scholarly journals Taking the HIGHWAY to Save Lives on Lake Victoria

2021 ◽  
pp. 1-65
Author(s):  
Rita D. Roberts ◽  
Steven J. Goodman ◽  
James W. Wilson ◽  
Paul Watkiss ◽  
Robert Powell ◽  
...  
Keyword(s):  
East Africa ◽  
Prediction Models ◽  
Lake Victoria ◽  
Weather Prediction ◽  
Warning System ◽  
Economic Benefits ◽  
Weather Forecasts ◽  
The People ◽  
Satellite Radar ◽  
Highway Project

AbstractUp to one thousand drowning deaths occur every year on Lake Victoria in East Africa. Nocturnal thunderstorms are one of the main culprits for the high winds and waves that cause fishing boats to capsize. The HIGHWAY project was established to develop an Early Warning System for Lake Victoria. Prior to HIGHWAY, weather forecasts for the lake were overly general and not trusted. Under the HIGHWAY project, forecasters from weather service offices in East Africa worked with leaders of fishing communities and Beach Management Units to develop marine forecasts and hazardous-weather warnings that were meaningful to fishermen and other stakeholders. Forecasters used high-resolution satellite, radar, and lightning observations collected during a HIGHWAY field campaign, along with guidance from numerical weather prediction models and a 4.4-km resolution Tropical Africa model, to produce specific forecasts and warnings for 10 zones over the lake. Forecasts were communicated to thousands of people by radio broadcasters, local intermediaries, and via smartphones using the WhatsApp application. Fishermen, ferry-boat operators, and lakeside communities used the new marine forecasts to plan their daytime and nighttime activities on the lake. A socio-economic benefits study conducted by HIGHWAY found that ~75% of the people are now using the forecasts to decide if and when to travel on the lake. Significantly, a 30% reduction in drowning fatalities on the lake is likely to have occurred, which when combined with the reduction in other weather-related losses, generates estimated socio-economic benefits of $44M/year due to the HIGHWAY project activities; the new marine forecasts and warnings are helping to save lives and property.

Forecasting of Tornadoes and Downbursts

2020 ◽  
Author(s):  
Djordje Romanic
Keyword(s):  
Chaos Theory ◽  
Prediction Models ◽  
Weather Prediction ◽  
Small Scale ◽  
Weather Forecasts ◽  
Wind Speeds ◽  
The Past ◽  
Extreme Wind ◽  
Extreme Wind Speeds ◽  
Numerical Weather Prediction Models

Tornadoes and downbursts cause extreme wind speeds that often present a threat to human safety, structures, and the environment. While the accuracy of weather forecasts has increased manifold over the past several decades, the current numerical weather prediction models are still not capable of explicitly resolving tornadoes and small-scale downbursts in their operational applications. This chapter describes some of the physical (e.g., tornadogenesis and downburst formation), mathematical (e.g., chaos theory), and computational (e.g., grid resolution) challenges that meteorologists currently face in tornado and downburst forecasting.

scholarly journals C-Band Dual-Doppler Retrievals in Complex Terrain: Improving the Knowledge of Severe Storm Dynamics in Catalonia

2020 ◽  
Vol 12 (18) ◽  
pp. 2930 ◽  
Author(s):  
Anna del Moral ◽  
Tammy M. Weckwerth ◽  
Tomeu Rigo ◽  
Michael M. Bell ◽  
María Carmen Llasat
Keyword(s):  
Complex Terrain ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Wind Fields ◽  
Severe Thunderstorm ◽  
Radar Network ◽  
Storm Dynamics ◽  
Storm Cells ◽  
First Time

Convective activity in Catalonia (northeastern Spain) mainly occurs during summer and autumn, with severe weather occurring 33 days per year on average. In some cases, the storms have unexpected propagation characteristics, likely due to a combination of the complex topography and the thunderstorms’ propagation mechanisms. Partly due to the local nature of the events, numerical weather prediction models are not able to accurately nowcast the complex mesoscale mechanisms (i.e., local influence of topography). This directly impacts the retrieved position and motion of the storms, and consequently, the likely associated storm severity. Although a successful warning system based on lightning and radar observations has been developed, there remains a lack of knowledge of storm dynamics that could lead to forecast improvements. The present study explores the capabilities of the radar network at the Meteorological Service of Catalonia to retrieve dual-Doppler wind fields to study the dynamics of Catalan thunderstorms. A severe thunderstorm that splits and a tornado-producing supercell that is channeled through a valley are used to demonstrate the capabilities of an advanced open source technique that retrieves dynamical variables from C-band operational radars in complex terrain. For the first time in the Iberian Peninsula, complete 3D storm-relative winds are obtained, providing information about the internal dynamics of the storms. This aids in the analyses of the interaction between different storm cells within a system and/or the interaction of the cells with the local topography.

