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Christoph Schlager, Gottfried Kirchengast, and Jürgen Fuchsberger

Abstract

An operational weather diagnostics application for automatic generation of wind fields in near–real time from observations delivered by the high-density WegenerNet meteorological station network in the Feldbach region of Austria is introduced. The purpose of the application is to empirically provide near-surface wind fields of very high spatial and temporal resolution for evaluating convection-permitting climate models as well as investigating weather and climate variability on a local scale. The diagnostic California Meteorological Model (CALMET) is used as the core tool. This model computes 3D wind fields based on observational weather data, a digital elevation model, and land-use categories. The application first produces the required input files from the WegenerNet stations and subsequently runs the CALMET model based on this input. In a third step the modeled wind fields are stored in the WegenerNet data archives every 30 min with a spatial resolution of 100 m × 100 m, while also generating averaged weather and climate products during postprocessing. The performance of the modeling against station observations, for which wind speeds were classified into weak and strong wind speeds, is evaluated and reasonably good results were found for both wind speed classes. The statistical agreement for the vector-mean wind speed is slightly better for weak wind speeds than for strong ones while the difference between modeled and observed wind directions is smaller for strong wind speeds than for weak ones. The application is also a valuable tool for other high-density networks.

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Gottfried Kirchengast, Thomas Kabas, Armin Leuprecht, Christoph Bichler, and Heimo Truhetz

The Feldbach region in southeast Austria, characteristic for experiencing a rich variety of weather and climate patterns, has been selected as the focus area for a pioneering weather and climate observation network at very high resolution: The WegenerNet comprises 151 meteorological stations measuring temperature, precipitation, and other parameters, in a tightly spaced grid within an area of about 20 km × 15 km centered near the city of Feldbach (46.93°N, 15.90°E). With its stations about every 2 km2, each with 5-min time sampling, the network provides regular measurements since January 2007, after a pilot phase, until 2010, meanwhile in an operational manner. Quality-controlled station time series and gridded field data (spacing 200 m × 200 m) are available in near–real time (data latency less than 1–2 h) for visualization and download via a data portal (www.wegenernet.org; detailed information is available via www.wegcenter.at/wegenernet).

This paper introduces the WegenerNet from its design and setup via its processing system and data products to showing example results. The latter include extreme weather event examples, climate variability over the 5-yr period from 2007 to 2011, and an example of calibration support to coupled climate–hydrology modeling. The network is set to serve as a long-term monitoring and validation facility for weather and climate research and applications. Uses include validation of nonhydrostatic models operated at 1-km-scale resolution and of statistical downscaling techniques (in particular for precipitation), validation of weather radar and satellite data, study of orography–climate relationships, and many others.

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Alexander Kann, Thomas Haiden, Klaus von der Emde, Christine Gruber, Thomas Kabas, Armin Leuprecht, and Gottfried Kirchengast

Abstract

Operational analyses of 2-m temperature, 2-m humidity, and 10-m wind speed are verified independently against observations obtained from the WegenerNet, an extremely high-density, grid-type surface station network in southeastern Austria with an average distance between stations of 1.4 km. The Integrated Nowcasting through Comprehensive Analysis (INCA) system provides high-resolution analyses in space (1 km) and time (1 h) over the eastern Alpine region and has been specially designed for use in complex terrain. The quality of the system is investigated within a small domain with gentle topography ranging in elevation from 250 to 500 m. A comprehensive validation of INCA relative to WegenerNet for a 3-yr period from December 2007 to November 2010 indicates high analysis skill during all seasons. A sensitivity study reveals the importance of a sufficiently dense station network used by the system and, even more important, the relevance of adequate representativeness of the observation data.

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Jackson Tan, Walter A. Petersen, Gottfried Kirchengast, David C. Goodrich, and David B. Wolff

Abstract

Precipitation profiles from the Global Precipitation Measurement (GPM) Core Observatory Dual-Frequency Precipitation Radar (DPR; Ku and Ka bands) form part of the a priori database used in the Goddard profiling algorithm (GPROF) for retrievals of precipitation from passive microwave sensors, which are in turn used as high-quality precipitation estimates in gridded products. As GPROF performs precipitation retrievals as a function of surface classes, error characteristics may be dependent on surface types. In this study, the authors evaluate the rainfall estimates from DPR Ku as well as GPROF estimates from passive microwave sensors in the GPM constellation. The evaluation is conducted at the level of individual satellite pixels (5–15 km) against three dense networks of rain gauges, located over contrasting land surface types and rainfall regimes, with multiple gauges per satellite pixel and precise accumulation about overpass time to ensure a representative comparison. As expected, it was found that the active retrievals from DPR Ku generally performed better than the passive retrievals from GPROF. However, both retrievals struggle under coastal and semiarid environments. In particular, virga appears to be a serious challenge for both DPR Ku and GPROF. The authors detected the existence of lag due to the time it takes for satellite-observed precipitation to reach the ground, but the precise delay is difficult to quantify. It was also shown that subpixel variability is a contributor to the errors in GPROF. These results can pinpoint deficiencies in precipitation algorithms that may propagate into widely used gridded products.

