scholarly journals The Role of Physical Parameterizations on the Numerical Weather Prediction: Impact of Different Cumulus Schemes on Weather Forecasting on Complex Orographic Areas

Atmosphere ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 616
Author(s):  
Giuseppe Castorina ◽  
Maria Teresa Caccamo ◽  
Franco Colombo ◽  
Salvatore Magazù
Keyword(s):  
Weather Forecasting ◽  
Weather Prediction ◽  
Wrf Model ◽  
Extreme Weather Events ◽  
Weather Research And Forecasting ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Weather Events ◽  
Cumulus Schemes

Numerical weather predictions (NWP) play a fundamental role in air quality management. The transport and deposition of all the pollutants (natural and/or anthropogenic) present in the atmosphere are strongly influenced by meteorological conditions such as, for example, precipitation and winds. Furthermore, the presence of particulate matter in the atmosphere favors the physical processes of nucleation of the hydrometeors, thus increasing the risk of even extreme weather events. In this framework of reference, the present work aimed to improve the quality of weather forecasts related to extreme events through the optimization of the weather research and forecasting (WRF) model. For this purpose, the simulation results obtained using the WRF model, where physical parametrizations of the cumulus scheme can be optimized, are reported. As a case study, we considered the extreme meteorological event recorded on 25 November 2016, which affected the whole territory of Sicily and, in particular, the area of Sciacca (Agrigento). In order, to evaluate the performance of the proposed approach, we compared the WRF model outputs with data obtained by a network of radar and weather stations. The comparison was performed through statistical methods on the basis of a “contingency table”, which allowed for ascertaining the best suited physical parametrizations able to reproduce this event.

scholarly journals A Fingerprinting Technique for Major Weather Events

2007 ◽  
Vol 46 (7) ◽  
pp. 1053-1066 ◽  
Author(s):  
Benjamin Root ◽  
Paul Knight ◽  
George Young ◽  
Steven Greybush ◽  
Richard Grumm ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Numerical Weather Prediction ◽  
Clustering Algorithm ◽  
Weather Prediction ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Middle Atlantic ◽  
Weather Events ◽  
Operational Forecasting ◽  
Multimodel Ensembles

Abstract Advances in numerical weather prediction have occurred on numerous fronts, from sophisticated physics packages in the latest mesoscale models to multimodel ensembles of medium-range predictions. Thus, the skill of numerical weather forecasts continues to increase. Statistical techniques have further increased the utility of these predictions. The availability of large atmospheric datasets and faster computers has made pattern recognition of major weather events a feasible means of statistically enhancing the value of numerical forecasts. This paper examines the utility of pattern recognition in assisting the prediction of severe and major weather in the Middle Atlantic region. An important innovation in this work is that the analog technique is applied to NWP forecast maps as a pattern-recognition tool rather than to analysis maps as a forecast tool. A technique is described that employs a new clustering algorithm to objectively identify the anomaly patterns or “fingerprints” associated with past events. The potential refinement and applicability of this method as an operational forecasting tool employed by comparing numerical weather prediction forecasts with fingerprints already identified for major weather events are also discussed.

scholarly journals Evaluation of IWV from the numerical weather prediction WRF model with PPP GNSS processing for Bulgaria

2016 ◽  
Author(s):  
Tzvetan Simeonov ◽  
Dmitry Sidorov ◽  
Felix Norman Teferle ◽  
Georgi Milev ◽  
Guergana Guerova
Keyword(s):  
Surface Pressure ◽  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Wrf Model ◽  
Mean Difference ◽  
Global Navigation Satellite Systems ◽  
Numerical Weather ◽  
Precipitation Efficiency ◽  
Good Agreement

Abstract. Global Navigation Satellite Systems (GNSS) meteorology is an established operational service providing hourly updated GNSS tropospheric products to the National Meteorologic Services (NMS) in Europe. In the last decade through the ground-based GNSS network densification and new processing strategies like Precise Point Positioning (PPP) it has become possible to obtain sub-hourly tropospheric products for monitoring severe weather events. In this work one year (January–December 2013) of sub-hourly GNSS tropospheric products (Zenith Total Delay) are computed using the PPP strategy for seven stations in Bulgaria. In order to take advantage of the sub-hourly GNSS data to derive Integrated Water Vapour (IWV) surface pressure and temperature with similar temporal resolution is required. As the surface observations are on 3 hourly basis the first step is to compare the surface pressure and temperature from numerical weather prediction model Weather Forecasting and Research (WRF) with observations at three synoptic stations in Bulgaria. The mean difference between the two data-sets for 1) surface pressure is less than 0.5 hPa and the correlation is over 0.989, 2) temperature the largest mean difference is 1.1 °C and the correlation coefficient is over 0.957 and 3) IWV mean difference is in range 0.1–1.1 mm. The evaluation of WRF on annual bases shows IWV underestimation between 0.5 and 1.5 mm at five stations and overestimation at Varna and Rozhen. Varna and Rozhen have also much smaller correlation 0.9 and 0.76. The study of the monthly IWV variation shows that at those locations the GNSS IWV has unexpected drop in April and March respectively. The reason for this drop is likely problems with station raw data. At the remaining 5 stations a very good agreement between GNSS and WRF is observed with high correlation during the cold part of 2013 i.e. March, October and December (0.95) and low correlation during the warm part of 2013 i.e. April to August (below 0.9). The diurnal cycle of the WRF model shows a dry bias in the range of 0.5-1.5 mm. Between 00 and 01 UTC the GNSS IWV tends to be underestimate IWV which is likely due to the processing window used. The precipitation efficiency from GNSS and WRF show very good agreement on monthly bases with a maximum in May-June and minimum in August–September. The annual precipitation efficiency in 2013 at Lovech and Burgas is about 6 %.

scholarly journals On Producing Reliable and Affordable Numerical Weather Forecasts on Public Cloud-Computing Infrastructure

2019 ◽  
Vol 36 (3) ◽  
pp. 491-509 ◽  
Author(s):  
Timothy C. Y. Chui ◽  
David Siuta ◽  
Gregory West ◽  
Henryk Modzelewski ◽  
Roland Schigas ◽  
...  
Keyword(s):  
Cloud Computing ◽  
Real Time ◽  
Virtual Machines ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Wrf Model ◽  
West African Monsoon ◽  
Computer Hardware ◽  
Potential Cost ◽  
Numerical Weather

AbstractCloud-computing resources are increasingly used in atmospheric research and real-time weather forecasting. The aim of this study is to explore new ways to reduce cloud-computing costs for real-time numerical weather prediction (NWP). One way is to compress output files to reduce data egress costs. File compression techniques can reduce data egress costs by over 50%. Data egress costs can be further minimized by postprocessing in the cloud and then exporting the smaller resulting files while discarding the bulk of the raw NWP output. Another way to reduce costs is to use preemptible resources, which are virtual machines (VMs) on the Google Cloud Platform (GCP) that clients can use at an 80% discount (compared to nonpreemptible VMs), but which can be turned off by the GCP without warning. By leveraging the restart functionality in the Weather Research and Forecasting (WRF) Model, preemptible resources can be used to save 60%–70% in weather simulation costs without compromising output reliability. The potential cost savings are demonstrated in forecasts over the Canadian Arctic and in a case study of NWP runs for the West African monsoon (WAM) of 2017. The choice in model physics, VM specification, and use of the aforementioned cost-saving measures enable simulation costs to be low enough such that the cloud can be a viable platform for running short-range ensemble forecasts when compared to the cost of purchasing new computer hardware.

scholarly journals The Potential Impact of Assimilating Synthetic Microwave Radiances Onboard a Future Geostationary Satellite on the Prediction of Typhoon Lekima Using the WRF Model

2021 ◽  
Vol 13 (5) ◽  
pp. 886
Author(s):  
Yuanbing Wang ◽  
Jieying He ◽  
Yaodeng Chen ◽  
Jinzhong Min
Keyword(s):  
Data Assimilation ◽  
Weather Prediction ◽  
Wrf Model ◽  
Geostationary Satellite ◽  
Weather Research And Forecasting ◽  
Observational System ◽  
Weather Events ◽  
High Impact Weather ◽  
New Perspective ◽  
Microwave Radiances

Geostationary meteorological satellites can provide continuous observations of high-impact weather events with a high temporal and spatial resolution. Sounding the atmosphere using a microwave instrument onboard a geostationary satellite has aroused great study interests for years, as it would increase the observational efficiency as well as provide a new perspective in the microwave spectrum to the measuring capability for the current observational system. In this study, the capability of assimilating future geostationary microwave sounder (GEOMS) radiances was developed in the Weather Research and Forecasting (WRF) model’s data assimilation (WRFDA) system. To investigate if these frequently updated and widely distributed microwave radiances would be beneficial for typhoon prediction, observational system simulation experiments (OSSEs) using synthetic microwave radiances were conducted using the mesoscale numerical model WRF and the advanced hybrid ensemble–variational data assimilation method for the Lekima typhoon that occurred in early August 2019. The results show that general positive forecast impacts were achieved in the OSSEs due to the assimilation of GEOMS radiances: errors of analyses and forecasts in terms of wind, humidity, and temperature were both reduced after assimilating GEOMS radiances when verified against ERA-5 data. The track and intensity predictions of Lekima were also improved before 68 h compared to the best track data in this study. In addition, rainfall forecast improvements were also found due to the assimilation impact of GEOMS radiances. In general, microwave observations from geostationary satellites provide the possibility of frequently assimilating wide-ranging microwave information into a regional model in a finer resolution, which can potentially help improve numerical weather prediction (NWP).

scholarly journals The Weather Research and Forecasting Model: Overview, System Efforts, and Future Directions

2017 ◽  
Vol 98 (8) ◽  
pp. 1717-1737 ◽  
Author(s):  
Jordan G. Powers ◽  
Joseph B. Klemp ◽  
William C. Skamarock ◽  
Christopher A. Davis ◽  
Jimy Dudhia ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Prediction Models ◽  
System Modeling ◽  
Weather Prediction ◽  
Wrf Model ◽  
Weather Research And Forecasting ◽  
Earth System Modeling ◽  
Numerical Weather ◽  
Wide Range ◽  
Weather Research

Abstract Since its initial release in 2000, the Weather Research and Forecasting (WRF) Model has become one of the world’s most widely used numerical weather prediction models. Designed to serve both research and operational needs, it has grown to offer a spectrum of options and capabilities for a wide range of applications. In addition, it underlies a number of tailored systems that address Earth system modeling beyond weather. While the WRF Model has a centralized support effort, it has become a truly community model, driven by the developments and contributions of an active worldwide user base. The WRF Model sees significant use for operational forecasting, and its research implementations are pushing the boundaries of finescale atmospheric simulation. Future model directions include developments in physics, exploiting emerging compute technologies, and ever-innovative applications. From its contributions to research, forecasting, educational, and commercial efforts worldwide, the WRF Model has made a significant mark on numerical weather prediction and atmospheric science.

scholarly journals The Impact of Emerging 5G Technology on U.S. Weather Prediction

2020 ◽  
Vol 17 (02) ◽  
Author(s):  
Sarah E. Benish ◽  
Graham H. Reid ◽  
Abhinav Deshpande ◽  
Shantam Ravan ◽  
Rachel Lamb
Keyword(s):  
Weather Forecasting ◽  
Weather Prediction ◽  
Forecast Accuracy ◽  
Fourth Generation ◽  
Weather Research And Forecasting ◽  
Weather Forecasts ◽  
Fifth Generation ◽  
Forecast Lead Time ◽  
The Impact ◽  
Roll Out

Fifth generation (5G) wireless networks promise to provide faster and more expansive data connectivity, exceeding thresholds from previous fourth generation (4G) technology. The deployment of 5G infrastructure requires allocating additional frequencies in radio bands at 24 gigahertz (GHz), potentially contaminating neighboring remote sensing bands used for weather forecasting and prediction. The current U.S. out-of-band emissions limit at 24 GHz of -20 dBW per 200 MHz is projected to degrade meteorological forecast accuracy by up to 30%, reducing the hurricane forecast lead time by 2 to 3 days, and endangering thousands of additional lives. Under the Weather Research and Forecasting Innovation Act of 2017 (Pub.L 115-25), the National Oceanic and Atmospheric Administration (NOAA) must develop more accurate and timely severe weather forecasts in order to protect life and property and reduce economic risk; however, the potential out-of-band interference from the roll out of 5G threatens this aim. Given U.S. economic reliance on accurate weather prediction (estimated to be in the trillions of dollars), we propose that Congress mandate stricter noise restrictions to adequately meet requirements of the Pub.L 115-25, while minimizing disruption to 5G deployment.

