scholarly journals Optimal interpolation analysis of high-latitude ionospheric electrodynamics using empirical orthogonal functions: Estimation of dominant modes of variability and temporal scales of large-scale electric fields

2005 ◽  
Vol 110 (A6) ◽  
Author(s):  
Tomoko Matsuo
Keyword(s):  
Electric Fields ◽  
High Latitude ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Optimal Interpolation ◽  
Temporal Scales ◽  
Orthogonal Functions ◽  
Modes Of Variability ◽  
Ionospheric Electrodynamics ◽  
Interpolation Analysis

scholarly journals Data-Driven Interpolation of Sea Level Anomalies Using Analog Data Assimilation

2019 ◽  
Vol 11 (7) ◽  
pp. 858 ◽  
Author(s):  
Redouane Lguensat ◽  
Phi Huynh Viet ◽  
Miao Sun ◽  
Ge Chen ◽  
Tian Fenglin ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Sea Level ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Data Driven ◽  
Optimal Interpolation ◽  
Observation System ◽  
Orthogonal Functions ◽  
Sea Level Anomalies ◽  
Recent Developments

From the recent developments of data-driven methods as a means to better exploit large-scale observation, simulation and reanalysis datasets for solving inverse problems, this study addresses the improvement of the reconstruction of higher-resolution Sea Level Anomaly (SLA) fields using analog strategies. This reconstruction is stated as an analog data assimilation issue, where the analog models rely on patch-based and Empirical Orthogonal Functions (EOF)-based representations to circumvent the curse of dimensionality. We implement an Observation System Simulation Experiment (OSSE) in the South China Sea. The reported results show the relevance of the proposed framework with a significant gain in terms of Root Mean Square Error (RMSE) for scales below 100 km. We further discuss the usefulness of the proposed analog model as a means to exploit high-resolution model simulations for the processing and analysis of current and future satellite-derived altimetric data with regard to conventional interpolation schemes, especially optimal interpolation.

scholarly journals A method to generate fully multi-scale optimal interpolation by combining efficient single process analyses, illustrated by a DINEOF analysis spiced with a local optimal interpolation

Ocean Science ◽  
2014 ◽  
Vol 10 (5) ◽  
pp. 845-862 ◽  
Author(s):  
J.-M. Beckers ◽  
A. Barth ◽  
I. Tomazic ◽  
A. Alvera-Azcárate
Keyword(s):  
Large Scale ◽  
Original Data ◽  
Empirical Orthogonal Functions ◽  
Optimal Interpolation ◽  
Test Case ◽  
Orthogonal Functions ◽  
Multi Scale ◽  
Large Scale Analysis ◽  
Single Process ◽  
Using Data

Abstract. We present a method in which the optimal interpolation of multi-scale processes can be expanded into a succession of simpler interpolations. First, we prove how the optimal analysis of a superposition of two processes can be obtained by different mathematical formulations involving iterations and analysis focusing on a single process. From the different mathematical equivalent formulations, we then select the most efficient ones by analyzing the behavior of the different possibilities in a simple and well-controlled test case. The clear guidelines deduced from this experiment are then applied to a real situation in which we combine large-scale analysis of hourly Spinning Enhanced Visible and Infrared Imager (SEVIRI) satellite images using data interpolating empirical orthogonal functions (DINEOF) with a local optimal interpolation using a Gaussian covariance. It is shown that the optimal combination indeed provides the best reconstruction and can therefore be exploited to extract the maximum amount of useful information from the original data.

scholarly journals A model intercomparison analysing the link between ozone and geopotential height anomalies in January

2007 ◽  
Vol 7 (6) ◽  
pp. 15409-15451 ◽  
Author(s):  
P. Braesicke ◽  
C. Brühl ◽  
M. Dameris ◽  
R. Deckert ◽  
V. Eyring ◽  
...  
Keyword(s):  
Geopotential Height ◽  
Climate Models ◽  
Horizontal Resolution ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Modes Of Variability ◽  
Statistical Framework ◽  
Column Ozone ◽  
Linear Correlations ◽  
Upper Boundary

Abstract. A statistical framework to evaluate the performance of chemistry-climate models with respect to the interaction between meteorology and ozone during northern hemisphere mid-winter, in particularly January, is used. Different statistical diagnostics from four chemistry-climate models (E39C, ME4C, UMUCAM, ULAQ) are compared with the ERA-40 re-analysis. First, we analyse vertical coherence in geopotential height anomalies as described by linear correlations between two different pressure levels (30 and 200 hPa) of the atmosphere. In addition, linear correlations between (partial) column ozone and geopotential height anomalies at 200 hPa are discussed to motivate a simple picture of the meteorological impacts on ozone on interannual timescales. Secondly, we discuss characteristic spatial structures in geopotential height and (partial) column ozone anomalies as given by their first two empirical orthogonal functions. Finally, we describe the covariance patterns between reconstructed anomalies of geopotential height and (partial) column ozone. In general we find good agreement between the models with higher horizontal resolution (E39C, ME4C, UMUCAM) and ERA-40. Some diagnostics seem to be capable of picking up model similarities (either that the models use the same dynamical core (E39C, ME4C), or that they have a high upper boundary (ME4C, UMUCAM)). The methodology allows to identify the leading modes of variability contributing to the overall ozone-geopotential height correlations and points to interesting differences between the chemistry-climate models and ERA-40. Those discrepancies have to be taken into account when providing confidence intervals for climate change integrations.

