Working Group: Convection resolving climate simulations
Co-Coordinator: Michael Haller
At present, regional climate models (RCMs) typically use a grid resolution in the order of 10 km to 20 km, at which the convective processes are parameterised. However, with more computational resources becoming available, several groups are investigating the added value of using RCMs on scales, where convection is (partly) resolved by the model (Convection Resolving Climate Simulations; CRCS). This development is driven by the need of kilometer-scale climate information for impact studies (hydrology, wind energy, agriculture, ...), but also by more generic research questions. CRCSs are promising research tools for understanding processes and to study feedback mechanisms acting at the local scale (for example feedbacks related to land-atmosphere interactions).
On the long term, model skill is expected to improve by explicitly resolving the convective processes. Currently, CRCSs are at an experimental stage and added value compared to coarser scale climate simulations has only partly been demonstrated yet (e.g., Hohenegger et al., 2008). Conventional evaluation datasets and evaluation methods are not necessarily adequate for evaluating CRCSs and substantial research activity is needed in this domain. Opportunities of using instruments that provide information at small spatial scales (like radar) should be explored. Significant research effort is needed to assess the reliability of CCLM at the convection resolving scale, to identify and trace back it’s deficiencies, and to support model development.
Coordinated Evaluation of Convection Permitting Climate simulations with COSMO-CLM5.0
Groups working on CLM_CRCS topics
Research groups related to CRCS can be found via the topic browser.
CLM_CRCS related projects
The CRCS related projects can be found via the topic browser.
CLM_CRCS Namelist Market
A market place with namelists that have been used for conducting CRCSs can be found in the redmine system.
Please add your namelists to this market place, so that modellers who are not that experienced may have a better starting point for their simulations.
High resolution observational and analyze data
The WegenerNet climate station network region Feldbach comprises 151 meteorological stations in a tightly spaced grid (~ 20 km x 15 km) in the southeastern part of Styria. Temperature, humidity, precipitation, and other parameters are measured with high accuracy and are provided on various temporal scales (from 5 minutes to annual) for both single stations (one station per ~2 km²;) and interpolated regular grids (UTM, 1 km x 1 km; latitude/longitude, 0.01° x 0.01°). For application purposes, all data is available for visualization and download via the WegenerNet data portal (www.wegenernet.org).
Kabas, T., A. Leuprecht, C. Bichler, and G. Kirchengast, WegenerNet climate station network region Feldbach, Austria: network structure, processing system, and example results, Adv. Sci. Res., 6, 49-54, 2011, doi: 10.5194/asr-6-49-2011
Integrated Nowcasting through Comprehensive Analysis (INCA)
The Integrated Nowcasting through Comprehensive Analysis (INCA) dataset is produced by the Central Institute for Meteorology and Geodynamics (ZAMG) (Haiden et al. 2011). It has a 1 km × 1 km resolution and covers entire Austria. The INCA dataset is derived via a combination of numerical weather predictions (NWPs) (ALADIN, ECMWF) with current observation data from stations, radar, and satellites, and is further reﬁned with highly resolved orographic information (http://www.zamg.ac.at/forschung/synoptik/inca/ ).
Haiden, T., A. Kann, C. Wittmann, G. Pistotnik, B. Bica, and C. Gruber, The Integrated Nowcasting through Comprehensive Analysis (INCA) System and Its Validation over the Eastern Alpine Region, Wea. Forecasting, 26, 2, 166-183, 2011, doi: 10.1175/2010WAF2222451.1
Rain gauges and weather radars both constitute important devices for operational precipitation monitoring. Gauges provide accurate yet spotty precipitation estimates, while radars offer high temporal and spatial resolution yet at a limited absolute accuracy. We propose a simple methodology to combine radar and daily rain-gauge data to build
up a precipitation dataset with hourly resolution covering a climatological time period. The methodology starts from a daily precipitation analysis, derived from a dense rain-gauge network. A sequence of hourly radar analyses is then used to disaggregate the daily analyses. The disaggregation is applied such as to retain the daily precipitation totals of the raingauge analysis, in order to reduce the impact of quantitative radar biases. Hence, only the radar’s advantage in terms of temporal resolution is exploited. In this article the disaggregation method is applied to derive a 15-year gridded precipitation dataset at hourly resolution for Switzerland at a spatial resolution of 2 km. Validation of this dataset indicates that errors in hourly intensity and frequency are lower than 25% on average over the Swiss Plateau. In Alpine valleys, however, errors are typically larger due to shielding effects of the radar and the corresponding underestimation of precipitation
periods by the disaggregation. For the ﬂatland areas of the Swiss Plateau, the new dataset offers an interesting quantitative description of high-frequency precipitation variations suitable for climatological analyses of heavy events, the evaluation of numerical weather forecasting models and the calibration/operation of hydrological runoff models.
