scholarly journals A Regularization-Based Big Data Framework for Winter Precipitation Forecasting on Streaming Data

Electronics ◽  
2021 ◽  
Vol 10 (16) ◽  
pp. 1872
Author(s):  
Andreas Kanavos ◽  
Maria Trigka ◽  
Elias Dritsas ◽  
Gerasimos Vonitsanos ◽  
Phivos Mylonas
Keyword(s):  
Weather Prediction ◽  
Ensemble Methods ◽  
Winter Precipitation ◽  
Numerical Weather Prediction Model ◽  
Streaming Data ◽  
Weather Data ◽  
Bayesian Decision ◽  
Regularization Technique ◽  
Data Framework ◽  
Distributed Framework

In the current paper, we propose a machine learning forecasting model for the accurate prediction of qualitative weather information on winter precipitation types, utilized in Apache Spark Streaming distributed framework. The proposed model receives storage and processes data in real-time, in order to extract useful knowledge from different sensors related to weather data. In following, the numerical weather prediction model aims at forecasting the weather type given three precipitation classes namely rain, freezing rain, and snow as recorded in the Automated Surface Observing System (ASOS) network. For depicting the effectiveness of our proposed schema, a regularization technique for feature selection so as to avoid overfitting is implemented. Several classification models covering three different categorization methods namely the Bayesian, decision trees, and meta/ensemble methods, have been investigated in a real dataset. The experimental analysis illustrates that the utilization of the regularization technique could offer a significant boost in forecasting performance.

scholarly journals Cloud Service for Analysis and Interactive Visualization of Weather Data in Armenia

2018 ◽  
pp. 49-57
Author(s):  
Hayk Grigoryan ◽  
Rita Abrahamyan
Keyword(s):  
Air Temperature ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Interactive Visualization ◽  
Cloud Service ◽  
Numerical Weather Prediction Model ◽  
Weather Data ◽  
Observation Data ◽  
Near Surface ◽  
Numerical Weather

The Lesser Caucasus Mountains are crossing through the territory of Armenia, creating vast differences in altitude, terrain, temperature and precipitation in provinces and towns. Even Armenia’s lowlands are 500 to 1500m above sea level. Armenias highlands extend up to Aragats mountain at 4090m where, 75% of the territory is above 1000m, 50% is above 2000m, and 3.4% is above 3000m. This paper presents a cloud service with interactive visualization and analytical capabilities for weather data in Armenia by integrating the two existing infrastructures for observational data and numerical weather prediction. The weather data used in the platform consist of near-surface atmospheric elements including air temperature, relative humidity, pressure, wind and precipitation. The visualization and analitycs have been implemented for 2m air temperature. Cloud service provides the Armenian State Hydrometeorological and Monitoring Service with analytical capabilities to make a comparative analysis between the observation data and the results of a numerical weather prediction model for per station and region for a given period.

scholarly journals Meteorological, elevation, and slope effects on surface hoar formation

2015 ◽  
Vol 9 (2) ◽  
pp. 1857-1885 ◽  
Author(s):  
S. Horton ◽  
M. Schirmer ◽  
B. Jamieson
Keyword(s):  
Snow Cover ◽  
Weather Prediction ◽  
Regional Scale ◽  
Absolute Humidity ◽  
Numerical Weather Prediction Model ◽  
Weather Data ◽  
Cold Surface ◽  
Columbia Mountains ◽  
Horizontal Grid

Abstract. Failure in layers of buried surface hoar crystals (frost) can cause hazardous snow slab avalanches. Surface hoar crystals form on the snow surface and are sensitive to micro-meteorological conditions. In this study, the role of meteorological and terrain factors were investigated for three surface hoar layers in the Columbia Mountains of Canada. The distribution of crystals was observed over different elevations and aspects during 20 days of field observations. The same layers were modelled on a 2.5 km horizontal grid by forcing the snow cover model SNOWPACK with forecast weather data from a numerical weather prediction model. The moisture content of the air (i.e. absolute humidity) had the largest impact on modelled surface hoar growth, with warm and moist air being favourable. Surface hoar was most developed at certain elevation bands, usually corresponding to elevations with warm humid air, light winds, and cold surface temperatures. SNOWPACK simulations on virtual slopes systematically predicted smaller surface hoar on south-facing slopes. In the field, a complex combination of surface hoar and sun crusts were observed, suggesting the model did not adequately resolve the surface energy balance on slopes. Overall, a coupled weather–snow cover model could benefit avalanche forecasters by predicting surface hoar layers on a regional scale over different elevation bands.