scholarly journals Geospatial Trends and Decadal Anomalies in Extreme Rainfall over Uganda, East Africa

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
Charles Onyutha
Keyword(s):  
North Atlantic Ocean ◽  
Rainfall Variability ◽  
Daily Rainfall ◽  
Extreme Rainfall ◽  
Empirical Orthogonal Functions ◽  
Orthogonal Functions ◽  
Indicator Method ◽  
Significance Level ◽  
Modes Of Variability ◽  
In Series

Trends and variability in series comprising the mean of fifteen highest daily rainfall intensities in each year were analyzed considering entire Uganda. The data were extracted from high-resolution (0.5° × 0.5°) gridded daily series of the Princeton Global Forcings covering the period 1948–2008. Variability was analyzed using nonparametric anomaly indicator method and empirical orthogonal functions. Possible drivers of the rainfall variability were investigated. Trends were analyzed using the cumulative rank difference approach. Generally, rainfall was above the long-term mean from the mid-1950s to the late 1960s and again in the 1990s. From around 1970 to the late 1980s, rainfall was characterized by a decrease. The first and second dominant modes of variability correspond with the variation in Indian Ocean Dipole and North Atlantic Ocean index, respectively. The influence of Niño 3 on the rainfall variability of some parts of the country was also evident. The southern and northern parts had positive and negative trends, respectively. The null hypothesisH0(no trend) was collectively rejected at the significance level of 5% in the series from 7 out of 168 grid points. The insights from the findings of this study are vital for planning and management of risk-based water resources applications.

scholarly journals The Influence of European Climate Variability Mechanism on Air Temperatures in Romania

2014 ◽  
Vol 8 (1) ◽  
pp. 5-16 ◽  
Author(s):  
Nicoleta Ionac ◽  
Monica Matei
Keyword(s):  
Time Series ◽  
Climate Variability ◽  
Large Scale ◽  
Empirical Orthogonal Functions ◽  
Spatial And Temporal Variability ◽  
Orthogonal Functions ◽  
Mean Values ◽  
European Climate ◽  
Air Temperatures ◽  
And Shifts

Abstract The present paper investigates on the spatial and temporal variability of maximum and minimum air-temperatures in Romania and their connection to the European climate variability. The European climate variability is expressed by large scale parameters, which are roughly represented by the geopotential height at 500 hPa (H500) and air temperature at 850 hPa (T850). The Romanian data are represented by the time series at 22 weather stations, evenly distributed over the entire country’s territory. The period that was taken into account was 1961-2010, for the summer and winter seasons. The method of empirical orthogonal functions (EOF) has been used, in order to analyze the connection between the temperature variability in Romania and the same variability at a larger scale, by taking into consideration the atmosphere circulation. The time series associated to the first two EOF patterns of local temperatures and large-scale anomalies were considered with regard to trends and shifts in their mean values. The non- Mann-Kendall and Pettitt parametric tests were used in this respect. The results showed a strong correlation between T850 parameter and minimum and maximum air temperatures in Romania. Also, the ample variance expressed by the first EOF configurations suggests a connection between local and large scale climate variability.

scholarly journals DINEOF reconstruction of clouded images including error maps – application to the Sea-Surface Temperature around Corsican Island

Ocean Science ◽  
2006 ◽  
Vol 2 (2) ◽  
pp. 183-199 ◽  
Author(s):  
J.-M. Beckers ◽  
A. Barth ◽  
A. Alvera-Azcárate
Keyword(s):  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Empirical Orthogonal Functions ◽  
Sea Surface ◽  
Optimal Interpolation ◽  
Orthogonal Functions ◽  
Ligurian Sea ◽  
Additional Information ◽  
Error Covariance ◽  
Data Coverage

Abstract. We present an extension to the Data INterpolating Empirical Orthogonal Functions (DINEOF) technique which allows not only to fill in clouded images but also to provide an estimation of the error covariance of the reconstruction. This additional information is obtained by an analogy with optimal interpolation. It is shown that the error fields can be obtained with a clever rearrangement of calculations at a cost comparable to that of the interpolation itself. The method is presented on the reconstruction of sea-surface temperature in the Ligurian Sea and around the Corsican Island (Mediterranean Sea), including the calculation of inter-annual variability of average surface values and their expected errors. The application shows that the error fields are not only able to reflect the data-coverage structure but also the covariances of the physical fields.