scholarly journals Predictability of flood events in view of current meteorology and hydrology in the conditions of the Czech Republic

2008 ◽  
Vol 2 (No. 4) ◽  
pp. 156-168 ◽  
Author(s):  
L. Březková ◽  
M. Šálek ◽  
E. Soukalová ◽  
M. Starý
Keyword(s):  
Czech Republic ◽  
Prediction Models ◽  
Weather Prediction ◽  
Warning System ◽  
Economic Losses ◽  
Hydrological Models ◽  
Flood Events ◽  
Common Error ◽  
The Czech Republic ◽  
Hydrological Forecast

In central Europe, floods are natural disasters causing the greatest economic losses. One way to reduce partly the flood-related damage, especially the loss of lives, is a functional objective forecasting and warning system that incorporates both meteorological and hydrological models. Numerical weather prediction models operate with horizontal spatial resolution of several dozens of kilometres up to several kilometres, nevertheless, the common error in the localisation of the heavy rainfall characteristic maxima is mostly several times as large as the grid size. The distributive hydrological models for the middle sized basins (hundreds to thousands of km<sup>2</sup>) operate with the resolution of hundreds of meters. Therefore, the (in) accuracy of the meteorological forecast can heavily influence the following hydrological forecast. In general, we can say that the shorter is the duration of the given phenomenon and the smaller area it hits, the more difficult is its prediction. The time and spatial distribution of the predicted precipitation is still one of the most difficult tasks of meteorology. Hydrological forecasts are created under the conditions of great uncertainty. This paper deals with the possibilities of the current hydrology and meteorology with regard to the predictability of the flood events. The Czech Hydrometeorological Institute is responsible by law for the forecasting flood service in the Czech Republic. For the precipitation and temperature forecasts, the outputs of the numerical model of atmosphere ALADIN are used. Moreover, the meteorological community has available operational outputs of many weather prediction models, being run in several meteorological centres around the world. For the hydrological forecast, the HYDROG and AQUALOG models are utilised. The paper shows examples of the hydrological flood forecasts from the years 2002&ndash;2006 in the Dyje catchment, attention being paid to floods caused by heavy rainfalls in the summer season. The results show that it is necessary to take into account the predictability of the particular phenomenon, which can be used in the decision making process during an emergency.

scholarly journals A Similarity-Based Implementation of the Schaake Shuffle

2016 ◽  
Vol 144 (5) ◽  
pp. 1909-1921 ◽  
Author(s):  
Roman Schefzik
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Similarity Criterion ◽  
Ensemble Prediction ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Prediction Horizon ◽  
Prediction Systems ◽  
Specific Implementation

Contemporary weather forecasts are typically based on ensemble prediction systems, which consist of multiple runs of numerical weather prediction models that vary with respect to the initial conditions and/or the parameterization of the atmosphere. Ensemble forecasts are frequently biased and show dispersion errors and thus need to be statistically postprocessed. However, current postprocessing approaches are often univariate and apply to a single weather quantity at a single location and for a single prediction horizon only, thereby failing to account for potentially crucial dependence structures. Nonparametric multivariate postprocessing methods based on empirical copulas, such as ensemble copula coupling or the Schaake shuffle, can address this shortcoming. A specific implementation of the Schaake shuffle, called the SimSchaake approach, is introduced. The SimSchaake method aggregates univariately postprocessed ensemble forecasts using dependence patterns from past observations. Specifically, the observations are taken from historical dates at which the ensemble forecasts resembled the current ensemble prediction with respect to a specific similarity criterion. The SimSchaake ensemble outperforms all reference ensembles in an application to ensemble forecasts for 2-m temperature from the European Centre for Medium-Range Weather Forecasts.

LIMITED AREA WEATHER PREDICTION MODELS VIEWED AS DYNAMICAL SYSTEMS

1999 ◽  
Vol 09 (05) ◽  
pp. 831-842 ◽  
Author(s):  
F. CHOMÉ ◽  
C. NICOLIS
Keyword(s):  
Prediction Models ◽  
Weather Prediction ◽  
Dynamical Behavior ◽  
Limited Area ◽  
Initial State ◽  
Weather Forecasts ◽  
Fine Mesh ◽  
Area Of Interest ◽  
Variable Mesh ◽  
High Resolution Models

