scholarly journals Numerical Weather Forecasts at Kilometer Scale in the French Alps: Evaluation and Application for Snowpack Modeling

2016 ◽  
Vol 17 (10) ◽  
pp. 2591-2614 ◽  
Author(s):  
Vincent Vionnet ◽  
Ingrid Dombrowski-Etchevers ◽  
Matthieu Lafaysse ◽  
Louis Quéno ◽  
Yann Seity ◽  
...  
Keyword(s):  
Snow Water Equivalent ◽  
Weather Prediction ◽  
High Elevation ◽  
Cold Bias ◽  
Positive Bias ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
French Alps ◽  
Significant Difference ◽  
Forecasting System

Abstract Numerical weather prediction (NWP) systems operating at kilometer scale in mountainous terrain offer appealing prospects for forecasting the state of snowpack in support of avalanche hazard warning, water resources assessment, and flood forecasting. In this study, daily forecasts of the NWP system Applications of Research to Operations at Mesoscale (AROME) at 2.5-km grid spacing over the French Alps were considered for four consecutive winters (from 2010/11 to 2013/14). AROME forecasts were first evaluated against ground-based measurements of air temperature, humidity, wind speed, incoming radiation, and precipitation. This evaluation shows a cold bias at high altitude partially related to an underestimation of cloud cover influencing incoming radiative fluxes. AROME seasonal snowfall was also compared against output from the Système d’Analyse Fournissant des Renseignements Atmosphériques à la Neige (SAFRAN) specially developed for alpine terrain. This comparison reveals that there are regions of significant difference between the two, especially at high elevation, and possible causes for these differences are discussed. Finally, AROME forecasts and SAFRAN reanalysis have been used to drive the snowpack model Surface Externalisée (SURFEX)/Crocus (SC) and to simulate the snowpack evolution over a 2.5-km grid covering the French Alps during four winters. When evaluated at the experimental site of Col de Porte, both simulations show good agreement with measurements of snow depth and snow water equivalent. At the scale of the French Alps, AROME-SC exhibits an overall positive bias, with the largest positive bias found in the northern and central French Alps. This study constitutes the first step toward the development of a distributed snowpack forecasting system using AROME.

scholarly journals Characterising extratropical near tropopause analysis humidity biases and their radiative effects on temperature forecasts

2021 ◽  
Author(s):  
Jake Bland ◽  
Suzanne Gray ◽  
John Methven ◽  
Richard Forbes
Keyword(s):  
Prediction Models ◽  
Initial Conditions ◽  
Weather Prediction ◽  
Specific Humidity ◽  
Cold Bias ◽  
Radiative Effects ◽  
Weather Forecasts ◽  
Forecast Models ◽  
Forecasting System ◽  
Temperature Errors

<p>A cold bias in the extratropical lowermost stratosphere in forecasts is one of the most prominent systematic temperature errors in numerical weather prediction models. Hypothesized causes of this bias include radiative effects from a collocated moist bias in model analyses. Such biases would be expected to affect extratropical dynamics and result in the misrepresentation of wave propagation at tropopause level. Here the extent to which these biases are connected is quantified. Observations from radiosondes are compared to operational analyses and forecasts from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) and Met Office Unified Model (MetUM) to determine the magnitude and vertical structure of these biases. Both operational models over-estimate lowermost stratospheric specific humidity by around 70% of the observed values on average, around 1km above the tropopause. This moist bias is already present in the initial conditions and changes little in forecasts over the first five days. Though temperatures are represented well in the analyses, the IFS forecasts anomalously cool in the lower stratosphere, relative to verifying radiosonde observations, by 0.2K per day. The IFS single column model is used to show this temperature change can be attributed to increased long-wave radiative cooling due to the lowermost stratospheric moist bias in the initial conditions. However, the MetUM temperature biases cannot be entirely attributed to the moist bias, and another significant factor must be present. These results highlight the importance of improving the humidity analysis to reduce the extratropical lowermost stratospheric cold bias in forecast models and the need to understand and mitigate the causes of the moist bias in these models.</p>

scholarly journals What Is the Predictability Limit of Midlatitude Weather?

