scholarly journals Experimental technology for very-short-range numerical weather prediction based on a nonhydrostatic mesoscale meteorological model with assimilation of radar and ground-based observational data

2020 ◽  
pp. 6-27
Author(s):  
V.D Zhupanov ◽  
◽  
V.I. Luk’yanov ◽  
E.V Vasil’ev ◽  
T.G. Dmitrieva ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Weather Station ◽  
Meteorological Model ◽  
Numerical Weather ◽  
Station Data ◽  
Mesoscale Meteorological Model ◽  
Weather Station Data ◽  
Skill Scores

A brief description of the very-short-range numerical weather prediction technology based on the WRF-ARW nonhydrostatic model is presented. Skill scores are provided for the short- and very-short-range forecasts of temperature, precipitation and wind of various intensity, which were calculated with this model as a result of its integration on the nested grid with a spacing of 3 km, with the direct simulation of deep convection and the assimilation of radar and ground-based weather station data. The forecasts for a location were verified for the central part of European Russia using radar and weather station data for the summer of 2020. It is demonstrated that the model adequately simulates mesoscale convective systems and the related zones of heavy precipitation, strong winds, and thunderstorms. Possible reasons for forecast biases and the ways to reduce the value of spatial and temporal errors are discussed. Keywords: numerical very-short-range forecasting, mesoscale meteorological model, radar data assimilation, precipitation, severe weather events, active convection

scholarly journals Short-Range Numerical Weather Prediction of Extreme Precipitation Events Using Enhanced Surface Data Assimilation

Atmosphere ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 587
Author(s):  
Magnus Lindskog ◽  
Tomas Landelius
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Heavy Precipitation ◽  
Numerical Weather ◽  
Extreme Precipitation Events ◽  
Surface Data ◽  
Enhanced Surface ◽  
Precipitation Events

A limited-area kilometre scale numerical weather prediction system is applied to evaluate the effect of refined surface data assimilation on short-range heavy precipitation forecasts. The refinements include a spatially dependent background error representation, use of a flow-dependent data assimilation technique, and use of data from a satellite-based scatterometer instrument. The effect of the enhancements on short-term prediction of intense precipitation events is confirmed through a number of case studies. Verification scores and subjective evaluation of one particular case points at a clear impact of the enhanced surface data assimilation on short-range heavy precipitation forecasts and suggest that it also tends to slightly improve them. Although this is not strictly statistically demonstrated, it is consistent with the expectation that a better surface state should improve rainfall forecasts.

scholarly journals Existence of multiple scales in uncertainty of numerical weather prediction

2019 ◽  
Vol 9 (1) ◽  
Author(s):  
Hyo-Jong Song
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Multiple Scales ◽  
Forecast Error ◽  
Weather Prediction ◽  
Initial Condition ◽  
Forecast Uncertainty ◽  
Essential Information ◽  
Numerical Weather ◽  
Sample Correlation

Abstract Numerical weather prediction provides essential information of societal influence. Advances in the initial condition estimation have led to the improvement of the prediction skill. The process to produce the better initial condition (analysis) with the combination of short-range forecast and observation over the globe requires information about uncertainty of the forecast results to decide how much observation is reflected to the analysis and how far the observation information should be propagated. Forecast ensemble represents the error of the short-range forecast at the instance. The influence of observation propagating along with forecast ensemble correlation needs to be restricted by localized correlation function because of less reliability of sample correlation. So far, solitary radius of influence is usually used since there has not been an understanding about the realism of multiple scales in the forecast uncertainty. In this study, it is explicitly shown that multiple scales exist in short-range forecast error and any single-scale localization approach could not resolve this situation. A combination of Gaussian correlation functions of various scales is designed, which more weighs observation itself near the data point and makes ensemble perturbation, far from the observation position, more participate in decision of the analysis. Its outstanding performance supports the existence of multi-scale correlation in forecast uncertainty.

scholarly journals Crowd‐sourced observations for short‐range numerical weather prediction: Report from EWGLAM / SRNWP Meeting 2019

2021 ◽  
Author(s):  
Kasper S. Hintz ◽  
Conor McNicholas ◽  
Roger Randriamampianina ◽  
Hywel T. P. Williams ◽  
Bruce Macpherson ◽  
...  
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Numerical Weather

Naissances du modèle et de la coopération Aladin

2021 ◽  
pp. 041
Author(s):  
András Horányi ◽  
Radmila Brožková
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Early Years ◽  
Pivotal Role ◽  
Personal Account ◽  
Numerical Weather ◽  
The Creation

Jean-François Geleyn a joué un rôle central dans la création et le fonctionnement de la coopération Aladin sur la prévision numérique du temps (PNT). Le projet Aladin a non seulement développé des outils de prévision numérique du temps à court terme, qui pouvaient être utilisés pour la prévision numérique opérationnelle, mais a également instauré un lien durable entre ses participants. Dans cet article, nous rendons hommage à Jean-François avec notre récit historique et parfois personnel des premières années de la coopération. Nous reconnaissons et soulignons que Jean-François n'a pas seulement créé et façonné la coopération elle-même, mais qu'il a également influencé la carrière et la vie de beaucoup des scientifiques appartenant aux instituts participant à ce projet. Jean-François Geleyn had a pivotal role in the creation and running of the Aladin Numerical Weather Prediction (NWP) cooperation. The Aladin project not only developed short-range NWP tools, which could be used for operational numerical forecasting, but also instilled a long-lasting bond among its participants. In this article we pay tribute to Jean-François with our historical and sometimes personal account of the early years of the cooperation. We acknowledge and stress that Jean-François not only created and shaped the cooperation itself, but also influenced the career and life of many scientists from the participating institutes.

