CNOP‐Based Adaptive Observation Network Designed for Improving Upstream Kuroshio Transport Prediction

Abstract To improve the prediction of Asian dust events on the Korean Peninsula, meteorological fields must be accurately predicted because dust transport models require them as input. Accurate meteorological forecasts could be obtained by integrating accurate initial conditions obtained from data assimilation processes in numerical weather prediction. In data assimilation, selecting the appropriate observation location is important to ensure that the initial conditions represent the surrounding meteorological flow. To investigate the effect of observation network configuration on meteorological forecasts during Asian dust events on the Korean Peninsula, observing system simulation experiments using several simulated and real observation networks were tested with the Weather Research and Forecasting modeling system for 11 Asian dust events affecting the Korean Peninsula during a recent 6-yr period. First, the characteristics of randomly fixed and adaptively selected observation networks were investigated with various observation densities. The adaptive observation strategy could reduce forecast errors more efficiently than the fixed observation strategy. For both the fixed and adaptive observation strategies, the mean forecast error reduction rates increased as the number of assimilated observations and the distance between observation sites increased up to 300 km. Second, the effects of redistributing the real observation sites and adding observation sites to the real observation network based on the adaptive observation strategy were investigated. Adding adaptive observation sites to the real observation network in statistically sensitive regions improved the forecast performance more than redistributing real observation sites did. The strategy of adding adaptive observation sites is used to suggest the optimal meteorological observation network for meteorological forecasts of Asian dust transport events on the Korean Peninsula.

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Application of conditional non-linear optimal perturbations to tropical cyclone adaptive observation using the Weather Research Forecasting (WRF) model

Tellus A Dynamic Meteorology and Oceanography ◽

10.3402/tellusa.v63i5.15874 ◽

2011 ◽

Author(s):

Hongli Wang ◽

Mu Mu ◽

Xiang-Yu Huang

Keyword(s):

Tropical Cyclone ◽

Wrf Model ◽

Adaptive Observation ◽

Non Linear ◽

Weather Research

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DESIGN AND IMPLEMENTATION OF THE OCEANOGRAPHIC MODELING AND OBSERVATION NETWORK (REMO) FOR PHYSICAL OCEANOGRAPHY STUDIES AND OCEAN FORECASTING

Brazilian Journal of Geophysics ◽

10.22564/rbgf.v31i2.18 ◽

2014 ◽

Vol 31 (2) ◽

Author(s):

Jose Antonio Moreira Lima

Keyword(s):

Atlantic Ocean ◽

Large Scale ◽

North Pacific Ocean ◽

Integrated Approach ◽

Sea Surface ◽

Global Ocean ◽

Height Anomaly ◽

The North ◽

Observation Network ◽

Ocean Forecasting

This paper is concerned with the planning, implementation and some results of the Oceanographic Modeling and Observation Network, named REMO, for Brazilian regional waters. Ocean forecasting has been an important scientific issue over the last decade due to studies related to climate change as well as applications related to short-range oceanic forecasts. The South Atlantic Ocean has a deficit of oceanographic measurements when compared to other ocean basins such as the North Atlantic Ocean and the North Pacific Ocean. It is a challenge to design an ocean forecasting system for a region with poor observational coverage of in-situ data. Fortunately, most ocean forecasting systems heavily rely on the assimilation of surface fields such as sea surface height anomaly (SSHA) or sea surface temperature (SST), acquired by environmental satellites, that can accurately provide information that constrain major surface current systems and their mesoscale activity. An integrated approach is proposed here in which the large scale circulation in the Atlantic Ocean is modeled in a first step, and gradually nested into higher resolution regional models that are able to resolve important processes such as the Brazil Current and associated mesoscale variability, continental shelf waves, local and remote wind forcing, and others. This article presents the overall strategy to develop the models using a network of Brazilian institutions and their related expertise along with international collaboration. This work has some similarity with goals of the international project Global Ocean Data Assimilation Experiment OceanView (GODAE OceanView).

