| Monday, 21 June, 2004 (Introduction to GPS) |
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| Introduction to GPS / GNSS in Atmospheric Sensing (#1) Tom Yunck, JPL (1) Brief description of GPS / GNSS Systems + Some History (2) Science applications of GPS/GNSS overview (3) Why suitable for atmospheric sensing - some history |
| The Physics of Atmospheric Microwave Propagation (#2) Sergey Sokolovskiy, UCAR (1) Atmospheric refractivity (2) Geometric optics and its applicability (3) Deriving ray equations from Fermat principle (4) Spherically symmetric atmosphere, Snell's law (5) Propagation effects: phase delay, ray bending, focusing/defocusing effect on amplitude, their typical magnitude and examples in the Earth's atmosphere |
| Description of GPS Signal Structure & How receivers work (#3) Larry Young, JPL (1) L1, L2, P1, P2, C/A, Ycode modulations, wavelengths, codes (2) Receiver description (3) Rinex data file description and explanation / Examples |
| Tuesday, 22 June, 2004 (Ground-based meteorology) |
| The GPS / GNSS Observation equation and data processing (#4) M. Rothacher, University of Munich, Germany (1) The observation equations / Zero/single/double differences (2) Ambiguities/Data inversion/Parameter Estimation (LSQ, Kalman filter) (3) GPS analysis software - what does it do? |
| Ground based Meteorology / Parameter Estimation / results (#5) Christian Rocken, UCAR (1) Wet delay / Dry Delay / Mapping functions (2) Estimation of tropospheric parameters - error sources (3) Ground based networks / Validation / Example results |
| New developments in Ground-based meteorology (#6) John Braun, UCAR (Slant Delays / assumptions and errors Tomography and 3-D moisture retrieval Validation / example results |
| Ground-based Seminar Presentations (#7) Ted Iwabutchi, UCAR, MRI - Geonet (#7a) Natalya Nicholson, Univ. Calgary, Alberta - Southern Alberta GPS Network (#7b) Victoria Hoyle, Univ. Calgary, Alberta - Retrieval of Atmospheric Water Vapor in Southern Alberta (#7c) Randolph Ware, UCAR - SuomiNet (#7d) |
| Wednesday, 23 June, 2004 (Ground-based ionosphere) |
| Ionosphere and Ionospheric delay (#8) Tony Mannucci, JPL (1) Intro to the Ionosphere, E, F2 layers basic physics (2) Dispersive delay / ionospheric refractivity / higher order effects (3) Measuring TEC absolute and relative / Errors |
| Ionospheric Scintillation and GPS (#10) Keith M. Groves, Hanscom Air Force Base (1) Scintialltion - what is it? What causes it? why does it matter? (2) Measurement of Scintillation S4 / ROTI etc. (3) Effect of scintillation on GPS - problems / solutions /examples |
| Mountaintop and airborne GPS observations, and effects of turbulence on RO signals (#11): Aoyama: Mountaintop GPS observations (#11a) Speaker (TBD): Airborne GPS observations (#11b) Larry Cornman, NCAR : The Effect of Turbulence on GPS Signals: Theory and Measurements(#11c) |
| Thursday, 24 June, 2004 (Radio Occultation I) |
| Introduction to Radio Occultation sounding (#12) E. Rob Kursinski, U. of Arizona (1) Overview of the main steps in processing RO signals. (2) Calculation of the bending angles from Doppler. (3) Abel inversion. (4) Characteristics of RO sounding: vertical and horizontal resolution. (5) Outline of the main difficulties of RO soundings (residual ionospheric noise, multipath.) |
| Extraction of the Atmospheric Excess Phase for RO Processing (#13a) Chris Rocken and Bill Schreiner, UCAR (1) Solving for satellite orbits and clocks. (2) The use of ground receivers and reference satellites. (3) Single vs. double differencing. (4) Noise reduction by applying modified ionospheric calibration for reference link. |
| Acquisition of the tropospheric RO signals (#14) Sergey Sokolovskiy, UCAR (1) Modeling of the tropospheric RO signals. (2) Structure of the tropospheric RO signals and their visualization by sliding spectrograms. (3) Principles of the open loop tracking of RO signals. (4) Post-processing of the acquired RO signals: correction of the mean frequency, upsampling, extraction of the phase and amplitude. |