Wüest, M., C. Frei, A. Altenhoff, M. Hagen, M. Litschi, C. Schär: A gridded hourly precipitation dataset for Switzerland using rain-gauge analysis and radar-based disaggregation, Int J Climatol, 30, 12, 1764–1775, 2010, doi: 10.1002/joc.2025
The Alpine precipitation grid dataset (EURO4M-APGD)
MeteoSwiss has developed a gridded analysis of daily precipitation, extending over the entire Alpine region. The dataset is based on measurements at high-resolution rain-gauge networks, encompassing more than 8500 stations from Austria, Croatia, France, Germany, Italy, Slovenia and Switzerland. The dataset was developed in the framework of EURO4M (European Reanalysis and Observations for Monitoring), a FP7 (seventh framework program) Collaborative Project of the European Union.
Isotta, F.A. et al. 2014: The climate of daily precipitation in the Alps: development and analysis of a high-resolution grid dataset from pan-Alpine rain-gauge data. Int. J. Climatol., 34: 1657-1675. doi: 10.1002/joc.3794.
The REGNIE dataset contains daily precipitation on a 1 km grid covering Germany within the period 1961 to 2016 (dataset description).
DWD (2009). Regionalisierte Niederschlagshöhen (REGNIE).
Daily precipitation data-set for Sweden on a 4 km grid for 1961 to 2010.
Johansson, B. (2002). Estimation of areal precipitation for hydrological modelling in Sweden. Ph.D.thesis A76. Earth Science Centre, Göteborg University.
Daily precipitation and temperature for Norway on a 1 km grid within the period 1957 to 2017.
Lussana et al (2019): seNorge_2018, daily precipitation, and temperature datasets over Norway
Daily precipitation on a 0.11° grid for Spain given from 1971 to 2011.
Herrera, S. et al. (2012). Development and analysis of a 50-year high-resolution daily gridded precipitation dataset over Spain (Spain02)". In: Int. J. Climatol. 32.1, pp. 74-85
Gridded multiple parameter dataset covering the Carpathian region with 10 km grid spacing from 1961 to 2010.
Szalai, S. et al. (2013). Climate of the Greater Carpathian Region. Final Technical Report. www.carpatclim-eu.org.
SAFRAN (Meteo France)
Gridded reanalysis for France providing multible sub-daily parameters on a 8 km grid from 1958 to 2013.
Quintana-Segui, P. et al. (2008). Analysis of near-surface atmospheric variables: Validation of the SAFRAN analysis over France. In: Journal of Applied Meteorology & Climatology 47.1
A broad buffet of different kind of observational data can be found under
- General Observation Period (GOP), covering 2007/2008
Crewell, S., M. Mech, T. Reinhardt, C. Selbach, H. -. Betz, E. Brocard, G. Dick, E. O'Connor, J. Fischer, T. Hanisch, T. Hauf, A. Hünerbein, L. Delobbe, A. Mathes, G. Peters, H. Wernli, M. Wiegner, and V. Wulfmeyer (2008), The general observation period 2007 within the priority program on quantitative precipitation forecasting: Concept and first results, Meteorol.Z., 17, 6, 849-866, doi: 10.1127/0941-2948/2008/0336.
