CURRENT DEVELOPMENTS OF LAPS INGEST PROCESSES AT METEOCAT Area of Applied Research and Modelling – SMC Jordi More & Abdel Sairouni.

Slides:



Advertisements
Similar presentations
WRF Modeling System V2.0 Overview
Advertisements

LAPS Analysis & Software by Steve Albers. 2 Basic Solution LAPS coupled with MM5 NWP model Use diabatic initialization (“hot start”) Utilize parallel.
Towards Rapid Update Cycling for Short Range NWP Forecasts in the HIRLAM Community WMO/WWRP Workshop on Use of NWP for Nowcasting UCAR Center Green Campus,
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss The Latent Heat Nudging Scheme of COSMO EWGLAM/SRNWP Meeting,
Improved Simulations of Clouds and Precipitation Using WRF-GSI Zhengqing Ye and Zhijin Li NASA-JPL/UCLA June, 2011.
S4D WorkshopParis, France Polar Meteorology Group, Byrd Polar Research Center, The Ohio State University, Columbus, Ohio A High-Resolution David H. Bromwich.
Assimilating Sounding, Surface and Profiler Observations with a WRF-based EnKF for An MCV Case during BAMEX Zhiyong Meng & Fuqing Zhang Texas A&M University.
Hector simulation We found simulation largely depending on: Model initialization scheme Lateral boundary conditions Physical processes represented in the.
ECMWF – 1© European Centre for Medium-Range Weather Forecasts Developments in the use of AMSU-A, ATMS and HIRS data at ECMWF Heather Lawrence, first-year.
Improving Cloud Simulation in Weather Research and Forecasting (WRF) Through Assimilation of GOES Satellite Observations Andrew White Advisor: Dr. Arastoo.
Dr Mark Cresswell Model Assimilation 69EG6517 – Impacts & Models of Climate Change.
Incorporation of TAMDAR into Real-time Local Modeling Tom Hultquist Science & Operations Officer NOAA/National Weather Service Marquette, MI.
Operational Global Model Plans John Derber. Timeline July 25, 2013: Completion of phase 1 WCOSS transition August 20, 2013: GDAS/GFS model/analysis upgrade.
Storm-scale data assimilation and ensemble forecasting for Warn-on- Forecast 5 th Warn-on-Forecast Workshop Dusty Wheatley 1, Kent Knopfmeier 2, and David.
Details for Today: DATE:18 th November 2004 BY:Mark Cresswell FOLLOWED BY:Literature exercise Model Assimilation 69EG3137 – Impacts & Models of Climate.
The Sensitivity of a Real-Time Four- Dimensional Data Assimilation Procedure to Weather Research and Forecast Model Simulations: A Case Study Hsiao-ming.
CODAR Ben Kravitz September 29, Outline What is CODAR? Doppler shift Bragg scatter How CODAR works What CODAR can tell us.
10° COSMO GENERAL MEETING Plans and State of Art at USAM/CNMCA Massimo Ferri.
Meso-γ 3D-Var Assimilation of Surface measurements : Impact on short-range high-resolution simulations Geneviève Jaubert, Ludovic Auger, Nathalie Colombon,
Oct October 2008 An update provided by Thomas Nehrkorn Atmospheric & Environmental Research, Inc. 131 Hartwell Ave Lexington, Massachusetts
Mesoscale model support for the 2005 MDSS demonstration Paul Schultz NOAA/Earth System Research Laboratory Global Systems Division (formerly Forecast Systems.
IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Allen Zhao 1, John Cook 1, Qin Xu 2, and.
Improvement of Short-term Severe Weather Forecasting Using high-resolution MODIS Satellite Data Study of MODIS Retrieved Total Precipitable Water (TPW)
Non-hydrostatic Numerical Model Study on Tropical Mesoscale System During SCOUT DARWIN Campaign Wuhu Feng 1 and M.P. Chipperfield 1 IAS, School of Earth.
LAPS __________________________________________ Analysis and nowcasting system for Finland/Scandinavia Finnish Meteorological Institute Erik Gregow.
Simulation of Intense Convective Precipitation Observed during ARMEX Someshwar Das, R. Ashrit, M. Dasgupta, Someshwar Das 1, R. Ashrit 1, M. Dasgupta 1,
WINDSOR TORNADO STUDY - STMAS (updated 9/16/2009 by Huiling Yuan and Yuanfu Xie)
AMB Verification and Quality Control monitoring Efforts involving RAOB, Profiler, Mesonets, Aircraft Bill Moninger, Xue Wei, Susan Sahm, Brian Jamison.
Meteorological Data Analysis Urban, Regional Modeling and Analysis Section Division of Air Resources New York State Department of Environmental Conservation.