| Radio-holographic inversions of the tropospheric RO signals (#15) Sergey Sokolovskiy, UCAR (1) Back propagation method. (2) Radio-optics (the sliding spectral) methods. (3) Methods utilizing transformation of the complex wave function to impact parameter representation (canonical transform, full spectrum inversion). |
| GEOS: a 3rd generation of RO science? Peter Milne, NSF |
| Friday, 25 June, 2004 (Radio Occultation II) |
| Ionospheric calibration of RO signals (#16) Stig Syndergaard, UCAR (1) Calibration of the phases and the bending angles. (2) Ionospheric calibration in case of very noisy L2. (3) Ionospheric calibration in the lower troposphere. (4) Higher-order effects, model-dependent ionospheric calibration. |
| The use of ancillary data (climatology) the upper stratosphere for noise reduction of RO inversions (#17) Sean Healy, ECMWF (1) A dominant source of noise in RO observables in the upper stratosphere. Downward error propagation after the Abel inversion. (2) Initialization of the Abel inversion by replacing the observables for the 1st guess (climatology) above some altitude. An optimal choice of the altitude. (3) Optimization of the obsertvational bending angles (mixing with the 1st guess according to estimated errors). (4) Individual estimation of noise for each occultation. (5) Account for the finite vertical error correlation of the observational bending angles and the 1st guess. |
| Challenge of RO in the PBL (#18) Sergey Sokolovskiy, UCAR (1) The structure of the moist PBL. (2) Superrefraction in geometric optics. (3) Inapplicability of the Abel inversion in case of the superrefraction. (4) Effect of the superrefraction on RO inversions. (5) Probability of the superrefraction in different regions over tropic ocean. (6) Superrefraction in radio-holographic inversions. (7) Effect of small-scale refractivity irregularities on radio-holographic inversions. (8) Parameters of RO signals that indicate high probability of the superrefraction. |
| COSMIC Data Analysis and Archive Center (CDAAC) demonstration and exercise (#20) Doug Hunt and Bill Schreiner, UCAR (Note: The original lecture planned for #19 is cancelled) |
| Monday, 28 June, 2004 (RO Results) |
| Error characterization of RO (#21) E. Rob Kursinski, U. of Arizona (1) Classification of the observational errors: measurement errors; and representativeness errors. (2) Main error sources: residual ionospheric noise at high altitudes; turbulence at mid and low altitudes; superrefraction in the lower troposphere. (3) Different approaches for error charcterization: theoretical, based on some parameters of RO signal (allows individual characterization of each occultation); empirical, based on comparison to ancillary data; empirical, based on comparison of close occultations. (4) Summary of the magnitude and altitude ranges for different types of errors. |
| Ionospheric observations from RO (#22) Paul Straus, The Aerospace Corporation (1) Uncalibrated and calibrated TEC. Calibration of TEC by use of pseudo-range data. Calibration of TEC by use of the ascending and descending parts of orbit. (2) Abel inversion of TEC below LEO orbit. Use of single- and dual-frequency data. Advantages and shortcomings of both approaches. Demonstration of results. (3) Overview of constrained inversions (demonstration of results). Overview of 3-D tomography, assimilation of TEC by dynamic ionospheric models. (4) Overview of correlative ionospheric data used for comparisons to GPS RO inversions (ionosondes, incoherent scatter radars, etc.) (5) Ionospheric scintillation. Its characterization. Localization of the regions with ionospheric irregularities by multiple occultations with crossed lines of sight and by back propagation of the complex RO signals from single occultations. |
| GPS RO Missions: Overview and Results from CHAMP, GPS/MET and SAC-C (#23) Jens Wickert, GFZ, Postdam, Germany (1) Overview: GPS RO mission (2) In more detail: GPS/MET & SAC-C (selected results) (3) CHAMP: The mission (4) CHAMP: Overview of results |