- Convectively and Orographically Induced Precipitation Study (COPS), covering the Black Forest (Germany) from June to August 2007
Wulfmeyer, V., A. Behrendt, H. -. Bauer, C. Kottmeier, U. Corsmeier, A. Blyth, G. Craig, U. Schumann, M. Hagen, S. Crewell, P. Di Girolamo, C. Flamant, M. Miller, A. Montani, S. Mobbs, E. Richard, M. W. Rotach, M. Arpagaus, H. Russchenberg, P. Schlüssel, M. König, V. Gärtner, R. Steinacker, M. Dorninger, D. D. Turner, T. Weckwerth, A. Hense, and C. Simmer (2008), RESEARCH CAMPAIGN: The Convective and Orographically Induced Precipitation Study, A Research and Development Project of the World Weather Research Program for Improving Quantitative Precipitation Forecasting in Low-Mountain Regions, Bull.Am.Meteorol.Soc., 89, 10, 1477-1486, doi: 10.1175/2008BAMS2367.1.
Ban, N., J. Schmidli, and C. Schär (2014), Evaluation of the convection-resolving regional climate modeling approach in decade-long simulations, J. Geophys. Res. Atmos., doi:10.1002/2014JD021478
Chan, S. C., Kendon, E. J., Fowler, H. J., Blenkinsop, S., Roberts, N. M., Ferro, C. A. (2014). The value of high-resolution Met Office regional climate
models in the simulation of multi-hourly precipitation extremes. Journal of Climate
Fosser G, Khodayar S, Berg P (2014) Benefit of convection permitting climate model simulations in the representation of convective precipitation, Climate
Froidevaux, P., L. Schlemmer, J. Schmidli, W. Langhans, C. Schär (2014): Influence of background wind on the local soil moisture-precipitation feedback. J. Atmos. Sci., 71, 782-799.
Grell, G. A., L. Schade, .R. Knoche, A. Pfeiffer, J Egger (2000): Nonhydrostatic climate simulations of precipitation over complex terrain, J Geophys Res-Atmos., 105 (D24), 29595-29608, doi: 10.1029/2000JD900445
Hohenegger C., P. Brockhaus, C. Schaer (2008): Towards climate simulations at cloud-resolving scales, Meteorol. Z., 17 (4), 383-394
P. Brockhaus, C. S. Bretherton and C. Schär (2009): The soil moisture-precipitation feedback in simulations with explicit and parameterized convection, J. Climate , 22 (19), 5003-5020
Junk, J., A. Matzarakis, A. Ferrone, A. Krein (2014), Evidence of past and future changes in health-related meteorological variables across Luxembourg, Air Qual Atmos Health, 7, 71–81, doi: 10.1007/s11869-013-0229-4
Kendon, E. J., N. M. Roberts, et al. (2014). "Heavier summer downpours with climate change revealed by weather forecast resolution model." Nature Clim. Change 4(7): 570-576.
Knote, C., G. Heinemann, B. Rockel, Changes in weather extremes: Assessment of return values using high resolution climate simulations at convection-resolving scale, Meteorol. Z.19 (1), 11-32, 2010, doi: 10.1127/0941-2948/2010/0424
Langhans, W., J. Schmidli, C. Schaer: Mesoscale Impacts of Explicit Numerical Diffusion in a Convection-Permitting Model, Mon.Weather Rev., 140(1), 226-244, 2012, doi: 10.1175/2011MWR3650.1
Langhans, W., J. Schmidli, C. Schaer: Bulk Convergence of Cloud-Resolving Simulations of Moist Convection over Complex Terrain, J. Atmos.Sci., 69(7), 2207-2228, 2012, doi: 10.1175/JAS-D-11-0252.1
Langhans, W., J. Schmidli, O. Fuhrer, S. Bieri, and C. Schaer (2013): Long-term simulations of thermally driven flows and orographic convection at convection-parameterizing and cloud-resolving resolutions. J. Appl. Meteor. Climatol., 52, 1490-1510.