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Topology detection of a distribution network based on adaptive state observer

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-214998 ◽

2021 ◽

pp. 1-9

Author(s):

Yong Xiao ◽

Yonggang Zeng ◽

Yun Zhao ◽

Yuxin Lu ◽

Weibin Lin

Keyword(s):

Smart Grid ◽

Distribution Network ◽

Detection Algorithm ◽

State Observer ◽

Matlab Simulation ◽

Unknown Parameters ◽

State Observation ◽

Observation Network ◽

Topology Information ◽

Adaptive State Observer

The traditional distribution network lacks real-time topology information, which makes the implementation of smart grid complicated. The smart grid needs to monitor and dispatch the grid to maintain the economic and safe operation of the system. In this paper, we propose a topology detection algorithm of the distribution network based on adaptive state observer. Based on the transient dynamic model of the distribution network, the line states of the distribution network are regarded as unknown parameters, a virtual adaptive state observation network is built, and the topology can be inferred by the changes of adaptive state parameters. Finally, the effectiveness of our algorithm is verified by the MATLAB simulation experiments.

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A Computational Methodology for the Calibration of Tephra Transport Nowcasting at Sakurajima Volcano, Japan

Atmosphere ◽

10.3390/atmos12010104 ◽

2021 ◽

Vol 12 (1) ◽

pp. 104

Author(s):

Alexandros P. Poulidis ◽

Atsushi Shimizu ◽

Haruhisa Nakamichi ◽

Masato Iguchi

Keyword(s):

Remote Sensing ◽

High Resolution ◽

Remote Sensing Data ◽

Volcanic Eruptions ◽

Weather Research And Forecasting ◽

Sakurajima Volcano ◽

Observation Network ◽

Complementary Strategy ◽

Physical Quantities ◽

Do So

Ground-based remote sensing equipment have the potential to be used for the nowcasting of the tephra hazard from volcanic eruptions. To do so raw data from the equipment first need to be accurately transformed to tephra-related physical quantities. In order to establish these relations for Sakurajima volcano, Japan, we propose a methodology based on high-resolution simulations. An eruption that occurred at Sakurajima on 16 July 2018 is used as the basis of a pilot study. The westwards dispersal of the tephra cloud was ideal for the observation network that has been installed near the volcano. In total, the plume and subsequent tephra cloud were recorded by 2 XMP radars, 1 lidar and 3 optical disdrometers, providing insight on all phases of the eruption, from plume generation to tephra transport away from the volcano. The Weather Research and Forecasting (WRF) and FALL3D models were used to reconstruct the transport and deposition patterns. Simulated airborne tephra concentration and accumulated load were linked, respectively, to lidar backscatter intensity and radar reflectivity. Overall, results highlight the possibility of using such a high-resolution modelling-based methodology as a reliable complementary strategy to common approaches for retrieving tephra-related quantities from remote sensing data.

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Gray Phonon Transport Prediction of Thermal Conductivity in Lithium Aluminate with Higher-Order Finite Elements on Meshes with Curved Surfaces

Journal of Computational and Theoretical Transport ◽

10.1080/23324309.2021.1900258 ◽

2021 ◽

pp. 1-25

Author(s):

Nicholas H. Whitman ◽

Todd S. Palmer ◽

P. Alex Greaney ◽

S. Aria Hosseini ◽

Douglas E. Burkes ◽

...

Keyword(s):

Thermal Conductivity ◽

Finite Elements ◽

Phonon Transport ◽

Higher Order ◽

Curved Surfaces ◽

Lithium Aluminate ◽

Higher Order Finite Elements ◽

Transport Prediction

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Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

International Journal of Biometeorology ◽

10.1007/s00484-021-02110-3 ◽

2021 ◽

Author(s):

Ye Yuan ◽

Stefan Härer ◽

Tobias Ottenheym ◽

Gourav Misra ◽

Alissa Lüpke ◽

...

Keyword(s):

Climate Change ◽

Time Series ◽

Strong Decrease ◽

Temperature Changes ◽

Interpolation Accuracy ◽

Observation Network ◽

Shiny App ◽

Long Term Trends ◽

Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

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