The Sea Surface Temperature in operational NWP model ALADIN

Slides:



Advertisements
Similar presentations
Basics of numerical oceanic and coupled modelling Antonio Navarra Istituto Nazionale di Geofisica e Vulcanologia Italy Simon Mason Scripps Institution.
Advertisements

Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.
Coastal Altimetry Meeting, Silver Spring, 5-7 February 2008Page n° 1/19 The wet tropospheric correction for coastal altimetry T. Strub S. Brown F. Mercier.
NOAA/NWS Change to WRF 13 June What’s Happening? WRF replaces the eta as the NAM –NAM is the North American Mesoscale “timeslot” or “Model Run”
M.Vicomte (1),C. Claud (1), M. Rojo (1), P.-E. Mallet (1), T. Laffineur (2) (1)CNRS/IPSL/LMD, Palaiseau, France (UPMC) (2) ENM, Météo France, Toulouse,
For the Lesson: Eta Characteristics, Biases, and Usage December 1998 ETA-32 MODEL CHARACTERISTICS.
Landslide Susceptibility Mapping to Inform Land-use Management Decisions in an Altered Climate Muhammad Barik and Jennifer Adam Washington State University,
Indirect Determination of Surface Heat Fluxes in the Northern Adriatic Sea via the Heat Budget R. P. Signell, A. Russo, J. W. Book, S. Carniel, J. Chiggiato,
Outline Further Reading: Chapter 05 of the text book - continental vs. marine regimes - temperature structure of the atmosphere - seasonal variations Natural.
Ensemble Post-Processing and it’s Potential Benefits for the Operational Forecaster Michael Erickson and Brian A. Colle School of Marine and Atmospheric.
1 Improved Sea Surface Temperature (SST) Analyses for Climate NOAA’s National Climatic Data Center Asheville, NC Thomas M. Smith Richard W. Reynolds Kenneth.
CARPE DIEM Centre for Water Resources Research NUID-UCD Contribution to Area-3 Dusseldorf meeting 26th to 28th May 2003.
Meteorological and Hydrological Service, Grič 3, HR Zagreb, Croatia FORECASTING BORA WIND AFTER THE COLD FRONT PASSAGE AT.
ALADIN/RC LACE Data Assimilation Mini-Workshop, Budapest, October 20 th -22 th Smoothing of Soil Wetness Index (SWI) in ALADIN/LACE domain Stjepan.
Effects of Ocean-Atmosphere Coupling in a Modeling Study of Coastal Upwelling in the Area of Orographically-Intensified Flow Natalie Perlin, Eric Skyllingstad,
Numerical simulations of the severe rainfall in Pula, Croatia, on 25 th September 2010 Antonio Stanešić, Stjepan Ivatek-Šahdan, Martina Tudor and Dunja.
The National Environmental Agency of Georgia L. Megrelidze, N. Kutaladze, Kh. Kokosadze NWP Local Area Models’ Failure in Simulation of Eastern Invasion.
A Comparison of the Northern American Regional Reanalysis (NARR) to an Ensemble of Analyses Including CFSR Wesley Ebisuzaki 1, Fedor Mesinger 2, Li Zhang.
LAPS __________________________________________ Analysis and nowcasting system for Finland/Scandinavia Finnish Meteorological Institute Erik Gregow.
Modeling the upper ocean response to Hurricane Igor Zhimin Ma 1, Guoqi Han 2, Brad deYoung 1 1 Memorial University 2 Fisheries and Oceans Canada.
Use of Dynamical Adaptation in Research Impact Studies Second Workshop on Statistical and Dynamical Adaptation May 2003, Vienna, Austria Martina.
26 th EWGLAM & 11 th SRNWP meetings, Oslo, Norway, 4 th - 7 th October 2004 Stjepan Ivatek-Šahdan RC LACE Data Manager Croatian Meteorological and Hydrological.
