Two adaptive radiation parameterisations Annika Schomburg 1), Victor Venema 1), Felix Ament 2), Clemens Simmer 1) 1) Department of Meteorology, University.

Slides:



Advertisements
Similar presentations
6. Equilibrium fluctuations for time-varying forcing. Values of constant term larger than expected from RCE run correspond to fluctuations greater than.
Advertisements

Predictable Chaotic Exhibits memory Equilibrium Towards non-equilibrium Acknowledgements LD is supported by NERC CASE award NER/S/A/2004/ Conclusions.
Quantifying sub-grid cloud structure and representing it GCMs
© Crown copyright Met Office Radiation Parametrisation Current development work with the UM James Manners, visit to Reading University on 19 th February.
Solar Energy Forecasting Using Numerical Weather Prediction (NWP) Models Patrick Mathiesen, Sanyo Fellow, UCSD Jan Kleissl, UCSD.
Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
30 th September 2010 Bannister & Migliorini Slide 1 of 9 High-resolution assimilation and weather forecasting Ross Bannister and Stefano Migliorini (NCEO,
© CNMCA Cap. Davide MELFI Italian Air Force Meteorological Service CNMCA An Italian tool for convection: NEFODINA Session 4 of Convection Week 2011, 6-9.
(Mt/Ag/EnSc/EnSt 404/504 - Global Change) Climate Models (from IPCC WG-I, Chapter 8) Climate Models Primary Source: IPCC WG-I Chapter 8 - Climate Models.
GIST 24/10/06 Jeff Settle, ESSC Reconciling Point and Area Data for RADAGAST GIST 24 October 2006.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss The Latent Heat Nudging Scheme of COSMO EWGLAM/SRNWP Meeting,
© Crown copyright Met Office Radiation developments Latest work on the radiation code in the Unified Model James Manners, Reading collaboration meeting.
Iterative and constrained algorithms to generate cloud fields with measured properties Victor Venema Clemens Simmer Susanne Crewell Bonn University
Princeton University Global Evaluation of a MODIS based Evapotranspiration Product Eric Wood Hongbo Su Matthew McCabe.
The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology The Effect of Turbulence on Cloud Microstructure,
Initial testing of longwave parameterizations for broken water cloud fields - accounting for transmission Ezra E. Takara and Robert G. Ellingson Department.
A new algorithm for the downscaling of 3-dimensional cloud fields Victor Venema Sebastián Gimeno García Clemens Simmer.
Results of an Adaptive Radiative Transfer Parameterisation for the Lokal-Modell LM-User-Seminar 5 th – 7 th March 2007, Langen Annika Schomburg 1), Victor.
Study of magnetic helicity in solar active regions: For a better understanding of solar flares Sung-Hong Park Center for Solar-Terrestrial Research New.
The Consideration of Noise in the Direct NWP Model Output Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.
4 th COPS Workshop, Hohenheim, 25 – 26 September 2006 Modeling and assimilation efforts at IPM in preparation of COPS Hans-Stefan Bauer, Matthias Grzeschik,
Dr Mark Cresswell Statistical Forecasting [Part 1] 69EG6517 – Impacts & Models of Climate Change.
World Renewable Energy Forum May 15-17, 2012 Dr. James Hall.
SLEPS First Results from SLEPS A. Walser, M. Arpagaus, C. Appenzeller, J. Quiby MeteoSwiss.
Development of WRF-CMAQ Interface Processor (WCIP)
By: Michael Kevin Hernandez Key JTWC ET onset JTWC Post ET  Fig. 1: JTWC best track data on TC Sinlaku (2008). ECMWF analysis ET completion ECMWF analysis.
What is a Climate Model?.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High-resolution data assimilation in COSMO: Status and.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High Resolution Snow Analysis for COSMO
Downscaling and its limitation on climate change impact assessments Sepo Hachigonta University of Cape Town South Africa “Building Food Security in the.
1st Progress meeting ELDAS The ELDORADO surface radiation system Bart vd Hurk & Dirk Meetschen et al (see ELDAS web-page)
Radar in aLMo Assimilation of Radar Information in the Alpine Model of MeteoSwiss Daniel Leuenberger and Andrea Rossa MeteoSwiss.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High resolution COSMO runs for dispersion applications.
Overview of the “Geostationary Earth Radiation Budget (GERB)” Experience. Nicolas Clerbaux Royal Meteorological Institute of Belgium (RMIB) In collaboration.
Mathematics of PCR and CCA Simon Mason Seasonal Forecasting Using the Climate Predictability Tool Bangkok, Thailand, 12 – 16 January.
