A comparison of NOAA/AVHRR derived cloud amount with MODIS and surface observation

Similar documents
Vulnerability assessment of ecosystem services for climate change impacts and adaptation (VACCIA)

Satellite remote sensing using AVHRR, ATSR, MODIS, METEOSAT, MSG

Cloud Masking and Cloud Products

Universidad Nacional de Rosario Facultad de Ciencias Exactas, Ingeniería y Agrimensura Centro de Sensores Remotos CONICET CONAE NASA

Studying cloud properties from space using sounder data: A preparatory study for INSAT-3D

GOES-R AWG Cloud Team: ABI Cloud Height

CERES Edition 2 & Edition 3 Cloud Cover, Cloud Altitude and Temperature

A SURVEY OF CLOUD COVER OVER MĂGURELE, ROMANIA, USING CEILOMETER AND SATELLITE DATA

3.4 Cryosphere-related Algorithms

Measurement of the effect of biomass burning aerosol on inhibition of cloud formation over the Amazon

Denis Botambekov 1, Andrew Heidinger 2, Andi Walther 1, and Nick Bearson 1

SYNERGISTIC USE OF IMAGER WINDOW OBSERVATIONS FOR CLOUD- CLEARING OF SOUNDER OBSERVATION FOR INSAT-3D

Suman Goyal Sc E /In-charge SYNOPTIC Application Unit Satellite Meteorology Davison

Active Fire Monitoring: Product Guide

Comparison of NOAA's Operational AVHRR Derived Cloud Amount to other Satellite Derived Cloud Climatologies.

ECMWF Aerosol and Cloud Detection Software. User Guide. version /01/2015. Reima Eresmaa ECMWF

Long term cloud cover trends over the U.S. from ground based data and satellite products

SAFNWC/MSG Cloud type/height. Application for fog/low cloud situations

CALCULATION OF CLOUD MOTION WIND WITH GMS-5 IMAGES IN CHINA. Satellite Meteorological Center Beijing , China ABSTRACT

Cloud detection and clearing for the MOPITT instrument

Evaluation of the Effect of Upper-Level Cirrus Clouds on Satellite Retrievals of Low-Level Cloud Droplet Effective Radius

REMOTE SENSING OF CLOUD-AEROSOL RADIATIVE EFFECTS FROM SATELLITE DATA: A CASE STUDY OVER THE SOUTH OF PORTUGAL

Present Status of Coastal Environmental Monitoring in Korean Waters. Using Remote Sensing Data

Validating MOPITT Cloud Detection Techniques with MAS Images

Advances in Cloud Imager Remote Sensing

STAR Algorithm and Data Products (ADP) Beta Review. Suomi NPP Surface Reflectance IP ARP Product

Quantifying Seasonal Variation in Cloud Cover with Predictive Models

Cloud Climatology for New Zealand and Implications for Radiation Fields

How To Understand Cloud Properties From Satellite Imagery

Radiative effects of clouds, ice sheet and sea ice in the Antarctic

NOAA NESDIS CENTER for SATELLITE APPLICATIONS and RESEARCH ALGORITHM THEORETICAL BASIS DOCUMENT. ABI Cloud Mask

163 ANALYSIS OF THE URBAN HEAT ISLAND EFFECT COMPARISON OF GROUND-BASED AND REMOTELY SENSED TEMPERATURE OBSERVATIONS

How to Use the NOAA Enterprise Cloud Mask (ECM) Andrew Heidinger, Tom Kopp, Denis Botambekov and William Straka JPSS Cloud Team August 29, 2015

The impact of window size on AMV

DISCRIMINATING CLEAR-SKY FROM CLOUD WITH MODIS ALGORITHM THEORETICAL BASIS DOCUMENT (MOD35) MODIS Cloud Mask Team

MSG-SEVIRI cloud physical properties for model evaluations

Solarstromprognosen für Übertragungsnetzbetreiber

The NASA NEESPI Data Portal to Support Studies of Climate and Environmental Changes in Non-boreal Europe

Satellite Snow Monitoring Activities Project CRYOLAND

AATSR Technical Note. Improvements to the AATSR IPF relating to Land Surface Temperature Retrieval and Cloud Clearing over Land

Best practices for RGB compositing of multi-spectral imagery

Night Microphysics RGB Nephanalysis in night time

DISCRIMINATING CLEAR-SKY FROM CLOUD WITH MODIS ALGORITHM THEORETICAL BASIS DOCUMENT (MOD35) MODIS Cloud Mask Team

Development of an Integrated Data Product for Hawaii Climate

SATELLITE IMAGES IN ENVIRONMENTAL DATA PROCESSING

Estimating Firn Emissivity, from 1994 to1998, at the Ski Hi Automatic Weather Station on the West Antarctic Ice Sheet Using Passive Microwave Data

Solar Irradiance Forecasting Using Multi-layer Cloud Tracking and Numerical Weather Prediction

Clear Sky Radiance (CSR) Product from MTSAT-1R. UESAWA Daisaku* Abstract

Combining Satellite High Frequency Microwave Radiometer & Surface Cloud Radar Data for Determination of Large Scale 3 D Cloud IWC 서은경

The Surface Energy Budget

Towards assimilating IASI satellite observations over cold surfaces - the cloud detection aspect

Adaptive HSI Data Processing for Near-Real-time Analysis and Spectral Recovery *

Analysis of Climatic and Environmental Changes Using CLEARS Web-GIS Information-Computational System: Siberia Case Study

Monitoring of Arctic Conditions from a Virtual Constellation of Synthetic Aperture Radar Satellites

Slide 1. Slide 2. Slide 3

Comparison between current and future environmental satellite imagers on cloud classification using MODIS

Satellite Remote Sensing of Volcanic Ash

SATELLITE OBSERVATION OF THE DAILY VARIATION OF THIN CIRRUS

Interactive comment on Total cloud cover from satellite observations and climate models by P. Probst et al.

