Ensemble-Based Data Assimilation of GPM/DP R Reflectivity into the Nonhydrostatic Icosahed ral Atmospheric Model NICAM
|
|
- Hope Carr
- 5 years ago
- Views:
Transcription
1 Ensemble-Based Data Assimilation of GPM/DP R Reflectivity into the Nonhydrostatic Icosahed ral Atmospheric Model NICAM Shunji Kotsuki1, Koji Terasaki1, Shigenori Otsuka1, Kenta Kurosawa1, and Takemasa Miyoshi1,2 Data Assimilation Research Team, RIKEN-AICS, Japan 2 University of Maryland, College Park, Maryland, USA 1 6th ISDA, March 09, Ludwig-Maximilians-Universität,
2 Project Overview GPM NICAM LETKF (JAXA) Local Ensemble Transform Kalman Filter (Hunt et al. 2007) Goal: Look for most effective use of GPM precipitation measurements.
3 NICAM-LETKF (NWP) system System developed (Terasaki et al. 2014, SOLA) AMSU-A assimilated (Terasaki and Miyoshi 2018, JMSJ) MHS assimilated (Chandramouli et al., in prep.) GSMaP assimilated (Kotsuki et al. 2017, JGR-A) Model parameter w/ GSMaP (Kotsuki et al., in revision) Adaptive-RTPP&RTPS (Kotsuki et al. 2017, QJRMS) EFSO implemented (Kotsuki et al., in prep.; Poster 5.10) Accounting for correlated R (Terasaki et al., in prep.) System accelerated (Yashiro et al. 2016, GMD) Extrapolation nowcast system System developed (Otsuka et al. 2016, WAF) This presentation Merging nowcast & NWP for global precip. FCST
4 Merging nowcast & NWP for global precipitation FCST
5 GSMaP: Global Satellite Mapping of Precipitation (NASA)
6 Extrapolation nowcast with DA (Otsuka, Kotsuki, and Miyoshi 2016, WAF)
7 GSMaP_RNC=NICAM-LETKF Seamless Precip. FCST Real-time GSMaP_NRT hourly, 0.10deg) 12hr 4hr GSMaP_RNC (hourly, 0.10deg)
8 GSMaP_RNC=NICAM-LETKF Seamless Precip. FCST Real-time 12hr GSMaP_NRT hourly, 0.10deg) 4hr GSMaP_RNC (hourly, 0.10deg) 120hr NICAM-LETKF (6-hourly, 112-km) 4hr Every-6hr 4hr NICAM-FCST (hourly, 112-km) 120hr GSMaP_RNC=NICAM-LETKF Seamless FCST (hourly, 0.10deg) MERGE grid wgrid NICAM grid (1 wgrid ) NOWCASTgrid
9 Local Threat Score (LTS) defined Global Threat Score GTS (t ) TPG (t ) TPG (t ) FPG (t ) FN G (t ) X G (t ) X i (t ) Si Obs. True Obs. False FCST Positive TP FP FCST Negative FN TN i global sampling, timedependent X j TPj, FPj, FN j, TPj S: pixel size (km2)
10 Local Threat Score (LTS) defined ti m e Grid i Di Global Threat Score GTS (t ) TPG (t ) TPG (t ) FPG (t ) FN G (t ) X G (t ) X i (t ) Si i Local Threat Score LTSi TPL,i TPL,i FPL,i FN L,i X L,i X j (t ) Sj global sampling, timedependent X j TPj, FPj, FN j, TPj S: pixel size (km2) t j Di local & temporal sampling
11 Spatially-estimated Weight (2014/09~2014/11) MERGE grid wgrid NICAM grid (1 wgrid ) NOWCASTgrid Found the best w from (0, 0.1,, 1.0 FT: 04-hr FT: 08-hr FT: 12-hr NOWCAST NICAM
12 Local Threat Scores (2014/09~2014/11) MERGE grid wgrid NICAM grid (1 wgrid ) NOWCASTgrid Found the best w from (0, 0.1,, 1.0 TP TS TP FP FN Obs. True Obs. False FCST Positive TP FP FCST Negative FN TN masked out if TN 0.99 TP FP FN TN
13 Local Threat Scores (2014/09~2014/11) MERGE grid wgrid NICAM grid (1 wgrid ) NOWCASTgrid Found the best w from (0, 0.1,, 1.0 MERGE NOWCAST
14 Local Threat Scores (2014/09~2014/11) MERGE grid wgrid NICAM grid (1 wgrid ) NOWCASTgrid Found the best w from (0, 0.1,, 1.0 MERGE NOWCAST & NICAM
15 Global Precip. FCST Scores Merged w/ spatial W Merged w/ global W NICAM GSMaP_RNC (nowcast)
16 Assimilating GPM/DPR reflect ivity
17 GPM/DPR (Ku and Ka bands) Sim. 3-D Radar Reflectivity Hou et al.(2014) Level 2 Normal Scan (NS) Matched Scan (MS) High-Sens. (HS) width 245 km 125 km 125 km Δx 5 km 5 km 5km Δz 250 m 250 m 500 m Band Ku Ka Ka
18 Assimilation of GPM/DPR by NICAM-LETKF Without DPR NICAM Ensemble Forecast (GL6;112 km) Xf OBSOPE Xa Xf (PREPBUFR, AMSU-A, GSMaP) X f, y o & HX f LETKF H yo : obs. operator : observation X f,a: guess, analysis (ensemble)
