Correlation study of time-varying multivariate climate data sets

2009 ◽  
Author(s):  
Jeffrey Sukharev ◽  
Chaoli Wang ◽  
Kwan-Liu Ma ◽  
Andrew T. Wittenberg
Keyword(s):  
Correlation Study ◽  
Data Sets ◽  
Time Varying ◽  
Climate Data

TV-MV Analytics: A visual analytics framework to explore time-varying multivariate data

2019 ◽  
Vol 19 (1) ◽  
pp. 3-23
Author(s):  
Aurea Soriano-Vargas ◽  
Bernd Hamann ◽  
Maria Cristina F de Oliveira
Keyword(s):  
Visual Analytics ◽  
Visual Analysis ◽  
Multivariate Data ◽  
Visual Exploration ◽  
Data Sets ◽  
Time Varying ◽  
Domain Experts ◽  
Data Mining Algorithms ◽  
Temporal Relationships ◽  
Visualization Techniques

We present an integrated interactive framework for the visual analysis of time-varying multivariate data sets. As part of our research, we performed in-depth studies concerning the applicability of visualization techniques to obtain valuable insights. We consolidated the considered analysis and visualization methods in one framework, called TV-MV Analytics. TV-MV Analytics effectively combines visualization and data mining algorithms providing the following capabilities: (1) visual exploration of multivariate data at different temporal scales, and (2) a hierarchical small multiples visualization combined with interactive clustering and multidimensional projection to detect temporal relationships in the data. We demonstrate the value of our framework for specific scenarios, by studying three use cases that were validated and discussed with domain experts.

scholarly journals A new pan-European dataset for gridded daily average wind speed based on in-situ observations

2021 ◽  
Author(s):  
Jouke de Baar ◽  
Gerard van der Schrier ◽  
Irene Garcia-Marti ◽  
Else van den Besselaar
Keyword(s):  
Spatial Distribution ◽  
High Resolution ◽  
Cross Validation ◽  
Data Sets ◽  
Optimal Prediction ◽  
Auxiliary Variables ◽  
Climate Data ◽  
Ensemble Spread ◽  
Wind Fields

<p><strong>Objective</strong></p><p>The purpose of the European Copernicus Climate Change Service (C3S) is to support society by providing information about the past, present and future climate. For the service related to <em>in-situ</em> observations, one of the objectives is to provide high-resolution (0.1x0.1 and 0.25x0.25 degrees) gridded wind speed fields. The gridded wind fields are based on ECA&D daily average station observations for the period 1970-2020.</p><p><strong>Research question</strong> </p><p>We address the following research questions: [1] How efficiently can we provide the gridded wind fields as a statistically reliable ensemble, in order to represent the uncertainty of the gridding? [2] How efficiently can we exploit high-resolution geographical auxiliary variables (e.g. digital elevation model, terrain roughness) to augment the station data from a sparse network, in order to provide gridded wind fields with high-resolution local features?</p><p><strong>Approach</strong></p><p>In our analysis, we apply greedy forward selection linear regression (FSLR) to include the high-resolution effects of the auxiliary variables on monthly-mean data. These data provide a ‘background’ for the daily estimates. We apply cross-validation to avoid FSLR over-fitting and use full-cycle bootstrapping to create FSLR ensemble members. Then, we apply Gaussian process regression (GPR) to regress the daily anomalies. We consider the effect of the spatial distribution of station locations on the GPR gridding uncertainty.</p><p>The goal of this work is to produce several decades of daily gridded wind fields, hence, computational efficiency is of utmost importance. We alleviate the computational cost of the FSLR and GPR analyses by incorporating greedy algorithms and sparse matrix algebra in the analyses.</p><p><strong>Novelty</strong>   </p><p>The gridded wind fields are calculated as a statistical ensemble of realizations. In the present analysis, the ensemble spread is based on uncertainties arising from the auxiliary variables as well as from the spatial distribution of stations.</p><p>Cross-validation is used to tune the GPR hyper parameters. Where conventional GPR hyperparameter tuning aims at an optimal prediction of the gridded mean, instead, we tune the GPR hyperparameters for optimal prediction of the gridded ensemble spread.</p><p>Building on our experience with providing similar gridded climate data sets, this set of gridded wind fields is a novel addition to the E-OBS climate data sets.</p>

scholarly journals Developing Gridded Climate Data Sets of Precipitation for Greece Based on Homogenized Time Series

Climate ◽  
2019 ◽  
Vol 7 (5) ◽  
pp. 68 ◽  
Author(s):  
Flora Gofa ◽  
Anna Mamara ◽  
Manolis Anadranistakis ◽  
Helena Flocas
Keyword(s):  
Climate Model ◽  
Interpolation Method ◽  
Region Of Interest ◽  
Data Sets ◽  
Monthly Precipitation ◽  
Climate Data ◽  
Variable Nature ◽  
Wide Range ◽  
Realistic Representation ◽  
Space Data