Different strategies for building high-resolution models providing a more detailed description of a limited area of interest as for example, in regional weather forecasts are developed. They are subsequently compared, on the basis of the dynamical behavior generated by the corresponding models. The statistical properties of the relevant fields are analyzed, and predictability experiments are performed on statistical ensembles of close lying trajectories whose mean distance represents the uncertainty in the initial state of the system. The results show that a global, variable-mesh model performs much better than a limited area fine mesh one embedded into a coarser global model.

scholarly journals Dual-Polarization Radar Observations of Deep Convection over Lake Victoria Basin in East Africa

Atmosphere ◽  
2019 ◽  
Vol 10 (11) ◽  
pp. 706 ◽  
Author(s):  
Pascal F. Waniha ◽  
Rita D. Roberts ◽  
James W. Wilson ◽  
Agnes Kijazi ◽  
Benedicto Katole
Keyword(s):  
East Africa ◽  
Lake Victoria ◽  
Deep Convection ◽  
Warning System ◽  
Lake Victoria Basin ◽  
Radar Observations ◽  
Low Level ◽  
Weather Events ◽  
Gust Fronts ◽  
Convection Initiation

Lake Victoria in East Africa supports the livelihood of thousands of fishermen and it is estimated that 3000–5000 human deaths occur per year over the lake. It is hypothesized that most of these fatalities are due to localized, severe winds produced by intense thunderstorms over the lake during the rainy season and larger scale, intense winds over the lake during the dry season. The intense winds produce a rough state of the lake (big wave heights) that cause fishing boats to capsize. In this region, weather radars have never been a primary tool for monitoring and nowcasting high impact weather. The Tanzania Meteorological Agency operates an S-band polarimetric radar in Mwanza, Tanzania, along the south shore of Lake Victoria. This radar collects high temporal and spatial resolution data that is now being used to detect and monitor the formation of deep convection over the lake and improve scientific understanding of storm dynamics and intensification. Nocturnal thunderstorms and convection initiation over the lake are well observed by the Mwanza radar and are strongly forced by lake and land breezes and gust fronts. Unexpected is the detection of clear air echo to ranges ≥100 km over the lake that makes it possible to observe low-level winds, gust fronts, and other convergence lines near the surface of the lake. The frequent observation of extensive clear air and low-level convergence lines opens up the opportunity to nowcast strong winds, convection initiation, and subsequent thunderstorm development and incorporate this information into a regional early warning system proposed for Lake Victoria Basin (LVB). Two weather events are presented illustrating distinctly different nocturnal convection initiation over the lake that evolve into intense morning thunderstorms. The evolution of these severe weather events was possible because of the Mwanza radar observations; satellite imagery alone was insufficient to provide prediction of storm initiation, growth, movement, and decay.

scholarly journals The Gridding of MOS

2009 ◽  
Vol 24 (2) ◽  
pp. 520-529 ◽  
Author(s):  
Bob Glahn ◽  
Kathryn Gilbert ◽  
Rebecca Cosgrove ◽  
David P. Ruth ◽  
Kari Sheets
Keyword(s):  
United States ◽  
Prediction Models ◽  
Weather Prediction ◽  
Correction Method ◽  
Maximum Temperature ◽  
Weather Variables ◽  
Weather Forecasts ◽  
Surface Weather ◽  
Model Output Statistics ◽  
Numerical Weather Prediction Models

Abstract Model output statistics (MOS) guidance forecasts have been produced at stations and provided to National Weather Service forecasters and private entities for over three decades. As the numerical weather prediction models became more accurate, MOS followed that trend. Up until a few years ago, the MOS produced at observation locations met the basic need for guidance. With the advent of the Interactive Forecast Preparation System and the National Digital Forecast Database, gridded MOS forecasts became needed as guidance for forecasters. One method of providing such grids is to objectively analyze the MOS forecasts for points. A basic successive correction method has been extended to analyze MOS forecasts and surface weather variables. This method is being applied to MOS forecasts to provide guidance for producing grids of sensible weather elements such as temperature, clouds, and snow amount. Guidance forecasts have been implemented for the conterminous United States for most weather elements contained in routine weather forecasts. This paper describes the method applied to daytime maximum temperature over the conterminous United States and gives example results.

scholarly journals Joint Exploitation of SAR and GNSS for Atmospheric Phase Screens Retrieval Aimed at Numerical Weather Prediction Model Ingestion

2020 ◽  
Vol 12 (4) ◽  
pp. 654 ◽  
Author(s):  
Marco Manzoni ◽  
Andrea Virgilio Monti-Guarnieri ◽  
Eugenio Realini ◽  
Giovanna Venuti
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
Weather Prediction ◽  
Numerical Weather Prediction Model ◽  
Fast Method ◽  
Total Delay ◽  
Dense Area ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Phase Screens