| Water vapor monitoring with RO (#24) E. Rob Kursinski, U. of Arizona (1) Ambiguity of interpretation of the retrieved refractivity in the presence of water vapor. The use of ancillary data for resolving the ambiguity. 1-D VAR. (2) Overview of GPS RO water vapor retrievals. Comparison to ancillary data (NWP models, radiosondes, etc.) (3) Independent retrievals of water vapor by use of additional frequencies near the absorption lines (X-link technology). Requirements for the accuracy of amplitude measurements. (4) Problems of the standard GPS RO and the X-link techniques in the lower troposphere. Demonstration of results of numerical simulations of the X-link technique. |
| Tuesday, 29 June, 2004 (Science Applications) |
| Climate Monitoring and RO (#25) Stephen Leroy, Harvard University (1) Issues of assessing climate change based on observations or global analyses (2) Projection of climate changes based on global climate models (3) Climate changes signals as detected by the GPS RO data (4) Issues and topics for future research |
| Complementary Radiometric Observations (#26) John Dykema, Harvard University (1) Explain TOVS, AIRS etc. (2) What is the true information from AIRS vs from GPS RO - importance of 1st guess (3) Discuss strengths and weaknesses of Radiometer data (4) Discuss how RO + nadir viewing data can be complementary |
| Radio Occultation and the Tropopause (#27) William Randel, NCAR (1) Transport of moisture through tropopause (2) Diagnosis of tropopause variations based on GPS RO data (3) Analysis of QBO based on GPS RO data (4) Potential future applications of GPS RO data |
| GPS reflections for oceanography, ice and surface hydrology (#28) Cinzia Zuffada, JPL (1) The concept of GPS reflected signals, bistatic radar geometry (2) GPS reflections from ocean - incoherent scattering - surface roughness characterization - ocean altimetry - GPS reflections mission concept (3) GPS reflections from land - relationship to soil moisture (4) GPS reflections from ice - relationship to ice thickness - ice altimetry |
| Wednesday, 30 June, 2004 (Data Assimilation) |
| Overview of Atmospheric Data Assimilation (#29) Chris Snyder, NCAR (1) Estimation theory (2) Different approaches to data assimilation (3DVAR, 4DVAR, EnKF) (3) Factors affecting the performance of atmospheric data assimilation (e.g., observation error statistics, background error statistics, observation operators, model errors, …etc) |
| Assimilation and Impact of Ground Based Observations (#30) Lidia Cucurull, UCAR/NCEP (1) Issues related to the assimilation of ground based GPS data (2) Different choices of observation operators (total zenith delay, slant delay, slant water vapor, precipitable water, …etc) (3) Variational assimilation of ground-based GPS data and other auxiliary data (4) Impact on precipitation forecasts |
| Assimilation and Impact of RO Observations (#31) Bill Kuo, UCAR (1) Issues related to the assimilation of GPS RO data (2) Different choices of observation operators (3) Variational (3DVAR and 4DVAR) assimilation of GPS RO data (4) Impact on prediction |
| GAIM and Ionospheric RO data assimilation (#32) George Hajj, JPL (1) Ionospheric Forward Model (2) GPS ionospheric occultation and other complementary data (3) 4DVAR (4) Validation |
| Thursday, 1 July, 2004 - Mini-Workshop on Atmospheric Remote Sensing Using GPS |
| Special Session on the Assimilation of GPS Radio Occultation Data |
| Forecast impact experiments with CHAMP RO measurements: Firsts results at ECMWF. Sean Healy, ECMWF |
| Use of COSMIC observations in the NOAA/NWS/NCEP data assimilation systems. Lidia Cucurull, UCAR and NOAA/NCEP |
| Assimilation of GPS RO data using the NCAR Data Assimilation Research Testbed. Hui Liu, NCAR |
| Assimilation of CHAMP and SAC-C Radio Occultation Data Using MM5 4DVAR. Tae-Kwon Wee, UCAR COSMIC |
| Impact of Assimilated GPS Occultation Refractivity on Typhoon Predictions. Ching-Yuan Huang, National Central University |