Changyong Cao 1, Pubu Ciren 2, Mitch Goldberg 1, and Fuzhong Weng 1. Introduction

Similar documents
Inter comparison of Terra and Aqua MODIS Reflective Solar Bands Using Suomi NPP VIIRS

Legacy of NOAA, NASA and NIST Cooperation in Developing Radiometric Calibration Standards Equipment and Methodologies. Raju Datla, Michael Weinreb

P5.15 ADDRESSING SPECTRAL GAPS WHEN USING AIRS FOR INTERCALIBRATION OF OPERATIONAL GEOSTATIONARY IMAGERS

984 IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL. 46, NO. 4, APRIL /$ IEEE

Preparations for NOAA-N

Suomi NPP VIIRS Calibration/ Validation Progress Update

GOES-16 ABI On-Orbit Performance

Frequency grid setups for microwave radiometers AMSU-A and AMSU-B

Comprehensive Vicarious Calibration and Characterization of a Small Satellite Constellation Using the Specular Array Calibration (SPARC) Method

IASI L0/L1 NRT Monitoring at EUMETSAT: Comparison of Level 1 Products from IASI and HIRS on Metop-A

Interactive comment on Radiometric consistency assessment of hyperspectral infrared sounders by L. Wang et al.

Bias estimation and correction for satellite data assimilation

AVHRR/3 Operational Calibration

A view from the Global Space-based Inter- Calibration System (GSICS. Mitch Goldberg, NOAA Chair of GSICS Executive Panel

University of Texas at San Antonio EES 5053 Term Project CORRELATION BETWEEN NDVI AND SURFACE TEMPERATURES USING LANDSAT ETM + IMAGERY NEWFEL MAZARI

Railroad Valley Playa for use in vicarious calibration of large footprint sensors

New Spectral Compensation Method for Intercalibration Using High Spectral Resolution Sounder

Kazuhiro TANAKA GCOM project team/jaxa April, 2016

Feedback on Level-1 data from CCI projects

Intersatellite Calibration of infrared sensors onboard Indian Geostationary Satellites using LEO Hyperspectral Observations

Measurements of Infrared Sources with the Missile Defense Transfer Radiometer

Performance status of IASI on MetOp-A and MetOp-B

Development of Chinese SI-traceable reference instruments and retrospective recalibration of historical satellite data

AIRS Version 4 Data. International TOVS Study Conference XIV Beijing, China May California Institute of Technology Jet Propulsion Laboratory

Recent developments in Deep Blue satellite aerosol data products from NASA GSFC

1. INTRODUCTION. GOCI : Geostationary Ocean Color Imager

NOAA JPSS and GOES Fire Products R. Bradley Pierce and Shobha Kondragunta NOAA/NESDIS/STAR

Climate Data Record (CDR) Program

Bias correction of satellite data at ECMWF. T. Auligne, A. McNally, D. Dee. European Centre for Medium-range Weather Forecast

Fundamentals of Remote Sensing

GSICS MVIRI-IASI Inter-calibration Uncertainty Evaluation

High Accuracy IR Radiances-CLARREO Slide 1

Looking at 637 nm VIIRS band, S-NPP

Evaluation of FLAASH atmospheric correction. Note. Note no SAMBA/10/12. Authors. Øystein Rudjord and Øivind Due Trier

RADIATION BUDGET INSTRUMENT (RBI): FINAL DESIGN AND INITIAL EDU TEST RESULTS

Compact High Resolution Imaging Spectrometer (CHRIS) siraelectro-optics

S3 Product Notice SLSTR

A New Lossless Compression Algorithm For Satellite Earth Science Multi-Spectral Imagers

Limb Correction of Infrared Imagery in Cloudy Regions for the Improved Interpretation of RGB Composites

Use of Apodization to Improve Quality of Radiometric Measurements from Interferometric Sounders (2/11/02)

THE MIAMI-2001 RADIOMETER INTERCOMPARISON

The AATSR LST retrieval: State of knowledge and current developments

Cross Track Infrared Sounder (CrIS) Flight Model 1 Test Results

Environmental Data Records from Special Sensor Microwave Imager and Sounder (SSMIS)

The Influence of Frequency Shifts in Microwave Sounder Channels on NWP Analyses and Forecasts

Microwave Sensors Subgroup (MSSG) Report

RADIOMETRIC PERFORMANCE OF THE CRIS INSTRUMENT FOR JPSS-1

Simulation study for the Stratospheric Inferred Wind (SIW) sub-millimeter limb sounder

AN INTRODUCTION TO MICROCARB, FIRST EUROPEAN PROGRAM FOR CO2 MONITORING.

