scholarly journals Comparison between Geostatistical Interpolation and Numerical Weather Model Predictions for Meteorological Conditions Mapping

2020 ◽  
Vol 5 (2) ◽  
pp. 15 ◽  
Author(s):  
Javier López Gómez ◽  
Francisco Troncoso Pastoriza ◽  
Enrique Granada Álvarez ◽  
Pablo Eguía Oller
Keyword(s):  
Local Level ◽  
Weather Prediction ◽  
Ground Truth ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Accurate Information ◽  
Numerical Weather ◽  
Weather Model ◽  
Geostatistical Interpolation ◽  
Solid Foundation

Mapping of meteorological conditions surrounding road infrastructures is a critical tool to identify high-risk spots related to harsh weather. However, local or regional data are not always available, and researchers and authorities must rely on coarser observations or predictions. Thus, choosing a suitable method for downscaling global data to local levels becomes essential to obtain accurate information. This work presents a deep analysis of the performance of two of these methods, commonly used in meteorology science: Universal Kriging geostatistical interpolation and Weather Research and Forecasting numerical weather prediction outputs. Estimations from both techniques are compared on 11 locations in central continental Portugal during January 2019, using measured data from a weather station network as the ground truth. Results show the different performance characteristics of both algorithms based on the nature of the specific variable interpolated, highlighting potential correlations to obtain the most accurate data for each case. Hence, this work provides a solid foundation for the selection of the most appropriate tool for mapping of weather conditions at the local level over linear transport infrastructures.

scholarly journals A Remotely Deployable Wind Sonic Anemometer

2017 ◽  
Vol 1 (2) ◽  
pp. 1
Author(s):  
Muhammad Awais ◽  
Syed Suleman Abbas Zaidi ◽  
Murk Marvi ◽  
Muhammad Khurram
Keyword(s):  
Wind Speed ◽  
Systems Engineering ◽  
Business Models ◽  
Short Term Memory ◽  
Weather Prediction ◽  
Sonic Anemometer ◽  
Ground Truth ◽  
Weather Conditions ◽  
Average Wind Speed ◽  
Ground Truth Data

Communication and computing shape up base for explosion of Internet of Things (IoT) era. Humans can efficiently control the devices around their environment as per requirements because of IoT, the communication between different devices brings more flexibility in surrounding. Useful data is also gathered from some of these devices to create Big Data; where, further analysis assist in making life easier by developing good business models corresponding to user needs, enhance scientific research, formulating weather prediction or monitoring systems and contributing in other relative fields as well. Thus, in this research a remotely deployable IoT enabled Wind Sonic Anemometer has been designed and deployed to calculate average wind speed, direction, and gust. The proposed design is remotely deployable, user-friendly, power efficient and cost-effective because of opted modules i.e., ultrasonic sensors, GSM module, and solar panel. The testbed was also deployed at the roof of Computer & Information Systems Engineering (CIS) department, NED UET. Further, its calibration has been carried out by using long short-term memory (LSTM), a deep learning technique; where ground truth data has been gathered from mechanical wind speed sensor (NRG-40 H) deployed at top of Industrial & Manufacturing (IM) department of NED UET. The obtained results are satisfactory and the performance of designed sensor is also good under various weather conditions.

scholarly journals MATISSE: an ArcGIS tool for monitoring and nowcasting meteorological hazards

2015 ◽  
Vol 12 (1) ◽  
pp. 163-169 ◽  
Author(s):  
V. Rillo ◽  
A. L. Zollo ◽  
P. Mercogliano
Keyword(s):  
Prediction Models ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Meteorological Conditions ◽  
Economic Losses ◽  
Statistical Characterization ◽  
Adverse Weather ◽  
Aviation Hazards ◽  
Meteorological Hazards

Abstract. Adverse meteorological conditions are one of the major causes of accidents in aviation, resulting in substantial human and economic losses. For this reason it is crucial to monitor and early forecast high impact weather events. In this context, CIRA (Italian Aerospace Research Center) has implemented MATISSE (Meteorological AviaTIon Supporting SystEm), an ArcGIS Desktop Plug-in able to detect and forecast meteorological aviation hazards over European airports, using different sources of meteorological data (synoptic information, satellite data, numerical weather prediction models data). MATISSE presents a graphical interface allowing the user to select and visualize such meteorological conditions over an area or an airport of interest. The system also implements different tools for nowcasting of meteorological hazards and for the statistical characterization of typical adverse weather conditions for the airport selected.