19 Assimilation of GPM/DPR by NICAM-LETKF With DPR NICAM Ensemble Forecast (GL8;28 km) Xf OBSOPE Xa Xf (PREPBUFR, AMSU-A, GSMaP) Xf Joint Simulator GPM/DP R (superob ) X f, y o & HX f LETKF H yo : obs. operator : observation X f,a: guess, analysis (ensemble)
20 First DA step 2014/06/16/0000UTC FG (NICAM) AN (DA) Obs. (GPM/DPR) dbz Column Maximum dbz
21 First DA step 2014/06/16/0000UTC FG (NICAM) AN (DA) Obs. (GPM/DPR) dbz Column Maximum dbz
22 First DA step 2014/06/16/0000UTC FG (NICAM) AN (DA) B A Obs. (GPM/DPR) B A B A dbz Column Maximum dbz
23 First DA step 2014/06/16/0000UTC FG (NICAM) AN (DA) Obs. (GPM/DPR) m KuPR A KuPR B A KuPR B A B m KaPR A KaPR B A KaPR B A dbz B
24 DA cycle experiment (vs. ERA Interim; 2014) RMSD: T RMSD 500 hpa slightly degraded RMSD: Qv RMSD 500 hpa comparable Spread Spread RMSD Spread 700 hpa slightly improved 700 hpa RMSD Spread slightly degraded : CTRL : w/ KuPR & KaPR (thinned by 3x3 grids) : w/ KuPR & KaPR (thinned by 5x5 grids)
25 Effective use of GPM/DPR GPM/DPR 6-hr coverage Maybe too sparse Parameter DA tested
26 Estimating Cloud Microphysic s Parameter with GPM/DPR
27 Single moment cloud microphysics NSW6 NSW6 (vapor, cloud, ice, rain, snow, graupel) Parameters : terminal velocity coefficients vt r,s, g ( D ) c r,s, g D d r,s, g 0 / 1/2 4 g g cg 3 C D 0 1/2 NICAM default NICAM for MJO Cr Cs CD
28 Single moment cloud microphysics NSW6 NSW6 (vapor, cloud, ice, rain, snow, graupel) Parameters : terminal velocity coefficients vt r,s, g ( D ) c r,s, g D d r,s, g 0 / 1/2 4 g g cg 3 C D 0 Cr 1/2 NICAM default NICAM for MJO Cr Cs CD Cs : mean : spread Cd
29 RMSD vs. ERA Interim (2014) RMSD: T Improved by parameter DA RMSD: Qv Degraded by parameter DA
30 Summary Merging nowcast and NWP Local threat score (LTS) defined Spatially-distributed weight estimated w/ LTS Merged Precip. FCST nowcast & NWP Assimilating GPM/DPR reflectivity NICAM-LETKF system updated (112-km 28-k m) Implementing Joint Simulator into NICAM-LETK F GPM/DPR assimilated, but impact is unclear
31 APPENDIX
32 Change in precipitation fields (2014/06/16) CTRL GSMaP Gauge (OBS) TEST (w/ parameter DA) mm/6h
33 Parameter Estimation in NICAM-LETKF 112-km
34 Parameter Estimation in NICAM-LETKF 112-km Online!
35 Estimated Parameter (large scale condensation) Berry (1967) s LSC scheme P B1 l 2 B2 B3 Nc l ρ : air density P: precipitation rate l : cloud water mixing ratio Nc: total # of cloud droplet Default Parameter Estimated Parameter : mean : spread (Kotsuki et al., in revision)
36 Estimated Parameter (large scale condensation) Berry (1967) s LSC scheme P B1 l 2 B2 B3 Nc l ρ : air density P: precipitation rate l : cloud water mixing ratio Nc: total # of cloud droplet Default Parameter Estimated Parameter : mean w/o Parameter DA w/ Parameter DA Precip. FCST improved by parameter DA : spread OBS (GSMaP_Gauge) (Kotsuki et al., in revision)
37 Estimated Parameter (large scale condensation) Berry (1967) s LSC scheme P B1 l 2 B2 B3 Nc l ρ : air density P: precipitation rate l : cloud water mixing ratio Nc: total # of cloud droplet Default Parameter Estimated Parameter : mean w/o Parameter DA w/ Parameter DA Precip. FCST improved by parameter DA : spread OBS (GSMaP_Gauge) (Kotsuki et al., in revision)
38 Satellite-simulator implemented Kotsuki et al. (2014, SOLA)
39 Satellite-simulator implemented Kotsuki et al. (2014, SOLA) : GPM/DPR bright band height (m), - - -: NICAM 0 height (m)
Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements
January 16, 2014, JAXA Joint PI Workshop, Tokyo Ensemble-based Data Assimilation of TRMM/GPM Precipitation Measurements PI: Takemasa Miyoshi RIKEN Advanced Institute for Computational Science Takemasa.Miyoshi@riken.jp
More informationBig Ensemble Data Assimilation
October 11, 2018, WWRP PDEF WG, JMA Tokyo Big Ensemble Data Assimilation Takemasa Miyoshi* RIKEN Center for Computational Science *PI and presenting, Takemasa.Miyoshi@riken.jp Data Assimilation Research
More informationGSMaP RIKEN Nowcast (GSMaP_RNC) Data Format Description