PLANET SURFACE REFLECTANCE PRODUCT

Aniekan Eyoh 1, Onuwa Okwuashi 2 1,2 Department of Geoinformatics & Surveying, University of UYO, Nigeria. IJRASET: All Rights are Reserved

Meteosat Third Generation (MTG) Lightning Imager (LI) instrument on-ground and in-flight calibration

On-Orbit Radiometric Performance of the Landsat 8 Thermal Infrared Sensor. External Editors: James C. Storey, Ron Morfitt and Prasad S.

STATUS OF THE SEVIRI LEVEL 1.5 DATA

Microwave Sensors Subgroup (MSSG) Report

New Satellite Method for Retrieving Precipitable Water Vapor over Land and Ocean

Radiometric performance of Second Generation Global Imager (SGLI) using integrating sphere

METimage Calibration & Performance Verification. Xavier Gnata ICSO 2016

Using Ground Targets for Sensor On orbit Calibration Support

LANDSAT 8 Level 1 Product Performance

CHAPTER 2 A NEW SCHEME FOR SATELLITE RAW DATA PROCESSING AND IMAGE REPRESENTATION

Historical radiometric calibration of Landsat 5

Technical Report Analysis of SSMIS data. Eva Howe. Copenhagen page 1 of 16

Using GPS-RO to evaluate Climate Data Records from MSU/AMSU. Carl Mears, Remote Sensing Systems

VICARIOUS CALIBRATION SITE SELECTION FOR RAZAKSAT MEDIUM-SIZED APERTURE CAMERA (MAC)

The Global Imager (GLI)

Receiver Design for Passive Millimeter Wave (PMMW) Imaging

OPAL Optical Profiling of the Atmospheric Limb

Outline. GPS RO Overview. COSMIC Overview. COSMIC-2 Overview. Summary 9/29/16

Multi-sensor data base over desert sites for calibration purpose. P. Henry ¹, X. Briottet ², C. Miesch ², F. Cabot ¹ ¹CNES, ²ONERA

Lecture 6: Multispectral Earth Resource Satellites. The University at Albany Fall 2018 Geography and Planning

Two-linear-polarization measurement of O 2 A band with TANSO-FTS onboard GOSAT

Estimation of outgoing longwave radiation from Atmospheric Infrared Sounder radiance measurements

Bias correction of satellite data at ECMWF

Microwave Sounding. Ben Kravitz October 29, 2009

CAL/VAL ACTIVITIES IN ROSHYDROMET. GSICS Executive Panel 14, Tokyo, 15 July. 2013

Characterising the FY-3A Microwave Temperature Sounder Using the ECMWF Model

Project Title: Validation and Correction for the MODIS Spatial Response. NASA Grant #: NAG Period: October 1, May 31, 1999

Progress towards an assimilation strategy for AIRS at ECMWF. Tony McNally, N. Fourrié, M. Matricardi, JN. Thépaut*, P. Watts

On the sensitivity of Land Surface Temperature estimates in arid irrigated lands using MODTRAN

Wind Imaging Spectrometer and Humidity-sounder (WISH): a Practical NPOESS P3I High-spatial Resolution Sensor

Project Overview The Development of AMSU FCDR s and TCDR s s for Hydrological Applications

ENMAP RADIOMETRIC INFLIGHT CALIBRATION, POST-LAUNCH PRODUCT VALIDATION, AND INSTRUMENT CHARACTERIZATION ACTIVITIES

Transfer Calibration from ERBS WFOV Nonscanner to NOAA-9 WFOV Nonscanner and to NOAA-9 Scanner

Radia%on at the Top of the Atmosphere

Status of Aqua MODIS Reflective Solar Bands Calibration and Performance

CIRiS: Compact Infrared Radiometer in Space August, 2017

Some Basic Concepts of Remote Sensing. Lecture 2 August 31, 2005

SATELLITE OCEANOGRAPHY

Japan's Greenhouse Gases Observation from Space

A Kalman-Filtering Approach to High Dynamic Range Imaging for Measurement Applications

Present and future of marine production in Boka Kotorska

Fires, Flares and Lights: Mapping Anthropogenic Emission Sources with Nighttime Low light Imaging Satellite Data

Preparing for the exploitation of Sentinel-2 data for agriculture monitoring. JACQUES Damien, DEFOURNY Pierre UCL-Geomatics Lab 2 octobre 2013

Nighttime VIIRS LCLUC Applications

Use of GNSS Radio Occultation data for Climate Applications Bill Schreiner Sergey Sokolovskiy, Doug Hunt, Ben Ho, Bill Kuo UCAR

Chapter 5 Nadir looking UV measurement.