scholarly journals Evaluation of numerical weather prediction model precipitation forecasts for use in short-term streamflow forecasting

2012 ◽  
Vol 9 (11) ◽  
pp. 12563-12611 ◽  
Author(s):  
D. L. Shrestha ◽  
D. E. Robertson ◽  
Q. J. Wang ◽  
T. C. Pagano ◽  
P. Hapuarachchi
Keyword(s):  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Lead Times ◽  
Streamflow Forecasting ◽  
Low Resolution ◽  
Short Term ◽  
Catchment Scale ◽  
Numerical Weather ◽  
Skill Scores ◽  
Systematic Biases

Abstract. The quality of precipitation forecasts from four Numerical Weather Prediction (NWP) models is evaluated over the Ovens catchment in southeast Australia. Precipitation forecasts are compared with observed precipitation at point and catchment scales and at different temporal resolutions. The four models evaluated are the Australian Community Climate Earth-System Simulator (ACCESS) including ACCESS-G with a 80 km resolution, ACCESS-R 37.5 km, ACCESS-A 12 km, and ACCESS-VT 5 km. The high spatial resolution NWP models (ACCESS-A and ACCESS-VT) appear to be relatively free of bias (i.e. <30%) for 24 h total precipitation forecasts. The low resolution models (ACCESS-R and ACCESS-G) have widespread systematic biases as large as 70%. When evaluated at finer spatial and temporal resolution (e.g. 5 km, hourly) against station observations, the precipitation forecasts appear to have very little skill. There is moderate skill at short lead times when the forecasts are averaged up to daily and/or catchment scale. The skill decreases with increasing lead times and the global model ACCESS-G does not have significant skill beyond 7 days. The precipitation forecasts fail to produce a diurnal cycle shown in observed precipitation. Significant sampling uncertainty in the skill scores suggests that more data are required to get a reliable evaluation of the forecasts. Future work is planned to assess the benefits of using the NWP rainfall forecasts for short-term streamflow forecasting. Our findings here suggest that it is necessary to remove the systematic biases in rainfall forecasts, particularly those from low resolution models, before the rainfall forecasts can be used for streamflow forecasting.

scholarly journals RainCast: A Rapid Update Rainfall Forecasting System for New Zealand

2021 ◽  
Author(s):  
Sijin Zhang ◽  
Gerard Barrow ◽  
Iman Soltanzadeh ◽  
Graham Rye ◽  
Yizhe Zhan ◽  
...  
Keyword(s):  
New Zealand ◽  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Rainfall Forecasting ◽  
Numerical Weather ◽  
Forecasting System

Abstract RainCast is a rapid update forecasting system that has been developed to improve short-range rainfall forecasting in New Zealand. This system blends extrapolated nowcast information with multiple forecasts from numerical weather prediction (NWP) models to generate updated rain forecasts every hour. It is demonstrated that RainCast is able to outperform the rainfall forecasts produced from NWP systems out to 24 hours, with the greatest improvement in the first 3-4 hours. The limitations of RainCast are also discussed, along with recommendations on how to further improve the system.

scholarly journals Impact of Doppler Radar Wind Observations on Australian High-Resolution Numerical Weather Prediction

2020 ◽  
Vol 35 (2) ◽  
pp. 309-324
Author(s):  
Susan Rennie ◽  
Lawrence Rikus ◽  
Nathan Eizenberg ◽  
Peter Steinle ◽  
Monika Krysta
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Doppler Radar ◽  
Weather Prediction ◽  
Limited Area ◽  
Observation Error ◽  
Numerical Weather ◽  
Skill Scores ◽  
The Impact

Abstract The impact of Doppler radar wind observations on forecasts from a developmental, high-resolution numerical weather prediction (NWP) system is assessed. The new 1.5-km limited-area model will be Australia’s first such operational NWP system to include data assimilation. During development, the assimilation of radar wind observations was trialed over a 2-month period to approve the initial inclusion of these observations. Three trials were run: the first with no radar data, the second with radial wind observations from precipitation echoes, and the third with radial winds from both precipitation and insect echoes. The forecasts were verified against surface observations from automatic weather stations, against rainfall accumulations using fractions skill scores, and against satellite cloud observations. These methods encompassed verification across a range of vertical levels. Additionally, a case study was examined more closely. Overall results showed little statistical difference in skill between the trials, and the net impact was neutral. While the new observations clearly affected the forecast, the objective and subjective analyses showed a neutral impact on the forecast overall. As a first step, this result is satisfactory for the operational implementation. In future, upgrades to the radar network will start to reduce the observation error, and further improvements to the data assimilation are planned, which may be expected to improve the impact.

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