GSMaP RIKEN Nowcast (GSMaP_RNC) Data Format Description 4 August 2017 This document describes data format and information of Global Satellite Mapping of Precipitation (GSMaP) RIKEN Nowcast provided by
More informationWRF-LETKF The Present and Beyond
November 12, 2012, Weather-Chaos meeting WRF-LETKF The Present and Beyond Takemasa Miyoshi and Masaru Kunii University of Maryland, College Park miyoshi@atmos.umd.edu Co-investigators and Collaborators:
More informationNumerical Weather Prediction Chaos, Predictability, and Data Assimilation
July 23, 2013, DA summer school, Reading, UK Numerical Weather Prediction Chaos, Predictability, and Data Assimilation Takemasa Miyoshi RIKEN Advanced Institute for Computational Science Takemasa.Miyoshi@riken.jp
More informationAssimilation of GPM/DPR at JMA
Assimilation of GPM/DPR at JMA Yasutaka Ikuta Numerical Prediction Division Japan Meteorological Agency Data Assimilation Seminar in RIKEN/AICS, Kobe, Japan, 13 Jun, 2016 1 OUTLINE 1. Introduction 2. Operational
More informationEnsemble Assimilation of Global Large-Scale Precipitation
Ensemble Assimilation of Global Large-Scale Precipitation Guo-Yuan Lien 1,2 in collaboration with Eugenia Kalnay 2, Takemasa Miyoshi 1,2 1 RIKEN Advanced Institute for Computational Science 2 University
More informationGPM/DPR V5 algorithm. Toshio Iguchi NICT JPST teleference 13 April 2017
GPM/DPR V5 algorithm Toshio Iguchi NICT JPST teleference 13 April 2017 Major changes that affect R estimates in V5 Calibration change in L1 (Positive effect) Zm increases from V4 by KuPR: +1.3 db, KaPR(MS):
More informationEnhancing information transfer from observations to unobserved state variables for mesoscale radar data assimilation
Enhancing information transfer from observations to unobserved state variables for mesoscale radar data assimilation Weiguang Chang and Isztar Zawadzki Department of Atmospheric and Oceanic Sciences Faculty
More informationAssimilation of radar reflectivity
Assimilation of radar reflectivity Axel Seifert Deutscher Wetterdienst, Offenbach, Germany Convective-scale NWP at DWD: Plans for 2020 Storm-scale ICON-RUC-EPS: hourly 12h ensemble forecasts based on short
More informationAPPENDIX 2 OVERVIEW OF THE GLOBAL PRECIPITATION MEASUREMENT (GPM) AND THE TROPICAL RAINFALL MEASURING MISSION (TRMM) 2-1
APPENDIX 2 OVERVIEW OF THE GLOBAL PRECIPITATION MEASUREMENT (GPM) AND THE TROPICAL RAINFALL MEASURING MISSION (TRMM) 2-1 1. Introduction Precipitation is one of most important environmental parameters.
More informationGPM-GSMaP data is now available from JAXA G-portal (https://www.gportal.jaxa.jp) as well as current GSMaP web site (http://sharaku.eorc.jaxa.
GPM-GSMaP data is now available from JAXA G-portal (https://www.gportal.jaxa.jp) as well as current GSMaP web site (http://sharaku.eorc.jaxa.jp/ GSMaP/). GPM Core GMI TRMM PR GPM era Precipitation Radar
More informationAN OBSERVING SYSTEM EXPERIMENT OF MTSAT RAPID SCAN AMV USING JMA MESO-SCALE OPERATIONAL NWP SYSTEM
AN OBSERVING SYSTEM EXPERIMENT OF MTSAT RAPID SCAN AMV USING JMA MESO-SCALE OPERATIONAL NWP SYSTEM Koji Yamashita Japan Meteorological Agency / Numerical Prediction Division 1-3-4, Otemachi, Chiyoda-ku,
More informationAssimilation of cloud information into the COSMO model with an Ensemble Kalman Filter. Annika Schomburg, Christoph Schraff
Assimilation of cloud information into the COSMO model with an Ensemble Kalman Filter Annika Schomburg, Christoph Schraff SRNWP-Workshop, Offenbach Mai 2013 annika.schomburg@dwd.de 1 Introduction LETKF
More informationAssimilating cloud information from satellite cloud products with an Ensemble Kalman Filter at the convective scale
Assimilating cloud information from satellite cloud products with an Ensemble Kalman Filter at the convective scale Annika Schomburg, Christoph Schraff This work was funded by the EUMETSAT fellowship programme.
