD-NIR-PWV was compared with both GNSS-PWV and ERA-PWV, and their mean bias and mean RMS were 2.9 and 3.8 mm (compared to GNSS-PWV) and 2.1 and 3.0 mm (compared to ERA-PWV), respectively. The positive bias values and the non-normal distribution of the differences between MOD-NIR-PWV and both reference datasets imply that a considerable systematic overestimation of MOD-NIR-PWV over China may exist. To mitigate the systematic bias, ERA-PWV was utilized as the sample data due to its spatial continuities, and a grid-based calibration model was developed based on the annual and semiannual periodicities in the differences between MOD-NIR-PWV and ERA-PWV at each grid point. After applying the calibration model to correct MOD-NIR-PWV, the calibrated MOD-NIR-PWV was compared with ERA-PWV and GNSS-PWV for precision and accuracy analysis, respectively. The comparison showed that the model could significantly improve the precision by 94% and accuracy by 53%, which manifested the effectiveness of the calibration model in improving the performance of MOD-NIR-PWV over China.

scholarly journals Rapid Update with EnVar Direct Radar Reflectivity Data Assimilation for the NOAA Regional Convection-Allowing NMMB Model over the CONUS: System Description and Initial Experiment Results

Atmosphere ◽  
2021 ◽  
Vol 12 (10) ◽  
pp. 1286
Author(s):  
Yongming Wang ◽  
Xuguang Wang
Keyword(s):  
Data Assimilation ◽  
Grid Point ◽  
Warm Season ◽  
Multiscale Model ◽  
Large Temperature ◽  
In Situ Observation ◽  
The North ◽  
Analysis System ◽  
Reflectivity Data ◽  
The Impact

This study first describes the extended Grid-Point Statistical Interpolation analysis system (GSI)-based ensemble-variational data assimilation (DA) system within the North American Mesoscale Rapid Refresh (NAMRR) system for the Nonhydrostatic Multiscale Model on the B grid (NMMB). Experiments were conducted to examine three critical aspects of data assimilation configuration in this system. Ten retrospective high-impact convective cases during the warm season of 2015–2016 were adopted for testing. A 10-member, 18 h ensemble forecast was launched for each experiment. Specifically, the experiment using horizontal (vertical) localization radii (Lr) of 300 km (0.55-scaled height measured in the nature log of pressure) overall had more skills than that of 500 km (1.1-scaled height) for conventional in-situ observation assimilation. Diagnostics suggest that the higher forecast skills could be attributed to applying smaller Lr in the boundary with large temperature and moisture gradients. For radar DA, the experiment was more skillful with horizontal (vertical) Lr of 15 km (1.1-scaled height) than that of 12 km (0.55-scaled height). Diagnostics suggest that the improved forecasts were achieved by using wider Lr to spread radar observations into unobserved areas more effectively. Slight forecast skill differences between the relaxation inflation factors of 95% and 65% are presented. The impact of varying inflation magnitudes primarily occurred in the upper-level spread.

scholarly journals A Seven-Year Wind Profiler–Based Climatology of the Windward Barrier Jet along California’s Northern Sierra Nevada

2010 ◽  
Vol 138 (4) ◽  
pp. 1206-1233 ◽  
Author(s):  
Paul J. Neiman ◽  
Ellen M. Sukovich ◽  
F. Martin Ralph ◽  
Mimi Hughes
Keyword(s):  
Sierra Nevada ◽  
Warm Season ◽  
Central Valley ◽  
Wind Profiler ◽  
Cool Season ◽  
Plan View ◽  
The North ◽  
Synoptic Conditions ◽  
Windward Slope ◽  
The Mean

Abstract This wind profiler–based study highlights key characteristics of the barrier jet along the windward slope of California’s Sierra Nevada. Between 2000 and 2007 roughly 10% of 100 000 hourly wind profiles, recorded at two sites, satisfied the sierra barrier jet (SBJ) threshold criteria described in the text. The mean magnitude of the terrain-parallel flow in the SBJ core (i.e., Vmax) was similar at both sites (∼17.5 m s−1) and at a comparable altitude, 500–1000 m above the surface. The cross-mountain wind speed was weak at the altitude of Vmax, consistent with blocked conditions. The seasonal cycle of SBJ occurrences showed a maximum during the cooler months and a minimum in summer. Additionally, the SBJ was stronger in winter than in summer. Because the warm-season (May–September) SBJs were different than their cool-season (October–April) counterparts and occurred during California’s dry season, they were not discussed in detail. An inventory of ∼250 cool-season SBJ cases from the two sites was generated (a case contains ≥8 consecutive SBJ profiles). Up to 60% of the nearby cool-season precipitation fell during SBJ cases, and these cases shifted the precipitation down the sierra’s windward slope and enhanced precipitation at the north end of the Central Valley (relative to non-SBJ conditions). The large number of cool-season SBJ cases was stratified by the mean strength and altitude of Vmax and by the case duration. Composite profiles of the along-barrier component for the top- and bottom-20 ranked cases in each of these three SBJ classes reveal stark differences in the magnitude and vertical positioning of the barrier jet. The three SBJ classes yielded uniquely different local precipitation characteristics in proximity to the wind profilers, with the strongest and longest-lived SBJs yielding the greatest precipitation. North American Regional Reanalysis plan-view composites were generated to explore the synoptic conditions responsible for, and to showcase the precipitation distributions associated with, the top- and bottom-20 ranked cases in each of the three classes of SBJs. The composite analyses yielded large contrasts between the SBJ classes that could prove useful in forecasting SBJs and their precipitation impacts. All SBJ classes occurred, on average, in the pre-cold-frontal environment of landfalling winter storms.

