Development of a one-dimensional version of the Hirlam-model in Sweden Background: This model has been run operationally for about nine years now. Mainly.

Slides:



Advertisements
Similar presentations
Heat and Air Temperature
Advertisements

Weather Part 2.
Institut für Meteorologie und Klimatologie Universität Hannover
Hirlam Physics Developments Sander Tijm Hirlam Project leader for physics.
Training course: boundary layer IV Parametrization above the surface layer (layout) Overview of models Slab (integral) models K-closure model K-profile.
Meteorology is the study of weather, weather phenomena, and weather forecasting. It uses two kinds of observations: Qualitative: not based on numbers.
Lidar-Based Microphysical Retrievals During M-PACE Gijs de Boer Edwin Eloranta The University of Wisconsin - Madison ARM CPMWG Meeting, October 31, 2006.
Section 04 Thermodynamics Adiabatic Processes Lesson 10/11.
Fog Forecasting at Roissy Airport (Paris) with 1D model Thierry Bergot and Joël Noilhan Météo-France 1) Methodology Cobel : 1D model of boundary layer.
Welcome to Weather Science Jeopardy GeneralKnowledge Weather Factors I Weather Factors II ForecastingTools Final Jeopardy.
The Rain Shadow Our Learning Target:
Introduction to surface ocean modelling SOPRAN GOTM School Warnemünde: Hans Burchard Baltic Sea Research Institute Warnemünde, Germany.
Rapid Update Cycle Model William Sachman and Steven Earle ESC452 - Spring 2006.
1 NGGPS Dynamic Core Requirements Workshop NCEP Future Global Model Requirements and Discussion Mark Iredell, Global Modeling and EMC August 4, 2014.
Current issues in GFS moist physics Hua-Lu Pan, Stephen Lord, and Bill Lapenta.
MDSS Challenges, Research, and Managing User Expectations - Weather Issues - Bill Mahoney & Kevin Petty National Center for Atmospheric Research (NCAR)
Lapse Rates and Stability of the Atmosphere
Fronts and Masses UNIT 4, LESSON 3. Warm Up – November 5 Right Now.
THE EFFECT OF THE SURFACE CHARACTERISTICS ON THE DICE RESULTS SEEN BY THE MESONH MODEL M. A. Jiménez, P. Le Moigne and J. Cuxart DICE workshop, October.
Overview of the present HIRLAM surface assimilation Mainly taken from: HIRLAM Technical Report No. 58.
Evaporation Slides prepared by Daene C. McKinney and Venkatesh Merwade
Moisture, Clouds, and Precipitation
Mesoscale Modeling Review the tutorial at: –In class.
Moisture, Clouds, and Precipitation
Moisture, Clouds, and Precipitation. Water in the Atmosphere  Precipitation is any form of water that falls from a cloud.  When it comes to understanding.
Verification and Case Studies for Urban Effects in HIRLAM Numerical Weather Forecasting A. Baklanov, A. Mahura, C. Petersen, N.W. Nielsen, B. Amstrup Danish.
Status report from the Lead Centre for Surface Processes and Assimilation E. Rodríguez-Camino (INM) and S. Gollvik (SMHI)
Chapter 09 Fog Formation Lesson 30/31 Types of Fog Radiation Fog Smoke Fog (Smog) Advection Fog Thaw Fog Arctic Sea Smoke (Steam Fog) Frontal Fog Hill.
Météo-France / CNRM – T. Bergot 1) Introduction 2) The methodology of the inter-comparison 3) Phase 1 : cases study Inter-comparison of numerical models.
Condensation in the Atmosphere The atmosphere contains a mixture of dry air and a variable amount of water vapor (0-4% or 0-30 g/kg) An air parcel is said.
Introduction to Cloud Dynamics We are now going to concentrate on clouds that form as a result of air flows that are tied to the clouds themselves, i.e.
Chapter 5. The Formation of Dew & Frost  Dew forms on objects near the ground surface when they cool below the dew point temperature. More likely on.
Modeling the Atmospheric Boundary Layer (2). Review of last lecture Reynolds averaging: Separation of mean and turbulent components u = U + u’, = 0 Intensity.
INSTYTUT METEOROLOGII I GOSPODARKI WODNEJ INSTITUTE OF METEOROLOGY AND WATER MANAGEMENT TITLE : IMPLEMENTATION OF MOSAIC APPROACH IN COSMO AT IMWM AUTHORS:
HNMS contribution to CONSENS Petroula Louka & Flora Gofa Hellenic National Meteorological Service
TOPIC 7. What is weather? Weather is the state or condition of the variables of the atmosphere at any given location for a short period of time.
Evaluating forecasts of the evolution of the cloudy boundary layer using radar and lidar observations Andrew Barrett, Robin Hogan and Ewan O’Connor Submitted.
Improved road weather forecasting by using high resolution satellite data Claus Petersen and Bent H. Sass Danish Meteorological Institute.
Henning Gisselø Danish Meteorological Institute in Copenhagen Operational forecaster for 17 years.
T. Bergot - Météo-France CNRM/GMME 1) Methodology 2) Results for Paris-CdG airport Improved site-specific numerical model of fog and low clouds -dedicated.
Low stratus (and fog) forecast for Central Europe introducing an empirical enhancement scheme for sub-inversion cloudiness Harald Seidl and Alexander Kann.
Simulating Stratocumulus clouds sensitivity to representation of: Drizzle Cloud top entrainment Cloud-Radiation interaction Large scale subsidence Vertical.
Lesson 1-1 Weather is the atmospheric conditions, along with short-term changes, of a certain place at a certain time.Weather Weather can change quickly.
HYDROMETCENTRE of RUSSIA Gdaly Rivin STC COSMO September 2008, Krakow, Poland Hydrometeorological centre of Russian Federation.
Deutscher Wetterdienst COSMO-ICON Physics Current Status and Plans Ulrich Schättler Source Code Administrator COSMO-Model.
Unit 7: Severe Weather Lecture 1 Objectives: E4.3f - Describe how mountains, frontal edging (including dry lines) convection, and convergence form clouds.
Atmospheric Moisture. Water in the Atmosphere Water vapor is the source of all condensation and precipitation Essentially all water on Earth is conserved.
The Arctic boundary layer: Characteristics and properties Steven Cavallo June 1, 2006 Boundary layer meteorology.
MODELING OF SUBGRID-SCALE MIXING IN LARGE-EDDY SIMULATION OF SHALLOW CONVECTION Dorota Jarecka 1 Wojciech W. Grabowski 2 Hanna Pawlowska 1 Sylwester Arabas.
A Case Study of Decoupling in Stratocumulus Xue Zheng MPO, RSMAS 03/26/2008.
Météo-France / CNRM – T. Bergot 1) Methodology 2) The assimilation procedures at local scale 3) Results for the winter season Improved Site-Specific.
Evaporation What is evaporation? How is evaporation measured? How is evaporation estimated? Reading for today: Applied Hydrology Sections 3.5 and 3.6 Reading.
Atmospheric Stability and Air Masses
Radiative-Convective Model. Overview of Model: Convection The convection scheme of Emanuel and Živkovic-Rothman (1999) uses a buoyancy sorting algorithm.
Chapter 18 Moisture, Clouds, & Precipitation Water in the Atmosphere When it comes to understanding atmospheric processes, water vapor is the most.
Hirlam1D; Statusreport Esbjörn Olsson SMHI/Sundsvall Presented at: Cost 722 Larnaca meeting may 2005.
A revised formulation of the COSMO surface-to-atmosphere transfer scheme Matthias Raschendorfer COSMO Offenbach 2009 Matthias Raschendorfer.
Water Vapor and Humidity D Where does water vapor come from?  When warm air touched cold glass, the air cools and droplets form  Water Vapor 
Meteorological Variables 1. Local right-hand Cartesian coordinate 2. Polar coordinate x y U V W O O East North Up Dynamic variable: Wind.
Condensation in the Atmosphere
Moisture, Clouds and Precipitation Standards: Concept 2: PO 14
Chapter 3 Thermodynamics.
Condensation in the Atmosphere
4.6 Investigating Weather
Radiation Fog Forecasting Using a 1-D Model
Matthias Raschendorfer 2007
Session 6: Atmospheric Boundary Layer
NRL POST Stratocumulus Cloud Modeling Efforts
WEATHER Unit 1b.
A buoyancy-based turbulence mixing length technique for cloudless and cloudy boundary layers in the COSMO model Veniamin Perov and Mikhail Chumakov.
Presentation transcript:

Development of a one-dimensional version of the Hirlam-model in Sweden Background: This model has been run operationally for about nine years now. Mainly for TAF-production, but also for other types of site- specific forecasting. The model is regarded as a useful tool in the forecast production, but of course there are occasions where it is not performing that well. The most problems lies of course in predicting the boundary layer structures (fog/low clouds). During the years the model has been updated at a number of occasions with new parameterisations developed within the Hirlam project.

Development of a one-dimensional version of the Hirlam-model in Sweden New update for the model: Surface parameterisation. An updated version of the ISBA surface scheme will be introduced to the 1D-model. In the original scheme each gridpoint is divided in five different tiles (water, ice, open land, open land with low vegetation and forest). For each of these tiles there is a separate calculation of all the surface fluxes of heat, moisture and momentum. The different fluxes are then area-weighted together to give the model the lower boundary conditions. The ISBA-scheme has not performed that well when the ground is covered with snow, so the scheme has recently been revised in Sweden with a new treatment of the snow. This is done by introducing a sixth tile; snow. The snow-covered parts of the tiles open land and open land with low vegetation are transferred to this snow-tile and separate calculation is done for this new tile.

Development of a one-dimensional version of the Hirlam-model in Sweden Surface parameterisation cont. Tests have shown that with this scheme it is possible for the model to better describe the extremely low temperatures that sometimes occurs over the snow-covered parts of northern Scandinavia. Turbulence For vertical diffusion processes in Hirlam there is at present a scheme that treats the turbulent kinetic energy (TKE) as a separate variable in the model. In this scheme the vertical mixing of the variables, temperature, moisture and cloud water is treated separately. In an updated moist variant of the scheme the mixing is done on the variables liquid potential temperature and total water content.

Development of a one-dimensional version of the Hirlam-model in Sweden Turbulence cont. These variables are conserved under non-precipitating moist adiabatic mixing. The primary benefit is that in-cloud buoyancy effect on TKE is incorporated so that cloudy turbulence is also properly predicted. The cloudy profiles are well mixed, i.e. they follow moist adiabats. This new moist turbulence scheme requires a very high vertical resolution, initially about 150 levels will be used with a 30-m resolution in the boundary layer. These two significant upgrades to the one-dimensional model will be implemented shortly and forthcoming tests will show how much better the model will be on forecasting fog and low clouds.

New Hirlam 1D-model Status report Dataassimilation (ISBA) using observed t2m and q2m. –Same as 3D-model. –Gives new values of surface temp and moisture. –Seems to be working well so far. Using observation to modify initial profile. –How to relate observed cloud amount with cloud water and specific humidity in the model? –Cloud tops?

New Hirlam 1D-model Status report New surface scheme with special snow treatment. –Seems to be performing well. –Snow evaporation? –A bit cold on sunny spring days. New turbulence scheme. –Using 140 vertical levels. –Still some tuning to do. –Problems with partial cloudiness.

New Hirlam 1D-model Vertical levels

New Hirlam 1D-model