More informationEvaluation and assimilation of all-sky infrared radiances of Himawari-8
Evaluation and assimilation of all-sky infrared radiances of Himawari-8 Kozo Okamoto 1,2, Yohei Sawada 1,2, Masaru Kunii 1, Tempei Hashino 3, Takeshi Iriguchi 1 and Masayuki Nakagawa 1 1: JMA/MRI, 2: RIKEN/AICS,
More informationThe Nowcasting Demonstration Project for London 2012
The Nowcasting Demonstration Project for London 2012 Susan Ballard, Zhihong Li, David Simonin, Jean-Francois Caron, Brian Golding, Met Office, UK Introduction The success of convective-scale NWP is largely
More informationComputational Challenges in Big Data Assimilation with Extreme-scale Simulations
May 1, 2013, BDEC workshop, Charleston, SC Computational Challenges in Big Data Assimilation with Extreme-scale Simulations Takemasa Miyoshi RIKEN Advanced Institute for Computational Science Takemasa.Miyoshi@riken.jp
More informationTowards the assimilation of all-sky infrared radiances of Himawari-8. Kozo Okamoto 1,2
Towards the assimilation of all-sky infrared radiances of Himawari-8 Kozo Okamoto 1,2 H. Ishimoto 1, M. Kunii 1,2, M. Otsuka 1,2, S. Yokota 1, H. Seko 1,2, and Y. Sawada 2 1: JMA/MRI, 2: RIKEN/AICS ISDA2016,
More informationGSMaP - Integrated application with developer and user collaboration -
WIGOS WORKSHOP 2019 Session 2.2 GSMaP - Integrated application with developer and user collaboration - Takuji Kubota and Moeka Yamaji Earth Observation Research Center (EORC) Japan Aerospace Exploration
More informationTangent-linear and adjoint models in data assimilation
Tangent-linear and adjoint models in data assimilation Marta Janisková and Philippe Lopez ECMWF Thanks to: F. Váňa, M.Fielding 2018 Annual Seminar: Earth system assimilation 10-13 September 2018 Tangent-linear
More informationOperational Use of Scatterometer Winds in the JMA Data Assimilation System
Operational Use of Scatterometer Winds in the Data Assimilation System Masaya Takahashi Numerical Prediction Division, Japan Meteorological Agency () International Ocean Vector Winds Science Team Meeting,
More informationData Assimilation Development for the FV3GFSv2
Data Assimilation Development for the FV3GFSv2 Catherine Thomas 1, 2, Rahul Mahajan 1, 2, Daryl Kleist 2, Emily Liu 3,2, Yanqiu Zhu 1, 2, John Derber 2, Andrew Collard 1, 2, Russ Treadon 2, Jeff Whitaker
More informationSupplementary Figure 1. Summer mesoscale convective systems rainfall climatology and trends. Mesoscale convective system (MCS) (a) mean total
Supplementary Figure 1. Summer mesoscale convective systems rainfall climatology and trends. Mesoscale convective system (MCS) (a) mean total rainfall and (b) total rainfall trend from 1979-2014. Total
More informationGlobal Precipitation Data Sets
Global Precipitation Data Sets Rick Lawford (with thanks to Phil Arkin, Scott Curtis, Kit Szeto, Ron Stewart, etc) April 30, 2009 Toronto Roles of global precipitation products in drought studies: 1.Understanding
More informationA new mesoscale NWP system for Australia
A new mesoscale NWP system for Australia www.cawcr.gov.au Peter Steinle on behalf of : Earth System Modelling (ESM) and Weather&Environmental Prediction (WEP) Research Programs, CAWCR Data Assimilation
More informationAdvances and Challenges in Ensemblebased Data Assimilation in Meteorology. Takemasa Miyoshi
January 18, 2013, DA Workshop, Tachikawa, Japan Advances and Challenges in Ensemblebased Data Assimilation in Meteorology Takemasa Miyoshi RIKEN Advanced Institute for Computational Science Takemasa.Miyoshi@riken.jp
More informationA workshop on the theme of clouds, circulation and climate sensitivity March in Schloss Ringberg, Germany
Clouds, circulation, and climate sensitivity simulated by NICAM Global nonhydrostatic model simulations with single and double momentum cloud microphysics schemes and evaluation using satellite simulators
More informationStudy on data assimilation to improve precipitation forecasts
Study on data assimilation to improve precipitation forecasts CI: Kozo Okamoto*, Kazumasa Aonashi, Seiji Origuchi, Toshiyuki Ishibashi (/MRI) Thanks to Takuji Kubota (JAXA), Akihiro Hahimoto (MRI) 14-17
More informationAssimilation of precipitation-related observations into global NWP models
Assimilation of precipitation-related observations into global NWP models Alan Geer, Katrin Lonitz, Philippe Lopez, Fabrizio Baordo, Niels Bormann, Peter Lean, Stephen English Slide 1 H-SAF workshop 4
More informationNRL Four-dimensional Variational Radar Data Assimilation for Improved Near-term and Short-term Storm Prediction
NRL Marine Meteorology Division, Monterey, California NRL Four-dimensional Variational Radar Data Assimilation for Improved Near-term and Short-term Storm Prediction Allen Zhao, Teddy Holt, Paul Harasti,
More informationUpdate on the KENDA project
Christoph Schraff Deutscher Wetterdienst, Offenbach, Germany and many colleagues from CH, D, I, ROM, RU Km-scale ENsemble-based Data Assimilation : COSMO priority project Local Ensemble Transform Kalman
More informationRadiance Data Assimilation with an EnKF
Radiance Data Assimilation with an EnKF Zhiquan Liu, Craig Schwartz, Xiangyu Huang (NCAR/MMM) Yongsheng Chen (York University) 4/7/2010 4th EnKF Workshop 1 Outline Radiance Assimilation Methodology Apply
More informationRainfall Observation from Space: A overview of GPM and GSMaP
Rainfall Observation from Space: A overview of GPM and GSMaP (July 2016 version) Earth Observation Research Center (EORC) Japan Aerospace Exploration Agency (JAXA) Rainfall Measurement and our life Rain
More informationAssimilation of GPM/DPR in Km-scale Hybrid-4DVar system
Assimilation of GPM/DPR in Km-scale Hybrid-4DVar system Yasutaka Ikuta Numerical Prediction Division / Japan Meteorological Agency 6th International Symposium on Data Assimilation, 5-9 March 208, Munich,
More informationRepresentation of model error for data assimilation on convective scale
Representation of model error for data assimilation on convective scale Yuefei Zenga,b, Tijana Janjicb, Alberto de Lozarc, Ulrich Blahakc, Hendrik Reichc, Axel Seifertc, Stephan Raspa, George Craiga a)
More informationEnsemble Kalman Filter Assimilation of Radar Data for a Convective Storm using a Two-moment Microphysics Scheme 04/09/10
Ensemble Kalman Filter Assimilation of Radar Data for a Convective Storm using a Two-moment Microphysics Scheme 04/09/10 Youngsun Jung 1, Ming Xue 1,2, and Mingjing Tong 3 CAPS 1 and School of Meteorology
More informationObserving system experiments of MTSAT-2 Rapid Scan Atmospheric Motion Vector for T-PARC 2008 using the JMA operational NWP system
Tenth International Winds Workshop 1 Observing system experiments of MTSAT-2 Rapid Scan Atmospheric Motion Vector for T-PARC 2008 using the JMA operational NWP system Koji Yamashita Japan Meteorological
More informationRecent progress in convective scale Arome NWP system and on-going research activities
Recent progress in convective scale Arome NWP system and on-going research activities P. Brousseau, P. Chambon, G. Faure, R. Honnert, A. Mary, N. Merlet, Y. Seity, B. Vié, E. Wattrelot (presented by F.
