16/06/20151 Validating the AVHRR Cloud Top Temperature and Height product using weather radar data COST 722 Expert Meeting 9.-11. 6. 2004 Sauli Joro.

Slides:



Advertisements
Similar presentations
Proposed new uses for the Ceilometer Network
Advertisements

Robin Hogan, Richard Allan, Nicky Chalmers, Thorwald Stein, Julien Delanoë University of Reading How accurate are the radiative properties of ice clouds.
Integrated Profiling at the AMF
Improved Automated Cloud Classification and Cloud Property Continuity Studies for the Visible/Infrared Imager/Radiometer Suite (VIIRS) Michael J. Pavolonis.
A thermodynamic model for estimating sea and lake ice thickness with optical satellite data Student presentation for GGS656 Sanmei Li April 17, 2012.
3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.
An overview of CM SAF cloud retrieval methods Karl-Göran Karlsson SMHI, Sweden Outline: How do we observe clouds from space? Which cloud properties can.
TRMM Tropical Rainfall Measurement (Mission). Why TRMM? n Tropical Rainfall Measuring Mission (TRMM) is a joint US-Japan study initiated in 1997 to study.
Understanding AMV errors using a simulation framework Peter Lean 1* Stefano Migliorini 1 and Graeme Kelly 2 * EUMETSAT Research Fellow, 1 University of.
The SMHI AVHRR & Cloud Type dataset for CNN-I/II & BBC Adam Dybbroe The AVHRR dataset Navigation, Coverage, Re-mapping, Resolution The Cloud Type product.
10/05/041 Utilisation of satellite data in the verification of HIRLAM cloud forecasts Christoph Zingerle and Pertti Nurmi.
Initial testing of longwave parameterizations for broken water cloud fields - accounting for transmission Ezra E. Takara and Robert G. Ellingson Department.
Xin Kong, Lizzie Noyes, Gary Corlett, John Remedios, Simon Good and David Llewellyn-Jones Earth Observation Science, Space Research Centre, University.
Satellite basics Estelle de Coning South African Weather Service
Motivation Many GOES products are not directly used in NWP but may help in diagnosing problems in forecasted fields. One example is the GOES cloud classification.
11/09/2015FINNISH METEOROLOGICAL INSTITUTE CARPE DIEM WP 7: FMI Progress Report Jarmo Koistinen, Heikki Pohjola Finnish Meteorological Institute.
Development of WRF-CMAQ Interface Processor (WCIP)
Applications and Limitations of Satellite Data Professor Ming-Dah Chou January 3, 2005 Department of Atmospheric Sciences National Taiwan University.
Diagnosing Climate Change from Satellite Sounding Measurements – From Filter Radiometers to Spectrometers William L. Smith Sr 1,2., Elisabeth Weisz 1,
1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)
1 GOES-R AWG Product Validation Tool Development Aviation Application Team – Volcanic Ash Mike Pavolonis (STAR)
USING OF METEOSAT SECOND GENERATION HIGH RESOLUTION VISIBLE DATA FOR THE IMPOVEMENT OF THE RAPID DEVELOPPING THUNDERSTORM PRODUCT Oleksiy Kryvobok Ukrainian.
COSMIC GPS Radio Occultation Temperature Profiles in Clouds L. LIN AND X. ZOU The Florida State University, Tallahassee, Florida R. ANTHES University Corporation.
Cloud Top Properties Bryan A. Baum NASA Langley Research Center Paul Menzel NOAA Richard Frey, Hong Zhang CIMSS University of Wisconsin-Madison MODIS Science.
Towards retrieving 3-D cloud fractions using Infrared Radiances from multiple sensors Dongmei Xu JCSDA summer colloquium, July August
WATER VAPOR RETRIEVAL OVER CLOUD COVER AREA ON LAND Dabin Ji, Jiancheng Shi, Shenglei Zhang Institute for Remote Sensing Applications Chinese Academy of.
Water Vapour & Cloud from Satellite and the Earth's Radiation Balance
10/05/041 Satellite Data in the Verification of Model cloud forecasts Christoph Zingerle Tartu, 24. – 26. Jan HiRLAM mini workshop on clouds and.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 Using CALIPSO to Explore the Sensitivity to Cirrus Height in the Infrared.
CBH statistics for the Provisional Review Curtis Seaman, Yoo-Jeong Noh, Steve Miller and Dan Lindsey CIRA/Colorado State University 12/27/2013.
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
Assimilating satellite cloud information with an Ensemble Kalman Filter at the convective scale Annika Schomburg, Christoph Schraff, Hendrik Reich, Roland.
Linear Optimization as a Solution to Improve the Sky Cover Guess, Forecast Jordan Gerth Cooperative Institute for Meteorological Satellite Studies University.