P1.85 DEVELOPMENT OF SIMULATED GOES PRODUCTS FOR GFS AND NAM Hui-Ya Chuang and Brad Ferrier Environmental Modeling Center, NCEP, Washington DC Introduction.
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.
Application of Cloud Analysis in GRAPES_RAFS Lijuan ZHU [1], Dehui CHEN [1], Zechun LI [1], Liping LIU [2], Zhifang XU [1], Ruixia LIU [3] [1] National.
WRF Four-Dimensional Data Assimilation (FDDA) Jimy Dudhia.
Status of improving the use of MODIS, AVHRR, and VIIRS polar winds in the GDAS/GFS David Santek, Brett Hoover, Sharon Nebuda, James Jung Cooperative Institute.
Improved road weather forecasting by using high resolution satellite data Claus Petersen and Bent H. Sass Danish Meteorological Institute.
5 th ICMCSDong-Kyou Lee Seoul National University Dong-Kyou Lee, Hyun-Ha Lee, Jo-Han Lee, Joo-Wan Kim Radar Data Assimilation in the Simulation of Mesoscale.
Evaluation of radiance data assimilation impact on Rapid Refresh forecast skill for retrospective and real-time experiments Haidao Lin Steve Weygandt Stan.
MCS Introduction Where? Observed reflectivity at 3km from
The Hyperspectral Environmental Suite (HES) and Advanced Baseline Imager (ABI) will be flown on the next generation of NOAA Geostationary Operational Environmental.
Assimilation of Lightning Data Using a Newtonian Nudging Method Involving Low-Level Warming Max R. Marchand Henry E. Fuelberg Florida State University.
MSG cloud mask initialisation in hydrostatic and non-hydrostatic NWP models Sibbo van der Veen KNMI De Bilt, The Netherlands EMS conference, September.
August 6, 2001Presented to MIT/LL The LAPS “hot start” Initializing mesoscale forecast models with active cloud and precipitation processes Paul Schultz.
Low-level Wind Analysis and Prediction During B08FDP 2006 Juanzhen Sun and Mingxuan Chen Other contributors: Jim Wilson Rita Roberts Sue Dettling Yingchun.
A physical initialization algorithm for non-hydrostatic NWP models using radar derived rain rates Günther Haase Meteorological Institute, University of.
Instruments. In Situ In situ instruments measure what is occurring in their immediate proximity. E.g., a thermometer or a wind vane. Remote sensing uses.
Local Analysis and Prediction System (LAPS) Technology Transfer NOAA – Earth System Research Laboratory Steve Albers, Brent Shaw, and Ed Szoke LAPS Analyses.
Local Analysis and Prediction System (LAPS) Purpose/HistoryDesign Implementation Details Dan Birkenheuer Forecast Systems Lab Boulder, CO
15 June 2005RSA TIM – Boulder, CO Hot-Start with RSA Applications by Steve Albers.
WINDSOR TORNADO STUDY - STMAS (updated 10/21/2009 by Huiling Yuan and Yuanfu Xie)
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Combining GOES Observations with Other Data to Improve Severe Weather Forecasts.
Applied Meteorology Unit 1 Observation Denial and Performance of a Local Mesoscale Model Leela R. Watson William H. Bauman.
2. WRF model configuration and initial conditions  Three sets of initial and lateral boundary conditions for Katrina are used, including the output from.
Coordination Group for Meteorological Satellites - CGMS Korea Meteorological Administration, May 2015 Satellite Data Application in KMA’s NWP Systems Presented.
Indirect impact of ozone assimilation using Gridpoint Statistical Interpolation (GSI) data assimilation system for regional applications Kathryn Newman1,2,
STMAS/LAPS for Convective Weather Analysis and Short-range Forecast
Xuexing Qiu and Fuqing Dec. 2014
Numerical Weather Forecast Model (governing equations)
Analysis and forecasting system for Finland/Scandinavia
WRF Four-Dimensional Data Assimilation (FDDA)
WRF model runs of 2 and 3 August
Better Forecasting Bureau
Daniel Leuenberger1, Christian Keil2 and George Craig2
Winter storm forecast at 1-12 h range
Local Analysis and Prediction System (LAPS)
Radiation fogs: WRF and LES numerical experiments
Exploring Application of Radio Occultation Data in Improving Analyses of T and Q in Radiosonde Sparse Regions Using WRF Ensemble Data Assimilation System.
Real-time WRF EnKF 36km outer domain/4km nested domain D1 (36km)
2007 Mei-yu season Chien and Kuo (2009), GPS Solutions
Start Hot-Start Section
Status of the Regional OSSE for Space-Based LIDAR Winds – Feb01
Presentation transcript:

CURRENT DEVELOPMENTS OF LAPS INGEST PROCESSES AT METEOCAT Area of Applied Research and Modelling – SMC Jordi More & Abdel Sairouni

Boulder, Colorado, October 2010 Introducció INTRODUCTION METEOCAT: METEOROLOGICAL SERVICE OF CATALONIA BARCELONA MEDITERRANEAN SEA CATALONIA: ~ km 2 ~ population of 7 milion

Boulder, Colorado, October 2010 Introducció LAPS CONFIGURATION Ingested data: LAPS SYSTEM MM5 METAR RADAR MSG AWS RAOB

Boulder, Colorado, October 2010 Introducció LAPS CONFIGURATION LAPS SYSTEM: Version: LAPS (with some modifications) Domains: 36 km & 12 km (22 levels) Platform: SGI Altix 350 with 24 processors, IFORT compiler Background: MM5 coarse (36 km, 26 levels) MM5 nested (12 km, 30 levels) DX = 36 km DX = 12 km

Boulder, Colorado, October 2010 Background model (LFMPOST.EXE): Modified interface for MM5 assimilation. Changes in the MSLP calculation using routines of MM5 (not included in LAPS versions). Surface (METARS, AWS) and Upper air data (RAOB ): New routines to process observational data and write LSO and SND files. Radar data (IRIS FORMAT): New interface for IRIS Doppler radar assimilation (reflectivity and radial velocity) using RSL (NASA TRMM). Data from 4 Radar (C band) available: LAPS MODIFICATIONS: INGEST

Boulder, Colorado, October 2010 LAPS MODIFICATIONS: INGEST SATELITE DATA (MSG 9): LAPS was conceived to use satellite data: GOES12 MSG (Meteosat Second Generation) requires major changes because: Different channels in both satellites. Different resolution at sub-satellite point and the zenith angle. Satellite data obtained from MSG (five channels are obtained): Visible, IR(12 micron), IR(3.9 micron), IR(10.8 micron) and Water Vapour(6.2 micron)

Boulder, Colorado, October 2010 LAPS MODIFICATIONS: CLOUD CLOUD ANALYSIS: Modification routines to process satellite and radar: Cloud top Original LAPS Modified LAPS Terrain T(klaps-3) T(klaps) T(klaps-1) T(klaps-2) kterrain What if Tsat < T(klaps)? frac=T(klaps) – T(klaps-1)/ Tsat – T(klaps) arg=H(klaps-1)+frac*(H(klaps)-H(klaps-1) Tsat Z(klaps) Z(klaps-1) Z(klaps-2) Z(klaps-3)

Boulder, Colorado, October 2010 CLOUD ANALYSIS: 09/13/2006 (00UT) Meteosat IR (10.8u)Meteosat IR (3.9u)Radar Ref. (700 hPa) CLOUD COVERCLOUD BASECLOUD TOP OBS. LAPS

Boulder, Colorado, October 2010 LAPS MODIFICATIONS: CLOUD CLOUD ANALYSIS EXAMPLE: Cloud top Original LAPS Modified LAPS 1.e-30

