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

ObjectiveThe 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 in-situ 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.Research question&#160;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?ApproachIn 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 &#8216;background&#8217; 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.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.Novelty&#160;&#160;&#160;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.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.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.

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Ultrastructural analysis of the spatial distribution of MRNA

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100123167 ◽

1992 ◽

Vol 50 (1) ◽

pp. 552-553

Author(s):

Gary Bassell ◽

Robert H. Singer

Keyword(s):

Electron Microscopy ◽

Spatial Distribution ◽

In Situ Hybridization ◽

High Resolution ◽

Nucleic Acids ◽

Colloidal Gold ◽

Dna Probe ◽

Nucleotide Analogs ◽

Gold Particles

We have been investigating the spatial distribution of nucleic acids intracellularly using in situ hybridization. The use of non-isotopic nucleotide analogs incorporated into the DNA probe allows the detection of the probe at its site of hybridization within the cell. This approach therefore is compatible with the high resolution available by electron microscopy. Biotinated or digoxigenated probe can be detected by antibodies conjugated to colloidal gold. Because mRNA serves as a template for the probe fragments, the colloidal gold particles are detected as arrays which allow it to be unequivocally distinguished from background.

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Impact of Satellite Winds on Marine Wind Simulations

Weather and Forecasting ◽

10.1175/2007waf2006093.1 ◽

2008 ◽

Vol 23 (2) ◽

pp. 290-303 ◽

Cited By ~ 7

Author(s):

Will Perrie ◽

Weiqing Zhang ◽

Mark Bourassa ◽

Hui Shen ◽

Paris W. Vachon

Keyword(s):

Field Data ◽

Cross Validation ◽

Optimum Combination ◽

Background Wind ◽

Wind Fields ◽

Input Field ◽

Spatial Derivatives ◽

Environmental Prediction ◽

Background Input

Abstract A variational data assimilation method is applied to remotely sensed wind data from Hurricanes Gustav (2002) and Isabel (2003) to produce enhanced marine wind estimates. The variational method utilizes constraints to ensure that an optimum combination of winds is determined, in the sense of minimization of a cost function measuring the misfit between observations and background input field data and constraining nongeophysical features in the spatial derivatives. Constraints are multiplied by weights, which are objectively determined by cross validation. Verification is obtained by comparison with available operational in situ buoy observations and analyses winds. It is shown that the newly constructed midlatitude wind fields represent an improvement relative to background wind field estimates and also relative to Quick Scatterometer–National Centers for Environmental Prediction reanalysis blended winds, and that the new winds have an impact on simulations of waves and upper-ocean currents.

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Sensitivity of ensemble Lagrangian reconstructions to assimilated wind time step resolution

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-9-8619-2009 ◽

2009 ◽

Vol 9 (2) ◽

pp. 8619-8633

Author(s):

I. Pisso ◽

V. Marécal ◽

B. Legras ◽

G. Berthet

Keyword(s):

High Resolution ◽

Spatial Resolution ◽

Time Resolution ◽

Wind Fields ◽

Time Step ◽

Coarse Resolution ◽

Temporal And Spatial ◽

Atmospheric Tracers

Abstract. The aim of this study is to define the optimal temporal and spatial resolution required for accurate offline diffusive Lagrangian reconstructions of high resolution in-situ tracers measurements based on meteorological wind fields and on coarse resolution 3-D tracer distributions. Increasing the time resolution of the advecting winds from three to one hour intervals has a modest impact on diffusive reconstructions in the case studied. This result is discussed in terms of the effect on the geometry of transported clouds of points in order to set out a method to assess the effect of meteorological flow on the transport of atmospheric tracers.

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Ten Priority Science Gaps in Assessing Climate Data Record Quality

Remote Sensing ◽

10.3390/rs11080986 ◽

2019 ◽

Vol 11 (8) ◽

pp. 986 ◽

Cited By ~ 8

Author(s):

Joanne Nightingale ◽

Jonathan P.D. Mittaz ◽

Sarah Douglas ◽

Dick Dee ◽

James Ryder ◽

...

Keyword(s):

Climate Change ◽

Quality Assurance ◽

Data Sets ◽

Climate Data ◽

Science Knowledge ◽

Knowledge Gaps ◽

Quality Aspects ◽

Data Products ◽

In Situ Observations

Decision makers need accessible robust evidence to introduce new policies to mitigate and adapt to climate change. There is an increasing amount of environmental information available to policy makers concerning observations and trends relating to the climate. However, this data is hosted across a multitude of websites often with inconsistent metadata and sparse information relating to the quality, accuracy and validity of the data. Subsequently, the task of comparing datasets to decide which is the most appropriate for a certain purpose is very complex and often infeasible. In support of the European Union’s Copernicus Climate Change Service (C3S) mission to provide authoritative information about the past, present and future climate in Europe and the rest of the world, each dataset to be provided through this service must undergo an evaluation of its climate relevance and scientific quality to help with data comparisons. This paper presents the framework for Evaluation and Quality Control (EQC) of climate data products derived from satellite and in situ observations to be catalogued within the C3S Climate Data Store (CDS). The EQC framework will be implemented by C3S as part of their operational quality assurance programme. It builds on past and present international investment in Quality Assurance for Earth Observation initiatives, extensive user requirements gathering exercises, as well as a broad evaluation of over 250 data products and a more in-depth evaluation of a selection of 24 individual data products derived from satellite and in situ observations across the land, ocean and atmosphere Essential Climate Variable (ECV) domains. A prototype Content Management System (CMS) to facilitate the process of collating, evaluating and presenting the quality aspects and status of each data product to data users is also described. The development of the EQC framework has highlighted cross-domain as well as ECV specific science knowledge gaps in relation to addressing the quality of climate data sets derived from satellite and in situ observations. We discuss 10 common priority science knowledge gaps that will require further research investment to ensure all quality aspects of climate data sets can be ascertained and provide users with the range of information necessary to confidently select relevant products for their specific application.

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Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

10.5194/amt-2018-31 ◽

2018 ◽

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger

Keyword(s):

High Resolution ◽

Wind Field ◽

Climate Models ◽

Meteorological Station ◽

Strong Wind ◽

Climate Data ◽

Wind Fields ◽

Station Network ◽

Weather And Climate ◽

Wind Events

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real time, recently developed by the authors (Schlager et al., 2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains eleven meteorological stations at elevations from about 600 m to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 minutes from the WegenerNet JBT, mean wind and wind gust fields at 10 m and 50 m height levels with a high spatial resolution of 100 × 100 m and a temporal resolution of 30 minutes. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. A main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance with somewhat better values for the strong wind conditions. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from five-year climate data of the WegenerNet JBT in comparison to nine-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al., (2017)In general, the five-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with and evaluation of wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

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Development and validation of the CHIRTS-daily quasi-global high-resolution daily temperature data set

Scientific Data ◽

10.1038/s41597-020-00643-7 ◽

2020 ◽

Vol 7 (1) ◽

Cited By ~ 2

Author(s):

Andrew Verdin ◽

Chris Funk ◽

Pete Peterson ◽

Martin Landsfeld ◽

Cascade Tuholske ◽

...

Keyword(s):

High Resolution ◽

Remotely Sensed ◽

Temperature Data ◽

Daily Temperature ◽

Good Representation ◽

Climate Data ◽

Data Set ◽

Weather Forecasts ◽

Development And Validation

Abstract We present a high-resolution daily temperature data set, CHIRTS-daily, which is derived by merging the monthly Climate Hazards center InfraRed Temperature with Stations climate record with daily temperatures from version 5 of the European Centre for Medium-Range Weather Forecasts Re-Analysis. We demonstrate that remotely sensed temperature estimates may more closely represent true conditions than those that rely on interpolation, especially in regions with sparse in situ data. By leveraging remotely sensed infrared temperature observations, CHIRTS-daily provides estimates of 2-meter air temperature for 1983–2016 with a footprint covering 60°S-70°N. We describe this data set and perform a series of validations using station observations from two prominent climate data sources. The validations indicate high levels of accuracy, with CHIRTS-daily correlations with observations ranging from 0.7 to 0.9, and very good representation of heat wave trends.