scholarly journals Using modern technology to protect vineyards from hail amid climate change

2021 ◽  
Vol 234 ◽  
pp. 00034
Author(s):  
Teimuraz Davitashvili ◽  
Inga Samkharadze ◽  
Lika Megreladze ◽  
Ramaz Kvatadze
Keyword(s):  
Weather Forecasting ◽  
Wrf Model ◽  
Modern Technology ◽  
Extreme Weather Events ◽  
Weather Events ◽  
Arw Model ◽  
Strong Convection ◽  
Southern Georgia ◽  
Wine Region ◽  
Effective Use

Over the past two decades, Georgia has faced increasingly heavy rainfall, hail and flooding, which especially devastated Kakheti wine region in Southern Georgia, causing severe damage to hundreds of vineyards. Since 2015, 85 anti-hail missile systems have been installed to protect entire Kakheti region, however, for the effective use of a modern anti-hail system, it became necessary to timely forecast extreme weather events of a regional and local scale. Thus, this article aims to develop timely forecasting of strong convection, dangerous precipitation and hail using modern weather forecasting models and radar technologies in Georgia. For this reasons various combinations of the physics parameterization schemes of the WRF-ARW model, the ARL READY system and the data of the modern meteorological radar Meteor 735CDP10 are used to predict the thermodynamic state of the atmosphere and assess the possible level of development of convective processes. The analysis of the calculated results showed that the variants of the microphysics parametrization schemes of the WRF model lead to significant variability in precipitation forecasts on complex terrain. Meanwhile, the upper-air diagrams of the READY system clearly showed the instability of the atmosphere for the cases discussed. Some results of these calculations are presented and analysed in this paper.

Reliability, Resilience and the Oncoming Wave of Retiring Baseload Units, Volume I: The Critical Role of Thermal Units During Extreme Weather Events

2018 ◽  
Author(s):  
Peter C. Balash, PhD ◽  
Kenneth C. Kern ◽  
John Brewer ◽  
Justin Adder ◽  
Christopher Nichols ◽  
...  
Keyword(s):  
Critical Role ◽  
Extreme Weather ◽  
Extreme Weather Events ◽  
Weather Events

scholarly journals “Snow Scenes”: Exploring the Role of Memory and Place in Commemorating Extreme Winters

2016 ◽  
Vol 8 (1) ◽  
pp. 5-19 ◽  
Author(s):  
Alexander Hall ◽  
Georgina Endfield
Keyword(s):  
Rural Communities ◽  
Local Level ◽  
Strong Association ◽  
Potential Effect ◽  
Cultural Dimensions ◽  
Extreme Weather Events ◽  
National Narratives ◽  
Local Climate ◽  
Weather Events

Abstract Scholars are increasingly focusing on the cultural dimensions of climate, addressing how individuals construct their understanding of climate through local weather. Research often focuses on the importance of widespread conceptualizations of mundane everyday weather, although attention has also been paid to extreme weather events and their potential effect on popular understandings of local climate. This paper introduces the “Snow Scenes” project, which aimed to engage rural communities in Cumbria, England, with their memories of extreme and severe past winter conditions in the region. Collating memories across a wide demographic, using a variety of methods, individual memories were analyzed alongside meteorological and historical records. By exploring these memories and their associated artifacts, this paper aims to better understand the role of memory and place in commemorating extreme winters. First, it is demonstrated how national narratives of exceptional winters are used by individuals as benchmarks against which to gauge conditions. Second, this paper identifies how specific locations and landmarks help to place memories and are shown to be important anchors for individuals’ understanding of their climate. Third, the paper considers how memories of severe winters are often nostalgic in their outlook, with a strong association between snowy winters, childhood, and childhood places. Fourth, it is illustrated how such events are regularly connected to important personal or familial milestones. Finally, the paper reflects on how these local-level experiences of historical extreme events may be central to the shaping of popular understandings of climate and also, by extension, climate change.

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