The Sydney Australia Wildfires of January 1994 - Meteorological Conditions and High Resolution Numerical Modeling Experiments

1996 ◽  
Vol 6 (3) ◽  
pp. 145 ◽  
Author(s):  
MS Speer ◽  
LM Leslie ◽  
JR Colquhoun ◽  
E Mitchell
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Detailed Examination ◽  
Meteorological Conditions ◽  
Limited Area ◽  
Research Activity ◽  
Southeastern Australia ◽  
South Wales ◽  
Resolution Model

Southeastern Australia is particularly vulnerable to wildfires during the spring and summer months, and the threat of devastation is present most years. In January 1994, the most populous city in Australia, Sydney, was ringed by wildfires, some of which penetrated well into suburban areas and there were many other serious fires in coastal areas of New South Wales (NSW). In recent years much research activity in Australia has focussed on the development of high resolution limited area models, for eventual operational prediction of meteorological conditions associated with high levels of wildfire risk. In this study, the period January 7-8, 1994 was chosen for detailed examination, as it was the most critical period during late December 1993/early January 1994 for the greater Sydney area. Routine forecast guidance from the Australian Bureau of Meteorology's operational numerical weather prediction (NWP) models was very useful in that both the medium and short range models predicted synoptic patterns suggesting extreme fire weather conditions up to several days in advance. However, vital information of a detailed nature was lacking. A new high resolution model was run at the operational resolution of 150 km and the much higher resolutions of 25 km and 5 km. The new model showed statistically significant greater skill in predicting details of wind, relative humidity and temperature patterns both near the surface and above the boundary layer. It also produced skilful predictions of the Forest Fire Danger Index.

scholarly journals Antarctic Verification of the Australian Numerical Weather Prediction Model

2019 ◽  
Vol 34 (4) ◽  
pp. 1081-1096
Author(s):  
Benjamin J. E. Schroeter ◽  
Phil Reid ◽  
Nathaniel L. Bindoff ◽  
Kelvin Michael
Keyword(s):  
Numerical Weather Prediction ◽  
Performance Metrics ◽  
Weather Prediction ◽  
Model Performance ◽  
Weather Conditions ◽  
Forecast Model ◽  
Forecast Horizon ◽  
Positive Screen ◽  
Numerical Weather ◽  
The Antarctic

Abstract The Australian Community Climate and Earth-System Simulator-Global (ACCESS-G) features an atmosphere-only numerical weather prediction (NWP) suite used operationally by the Australian Bureau of Meteorology to forecast weather conditions for the Antarctic. The current operational version of the forecast model, the Australian Parallel Suite v2 (APS2), has been used operationally since early 2016. To date, the performance of the model has been largely unverified for the Antarctic and anecdotal reports suggest challenges for model performance in the region. This study investigates the performance of ACCESS-G south of 50°S over 2017 and finds that model performance degrades toward the poles and in proportion to forecast horizon against a range of performance metrics. The model exhibits persistent negative surface and mean sea level pressure biases around the Adelie Land coast, which is linked to the underrepresentation of model winds to the west, and driven by positive screen temperature biases that inhibit modeled katabatic outflow. These results suggest that an improved representation of boundary layer parameterization could be implemented to improve model performance in the region.

scholarly journals Hydrometeorological evaluation of two nowcasting systems for Mediterranean heavy precipitation events with operational considerations

2020 ◽  
Author(s):  
Alexane Lovat ◽  
Béatrice Vincendon ◽  
Véronique Ducrocq
Keyword(s):  
Emergency Services ◽  
Flash Flood ◽  
Weather Prediction ◽  
Coupled System ◽  
Heavy Precipitation ◽  
Point Of View ◽  
Lead Times ◽  
Numerical Weather ◽  
Weather Model ◽  
Precipitation Events