Mass, F. C., D. Ovens, K. Westrick, B. A. Colle: Does increasing horizontal resolution produce more skillful forecasts? The results of two years of real-time numerical weather prediction over the Pacific Northwest, B Am Meteorol Soc , 407–430, 2002
Prein, A. F., and A. Gobiet, NHCM-1: Non-hydrostatic climate modelling. Part I: Defining and Detecting Added Value in Cloud Resolving Climate Simulations, Sci. Rep., 39-2011, 74 pp., 2011, Wegener Center Verlag, Graz, Austria, ISBN 978-3-9502940-6-4, available.
Prein, A. F., M. Suklitsch, H. Truhetz, and A. Gobiet (2011), NHCM-1: Non-hydrostatic climate modelling. Part III: Evaluation of the LocMIP simulations, Sci. Rep., 41-2011, 128 pp., 2011, Wegener Center Verlag, Graz, Austria, ISBN 978-3-9502940-8-8, avaiable.
Prein, A. F., A. Gobiet, M. Suklitsch, H. Truhetz, N. K. Awan, K. Keuler, and G. Georgievski (2013) Added Value of Convection Permitting Seasonal Simulations. Clim. Dyn., doi: 10.1007/s00382-013-1744-6
Prein, A. F., G. J. Holland, R. M. Rasmussen, J. Done, K. Ikeda, M. P. Clark, and C. H. Liu (2013) Importance of Regional Climate Model Grid Spacing for the Simulation of Precipitation Extremes. J. Climate, doi: 10.1175/JCLI-D-12-00727.1
Suklitsch, M., A. F. Prein, H. Truhetz, and A. Gobiet, NHCM-1: Non-hydrostatic climate modelling. Part II: Current state of selected cloud-resolving regional climate models and their error characteristics, Sci. Rep., 40-2011, 90 pp., 2011, Wegener Center Verlag, Graz, Austria, ISBN 978-3-9502940-7-1, available
Tölle, M. H., O. Gutjahr, G. Busch, and J. C. Thiele (2014), Increasing bioenergy production on arable land: Does the regional and local climate respond? Germany as a case study, J. Geophys. Res. Atmos., 119, 2711–2724, doi:10.1002/2013JD020877
Van Weverberg, K., Goudenhoofdt, E., Blahak, U., Brisson, E., Demuzere, M., Marbaix, P., & van Ypersele, J. P. (2014). Comparison of one-moment and two-moment bulk microphysics for high-resolution climate simulations of intense precipitation. Atmospheric Research, 147, 145-161.
WG Annual Reports
Bodo Ahrens (Uni Frankfurt), Ivonne Anders (ZAMG Vienna), Nikolina Ban (ETH Zurich), Nico Becker (FU Berlin), Benedikt Brecht (KIT), Marcus Breil (KIT Karlsrue), Christoph Brendel (Uni Frankfurt), Susanne Brienen (DWD Offenbach), Erwan Brisson (KU Leuven), Fabien Chatterjee (KULeuven), Andras Csaki (Uni Graz), Matthias Demuzere (KU Leuven), Hendrik Feldmann (KIT Karlsruhe), Barbara Früh (DWD), Beate Geyer (HZG), Irina Gorodetskaya (KU Leuven), Oliver Gutjahr (Uni Trier), Ha Ho (HZG), Michael Keller (ETH Zurich), Klaus Keuler (BTU Cottbus), Stefan Lange (PIK Potsdam), Wolfgang Langhans (ETH Zurich), David Leutwyler (ETH Zurich), Edmund Meredith (Fu Berlin), Hans-Jürgen Panitz (KIT), Burkhardt Rockel (HZG), Abdoulaye Sarr (ANAMS Dakar), Gerd Schaedler (KIT), Christoph Schär (ETH Zurich), Lukas Schefczyk (Uni Trier), Jürg Schmidli (ETH Zurich), Sebastian Schubert (HU-Berlin), Wim Thiery (KU Leuven), Heimo Truhetz (Uni Graz), Kristina Trusilova (DWD Offenbach), Merja Tölle (Uni Göttingen), Nicole van Lipzig (KU Leuven), Zhou Weidan (Nanjing Univ.), Andreas Will (BTU Cottbus), Hendrik Wouters (KU Leuven), Johann Züger (AIT Vienna), Maja Žuvela-Aloise (ZAMG Vienna)
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