scholarly journals Meteorological, elevation, and slope effects on surface hoar formation

2015 ◽  
Vol 9 (4) ◽  
pp. 1523-1533 ◽  
Author(s):  
S. Horton ◽  
M. Schirmer ◽  
B. Jamieson
Keyword(s):  
Snow Cover ◽  
Weather Prediction ◽  
Regional Scale ◽  
Numerical Weather Prediction Model ◽  
Weather Data ◽  
Cold Surface ◽  
Wind Speeds ◽  
Air Temperatures ◽  
Columbia Mountains ◽  
Strong Winds

Abstract. Failure in layers of buried surface hoar crystals (frost) can cause hazardous snow slab avalanches. Surface hoar crystals form on the snow surface and are sensitive to micro-meteorological conditions. In this study, the role of meteorological and terrain factors was investigated for three layers of surface hoar in the Columbia Mountains of Canada. The distribution of crystals over different elevations and aspects was observed on 20 days of field observations during a period of high pressure. The same layers were modelled over simplified terrain on a 2.5 km horizontal grid by forcing the snow cover model SNOWPACK with forecast weather data from a numerical weather prediction model. Modelled surface hoar growth was associated with warm air temperatures, high humidity, cold surface temperatures, and low wind speeds. Surface hoar was most developed in regions and elevation bands where these conditions existed, although strong winds at high elevations caused some model discrepancies. SNOWPACK simulations on virtual slopes systematically predicted smaller surface hoar on south-facing slopes. In the field, a complex combination of surface hoar and sun crusts were observed, suggesting the simplified model did not adequately resolve the surface energy balance on slopes. Overall, a coupled weather–snow cover model could benefit avalanche forecasters by predicting surface hoar layers on a regional scale over different elevation bands.

scholarly journals Parameterization of mires in a numerical weather prediction model

2014 ◽  
Vol 50 (11) ◽  
pp. 8982-8996 ◽  
Author(s):  
Alla Yurova ◽  
Mikhail Tolstykh ◽  
Mats Nilsson ◽  
Andrey Sirin
Keyword(s):  
Prediction Model ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Numerical Weather

scholarly journals Evaluation of the WRF Model to Simulate a High-Intensity Rainfall Event over Kampala, Uganda

Water ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 873
Author(s):  
Yakob Umer ◽  
Janneke Ettema ◽  
Victor Jetten ◽  
Gert-Jan Steeneveld ◽  
Reinder Ronda
Keyword(s):  
High Intensity ◽  
Flood Hazard ◽  
Weather Prediction ◽  
Wrf Model ◽  
Numerical Weather Prediction Model ◽  
Rainfall Event ◽  
Weather Research And Forecasting ◽  
Event Triggering ◽  
Rain Events

Simulating high-intensity rainfall events that trigger local floods using a Numerical Weather Prediction model is challenging as rain-bearing systems are highly complex and localized. In this study, we analyze the performance of the Weather Research and Forecasting (WRF) model’s capability in simulating a high-intensity rainfall event using a variety of parameterization combinations over the Kampala catchment, Uganda. The study uses the high-intensity rainfall event that caused the local flood hazard on 25 June 2012 as a case study. The model capability to simulate the high-intensity rainfall event is performed for 24 simulations with a different combination of eight microphysics (MP), four cumulus (CP), and three planetary boundary layer (PBL) schemes. The model results are evaluated in terms of the total 24-h rainfall amount and its temporal and spatial distributions over the Kampala catchment using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) analysis. Rainfall observations from two gauging stations and the CHIRPS satellite product served as benchmark. Based on the TOPSIS analysis, we find that the most successful combination consists of complex microphysics such as the Morrison 2-moment scheme combined with Grell-Freitas (GF) and ACM2 PBL with a good TOPSIS score. However, the WRF performance to simulate a high-intensity rainfall event that has triggered the local flood in parts of the catchment seems weak (i.e., 0.5, where the ideal score is 1). Although there is high spatial variability of the event with the high-intensity rainfall event triggering the localized floods simulated only in a few pockets of the catchment, it is remarkable to see that WRF is capable of producing this kind of event in the neighborhood of Kampala. This study confirms that the capability of the WRF model in producing high-intensity tropical rain events depends on the proper choice of parametrization combinations.

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