FY-3 Data Quality and Assimilation in NWP

Sustained Ocean Color Research and Operations

The Radiation Balance

Transcription:

Intersatellite Calibration of HIRS from 1980 to 2003 Using the Simultaneous Nadir Overpass (SNO) Method for Improved Consistency and Quality of Climate Data Changyong Cao 1, Pubu Ciren 2, Mitch Goldberg 1, and Fuzhong Weng 1 1 NOAA/NESDIS/ORA/STAR, Camp Springs, Maryland, USA; 2 QSS Group Inc. Lanham, Maryland, USA Abstract The Simultaneous Nadir Overpass (SNO) method developed at NOAA/NESDIS has been used for the intersatellite calibration in the last few years with excellent results. The objective of this project is to establish and apply an accurate intersatellite calibration procedure to develop inter-satellite calibration data sets and characterize inter-satellite calibration bias, which will be useful for data users in developing time series from NOAA satellite data from 1980 to 2003. It uses the novel approach that takes advantage of inter-satellite calibration using simultaneous nadir overpass (SNO) observations at the orbital intersections between each succeeding pair of satellites. This will ensure the calibration consistency and quality for long-term climate studies, reduce the uncertainties about critical climate trends, and therefore facilitate the construction of long-term climate data records. Introduction The objective of this project is to establish and apply an accurate intersatellite calibration procedure to develop inter-satellite calibration data sets and characterize inter-satellite calibration bias, which will be useful for data users in developing time series from NOAA satellite data from 1980 to 2003. It uses a novel approach that takes advantage of inter-satellite calibration using simultaneous nadir overpass (SNO) observations at the orbital intersections between each succeeding pair of satellites (Figure 1). This will ensure the calibration consistency and quality for long-term climate studies, reduce the uncertainties about critical climate trends, and therefore facilitate the construction of long-term climate data records. Figure 1. Example Simultaneous Nadir Overpass (SNO) between NOAA-15 and -16

Methodology The Simultaneous Nadir Overpass (SNO) methodology is relatively straight forward and the results are highly repeatable. Detailed procedures are as follows: 1. Predict Simultaneous Nadir Overpasses (SNOs) between each succeeding pairs of NOAA satellites using the orbital perturbation model SGP4 and historical two-line-elements (TLEs) (Cao, et al., 2004, 2005b) 2. Level 1B data that contain SNO observations from NOAA-6 to NOAA-17. Criteria for the SNOs: 1). At the SNO, the distance between nadir pixels of two orbits < 20 km. 2). time difference between nadir pixels of the two orbits is less than 30 seconds. 3. A subset data (56 columns by 31 rows) is extracted initially, and the data converted to both radiance and brightness temperature. 4. A pixel by pixel match is performed for the matchup subset data between the two satellites based on the latitude and longitude of each pixel. 5. Statistics of the biases in radiance and brightness temperature between two succeeding NOAA satellites are calculated based on a 4 by 5 box at nadir, or within +/- 5 degrees from nadir. 6. The time series of the biases are plotted. The results are then analyzed. Further analysis maybe needed to find the root cause of the biases between satellite before the bias can be used for intersatellite bias adjustments. Data Processing All historical SNOs for NOAA-6 to NOAA-17 satellites have been calculated, cross-checked with multiple runs of the software, and validated against HIRS level 1b match-up data sets. The algorithm for predicting the SNOs has been published in Cao, et al., 2004. All predicted SNOs for NOAA-6 to NOAA-17 have been documented in the report by Cao et al., 2005b. Intersatellite calibration of HIRS with the SNO method for NOAA-6 to -17 have been generated. Intersatellite radiance biases have been calculated for each channel of HIRS. The biases have been characterized in correlation plots between pairs of satellites, tables, and time series plots. Forward calculations with the line-by-line radiative transfer code LBLRTM have been performed for all satellites, with sample Arctic, Antarctic, and tropical atmospheric profiles. Further detailed studies on the radiance biases and possible causes have been conducted with NOAA-15 to -17 data and the results documented in the paper by Cao, et al., 2005a. Efforts have been made to find the source of radiance biases by working with the National Institute of Standards and Technology and the HIRS instrument manufacturer with regard to the measurement uncertainties of the HIRS spectral response function (Kaplan, 2002). The following bias model has been developed: Where: β= f(τ, ε, ι, ν, e, g, s, α, o) (1) β = radiance bias τ =observation time differences (this is reduced to a negligible level with the SNO method). ε =blackbody spectral emissivity and discrepancies between skin and bulk temperatures

ι =nonlinearity ν =spectral uncertainty e=calibration algorithm g=geolocation, including location differences and navigation errors. s=scene uniformity and sensor modulation transfer functions (MTF) α=calibration anomaly o=other factors Preliminary Results Time series plots of radiance bias for each channel show the consistency of the biases during the study period. Arctic and Antarctic data are shown with distinct symbols, while colors represent different satellite combinations. Note that there is a gap for data involving NOAA-8 (around 1984 to 1986) due to the lack of SNO data and the relative short operating period of this satellite Figure 2. Time Series of Radiance Biases for HIRS Channel 8 (1980 to 2004) that shows small median biases Preliminary analysis of these biases suggests the following: a). For the window channels such as ch8 and ch19 (Figure 2), the intersatellite biases (median values) are small (< 0.3K) for all the satellite pairs. This is important because in the longwave, channels 1-12 share the same HgCdTe detector, and in the shortwave, channels 13-19 share the same InSb detector. Good agreement for the window channels may suggest that the blackbody calibration is relatively reliable for all channels, assuming the blackbody spectral emissivity is relatively flat over the entire spectral region of HIRS.

