The WegenerNet 3D Open-Air Laboratory for Climate Change Research: A unique facility for high-resolution precipitation studies

The WegenerNet Feldbach Region is a unique weather and climate observation network comprising 155 hydrometeorological stations measuring temperature, humidity, precipitation, and at particular locations wind speed and direction as well as other parameters, in a tightly spaced grid within a core area of 22 km x 16 km centered near the city of Feldbach (46.93&#176;N, 15.90&#176;E), in southeastern Austria.With about one station every two square-km (area of about 300 square-km in total), and each station with 5-min time sampling, the network provides fully automated regular measurements since January 2007.In 2020, the station network was expanded by three major new components, expanding it from a 2D ground station hydrometeorological network into a 3D open-air laboratory for climate change research at very high resolution. &#160;These new atmospheric 3D-observation components consist of:1. A polarimetric X-band Doppler weather radar for studying precipitation parameters in the troposphere above the ground network, such as rain rate, hydrometeor classification, Doppler velocity, and approximate drop size distribution and number: it can provide 3D volume data (at about 1 km x 1 km horizontal and 500 m vertical resolution and 2.5 min time sampling) for moderate to strong precipitation. Together with the dense ground network, this allows detailed studies of heavy precipitation events with high resolution and accuracy.2. A radiometer pair consisting of two azimuth- and elevation-steerable radiometers: (1) a microwave atmospheric-profiling radiometer with built-in auxiliary infrared radiometer for vertical profiling of temperature, humidity, and cloud liquid water in the troposphere above the WegenerNet area (with about 100 m to 1 km vertical resolution and 5 to 10 min time sampling), also capable of measuring cloud-base heights, vertically integrated water vapor (IWV), and slant IWV along line-of-sight paths towards Global Navigation Satellite System (GNSS) satellites, and (2) a complementary infrared cloud structure radiometer at similar spatiotemporal sampling for further refining gridded cloud-base height calculations and enabling multi-layer cloud-field reconstruction over the WegenerNet area, providing 3D cloud-field (multi-layered cloud fraction) estimates.3. A water-vapor-mapping high-resolution GNSS station network named GNSS-StarNet, comprising six ground stations and spatially forming two star-shaped subnets across the WegenerNet area (one with about 10 km interstation distance and one embedded with about 5 km interstation distance), for providing slant IWV, vertical IWV, and precipitable water, among other parameters, at 2.5 to 15 min time sampling.The new components, together with the existing ground network, provide a unique setup for studying extreme meteorological events such as heavy precipitation, hailstorms, droughts, and heat waves at very high resolution. We will present the up-to-date status of the WegenerNet and highlight recent uses in precipitation, hydrology and climate-related studies.

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The WegenerNet 3D weather and climate research facility: A unique open-air laboratory for high-resolution precipitation studies

10.5194/egusphere-egu2020-1417 ◽

2020 ◽

Author(s):

Jürgen Fuchsberger ◽

Gottfried Kirchengast ◽

Christoph Bichler

Keyword(s):

