FROST-2014: The Sochi Winter Olympics International Project

2017 ◽  
Vol 98 (9) ◽  
pp. 1908-1929 ◽  
Author(s):  
Dmitry Kiktev ◽  
Paul Joe ◽  
George A. Isaac ◽  
Andrea Montani ◽  
Inger-Lise Frogner ◽  
...  
Keyword(s):  
Prediction Models ◽  
Deep Convection ◽  
Weather Prediction ◽  
Ensemble Prediction ◽  
Winter Olympics ◽  
Systems Model ◽  
Weather Observations ◽  
Prediction Systems ◽  
Development And Validation

Abstract The World Meteorological Organization (WMO) World Weather Research Programme’s (WWRP) Forecast and Research in the Olympic Sochi Testbed program (FROST-2014) was aimed at the advancement and demonstration of state-of-the-art nowcasting and short-range forecasting systems for winter conditions in mountainous terrain. The project field campaign was held during the 2014 XXII Olympic and XI Paralympic Winter Games and preceding test events in Sochi, Russia. An enhanced network of in situ and remote sensing observations supported weather predictions and their verification. Six nowcasting systems (model based, radar tracking, and combined nowcasting systems), nine deterministic mesoscale numerical weather prediction models (with grid spacings down to 250 m), and six ensemble prediction systems (including two with explicitly simulated deep convection) participated in FROST-2014. The project provided forecast input for the meteorological support of the Sochi Olympic Games. The FROST-2014 archive of winter weather observations and forecasts is a valuable information resource for mesoscale predictability studies as well as for the development and validation of nowcasting and forecasting systems in complex terrain. The resulting innovative technologies, exchange of experience, and professional developments contributed to the success of the Olympics and left a post-Olympic legacy.

scholarly journals PostProcessing and Visualization Techniques for Convection-Allowing Ensembles

2019 ◽  
Vol 100 (7) ◽  
pp. 1245-1258 ◽  
Author(s):  
Brett Roberts ◽  
Israel L. Jirak ◽  
Adam J. Clark ◽  
Steven J. Weiss ◽  
John S. Kain
Keyword(s):  
Real Time ◽  
Data Extraction ◽  
Deep Convection ◽  
Weather Prediction ◽  
Ensemble Forecast ◽  
Ensemble Prediction ◽  
Data Volume ◽  
Explicit Simulation ◽  
Prediction Systems ◽  
Visualization Techniques

AbstractSince the early 2000s, growing computing resources for numerical weather prediction (NWP) and scientific advances enabled development and testing of experimental, real-time deterministic convection-allowing models (CAMs). By the late 2000s, continued advancements spurred development of CAM ensemble forecast systems, through which a broad range of successful forecasting applications have been demonstrated. This work has prepared the National Weather Service (NWS) for practical usage of the High Resolution Ensemble Forecast (HREF) system, which was implemented operationally in November 2017. Historically, methods for postprocessing and visualizing products from regional and global ensemble prediction systems (e.g., ensemble means and spaghetti plots) have been applied to fields that provide information on mesoscale to synoptic-scale processes. However, much of the value from CAMs is derived from the explicit simulation of deep convection and associated storm-attribute fields like updraft helicity and simulated reflectivity. Thus, fully exploiting CAM ensembles for forecasting applications has required the development of fundamentally new data extraction, postprocessing, and visualization strategies. In the process, challenges imposed by the immense data volume inherent to these systems required new approaches when considering diverse factors like forecaster interpretation and computational expense. In this article, we review the current state of postprocessing and visualization for CAM ensembles, with a particular focus on forecast applications for severe convective hazards that have been evaluated within NOAA’s Hazardous Weather Testbed. The HREF web viewer implemented at the NWS Storm Prediction Center (SPC) is presented as a prototype for deploying these techniques in real time on a flexible and widely accessible platform.

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.

scholarly journals Coarse Graining the Vorticity Equation in the ECMWF Integrated Forecasting System: The Search for Kinetic Energy Backscatter

2013 ◽  
Vol 70 (4) ◽  
pp. 1233-1241 ◽  
Author(s):  
G. J. Shutts
Keyword(s):  
Kinetic Energy ◽  
Prediction Models ◽  
Weather Prediction ◽  
Vorticity Equation ◽  
Ensemble Prediction ◽  
Spectral Energy ◽  
Net Energy ◽  
Parameterization Schemes ◽  
Advection Schemes ◽  
Prediction Systems

