Feasibility of Short-Range Numerical Weather Prediction Using Observations from a Network of Profilers

1987 ◽  
Vol 115 (10) ◽  
pp. 2402-2427 ◽  
Author(s):  
Ying-Hwa Kuo ◽  
Evelyn G. Donall ◽  
Melvyn A. Shapiro
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Weather Prediction ◽  
Numerical Weather

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 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.

Forming of the products of non-hydrostatic modelling systems for short-range weather prediction COSMO-RuBy (Hydrometcentre of Russia) и WRF-ARW (Belhydromet)

2021 ◽  
pp. 6-29
Author(s):  
I.А. Rozinkina ◽  
◽  
G.S . Rivin ◽  
R.N. Burak ◽  
Е.D. Аstakhova ◽  
...  
Keyword(s):  
Machine Learning ◽  
Numerical Weather Prediction ◽  
Web Site ◽  
Short Range ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Grid Spacing ◽  
Numerical Weather ◽  
Prediction Systems

The paper considers the results of activities on the development of output products for the non-hydrostatic short-range numerical weather prediction systems: COSMO-RuBy with a grid spacing of 2.2 km at the Hydrometcentre of Russia and WRF-ARW with a grid spacing of 3 km in Belhydromet. The important results of the activities are the organization of the exchange of unified products between the countries and the development at the Hydrometcentre of Russia of two technologies for obtaining the unified products: the multi-model lagged ensemble system and the system for the complex correction based on machine learning of model results. A specialized web-site providing convenient work of forecasters with the COSMO-RuBy results and unified products was created at the Hydrometcentre of Russia based on the feedback from forecasters. The systems of common visualization and verification of COSMO-RuBy and WRF-ARW results are implemented in Belhydromet. Keywords: numerical weather prediction, ensemble forecasting, visualization, machine learning

scholarly journals Impacts of increasing the aerosol complexity in the Met Office global numerical weather prediction model

2014 ◽  
Vol 14 (9) ◽  
pp. 4749-4778 ◽  
Author(s):  
J. P. Mulcahy ◽  
D. N. Walters ◽  
N. Bellouin ◽  
S. F. Milton
Keyword(s):  
West Africa ◽  
Direct Effect ◽  
Numerical Weather Prediction ◽  
Forest Fires ◽  
Short Range ◽  
Weather Prediction ◽  
Dust Storms ◽  
Surface Radiation ◽  
Wave Radiation ◽  
Numerical Weather

Abstract. The inclusion of the direct and indirect radiative effects of aerosols in high-resolution global numerical weather prediction (NWP) models is being increasingly recognised as important for the improved accuracy of short-range weather forecasts. In this study the impacts of increasing the aerosol complexity in the global NWP configuration of the Met Office Unified Model (MetUM) are investigated. A hierarchy of aerosol representations are evaluated including three-dimensional monthly mean speciated aerosol climatologies, fully prognostic aerosols modelled using the CLASSIC aerosol scheme and finally, initialised aerosols using assimilated aerosol fields from the GEMS project. The prognostic aerosol schemes are better able to predict the temporal and spatial variation of atmospheric aerosol optical depth, which is particularly important in cases of large sporadic aerosol events such as large dust storms or forest fires. Including the direct effect of aerosols improves model biases in outgoing long-wave radiation over West Africa due to a better representation of dust. However, uncertainties in dust optical properties propagate to its direct effect and the subsequent model response. Inclusion of the indirect aerosol effects improves surface radiation biases at the North Slope of Alaska ARM site due to lower cloud amounts in high-latitude clean-air regions. This leads to improved temperature and height forecasts in this region. Impacts on the global mean model precipitation and large-scale circulation fields were found to be generally small in the short-range forecasts. However, the indirect aerosol effect leads to a strengthening of the low-level monsoon flow over the Arabian Sea and Bay of Bengal and an increase in precipitation over Southeast Asia. Regional impacts on the African Easterly Jet (AEJ) are also presented with the large dust loading in the aerosol climatology enhancing of the heat low over West Africa and weakening the AEJ. This study highlights the importance of including a more realistic treatment of aerosol–cloud interactions in global NWP models and the potential for improved global environmental prediction systems through the incorporation of more complex aerosol schemes.

An Update on the Impact of Aeolus Doppler Wind Lidar Observations for Use in Numerical Weather Prediction at ECMWF

2021 ◽  
Author(s):  
Michael P. Rennie ◽  
Lars Isaksen
Keyword(s):  
Numerical Weather Prediction ◽  
Short Range ◽  
Lower Stratosphere ◽  
Positive Impact ◽  
Weather Prediction ◽  
Horizontal Line ◽  
Numerical Weather ◽  
Wind Retrievals ◽  
Atmospheric Backscatter ◽  
The Impact

<p>The latest results on the assessment of the impact of Aeolus Level-2B horizontal line-of-sight wind retrievals in global Numerical Weather Prediction at ECMWF will be presented.  Aeolus has been operationally assimilated at ECMWF since 9 January 2020.<br>Random and systematic error estimates were derived from observation minus background departure statistics.  The HLOS wind random error standard deviation is estimated to vary over the range 4.0-7.0 m/s for the Rayleigh-clear and 2.8-3.6 m/s for the Mie-cloudy; depending on atmospheric signal levels which in turn depends on instrument performance, atmospheric backscatter properties and the processing algorithms.<br>In Observing System Experiments (OSEs) Aeolus provides statistically significant improvement in short-range forecasts as verified by observations sensitive to temperature, wind and humidity.  Longer forecast range verification shows positive impact that is strongest at the 2-3 day forecast range; ~2% improvement in root mean square error for vector wind and temperature in the tropical upper troposphere and lower stratosphere and polar troposphere.  Positive impact up to 9 days is found in the tropical lower stratosphere.  Both Rayleigh-clear and Mie-cloudy winds provide positive impact, but the Rayleigh accounts for most tropical impact. The Forecast Sensitivity Observation Impact (FSOI) metric is available since Aeolus was operationally assimilated, which confirms Aeolus is a useful contribution to the global observing system; with the Rayleigh-clear and Mie-cloudy winds providing similar overall short-range impact in 2020.  If the OSEs are ready in time, we will present the impact of the first reprocessed Aeolus data for the July-December 2019 period.</p>

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