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 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 Probabilistic forecasting of shallow, rainfall-triggered landslides using real-time numerical weather predictions

2008 ◽  
Vol 8 (2) ◽  
pp. 349-357 ◽  
Author(s):  
J. Schmidt ◽  
G. Turek ◽  
M. P. Clark ◽  
M. Uddstrom ◽  
J. R. Dymond
Keyword(s):  
Soil Moisture ◽  
New Zealand ◽  
Real Time ◽  
Numerical Weather Prediction ◽  
Weather Prediction ◽  
Hydrologic Model ◽  
Local Scale ◽  
Weather Forecasts ◽  
Numerical Weather ◽  
Infinite Slope Stability Model

Abstract. A project established at the National Institute of Water and Atmospheric Research (NIWA) in New Zealand is aimed at developing a prototype of a real-time landslide forecasting system. The objective is to predict temporal changes in landslide probability for shallow, rainfall-triggered landslides, based on quantitative weather forecasts from numerical weather prediction models. Global weather forecasts from the United Kingdom Met Office (MO) Numerical Weather Prediction model (NWP) are coupled with a regional data assimilating NWP model (New Zealand Limited Area Model, NZLAM) to forecast atmospheric variables such as precipitation and temperature up to 48 h ahead for all of New Zealand. The weather forecasts are fed into a hydrologic model to predict development of soil moisture and groundwater levels. The forecasted catchment-scale patterns in soil moisture and soil saturation are then downscaled using topographic indices to predict soil moisture status at the local scale, and an infinite slope stability model is applied to determine the triggering soil water threshold at a local scale. The model uses uncertainty of soil parameters to produce probabilistic forecasts of spatio-temporal landslide occurrence 48~h ahead. The system was evaluated for a damaging landslide event in New Zealand. Comparison with landslide densities estimated from satellite imagery resulted in hit rates of 70–90%.

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