An Analog Method for Real-Time Forecasting of Summer Monsoon Subseasonal Variability

2007 ◽  
Vol 135 (12) ◽  
pp. 4149-4160 ◽  
Author(s):  
Prince K. Xavier ◽  
B. N. Goswami
Keyword(s):  
Real Time ◽  
Summer Monsoon ◽  
Large Scale ◽  
Initial Conditions ◽  
Empirical Orthogonal Functions ◽  
Initial Condition ◽  
Analog Method ◽  
Initial State ◽  
Orthogonal Functions ◽  
Modeling Data

Abstract A physically based empirical real-time forecasting strategy to predict the subseasonal variations of the Indian summer monsoon up to four–five pentads (20–25 days) in advance has been developed. The method is based on the event-to-event similarity in the properties of monsoon intraseasonal oscillations (ISOs). This two-tier analog method is applied to NOAA outgoing longwave radiation (OLR) pentad averaged data that have sufficiently long records of observation and are available in nearly real time. High-frequency modes in the data are eliminated by reconstructing the data using the first 10 empirical orthogonal functions (EOFs), which together explain about 75% of the total variance. In the first level of the method, the spatial analogs of initial condition pattern are identified from the modeling data. The principal components (PCs) of these spatial analogs, whose evolution history of the latest five pentads matches that of the initial condition pattern, are considered the temporal PC analogs. Predictions are generated for each PC as the average evolution of PC analogs for the given lead time. Predicted OLR values are constructed using the EOFs and predicted PCs. OLR data for 1979–99 are used as the modeling data and independent hindcasts are generated for the period 2000–05. The skill of anomaly predictions is rather high over the central and northern Indian region for lead times of four–five pentads. The phases and amplitude of intraseasonal convective spells are predicted well, especially the long midseason break of 2002 that resulted in large-scale drought conditions. Skillful predictions can be made up to five pentads when started from an active initial state, whereas the limit of useful predictions is about two–three pentads when started from break initial conditions. An important feature of this method is that unlike some other empirical methods to forecast monsoon ISOs, it uses minimal time filtering to avoid any possible endpoint effects and hence may be readily used for real-time applications. Moreover, as the modeling data grow with time as a result of the increased number of observations, the number of analogs would also increase and eventually the quality of forecasts would improve.

scholarly journals Sea level variability at Adriatic coast and its relationship to atmospheric forcing

2005 ◽  
Vol 23 (6) ◽  
pp. 1997-2010 ◽  
Author(s):  
K. Bergant ◽  
M. Sušnik ◽  
I. Strojan ◽  
A. G. P. Shaw
Keyword(s):  
Sea Level ◽  
Large Scale ◽  
Tide Gauge ◽  
Grid Point ◽  
Principal Component ◽  
Empirical Orthogonal Functions ◽  
Seasonal Cycles ◽  
Atmospheric Forcing ◽  
Orthogonal Functions ◽  
Adriatic Coast

Abstract. Sea level (SLH) variability at the Adriatic coast was investigated for the period 1872–2001 using monthly average values of observations at 13 tide gauge stations. Linear trends and seasonal cycles were investigated first and removed afterwards from the data. Empirical Orthogonal Functions (EOF) analysis was used further on remaining anomalies (SLA) to extract the regional intermonthly variability of SLH. It was found that the leading EOF and its principal component (PC) explain a major part of SLA variability (92%). The correlation between the reconstructed SLA, based on leading EOF and its PC, and overlapping observed SLA values for selected tide gauge stations is between 0.93 and 0.99. Actual SLH values at tide gauge stations can be reconstructed and some gaps in the data can be filled in on the basis of estimated SLA values if reasonable estimates of long-term trends and seasonal cycles are also available. A strong, seasonally dependent relationship between SLA at the Adriatic coast and atmospheric forcing, represented by sea level pressure (SLP) fields, was also found. Comparing the time series of leading PC and gridded SLP data for the period 1948–2001, the highest correlation coefficients (r) of –0.92 in winter, –0.84 in spring, –0.66 in summer, and –0.91 in autumn were estimated for a SLP grid point located in northern Italy. The SLP variability on this grid point contains information about the isostatic response of the sea level at the Adriatic coast, but can also be treated as a sort of teleconnection index representing the large-scale SLP variability across central and southern Europe. To some extent the large-scale SLP variability that affects the SLA at the Adriatic coast can be related to the North Atlantic Oscillation (NAO), because significant correlations were found between the NAO index and the first PC of SLA (rwinter=–0.56, rspring=–0.45, rsummer=–0.48, and rautumn=–0.43) for the period 1872–2001. The use of partial least-squares (PLS) regression between large-scale SLP fields and SLA only slightly improved the description of the SLA dependence on SLP forcing in comparison to the single grid point approach. A strong relationship between atmospheric pressure and the sea level could represent an additional possibility for filling in the gaps in the tide gauge data. Keywords. Oceanography: general (Climate and interannual variability) – Oceanography: physical (Air-sea interactions; sea level variations)

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