More information16. Data Assimilation Research Team
16. Data Assimilation Research Team 16.1. Team members Takemasa Miyoshi (Team Leader) Shigenori Otsuka (Postdoctoral Researcher) Juan J. Ruiz (Visiting Researcher) Keiichi Kondo (Student Trainee) Yukiko
More informationCloud detection using SEVIRI IR channels
Cloud detection using SEVIRI IR channels Alessandro.Ipe@oma.be & Luis Gonzalez Sotelino Royal Meteorological Institute of Belgium GERB Science Team Meeting @ London September 9 10 2009 1 / 19 Overview
More informationGlobal Precipitation Measurement (GPM) for Science and Society
Global Precipitation Measurement (GPM) for Science and Society Gail Skofronick-Jackson GPM Project Scientist NASA Goddard Space Flight Center Radar Observation of Rain from Space Tokyo, Japan 29 November
More informationWelcome and Introduction
Welcome and Introduction Riko Oki Earth Observation Research Center (EORC) Japan Aerospace Exploration Agency (JAXA) 7th Workshop of International Precipitation Working Group 17 November 2014 Tsukuba International
More informationOverview on Data Assimilation Activities in COSMO
Overview on Data Assimilation Activities in COSMO Deutscher Wetterdienst, D-63067 Offenbach, Germany current DA method: nudging PP KENDA for (1 3) km-scale EPS: LETKF radar-derived precip: latent heat
More informationEnhancing the Barcelona Supercomputing Centre chemical transport model with aerosol assimilation
www.bsc.es Enhancing the Barcelona Supercomputing Centre chemical transport model with aerosol assimilation Enza Di Tomaso 1, Nick Schutgens 2, Oriol Jorba 1, George Markomanolis 1 1 Earth Sciences Department,
More informationDirect assimilation of all-sky microwave radiances at ECMWF
Direct assimilation of all-sky microwave radiances at ECMWF Peter Bauer, Alan Geer, Philippe Lopez, Deborah Salmond European Centre for Medium-Range Weather Forecasts Reading, Berkshire, UK Slide 1 17
More informationData Assimilation of Satellite Lidar Aerosol Observations
Data Assimilation of Satellite Lidar Aerosol Observations Thomas Sekiyama Meteorological Research Institute Japan Meteorological Agency (MRI/JMA) AICS Data Assimilation Workshop, 27 February 2013, Kobe,
More informationMasahiro Kazumori, Takashi Kadowaki Numerical Prediction Division Japan Meteorological Agency
Development of an all-sky assimilation of microwave imager and sounder radiances for the Japan Meteorological Agency global numerical weather prediction system Masahiro Kazumori, Takashi Kadowaki Numerical
More informationAssimilation of MSG visible and near-infrared reflectivity in KENDA/COSMO
Assimilation of MSG visible and near-infrared reflectivity in KENDA/COSMO Leonhard Scheck1,2, Tobias Necker1,2, Pascal Frerebeau2, Bernhard Mayer2, Martin Weissmann1,2 1) Hans-Ertl-Center for Weather Research,
More information3.23 IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL
3.23 IMPROVING VERY-SHORT-TERM STORM PREDICTIONS BY ASSIMILATING RADAR AND SATELLITE DATA INTO A MESOSCALE NWP MODEL Q. Zhao 1*, J. Cook 1, Q. Xu 2, and P. Harasti 3 1 Naval Research Laboratory, Monterey,
More informationExtending the use of surface-sensitive microwave channels in the ECMWF system
Extending the use of surface-sensitive microwave channels in the ECMWF system Enza Di Tomaso and Niels Bormann European Centre for Medium-range Weather Forecasts Shinfield Park, Reading, RG2 9AX, United
More informationWG1 Overview. PP KENDA for km-scale EPS: LETKF. current DA method: nudging. radar reflectivity (precip): latent heat nudging 1DVar (comparison)
WG1 Overview Deutscher Wetterdienst, D-63067 Offenbach, Germany current DA method: nudging PP KENDA for km-scale EPS: LETKF radar reflectivity (precip): latent heat nudging 1DVar (comparison) radar radial
More informationImplementation and evaluation of a regional data assimilation system based on WRF-LETKF
Implementation and evaluation of a regional data assimilation system based on WRF-LETKF Juan José Ruiz Centro de Investigaciones del Mar y la Atmosfera (CONICET University of Buenos Aires) With many thanks
More informationRelease Notes for the DPR Level 2 and 3 products
Release Notes for the DPR Level 2 and 3 products DPR Level 2 and 3 Version 4 products have been released to public users since March 2016. Caveats for these products are described as follows. All users
More informationDevelopment of 3D Variational Assimilation System for ATOVS Data in China
Development of 3D Variational Assimilation System for ATOVS Data in China Xue Jishan, Zhang Hua, Zhu Guofu, Zhuang Shiyu 1) Zhang Wenjian, Liu Zhiquan, Wu Xuebao, Zhang Fenyin. 2) 1) Chinese Academy of