The creation of realistic gridded precipitation fields improves our understanding of the observed climate and is necessary for validating climate model output for a wide range of applications. The challenge in trying to represent the highly variable nature of precipitation is to overcome the lack of density of observations in both time and space. Data sets of mean monthly and annual precipitations were developed for Greece in gridded format with an analysis of 30 arcsec (∼800 m) based on data from 1971 to 2000. One hundred and fifty-seven surface stations from two different observation networks were used to cover a satisfactory range of elevations. Station data were homogenized and subjected to quality control to represent changes in meteorological conditions rather than changes in the conditions under which the observations were made. The Meteorological Interpolation based on Surface Homogenized Data Basis (MISH) interpolation method was used to develop data sets that reproduce, as closely as possible, the spatial climate patterns over the region of interest. The main geophysical factors considered for the interpolation of mean monthly precipitation fields were elevation, latitude, incoming solar irradiance, Euclidian distance from the coastline, and land-to-sea percentage. Low precipitation interpolation uncertainties estimated with the cross-validation method provided confidence in the interpolation method. The resulting high-resolution maps give an overall realistic representation of precipitation, especially in fall and winter, with a clear longitudinal dependence on precipitation decreasing from western to eastern continental Greece.

scholarly journals Climatology and interannual variability of dynamic variables in multiple reanalyses evaluated by the SPARC Reanalysis Intercomparison Project (S-RIP)

2017 ◽  
Vol 17 (23) ◽  
pp. 14593-14629 ◽  
Author(s):  
Craig S. Long ◽  
Masatomo Fujiwara ◽  
Sean Davis ◽  
Daniel M. Mitchell ◽  
Corwin J. Wright
Keyword(s):  
Trend Detection ◽  
Data Sets ◽  
Temperature Structure ◽  
Climate Data ◽  
Quasi Biennial Oscillation ◽  
Temperature Bias ◽  
Independent Observations ◽  
Intercomparison Project ◽  
Reanalysis Intercomparison ◽  
Good Agreement

Abstract. Two of the most basic parameters generated from a reanalysis are temperature and winds. Temperatures in the reanalyses are derived from conventional (surface and balloon), aircraft, and satellite observations. Winds are observed by conventional systems, cloud tracked, and derived from height fields, which are in turn derived from the vertical temperature structure. In this paper we evaluate as part of the SPARC Reanalysis Intercomparison Project (S-RIP) the temperature and wind structure of all the recent and past reanalyses. This evaluation is mainly among the reanalyses themselves, but comparisons against independent observations, such as HIRDLS and COSMIC temperatures, are also presented. This evaluation uses monthly mean and 2.5° zonal mean data sets and spans the satellite era from 1979–2014. There is very good agreement in temperature seasonally and latitudinally among the more recent reanalyses (CFSR, MERRA, ERA-Interim, JRA-55, and MERRA-2) between the surface and 10 hPa. At lower pressures there is increased variance among these reanalyses that changes with season and latitude. This variance also changes during the time span of these reanalyses with greater variance during the TOVS period (1979–1998) and less variance afterward in the ATOVS period (1999–2014). There is a distinct change in the temperature structure in the middle and upper stratosphere during this transition from TOVS to ATOVS systems. Zonal winds are in greater agreement than temperatures and this agreement extends to lower pressures than the temperatures. Older reanalyses (NCEP/NCAR, NCEP/DOE, ERA-40, JRA-25) have larger temperature and zonal wind disagreement from the more recent reanalyses. All reanalyses to date have issues analysing the quasi-biennial oscillation (QBO) winds. Comparisons with Singapore QBO winds show disagreement in the amplitude of the westerly and easterly anomalies. The disagreement with Singapore winds improves with the transition from TOVS to ATOVS observations. Temperature bias characteristics determined via comparisons with a reanalysis ensemble mean (MERRA, ERA-Interim, JRA-55) are similarly observed when compared with Aura HIRDLS and Aura MLS observations. There is good agreement among the NOAA TLS, SSU1, and SSU2 Climate Data Records and layer mean temperatures from the more recent reanalyses. Caution is advised for using reanalysis temperatures for trend detection and anomalies from a long climatology period as the quality and character of reanalyses may have changed over time.

Guidelines for assessing the suitability of spatial climate data sets

2006 ◽  
Vol 26 (6) ◽  
pp. 707-721 ◽  
Author(s):  
Christopher Daly
Keyword(s):  
Data Sets ◽  
Climate Data

scholarly journals Development of a Seamless Forecast for Solar Radiation Using ANAKLIM++

2020 ◽  
Vol 12 (21) ◽  
pp. 3672
Author(s):  
Isabel Urbich ◽  
Jörg Bendix ◽  
Richard Müller
Keyword(s):  
Solar Radiation ◽  
Weather Prediction ◽  
Software Tool ◽  
Absolute Error ◽  
Shortwave Radiation ◽  
Data Sets ◽  
Climate Data ◽  
Forecast Horizon ◽  
Novel Approach ◽  
Forecasting System

