Recent developments for a forward operator for GPS RO Lidia Cucurull NOAA GPS RO Program Scientist NOAA/NWS/NCEP/EMC NCU, Taiwan, 16 August 2010 1.

Slides:



Advertisements
Similar presentations
Characterization of ATMS Bias Using GPSRO Observations Lin Lin 1,2, Fuzhong Weng 2 and Xiaolei Zou 3 1 Earth Resources Technology, Inc.
Advertisements

Challenges in Using GOES Data Within Operational Numerical Models Dr. Louis W. Uccellini Director National Centers for Environmental Prediction NASA Science.
Chapter 8 Coordinate Systems.
Satellite observation systems and reference systems (ae4-e01) Signal Propagation E. Schrama.
Improved NCEP SST Analysis
Radio Occultation Atmospheric Profiling with Global Navigation Satellite Systems (GNSS)
Forecast impact experiments with CHAMP RO measurements Sean Healy Acknowledgements Jean-Noël Thépaut, Sami Saarinen, Niels Bormann, Lars Isaksen, Adrian.
GPS / RO for atmospheric studies Dept. of Physics and Astronomy GPS / RO for atmospheric studies Panagiotis Vergados Dept. of Physics and Astronomy.
COSMIC / FormoSat 3 Overview, Status, First results, Data distribution.
Radio Occultation From GPS/MET to COSMIC.
1 Data Impact Experiments at the JCSDA and NCEP/EMC S. Lord (NCEP/EMC) L.P. Riishojgaard (JCSDA) Contributions by: L. Cucurull, J. Jung, L. Bi, D. Kleist,
GPS radio occultation Sean Healy DA lecture, 28th April, 2008.
Use of GPS RO in Operations at NCEP
Impact of Infrared, Microwave and Radio Occultation Satellite Observations on Operational Numerical Weather Prediction Lidia Cucurull (1) and Richard A.
Assimilation of GOES Hourly and Meteosat winds in the NCEP Global Forecast System (GFS) Assimilation of GOES Hourly and Meteosat winds in the NCEP Global.
2nd GRAS SAF User Workshop, June 2003, Helsingør, Denmark. 1Introduction to data assimilation An introduction to data assimilation Xiang-Yu Huang.
The fear of the LORD is the beginning of wisdom 陳登舜 ATM NCU Group Meeting REFERENCE : Liu., H., J. Anderson, and Y.-H. Kuo, 2012: Improved analyses.
CGMS-40, November 2012, Lugano, Switzerland Coordination Group for Meteorological Satellites - CGMS IROWG - Overview of and Plans for the Newest CGMS Working.
Simulation Studies on the Analysis of Radio Occultation Data Andrea K. Steiner, Ulrich Foelsche, Andreas Gobiet, and Gottfried Kirchengast Institute for.
Different options for the assimilation of GPS Radio Occultation data within GSI Lidia Cucurull NOAA/NWS/NCEP/EMC GSI workshop, Boulder CO, 28 June 2011.
June, 2003EUMETSAT GRAS SAF 2nd User Workshop. 2 The EPS/METOP Satellite.
1 Tropospheric Signal Delay Corrections Seth I. Gutman NOAA Earth System Research Laboratory Boulder, CO USA NOAA/LSU Workshop on Benefits to the.
Status of the assimilation of GPS RO observations: the COSMIC Mission L. Cucurull JCSDA/UCAR J.C. Derber, R. Treadon, and R.J. Purser.
BoM/GNSS RO ACTIVITIES and PLANS John Le Marshall Director, JCSDA CAWCR
Linear and nonlinear representations of wave fields and their application to processing of radio occultations M. E. Gorbunov, A. V. Shmakov Obukhov Institute.
ROSA – ROSSA Validation results R. Notarpietro, G. Perona, M. Cucca
Data assimilation and forecasting the weather (!) Eugenia Kalnay and many friends University of Maryland.
Climate Monitoring with Radio Occultation Data Systematic Error Sources C. Rocken, S. Sokolovskiy, B. Schreiner, D. Hunt, B. Ho, B. Kuo, U. Foelsche.
1 Hyperspectral Infrared Water Vapor Radiance Assimilation James Jung Cooperative Institute for Meteorological Satellite Studies Lars Peter Riishojgaard.
COST 723 Training School - Cargese October 2005 KEY 1 Radiative Transfer Bruno Carli.
April 16, 2009ATMO/OPTI 656bKursinski et al. 1 GPS Occultation Introduction and Overview R. Kursinski Dept. of Atmospheric Sciences, University of Arizona,
Retrieval of Moisture from GPS Slant-path Water Vapor Observations using 3DVAR and its Impact on the Prediction of Convective Initiation and Precipitation.
Lennart Bengtsson ESSC, Uni. Reading THORPEX Conference December 2004 Predictability and predictive skill of weather systems and atmospheric flow patterns.