Improvement of Cold Season Land Precipitation Retrievals Through The Use of Field Campaign Data and High Frequency Microwave Radiative Transfer Model IPWG.
Use of Solar Reflectance Hyperspectral Data for Cloud Base Retrieval Andrew Heidinger, NOAA/NESDIS/ORA Washington D.C, USA Outline " Physical basis for.
METR February Radar Products More Radar Background Precipitation Mode: -Volume Coverage Patterns (VCP) 21: 9 elevation angles with a complete.
Response of active and passive microwave sensors to precipitation at mid- and high altitudes Ralf Bennartz University of Wisconsin Atmospheric and Oceanic.
Use of a high-resolution cloud climate data set for validation of Rossby Centre climate simulations Presentation at the EUMETSAT Meteorological Satellite.
Satellite based instability indices for very short range forecasting of convection Estelle de Coning South African Weather Service Contributions from Marianne.
Developers: John Walker, Chris Jewett, John Mecikalski, Lori Schultz Convective Initiation (CI) GOES-R Proxy Algorithm University of Alabama in Huntsville.
1 Summary of NWCSAF/PPS PAR User Survey Presented during the NWCSAF Product Assessment Review Workshop October 2005 Prepared by : Anke Thoss, Angela.
SAF - Nowcasting Product Assessment Review Worshop (Madrid 17 – 18 – 19 0ctober 2005 Yann Guillou Météo-France (DPR) Page 1/8 Long duration validation.
ITSC-1227 February-5 March 2002 Use of advanced infrared sounders in cloudy conditions Nadia Fourrié and Florence Rabier Météo France Acknowledgement G.
Validation of Cloud Top Temperature with CLOUDNET cloud radar data during the BBC2 Period Erwin Wolters (D. Donovan Presenting)
Cloud property retrieval from hyperspectral IR measurements Jun Li, Peng Zhang, Chian-Yi Liu, Xuebao Wu and CIMSS colleagues Cooperative Institute for.
Retrieval of cloud parameters from the new sensor generation satellite multispectral measurement F. ROMANO and V. CUOMO ITSC-XII Lorne, Victoria, Australia.
UCLA Vector Radiative Transfer Models for Application to Satellite Data Assimilation K. N. Liou, S. C. Ou, Y. Takano and Q. Yue Department of Atmospheric.
ITSC-12 Cloud processing in IASI context Lydie Lavanant Météo-France, Centre de Météorologie Spatiale, BP 147, Lannion Cedex France Purpose: Retrieval.
Preliminary results from the new AVHRR Pathfinder Atmospheres Extended (PATMOS-x) Data Set Andrew Heidinger a, Michael Pavolonis b and Mitch Goldberg a.
MODIS, AIRS, and Midlevel Cloud Phase Shaima Nasiri CIMSS/SSEC, UW-Madison Brian Kahn Jet Propulsion Laboratory MURI Hyperspectral Workshop 7-9 June, 2005.
Shaima Nasiri University of Wisconsin-Madison Bryan Baum NASA - Langley Research Center Detection of Overlapping Clouds with MODIS: TX-2002 MODIS Atmospheres.
NASA, CGMS-44, 7 June 2016 Coordination Group for Meteorological Satellites - CGMS LIMB CORRECTION OF POLAR- ORBITING IMAGERY FOR THE IMPROVED INTERPRETATION.
The assimilation of satellite radiances in LM F. Di Giuseppe, B. Krzeminski,R. Hess, C. Shraff (1) ARPA-SIM Italy (2) IMGW,Poland (3)DWD, Germany.
Radiance Simulation System for OSSE  Objectives  To evaluate the impact of observing system data under the context of numerical weather analysis and.
Cloud Detection: Optical Depth Thresholds and FOV Considerations Steven A. Ackerman, Richard A. Frey, Edwin Eloranta, and Robert Holz Cloud Detection Issues.
SIGMA: Diagnosis and Nowcasting of In-flight Icing – Improving Aircrew Awareness Through FLYSAFE Christine Le Bot Agathe Drouin Christian Pagé.
Characterising AMV height Characterising AMV height assignment errors in a simulation study Peter Lean 1* Stefano Migliorini 1 and Graeme Kelly 2 * EUMETSAT.
© Crown copyright Met Office Assimilating infra-red sounder data over land John Eyre for Ed Pavelin Met Office, UK Acknowledgements: Brett Candy DAOS-WG,
The Convective Rainfall Rate in the NWCSAF
Tae Young Kim and Myung jin Choi
CMa & CT Cloud mask and type
Precipitation Classification and Analysis from AMSU
CTTH Cloud Top Temperature and Height
SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations
AVHRR operational cloud masks intercomparison
PGE06 TPW Total Precipitable Water
Igor Appel Alexander Kokhanovsky
Studying the cloud radiative effect using a new, 35yr spanning dataset of cloud properties and radiative fluxes inferred from global satellite observations.
Fabien Carminati, Stefano Migliorini, & Bruce Ingleby
Presentation transcript:

16/06/20151 Validating the AVHRR Cloud Top Temperature and Height product using weather radar data COST 722 Expert Meeting Sauli Joro

16/06/20152 Introduction Weather radar & TOPS product Cloud Top Temperature and Height product Data Product comparison Results Summary Outline

16/06/20153 Visiting Scientist Activity within EUMETSAT SAFNWC framework EUMETSAT Satellite Application Facilities (SAFs) are programs specialized in developing and processing satellite data SAFNWC is consentrated on nowcasting and very short range forecasting Introduction

16/06/20154 The Cloud Top Temperature and Height (CTTH) product is developed within the SAFNWC framework Objective is to validate CTTH product with weather radar data – known to be very ambitious Study mainly concentrated on opaque and semi-transparent high clouds (clouds containing ice)

16/06/20155 Weather radar & TOPS product ”Direct” cloud measurement with good temporal and spatial resolution

16/06/20156 Experimental ice particle distributions in different cirrus clouds (Liou, 1992). water: 0.93 ice: 0.176

16/06/20157 ”What is the threshold value for an echo in dBZ to be considered as the cloud top?” FMI & Finnish Air Force: ”in case of ’raining cloud’ treshold value of –10 dBZ should give reliable cloud top heights” Poutiainen (1999): ”Various cirrus types can create Z values between – dBZ -5 dBZ and -10 dBZ selected to represent the true tops of ice clouds

16/06/20158 Thick cirrus on RHI screen taken from Radar Vantaa on 25 June 1998 at 10:59 UTC (Poutiainen, 1999)

16/06/20159 Thin cirrus on RHI screen taken from Radar Vantaa on 22 July 1998 at 07:29 UTC (Poutiainen, 1999)

16/06/ Uses 3-D weather radar data Vertical resolution 100 m TOPS product For each pixel downward search in cylindrical coordinates at constant range for the chosen dBZ treshold value and determines if it is crossed -> interpolation

16/06/ TOPS underestimation

16/06/ CTTH product Aims providing reliable estimates of the cloud top temperature and height for all the cloudy pixels within an AVHRR scene Vertical resolution 200 m Consists of two algorithms: one for opaque and one for semi-transparent cloudiness Results of Cloud Mask and Cloud Type products used as input data