2019 ◽  
Vol 76 (4) ◽  
pp. 1077-1091 ◽  
Author(s):  
Fuqing Zhang ◽  
Y. Qiang Sun ◽  
Linus Magnusson ◽  
Roberto Buizza ◽  
Shian-Jiann Lin ◽  
...  
Keyword(s):  
Initial Conditions ◽  
Weather Prediction ◽  
Forecast Model ◽  
Small Scale ◽  
Lead Times ◽  
Winter Storms ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Forecast Lead Time ◽  
Order Of Magnitude

Abstract Understanding the predictability limit of day-to-day weather phenomena such as midlatitude winter storms and summer monsoonal rainstorms is crucial to numerical weather prediction (NWP). This predictability limit is studied using unprecedented high-resolution global models with ensemble experiments of the European Centre for Medium-Range Weather Forecasts (ECMWF; 9-km operational model) and identical-twin experiments of the U.S. Next-Generation Global Prediction System (NGGPS; 3 km). Results suggest that the predictability limit for midlatitude weather may indeed exist and is intrinsic to the underlying dynamical system and instabilities even if the forecast model and the initial conditions are nearly perfect. Currently, a skillful forecast lead time of midlatitude instantaneous weather is around 10 days, which serves as the practical predictability limit. Reducing the current-day initial-condition uncertainty by an order of magnitude extends the deterministic forecast lead times of day-to-day weather by up to 5 days, with much less scope for improving prediction of small-scale phenomena like thunderstorms. Achieving this additional predictability limit can have enormous socioeconomic benefits but requires coordinated efforts by the entire community to design better numerical weather models, to improve observations, and to make better use of observations with advanced data assimilation and computing techniques.

Using neural networks for postprocessing of numerical weather predictions in complex terrain

2020 ◽  
Author(s):  
Jonas Bhend ◽  
Christoph Spirig ◽  
Max Hürlimann ◽  
Lionel Moret ◽  
Mark Liniger
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Complex Terrain ◽  
Weather Prediction ◽  
Lessons Learned ◽  
Swiss Alps ◽  
Local Effects ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Available Information

<p>Weather forecasts have been steadily improving in quality over the last decades. These ongoing improvements are due to advances in numerical weather prediction (NWP) and the advent of ever more powerful supercomputers that allow simulating future weather and its uncertainty with increasing resolution and using ensemble approaches. Such physics-based computer models, however, are not free of systematic errors. Statistical postprocessing can be used to calibrate NWP forecasts to further improve forecast quality and better exploit the available information. Here we present results from several explorative deep learning studies using artificial neural networks (ANN) to calibrate high resolution forecasts of temperature, precipitation, wind, and cloud cover in Switzerland. These first attempts at ANN-based postprocessing help us to understand the strengths and weaknesses of machine learning and are the basis to build more complex and comprehensive statistical models accounting for local effects in complex terrain such as the Swiss Alps. In all cases, ANN leads to significant improvements over the direct NWP output. While the improvement is comparable in magnitude with improvements achieved with conventional postprocessing approaches, ANN-based postprocessing is easier to generalize in space for a calibration of forecasts also at unobserved sites. In addition to the results of the postprocessing, we will also discuss the lessons learned so far in using machine learning for this particular problem.</p>

Severe Weather Prediction Using Storm Surrogates from an Ensemble Forecasting System

2016 ◽  
Vol 31 (1) ◽  
pp. 255-271 ◽  
Author(s):  
Ryan A. Sobash ◽  
Craig S. Schwartz ◽  
Glen S. Romine ◽  
Kathryn R. Fossell ◽  
Morris L. Weisman
Keyword(s):  
Extreme Values ◽  
Weather Prediction ◽  
Severe Weather ◽  
Model Output ◽  
Ensemble Forecasts ◽  
Weather Forecasts ◽  
Smoothing Kernel ◽  
Risk Areas ◽  
Forecasting System ◽  
Computational Resources