January 14, 2003GPS Meteorology Workshop1 Information from a Numerical Weather Model for Improving Atmosphere Delay Estimation in Geodesy Arthur Niell.
Application of COSMIC refractivity in Improving Tropical Analyses and Forecasts H. Liu, J. Anderson, B. Kuo, C. Snyder, and Y. Chen NCAR IMAGe/COSMIC/MMM.
WP 3: DATA ASSIMILATION SMHI/FMI Status report 3rd CARPE DIEM meeting, University of Essex, Colchester, 9-10 January 2003 Structure SMHI/FMI plans for.
Impact of FORMOSAT-3 GPS Data Assimilation on WRF model during 2007 Mei-yu season in Taiwan Shyuan-Ru Miaw, Pay-Liam Lin Department of Atmospheric Sciences.
Introduction of temperature observation of radio-sonde in place of geopotential height to the global three dimensional variational data assimilation system.
Rosetta_CD\PR\what_is_RS.ppt, :26AM, 1 Mars Express Radio Science Experiment MaRS MaRS Radio Science Data: Level 3 & 4 Basics S.Tellmann,
Key RO Advances Observation –Lower tropospheric penetration (open loop / demodulation) –Larger number of profiles (rising & setting) –Detailed precision.
2 nd GRAS-SAF USER WORKSHOP Assimilation of GPS radio occultation measurements at DAO (soon GMAO) P. Poli 1,2 and J. Joiner 3 Data Assimilation Office.
Improving the Assimilation of GPS RO Data in the Tropical Lower Troposphere Bill Kuo and Hui Liu UCAR COSMIC.
ECMWF reanalysis using GPS RO data Sean Healy Shinya Kobayashi, Saki Uppala, Mark Ringer and Mike Rennie.
Preliminary results from assimilation of GPS radio occultation data in WRF using an ensemble filter H. Liu, J. Anderson, B. Kuo, C. Snyder, A. Caya IMAGe.
Improved Radio Occultation Observations for a COSMIC Follow-on Mission C. Rocken, S. Sokolovskiy, B. Schreiner UCAR / COSMIC D. Ector NOAA.
COSMIC Update and Highlights 8 November
Sean Healy Presented by Erik Andersson
Impact of Blended MW-IR SST Analyses on NAVY Numerical Weather Prediction and Atmospheric Data Assimilation James Cummings, James Goerss, Nancy Baker Naval.
Data Assimilation Retrieval of Electron Density Profiles from Radio Occultation Measurements Xin’an Yue, W. S. Schreiner, Jason Lin, C. Rocken, Y-H. Kuo.
One-dimensional assimilation method for the humidity estimation with the wind profiling radar data using the MSM forecast as the first guess Jun-ichi Furumoto,
1 3D-Var assimilation of CHAMP measurements at the Met Office Sean Healy, Adrian Jupp and Christian Marquardt.
FORMOSAT-3 AMS Breakfast Meeting FORMOSAT-3/COSMIC Mission 15 January 2007 Welcome to a new era of Earth observations! FORMOSAT-3/COSMIC launch picture.
COSMIC Ionospheric measurements Jiuhou Lei NCAR ASP/HAO Research review, Boulder, March 8, 2007.
© Copyright QinetiQ limited 2006 On the application of meteorological data assimilation techniques to radio occultation measurements of.
Towards Assimilation of GOES Hourly winds in the NCEP Global Forecast System (GFS) Xiujuan Su, Jaime Daniels, John Derber, Yangrong Lin, Andy Bailey, Wayne.
GPS Radio-Occultation data (COSMIC mission) Lidia Cucurull NOAA Joint Center for Satellite Data Assimilation.
Xiujuan Su 1, John Derber 2, Jaime Daniel 3,Andrew Collard 1 1: IMSG, 2: EMC/NWS/NOAA, 3.NESDIS Assimilation of GOES hourly shortwave and visible AMVs.
Assimilation experiments with CHAMP GPS radio occultation measurements By S. B. HEALY and J.-N. THÉPAUT European Centre for Medium-Range Weather Forecasts,
Observational Error Estimation of FORMOSAT-3/COSMIC GPS Radio Occultation Data SHU-YA CHEN AND CHING-YUANG HUANG Department of Atmospheric Sciences, National.
TIMN seminar GNSS Radio Occultation Inversion Methods Thomas Sievert September 12th, 2017 Karlskrona, Sweden.
Hui Liu, Jeff Anderson, and Bill Kuo
Lidia Cucurull, NCEP/JCSDA
Item Taking into account radiosonde position in verification
Assimilation of Global Positioning System Radio Occultation Observations Using an Ensemble Filter in Atmospheric Prediction Models Hui Liu, Jefferey Anderson,
Satellite Foundational Course for JPSS (SatFC-J)
Data Assimilation Initiative, NCAR
Effects and magnitudes of some specific errors
Scientific challenges in GPS RO assimilation for weather forecasting
Challenges of Radio Occultation Data Processing
Observational Data Source Impacts In The NCEP GDAS
Presentation transcript:

Recent developments for a forward operator for GPS RO Lidia Cucurull NOAA GPS RO Program Scientist NOAA/NWS/NCEP/EMC NCU, Taiwan, 16 August

n Introduction n 3-term Refractivity expression n Bending angle n Effects of including compressibility factors (Yu-Chun Chen) n Summary and future work Outline 2

Radio Occultation concept LEO Occulting GPS Ionosphere Neutral atmosphere Earth Raw measurement: change of the delay (phase) of the signal path between the GPS and LEO during the occultation. (It includes the effect of the atmosphere) GPS transmits at two different frequencies: ~1.6 GHz (L1) and ~1.3 GHz (L2). n An occultation occurs when a GPS (GNSS) satellite rises or sets across the limb wrt to a LEO satellite n A ray passing through the atmosphere is refracted due to the vertical gradient of refractivity (density) n During an occultation (~ 3min) the ray path slices through the atmosphere 3

s 1, s 2,  1,  2  N T, P w, P Raw measurements of phase of the two signals (L1 and L2) Bending angles of L1 and L2 (neutral) bending angle Refractivity Ionospheric correction Abel transfrom Hydrostatic equilibrium, eq of state, apriori information Clocks correction, orbits determination, geometric delay choice of ‘observations’ Atmospheric products 4

Choice of observation operators Complexity L1, L2 phase L1, L2 bending angle Neutral atmosphere bending angle (ray-tracing) Linearized nonlocal observation operator (distribution around TP) Local refractivity, Local bending angle (single value at TP) Retrieved T, q, and P Not practical Not good enough Possible choices 5

Introduction n At microwave wavelengths (GPS), the dependence of N on atmospheric variables can be expressed as: Hydrostatic balance P is the total pressure (mb) T is the temperature (K) Scattering terms W w and W i are the liquid water and ice content (gr/m 3 ) Moisture P w is the water vapor pressure (mb) Ionosphere f is the frequency (Hz) n e electron density(m -3 ) – important in the troposphere for T> 240K –can contribute up to 30% of the total N in the tropical LT. –can dominate the bending in the LT. Contributions from liquid water & ice to N are very small and the scattering terms can be neglected RO technology is almost insensitive to clouds. 6