Abstract. Heavy precipitation events and subsequent flash floods regularly affect the Mediterranean coastal regions. In these situations, forecasting rainfall and river discharges is crucial especially up to six hours, which is a relevant lead time for emergency services in crisis time. The present study investigates the hydrometeorological skills of two new nowcasting systems: a numerical weather model AROME-NWC and a nowcasting system blending numerical weather prediction and extrapolation of radar estimation called PIAF. Their performance is assessed for 10 past heavy precipitation events that occured in southeastern France. Precipitation forecasts are evaluated at a 15 min time resolution and the availability times of forecasts, based on the operational Météo-France suites, are taken into account when performing the evaluation. Rainfall observations and forecasts were first compared using a point-to-point approach. Then the evaluation was conducted from an hydrologic point of view, by comparing observed and forecast precipitation over watersheds affected by floods. In general, the results led to the same conclusions for both evaluations. On the very first lead times, up to 1 h 15/1 h 30 of forecast, the performance of PIAF is higher than AROME-NWC. For longer lead times (up to 3 h) their performance are equivalent in general. An assessment of river discharges simulated with the ISBA-TOP coupled system, which is dedicated to Mediterranean flash-flood simulations, forced by AROME-NWC and PIAF rainfall forecasts, was also performed on two exceptional past flash flood events. The results obtained for these two events show that using AROME-NWC or PIAF rainfall forecasts is promising for flash-flood forecasting in terms of peak intensity, timing, and first rise of discharge, with an anticipation of these phenomena that can reach several hours.

Ground-based GNSS ZTD/IWV estimation system for numerical weather prediction in challenging weather conditions

2014 ◽  
Vol 138 ◽  
pp. 414-426 ◽  
Author(s):  
Witold Rohm ◽  
Yubin Yuan ◽  
Bertukan Biadeglgne ◽  
Kefei Zhang ◽  
John Le Marshall
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Numerical Weather ◽  
Estimation System

scholarly journals Evaluating the Economic Impacts of Improvements to the High-Resolution Rapid Refresh (HRRR) Numerical Weather Prediction Model

2021 ◽  
pp. 1-36
Author(s):  
David D. Turner ◽  
Harvey Cutler ◽  
Martin Shields ◽  
Rebecca Hill ◽  
Brad Hartman ◽  
...  
Keyword(s):  
High Resolution ◽  
Numerical Weather Prediction ◽  
Economic Impacts ◽  
Weather Prediction ◽  
Critical Role ◽  
Weather Conditions ◽  
Numerical Weather Prediction Model ◽  
Economic Gain ◽  
Numerical Weather ◽  
The U.S

AbstractForecasts from numerical weather prediction (NWP) models play a critical role in many sectors of the American economy. Improvements to operational NWP model forecasts are generally assumed to provide significant economic savings through better decision making. But is this true? Since 2014, several new versions of the High-Resolution Rapid Refresh (HRRR) model were released into operation within the National Weather Service. Practically, forecasts have an economic impact only if they lead to a different action than what would be taken under an alternative information set. And in many sectors, these decisions only need to be considered during certain weather conditions. We estimate the economic impacts of improvements made to the HRRR, using 12-hour wind, precipitation, and temperature forecasts in several cases where they can have “economically meaningful” behavioral consequences. We examine three different components of the U.S. economy where such information matters: 1) better integration of wind energy resources into the electric grid, 2) increased worker output due to better precipitation forecasts that allow workers to arrive to their jobs on time, and 3) better decisions by agricultural producers in preparing for freezing conditions. These applications demonstrate some of the challenges in ascertaining the economic impacts of improved weather forecasts, including highlighting key assumptions that must be made to make the problem tractable. For these sectors, we demonstrate that there was a marked economic gain for the U.S. between HRRR versions 1 and 2, and a smaller, but still appreciable economic gain between versions 2 and 3.

scholarly journals Effectiveness of short-term numerical weather prediction in predicting growing degree days and meteorological conditions for apple scab appearance

2015 ◽  
Vol 23 (1) ◽  
pp. 50-56 ◽  
Author(s):  
Branislava Lalic ◽  
Maria Francia ◽  
Josef Eitzinger ◽  
Zorica Podraščanin ◽  
Ilija Arsenić
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Apple Scab ◽  
Meteorological Conditions ◽  
Growing Degree Days ◽  
Degree Days ◽  
Short Term ◽  
Numerical Weather
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