Abstract Probabilistic severe weather forecasts for days 1 and 2 were produced using 30-member convection-allowing ensemble forecasts initialized by an ensemble Kalman filter data assimilation system during a 32-day period coinciding with the Mesoscale Predictability Experiment. The forecasts were generated by smoothing the locations where model output indicated extreme values of updraft helicity, a surrogate for rotating thunderstorms in model output. The day 1 surrogate severe probability forecasts (SSPFs) produced skillful and reliable predictions of severe weather during this period, after an appropriate calibration of the smoothing kernel. The ensemble SSPFs exceeded the skill of SSPFs derived from two benchmark deterministic forecasts, with the largest differences occurring on the mesoscale, while all SSPFs produced similar forecasts on synoptic scales. While the deterministic SSPFs often overforecasted high probabilities, the ensemble improved the reliability of these probabilities, at the expense of producing fewer high-probability values. For the day 2 period, the SSPFs provided competitive guidance compared to the day 1 forecasts, although additional smoothing was needed to produce the same level of skill, reducing the forecast sharpness. Results were similar using 10 ensemble members, suggesting value exists when running a smaller ensemble if computational resources are limited. Finally, the SSPFs were compared to severe weather risk areas identified in Storm Prediction Center (SPC) convective outlooks. The SSPF skill was comparable to the SPC outlook skill in identifying regions where severe weather would occur, although performance varied on a day-to-day basis.

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 SINGV – the Convective-Scale Numerical Weather Prediction System for Singapore

2019 ◽  
Vol 36 (3) ◽  
Author(s):  
Xiang-Yu Huang ◽  
Dale Barker ◽  
Stuart Webster ◽  
Anurag Dipankar ◽  
Adrian Lock ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Extreme Rainfall ◽  
Prediction System ◽  
Local Data ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
The United Kingdom ◽  
Convective Scale ◽  
The Impact

Extreme rainfall is one of the primary meteorological hazards in Singapore, as well as elsewhere in the deep tropics, and it can lead to significant local flooding. Since 2013, the Meteorological Service Singapore (MSS) and the United Kingdom Met Office (UKMO) have been collaborating to develop a convective-scale Numerical Weather Prediction (NWP) system, called SINGV. Its primary aim is to provide improved weather forecasts for Singapore and the surrounding region, with a focus on improved short-range prediction of localized heavy rainfall. This paper provides an overview of the SINGV development, the latest NWP capabilities at MSS and some key results of evaluation. The paper describes science advances relevant to the development of any km-scale NWP suitable for the deep tropics and provides some insights into the impact of local data assimilation and utility of ensemble predictions.

scholarly journals Compressible EULAG dynamical core in COSMO: convective-scale Alpine weather forecasts

2021 ◽  
Author(s):  
Michał Z. Ziemiański ◽  
Damian K. Wójcik ◽  
Bogdan Rosa ◽  
Zbigniew P. Piotrowski
Keyword(s):  
High Resolution ◽  
Weather Prediction ◽  
Small Scale ◽  
Dynamical Core ◽  
Weather Forecasts ◽  
The Core ◽  
Convective Scale ◽  
Realistic Representation ◽  
General Improvement ◽  
Forecasting System

AbstractThis paper presents the semi-implicit compressible EULAG as a new dynamical core for convective-scale numerical weather prediction. The core is implemented within the infrastructure of the operational model of the Consortium for Small Scale Modeling (COSMO), forming the NWP COSMO-EULAG model (CE). This regional high-resolution implementation of the dynamical core complements its global implementation in the Finite-Volume Module of ECMWF’s Integrated Forecasting System. The paper documents the first operational-like application of the dynamical core for realistic weather forecasts. After discussing the formulation of the core and its coupling with the host model, the paper considers several high-resolution prognostic experiments over complex Alpine orography. Standard verification experiments examine the sensitivity of the CE forecast to the choice of the advection routine and assess the forecast skills against those of the default COSMO Runge-Kutta dynamical core at 2.2 km grid size showing a general improvement. The skills are also compared using satellite observations for a weak-flow convective Alpine weather case-study, showing favorable results. Additional validation of the new CE framework for partly convection-resolving forecasts using 1.1 km, 0.55 km, 0.22 km, and 0.1 km grids, designed to challenge its numerics and test the dynamics-physics coupling, demonstrates its high robustness in simulating multi-phase flows over complex mountain terrain, with slopes reaching 85 degrees, and the flow’s realistic representation.

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