Modulation of the Diurnal Cycle of Warm-Season Precipitation by Short-Wave Troughs

2013 ◽  
Vol 70 (6) ◽  
pp. 1710-1726 ◽  
Author(s):  
John D. Tuttle ◽  
Chris A. Davis
Keyword(s):  
Diurnal Cycle ◽  
North American ◽  
Phase Speed ◽  
Warm Season ◽  
Short Wave ◽  
Short Waves ◽  
The North ◽  
The Mean ◽  
Regional Reanalysis ◽  
Wave Passage

Abstract Traveling deep tropospheric disturbances of wavelengths ~1500 km (short waves) have long been known to play an important role in the initiation and maintenance of warm-season convection. To date, relatively few studies have formally documented the climatology of short waves and their relationship to the diurnal heating cycle, the topography, and the diurnal cycle of precipitation. Those that did had to rely on low-resolution global analyses and often could not track short waves across mountain barriers. In this study, 10 yr of the (32 km) NCEP North American Regional Reanalysis (NARR) are used to objectively identify and track short waves in the North American domain. Statistics of short-wave span, duration, phase speed, latitudinal extent, and locations of preferred intensification/decay are presented. Some of the key findings from the climatology include that the lee (windward) of mountain barriers are preferred regions of intensification (decay) and short waves show little evidence of a diurnal cycle and can pass a given point at any time of the day. The second part of the study focuses on the role that short waves play in modulating the diurnal cycle of propagating convection east of the Rocky Mountains. Depending on the timing of short-wave passage, short waves may either significantly enhance the precipitation above the mean or completely disrupt the normal diurnal cycle, causing precipitation to develop at times and locations where it normally does not. While short waves play an important role in modulating the mean precipitation patterns their role is considered to be secondary in nature. The diurnal precipitation signature is prominent even when short waves are not present.

scholarly journals Persistent heat waves projected for Middle East and North Africa by the end of the 21st century

PLoS ONE ◽  
2020 ◽  
Vol 15 (11) ◽  
pp. e0242477
Author(s):  
R. Varela ◽  
L. Rodríguez-Díaz ◽  
M. deCastro
Keyword(s):  
Middle East ◽  
North Africa ◽  
Heat Waves ◽  
Regional Climate ◽  
Warm Season ◽  
Mitigation Strategies ◽  
Mena Region ◽  
The North ◽  
The Mean ◽  
Using Data

The duration and intensity of future heat waves are analyzed for 53 cities in the Middle East and the North Africa (MENA) region for the 21st century under two different scenarios (RCP4.5 and RCP8.5). A consistent approach is carried out using data from 13 Regional models within the framework of the Coordinated Regional Climate Downscaling Experiment (CORDEX). By the end of the century, 80% of the most populated MENA cities are expected to be at least 50% of the days under heat wave conditions during the warm season. In addition, the mean and maximum intensity of the heat waves will also increase. Changes in the duration and intensity of heat waves have shown to be negatively correlated. Therefore, the vulnerability of the MENA cities to future heat waves was determined using a cumulative index (CI) that takes into account both duration and intensity. This CI indicates that Middle East and the eastern part of Africa will suffer the most unfavorable temperature conditions in the future. Assuming no intervention trough adaptation/mitigation strategies, these results, together with the particular properties of the MENA region, such as aridity or lack of precipitation, make it likely that the area will be affected by disease or famine.

scholarly journals HGPT2: An ERA5-Based Global Model to Estimate Relative Humidity

2021 ◽  
Vol 13 (11) ◽  
pp. 2179
Author(s):  
Pedro Mateus ◽  
Virgílio B. Mendes ◽  
Sandra M. Plecha
Keyword(s):  
Water Vapor ◽  
Relative Humidity ◽  
Real Time ◽  
Prediction Models ◽  
Weather Prediction ◽  
Precipitable Water ◽  
Precipitable Water Vapor ◽  
Global Navigation Satellite Systems ◽  
International Gnss Service ◽  
Weighted Mean Temperature