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A High-Resolution 1983–2016 Tmax Climate Data Record Based on Infrared Temperatures and Stations by the Climate Hazard Center

Journal of Climate ◽

10.1175/jcli-d-18-0698.1 ◽

2019 ◽

Vol 32 (17) ◽

pp. 5639-5658 ◽

Cited By ~ 9

Author(s):

Chris Funk ◽

Pete Peterson ◽

Seth Peterson ◽

Shraddhanand Shukla ◽

Frank Davenport ◽

...

Keyword(s):

High Resolution ◽

Heat Waves ◽

Climate Extremes ◽

Climate Science ◽

Climatic Research Unit ◽

Climate Extreme ◽

Vapor Pressures ◽

Climate Data ◽

In Situ Observations

Abstract Understanding the dynamics and physics of climate extremes will be a critical challenge for twenty-first-century climate science. Increasing temperatures and saturation vapor pressures may exacerbate heat waves, droughts, and precipitation extremes. Yet our ability to monitor temperature variations is limited and declining. Between 1983 and 2016, the number of observations in the University of East Anglia Climatic Research Unit (CRU) Tmax product declined precipitously (5900 → 1000); 1000 poorly distributed measurements are insufficient to resolve regional Tmax variations. Here, we show that combining long (1983 to the near present), high-resolution (0.05°), cloud-screened archives of geostationary satellite thermal infrared (TIR) observations with a dense set of ~15 000 station observations explains 23%, 40%, 30%, 41%, and 1% more variance than the CRU globally and for South America, Africa, India, and areas north of 50°N, respectively; even greater levels of improvement are shown for the 2011–16 period (28%, 45%, 39%, 52%, and 28%, respectively). Described here for the first time, the TIR Tmax algorithm uses subdaily TIR distributions to screen out cloud-contaminated observations, providing accurate (correlation ≈0.8) gridded emission Tmax estimates. Blending these gridded fields with ~15 000 station observations provides a seamless, high-resolution source of accurate Tmax estimates that performs well in areas lacking dense in situ observations and even better where in situ observations are available. Cross-validation results indicate that the satellite-only, station-only, and combined products all perform accurately (R ≈ 0.8–0.9, mean absolute errors ≈ 0.8–1.0). Hence, the Climate Hazards Center Infrared Temperature with Stations (CHIRTSmax) dataset should provide a valuable resource for climate change studies, climate extreme analyses, and early warning applications.

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Extending Limited In Situ Mountain Weather Observations to the Baseline Climate: A True Verification Case Study

Atmosphere ◽

10.3390/atmos11111256 ◽

2020 ◽

Vol 11 (11) ◽

pp. 1256

Author(s):

Marlis Hofer ◽

Johannes Horak

Keyword(s):