High Resolution ◽

Precipitable Water ◽

Heavy Precipitation ◽

Satellite System ◽

Doppler Velocity ◽

Time Sampling ◽

Volume Data ◽

Station Network ◽

Climate Research ◽

Weather And Climate

The WegenerNet Feldbach Region is a unique weather and climate observation facility comprising 155 meteorological stations measuring temperature, humidity, precipitation, and other parameters, in a tightly spaced grid within a core area of 22 km &#215; 16 km centered near the city of Feldbach (46.93&#176;N, 15.90&#176;E). With its stations every about two square-km (area of about 300 square-km in total), and each station with 5-min time sampling, the network provides regular measurements since January 2007. In 2020, the station network will be expanded by three major new components, converting it from a 2D ground station network into a 3D open-air laboratory for weather and climate research at very high resolution. The following new observing components will start operations by spring 2020:<ol><li>A polarimetric X-band Doppler weather radar for studying precipitation parame- ters in the troposphere above the ground network, such as rain rate, hydrometeor classification, Doppler velocity, and approximate drop size and number. It can provide 3D volume data (at about 1 km &#215; 1 km horizontal and 500 m vertical res- olution, and 5-min time sampling) for moderate to strong precipitation. Together with the dense ground network this allows detailed studies of heavy precipitation events at high accuracy.</li> <li>An azimuth-steerable microwave/IR radiometer for vertical profiling of temperature, humidity, and cloud liquid water in the troposphere (with 200 m to 1 km vertical resolution, and 5-min time sampling), also capable of measuring integrated water vapor (IWV) along line-of-sight paths towards Global Navigation Satellite System (GNSS) satellites.</li> <li>A water vapor mapping high-resolution GNSS station network, named GNSS StarNet, comprising six ground stations, spatially forming two star-shaped subnets (one with &#8764;10 km interstation distance, and one embedded with &#8764;5 km distance), for providing slant IWV, vertical IWV, and precipitable water, among other parame- ters, at 5-min time sampling.</li> </ol>We will present a detailed overview of the new components, their location, specifica- tion, and output data products.

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Recalibration of the advanced very high resolution radiometer for climate change research

Advances in Space Research ◽

10.1016/0273-1177(94)90359-x ◽

1994 ◽

Vol 14 (1) ◽

pp. 117-120 ◽

Cited By ~ 3

Author(s):

C.R. Nagaraja Rao ◽

M.P. Weinreb ◽

Jianhua Chen

Keyword(s):

Climate Change ◽

High Resolution ◽

Climate Change Research ◽

Very High

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2D to 3D high-resolution seismic data conversion: imaging a shallow water metal bearing mine tailings deposit in Portmán Bay, Spain

10.5194/egusphere-egu2020-3598 ◽

2020 ◽

Author(s):

Emma Soldevila ◽

Ramon Carbonell ◽

David Amblas ◽

Miquel Canals

Keyword(s):

High Resolution ◽

Mine Tailings ◽

Single Channel ◽

Vertical Resolution ◽

3D Seismic ◽

Data Volume ◽

Very High ◽

Parametric Source ◽

3D Interpolation ◽

2D To 3D

High-resolution (HR) 3D seismic acquisition is expensive and often not available due to a variety of reasons. This work builds an optimized workflow to convert a dense 2D HR seismic grid into a 3D seismic volume. The task has been developed within a broader project, NUREIEVA, which aims at characterizing a metal-rich onshore and shallow marine mine tailings deposit in Portm&#225;n Bay, Murcia, Spain, which developed from 1957 to 1990. Hence, in the framework of the NUREIEVA project a very dense set of 2D HR seismic lines was acquired. The geophysical equipment used to capture the submarine extent, thickness and internal structure of the mine tailings deposit was a hull-mounted Kongsberg TOPAS PS18 single-channel parametric source. The seismic grid thus acquired consisted of 1309 2D lines, with an approximate distance between lines of 10 m, covering an area of 7.45 km2. The parametric source yielded a vertical resolution of 15 cm, which is very high if compared with conventional seismic reflection data.&#160;In order to visualize the internal architecture of the mine tailings deposit in all directions, it is desirable to convert the dense 2D network of lines into a full 3D data volume. Such a data volume is intended to assist reaching faster deposit delimitation and more accurate volumetric calculations. For this purpose, a new optimized 2D to 3D conversion processing flow including a 3D interpolation scheme has been designed. Given the specific characteristics of the input data, a number of challenges had to be addressed, namely: (i) a very high vertical resolution that differs by at least two orders of magnitude from the horizontal resolution; (ii) a large data volume (2 TB), which involves extensive computing time; (iii) the heterogeneity in the acquisition parameters. Because of this, the lines had to be processed previously to the 3D interpolation to homogenize the imaging characteristics and signal content. This new methodology can be now applied for obtaining a 3D volume to any case where a single channel dense 2D seismic grid is available. Furthermore, the new methodology, duly adapted to each particular scenario, represents a low cost alternative to conventional HR 3D seismic and could prevent further seismic shooting in areas when 2D data is already available.

