scholarly journals Machine learning emulation of gravity wave drag in numerical weather forecasting

2021 ◽  
Author(s):  
Matthew Chantry ◽  
Sam Hatfield ◽  
Peter Duben ◽  
Inna Polichtchouk ◽  
Tim Palmer
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Data Assimilation ◽  
Gravity Wave ◽  
Weather Forecasting ◽  
Wave Drag ◽  
Seasonal Forecasting ◽  
Numerical Weather ◽  
Forecasting System ◽  
Numerical Weather Forecasting

<p>We assess the value of machine learning as an accelerator for a kernel of an operational weather forecasting system, specifically the parameterisation of non-orographic gravity wave drag. Emulators of this scheme can be trained that produce stable and accurate results up to seasonal forecasting timescales. By training on an increased complexity version of the parameterisation scheme we build emulators that produce more accurate forecasts than the existing parameterisation scheme. Leveraging the differentiability of neural networks we generate tangent linear and adjoint versions of our parameterisation, key components in 4D-var data-assimilation. We test our tangent linear and adjoint codes within an operational-like 4D-var setup and find no degradation in skill vs hand-written tangent-linear and adjoint codes.</p>

scholarly journals Beating the Uncertainties: Ensemble Forecasting and Ensemble-Based Data Assimilation in Modern Numerical Weather Prediction

2010 ◽  
Vol 2010 ◽  
pp. 1-10 ◽  
Author(s):  
Hailing Zhang ◽  
Zhaoxia Pu
Keyword(s):  
Data Assimilation ◽  
Numerical Weather Prediction ◽  
Weather Forecasting ◽  
Weather Prediction ◽  
Ensemble Forecasting ◽  
Atmospheric Flow ◽  
Numerical Weather ◽  
Recent Developments ◽  
Numerical Weather Forecasting ◽  
Chaotic Nature

Accurate numerical weather forecasting is of great importance. Due to inadequate observations, our limited understanding of the physical processes of the atmosphere, and the chaotic nature of atmospheric flow, uncertainties always exist in modern numerical weather prediction (NWP). Recent developments in ensemble forecasting and ensemble-based data assimilation have proved that there are promising ways to beat the forecast uncertainties in NWP. This paper gives a brief overview of fundamental problems and recent progress associated with ensemble forecasting and ensemble-based data assimilation. The usefulness of these methods in improving high-impact weather forecasting is also discussed.

TOMOREF operator as a tool to improve weather forecasts

2021 ◽  
Author(s):  
Natalia Hanna ◽  
Estera Trzcina ◽  
Maciej Kryza ◽  
Witold Rohm
Keyword(s):  
Data Assimilation ◽  
Weather Forecasting ◽  
Positive Impact ◽  
Precipitation Event ◽  
Initial State ◽  
Total Delay ◽  
Numerical Weather ◽  
Numerical Weather Model ◽  
Weather Model ◽  
Tomography Data

<p>The numerical weather model starts from the initial state of the Earth's atmosphere in a given place and time. The initial state is created by blending the previous forecast runs (first-guess), together with observations from different platforms. The better the initial state, the better the forecast; hence, it is worthy to combine new observation types. The GNSS tomography technique, developed in recent years, provides a 3-D field of humidity in the troposphere. This technique shows positive results in the monitoring of severe weather events. However, to assimilate the tomographic outputs to the numerical weather model, the proper observation operator needs to be built.</p><p>This study demonstrates the TOMOREF operator dedicated to the assimilation of the GNSS tomography‐derived 3‐D fields of wet refractivity in a Weather Research and Forecasting (WRF) Data Assimilation (DA) system. The new tool has been tested based on wet refractivity fields derived during a very intense precipitation event. The results were validated using radiosonde observations, synoptic data, ERA5 reanalysis, and radar data. In the presented experiment, a positive impact of the GNSS tomography data assimilation on the forecast of relative humidity (RH) was noticed (an improvement of root‐mean‐square error up to 0.5%). Moreover, within 1 hour after assimilation, the GNSS data reduced the bias of precipitation up to 0.1 mm. Additionally, the assimilation of GNSS tomography data had more influence on the WRF model than the Zenith Total Delay (ZTD) observations, which confirms the potential of the GNSS tomography data for weather forecasting.</p>

Verification and Case Studies for Urban Effects in HIRLAM Numerical Weather Forecasting

2009 ◽  
pp. 143-150 ◽  
Author(s):  
Alexander Mahura ◽  
Alexander Baklanov ◽  
Claus Petersen ◽  
Niels W. Nielsen ◽  
Bjarne Amstrup
Keyword(s):  
Case Studies ◽  
Weather Forecasting ◽  
Numerical Weather ◽  
Numerical Weather Forecasting ◽  
Urban Effects

Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation

2017 ◽  
Vol 107 ◽  
pp. 340-351 ◽  
Author(s):  
William Y.Y. Cheng ◽  
Yubao Liu ◽  
Alfred J. Bourgeois ◽  
Yonghui Wu ◽  
Sue Ellen Haupt
Keyword(s):  
Data Assimilation ◽  
Wind Turbine ◽  
Weather Forecasting ◽  
Short Term ◽  
Forecasting System
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