An ensemble study of HyMeX IOP6 and IOP7a Alan Hally (1,2), Evelyne Richard (1), Véronique Ducrocq (2) (1)LA, University of Toulouse, France (2)CNRM, Météo-France,
Operational ALADIN forecast in Croatian Meteorological and Hydrological Service 26th EWGLAM & 11th SRNWP meetings 4th - 7th October 2004,Oslo, Norway Zoran.
P1.7 The Real-Time Mesoscale Analysis (RTMA) An operational objective surface analysis for the continental United States at 5-km resolution developed by.
Pre-operational testing of Aladin physics Martina Tudor 1, Ivana Stiperski 1, Vlasta Tutiš 1, Dunja Drvar 1 and Filip Vana 2 1 Meteorological and Hydrological.
Improved road weather forecasting by using high resolution satellite data Claus Petersen and Bent H. Sass Danish Meteorological Institute.
Impact of a Last Glacial Maximum sea-level drop on the circulation of the Mediterranean Sea Abstract: During the Last Glacial Maximum (LGM), the global.
Air-sea fluxes over the ITHACA region Grbec, B. and Matić, F. Institute of oceanography and fisheries - Split.
Experience with ROMS for Downscaling IPCC Climate Models 2008 ROMS/TOMS European Workshop, Grenoble, 6-8 October Bjørn Ådlandsvik, Paul Budgell, Vidar.
One-year re-forecast ensembles with CCSM3.0 using initial states for 1 January and 1 July in Model: CCSM3 is a coupled climate model with state-of-the-art.
General Meeting Moscow, 6-10 September 2010 High-Resolution verification for Temperature ( in northern Italy) Maria Stefania Tesini COSMO General Meeting.
NWP models. Strengths and weaknesses. Morten Køltzow, met.no NOMEK
CMEMS Mediterranean MFC. Update on specific development for CMEMS Med-MFC V2 Analysis and Forecast Product 1.Data Assimilation: Grid point EOFs ( * )
Evaluation of cloudy convective boundary layer forecast by ARPEGE and IFS Comparisons with observations from Cabauw, Chilbolton, and Palaiseau  Comparisons.
EVALUATION OF A GLOBAL PREDICTION SYSTEM: THE MISSISSIPPI RIVER BASIN AS A TEST CASE Nathalie Voisin, Andy W. Wood and Dennis P. Lettenmaier Civil and.
The presence of sea ice on the ocean’s surface has a significant impact on the air-sea interactions. Compared to an open water surface the sea ice completely.
Modelling activities at Institute of Oceanography and Fisheries (IOF), Split within ADRICOSM-EXT project Gordana Beg Paklar Institute of Oceanography and.
Analysis of the weather situation prior to the garbage accumulation on the south-eastern Adriatic coast in Croatia Martina Tudor, Meteorological and Hydrological.
Numerical simulations of the severe rainfall in Pula, Croatia, on 25 th September 2010 Antonio Stanešić, Stjepan Ivatek-Šahdan, Martina Tudor and Dunja.
Unit 2 World Geography Review. Relationships Weather vs climate Weather = the state of the atmosphere at any one place or time. (short term) Climate =
High-resolution operational NWP for forecasting meteotsunamis
V. Vionnet1, L. Queno1, I. Dombrowski Etchevers2, M. Lafaysse1, Y
Towards development of a Regional Arctic Climate System Model ---
Monday’s lesson (At the end the lesson you will be able to…) Describe the changes in temperature with height through the lower layers of the atmosphere.
Does nudging squelch the extremes in regional climate modeling?
The Sea Surface Temperature in operational NWP model ALADIN
Smoothing of Soil Wetness Index (SWI) in ALADIN/LACE domain
Meteorological and Hydrological Service of Croatia
High-resolution operational NWP for forecasting meteotsunamis
Performance of ALARO0 baseline in pre-operational testing
ALADIN / HIRLAM 19th Workshop / All-Staff Meeting Utrecht, May 2009
High-resolution air-sea modeling of the Philippines winter monsoon
The ability for the ocean to absorb and store energy from the sun is due to… The transparency of the water that allows the sun’s ray to penetrate deep.