This paper proposes a simple and fast method to estimate Atmospheric Phase Screens (APSs) by jointly exploit a stack of Synthetic Aperture Radar (SAR) images and a dataset of GNSS-derived atmospheric product. The output of this processing is conceived to be ingested by Numerical Weather Prediction Models (NWPMs) to improve weather forecasts. In order to provide wide and dense area coverage and to respect requirements in terms of spatial resolution of ingestion products in NWPMs, both Permanent Scatterers (PSs) and Distributed Scatterers (DSs) are jointly exploited. While the formers are by definition stable targets, but unevenly distributed, the latter are ubiquitous but stable only within a certain temporal baseline that can vary depending on the operational frequency of the radar. The proposed method is thus particularly suited for C, L, and P band missions with low temporal baseline between two consecutive acquisitions of the same scene: these conditions, that are both necessary to provide the dense space-time coverage required by meteorologists, allow for a reliable and robust estimation of APSs thanks to the intrinsic limitation of temporal decorrelation. The proposed technique integrates Zenith Total Delay (ZTD) products computed on a very sparse grid from a network of GNSS stations to correct for SAR orbital errors and to provide the missing phase constant from the derived APS map. In this paper, the complete workflow is explained, and a comparison of the derived APSs is performed with phase screens derived from state-of-the-art SAR processing workflow (SqueeSAR®).

scholarly journals What Is the Added Value of a Convection-Permitting Model for Forecasting Extreme Rainfall over Tropical East Africa?

2018 ◽  
Vol 146 (9) ◽  
pp. 2757-2780 ◽  
Author(s):  
Beth J. Woodhams ◽  
Cathryn E. Birch ◽  
John H. Marsham ◽  
Caroline L. Bain ◽  
Nigel M. Roberts ◽  
...  
Keyword(s):  
East Africa ◽  
Lake Victoria ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Forecast Model ◽  
Time Of Day ◽  
Global Model ◽  
Added Value ◽  
Forecast Lead Time ◽  
The Tropics

ABSTRACT Forecasting convective rainfall in the tropics is a major challenge for numerical weather prediction. The use of convection-permitting (CP) forecast models in the tropics has lagged behind the midlatitudes, despite the great potential of such models in this region. In the scientific literature, there is very little evaluation of CP models in the tropics, especially over an extended time period. This paper evaluates the prediction of convective storms for a period of 2 years in the Met Office operational CP model over East Africa and the global operational forecast model. A novel localized form of the fractions skill score is introduced, which shows variation in model skill across the spatial domain. Overall, the CP model and the global model both outperform a 24-h persistence forecast. The CP model shows greater skill than the global model, in particular on subdaily time scales and for storms over land. Forecasts over Lake Victoria are also improved in the CP model, with an increase in hit rate of up to 20%. Contrary to studies in the midlatitudes, the skill of both models shows a large dependence on the time of day and comparatively little dependence on the forecast lead time within a 48-h forecast. Although these results provide more motivation for forecasters to use the CP model to produce subdaily forecasts with increased detail, there is a clear need for more in situ observations for data assimilation into the models and for verification. A move toward ensemble forecasting could have further benefits.

scholarly journals A web-based decision support system for smart dam operations using weather forecasts

2019 ◽  
Vol 21 (5) ◽  
pp. 687-707 ◽  
Author(s):  
Shahryar Khalique Ahmad ◽  
Faisal Hossain
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Open Source ◽  
Support System ◽  
Prediction Models ◽  
Weather Prediction ◽  
Atmospheric Modeling ◽  
Modeling Framework ◽  
Web Based ◽  
Weather Forecasts

Abstract A web-based open-source decision support system (DSS) was developed to facilitate real-world engagement with dam-operating agencies in the decision-making process involving atmospheric modeling, hydrologic modeling, and web technology. The development process was decoupled into the container (frontend) and the modeling framework for the content (backend), to arrive at an intelligent system that improves the productivity and independent reuse of each component. The backend framework uses the weather forecasts from Numerical Weather Prediction models, downscales to a finer resolution, and simulates hydrologic and data-based artificial neural network models to optimize operations. The frontend architecture disseminates the forecasted meteorological variables, reservoir inflow, optimized operations, and retrospective weekly assessment of forecasts and hydropower benefits. The framework is automated and operationalized over the Detroit dam (Oregon) to generate the daily optimized release decisions. However, backend scripts and frontend elements are flexible and customizable enough that the DSS can be reproduced for other dams. The optimization of reservoir operations based on weather forecasts results in significant additional hydropower benefit without compromising other objectives when compared to the conventional operations. More importantly, the platform helps visualize for the dam operator how much more ‘smarter’ operations can be if weather forecasts and open-source technology are used.

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