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 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 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

scholarly journals Reduced non-Gaussianity by 30-second rapid update inconvective-scale numerical weather prediction

2021 ◽  
Author(s):  
Juan Ruiz ◽  
Guo-Yuan Lien ◽  
Keiichi Kondo ◽  
Shigenori Otsuka ◽  
Takemasa Miyoshi
Keyword(s):  
Kalman Filter ◽  
Numerical Weather Prediction ◽  
Forecast Error ◽  
Weather Prediction ◽  
Numerical Weather ◽  
Convective Clouds ◽  
Environment Model ◽  
Error Distributions ◽  
Ensemble Transform Kalman Filter ◽  
Non Gaussian

Abstract. Non-Gaussian forecast error is a challenge for ensemble-based data assimilation (DA), particularly for more nonlinear convective dynamics. In this study, we investigate the degree of non-Gaussianity of forecast error distributions at 1-km resolution using a 1000-member ensemble Kalman filter, and how it is affected by the DA update frequency and observation number. Regional numerical weather prediction experiments are performed with the SCALE (Scalable Computing for Advanced Library and Environment) model and the LETKF (Local Ensemble Transform Kalman Filter) assimilating every-30-second phased array radar observations. The results show that non-Gaussianity develops rapidly within convective clouds and is sensitive to the DA frequency and the number of assimilated observations. The non-Gaussianity is reduced by up to 40 % when the assimilation window is shortened from 5 minutes to 30 seconds, particularly for vertical velocity and radar reflectivity.

scholarly journals EuLerian Identification of ascending Air Streams (ELIAS 2.0) in Numerical Weather Prediction and Climate Models. Part I: Development of deep learning model

2021 ◽  
Author(s):  
Julian Francesco Quinting ◽  
Christian M. Grams
Keyword(s):  
Logistic Regression ◽  
Deep Learning ◽  
Numerical Weather Prediction ◽  
Climate Models ◽  
Weather Prediction ◽  
Data Sets ◽  
Forecast Uncertainty ◽  
Numerical Weather ◽  
Process Oriented ◽  
Air Streams

Abstract. Physical processes on the synoptic scale are important modulators of the large-scale extratropical circulation. In particular, rapidly ascending air streams in extratropical cyclones, so-called warm conveyor belts (WCBs), modulate the upper-tropospheric Rossby wave pattern and are sources and magnifiers of forecast uncertainty. Thus, from a process-oriented perspective, numerical weather prediction (NWP) and climate models should adequately represent WCBs. The identification of WCBs usually involves Lagrangian air parcel trajectories that ascend from the lower to the upper troposphere within two days. This requires numerical data with high spatial and temporal resolution which is often not available from standard output and requires expensive computations. This study introduces a novel framework that aims to predict the footprints of the WCB inflow, ascent, and outflow stages over the Northern Hemisphere from instantaneous gridded fields using convolutional neural networks (CNNs). With its comparably low computational costs and relying on standard model output alone the new diagnostic enables the systematic investigation of WCBs in large data sets such as ensemble reforecast or climate model projections which are mostly not suited for trajectory calculations. Building on the insights from a logistic regression approach of a previous study, the CNNs are trained using a combination of meteorological parameters as predictors and trajectory-based WCB footprints as predictands. Validation of the networks against the trajectory-based data set confirms that the CNN models reliably replicate the climatological frequency of WCBs as well as their footprints at instantaneous time steps. The CNN models significantly outperform previously developed logistic regression models. Including time-lagged information on the occurrence of WCB ascent as a predictor for the inflow and outflow stages further improves the models' skill considerably. A companion study demonstrates versatile applications of the CNNs in different data sets including the verification of WCBs in ensemble forecasts. Overall, the diagnostic demonstrates how deep learning methods may be used to investigate the representation of weather systems and of their related processes in NWP and climate models in order to shed light on forecast uncertainty and systematic biases from a process-oriented perspective.

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 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.

Sign in / Sign up

Export Citation Format

Share Document