More informationIntercomparison of Satellite Precipitation Products for Different Cloud Types
8 th IPWG & 5 th IWSSM Joint Workshop Bologna, 3-7 October, 2016 Intercomparison of Satellite Precipitation Products for Different Cloud Types Nobuyuki UTSUMI Hyungjun Kim, Taikan OKI (IIS, The University
More informationReal time Quality Control of Phased Array Weather Radar Data Observed Every 30 Seconds
Real time Quality Control of Phased Array Weather Radar Data Observed Every 30 Seconds Shinsuke Satoh, Fusako Isoda, Tetsuya Sano, Hiroshi Hanado (NICT), Tomoo Ushio (Tokyo Metropolitan Univ.), Shigenori
More informationConvective-Scale Data Assimilation
Convective-Scale Data Assimilation Dale Barker, with contributions from Met Office colleagues, and: Jelena Bolarova (HIRLAM), Yann Michel (Meteo France), Luc Fillion (Environment Canada), Kazuo Saito (JMA/MRI)
More informationScatterometer Wind Assimilation at the Met Office
Scatterometer Wind Assimilation at the Met Office James Cotton International Ocean Vector Winds Science Team (IOVWST) meeting, Brest, June 2014 Outline Assimilation status Global updates: Metop-B and spatial
More informationAssimilation of precipitation-affected radiances in a cloud-resolving. WRF ensemble data assimilation system
Assimilation of precipitation-affected radiances in a cloud-resolving WRF ensemble data assimilation system Sara Q. Zhang 1, Milija Zupanski 2, Arthur Y. Hou 1, Xin Lin 1, and Samson H. Cheung 3 1 NASA
More informationImproved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter
Improved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter Hong Li, Junjie Liu, and Elana Fertig E. Kalnay I. Szunyogh, E. J. Kostelich Weather and Chaos Group
More informationJP1.15 PHYSICAL INITIALIZATION TO INCORPORATE RADAR PRECIPITATION DATA INTO A NUMERICAL WEATHER PREDICTION MODEL (Lokal Model)
JP1.15 PHYSICAL INITIALIZATION TO INCORPORATE RADAR PRECIPITATION DATA INTO A NUMERICAL WEATHER PREDICTION MODEL (Lokal Model) M. Milan 1, F. Ament 1, V. Venema 1, A. Battaglia 1, C. Simmer 1 1 Meteorological
More informationAssimilation of Himawari-8 Atmospheric Motion Vectors into the Numerical Weather Prediction Systems of Japan Meteorological Agency
Assimilation of Himawari-8 Atmospheric Motion Vectors into the Numerical Weather Prediction Systems of Japan Meteorological Agency Koji Yamashita Japan Meteorological Agency kobo.yamashita@met.kishou.go.jp,
More informationDeterministic and Ensemble Storm scale Lightning Data Assimilation
LI Mission Advisory Group & GOES-R Science Team Workshop 27-29 May 2015 Deterministic and Ensemble Storm scale Lightning Data Assimilation Don MacGorman, Ted Mansell (NOAA/National Severe Storms Lab) Alex
More informationGlobal Rainfall Map Realtime version (GSMaP_NOW) Data Format Description
updated 01 November 2018 2 November 2015 Global Rainfall Map Realtime version (GSMaP_NOW) Data Format Description This document describes data format and information of Global Rainfall Map Realtime version
More informationDoppler radial wind spatially correlated observation error: operational implementation and initial results
Doppler radial wind spatially correlated observation error: operational implementation and initial results D. Simonin, J. Waller, G. Kelly, S. Ballard,, S. Dance, N. Nichols (Met Office, University of
More informationValue of Satellite Observation Sensitive to Humidity and Precipitation in JMA s Operational Numerical Weather Prediction
地球観測衛星 30 周年記念シンポジウム JAXA Symposium for earth observing satellites 気象庁の現業数値気象予報における衛星観測データの水蒸気及び降水の解析予測精度への貢献 Value of Satellite Observation Sensitive to Humidity and Precipitation in JMA s Operational
More informationSatellite Soil Moisture Content Data Assimilation in Operational Local NWP System at JMA
Satellite Soil Moisture Content Data Assimilation in Operational Local NWP System at JMA Yasutaka Ikuta Numerical Prediction Division Japan Meteorological Agency Acknowledgment: This research was supported
More informationEFSO and DFS diagnostics for JMA s global Data Assimilation System: their caveats and potential pitfalls
EFSO and DFS diagnostics for JMA s global Data Assimilation System: their caveats and potential pitfalls Daisuke Hotta 1,2 and Yoichiro Ota 2 1 Meteorological Research Institute, Japan Meteorological Agency
More informationECMWF snow data assimilation: Use of snow cover products and In situ snow depth data for NWP