16/06/ CTTH opaque retrieval Applied to all pixels classified as ’opaque’ by the Cloud Type product Based on Radiative Transfer Model simulations and results of NWP model For each pressure level the RTM simulates the AVHRR channel 4 cloudy brightness temperature -> the best fit to the measurement is selected -> CTH from NWP

16/06/ CTTH semi-transparent retrieval Measurement itself is ”contaminated” as the radiation from the surface is partly passed through the cloud -> brightness temperatures too warm Method uses AVHRR channels 4 and 5 brightness temperatures Result is applied to all pixels within an image segment classified as semi- transparent

16/06/ Example distribution of 32x32 pixel size image segment. semi-transparent opaque clear Single cloud layer Constant absorption coefficient throughout the cloud layer Brightness temperature depends linearly on radiance No atmospheric absorption Local thermodynamic equilibrium thermodynamic cloud top temperature

16/06/ Data Selected time period April and May satellite overpasses FMI radar data, 15min temporal resolution NWP used: HIRLAM RTM used: RTTOV

16/06/ Maximum elevation angle sets a certain minimum distance for cloud top height detection Radar sensitivity sets a maximum distance for each dBZ value ”Donuts” around radar sites Proper –10 dBZ cases only from Radar Utajärvi Cases divided into two sub- categories -5 dBZ and -10 dBZ Radar data features

16/06/ Product comparison Cases selected subjectively from AVHRR imagery together with Cloud Type and CTTH product outputs

16/06/ Simple comparison method used – data may not be normally distributed Pixel-by-pixel comparison not reasonable -> data needs to be averaged Data is classified to histogram with 200 m class intervals -> resolution difference vanishes Modes are taken as the results of the products Mode difference = TOPS – CTTH Comparison is considered to be successful if the absolute value of mode difference is less than 500 m

16/06/ Results TOPS-CTTH both -5 dBZ and -10 dBZ cases 41% success

16/06/ TOPS-CTTH radar echoes below 2000m discarded -10 dBZ applied to all cases 57% success

16/06/ TOPS-CTTH no discarded echoes cases within r -10 coverage area 75% success

16/06/ Case A: Opaque high cloud from Radar Ikaalinen -5 dBZ coverage. Satellite over pass is received on 1 May 2003 at 10:43 UTC. timing -2 minutes

16/06/ Case A frequency distributions and HP’s.

16/06/ Case B: Opaque high cloud from Radar Anjalankoski -5 dBZ coverage. Satellite over pass is received on 29 April 2003 at 19:15 UTC. timing -3 minutes

16/06/ Case B frequency distributions and HP’s.

16/06/ TOPS-CTTH

16/06/ Case C: Semi-transparent high cloud from Radar Utajärvi -10 dBZ coverage. Satellite over pass is received on 13 May 2003 at 10:47 UTC. timing -6 minutes

16/06/ Case C frequency distributions and HP’s.

16/06/ Summary A new approach for the CTTH product validation presented The approach presented here uses 3D radar data Result interpretation very challenging as both methods are based on numerous assumptions – lots of different error sources

16/06/ Results on the opaque high cloud categoty promising – more than 55% of the comparisons successful Still lots of unsuccessful comparisons with no explicit explanation, also Cloud Type misclassifications occurred -10 dBZ proved to be a good first guess for the top heights of thick ice clouds

16/06/ Results on semi-transparent high cloud category disappointing with only three successfull comparisons -10 dBZ threshold turned out to be too high for the semi-transparent high clouds and more sensitive values should be applied in the future

16/06/ A weather radar is not the best possible tool for the CTH validation, however, it can offer valuable information about the cloud tops in various situations being at its best when the cloud consists lots of ice particles. The lack of 45-degree elevation angles limited proper -10 dBZ cases to Radar Utajärvi Conclusion

16/06/ Report available at