Towards the operational cloud classification in Finland Otto Hyvärinen COST 722 Expert Meeting, Helsinki, 9 June 2004.

Slides:



Advertisements
Similar presentations
Rainfall estimation for food security in Africa, using the Meteosat Second Generation (MSG) satellite. Robin Chadwick.
Advertisements

Precipitation Products PPS Anke Thoss, SMHI User Workshop, February 2015, Madrid.
Affinity Set and Its Applications Moussa Larbani and Yuh-Wen Chen.
VIIRS LST Uncertainty Estimation And Quality Assessment of Suomi NPP VIIRS Land Surface Temperature Product 1 CICS, University of Maryland, College Park;
3D Radiative Transfer in Cloudy Atmospheres: Diffusion Approximation and Monte Carlo Simulation for Thermal Emission K. N. Liou, Y. Chen, and Y. Gu Department.
Northern hemisphere Fractional Snow mapping with VIIRS - First experiments in ESA DUE GlobSnow-2 Sari Metsämäki 1), Kari Luojus 2), Jouni Pulliainen 2),
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.
Accuracy Assessment of Thematic Maps
1 Operational low visibility statistical prediction Frédéric Atger (Météo-France)
Multiple Criteria for Evaluating Land Cover Classification Algorithms Summary of a paper by R.S. DeFries and Jonathan Cheung-Wai Chan April, 2000 Remote.
Revolutions in Remote Sensing Greatly Enhanced Weather Prediction from the 1950s Through Today.
16/06/20151 Validating the AVHRR Cloud Top Temperature and Height product using weather radar data COST 722 Expert Meeting Sauli Joro.
Measuring Model Complexity (Textbook, Sections ) CS 410/510 Thurs. April 27, 2007 Given two hypotheses (models) that correctly classify the training.
10/05/041 Utilisation of satellite data in the verification of HIRLAM cloud forecasts Christoph Zingerle and Pertti Nurmi.
04/07/04THE FINNISH METEOROLOGICAL INSTITUTE Wednesday Opening of meeting Presentations and discussions Case Study of Fog - FMI
Classification for High Dimensional Problems Using Bayesian Neural Networks and Dirichlet Diffusion Trees Radford M. Neal and Jianguo Zhang the winners.
COST-722 Short range forecasting methods of fog, visibility and low clouds.
FMI contribution to WGii first report Requirements from the forecasters and from the cuntomers Janne Kotro Jukka Julkunen Juha Kilpinen.
12/03/041 Remote sensing in Weather applications We produce algorithms and prototypes of products for FMI forecasters and customers. Our tools include.
SPS ITRT April Computation/ Estimation Number & Number Sense Measurement/ Geometry Probability/ Statistics Patterns, Functions, & Algebra
1 Satellite Remote Sensing of Particulate Matter Air Quality ARSET Applied Remote Sensing Education and Training A project of NASA Applied Sciences Pawan.
0 Pattern Classification, Chapter 3 0 Pattern Classification All materials in these slides were taken from Pattern Classification (2nd ed) by R. O. Duda,
A Simple Method to Extract Fuzzy Rules by Measure of Fuzziness Jieh-Ren Chang Nai-Jian Wang.
1 Department of Agriculture Animal Production and Aquatic Environment 2 Department of Management of Environment and Natural Resources University of Thessaly.
Center for Satellite Applications and Research (STAR) Review 09 – 11 March 2010 CLOUD MASK AND QUALITY CONTROL FOR SST WITHIN THE ADVANCED CLEAR SKY PROCESSOR.
OC3522Summer 2001 OC Remote Sensing of the Atmosphere and Ocean - Summer 2001 Scattering by Clouds & Applications.
Example of Fog over Northern Europe  24 Feb 2004; a winter case, fog over cold snow surface  29 May 2004; fog over sea drifting ashore Pirkko Pylkkö.
Mestrado em Ciência de Computadores Mestrado Integrado em Engenharia de Redes e Sistemas Informáticos VC 14/15 – TP19 Neural Networks & SVMs Miguel Tavares.
Challenges with AVHRR retrieval at high-latitudes ESA LTDP meeting DLR, Munich April 2015 Karl-Göran Karlsson SMHI, Norrköping, Sweden Öystein Godöy.
Some working definitions…. ‘Data Mining’ and ‘Knowledge Discovery in Databases’ (KDD) are used interchangeably Data mining = –the discovery of interesting,
Federal Department of Home Affairs FDHA Federal Office of Meteorology and Climatology MeteoSwiss High Resolution Snow Analysis for COSMO
Satellite and Aerial Image Analysis. Remote Sensing Earth Observation Photogrammetry From the Cold War to Spaceship Earth Application Areas: anything.
Digital Image Processing
Estimating the radiative impacts of aerosol using GERB and SEVIRI H. Brindley Imperial College.
Overview WP3000 Arnout Feijt AVHRR and AMSU analysis Data acquisition Standard processing Cloud Type Classifications AMSU AVHRR Quantitative Cloud Analysis.
Satellite Imagery Another type of “remote sensing” observation.
Digital Image Processing Lecture 25: Object Recognition Prof. Charlene Tsai.
1 RTM/NWP-BASED SST ALGORITHMS FOR VIIRS USING MODIS AS A PROXY B. Petrenko 1,2, A. Ignatov 1, Y. Kihai 1,3, J. Stroup 1,4, X. Liang 1,5 1 NOAA/NESDIS/STAR,
Cloud Mask: Results, Frequency, Bit Mapping, and Validation UW Cloud Mask Working Group.
Mesoscale Observing Network Course The Sea Breeze Project Reijo Hyvönen, FMI Christer Helenelund, Vaisala.
Chapter 20 Classification and Estimation Classification – Feature selection Good feature have four characteristics: –Discrimination. Features.
2008/9/15fuzzy set theory chap01.ppt1 Introduction to Fuzzy Set Theory.
Low stratus (and fog) forecast for Central Europe introducing an empirical enhancement scheme for sub-inversion cloudiness Harald Seidl and Alexander Kann.
Using MODIS and AIRS for cloud property characterization Jun W. Paul Menzel #, Steve Chian-Yi and Institute.
How Good is a Model? How much information does AIC give us? –Model 1: 3124 –Model 2: 2932 –Model 3: 2968 –Model 4: 3204 –Model 5: 5436.
VIIRS in McIDAS-V East Coast Power Outage Images produced by William Straka III.
1 PGE04-MSG Precipitating Clouds Product Presented during the NWCSAF Product Assessment Review Workshop October 2005 Prepared by : Anke Thoss, Anna.
15 th EMS Annual Meeting & 12 th European Conference on Applications of Meteorology, 7-11 September 2015, Sofia, Bulgaria Introduction We present a sophisticated.
Infrared and Microwave Remote Sensing of Sea Surface Temperature Gary A. Wick NOAA Environmental Technology Laboratory January 14, 2004.
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.
Assimilating Cloudy Infrared Brightness Temperatures in High-Resolution Numerical Models Using Ensemble Data Assimilation Jason A. Otkin and Rebecca Cintineo.
Global vs mesoscale ATOVS assimilation at the Met Office Global Large obs error (4 K) NESDIS 1B radiances NOAA-15 & 16 HIRS and AMSU thinned to 154 km.
Assessment on Phytoplankton Quantity in Coastal Area by Using Remote Sensing Data RI Songgun Marine Environment Monitoring and Forecasting Division State.
Accuracy Assessment of Thematic Maps THEMATIC ACCURACY.
Cloud and sea ice masking at high latitudes Steinar Eastwood (met.no)
Calculation of Sea Surface Temperature Forward Radiative Transfer Model Approach Alec Bogdanoff, Florida State University Carol Anne Clayson and Brent.
Ocean Report Australia – Ocean Colour & SST
Precipitation Classification and Analysis from AMSU
CS 9633 Machine Learning Inductive-Analytical Methods
Accuracy Assessment of Thematic Maps
METEOSAT SECOND GENERATION (MSG)
15 October 2004 IPWG-2, Monterey Anke Thoss
APOLLO_NG A new cloud retrieval for the CAMS radiation service
AVHRR operational cloud masks intercomparison
PGE06 TPW Total Precipitable Water
Computer Science Department Brigham Young University
Generation of Cloud Products from NOAA’s Operational Satellite Imagers
Visualization of Model Forecasts as Satellite Visible Imagery
METEOSAT SECOND GENERATION (MSG)
AWS Computing NTEG June 2019 New Technology Exploration Group.
Presentation transcript:

Towards the operational cloud classification in Finland Otto Hyvärinen COST 722 Expert Meeting, Helsinki, 9 June 2004

Contents Cloud classification work with NOAA/AVHRR –in FMI –in Nowcasting SAF (PPS) Problems Future plans

FMI approach to cloud classification with NOAA/AVHRR The statistical pattern recognition approach –Collect a lot of training data –Approximate posterior probabilities with neural networks –Use a principled way of handling the uncertainty No physics, only data! –Different models for day, night and twilight Unfortunately, the development has stalled...

20 September 2001, 5:54 UTC

Cloud Classification

Goodness of Cloud/Surface Separation

Nowcasting SAF approach with NOAA/AVHRR Developed mostly in SMHI Traditional thresholding –Thresholds computed with the help of the radiative transfer models (RTTOV) Uncertainty handled more ad hoc

An example from 8 June 2004, 06:20 UTC Cloud MaskCloud Classification

How do they compare? Comparison against SYNOP observations of total cloudiness All stationsStations from "the training area"

Problems? Common problems –Twilight! –AVHRR FMI problems – no physics  should explore RTMs Nowcasting SAF problems – bad decision making methods  should explore pattern recognition methods

Towards future Is Nowcasting SAF method for AVHRR "good enough" for us? From AVHRR to MODIS, VIIRS, etc –how to make this as least painful as possible? –SEVIRI, how useful is it in Finland, really? From the satellite cloud classification to the cloud analysis –how to combine remote sensing and in-situ observations with the model data to real 3D analysis of clouds, fog, and visibility?