scholarly journals Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 484 ◽  
Author(s):  
Ana Firanj Sremac ◽  
Branislava Lalić ◽  
Milena Marčić ◽  
Ljiljana Dekić
Keyword(s):  
Downy Mildew ◽  
Fire Blight ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Plasmopara Viticola ◽  
Forecasting System ◽  
Nwp Model ◽  
The Impact

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

Building damage impact forecasting for winter windstorms in Switzerland

2021 ◽  
Author(s):  
Thomas Röösli ◽  
David N. Bresch
Keyword(s):  
False Alarm ◽  
Meteorological Data ◽  
Weather Forecast ◽  
Building Damage ◽  
False Alarms ◽  
Preventive Action ◽  
Wind Gusts ◽  
Geographical Regions ◽  
Forecasting System ◽  
The Impact

<p>Weather extremes can have high socio-economic impacts. Better impact forecasting and preventive action help to reduce these impacts. In Switzerland, the winter windstorms caused high building damage, felled trees and interrupted traffic and power. Events such as Burglind-Eleanor in January 2018 are a learning opportunity for weather warnings, risk modelling and decision-making.</p><p>We have developed and implemented an operational impact forecasting system for building damage due to wind events in Switzerland. We use the ensemble weather forecast of wind gusts produced by the national meteorological agency MeteoSwiss. We couple this hazard information with a spatially explicit impact model (CLIMADA) for building damages due to winter windstorms. Each day, the impact forecasting system publishes a probabilistic forecast of the expected building damages on a spatial grid.</p><p>This system produces promising results for major historical storms when compared to aggregated daily building insurance claims data from a public building insurer of the canton of Zurich. The daily impact forecasts were qualitatively categorized as (1) successful (2) miss or (3) false alarm. The impacts of windstorm Burglind-Eleanor and five other winter windstorms were forecasted reasonably well, with four successful forecasts, one miss and one false alarm.</p><p> The building damage due to smaller storm extremes was not as successfully forecasted. Thunderstorms are not as well forecasted with 2 days’ lead time and as a result the impact forecasting system produces more misses and false alarms outside the winter storm season. For the Alpine-specific southerly Foehn winds, the impact forecasts produce many false alarms, probably caused by an overestimation of wind gusts in the weather forecast.</p><p>The forecasting system can be used to improve weather warnings and allocate resources and staff in the claims handling process of building insurances. This will help to improve recovery time and costs to institutions and individuals. The open-source code and open meteorological data makes this implementation transferable to other hazard types and other geographical regions.</p>

scholarly journals Automatic Regional Interpretation and Forecasting System Supported by Machine Learning

Atmosphere ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 793
Author(s):  
Chao Yan ◽  
Jing Feng ◽  
Kaiwen Xia ◽  
Chaofan Duan
Keyword(s):  
Machine Learning ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Forecast Model ◽  
Support Vector ◽  
Observation Data ◽  
Forecasting System ◽  
Weather Changes ◽  
Model Output Statistics

The Model Output Statistics (MOS) model is a dynamic statistical weather forecast model based on multiple linear regression technology. It is greatly affected by the selection of parameters and predictors, especially when the weather changes drastically, or extreme weather occurs. We improved the traditional MOS model with the machine learning method to enhance the capabilities of self-learning and generalization. Simultaneously, multi-source meteorological data were used as the input to the model to improve the data quality. In the experiment, we selected the four areas of Nanjing, Beijing, Chengdu, and Guangzhou for verification, with the numerical weather prediction (NWP) products and observation data from automatic weather stations (AWSs) used to predict the temperature and wind speed in the next 24 h. From the experiment, it can be seen that the accuracy of the prediction values and speed of the method were improved by the ML-MOS. Finally, we compared the ML-MOS model with neural networks and support vector machine (SVM), the results show that the prediction result of the ML-MOS model is better than that of the above two models.