Boulder, Colorado, October 2010 CLOUD ANALYSIS: 09/13/2006 (00UT) Meteosat IR (10.8u)Meteosat IR (3.9u) CLOUD COVERCLOUD BASECLOUD TOP OBS. MOD. C.T. Radar Ref. (700 hPa)

Cloud base_init= cloud_top m Boulder, Colorado, October 2010 LAPS MODIFICATIONS: CLOUD CLOUD ANALYSIS: Modification routines to process satellite and radar: Cloud base Original LAPS Modified LAPS Cloud top Cloud base init Terrain 1500 m Terrain Cloud top LCL base Cloud base_init = f(cloud top,LCL)

Boulder, Colorado, October 2010 CLOUD ANALYSIS: 09/13/2006 (00UT) Meteosat IR (10.8u)Meteosat IR (3.9u) CLOUD COVERCLOUD BASECLOUD TOP OBS. MOD. C.T. & C.B. Radar Ref. (700 hPa)

Boulder, Colorado, October 2010 LAPS MODIFICATIONS: WIND WIND ANALYSIS: Minor modification in multiwind_noZ.f for radar wind computation (only when multi-radar n=4) RADAR WIND OBSERVATIONS RADAR POSITION 850 hPa700 hPa500 hPa

Boulder, Colorado, October 2010 WIND ANALYSIS: 03/03/2010 (12UT) 850 hPa 500 hPa CONV. OBS.+ RAD. WIND+ BALANCE

Boulder, Colorado, October 2010 WIND ANALYSIS: 03/03/2010 (12UT) ZONAL WIND COMPONENT (m/s) MERIDIONAL WIND COMPONENT (m/s) MERIDIONAL WIND COMPONENT (m/s) VERIFICATION: BCN SOUNDING WITH BCN SOUND WITHOUT ZONAL WIND COMPONENT (m/s)

Boulder, Colorado, October 2010 OPERATIONAL LAPS Observational data operationally assimilated: - MSG (1)(1)lvd - RADAR (4)(4)vrz, v01, v02, v03, v04 - METAR (~35)(~400)lso - RAOB (~5)(~50)snd - AWS (~200)lso - NWPMM5-12kmMM5-36kmlga, lgb Coupling LAPS with MM5 procedures: WARM:update every 3 hours (nudging) HOT:update every 1 hour LAPS 12 km # obs LAPS 36 km # obs LAPS intermediate files

Boulder, Colorado, October 2010 OPERATIONAL LAPS LAPS-MM5 36 km (BC) LAPS-MM5 12 km (3h-Nudging) Nudging LAPS LAPS LAPS-MM5 36 km (BC) LAPS-MM5 12 km (3h-Nudging) LAPS-MM5 12 km WARM HOT

Boulder, Colorado, October 2010 LAPS CURRENT MODIFICATIONS Impact of LAPS to improve short-term QPF : Case study 13 September 2006 OBS. RADARMM5 (control)MM5-LAPS (3h-nudging) Z Z OPERATIONAL LAPS

Boulder, Colorado, October 2010 LAPS CURRENT MODIFICATIONSOPERATIONAL LAPS MM5 (control) HOT (MM5-LAPS) WARM (3h Nudge) MM5 (control) HOT (MM5-LAPS) WARM (3h Nudge) Impact of LAPS to improve short-term QPF : Case study 13 September 2006

Boulder, Colorado, October 2010 CONCLUSIONS & FUTURE WORK Summary & Future work: Modifications in cloud top and cloud base calculation improve the quality of cloud cover and HUMIDITY analysis. A necessary effort is needed to improve the CLOUD analysis (cloud base). LAPS system coupled with MM5 produce better results in QPF during the first 6 hours. Radial velocity wind from radar data sometimes can deteriorate the WIND analysis. Some improvements are needed. LAPS upgrades are difficult due to the modifications required to ingest MSG satellite data.

Boulder, Colorado, October 2010 CONCLUSIONS & FUTURE WORK Simulations in testing phase: Increase resolution in LAPS domain (4 km). Coupling LAPS with WRF. Test ECMWF as a background in the LAPS system. Quality control in radar wind: long-term verification. Study LAPS surface analysis when using AWS local network. On-line verification of QPF using LAPS-MM5 system (fuzzy verificaction).

Boulder, Colorado, October 2010 THE END THANK YOU !