Coupled atmosphere-ocean simulation on hurricane forecast
Regional and Global Ramifications of Boundary Current Upwelling
Meteorological and Hydrological Service of Croatia
Recent changes in the ALADIN operational suite
IMPROVING HURRICANE INTENSITY FORECASTS IN A MESOSCALE MODEL VIA MICROPHYSICAL PARAMETERIZATION METHODS By Cerese Albers & Dr. TN Krishnamurti- FSU Dept.
Jackie May* Mark Bourassa * Current affilitation: QinetiQ-NA
Weather forecasting in a coupled world
Jeff Key*, Aaron Letterly+, Yinghui Liu+
Mark A. Bourassa and Qi Shi
El Niño-Southern Oscillation
RegCM3 Lisa C. Sloan, Mark A. Snyder, Travis O’Brien, and Kathleen Hutchison Climate Change and Impacts Laboratory Dept. of Earth and Planetary Sciences.
Marine Environment Radio-Sonde Verification of High Resolution Mesoscale MM5 Model Runs   OC 3570 Project By LCDR Jimmy Horne.
Understanding and forecasting seasonal-to-decadal climate variations
Factors that Affect Climate
REGIONAL AND LOCAL-SCALE EVALUATION OF 2002 MM5 METEOROLOGICAL FIELDS FOR VARIOUS AIR QUALITY MODELING APPLICATIONS Pat Dolwick*, U.S. EPA, RTP, NC, USA.
A Coastal Forecasting System
Presentation transcript:

The Sea Surface Temperature in operational NWP model ALADIN Martina Tudor, Antonio Stanešić, Stjepan Ivatek-Šahdan and Ivica Janeković 1 Croatian Meteorological and Hydrological Service, Grič3, Zagreb, Croatia 2 Ruđer Bošković Institute, Bijenička 54, Zagreb, Croatia 3 The University of Western Australia, School of Civil, Environmental and Mining Engineering & UWA Oceans Institute, Crawley, WA 6009, Australia SST shift In the first set of experiments, the SST field in the initial file by shifting the SST field uniformly. For each model forecast, the operational SST field is modified by increasing or decreasing SST values by 2K and 5K. These values have been chosen on the basis of evaluation of model and analyzed SST against in situ data. The effect is not uniform, but it also affects precipitation above land, relatively far from the coastline. ABSTRACT Sea surface temperature (SST) influences the model forecast. For example it is important for the correct modelling of land/sea breeze and influences the intensity of precipitation downstream. In operational forecast using numerical weather prediction (NWP) model ALADIN, SST is taken from initial file and remains constant during the model forecast (up to 72 hours). There are two sets of SST fields provided in the coupling files from operational forecasts of IFS and ARPEGE, provided by ECMWF and Meteo-France respectively. In this study we used SST measured in situ on a number of stations in Croatia and Italy. The ARPEGE operational SST analysis combines AVHRR satellite data and in situ measurements in the operational oceanographic model Mercator. The SST from IFS forecast is derived from the Operational Sea Surface Temperature and Sea Ice Analysis (OSTIA) analysis. SST from the Regional Ocean Modelling System (ROMS) was used over the Adriatic Sea with OSTIA analysis over the rest of the Mediterranean. The impact of SST on the intensity and location of intensive rainfall is investigated by using alternative SST fields in the initial conditions, first from ARPEGE and IFS. In the first set of experiments, SST effects on forecast precipitation are analysed by modifying the SST field in the initial file by shifting the SST field uniformly. For each model forecast, the SST field obtained from ARPEGE is modified by increasing or decreasing SST values by 2K and 5K and finally decreasing by 10K for all sea points in the model domain. These values have been chosen on the basis of evaluation of model and analysed SST against in situ data. In the further set of experiments, SST in the model was replaced using OSTIA and MUR analyses as well as ROMS model output. In one experiment we also nudged the SST field towards the measurements in order to test if precipitation forecast can be improved when SST is based on measurements. Here we briefly analyse the SST fields from the coupling files and