Impact of ASCAT Scatterometer Wind Observations on the High-Resolution Limited-Area Model (HIRLAM) within an Operational Context

2013 ◽  
Vol 28 (2) ◽  
pp. 489-503 ◽  
Author(s):  
Siebren de Haan ◽  
Gert-Jan Marseille ◽  
Paul de Valk ◽  
John de Vries
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Limited Area ◽  
Limited Area Model ◽  
Area Model ◽  
Forecasting System ◽  
Nwp Model ◽  
The Impact ◽  
Forecast Quality

Abstract Denial experiments, also denoted observing system experiments (OSEs), are used to determine the impact of an observing system on the forecast quality of a numerical weather prediction (NWP) model. When the impact is neutral or positive, new observations from this observing system may be admitted to an operational forecasting system based on that NWP model. A drawback of the method applied in most denial experiments is that it neglects the operational time constraint on the delivery of observations. In a 10-week twin experiment with the operational High-Resolution Limited-Area Model (HIRLAM) at KNMI, the impact of additional ocean surface wind observations from the Advanced Scatterometer (ASCAT) on the forecast quality of the model has been verified under operational conditions. In the experiment, the operational model was used as reference, parallel to an augmented system in which the ASCAT winds were assimilated actively. Objective verification of the forecast with independent wind observations from moored buoys and ASCAT winds revealed a slight improvement in forecast skill as measured by a decrease in observation-minus-forecast standard deviation in the wind components for the short range (up to 24 h). A subjective analysis in a case study showed a realistic deepening of a low pressure system over the North Atlantic near the coast of Ireland through the assimilation of scatterometer data that were verified with radiosonde observations over Ireland. Based on these results, the decision was made to include ASCAT in operations at the next upgrade of the forecasting system.

scholarly journals On the Impact and Benefits of AMDAR Observations in Operational Forecasting—Part I: A Review of the Impact of Automated Aircraft Wind and Temperature Reports

2016 ◽  
Vol 97 (4) ◽  
pp. 585-602 ◽  
Author(s):  
Ralph Alvin Petersen
Keyword(s):  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Cost Effective ◽  
Forecast Errors ◽  
Commercial Aircraft ◽  
Weather Forecasts ◽  
Fuel Savings ◽  
Temperature Forecast ◽  
The Impact

Abstract This paper reviews the impact of World Meteorological Organization (WMO) Aircraft Meteorological Data Relay (AMDAR) observations on operational numerical weather prediction (NWP) forecasts at both regional and global scales that support national and local weather forecast offices across the globe. Over the past three decades, data collected from commercial aircraft have helped reduce flight-level wind and temperature forecast errors by nearly 50%. Improvements are largest in 3–48-h forecasts and in regions where the automated reports 1) are most numerous, 2) cover a broad area, and 3) are available at multiple levels (e.g., made during aircraft ascent and descent). Improvements in weather forecasts due to these data have already had major impacts on a variety of aspects of airline operations, ranging from fuel savings from improved wind and temperature forecasts used in flight planning to passenger comfort and safety due to better awareness of en route and near-terminal weather hazards. Aircraft wind and temperature observations now constitute the third most important dataset for global NWP and, in areas of ample reports, have become the single most important dataset for use in shorter-term, regional NWP applications. Automated aircraft reports provide the most cost-effective data source for improving NWP, being more than 5 times more cost effective than any other major-impact observing system. They also present an economical alternative for obtaining tropospheric profiles both in areas of diminishing conventional observation and as a supplement to existing datasets, both in time and space. An evaluation of moisture observations becoming available from an increasing number of AMDAR-equipped aircraft will be presented in Part II of this paper, including examples of the use of the full array of AMDAR observations in a variety of forecasting situations.

UTCI as the NWP model ALADIN (CHMI) output – first experiences

2021 ◽  
Vol 94 (2) ◽  
pp. 237-249
Author(s):  
Martin Novák
Keyword(s):  
Numerical Weather Prediction ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Climate Index ◽  
Basic Information ◽  
Universal Thermal Climate Index ◽  
Numerical Weather ◽  
Weather Forecasters ◽  
Thermal Climate ◽  
Nwp Model

The article includes a summary of basic information about the Universal Thermal Climate Index (UTCI) calculation by the numerical weather prediction (NWP) model ALADIN of the Czech Hydrometeorological Institute (CHMI). Examples of operational outputs for weather forecasters in the CHMI are shown in the first part of this work. The second part includes results of a comparison of computed UTCI values by ALADIN for selected place with UTCI values computed from real measured meteorological data from the same place.

scholarly journals Detection and thermal description of medicanes from numerical simulation

2014 ◽  
Vol 14 (5) ◽  
pp. 1059-1070 ◽  
Author(s):  
M. A. Picornell ◽  
J. Campins ◽  
A. Jansà
Keyword(s):  
Numerical Simulation ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Small Scale ◽  
Medium Range Weather Forecast ◽  
Warm Core ◽  
Strong Winds ◽  
Nwp Model ◽  
Ecmwf Model

Abstract. Tropical-like cyclones rarely affect the Mediterranean region but they can produce strong winds and heavy precipitations. These warm-core cyclones, called MEDICANES (MEDIterranean hurriCANES), are small in size, develop over the sea and are infrequent. For these reasons, the detection and forecast of medicanes are a difficult task and many efforts have been devoted to identify them. The goals of this work are to contribute to a proper description of these structures and to develop some criteria to identify medicanes from numerical weather prediction (NWP) model outputs. To do that, existing methodologies for detecting, characterizating and tracking cyclones have been adapted to small-scale intense cyclonic perturbations. First, a mesocyclone detection and tracking algorithm has been modified to select intense cyclones. Next, the parameters that define the Hart's cyclone phase diagram are tuned and calculated to examine their thermal structure. Four well-known medicane events have been described from numerical simulation outputs of the European Centre for Medium-Range Weather Forecast (ECMWF) model. The predicted cyclones and their evolution have been validated against available observational data and numerical analyses from the literature.

scholarly journals QuikSCAT Impacts on Coastal Forecasts and Warnings: Operational Utility of Satellite Ocean Surface Vector Wind Data

2008 ◽  
Vol 23 (5) ◽  
pp. 878-890 ◽  
Author(s):  
Ralph F. Milliff ◽  
Peter A. Stamus
Keyword(s):  
Survey Design ◽  
Weather Prediction ◽  
Winter Season ◽  
Late Summer ◽  
Weather Forecast ◽  
Ocean Surface ◽  
Site Visit ◽  
Island States ◽  
The Impact ◽  
Vector Wind

Abstract This study reports on the operational utility of ocean surface vector wind (SVW) data from Quick Scatterometer (QuikSCAT) observations in the National Oceanic and Atmospheric Administration (NOAA) National Weather Service (NWS) Weather Forecast Offices (WFOs) covering the coastal United States, including island states and territories. Thirty-three U.S. coastal WFOs were surveyed, and 16 WFO site visits were conducted, from late summer 2005 to the 2005/06 winter season, in order to quantify the impact of QuikSCAT SVW data on forecasts and warnings, with a particular focus on operations affecting marine users. Details of the survey design and site visit strategies are described. Survey results are quantified and site visit impressions are discussed. Key findings include (i) QuikSCAT data supplement primary datasets and numerical weather prediction fields, in the manual production of local public (weather) and marine forecasts and warnings; (ii) operational utility of satellite SVW data would be enhanced by SVW retrievals of finer temporal resolution, closer to the coasts; and (iii) rain flags in the SVW data have little impact on utility for WFO operations.

Urban Finescale Forecasting Reveals Weather Conditions with Unprecedented Detail

2017 ◽  
Vol 98 (12) ◽  
pp. 2675-2688 ◽  
Author(s):  
R. J. Ronda ◽  
G. J. Steeneveld ◽  
B. G. Heusinkveld ◽  
J. J. Attema ◽  
A. A. M. Holtslag
Keyword(s):  
Local Governments ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Morphological Characteristics ◽  
Weather Conditions ◽  
Human Thermal Comfort ◽  
Forecasting System ◽  
The Impact ◽  
Urban Weather ◽  
Neighborhood Level

Abstract Urban landscapes impact the lives of urban dwellers by influencing local weather conditions. However, weather forecasting down to the street and neighborhood scale has been beyond the capabilities of numerical weather prediction (NWP) despite the fact that observational systems are now able to monitor urban climate at these scales. In this study, weather forecasts at intra-urban scales were achieved by exploiting recent advances in topographic element mapping and aerial photography as well as looking at detailed mappings of soil characteristics and urban morphological properties, which were subsequently incorporated into a specifically adapted Weather Research and Forecasting (WRF) Model. The urban weather forecasting system (UFS) was applied to the Amsterdam, Netherlands, metropolitan area during the summer of 2015, where it produced forecasts for the city down to the neighborhood level (a few hundred meters). Comparing these forecasts to the dense network of urban weather station observations within the Amsterdam metropolitan region showed that the forecasting system successfully determined the impact of urban morphological characteristics and urban spatial structure on local temperatures, including the cooling effect of large water bodies on local urban temperatures. The forecasting system has important practical applications for end users such as public health agencies, local governments, and energy companies. It appears that the forecasting system enables forecasts of events on a neighborhood level where human thermal comfort indices exceeded risk thresholds during warm weather episodes. These results prove that worldwide urban weather forecasting is within reach of NWP, provided that appropriate data and computing resources become available to ensure timely and efficient forecasts.