A novel approach for a blending between nowcasting and numerical weather prediction (NWP) for the surface incoming shortwave radiation (SIS) for a forecast horizon of 1–5 h is presented in this study. The blending is performed with a software tool called ANAKLIM++ (Adjustment of Assimilation Software for the Reanalysis of Climate Data) which was originally designed for the efficient assimilation of two-dimensional data sets using a variational approach. A nowcasting for SIS was already presented and validated in earlier publications as seamless solar radiation forecast (SESORA). For our blending, two NWP models, namely the ICON (Icosahedral Non-hydrostatic model) from the German weather Service (DWD) and the IFS (Integrated Forecasting System) from the European Centre for Medium-Range Weather Forecasts (ECMWF), were used. The weights for the input data for ANAKLIM++ vary for every single forecast time and pixel, depending on the error growth of the nowcasting. The results look promising, since the root mean square error (RMSE) and mean absolute error (MAE) of the blending are smaller than the error measures of the nowcasting or NWP models, respectively.

A performance evaluation of surplus production models with time-varying intrinsic growth in dynamic ecosystems

2019 ◽  
Vol 76 (12) ◽  
pp. 2245-2255 ◽  
Author(s):  
Geneviève M. Nesslage ◽  
Michael J. Wilberg
Keyword(s):  
Growth Rate ◽  
Time Series ◽  
Survey Data ◽  
Atlantic Menhaden ◽  
Data Sets ◽  
Time Varying ◽  
Intrinsic Growth Rate ◽  
Surplus Production ◽  
Production Models ◽  
Exploitation Rate

We conducted a simulation study to evaluate performance of surplus production models (SPMs) with a time-varying intrinsic growth rate (SPMTVr) for stocks with predation-driven changes in productivity. Data sets were simulated using an age-structured, linked, predator–prey model of Atlantic menhaden (Brevoortia tyrannus), a forage fish native to the Northwest Atlantic, and Atlantic striped bass (Morone saxatilis), its primary predator, with differing time series of fishing mortality on both predator and prey. Simulations generated test data sets for Atlantic menhaden SPMs that included either a static or time-varying intrinsic growth rate parameter. The SPMTVr largely produced more accurate, less variable estimates of exploitation rate and biomass than models with static intrinsic growth. We also applied SPMTVr to empirical Atlantic menhaden catch and survey data for 1964–2016. The SPMTVr fit the survey data well, estimated an intrinsic growth rate time series that mirrored long-term juvenile survey trends, and produced biomass and exploitation rate trends that mirrored a statistical catch-at-age model. The SPMTVr estimated dynamic, maximum sustainable yield (MSY)-based reference points that reflected changing stock productivity.

scholarly journals A method for merging nadir-sounding climate records, with an application to the global-mean stratospheric temperature data sets from SSU and AMSU

2015 ◽  
Vol 15 (16) ◽  
pp. 9271-9284 ◽  
Author(s):  
C. McLandress ◽  
T. G. Shepherd ◽  
A. I. Jonsson ◽  
T. von Clarmann ◽  
B. Funke
Keyword(s):  
Michelson Interferometer ◽  
Data Sets ◽  
Climate Data ◽  
Data Set ◽  
Linear Temperature ◽  
Temperature Changes ◽  
Stratospheric Temperature ◽  
Temperature Trends ◽  
Microwave Sounding ◽  
Global Mean

Abstract. A method is proposed for merging different nadir-sounding climate data records using measurements from high-resolution limb sounders to provide a transfer function between the different nadir measurements. The two nadir-sounding records need not be overlapping so long as the limb-sounding record bridges between them. The method is applied to global-mean stratospheric temperatures from the NOAA Climate Data Records based on the Stratospheric Sounding Unit (SSU) and the Advanced Microwave Sounding Unit-A (AMSU), extending the SSU record forward in time to yield a continuous data set from 1979 to present, and providing a simple framework for extending the SSU record into the future using AMSU. SSU and AMSU are bridged using temperature measurements from the Michelson Interferometer for Passive Atmospheric Sounding (MIPAS), which is of high enough vertical resolution to accurately represent the weighting functions of both SSU and AMSU. For this application, a purely statistical approach is not viable since the different nadir channels are not sufficiently linearly independent, statistically speaking. The near-global-mean linear temperature trends for extended SSU for 1980–2012 are −0.63 ± 0.13, −0.71 ± 0.15 and −0.80 ± 0.17 K decade−1 (95 % confidence) for channels 1, 2 and 3, respectively. The extended SSU temperature changes are in good agreement with those from the Microwave Limb Sounder (MLS) on the Aura satellite, with both exhibiting a cooling trend of ~ 0.6 ± 0.3 K decade−1 in the upper stratosphere from 2004 to 2012. The extended SSU record is found to be in agreement with high-top coupled atmosphere–ocean models over the 1980–2012 period, including the continued cooling over the first decade of the 21st century.

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