Continuous Humidity Profiling using a Wind- Profiler Radar in the UHF band (continued) F. Saïd* (1), B. Campistron (1), D. Bengochea (1), O. Bock (2),

Slides:

Advertisements

Similar presentations

Filterbank Radiometers for Atmospheric Profiling

Advertisements

Introduction to data assimilation in meteorology Pierre Brousseau, Ludovic Auger ATMO 08,Alghero, september 2008.

C. Flamant (1), C. Champollion (1,2), S. Bastin (1) and E. Richard (3) (1) Institut Pierre-Simon Laplace, UPMC/CNRS/UVSQ (2) Géosciences Montpellier UM2/CNRS,

A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.

Operational bias humidity correction applied to HyMeX radiosounding M.Nuret (1), O. Bock (2) and N. Fourrié (1) (1)METEO-France, CNRM-GAME (2)IGN LAREG.

GLOBAL CLIMATES & BIOMES

Shortwave Radiation Options in the WRF Model

Atmospheric Destabilization Processes Upper Level Mixed Layer Synoptic Lifting Dynamic Destabilization Differential Advection.

Water vapor estimates using simultaneous S and Ka band radar measurements Scott Ellis, Jothiram Vivekanandan NCAR, Boulder CO, USA.

Skyler Goldman, Meteorology, DMES RELATIONSHIP BETWEEN ROUGHNESS LENGTH, STATIC STABILITY, AND DRAG COEFFICIENT IN A DUNE ENVIRONMENT.

Measured parameters: particle backscatter at 355 and 532 nm, particle extinction at 355 nm, lidar ratio at 355 nm, particle depolarization at 355 nm, atmospheric.

Chapter 8 Coordinate Systems.

Raman Lidar observations of a MCS on July 20th Rohini Bhawar (1), Paolo Di Girolamo (1), Donato Summa (1), Tatiana Di Iorio (2), Belay B. Demoz (3,4) (1)

Lidar and radar measurements of the melting layer at Supersite R: observations dark and bright band phenomena Donato Summa(1), Paolo Di Girolamo(1), Rohini.

NDACC Working Group on Water Vapor NDACC Working Group on Water Vapor Bern, July 5 -7, 2006 Raman Lidar activities at Rome - Tor Vergata F.Congeduti, F.Cardillo,

Unstable Science Question 2 John Hanesiak CEOS, U. Manitoba Unstable Workshop, Edmonton, AB April 18-19, 2007.

Humidity. Water Vapor Can make up as little as 1/10 th of 1% to 4% of the atmosphere. Scientists agree that it is the most important atmospheric gas when.

July 2001Zanjan, Iran1 Atmospheric Profilers Marc Sarazin (European Southern Observatory)

Water Vapour Intercomparison Effort in the Frame of the Convective and Orographically-Induced Precipitation Study: Airborne-to-Ground-based and airborne-to-airborne.

Observation of a Saharan dust outbreak on 1-2 August 2007: determination of microphysical particle parameters Paolo Di Girolamo 1, Donato Summa 1, Rohini.

Chapter 11 Section 2 State of Atmosphere. Temperature vs. Heat Temperature: measures the movement of molecules  Faster = Warmer  Slower = Colder  Measured.

Raman lidar characterization of PBL structure during COPS Donato Summa a, Paolo Di Girolamo a, Dario Stelitano a, Tatiana Di Iorio b a DIFA, Univ. della.

1 The Thermodynamic Diagram Adapted by K. Droegemeier for METR 1004 from Lectures Developed by Dr. Frank Gallagher III OU School of Meteorology.

June, 2003EUMETSAT GRAS SAF 2nd User Workshop. 2 The EPS/METOP Satellite.

Boundary layer temperature profile observations using ground-based microwave radiometers Bernhard Pospichal, ISARS 2006 Garmisch-Partenkirchen AMMA - Benin.

IHOP Workshop, Boulder, CO, March, 2003 DLR-DIAL Observations Instrument PI: Gerhard Ehret Instrument operation: Gorazd Poberaj, Andreas Fix, Martin.

Introduction to Cloud Dynamics We are now going to concentrate on clouds that form as a result of air flows that are tied to the clouds themselves, i.e.

COSMIC GPS Radio Occultation Temperature Profiles in Clouds L. LIN AND X. ZOU The Florida State University, Tallahassee, Florida R. ANTHES University Corporation.

ARM Data Overview Chuck Long Jim Mather Tom Ackerman.

AROME WMED, a real-time mesoscale model designed for the HyMeX Special Observation Periods N. Fourrié,, E. Bresson, M. Nuret, C. Jany, P. Brousseau, A.

Wu Sponsors: National Aeronautics and Space Administration (NASA) Goddard Space Flight Center (GSFC) Goddard Institute for Space Studies (GISS) New York.

Evaluation of the WVSS-II Sensor Using Co-located In-situ and Remotely Sensed Observations Sarah Bedka, Ralph Petersen, Wayne Feltz, and Erik Olson CIMSS.

RAdio Detection And Ranging. Was originally for military use 1.Sent out electromagnetic radiation (Active) 2.Bounced off an object and returned to a listening.

Evaluating forecasts of the evolution of the cloudy boundary layer using radar and lidar observations Andrew Barrett, Robin Hogan and Ewan O’Connor Submitted.

AROME_WMED for HyMeX M.Nuret, N. Fourrié, E. Bresson, C. Jany, P. Brousseau, A. Doerenbecher and colleagues Météo-France / CNRM-GAME.

Toulouse IHOP meeting 15 June 2004 Water vapour variability within the growing convective boundary layer of 14 June 2002 with large eddy simulations and.

Verification Verification with SYNOP, TEMP, and GPS data P. Kaufmann, M. Arpagaus, MeteoSwiss P. Emiliani., E. Veccia., A. Galliani., UGM U. Pflüger, DWD.

1 Atmospheric profiling to better understand fog and low level cloud life cycle ARM/EU workshop on algorithms, May 2013 J. Delanoe (LATMOS), JC.

Toward Correcting InSAR Images for Tropospheric Delay A.W. Moore, S.L. Granger, S.E. Owen, F.H. Webb, E.J. Fetzer, E.J. Fielding, E.F. Fishbein Jet Propulsion.

ATM 301 Lecture #11 (sections ) E from water surface and bare soil.

T. Bergot - Météo-France CNRM/GMME 1) Methodology 2) Results for Paris-CdG airport Improved site-specific numerical model of fog and low clouds -dedicated.

Preliminary LES simulations with Méso-NH to investigate water vapor variability during IHOP_2002 F. Couvreux F. Guichard, V.

SRL/Reference sonde P. Di Girolamo, D. Whiteman, B. Demoz, J. Wang, K. Beierle, T. Weckwerth The Reference sonde (C34) consists of a Snow White (SW) chilled-mirror.

EARTH SCIENCE Prentice Hall EARTH SCIENCE Tarbuck Lutgens 

Airborne/ground-based sensor intercomparison: SRL/LASE Paolo Di Girolamo, Domenico Sabatino, David Whiteman, Belay Demoz, Edward Browell, Richard Ferrare.

A new method for first-principles calibration

Moist processes involved in IOP13 and IOP16. Fanny DUFFOURG Olivier NUISSIER Christine LAC CNRM-GAME / Météo-France & CNRS HyMeX ST-WV meeting, Toulouse,

State of the Atmosphere. Temperature is a measurement of how rapidly or slowly molecules move around.

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,

METR Introduction to Synoptic Meteorology Other upper air sounding systems, apart from radiosondes University of Oklahoma 2004.

Météo-France / CNRM – T. Bergot 1) Methodology 2) The assimilation procedures at local scale 3) Results for the winter season Improved Site-Specific.

Jonathan Pleim NOAA/ARL* RTP, NC A NEW COMBINED LOCAL AND NON-LOCAL PBL MODEL FOR METEOROLOGY AND AIR QUALITY MODELING * In Partnership with the U.S. Environmental.

UNIVERSITY OF BASILICATA CNR-IMAA (Consiglio Nazionale delle Ricerche Istituto di Metodologie per l’Analisi Ambientale) Tito Scalo (PZ) Analysis and interpretation.

Estimation of temperature and humidity with a wind profiling radar-RASS measurements Toshitaka Tsuda Research Institute for Sustainable Humanosphere, Kyoto.

Chapter 5 Cloud Development and Precipitation Adiabatic Changes in a Rising Air Parcel Adiabatic- no energy exchange with environment Adiabatic- no energy.

Spain Algeria France Italy

COAMPS ® Ducting Validation Wallops-2000 William Thompson and Tracy Haack Naval Research Laboratory Marine Meteorology Division Monterey, CA COAMPS ® is.

Development of Assimilation Methods for Polarimetric Radar Data

The second AROME-WMED reanalysis of SOP1

sun- (/sky-) photometer ground-networks

Relationships inferred from AIRS-CALIPSO synergy

Analysis of Refractivity Measurements: Progress and Plans

Weather balloons are launched twice a day

Local Analysis and Prediction System (LAPS)

Nonlinear modulation of O3 and CO induced by mountain waves in the UTLS region during TREX Mohamed Moustaoui(1), Alex Mahalov(1), Hector Teitelbaum(2)

1. Transformations of Moist Air

Refractivity During IHOP_2002

M. De Graaf1,2, K. Sarna2, J. Brown3, E. Tenner2, M. Schenkels4, and D

Forecast Verification time!

Presentation transcript:

Continuous Humidity Profiling using a Wind- Profiler Radar in the UHF band (continued) F. Saïd* (1), B. Campistron (1), D. Bengochea (1), O. Bock (2), P. Di Girolamo (3) and D. Legain (4) (1) Laboratoire d’Aérologie, Université de Toulouse, UMR CNRS 5560, Toulouse, France, (2) IGN-LAREG Paris, France, (3) Scuola di Ingegneria, Universita degli Studi della Basilicata, Potenza, Italy. (4) CNRM, Météo-France, Toulouse, France. HYMEX 9 th International Conference, Mykonos, sept 2015 CNRM/Météo-France UHF wind profiler and radiosounding balloons (photo: BLLAST 2011)

-Objective  Determine (indirectly) atmospheric water vapor profiles at a fine time resolution by using a Doppler wind profiler radar (and ancillary data) -Theoretical basis -1 st step: Calibration and optimization -2 nd step: Fine resolution profiles

THEORETICAL BASIS : to retrieve q, the humidity mixing ratio, at level Z, we use : vertical profile of refractive index M 2, and not M, is provided by the radar P, T are provided by a RS We also need initial conditions when integrating the differential equation

1 st step: CALIBRATION and OPTIMIZATION (1/4) Initial value from RS radar reflectivity reflectivity maximum (usually a transition between ≠ humidity conditions) Novelty (vs litter.)  2 integrations down and up to the Zi level.  varying calibration coefficients from one profile to the other Zi level

1 st step: CALIBRATION and OPTIMIZATION (2/4) Indetermination on M sign  use the RS sign for M Before After M>0

1 st step: CALIBRATION and OPTIMIZATION (3/4) q larger than the saturation threshold  limitate profiles to q sat Before After saturated mixing ratio q sat (from RS)

1 st step: CALIBRATION and OPTIMIZATION: results (4/4) Bias (q RS – q radar ) and standard deviation before and after the improvements (October 2012): std decreases a lot;|bias| is the same and shows a weak underestimation from the radar at mid-levels. Before (28 profiles) After the radar is 1 g/kg larger than the RS

2 nd step: FINE RESOLUTION PROFILES (1/5) RS1+ RS2 or RS1+RS3 provide RS1 RS2 RS3 q initial conditions calibration coefficients P and T profiles M sign ↓ that are interpolated 15-min RADAR obs provide Cn 2, Zi, windshear, ε RADAR 15 min q P R O F I L E S

2 nd step: FINE RESOLUTION PROFILES (2/5) RS1 (15h) RS2 (21h) saturated profiles 15-min radar profiles RS1 RS2 15-min lidar profiles (BASIL) interpolated RS profiles The radar shows a cloud between 16-18h ( m) that appears and vanishes between the 15h and 21h RS. The lidar signal is attenuated above 2000m, where saturation starts.

2 nd step: FINE RESOLUTION PROFILES (3/5) RS1 (3h) RS2 (9h) RS1 is dry over 2000m at 3h. An advection of moist air (the wind direction has shifted) increases q between 2000 and 3000m  well seen by the radar RS1 (3h) RS2 (9h) saturated profiles 15-min radar profiles the radar is wetter here (confirmed by W) moisture advection

2 nd step: FINE RESOLUTION PROFILES (4/5) RS1 (3h) RS2 (9h) saturated profiles drying RS1 RS2 15-min lidar profiles interpolated RS profiles The radar also shows the drying between the 3h and 9h profiles and the development of the boundary layer, a little more accurately that the interpolated RS (dashed lines on the right). BL development

2 nd step: FINE RESOLUTION PROFILES (5/5) RS1 (3h) RS2 (9h) this part of the virga and the moistening below are well documented with the radar but are not seen by the RS.

CONCLUSION The retrieval of air humidity profiles from radar reflectivity is an ill-posed problem because extra parameter are needed  limit the ambitions of the profiler retrieval. Using RS at two different times, the profiler is able to retreive humidity at intermediate time (at a finer temporal resolution). In that configuration the profiler works as a temporal interpolator. The original and most important improvement: the double vertical integration (upward and downward) heading to the Zi position provided by the profiler + specific calibration coefficients Operational use  requires some refining. However, tests have to be made in different weather conditions (dry), and the lidar would be perfect to assess the results. [IWV from GPS could not help (to constrain the profiles) since their variation was not consistent with the RS variation]