scholarly journals Application of Boyd’s Periodization and Relaxation Method in a Spectral Atmospheric Limited-Area Model. Part II: Accuracy Analysis and Detailed Study of the Operational Impact

2012 ◽  
Vol 140 (10) ◽  
pp. 3149-3162 ◽  
Author(s):  
Daan Degrauwe ◽  
Steven Caluwaerts ◽  
Fabrice Voitus ◽  
Rafiq Hamdi ◽  
Piet Termonia
Keyword(s):  
Weather Prediction ◽  
Relaxation Method ◽  
Companion Paper ◽  
Water Model ◽  
Limited Area ◽  
Perfect Model ◽  
Depth Study ◽  
Nwp Model ◽  
The Impact ◽  
Operational Impact

Abstract Spectral limited-area models face a particular challenge at their lateral boundaries: the fields need to be made periodic. Boyd proposed a windowing-based method to improve the periodization and relaxation. In a companion paper, the implementation of this windowing method in the operational semi-implicit semi-Lagrangian spectral HARMONIE system was described and some first reproducibility tests, comparing this method to the old existing one, were presented. The present paper provides an in-depth study of the impact of this method for different configurations of the implementation. This is carried out in three steps in well-controlled experimental setups of increasing complexity. First, different aspects of Boyd’s method are analyzed in an idealized perfect-model test using a representative 1D shallow-water model. Second, the implementation is tested in an adiabatic 3D numerical weather prediction (NWP) model with perfect-model experiments. Finally, the impact of using Boyd’s method in a more operational-like NWP context is investigated as well. The presented tests show that, while the implementation of Boyd’s method is neutral in terms of scores, it is superior to the existing spline method in the case of strong dynamical forcings at the lateral boundaries.

scholarly journals Evaluation of Combined Satellite and Radar Data Assimilation with POD-4DEnVar Method on Rainfall Forecast

2020 ◽  
Vol 10 (16) ◽  
pp. 5493 ◽  
Author(s):  
Jingnan Wang ◽  
Lifeng Zhang ◽  
Jiping Guan ◽  
Mingyang Zhang
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Radar Data ◽  
Precipitation Forecast ◽  
Rainfall Forecast ◽  
Skill Scores ◽  
The Individual ◽  
The Impact ◽  
Radar Data Assimilation

Satellite and radar observations represent two fundamentally different remote sensing observation types, providing independent information for numerical weather prediction (NWP). Because the individual impact on improving forecast has previously been examined, combining these two resources of data potentially enhances the performance of weather forecast. In this study, satellite radiance, radar radial velocity and reflectivity are simultaneously assimilated with the Proper Orthogonal Decomposition (POD)-based ensemble four-dimensional variational (4DVar) assimilation method (referred to as POD-4DEnVar). The impact is evaluated on continuous severe rainfall processes occurred from June to July in 2016 and 2017. Results show that combined assimilation of satellite and radar data with POD-4DEnVar has the potential to improve weather forecast. Averaged over 22 forecasts, RMSEs indicate that though the forecast results are sensitive to different variables, generally the improvement is found in different pressure levels with assimilation. The precipitation skill scores are generally increased when assimilation is carried out. A case study is also examined to figure out the contributions to forecast improvement. Better intensity and distribution of precipitation forecast is found in the accumulated rainfall evolution with POD-4DEnVar assimilation. These improvements are attributed to the local changes in moisture, temperature and wind field. In addition, with radar data assimilation, the initial rainwater and cloud water conditions are changed directly. Both experiments can simulate the strong hydrometeor in the precipitation area, but assimilation spins up faster, strengthening the initial intensity of the heavy rainfall. Generally, the combined assimilation of satellite and radar data results in better rainfall forecast than without data assimilation.

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