find that errors in SST over Adriatic can exceed 10K. In reality, Kvarner Bay and Velebit Channel are often much colder than the rest of the Adriatic. In winter, western Adriatic current (WAC) is much colder too. The sea surface is too warm in the model and, consequently, the evaporation is much stronger yielding excess precipitation on the coastal mountains. Turbulent fluxes of heat are also too strong above the sea surface. Colder SST in coastal areas reduces the precipitation on the mountain. Reference Acc. Prec. 24h Prec 2km run SST-5K SST-5K SST-5K Warmer SST yields more precipitation Accumulated 24 hourly prec. until 06 UTC 13 September 2012 for reference run (top), when SST is reduced by 5K (top right) and increased by 5K (right) for 2km resolution forecast initialized at 06 UTC 12 September 2012. SST+5K Map of Adriatic and Italian area with the locations of stations. The names of stations in Croatia are truncated due to large spatial density. The names are explained in the lower left corner in the figure. The background is terrain height from 2km resolution ALADIN file, white means that land-sea mask is zero (sea or lake point in the model). IFS Impact on surface fluxes Warmer sea means warmer surface and less stable atmospheric layer above. The turbulent fluxes depend on atmospheric stability. Less stable atmosphere allows for stronger fluxes. With too warm SST, the fluxes of heat and momentum were too strong. Using more realistic (and in this case colder) SST improved the model fluxes (that were too strong to begin with). a warm bias in SST from IFS Using oper SST Using ROMS SST Accumulated heat fluxes from the reference run in 2km resolution, using SST obtained in the coupling files of the operational IFS (left) and from the run using ROMS model SST and OSTIA analysis (right). SST from the closest sea point from the initial coupling files of ARPEGE (cyan), IFS (pink), when interpolated to the model grid from the closest sea point of OSTIA (blue) and ROMS (green) and measured in-situ (red) for several stations along the western Adriatic coastline during the period from 27th October 2010 to 6 February 2016. Using oper SST Using ROMS SST IFS Example of operational SST from IFS. Accumulated turbulent fluxes from the reference run in 2km resolution, using SST obtained in the coupling files of the operational IFS (left) and from the run using ROMS model SST and OSTIA analysis (right). Measured OSTIA ROMS IFS ARPEGE Impact on precipitation The operational forecast in 2km resolution often produced bogus rainfall over Velebit mountain. A blob of weak to moderate rainfall would be there (in 24 hourly accumulated precipitation) for days and sometimes weeks. Using more realistic SST from OSTIA analysis and ROMS ocean model removed this precipitation. Example of operational SST from ARPEGE. ARPEGE oper OSTIA+ROMS Alternative SST-s Complete report http://radar.dhz.hr/~tudor/sst/sst_coupl_files2.pdf SST analysis OSTIA ~6.5 km SST analysis MUR ~1 km OSTIA has no data in the Velebit channel. MUR treats the islands as the sea surface. Accumulated 24 hourly precipitation forecast from 06 UTC 10 September 2012 from the reference run in 2km resolution, using SST obtained in the coupling files of the operational IFS (left) and from the run using ROMS model SST and OSTIA analysis (right). Warm SST in Velebit channel was the cause of wrong precipitation forecast over Velebit! ROMS characteristics - 2km resolution, 20 S levels - covers only Adriatic so something else needed on the rest of the sea surface and smooth transition at Otranto - details in Janeković et al. JGR-Oceans 2014 SST is colder along coastlines but warmer in the open sea. Higher spatial variability than higher resolution analysis. OPER ROMS OPER-ROMS ROMS model Forecast ~2 km SST at 10th September 2012 from the operational run in 2 km resolution (left) and when SST from ROMS was used over Adriatic (centre) and their difference (OPER-ROMS, right). The SST in the Velebit channel is considerably colder in ROMS.