Abstract Stochastic kinetic energy backscatter parameterization schemes are now widely used in ensemble prediction systems to account for random error associated with excessive dissipation and unrepresented energy backscatter in numerical weather prediction models. This dissipation arises from numerical advection schemes and explicit diffusion terms and is also implicit in some parameterization schemes. In the absence of a backscatter theory applicable to the convective scale and mesoscale, current parameterization methods are based on simple heuristic models designed to scale the energy input in proportion to a suitable measure of net energy dissipation rate. Free parameters in the formulation of backscatter tend to be tuned for the optimal performance of ensemble prediction systems, yet other forms of uncertainty represented in these forecasting systems make that task harder. Results are presented here from a study that aims to characterize the form and magnitude of kinetic energy backscatter within a global spectral framework. This is achieved by comparing a high-resolution “truth” model with a spectrally truncated version of the model for which the effect of the missing scales of motion is to be gauged. Energy exchange between these omitted scales and the resolved scales of the truncated representation is computed for the dominant terms in the vorticity equation. It is found that although there is a nonlocal spectral energy flux to low wavenumbers due to the purely rotational part of the flow, it is much smaller than the dissipative effect associated with terms involving the divergent part of the flow. Biharmonic horizontal diffusion is found to contribute significantly as an energy sink across the entire wavenumber spectrum.

Differentiating Freezing Drizzle and Freezing Rain in HRRR Model Forecasts

2021 ◽  
Author(s):  
Sarah Tessendorf ◽  
Allyson Rugg ◽  
Alexei Korolev ◽  
Ivan Heckman ◽  
Courtney Weeks ◽  
...  
Keyword(s):  
Drop Size ◽  
Prediction Models ◽  
Water Drop ◽  
Weather Prediction ◽  
Supercooled Liquid ◽  
Drop Diameter ◽  
Aircraft Icing ◽  
Freezing Rain ◽  
Primary Focus

AbstractSupercooled large drop (SLD) icing poses a unique hazard for aircraft and has resulted in new regulations regarding aircraft certification to fly in regions of known or forecast SLD icing conditions. The new regulations define two SLD icing categories based upon the maximum supercooled liquid water drop diameter (Dmax): freezing drizzle (100–500 μm) and freezing rain (> 500 μm). Recent upgrades to U.S. operational numerical weather prediction models lay a foundation to provide more relevant aircraft icing guidance including the potential to predict explicit drop size. The primary focus of this paper is to evaluate a proposed method for estimating the maximum drop size from model forecast data to differentiate freezing drizzle from freezing rain conditions. Using in-situ cloud microphysical measurements collected in icing conditions during two field campaigns between January and March 2017, this study shows that the High-Resolution Rapid Refresh model is capable of distinguishing SLD icing categories of freezing drizzle and freezing rain using a Dmax extracted from the rain category of the microphysics output. It is shown that the extracted Dmax from the model correctly predicted the observed SLD icing category as much as 99% of the time when the HRRR accurately forecast SLD conditions; however, performance varied by the method to define Dmax and by the field campaign dataset used for verification.

Exploring trends for the H SAF ASCAT root-zone soil moisture data records

2021 ◽  
Author(s):  
David Fairbairn ◽  
Patricia de Rosnay ◽  
Peter Weston
Keyword(s):  
Soil Moisture ◽  
Root Zone ◽  
Weather Prediction ◽  
Climate Change Impacts ◽  
Soil Moisture Data ◽  
Negative Root ◽  
In Situ Observations ◽  
Prediction Systems ◽  
Water Scarce

<p>Environmental (e.g. floods, droughts) and weather prediction systems rely on an accurate representation of soil moisture (SM). The EUMETSAT H SAF aims to provide high quality satellite-based hydrological products, including SM.<br>ECMWF is producing ASCAT root zone SM for H SAF. The production relies on an Extended Kalman filter to retrieve root zone SM from surface SM satellite data. A 10 km sampling reanalysis product (1992-2020) forced by ERA5 atmospheric fields (H141/H142) is produced for H SAF, which assimilates ERS/SCAT (1992-2006) and ASCAT-A/B/C (2007-2020) derived surface SM. The root-zone SM performance is validated using sparse in situ observations globally and generally demonstrates a positive and consistent correlation over the period. A negative trend in root-zone SM is found during summer and autumn months over much of Europe during the period (1992-2020). This is consistent with expected climate change impacts and is particularly alarming over the water-scarce Mediterranean region. The recent hot and dry summer of 2019 and dry spring of 2020 are well captured by negative root-zone SM anomalies. Plans for the future H SAF data record products will be presented, including the assimilation of high-resolution EPS-SCA-derived soil moisture data.</p>

15 year re-analysis of the urban climate of Amsterdam using WRF

2020 ◽  
Author(s):  
Sytse Koopmans ◽  
Gert-Jan Steeneveld ◽  
Ronald van Haren ◽  
Albert Holtslag
Keyword(s):  
Data Assimilation ◽  
The Netherlands ◽  
Thermal Comfort ◽  
Prediction Models ◽  
Weather Prediction ◽  
Urban Climate ◽  
Urban Canopy ◽  
Human Thermal Comfort ◽  
Weather Observations ◽  
Simulated Precipitation