snow data assimilation: Use of snow cover products and In situ snow depth data for NWP Patricia de Rosnay Thanks to: Ioannis Mallas, Gianpaolo Balsamo, Philippe Lopez, Anne Fouilloux, Mohamed Dahoui, Lars
More informationSeamless nowcasting. Open issues
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss Seamless nowcasting INCA Open issues Pierre Eckert Matteo Buzzi, Marco Sassi, Guido della Bruna, Marco Gaia
More informationPassive and Active Microwave Sensors for Precipitation Research
Passive and Active Microwave Sensors for Precipitation Research Joe Turk Jet Propulsion Laboratory California Institute of Technology Pasadena, CA jturk@jpl.nasa.gov Characteristics of Precipitation Wide
More informationThe convection-permitting COSMO-DE-EPS and PEPS at DWD
Deutscher Wetterdienst The convection-permitting COSMO-DE-EPS and PEPS at DWD Detlev Majewski based on Chr. Gebhardt, S. Theis, M. Paulat, M. Buchhold and M. Denhard Deutscher Wetterdienst The model COSMO-DE
More information0-6 hour Weather Forecast Guidance at The Weather Company. Steven Honey, Joseph Koval, Cathryn Meyer, Peter Neilley The Weather Company
1 0-6 hour Weather Forecast Guidance at The Weather Company Steven Honey, Joseph Koval, Cathryn Meyer, Peter Neilley The Weather Company TWC Forecasts: Widespread Adoption 2 0-6 Hour Forecast Details 3
More informationConvective-scale data assimilation in the Weather Research and Forecasting model using a nonlinear ensemble filter
Convective-scale data assimilation in the Weather Research and Forecasting model using a nonlinear ensemble filter Jon Poterjoy, Ryan Sobash, and Jeffrey Anderson National Center for Atmospheric Research
More informationAircraft-based Observations: Impact on weather forecast model performance
Aircraft-based Observations: Impact on weather forecast model performance Stephen S. Weygandt Eric James, Stan Benjamin, Bill Moninger, Brian Jamison, Geoff Manikin* NOAA Earth System Research Laboratory
More informationData Assimilation Research Team
Data Assimilation Research Team 1. Team members Takemasa Miyoshi (Team Leader) Shigenori Otsuka (Postdoctoral Researcher) Koji Terasaki (Postdoctoral Researcher) Keiichi Kondo (Postdoctoral Researcher)
More information14B.1 Evaluating cloud microphysics schemes in nested NMMB forecasts
14B.1 Evaluating cloud microphysics schemes in nested NMMB forecasts Brad Ferrier1,2 Weiguo Wang1,2 Edward Colón1,2 NOAA/NWS/NCEP Environmental Modeling Center (EMC) 2 IM Systems Group, Inc. (IMSG) 1 1
More information1. Current atmospheric DA systems 2. Coupling surface/atmospheric DA 3. Trends & ideas
1 Current issues in atmospheric data assimilation and its relationship with surfaces François Bouttier GAME/CNRM Météo-France 2nd workshop on remote sensing and modeling of surface properties, Toulouse,
More informationUpdate on CoSPA Storm Forecasts
Update on CoSPA Storm Forecasts Haig August 2, 2011 This work was sponsored by the Federal Aviation Administration under Air Force Contract No. FA8721-05-C-0002. Opinions, interpretations, conclusions,
More informationC. Gebhardt, S. Theis, R. Kohlhepp, E. Machulskaya, M. Buchhold. developers of KENDA, ICON-EPS, ICON-EDA, COSMO-D2, verification
Convective-scale EPS at DWD status, developments & plans C. Gebhardt, S. Theis, R. Kohlhepp, E. Machulskaya, M. Buchhold developers of KENDA, ICON-EPS, ICON-EDA, COSMO-D2, verification 1 Outline new operational
More informationH-SAF future developments on Convective Precipitation Retrieval
H-SAF future developments on Convective Precipitation Retrieval Francesco Zauli 1, Daniele Biron 1, Davide Melfi 1, Antonio Vocino 1, Massimiliano Sist 2, Michele De Rosa 2, Matteo Picchiani 2, De Leonibus
More informationSome Applications of WRF/DART
Some Applications of WRF/DART Chris Snyder, National Center for Atmospheric Research Mesoscale and Microscale Meteorology Division (MMM), and Institue for Mathematics Applied to Geoscience (IMAGe) WRF/DART
More informationRelationship between Singular Vectors, Bred Vectors, 4D-Var and EnKF
Relationship between Singular Vectors, Bred Vectors, 4D-Var and EnKF Eugenia Kalnay and Shu-Chih Yang with Alberto Carrasi, Matteo Corazza and Takemasa Miyoshi 4th EnKF Workshop, April 2010 Relationship
More informationMet Office convective-scale 4DVAR system, tests and improvement
Met Office convective-scale 4DVAR system, tests and improvement Marco Milan*, Marek Wlasak, Stefano Migliorini, Bruce Macpherson Acknowledgment: Inverarity Gordon, Gareth Dow, Mike Thurlow, Mike Cullen
