Abstract
The aim of this study is to assess the behavior of the high-resolution COSMO model, having convection explicitly solved, to different soil moisture initializations. This work is devoted to understand if and how the model is sensitive to the initial condition of the soil water content. Four different case studies, representing four different meteorological regimes, have been selected for such a purpose using different soil moisture fields prepared ad hoc. Results confirm that the model is very sensitive to the perturbation of the initial conditions of the soil water content, showing a significant spread increase in main prognostic variables, near the soil as well as upper in the atmosphere. These results suggest that an ensemble system, based on a high-resolution convection-permitting model, could benefit in terms of spread increase if a perturbation of the initial conditions of the soil moisture would be added to the classic perturbation of the atmosphere. For an evaluation of the performances of this kind of ensemble system, a comparison with observations should be performed.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Aligo EA, Gallus WA, Segal M (2007) Summer rainfall forecast spread in an ensemble initialized with different soil moisture analyses. Weather Forecast 22:299–314. doi:10.1175/WAF995.1
Atger F (1999) The skill of ensemble prediction systems. Mon Weather Rev 127:1941–1953. doi:10.1175/1520-0493(1999)127<1941:TSOEPS>2.0.CO;2
Balsamo G, Mahfouf JF, Bélair S, Deblonde G (2007) A land data assimilation system for soil moisture and temperature: an information content study. J Hydrometerorol 8:1225–1242. doi:10.1175/2007JHM819.1
Bouttier F, Vié B, Nuissier O, Raynaud L (2012) Impact of stochastic physics in a convection-permitting ensemble. Mon Weather Rev 140:3706–3721. doi:10.1175/MWR-D-12-00031.1
Bowler NE, Arribas A, Mylne KR, Robertson KB, Beare SE (2008) The MOGREPS short-range ensemble prediction system. Q J R Meteorol Soc 134:703–722. doi:10.1002/qj.234
Buizza R, Barkmeijer J, Palmer TN, Richardson D (2000) Current status and future developments of the ECMWF ensemble prediction system. Meteorol Appl 6:1–14. doi:10.1017/S1350482700001456
Caparrini F, Castelli F, Entekhabi D (2004) Estimation of surface turbulent fluxes through assimilation of radiometric surface temperature sequences. J Hydrometeorol 5:145–159. doi:10.1175/1525-7541(2004)005<0145:EOSTFT>2.0.CO;2
Cassardo C (2006) The Land Surface Process Model (LSPM) version 2006. The complete manual-Internal Report, DFG 1/2006, Department of General Physics “Amedeo Avogadro”. University of Torino, Torino, p 62
Doms G, Baldauf M (2015) A Description of the Nonhydrostatic Regional COSMO Model. Part I: Dynamics and Numerics. COSMO model documentation. http://www2.cosmo-model.org/content/model/documentation/core/cosmoDyncsNumcs.pdf
Doms G, Förstner J, Heise E, Herzog HJ, Mironov D, Raschendorfer M, Reinhardt T, Ritter B, Schrodin R, Schulz JP, Vogel G (2011) A Description of the Nonhydrostatic Regional COSMO Model. Part II: Physical Parameterization. COSMO model documentation. http://www.cosmo-model.org/content/model/documentation/core/cosmoPhysParamtr.pdf
Du J, DiMego G, Tracton MS, Zhou B (2003) NCEP short-range ensemble forecasting (SREF) system: multi-IC, multimodel and multi-physics approach. Research Activities in Atmospheric and Oceanic Modelling (edited by J. Cote), Report 33, CAS/JSC Working Group Numerical Experimentation (WGNE), WMO/TD-No. 1161: 5.09–5.10. http://www.emc.ncep.noaa.gov/mmb/SREF/reference.html
Ebert EE (2001) Ability of a poor man’s ensemble to predict the probability and distribution of precipitation. Mon Weather Rev 129:2461–2480. doi:10.1175/1520-0493(2001)129<2461:AOAPMS>2.0.CO;2
Eckel FA, Mass CF (2005) Aspects of effective mesoscale, short-range ensemble forecasting. Weather Forecast 20:328–350. doi:10.1175/WAF843.1
Fritsch JM, Carbone RE (2004) Improving quantitative precipitation forecasts in the warm season: A USWRP research and development strategy. B Am Meteorol Soc 85:955–965. doi:10.1175/BAMS-85-7-955
Hacker JP, Ha SY, Snyder C, Berner J, Eckel FA, Kuchera E, Pocernich M, Rugg S, Schramm J, Wang X (2011) The US Air Force Weather Agency’s mesoscale ensemble: Scientific description and performance results. Tellus 63A:625–641. doi:10.1111/j.1600-0870.2010.00497.x
Hamill TM, Colucci SJ (1997) Verification of Eta-RSM short-range ensemble forecasts. Mon Weather Rev 125:1312–1327. doi:10.1175/1520-0493(1997)125<1312:VOERSR>2.0.CO;2
Hess R (2001) Assimilation of screen-level observations by variational soil moisture analysis. Meteorol Atmos Phys 77:145–154. doi:10.1007/s007030170023
Huffman GJ, Bolvin DT, Nelkin EJ, Wolff DB, Adler RF, Gu G, Hong Y, Bowman KP, Stocker EF (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55. doi:10.1175/JHM560.1
