Interpreting an evaluation of the ECMWF global model with CloudSat observations: ambiguities due to radar reflectivity forward operator uncertainties

2012 ◽  
Vol 138 (669) ◽  
pp. 2047-2065 ◽  
Author(s):  
Sabatino Di Michele ◽  
Maike Ahlgrimm ◽  
Richard Forbes ◽  
Mark Kulie ◽  
Ralf Bennartz ◽  
...  
Keyword(s):  
Global Model ◽  
Radar Reflectivity ◽  
Forward Operator

scholarly journals FedPSO: Federated Learning Using Particle Swarm Optimization to Reduce Communication Costs

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 600
Author(s):  
Sunghwan Park ◽  
Yeryoung Suh ◽  
Jaewoo Lee
Keyword(s):  
Particle Swarm Optimization ◽  
Data Privacy ◽  
Particle Swarm ◽  
Global Model ◽  
Network Communication ◽  
Improve Performance ◽  
Communication Performance ◽  
Swarm Optimization ◽  
Model Update ◽  
Network Environments

Federated learning is a learning method that collects only learned models on a server to ensure data privacy. This method does not collect data on the server but instead proceeds with data directly from distributed clients. Because federated learning clients often have limited communication bandwidth, communication between servers and clients should be optimized to improve performance. Federated learning clients often use Wi-Fi and have to communicate in unstable network environments. However, as existing federated learning aggregation algorithms transmit and receive a large amount of weights, accuracy is significantly reduced in unstable network environments. In this study, we propose the algorithm using particle swarm optimization algorithm instead of FedAvg, which updates the global model by collecting weights of learned models that were mainly used in federated learning. The algorithm is named as federated particle swarm optimization (FedPSO), and we increase its robustness in unstable network environments by transmitting score values rather than large weights. Thus, we propose a FedPSO, a global model update algorithm with improved network communication performance, by changing the form of the data that clients transmit to servers. This study showed that applying FedPSO significantly reduced the amount of data used in network communication and improved the accuracy of the global model by an average of 9.47%. Moreover, it showed an improvement in loss of accuracy by approximately 4% in experiments on an unstable network.

scholarly journals Effect of the Assimilation Frequency of Radar Reflectivity on Rain Storm Prediction by Using WRF-3DVAR

2021 ◽  
Vol 13 (11) ◽  
pp. 2103
Author(s):  
Yuchen Liu ◽  
Jia Liu ◽  
Chuanzhe Li ◽  
Fuliang Yu ◽  
Wei Wang
Keyword(s):  
Data Assimilation ◽  
Northern China ◽  
Horizontal Resolution ◽  
Radar Reflectivity ◽  
Small Scale ◽  
Time Interval ◽  
Outer Domain ◽  
Storm Prediction ◽  
The Impact ◽  
Assimilation Time

An attempt was made to evaluate the impact of assimilating Doppler Weather Radar (DWR) reflectivity together with Global Telecommunication System (GTS) data in the three-dimensional variational data assimilation (3DVAR) system of the Weather Research Forecast (WRF) model on rain storm prediction in Daqinghe basin of northern China. The aim of this study was to explore the potential effects of data assimilation frequency and to evaluate the outputs from different domain resolutions in improving the meso-scale NWP rainfall products. In this study, four numerical experiments (no assimilation, 1 and 6 h assimilation time interval with DWR and GTS at 1 km horizontal resolution, 6 h assimilation time interval with radar reflectivity, and GTS data at 3 km horizontal resolution) are carried out to evaluate the impact of data assimilation on prediction of convective rain storms. The results show that the assimilation of radar reflectivity and GTS data collectively enhanced the performance of the WRF-3DVAR system over the Beijing-Tianjin-Hebei region of northern China. It is indicated by the experimental results that the rapid update assimilation has a positive impact on the prediction of the location, tendency, and development of rain storms associated with the study area. In order to explore the influence of data assimilation in the outer domain on the output of the inner domain, the rainfall outputs of 3 and 1 km resolution are compared. The results show that the data assimilation in the outer domain has a positive effect on the output of the inner domain. Since the 3DVAR system is able to analyze certain small-scale and convective-scale features through the incorporation of radar observations, hourly assimilation time interval does not always significantly improve precipitation forecasts because of the inaccurate radar reflectivity observations. Therefore, before data assimilation, the validity of assimilation data should be judged as far as possible in advance, which can not only improve the prediction accuracy, but also improve the assimilation efficiency.

scholarly journals Targeted covariance inflation for 3D‐volume radar reflectivity assimilation with the LETKF

2021 ◽  
Author(s):  
Klaus Vobig ◽  
Klaus Stephan ◽  
Ulrich Blahak ◽  
Kobra Khosravian ◽  
Roland Potthast
Keyword(s):  
Radar Reflectivity ◽  
3D Volume

Global model analysis of Ar inductively coupled plasma driven by a 150 kHz-band high-power pulse burst

2019 ◽  
Vol 58 (SA) ◽  
pp. SAAB06
Author(s):  
Yuma Saito ◽  
Kodai Shibata ◽  
Katsuyuki Takahashi ◽  
Seiji Mukaigawa ◽  
Koichi Takaki ◽  
...  
Keyword(s):  
High Power ◽  
Inductively Coupled Plasma ◽  
Model Analysis ◽  
Global Model ◽  
Pulse Burst ◽  
Inductively Coupled ◽  
Power Pulse ◽  
High Power Pulse
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