Forward Model for refractivity n (1) Geometric height of observation is converted to geopotential height. n (2) Observation is located between two model levels. n (3) Model variables of pressure, (virtual) temperature and specific humidity are interpolated to observation location. n (4) Model refractivity is computed from the interpolated values. n The assimilation algorithm produces increments of –surface pressure –water vapor of levels surrounding the observation –(virtual) temperature of levels surrounding the observation and all levels below the observation (ie. an observation is allowed to modify its position in the vertical) n Each observation is treated independently and we account for the drift of the tangent point within a profile 7

k1 k1-1 surface k2 k1-2 obs

Pre-operational implementation run n PRYnc (assimilation of operational obs ), n PRYc (PRYnc + COSMIC refractivity) n We assimilated around 1,000 COSMIC profiles per day Anomaly correlation as a function of forecast day (geopotential height) rms error (wind) 9

Dashed lines: PRYnc Solid lines: PRYc (with COSMIC) Red: 6-hour forecast Black: analysis Pre-operational implementation run (cont’d) 10

n More accurate forward operator for refractivity –Three term expression –Analysis of different sets of refractive indexes n Update of the quality control procedures –More observations (in particular in tropical latitudes) n Optimal observation error characterization (Desroziers 2005) –Smoother normalized differences –No empirical tuning n Changes resulted in an improvement in model skill in SH (mass fields) and reduction of the low- and high-level tropical wind errors n These changes were implemented operationally at NCEP in Dec 2009 n Detailed description of the changes and results can be found in Cucurull 2010, WAF, 25,2, Improved algorithms for N

3-term Forward Operator for refractivity n (1) Geometric height of observation is converted to geopotential height. n (2) Observation is located between two model levels. n (3) Model variables of pressure, (virtual) temperature and specific humidity are interpolated to observation location. n (4) Model refractivity is computed from the interpolated values. n The assimilation algorithm produces increments of –surface pressure –water vapor of levels surrounding the observation –(virtual) temperature of levels surrounding the observation and all levels below the observation (ie. an observation is allowed to modify its position in the vertical) n Each observation is treated independently and we account for the drift of the tangent point within a profile 12

13

original (ops) QC & errormodified QC & error (O-B)/O_err Errors too small Many more Observations !! Very few observations NH TR SH

Impact with COSMIC n AC scores (the higher the better) as a function of the forecast day for the 500 mb gph in Southern Hemisphere n 40-day experiments: –expx (NO COSMIC) –cnt (old RO assimilation code - with COSMIC) –exp (ops –- with COSMIC) COSMIC provides 8 hours of gain in model forecast skill starting at day 4 !!! Cucurull 2010 (WAF)

Forward Model for bending angle n Make-up of the integral: –Change of variable to avoid the singularity –Choose an equally spaced grid to evaluate the integral by applying the trapezoid rule 16

n Compute model geopotential heights and refractivities at the location of the observation n Convert geopotential heights to geometric heights n Add radius of curvature to the geometric heights to get the radius: r n Convert refractivity to index of refraction: n n Get refractional radius (x=nr) and dln(n)/dx at model levels and evaluate them in the new grid. We make use of the smoothed Lagrange-polynomial interpolators to assure the continuity of the FM wrt perturbations in model variables. n Evaluate the integral in the new grid. n Each observation is treated independently and we account for the drift of the tangent point within a profile Forward Model for bending angle (cont’d) 17

QC 18 NH TR SH NH TR SH

QC (model level) 19 NH TR SH NH TR SH

N vs BA (single case, T62L64) 20 N BA

21 N BA

22 N BA

Assimilation algorithm 23 0:gps E :gps E :gps E :gps E :gps E :gps E Counts J J/counts N BA

Experiments setup Case: 2010/02/01 12Z CTRL : no compressibility factor, old coefficient for N EXP0 : Compressibility Factor + old coefficient for N EXP1 : Compressibility Factor + Rueger’s Coefficient for N EXP2 : (Compressibility Factor + Rueger’s Coefficient for N) for GPS only EXP0 V.S. CTRL EXP1 V.S. CTRL Northern Hemisphere Yu-Chun Chen

CTRL anl V.S. EXP1 anl CTRL anl V.S. EXP2 anl

EXP1 anl V.S. EXP2 anl Small differences 0.3%~0.7%

Summary n NCEP’s operational assimilation algorithm for GPS RO makes use of a three-term forward operator for refractivity n Current work focuses on the use of a (local) bending angle operator n Compressibility factors will be further evaluated and tested in a future parallel run n Future work should address the horizontal gradients of refractivity (non-local operators) 27