VIIRS-CrIS mapping. NWP SAF AAPP VIIRS-CrIS Mapping

Evaluation of Wildfire Duration Time Over Asia using MTSAT and MODIS

16 th IOCCG Committee annual meeting. Plymouth, UK February mission: Present status and near future

GOES-R Advanced Baseline Imager (ABI) Algorithm Theoretical Basis Document For Low Cloud and Fog

Moderate- and high-resolution Earth Observation data based forest and agriculture monitoring in Russia using VEGA Web-Service

How To Find Natural Oil Seepage In The Dreki Area Using An Envisat Image

EUMETSAT Satellite Programmes

Generation of Cloud-free Imagery Using Landsat-8

Number of activated CCN as a key property in cloud-aerosol interactions. Or, More on simplicity in complex systems

Improved diagnosis of low-level cloud from MSG SEVIRI data for assimilation into Met Office limited area models

IMPACTS OF IN SITU AND ADDITIONAL SATELLITE DATA ON THE ACCURACY OF A SEA-SURFACE TEMPERATURE ANALYSIS FOR CLIMATE

Land-surface emissivity maps based on MSG/SEVIRI information

Guy Carpenter Asia-Pacific Climate Impact Centre, School of energy and Environment, City University of Hong Kong

Diurnal Cycle: Cloud Base Height clear sky

Reply to No evidence for iris

Nowcasting applications. Hungarian Meteorological Service

Daily High-resolution Blended Analyses for Sea Surface Temperature

Forest Fire Information System (EFFIS): Rapid Damage Assessment

Vaisala 3TIER Services Global Solar Dataset / Methodology and Validation

A system of direct radiation forecasting based on numerical weather predictions, satellite image and machine learning.

Temporal variation in snow cover over sea ice in Antarctica using AMSR-E data product

Sodankylä National Satellite Data Center (NSDC): Current and Future Satellite Missions and Products

Transcription:

A comparison of NOAA/AVHRR derived cloud amount with MODIS and surface observation LIU Jian YANG Xiaofeng and CUI Peng National Satellite Meteorological Center, CMA, CHINA

outline 1. Introduction 2. Data & Methods 3. Validation 4. Conclusion

1. Introduction 1. ISCCP data 1983-2009 2.5 2.5 2. MOD06 cloud product 2000- now 0.01 0.01 / 0.05 0.05 3. NOAA / AVHRR data 1989-now 0.01 0.01

2. Data & Methods Data: NOAA/AVHRR 10-60 N 65-145 E 1989-2008 Method Cloud detection Cloud Amount/ cloud fraction

2. Data & Methods Cloud detection " Dynamic threshold " Background clear fields " DEM

2. Data & Methods cloud amount A c is pixel s cloud amount. BT is pixel s brightness temperature at10.3µm channel. BT cld is brightness temperature of full cloud cover pixel at 10.3µm channel. BT clr is brightness temperature of full clear pixel at 10.3µm channel.

2. Data & Methods Cloud Fraction " Based on a pixel level cloud detection result " cloud fraction CF=N cloud /N total N cloud : cloudy pixel number N total : total pixel number

3. Validation_cloud detection cloud detection Surface - clear Surface- cloudy Satellite - clear A B Satellite- cloudy C D " ACR(the accuracy rate of cloud detection) ACR=(A+D)/(A+B+C+D). " ERR (Error rate) ERR= (B+C) /( A+B+C+D).

3. Validation_cloud detection Data Accuracy Rate Error Rate MODIS 74.34% 25.66% AVHRR 77.12% 22.88% The time series change of the accuracy (left) and error (right) of cloud detection for AVHRR and MODIS

3. Validation_ cloud amount AVHRR MODIS

3. Validation_ cloud amount the relationship between AVHRR derived cloud amount and surface observation in different seasons.a spring, b summer c autumn d winter

3. Validation_ cloud amount the relationship between MODIS cloud amount and surface observation in different seasons.a spring, b summer c autumn d winter

3. Validation_ cloud amount the time coefficient of the first EOF eigenvector for station, AVHRR and MODIS s total cloud amount

3. Validation_ cloud amount the diagram of the first EOF eigenvector for NOAA/AVHRR, MODIS and station s total cloud amount

3. Validation_ cloud amount the time coefficient of the first EOF eigenvector for station,avhrr and MODIS s total cloud amount in winter

3. Validation_ cloud amount the diagram of the first EOF eigenvector for NOAA/AVHRR, MODIS and station s total cloud amount in winter time

3. Validation_ cloud amount AVHRR MODIS the time evolution of SVD time coefficient

3. Validation_ cloud amount The correlation coefficients 1 0.95 2 0.95 3 0.94 4 0.92 5 0.88. SVD heterogeneous correlation map of the first (up) and the second (down) mode, left vector field (AVHRR),right vector field (station observation)

3. Validation_ cloud amount The correlation coefficients 1 0.89 2 0.91 3 0.90 4 0.88 5 0.74. SVD heterogeneous correlation map of the first (up) and the second (down) left vector field (MODIS),right vector field (station observation)

4. Conclusion The value of AVHRR derived cloud amount is lower than surface observations. Compared with MODIS 74.34% cloud detection accuracy rate, processed AVHRR data get 77.12% cloud detection accuracy rate. update cloud detection algorithm to distinguish cloud and snow and get a good results.

Thanks for your Attention