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Bettina C. Lackner, Andrea K. Steiner, Gabriele C. Hegerl, and Gottfried Kirchengast
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Bettina C. Lackner, Andrea K. Steiner, Gabriele C. Hegerl, and Gottfried Kirchengast

Abstract

The detection of climate change signals in rather short satellite datasets is a challenging task in climate research and requires high-quality data with good error characterization. Global Navigation Satellite System (GNSS) radio occultation (RO) provides a novel record of high-quality measurements of atmospheric parameters of the upper-troposphere–lower-stratosphere (UTLS) region. Because of characteristics such as long-term stability, self calibration, and a very good height resolution, RO data are well suited to investigate atmospheric climate change. This study describes the signals of ENSO and the quasi-biennial oscillation (QBO) in the data and investigates whether the data already show evidence of a forced climate change signal, using an optimal-fingerprint technique. RO refractivity, geopotential height, and temperature within two trend periods (1995–2010 intermittently and 2001–10 continuously) are investigated. The data show that an emerging climate change signal consistent with the projections of three global climate models from the Coupled Model Intercomparison Project cycle 3 (CMIP3) archive is detected for geopotential height of pressure levels at a 90% confidence level both for the intermittent and continuous period, for the latter so far in a broad 50°S–50°N band only. Such UTLS geopotential height changes reflect an overall tropospheric warming. 90% confidence is not achieved for the temperature record when only large-scale aspects of the pattern are resolved. When resolving smaller-scale aspects, RO temperature trends appear stronger than GCM-projected trends, the difference stemming mainly from the tropical lower stratosphere, allowing for climate change detection at a 95% confidence level. Overall, an emerging trend signal is thus detected in the RO climate record, which is expected to increase further in significance as the record grows over the coming years. Small natural changes during the period suggest that the detected change is mainly caused by anthropogenic influence on climate.

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Florian Ladstädter, Andrea K. Steiner, Bettina C. Lackner, Barbara Pirscher, Gottfried Kirchengast, Johannes Kehrer, Helwig Hauser, Philipp Muigg, and Helmut Doleisch

Abstract

In atmospheric and climate research, the increasing amount of data available from climate models and observations provides new challenges for data analysis. The authors present interactive visual exploration as an innovative approach to handle large datasets. Visual exploration does not require any previous knowledge about the data, as is usually the case with classical statistics. It facilitates iterative and interactive browsing of the parameter space to quickly understand the data characteristics, to identify deficiencies, to easily focus on interesting features, and to come up with new hypotheses about the data. These properties extend the common statistical treatment of data, and provide a fundamentally different approach. The authors demonstrate the potential of this technology by exploring atmospheric climate data from different sources including reanalysis datasets, climate models, and radio occultation satellite data. Results are compared to those from classical statistics, revealing the complementary advantages of visual exploration. Combining both the analytical precision of classical statistics and the holistic power of interactive visual exploration, the usual workflow of studying climate data can be enhanced.

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Juha-Pekka Luntama, Gottfried Kirchengast, Michael Borsche, Ulrich Foelsche, Andrea Steiner, Sean Healy, Axel von Engeln, Eoin O'Clerigh, and Christian Marquardt

Global Navigation Satellite System (GNSS) Receiver for Atmospheric Sounding (GRAS) is a radio occupation instrument especially designed and built for operational meteorological missions. GRAS has been developed by the European Space Agency (ESA) and the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) in the framework of the EUMETSAT Polar System (EPS). The GRAS instrument is already flying on board the first MetOp satellite (.MetOp-A) that was launched in October 2006. It will also be on board two other MetOp satellites (MetOp-B and MetOp-C) that will successively cover the total EPS mission lifetime of over 14 yr. GRAS provides daily about 600 globally distributed occultation measurements and the GRAS data products are disseminated to the users in near-real time (NRT) so that they can be assimilated into numerical weather prediction (NWP) systems. All GRAS data and products are permanently archived and made available to the users for climate applications and scientific research through the EUMETSAT Unified Meteorological Archive and Retrieval Facility (U-MARF) and the GRAS Meteorology Satellite Application Facility (SAF) Archive and Retrieval Facility (GARF). The GRAS navigation data can be used in space weather applications.

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