The neutral atmospheric delay is one of the major error sources in Space Geodesy techniques such as Global Navigation Satellite Systems (GNSS), and its modeling for high accuracy applications can be challenging. Improving the modeling of the atmospheric delays (hydrostatic and non-hydrostatic) also leads to a more accurate and precise precipitable water vapor estimation (PWV), mostly in real-time applications, where models play an important role, since numerical weather prediction models cannot be used for real-time processing or forecasting. This study developed an improved version of the Hourly Global Pressure and Temperature (HGPT) model, the HGPT2. It is based on 20 years of ERA5 reanalysis data at full spatial (0.25° × 0.25°) and temporal resolution (1-h). Apart from surface air temperature, surface pressure, zenith hydrostatic delay, and weighted mean temperature, the updated model also provides information regarding the relative humidity, zenith non-hydrostatic delay, and precipitable water vapor. The HGPT2 is based on the time-segmentation concept and uses the annual, semi-annual, and quarterly periodicities to calculate the relative humidity anywhere on the Earth’s surface. Data from 282 moisture sensors located close to GNSS stations during 1 year (2020) were used to assess the model coefficients. The HGPT2 meteorological parameters were used to process 35 GNSS sites belonging to the International GNSS Service (IGS) using the GAMIT/GLOBK software package. Results show a decreased root-mean-square error (RMSE) and bias values relative to the most used zenith delay models, with a significant impact on the height component. The HGPT2 was developed to be applied in the most diverse areas that can significantly benefit from an ERA5 full-resolution model.

scholarly journals Models for predicting treatment efficacy of antiepileptic drugs and prognosis of treatment withdrawal in epilepsy patients

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Shijun Yang ◽  
Bin Wang ◽  
Xiong Han
Keyword(s):  
Antiepileptic Drugs ◽  
Statistical Models ◽  
Prediction Models ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Treatment Withdrawal ◽  
Patient Treatment ◽  
Recent Developments ◽  
Progression Of Disease ◽  
Patients With Epilepsy

AbstractAlthough antiepileptic drugs (AEDs) are the most effective treatment for epilepsy, 30–40% of patients with epilepsy would develop drug-refractory epilepsy. An accurate, preliminary prediction of the efficacy of AEDs has great clinical significance for patient treatment and prognosis. Some studies have developed statistical models and machine-learning algorithms (MLAs) to predict the efficacy of AEDs treatment and the progression of disease after treatment withdrawal, in order to provide assistance for making clinical decisions in the aim of precise, personalized treatment. The field of prediction models with statistical models and MLAs is attracting growing interest and is developing rapidly. What’s more, more and more studies focus on the external validation of the existing model. In this review, we will give a brief overview of recent developments in this discipline.

Explanation Plus Prediction—The Logical Focus of Project Management Research

2021 ◽  
pp. 875697282199994
Author(s):  
Joseph F. Hair ◽  
Marko Sarstedt
Keyword(s):  
Project Management ◽  
Statistical Models ◽  
Predictive Power ◽  
Mean Absolute Error ◽  
Explanatory Power ◽  
Absolute Error ◽  
Model Parameters ◽  
Mean Square ◽  
Management Research ◽  
The Mean

Most project management research focuses almost exclusively on explanatory analyses. Evaluation of the explanatory power of statistical models is generally based on F-type statistics and the R 2 metric, followed by an assessment of the model parameters (e.g., beta coefficients) in terms of their significance, size, and direction. However, these measures are not indicative of a model’s predictive power, which is central for deriving managerial recommendations. We recommend that project management researchers routinely use additional metrics, such as the mean absolute error or the root mean square error, to accurately quantify their statistical models’ predictive power.

scholarly journals Mudrock Microstructure: A Technique for Distinguishing between Deep-Water Fine-Grained Sediments

Minerals ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 653
Author(s):  
Shereef Bankole ◽  
Dorrik Stow ◽  
Zeinab Smillie ◽  
Jim Buckman ◽  
Helen Lever
Keyword(s):  
Grain Size ◽  
Deep Water ◽  
Sedimentary Facies ◽  
X Ray Diffraction ◽  
Weak Current ◽  
Large Area ◽  
Fine Grained ◽  
Mean Size ◽  
The Mean ◽  
Water Facies

Distinguishing among deep-water sedimentary facies has been a difficult task. This is possibly due to the process continuum in deep water, in which sediments occur in complex associations. The lack of definite sedimentological features among the different facies between hemipelagites and contourites presented a great challenge. In this study, we present detailed mudrock characteristics of the three main deep-water facies based on sedimentological characteristics, laser diffraction granulometry, high-resolution, large area scanning electron microscopy (SEM), and the synchrotron X-ray diffraction technique. Our results show that the deep-water microstructure is mainly process controlled, and that the controlling factor on their grain size is much more complex than previously envisaged. Retarding current velocity, as well as the lower carrying capacity of the current, has an impact on the mean size and sorting for the contourite and turbidite facies, whereas hemipelagite grain size is impacted by the natural heterogeneity of the system caused by bioturbation. Based on the microfabric analysis, there is a disparate pattern observed among the sedimentary facies; turbidites are generally bedding parallel due to strong currents resulting in shear flow, contourites are random to semi-random as they are impacted by a weak current, while hemipelagites are random to oblique since they are impacted by bioturbation.

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