Air Temperature ◽

Cross Validation ◽

Reference Data ◽

Reanalysis Data ◽

High Mountain ◽

Monitoring Site ◽

Data Sets ◽

Evaluation Period ◽

Baseline Climate

The availability of in situ atmospheric observations decreases with elevation and topographic complexity. Data sets based on numerical atmospheric modeling, such as reanalysis data sets, represent an alternative source of information, but they often suffer from inaccuracies, e.g., due to insufficient spatial resolution. sDoG (statistical Downscaling for Glacierized mountain environments) is a reanalysis data postprocessing tool designed to extend short-term weather station data from high mountain sites to the baseline climate. In this study, sDoG is applied to ERA-Interim predictors to produce a retrospective forecast of daily air temperature at the Vernagtbach climate monitoring site (2640 MSL) in the Central European Alps. First, sDoG is trained and cross-validated using observations from 2002 to 2012 (cross-validation period). Then, the sDoG retrospective forecast and its cross-validation-based uncertainty estimates are evaluated for the period 1979–2001 (hereafter referred to as the true evaluation period). We demonstrate the ability of sDoG to model air temperature in the true evaluation period for different temporal scales: day-to-day variations, year-to-year and season-to-season variations, and the 23-year mean seasonal cycle. sDoG adds significant value over a selection of reference data sets available for the site at different spatial resolutions, including state-of-the-art global and regional reanalysis data sets, output by a regional climate model, and an observation-based gridded product. However, we identify limitations of sDoG in modeling summer air temperature variations particularly evident in the first part of the true evaluation period. This is most probably related to changes of the microclimate around the Vernagtbach climate monitoring site that violate the stationarity assumption underlying sDoG. When comparing the performance of the considered reference data sets, we cannot demonstrate added value of the higher resolution data sets over the data sets with lower spatial resolution. For example, the global reanalyses ERA5 (31 km resolution) and ERA-Interim (80 km resolution) both clearly outperform the higher resolution data sets ERA5-Land (9 km resolution), UERRA HARMONIE (11 km resolution), and UERRA MESCAN-SURFEX (5.5 km resolution). Performance differences among ERA5 and ERA-Interim, by contrast, are comparably small. Our study highlights the importance of station-scale uncertainty assessments of atmospheric numerical model output and downscaling products for high mountain areas both for data users and model developers.

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Empirical high-resolution wind field and gust model in mountainous and hilly terrain based on the dense WegenerNet station networks

Atmospheric Measurement Techniques ◽

10.5194/amt-11-5607-2018 ◽

2018 ◽

Vol 11 (10) ◽

pp. 5607-5627 ◽

Cited By ~ 1

Author(s):

Christoph Schlager ◽

Gottfried Kirchengast ◽

Juergen Fuchsberger

Keyword(s):

High Resolution ◽

Climate Models ◽

Meteorological Station ◽

Strong Wind ◽

Mountainous Region ◽

Climate Data ◽

Wind Fields ◽

Station Network ◽

Weather And Climate ◽

Wind Events

Abstract. A weather diagnostic application for automatic generation of gridded wind fields in near-real-time, recently developed by the authors Schlager et al. (2017), is applied to the WegenerNet Johnsbachtal (JBT) meteorological station network. This station network contains 11 meteorological stations at elevations from about 600 to 2200 m in a mountainous region in the north of Styria, Austria. The application generates, based on meteorological observations with a temporal resolution of 10 min from the WegenerNet JBT, mean wind and wind gust fields at 10 and 50 m height levels with a high spatial resolution of 100 m × 100 m and a temporal resolution of 30 min. These wind field products are automatically stored to the WegenerNet data archives, which also include long-term averaged weather and climate datasets from post-processing. The main purpose of these empirically modeled products is the evaluation of convection-permitting dynamical climate models as well as investigating weather and climate variability on a local scale. The application's performance is evaluated against the observations from meteorological stations for representative weather conditions, for a month including mainly thermally induced wind events (July 2014) and a month with frequently occurring strong wind events (December 2013). The overall statistical agreement, estimated for the vector-mean wind speed, shows a reasonably good modeling performance. Due to the spatially more homogeneous wind speeds and directions for strong wind events in this mountainous region, the results show somewhat better performance for these events. The difference between modeled and observed wind directions depends on the station location, where locations along mountain slopes are particularly challenging. Furthermore, the seasonal statistical agreement was investigated from 5-year climate data of the WegenerNet JBT in comparison to 9-year climate data from the high-density WegenerNet meteorological station network Feldbach Region (FBR) analyzed by Schlager et al. (2017). In general, the 5-year statistical evaluation for the JBT indicates similar performance as the shorter-term evaluations of the two representative months. Because of the denser WegenerNet FBR network, the statistical results show better performance for this station network. The application can now serve as a valuable tool for intercomparison with, and evaluation of, wind fields from high-resolution dynamical climate models in both the WegenerNet FBR and JBT regions.

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