<p><strong>15 year re-analysis of the urban climate of Amsterdam using WRF </strong></p><p><strong> </strong></p><p>Sytse Koopmans<sup>1</sup> ([email protected]), Gert-Jan Steeneveld<sup>1</sup>, Ronald van Haren<sup>2</sup>, Albert A.M. Holtslag<sup>1</sup>.</p><p> </p><p><sup>1</sup> Wageningen University and Research, the Netherlands:</p><p><sup>2 </sup>Netherlands eScience Center, the Netherlands:</p><p> </p><p> </p><p>Ongoing world-wide climate change and urbanization illustrate the need to understand urban hydrometeorology and its implications for human thermal comfort and water management. Numerical weather prediction models can assist to understand these issues, as they progress increasingly towards finer scales. With high model resolutions (grid spacing of 100m), effective representation of cities becomes crucial. The complex structures of cities, configuration of buildings, streets and scattered vegetation, require a different modelling approach than the homogeneous rural surroundings. The current urban canopy-layer schemes account for these city specific characteristics, but differ substantially amongst each other due to uncertainty in land use parameters and incomplete physical understanding. Therefore, the hindcasting of the urban environment needs improvement.</p><p>In this study, we improve the WRF (Weather Research and Forecasting) mesoscale model performance by incorporating observations of a variety of sources using data assimilation (WRF-3DVAR) and nudging techniques on a resolution up to 167 meter. Data assimilation aims to accurately describe the most probable atmospheric state by steering the model fields in the direction of the observations. Specific to urban boundary layers, a novel approach has been developed to nudge modelled urban canyon temperatures with quality controlled urban weather observations. Adjusting the urban fabric accordingly is crucial, because of the large heat storage within urban canopies. The road and wall layers of the urban canopy are adjusted depending on the bulk heat transfer coefficient and urban geometry. Other data assimilation sources consists of WMO synoptic weather observations and volume radar data.</p><p>The results of the 15-year climatological urban re-analysis are here presented and it is subdivided in three key questions. First, we attempt to answer how large the trends are in human thermal comfort over the 15 year period. Second, we investigate if there are seasonality’s detected in maximum urban heat island intensities. Earlier found hysteresis-like curves were reproduced to a large extent for for pedestrian level air temperatures. Lastly, we analyse trends in extreme precipitation using simulated precipitation data on one second interval.</p>

scholarly journals Comparison of Snow Cover from Satellite and Numerical Weather Prediction Models in the Northern Hemisphere and Northern Europe

2009 ◽  
Vol 48 (6) ◽  
pp. 1199-1216 ◽  
Author(s):  
Otto Hyvärinen ◽  
Kalle Eerola ◽  
Niilo Siljamo ◽  
Jarkko Koskinen
Keyword(s):  
Snow Cover ◽  
Prediction Models ◽  
Weather Prediction ◽  
Ground Truth ◽  
In Situ Measurements ◽  
Lower Atmosphere ◽  
Limited Area ◽  
In Situ Observations ◽  
Using Data

Abstract Snow cover has a strong effect on the surface and lower atmosphere in NWP models. Because the progress of in situ observations has stalled, satellite-based snow analyses are becoming increasingly important. Currently, there exist several products that operationally map global or continental snow cover. In this study, satellite-based snow cover analyses from NOAA, NASA, and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT), and NWP snow analyses from the High-Resolution Limited-Area Model (HIRLAM) and ECMWF, were compared using data from January to June 2006. Because no analyses were independent and since available in situ measurements were already used in the NWP analyses, no independent ground truth was available and only the consistency between analyses could be compared. Snow analyses from NOAA, NASA, and ECMWF were similar, but the analysis from NASA was greatly hampered by clouds. HIRLAM and EUMETSAT deviated most from other analyses. Even though the analysis schemes of HIRLAM and ECMWF were quite similar, the resulting snow analyses were quite dissimilar, because ECMWF used the satellite information of snow cover in the form of NOAA analyses, while HIRLAM used none. The differences are especially prominent in areas around the snow edge where few in situ observations are available. This suggests that NWP snow analyses based only on in situ measurements would greatly benefit from inclusion of satellite-based snow cover information.

scholarly journals Driftsondes: Providing In Situ Long-Duration Dropsonde Observations over Remote Regions

2013 ◽  
Vol 94 (11) ◽  
pp. 1661-1674 ◽  
Author(s):  
Stephen A. Cohn ◽  
Terry Hock ◽  
Philippe Cocquerez ◽  
Junhong Wang ◽  
Florence Rabier ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Field Experiments ◽  
Prediction Models ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Remote Sensors ◽  
Long Duration ◽  
Level Data ◽  
Research Aircraft

Constellations of driftsonde systems— gondolas floating in the stratosphere and able to release dropsondes upon command— have so far been used in three major field experiments from 2006 through 2010. With them, high-quality, high-resolution, in situ atmospheric profiles were made over extended periods in regions that are otherwise very difficult to observe. The measurements have unique value for verifying and evaluating numerical weather prediction models and global data assimilation systems; they can be a valuable resource to validate data from remote sensing instruments, especially on satellites, but also airborne or ground-based remote sensors. These applications for models and remote sensors result in a powerful combination for improving data assimilation systems. Driftsondes also can support process studies in otherwise difficult locations—for example, to study factors that control the development or decay of a tropical disturbance, or to investigate the lower boundary layer over the interior Antarctic continent. The driftsonde system is now a mature and robust observing system that can be combined with flight-level data to conduct multidisciplinary research at heights well above that reached by current research aircraft. In this article we describe the development and capabilities of the driftsonde system, the exemplary science resulting from its use to date, and some future applications.

scholarly journals The S2M meteorological and snow cover reanalysis over the French mountainous areas, description and evaluation (1958–2020)

2021 ◽  
Author(s):  
Matthieu Vernay ◽  
Matthieu Lafaysse ◽  
Diego Monteiro ◽  
Pascal Hagenmuller ◽  
Rafife Nheili ◽  
...  
Keyword(s):  
Snow Cover ◽  
Land Surface ◽  
Prediction Models ◽  
Weather Prediction ◽  
Scientific Publications ◽  
Mountain Areas ◽  
Data Set ◽  
Meteorological Observations ◽  
Surface Scheme

Abstract. This work introduces the S2M (SAFRAN - SURFEX/ISBA-Crocus - MEPRA) meteorological and snow cover reanalysis in the French Alps, Pyrenees and Corsica, spanning the time period from 1958 to 2020. The simulations are made over elementary areas, referred to as massifs, designed to represent the main drivers of the spatial variability observed in mountain ranges (elevation, slope and aspect). The meteorological reanalysis is performed by the SAFRAN system, which combines information from numerical weather prediction models (ERA-40 reanalysis from 1958 to 2002, ARPEGE from 2002 to 2020) and the best possible set of available in-situ meteorological observations. SAFRAN outputs are used to drive the Crocus detailed snow cover model, which is part of the land surface scheme SURFEX/ISBA. This model chain provides simulations of the evolution of the snow cover, underlying ground, and the associated avalanche hazard using the MEPRA model. This contribution describes and discusses the main climatological characteristics (climatology, variability and trends), and the main limitations of this dataset. We provide a short overview of the scientific applications using this reanalysis in various scientific fields related to meteorological conditions and the snow cover in mountain areas. An evaluation of the skill of S2M is also displayed, in particular through comparison to 665 independent in-situ snow depth observations. Further, we describe the technical handling of this open access data set, available at this address: http://dx.doi.org/10.25326/37#v2020.2. Scientific publications using this dataset must mention in the acknowledgments: "The S2M data are provided by Météo-France - CNRS, CNRM Centre d’Etudes de la Neige, through AERIS" and refer to it as Vernay et al. (2020).

scholarly journals Spatiotemporal characterization of Ensemble Prediction Systems – the Mean-Variance of Logarithms (MVL) diagram

2008 ◽  
Vol 15 (1) ◽  
pp. 109-114 ◽  
Author(s):  
J. M. Gutiérrez ◽  
C. Primo ◽  
M. A. Rodríguez ◽  
J. Fernández
Keyword(s):  
Weather Prediction ◽  
The Other ◽  
Ensemble Prediction ◽  
Spatiotemporal Evolution ◽  
Novel Approach ◽  
The Mean ◽  
The Ensemble Method ◽  
Prediction Systems ◽  
Mean Variance

Abstract. We present a novel approach to characterize and graphically represent the spatiotemporal evolution of ensembles using a simple diagram. To this aim we analyze the fluctuations obtained as differences between each member of the ensemble and the control. The lognormal character of these fluctuations suggests a characterization in terms of the first two moments of the logarithmic transformed values. On one hand, the mean is associated with the exponential growth in time. On the other hand, the variance accounts for the spatial correlation and localization of fluctuations. In this paper we introduce the MVL (Mean-Variance of Logarithms) diagram to intuitively represent the interplay and evolution of these two quantities. We show that this diagram uncovers useful information about the spatiotemporal dynamics of the ensemble. Some universal features of the diagram are also described, associated either with the nonlinear system or with the ensemble method and illustrated using both toy models and numerical weather prediction systems.

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