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Regional Dynamical Downscaling

Oxford Research Encyclopedia of Climate Science ◽

10.1093/acrefore/9780190228620.013.784 ◽

2020 ◽

Author(s):

Filippo Giorgi

Keyword(s):

Climate Change ◽

High Resolution ◽

Dynamical Downscaling ◽

Regional Climate ◽

Added Value ◽

Climate Information ◽

Atmospheric Models ◽

Future Directions ◽

Climate Change Research ◽

Climate Downscaling

Dynamical downscaling has been used for about 30 years to produce high-resolution climate information for studies of regional climate processes and for the production of climate information usable for vulnerability, impact assessment and adaptation studies. Three dynamical downscaling tools are available in the literature: high-resolution global atmospheric models (HIRGCMs), variable resolution global atmospheric models (VARGCMs), and regional climate models (RCMs). These techniques share their basic principles, but have different underlying assumptions, advantages and limitations. They have undergone a tremendous growth in the last decades, especially RCMs, to the point that they are considered fundamental tools in climate change research. Major intercomparison programs have been implemented over the years, culminating in the Coordinated Regional climate Downscaling EXperiment (CORDEX), an international program aimed at producing fine scale regional climate information based on multi-model and multi-technique approaches. These intercomparison projects have lead to an increasing understanding of fundamental issues in climate downscaling and in the potential of downscaling techniques to provide actionable climate change information. Yet some open issues remain, most notably that of the added value of downscaling, which are the focus of substantial current research. One of the primary future directions in dynamical downscaling is the development of fully coupled regional earth system models including multiple components, such as the atmosphere, the oceans, the biosphere and the chemosphere. Within this context, dynamical downscaling models offer optimal testbeds to incorporate the human component in a fully interactive way. Another main future research direction is the transition to models running at convection-permitting scales, order of 1–3 km, for climate applications. This is a major modeling step which will require substantial development in research and infrastructure, and will allow the description of local scale processes and phenomena within the climate change context. Especially in view of these future directions, climate downscaling will increasingly constitute a fundamental interface between the climate modeling and end-user communities in support of climate service activities.

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Water vapor correction method for advanced very high resolution radiometer data

Journal of Geophysical Research Atmospheres ◽

10.1029/93jc01837 ◽

1993 ◽

Vol 98 (C12) ◽

pp. 22817 ◽

Cited By ~ 8

Author(s):

Moira L. Steyn-Ross ◽

D. A. Steyn-Ross ◽

P. J. Smith ◽

J. D. Shepherd ◽

J. Reid ◽

...

Keyword(s):

Water Vapor ◽

High Resolution ◽

Correction Method ◽

Very High

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A new technique for estimating outgoing longwave radiation using infrared window and water vapor radiances from Kalpana very high resolution radiometer

Geophysical Research Letters ◽

10.1029/2007gl031715 ◽

2007 ◽

Vol 34 (23) ◽

pp. n/a-n/a ◽

Cited By ~ 9

Author(s):

Randhir Singh ◽

P. K. Thapliyal ◽

C. M. Kishtawal ◽

P. K. Pal ◽

P. C. Joshi

Keyword(s):

Water Vapor ◽

High Resolution ◽

Outgoing Longwave Radiation ◽

Longwave Radiation ◽

New Technique ◽

A New Technique ◽

Very High ◽

Infrared Window

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Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas

The Cryosphere ◽

10.5194/tc-15-4261-2021 ◽

2021 ◽

Vol 15 (9) ◽

pp. 4261-4279

Author(s):

Xiaodan Wu ◽

Kathrin Naegeli ◽

Valentina Premier ◽

Carlo Marin ◽

Dujuan Ma ◽

...