Iversen T, Deckmyn A, Santos C, Sattler K, Bremnes JB, Feddersen H, Frogner I-L (2011) Evaluation of ‘GLAMEPS’—a proposed multimodel EPS for short range forecasting. Tellus A 63:513–530. doi:10.1111/j.1600-0870.2010.00507.x
Klüpfel V, Kalthoff N, Gantner L, Kottmeier C (2011) Evaluation of soil moisture ensemble runs to estimate precipitation variability in convection-permitting model simulations for West Africa. Atmos Res 101(1–2):178–193. doi:10.1016/j.atmosres.2011.02.008
Lavaysse CM, Carrera S, Bélair N, Gagnon R, Frenette M, Charron M, Yau MK (2013) Impact of surface parameter uncertainties within the Canadian regional ensemble prediction system. Mon Weather Rev 141:1506–1526. doi:10.1175/MWR-D-11-00354.1
Mahmood R, Hubbard KG (2004) An analysis of simulated long-term soil moisture data for three land uses under contrasting hydroclimatic conditions in the Northern Great Plains. J Hydrometeorol 5:160–179. doi:10.1175/1525-7541(2004)005<0160:AAOSLS>2.0.CO;2
Marsigli C, Montani A, Nerozzi F, Paccagnella T, Tibaldi S, Molteni F, Buizza R (2001) A strategy for high-resolution ensemble prediction. II: Limited–area experiments in four Alpine flood events. Q J R Meteorol Soc 127:2095–2115. doi:10.1002/qj.49712757613
Migliorini S, Dixon M, Bannister R, Ballard S (2011) Ensemble prediction for nowcasting with a convection-permitting model-I: description of the system and the impact of radar-derived surface precipitation rates. Tellus A 63(3):468–496. doi:10.1111/j.1600-0870.2010.00503.x
Molteni F, Buizza R, Marsigli C, Montani A, Nerozzi F, Paccagnella T (2001) A strategy for high–resolution ensemble prediction. Part I: Definition of representative members and global model experiments. Q J R Meteorol Soc 127:2069–2094. doi:10.1002/qj.49712757612
Montani A, Marsigli C, Nerozzi F, Paccagnella T, Tibaldi S, Buizza R (2003a) The Soverato flood in Southern Italy: performance of global and limited-area ensemble forecasts. Nonlinear Process Geophys 10:261–274. doi:10.5194/npg-10-261-2003
Montani A, Capaldo M, Cesari D (2003b) Operational limited-area ensemble forecasts based on the Lokal Model. ECMWF Newsletter 98:2–7
Montani A, Cesari D, Marsigli C, Paccagnella T (2011) Seven years of activity in the field of mesoscale ensemble forecasting by the COSMO-LEPS system: main achievements and open challenges. Tellus A 63:605–624. doi:10.1111/j.1600-0870.2010.00499.x
Mullen SL, Buizza R (2001) Quantitative precipitation forecasts over the United States by the ECMWF ensemble prediction system. Mon Weather Rev 129:638–663. doi:10.1175/1520-0493(2001)129<0638:QPFOTU>2.0.CO;2
Pielke Sr RA (2001) Influence of the spatial distribution of vegetation and soils on the prediction of cumulus convective rainfall. Rev Geophys 39:151–177. doi:10.1029/1999RG000072
Quintanar AI, Mahmood R (2012) Ensemble forecast spread induced by soil moisture changes over mid-south and neighbouring mid-western region of the USA. Tellus A 64:17156. doi:10.3402/tellusa.v64i0.17156
Rodell M, Houser PR, Jambor U, Gottschalck J, Mitchell K, Meng CJ, Arsenault K, Cosgrove B, Radakovich J, Bosilovich M, Entin JK, Walker JP, Lohmann D, Toll D (2004) The global land data assimilation system. Bull Am Meteorol Soc 85:381–394. doi:10.1175/BAMS-85-3-381
Schraff C, Hess R (2012) A Description of the Nonhydrostatic Regional COSMO Model. Part III: Data Assimilation. COSMO model documentation. http://www2.cosmo-model.org/content/model/documentation/core/cosmoAssim.pdf
Sutton C, Hamill TM, Warner TT (2006) Will perturbing soil moisture improve warm-season ensemble forecasts? A proof of concept. Mon Weather Rev 134:3174–3189. doi:10.1175/MWR3248.1
Tennant WS, Beare S (2014) New schemes to perturb sea-surface temperature and soil moisture content in MOGREPS. Q J R Meteorol Soc 140:1150–1160. doi:10.1002/qj.2202
Wang Y, Kann A, Bellus M, Pailleux J, Wittmann C (2010) A strategy for perturbing surface initial conditions in LAMEPS. Atmos Sci Lett 11:108–113. doi:10.1002/asl.260
Wang Y, Bellus M, Wittmann C, Steinheimer M, Weidle F, Kann A, Ivatek-Sahdan S, Tian W, Ma X, Tascuf S, Bazileg E (2011) The central European limited-area ensemble forecasting system: ALADIN-LAEF. Q J R Meteorol Soc 137:483–502. doi:10.1002/qj.751
Author information
Authors and Affiliations
Corresponding author
Additional information
Responsible Editor: F. Mesinger.
Rights and permissions
About this article
Cite this article
Bonanno, R., Loglisci, N. Introducing lower boundary conditions perturbations in a convection-permitting ensemble system: sensitivity to soil moisture perturbation. Meteorol Atmos Phys 130, 67–80 (2018). https://doi.org/10.1007/s00703-017-0505-1
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00703-017-0505-1