More informationToward improved initial conditions for NCAR s real-time convection-allowing ensemble. Ryan Sobash, Glen Romine, Craig Schwartz, and Kate Fossell
Toward improved initial conditions for NCAR s real-time convection-allowing ensemble Ryan Sobash, Glen Romine, Craig Schwartz, and Kate Fossell Storm-scale ensemble design Can an EnKF be used to initialize
More informationSTRONGLY COUPLED ENKF DATA ASSIMILATION
STRONGLY COUPLED ENKF DATA ASSIMILATION WITH THE CFSV2 Travis Sluka Acknowledgements: Eugenia Kalnay, Steve Penny, Takemasa Miyoshi CDAW Toulouse Oct 19, 2016 Outline 1. Overview of strongly coupled DA
More informationRecent Data Assimilation Activities at Environment Canada
Recent Data Assimilation Activities at Environment Canada Major upgrade to global and regional deterministic prediction systems (now in parallel run) Sea ice data assimilation Mark Buehner Data Assimilation
More informationOperational Use of Scatterometer Winds at JMA
Operational Use of Scatterometer Winds at JMA Masaya Takahashi Numerical Prediction Division, Japan Meteorological Agency (JMA) 10 th International Winds Workshop, Tokyo, 26 February 2010 JMA Outline JMA
More informationComparing Local Ensemble Transform Kalman Filter with 4D-Var in a Quasi-geostrophic model
Comparing Local Ensemble Transform Kalman Filter with 4D-Var in a Quasi-geostrophic model Shu-Chih Yang 1,2, Eugenia Kalnay 1, Matteo Corazza 3, Alberto Carrassi 4 and Takemasa Miyoshi 5 1 University of
More informationRetrieving Snowfall Rate with Satellite Passive Microwave Measurements
Retrieving Snowfall Rate with Satellite Passive Microwave Measurements Huan Meng 1, Ralph Ferraro 1, Banghua Yan 1, Cezar Kongoli 2, Nai-Yu Wang 2, Jun Dong 2, Limin Zhao 1 1 NOAA/NESDIS, USA 2 Earth System
More informationThe Impacts of GPSRO Data Assimilation and Four Ices Microphysics Scheme on Simulation of heavy rainfall Events over Taiwan during June 2012
The Impacts of GPSRO Data Assimilation and Four Ices Microphysics Scheme on Simulation of heavy rainfall Events over Taiwan during 10-12 June 2012 Pay-Liam LIN, Y.-J. Chen, B.-Y. Lu, C.-K. WANG, C.-S.
More informationImproving the use of satellite winds at the Deutscher Wetterdienst (DWD)
Improving the use of satellite winds at the Deutscher Wetterdienst (DWD) Alexander Cress Deutscher Wetterdienst, Frankfurter Strasse 135, 63067 Offenbach am Main, Germany alexander.cress@dwd.de Ø Introduction
More informationData Assimilation: Finding the Initial Conditions in Large Dynamical Systems. Eric Kostelich Data Mining Seminar, Feb. 6, 2006
Data Assimilation: Finding the Initial Conditions in Large Dynamical Systems Eric Kostelich Data Mining Seminar, Feb. 6, 2006 kostelich@asu.edu Co-Workers Istvan Szunyogh, Gyorgyi Gyarmati, Ed Ott, Brian
More informationJi-Sun Kang. Pr. Eugenia Kalnay (Chair/Advisor) Pr. Ning Zeng (Co-Chair) Pr. Brian Hunt (Dean s representative) Pr. Kayo Ide Pr.
Carbon Cycle Data Assimilation Using a Coupled Atmosphere-Vegetation Model and the LETKF Ji-Sun Kang Committee in charge: Pr. Eugenia Kalnay (Chair/Advisor) Pr. Ning Zeng (Co-Chair) Pr. Brian Hunt (Dean
More informationThunderstorm-Scale EnKF Analyses Verified with Dual-Polarization, Dual-Doppler Radar Data
Thunderstorm-Scale EnKF Analyses Verified with Dual-Polarization, Dual-Doppler Radar Data David Dowell and Wiebke Deierling National Center for Atmospheric Research, Boulder, CO Ensemble Data Assimilation
More informationAssessment of AHI Level-1 Data for HWRF Assimilation
Assessment of AHI Level-1 Data for HWRF Assimilation Xiaolei Zou 1 and Fuzhong Weng 2 1 Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 2 Satellite Meteorology
More informationSnow Analysis for Numerical Weather prediction at ECMWF
Snow Analysis for Numerical Weather prediction at ECMWF Patricia de Rosnay, Gianpaolo Balsamo, Lars Isaksen European Centre for Medium-Range Weather Forecasts IGARSS 2011, Vancouver, 25-29 July 2011 Slide
More informationGuo-Yuan Lien*, Eugenia Kalnay, and Takemasa Miyoshi University of Maryland, College Park, Maryland 2. METHODOLOGY
9.2 EFFECTIVE ASSIMILATION OF GLOBAL PRECIPITATION: SIMULATION EXPERIMENTS Guo-Yuan Lien*, Eugenia Kalnay, and Takemasa Miyoshi University of Maryland, College Park, Maryland 1. INTRODUCTION * Precipitation
More informationGlobal and Regional OSEs at JMA
Global and Regional OSEs at JMA Yoshiaki SATO and colleagues Japan Meteorological Agency / Numerical Prediction Division 1 JMA NWP SYSTEM Global OSEs Contents AMSU A over coast, MHS over land, (related
More information