Keyword(s):

Climate Change ◽

High Resolution ◽

Land Cover ◽

Snow Cover ◽

Area Coverage ◽

Slope Aspect ◽

Hindu Kush ◽

Very High

Abstract. Long-term monitoring of snow cover is crucial for climatic and hydrological studies. The utility of long-term snow-cover products lies in their ability to record the real states of the earth's surface. Although a long-term, consistent snow product derived from the ESA CCI+ (Climate Change Initiative) AVHRR GAC (Advanced Very High Resolution Radiometer global area coverage) dataset dating back to the 1980s has been generated and released, its accuracy and consistency have not been extensively evaluated. Here, we extensively validate the AVHRR GAC snow-cover extent dataset for the mountainous Hindu Kush Himalayan (HKH) region due to its high importance for climate change impact and adaptation studies. The sensor-to-sensor consistency was first investigated using a snow dataset based on long-term in situ stations (1982–2013). Also, this includes a study on the dependence of AVHRR snow-cover accuracy related to snow depth. Furthermore, in order to increase the spatial coverage of validation and explore the influences of land-cover type, elevation, slope, aspect, and topographical variability in the accuracy of AVHRR snow extent, a comparison with Landsat Thematic Mapper (TM) data was included. Finally, the performance of the AVHRR GAC snow-cover dataset was also compared to the MODIS (MOD10A1 V006) product. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006. Using a ±3 d temporal filter caused a slight decrease in accuracy (from 94 % to 92 %). Validation against Landsat TM data over the area with a wide range of conditions (i.e., elevation, topography, and land cover) indicated overall root mean square errors (RMSEs) of about 13.27 % and 16 % and overall biases of about −5.83 % and −7.13 % for the AVHRR GAC raw and gap-filled snow datasets, respectively. It can be concluded that the here validated AVHRR GAC snow-cover climatology is a highly valuable and powerful dataset to assess environmental changes in the HKH region due to its good quality, unique temporal coverage (1982–2019), and inter-sensor/satellite consistency.

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A dynamic water vapor correction method for the retrieval of land surface temperatures from the advanced very high resolution radiometer

Journal of Geophysical Research Atmospheres ◽

10.1029/97jd00776 ◽

1997 ◽

Vol 102 (D16) ◽

pp. 19629-19643 ◽

Cited By ~ 3

Author(s):

Moira L. Steyn-Ross ◽

D. Alistair Steyn-Ross ◽

W. J. Emery

Keyword(s):

Water Vapor ◽

High Resolution ◽

Land Surface ◽

Correction Method ◽

Surface Temperatures ◽

Land Surface Temperatures ◽

Very High

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Detection and Attribution of Atmospheric Precipitable Water Changes since the 1970s over China

Scientific Reports ◽

10.1038/s41598-019-54185-z ◽

2019 ◽

Vol 9 (1) ◽

Cited By ~ 1

Author(s):

Jingpeng Zhang ◽

Tianbao Zhao ◽

Aiguo Dai ◽

Wenyu Zhang

Keyword(s):

Climate Change ◽

Water Vapor ◽

Signal Analysis ◽

Eastern China ◽

Precipitable Water ◽

Atmospheric Water Vapor ◽

Cmip5 Model ◽

Atmospheric Water ◽

Climate Change Research ◽

Detection And Attribution

AbstractAtmospheric water vapor increases as air temperature rises, which causes further warming. Thus, understanding the underlying causes of atmospheric water vapor change is vital in climate change research. Here, we conducted detection and attribution analyses of atmospheric precipitable water (PW) changes from 1973–2012 over China using an optimal fingerprinting method by comparing the homogenized radiosonde humidity data with CMIP5 model simulations. Results show that the increase in water vapor can be largely attributed to human activities. The effect of anthropogenic forcing (ANT) can be robustly detected and separated from the response to the natural external forcing (NAT) in the two-signal analysis. The moistening attributable to the ANT forcing explains most of the observed PW increase, while the NAT forcing leads to small moistening. GHGs are the primary moistening contributor responsible for the anthropogenic climate change, and the effect of GHGs can be also clearly detected and successfully attributed to the observed PW increases in a three-signal analysis. The scaling factor is used to adjust the CMIP5 model-projected PW changes over China and the observation-constrained future projections suggest that atmospheric water vapor may increase faster (slower) than that revealed by the raw simulations over whole (eastern) China.

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