Figure 3. Time Series of Radiance Biases for HIRS Channel 3 that shows seasonal radiance biases between satellites. Figure 4. Seasonal biases are highly correlated with the lapse rate

b). The seasonal biases for the stratosphere channels are probably caused by the differences in the spectral response functions and the fact that the atmosphere changes with season. Further analysis shows that the seasonal biases are highly correlated with the lapse rate, suggesting that the small difference in the spectral response functions plays an important role for the biases (Figure 4). c). The large biases in Channel 1 can not be fully explained by the forward calculations. Based on past studies, the large biases for this channel are probably caused by the measurement inaccuracy and true differences of the HIRS spectral response functions for this channel (Figure 5). Figure 5. Radiance biases for channel 1 on HIRS NOAA-6 to NOAA-17 d). Radiance biases for channel 10 are not very meaningful for several satellites because the channel was changed to a different spectral region for some succeeding satellites. e). Results for the short-wave channels are less reliable due to the low signal to noise ratio at typical polar temperatures, and reflected solar radiance in these channels. Conclusions The Simultaneous Nadir Overpass (SNO) method is used in this study to characterize the intersatellite calibration biases for HIRS onboard NOAA-6 to NOAA-17 from 1980 to 2003. The SNO method takes advantage of intersatellite calibration with nadir observations that are taken

within seconds at the orbital intersections between each succeeding pair of satellites. The low uncertainty using this approach allows us to study subtle radiometric and spectral calibration differences for HIRS on different satellites. This provides us an independent check of the instrument performance, and helps users better understand the nature of the intersatellite biases in constructing long-term time series of satellite data. Analysis of such datasets from 1980 to 2003 reveals unambiguous intersatellite radiance differences, as well as calibration anomalies. The results show that in general, the intersatellite relative biases are relatively small for most HIRS channels. The large biases in some channels differ in both magnitude and sign, and are likely caused by the differences and measurement uncertainties in the HIRS spectral response functions. The seasonal bias variations are found to be highly correlated with the lapse rate factor, approximated by adjacent channel radiance differences. The method presented in this study works particularly well for channels sensing the stratosphere because of the relative spatial uniformity and stability of the stratosphere, for which the intercalibration accuracy and precision are mostly limited by the instrument noise. The SNO method is simple, robust, and the results are highly repeatable and unambiguous. Intersatellite radiance calibration with this method is very useful for the on-orbit verification and monitoring of instrument performance, and is potentially useful for constructing long-term time series for climate studies. Acknowledgments and Disclaimer - The authors wish to thank Drs. Larry McMillin and Tom Kleespies of NOAA/NESDIS/ORA for their comments, suggestions, and support. This study is partially funded by the Environmental Services Data and Information Management (ESDIM) of NOAA s GeoSpatial Data and Climate Services (GDCS) group. The manuscript contents are solely the opinions of the authors and do not constitute a statement of policy, decision, or position on behalf of NOAA or the U. S. Government. REFERENCES Cao, C., P. Ciren, M. Goldberg, F. Weng, and C. Zou, 2005b, Simultaneous Nadir Overpasses for NOAA-6 to NOAA-17 Satellites from 1980 to 2003 for the Intersatellite Calibration of Radiometers. NOAA Technical Report, In press.. Cao, C., H. Xu, J. Sullivan, L. McMillin, P. Ciren, and Y. Hou, 2005a, Intersatellite radiance biases for the High Resolution Infrared Radiation Sounders (HIRS) onboard NOAA-15, -16, and -17 from simultaneous nadir observations. Journal of Atmospheric and Oceanic Technology, Vol 22, No. 4, pp. 381-395. Cao, C., M. Weinreb, and H. Xu, 2004, Predicting simultaneous nadir overpasses among polarorbiting meteorological satellites for the inter-satellite calibration of radiometers, Journal of Atmospheric and Oceanic Technology, Vol. 21, April, 2004. Kaplan, S., 2002, Transmittance Measurements of 19 Witness Infrared Bandpass Filters from the HIRS/4 Instrument, Test Report, Optical Technology Division, NIST.