Research and realization of geophysical system for HJ-2 satellite camera controller

<p>Model-free gradation of predictability of a geophysical system is essential to quantify how much inherent information is contained within the system and evaluate different forecasting methods' performance to get the best possible prediction. We conjecture that Multiscale Information enclosed in a given geophysical time series is the only input source for any forecast model. In the literature, established entropic measures dealing with grading the predictability of a time series at multiple time scales are limited. Therefore, we need an additional measure to quantify the information at multiple time scales, thereby grading the predictability level. This study introduces a novel measure, Wavelet Entropy Energy Measure (WEEM), based on Wavelet entropy to investigate a time series's energy distribution. From the WEEM analysis, predictability can be graded low to high. The difference between the entropy of a wavelet energy distribution of a time series and entropy of wavelet energy of white noise is the basis for gradation. The metric quantifies the proportion of the deterministic component of a time series in terms of energy concentration, and its range varies from zero to one. One corresponds to high predictable due to its high energy concentration and zero representing a process similar to the white noise process having scattered energy distribution. The proposed metric is normalized, handles non-stationarity, independent of the length of the data. Therefore, it can explain the evolution of predictability for any geophysical time series (ex: precipitation, streamflow, paleoclimate series) from past to the present. WEEM metric's performance can guide the forecasting models in getting the best possible prediction of a geophysical system by comparing different methods.&#160;</p>

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Introduction

Empirical Methods in Short-Term Climate Prediction ◽

10.1093/oso/9780199202782.003.0008 ◽

2006 ◽

Author(s):

Huug van den Dool

Keyword(s):

Physical System ◽

Weather Prediction ◽

Climate Prediction ◽

Lead Times ◽

Prediction Methods ◽

Data Sets ◽

Short Term ◽

Global Data Sets ◽

Geophysical System

This is first and foremost a book about short-term climate prediction. The predictions we have in mind are for weather/climate elements, mainly temperature (T) and precipitation (P), at lead times longer than two weeks, beyond the realm of detailed Numerical Weather Prediction (NWP), i.e. predictions for the next month and the next seasons out to at most a few years. call this short-term climate so as to distinguish it from long-term climate change which is not the main subject of this book. A few decades ago “short-term climate prediction” was known as “longrange weather prediction”. In order to understand short-term climate predictions, their skill and what they reveal about the atmosphere, ocean and land, several chapters are devoted to constructing prediction methods. The approach taken is mainly empirical, which means literally that it is based in experience. We will use global data sets to represent the climate and weather humanity experienced (and measured!) in the past several decades. The idea is to use these existing data sets in order to construct prediction methods. In doing so we want to acknowledge that every measurement (with error bars) is a monument about the workings of Nature. We thought about using the word “statistical” instead of “empirical” in the title of the book. These two notions overlap, obviously, but we prefer the word “empirical” because we are driven more by intuition than by a desire to apply existing or developing new statistical theory. While constructing prediction methods we want to discover to the greatest extent possible how the physical system works from observations. While not mentioned in the title, diagnostics of the physical system will thus be an important part of the book as well. We use a variety of classical tools to diagnose the geophysical system. Some of these tools have been developed further and/or old tools are applied in novel ways. We do not intend to cover all diagnostics methods, only those that relate closely to prediction. There will be an emphasis on methods used in operational prediction. It is quite difficult to gain a comprehensive idea from existing literature about methods used in operational short-term climate prediction.

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Estimating the state of a geophysical system with sparse observations: time delay methods to achieve accurate initial states for prediction

Nonlinear Processes in Geophysics ◽

10.5194/npg-24-9-2017 ◽

2017 ◽

Vol 24 (1) ◽

pp. 9-22 ◽

Cited By ~ 5

Author(s):

Zhe An ◽

Daniel Rey ◽

Jingxin Ye ◽

Henry D. I. Abarbanel

Keyword(s):

Time Delays ◽

Time Series Data ◽

Chaotic Behavior ◽

Weather Prediction ◽

Series Data ◽

State Variables ◽

Nonlinear Shallow Water Equations ◽

Delayed Measurements ◽

Lagrangian Drifter ◽

Geophysical System

Abstract. The problem of forecasting the behavior of a complex dynamical system through analysis of observational time-series data becomes difficult when the system expresses chaotic behavior and the measurements are sparse, in both space and/or time. Despite the fact that this situation is quite typical across many fields, including numerical weather prediction, the issue of whether the available observations are "sufficient" for generating successful forecasts is still not well understood. An analysis by Whartenby et al. (2013) found that in the context of the nonlinear shallow water equations on a β plane, standard nudging techniques require observing approximately 70 % of the full set of state variables. Here we examine the same system using a method introduced by Rey et al. (2014a), which generalizes standard nudging methods to utilize time delayed measurements. We show that in certain circumstances, it provides a sizable reduction in the number of observations required to construct accurate estimates and high-quality predictions. In particular, we find that this estimate of 70 % can be reduced to about 33 % using time delays, and even further if Lagrangian drifter locations are also used as measurements.

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Investigation of Hazardous Waste Sites and their Environment Using the BGR Helicopter-Borne Geophysical System

Journal of Environmental and Engineering Geophysics ◽

10.4133/jeeg7.4.169 ◽

2002 ◽

Vol 7 (4) ◽

pp. 169-181 ◽

Cited By ~ 16

Author(s):

Bernhard Siemon ◽

Christiane Stuntebeck ◽

Klaus-Peter Sengpiel ◽

Bernd Röttger ◽

Hans-Joachim Rehli ◽

...

Keyword(s):

Hazardous Waste ◽

Hazardous Waste Sites ◽

Geophysical System

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GEODYNAMICS

GEODYNAMICS ◽

10.23939/jgd2011.01.121 ◽

2011 ◽

Vol 1(10)2011 (1(10)) ◽

pp. 121-126

Author(s):

Yu.О. Gordienko ◽

◽

Yu.A. Andruschenko ◽

A.I. Solonec ◽

◽

...

Keyword(s):

Monitoring System ◽

Seismic Event ◽

Time Behavior ◽

Prognostic Parameters ◽

Physical Fields ◽

Geophysical System ◽

Main Center

In the article the possibility of creation of seismic situation monitoring system on territory of Ukraine and adjacent countries by using of complex of geophysical system of the Main center of special monitoring of NSAU is considered. It is entered the concept of system precursor of earthquake which will allow to correlate the sequence of disturbances of controlled physical fields to unique geophysical process and to identify the similar in time behavior of anomalies of various nature as preparation of the seismic event in the near area and to estimate his prognostic parameters – location, time, magnitude.

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