Update on model development Meteo-France Meteo-France CLOUDNET workshop - Exeter 5-6/04/2004 François Bouyssel.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Accounting for Change: Local wind forecasts from the high-
Sensitivity Analysis of Mesoscale Forecasts from Large Ensembles of Randomly and Non-Randomly Perturbed Model Runs William Martin November 10, 2005.
The climate and climate variability of the wind power resource in the Great Lakes region of the United States Sharon Zhong 1 *, Xiuping Li 1, Xindi Bian.
Georgia Institute of Technology Initial Application of the Adaptive Grid Air Quality Model Dr. M. Talat Odman, Maudood N. Khan Georgia Institute of Technology.
OWEMES 2006, Civitavecchia, Italy Accuracy of Short-Term Predictions for 25 GW Offshore Wind Power in Germany Jens Tambke, L. v. Bremen, N. Saleck, U.
BBC Workshop DeBilt, 18.–19. October 2004 Reconstruction of three dimensional cloud fields from two dimensional input datasets Klemens Barfus & Franz H.
© Imperial College LondonPage 1 Cloud Forcing Studies using CERES and GERB(-like) data Joanna Futyan, Jacqui Russell and John Harries GIST 22 RMIB, Brussels,
Retrieval of Moisture from GPS Slant-path Water Vapor Observations using 3DVAR and its Impact on the Prediction of Convective Initiation and Precipitation.
Testing LW fingerprinting with simulated spectra using MERRA Seiji Kato 1, Fred G. Rose 2, Xu Liu 1, Martin Mlynczak 1, and Bruce A. Wielicki 1 1 NASA.
10 th COSMO General Meeting, Krakow, September 2008 Recent work on pressure bias problem Lucio TORRISI Italian Met. Service CNMCA – Pratica di Mare.
Page 1© Crown copyright 2004 SRNWP Lead Centre Report on Data Assimilation 2005 for EWGLAM/SRNWP Annual Meeting October 2005, Ljubljana, Slovenia.
Application of an adaptive radiative transfer parameterisation in a mesoscale numerical weather prediction model DWD Extramural research Annika Schomburg.
RMIB involvement in the Geostationary Earth Radiation Budget (GERB) and Climate Monitoring SAF projects Nicolas Clerbaux Remote sensing from Space Division.
Forecasting Fine-Grained Air Quality Based on Big Data Date: 2015/10/15 Author: Yu Zheng, Xiuwen Yi, Ming Li1, Ruiyuan Li1, Zhangqing Shan, Eric Chang,
Developement of exact radiative transfer methods Andreas Macke, Lüder von Bremen, Mario Schewski Institut für Meereskunde, Uni Kiel.
LM-PAFOG: three dimensional fog forecasting with the “Lokal Modell” of the German Weather Service Matthieu Masbou 1,2 & Andreas Bott 1 1 Meteorological.
Statistical Postprocessing of Surface Weather Parameters Susanne Theis Andreas Hense Ulrich Damrath Volker Renner.
15 th EMS Annual Meeting & 12 th European Conference on Applications of Meteorology, 7-11 September 2015, Sofia, Bulgaria Introduction We present a sophisticated.
Development and testing of the moist second-order turbulence-convection model including transport equations for the scalar variances Ekaterina Machulskaya.
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Component testing of the COSMO model’s turbulent diffusion.
An Overview of Satellite Rainfall Estimation for Flash Flood Monitoring Timothy Love NOAA Climate Prediction Center with USAID- FEWS-NET, MFEWS, AFN Presented.
Cloud Fraction from Cloud Mask vs Total Sky Imager Comparison of 1 and 4 km Data The native resolution of the vis channel on GOES Imager is roughly 1 km.
A physical initialization algorithm for non-hydrostatic NWP models using radar derived rain rates Günther Haase Meteorological Institute, University of.
Downscaling Global Climate Model Forecasts by Using Neural Networks Mark Bailey, Becca Latto, Dr. Nabin Malakar, Dr. Barry Gross, Pedro Placido The City.
Département fédéral de l‘intérieur DFI Office fédéral de météorologie et de climatologie MétéoSuisse Postprocessing methods Pierre Eckert MeteoSwiss, Geneva.
Limitations of ’column physics’ for radiation computations in atmospheric models Bent H Sass Danish Meteorological Institute 1 May 2009 As the horizontal.
Daiwen Kang 1, Rohit Mathur 2, S. Trivikrama Rao 2 1 Science and Technology Corporation 2 Atmospheric Sciences Modeling Division ARL/NOAA NERL/U.S. EPA.
Development of the two-equation second-order turbulence-convection model (dry version): analytical formulation, single-column numerical results, and.
Investigating Cloud Inhomogeneity using CRM simulations.
WindNinja Model Domain/Objective
A. Topographic radiation correction in COSMO: gridscale or subgridscale? B. COSMO-2: convection resolving or convection inhibiting model? Matteo Buzzi.
Peter Lean1 Suzanne Gray1 Peter Clark2
transport equations for the scalar variances
interannual variability and the impact of cloud cover
Presentation transcript:

Two adaptive radiation parameterisations Annika Schomburg 1), Victor Venema 1), Felix Ament 2), Clemens Simmer 1) 1) Department of Meteorology, University of Bonn, Germany 2) MeteoSwiss, Switzerland

Introduction Today: accurate radiation schemes used in weather- prediction models -> computationally expensive Problem: radiative fluxes can not be updated at each time-step, are kept constant in between Well justified practice for large-scale models, where no large cloud cover changes on timescale of update interval Assumption of persistence is not suitable for models with a horizontal grid spacing of few kilometres

Adaptive parameterisation: Scheme I (Temporal scheme) calculate error- estimator based on a simple radiation scheme for each grid point Grid points where… …Δ ‘large‘ …Δ ‘ small‘ Apply „perturbation method“ for surface fluxes Recalculate 3D-radiation fluxes with exact scheme Perturbation method:

Simple radiation scheme: → Multivariate linear regression Predictands: – longwave: – shortwave: transmissivity: Distinction of 4 categories, with different sets of predictors: solar cloud free infrared cloud free solar cloudy infrared cloudy Scheme I (Temporal scheme)

Adaptive RT parameterisation II: Spatial Scheme uses spatial correlations update every 5 minutes one out of 4x4 columns for other 15 columns: search for similar column in the vicinity (search region 7x7 pixels) similarity index to be minimised:

The Model: Cosmo-LM Non hydrostatic Horizontal resolution: –Operational: 7km –Here: 2.8km Updating of radiation scheme once per forecast- hour Radiation Scheme of the LM (Ritter and Geyleyn 1992) Delta-Two-Stream Approximation Three intervals in the solar part of the spectrum and five intervals in the thermal part Model-domain Case study: 19th September 2001, a day characterised by much convection

RMSE for 12:30: Solar

RMSE for 12:30: Infrared

Results: Improvements of model consistency Total surface net flux: solar + IR [W/m²] 21 June 2004 Adaptive approach leads to a considerable reduction of unrealistic situations 1h-update 2.5 min- update Adaptive Median and 0.25 quantiles

RMS error as function of relative number of intrinsic calculations The number of calls to the δ-two-stream scheme is normalised by the number of calculations for the full field once per hour. The blue dotted line denotes the spatial scheme with the weights of the standard scheme. The red line designates the spatial scheme where the weights are optimised for each number of function calls. solarinfrared

Conclusions Adaptive Schemes significantly reduce RMSE: –SW: 44% for temporal scheme, 60% for spatial scheme –LW: 39% for temporal scheme, 58% for spatial scheme Smaller correlation length of error fields Significant reductions of exact calculations leads only to small increases of errors –This increase in computational efficiency can be utilised to employ more complex parameterisation schemes

Outlook Implement both schemes in model itself Perform full day case studies Other simple radiation scheme instead of regression : –very simple physical scheme –neural network –or online learning regression Application to whole vertical column, not only to surface fluxes Combine both schemes Application to other parts of model physics

Thank you for your attention! For further information see also: