Daily High-resolution Blended Analyses for Sea Surface Temperature



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

Huai-Min Zhang & NOAAGlobalTemp Team

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

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

NCDC s SATELLITE DATA, PRODUCTS, and SERVICES

Satellite SST Product Development Proposal

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

Joint Polar Satellite System (JPSS)

HFIP Web Support and Display and Diagnostic System Development

Precipitation Remote Sensing

Reply to No evidence for iris

2.8 Objective Integration of Satellite, Rain Gauge, and Radar Precipitation Estimates in the Multisensor Precipitation Estimator Algorithm

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

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

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

Near Real Time Blended Surface Winds

Scholar: Elaina R. Barta. NOAA Mission Goal: Climate Adaptation and Mitigation

IOMASA DTU Status October 2003

Development of an Integrated Data Product for Hawaii Climate

Slide 1. Slide 2. Slide 3

SPATIAL DISTRIBUTION OF NORTHERN HEMISPHERE WINTER TEMPERATURES OVER THE SOLAR CYCLE DURING THE LAST 130 YEARS

USING THE GOES 3.9 µm SHORTWAVE INFRARED CHANNEL TO TRACK LOW-LEVEL CLOUD-DRIFT WINDS ABSTRACT

Evaluation of sea surface salinity observed by Aquarius and SMOS

A Microwave Retrieval Algorithm of Above-Cloud Electric Fields

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

Description of Scatterometer Data Products

Monsoon Variability and Extreme Weather Events

Clouds and the Energy Cycle

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

Southern AER Atmospheric Education Resource

How to analyze synoptic-scale weather patterns Table of Contents

Climate Extremes Research: Recent Findings and New Direc8ons

Cloud detection and clearing for the MOPITT instrument

Visualizing of Berkeley Earth, NASA GISS, and Hadley CRU averaging techniques

South Africa. General Climate. UNDP Climate Change Country Profiles. A. Karmalkar 1, C. McSweeney 1, M. New 1,2 and G. Lizcano 1


TECHNICAL REPORTS. Authors: Tatsuhiro Noguchi* and Takaaki Ishikawa*

Real-time Ocean Forecasting Needs at NCEP National Weather Service

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

Surface-Based Remote Sensing of the Aerosol Indirect Effect at Southern Great Plains

II. Related Activities

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

Active Fire Monitoring: Product Guide

SATELLITE IMAGES IN ENVIRONMENTAL DATA PROCESSING

GLOBAL FORUM London, October 24 & 25, 2012

IGAD CLIMATE PREDICTION AND APPLICATION CENTRE

5200 Auth Road, Camp Springs, MD USA 2 QSS Group Inc, Lanham, MD, USA

Observed Cloud Cover Trends and Global Climate Change. Joel Norris Scripps Institution of Oceanography

Hyperspectral Satellite Imaging Planning a Mission

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

COASTAL WIND ANALYSIS BASED ON ACTIVE RADAR IN QINGDAO FOR OLYMPIC SAILING EVENT

Queensland rainfall past, present and future

Mapping of Antarctic sea ice in the depletion phase: an indicator of climatic change?

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

NASA Earth System Science: Structure and data centers

Satellite Snow Monitoring Activities Project CRYOLAND

Welcome to NASA Applied Remote Sensing Training (ARSET) Webinar Series

Developing Continuous SCM/CRM Forcing Using NWP Products Constrained by ARM Observations

User Perspectives on Project Feasibility Data

Intra-seasonal and Annual variability of the Agulhas Current from satellite observations

Chapter 2 False Alarm in Satellite Precipitation Data

David P. Ruth* Meteorological Development Laboratory Office of Science and Technology National Weather Service, NOAA Silver Spring, Maryland

DIURNAL CYCLE OF CLOUD SYSTEM MIGRATION OVER SUMATERA ISLAND

Technical note on MISR Cloud-Top-Height Optical-depth (CTH-OD) joint histogram product

Future needs of remote sensing science in Antarctica and the Southern Ocean: A report to support the Horizon Scan activity of COMNAP and SCAR

Thomas Fiolleau Rémy Roca Frederico Carlos Angelis Nicolas Viltard.

Chapter Overview. Seasons. Earth s Seasons. Distribution of Solar Energy. Solar Energy on Earth. CHAPTER 6 Air-Sea Interaction

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

Parameterization of Cumulus Convective Cloud Systems in Mesoscale Forecast Models

Very High Resolution Arctic System Reanalysis for

Improved Meteorological Measurements from Merchant Ships. Peter K. Taylor and Elizabeth C. Kent Southampton Oceanography Centre, UK

Ensuring the Preparedness of Users: NOAA Satellites GOES R, JPSS Laura K. Furgione

Heavy Rainfall from Hurricane Connie August 1955 By Michael Kozar and Richard Grumm National Weather Service, State College, PA 16803

Lecture 4: Pressure and Wind

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

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

Automated Spacecraft Scheduling The ASTER Example

Remote Sensing Satellite Information Sheets Geophysical Institute University of Alaska Fairbanks

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

New Precipitation products (GPM, GCOM-W) and more

Transcription:

Daily High-resolution Blended Analyses for Sea Surface Temperature by Richard W. Reynolds 1, Thomas M. Smith 2, Chunying Liu 1, Dudley B. Chelton 3, Kenneth S. Casey 4, and Michael G. Schlax 3 1 NOAA National Climatic Data Center, Asheville, North Carolina 2 NOAA National Climatic Data Center, College Park, Maryland 3 Oregon State University, Corvallis, Oregon 4 NOAA National Oceanographic Data Center, Silver Spring, Maryland 1. Introduction Two new high resolution sea surface temperature (SST) analysis products have been developed using optimum interpolation (OI). The analyses have a spatial grid resolution of 0.25 and temporal resolution of 1 day. One product uses Advanced Very High Resolution Radiometer (AVHRR) infrared satellite SST data. The other uses AVHRR and Advanced Microwave Scanning Radiometer (AMSR) on the NASA Earth Observing System satellite SST data. Both products also use in situ data from ships and buoys and include a large-scale adjustment of satellite biases with respect to the in situ data. Two products are needed because there is an increase in signal variance when AMSR became available in June 2002 due to its near all-weather coverage. AVHRR infrared (IR) instruments with multichannel capabilities have been available on NOAA polar orbiters since November 1981. AVHHR requires a cloud-free view to retrieve SSTs and one of the biggest challenges is to eliminate cloud contamination. Complete details on the operational algorithm can be found in May et al (1998). A delayed mode AVHRR product, Pathfinder, is produced by the University of Miami and the NOAA National Oceanographic Data Center with twice daily gridded averages on a 4.6 km grid and represents an improvement over the previously available Version 4 Pathfinder data (Kilpatrick et. al, 2001). As described by Chelton and Wentz (2005), AMSR SST retrievals are made along with several other variables including wind speed and precipitation. The AMSR SST retrievals have a footprint size of 56 km and are contaminated within about 75 km of land or ice and during precipitation events. Except in the Intertropical Convergence Zone (ITCZ) where precipitation is persistent, only a few percent of microwave (MW) SSTs are lost due to precipitation contamination. The primary advantage of AMSR data is thus the near all-weather measurement capability. AMSR data are available from Remote Sensing Systems as twice daily gridded averages on a 0.25 grid (http://www.ssmi.com/amsr/amsr_browse.html). 1

The AVHRR-only daily OI analysis uses Pathfinder AVHRR data (currently available from January 1985 through December 2005) and operational AVHRR data for 2006 onwards. Pathfinder AVHRR was chosen, where available, because it agrees better with the in situ data. The AMSR+AVHRR daily OI analysis begins with the start of AMSR in June 2002. In this product, the primary AVHRR contribution is in regions near land where AMSR is not available. However, in cloud-free regions, use of both IR and MW instruments can reduce systematic biases because their error characteristics are independent. 2. Comparisons To evaluate the new daily OI analyses, the input data and the analyses were compared with each other and other analyses. For this comparison, the weekly OI of Reynolds et al. (2002), henceforth referred to as the OI version 2 (OI.v2) was used. This analysis use infrared (IR) satellite data from AVHRR and in situ data from ships and buoys and is performed weekly on a 1 spatial grid from November 1981 to present. An additional analysis was obtained from the National Center for Environmental Prediction daily Real Time Global SST (RTG_SST) analysis (Thiébaux et al., 2003). The RTG_SST analysis uses the same data used in the OI.v2. However, the RTG_SST is run daily beginning on 30 January 2001 on a 1/2 grid and uses smaller spatial error correlation scales than those used in the OI.v2. Chelton and Wentz (2005) compared these two analyses with AVHRR and AMSR data with particular focus on 6 regions with strong SST fronts, including the Gulf Stream. The Gulf Stream gradients are presented in Figure 1 following Chelton and Wentz with the addition of the two new daily OI analyses. The figure shows the magnitude of the 3-day mean Gulf Stream SST gradients centered on 1 October 2003. The AVHRR data panel shows high resolution details in cloud-free regions, although the coverage for AVHRR data is less than half of the possible number of ocean grid points. The AMSR data panel shows smoother details because of the coarser footprint but with the expected better coverage. The analyses fill in the missing AMSR and AVHRR data gaps with different smoothing. In particular, note the region of missing AMSR data due to precipitation contamination between 35 N and 45 N along 60 W. The OI.v2 is heavily smoothed as reported by Chelton and Wentz. The RTG_SST and AVHRR OI are similar, showing much more detail. Here the RTG_SST slightly smoother than the AVHRR OI. The best analysis resolution is shown by the AMSR+AVHRR analysis, which is similar to the AMSR data in most of the offshore regions. All statistical parameters in the daily OI are the same for both products. Thus, the improvement in the AMSR+AVHRR analysis is due to the better AMSR coverage. It is useful to also examine the coverage of AMSR and of the two AVHRR versions: operational and Pathfinder. Figure 2 shows the percentage of daily oceanic 1/4 grid boxes that have either daytime or nighttime observations for 2003. For AVHRR, the results show that the average day and night operational AVHRR coverage is 8%, while the day and night Pathfinder coverage is 13% and 12%, respectively. (If day and night are 2

combined, the operational and pathfinder AVHRR coverage increases to 16% and 25%, respectively.) Because this is the same sensor, the different coverage is evidently due to different cloud masking. Figure 2 also shows that AMSR coverage has a clear advantage over AVHRR, as expected. AMSR raises the daily coverage for day and night to 40% and 46%, respectively. Note that there are dropouts of AMSR data shown in the figure. The largest one occurred between 30 October and 5 November 2003 due to spacecraft problem during which none of the onboard sensors were operational. To better examine the gradients over time, gradient indices were computed. The index for the Gulf Stream was computed from the daily magnitude of the SST gradients from June 2002 through December 2004 for daily OI runs: AVHRR-only and AMSR+AVHRR and for the OI.v2 and RTG_SST analyses. For the Gulf Stream the maximum gradient value was determined along lines of longitudes from 70 W - 40 W between 35 N and 50 N ; these maximum values were then averaged over longitude. The daily index is shown for the Gulf Stream in Fig. 3. The results show that the OI.v2 index is much lower than the others as expected from Fig. 1. Also as expected, the AVHRR-only and the RTG_SST indices are generally quite similar. Perhaps the most interesting difference occurs between the AVHRR-only and AMSR+AVHRR gradient indices. These indices are similar in August and September with the AMSR+AVHRR gradient index only slightly stronger. The differences gradually increase from September to roughly March and then decrease again to the August minimums. In winter the AMSR+AVHRR gradient index is almost double the AVHRR-only index. The results show that the seasonal cycle of the index is under represented by AVHRR alone, because cloud cover tends to be more pervasive in winter. Indices for the Kuroshio region (not shown) show a similar seasonal cycle although both the average and seasonal amplitude are weaker. The Kuroshio index is again stronger for the AMSR+AVHRR product in winter because of reduced AVHRR coverage due to pervasive cloud cover. The large-scale biases for the January 2003 - December 2005 period are now examined where the AMSR+AVHRR daily OI analysis is used as a reference. Two special AVHRR-only and AMSR+AVHRR daily OI analyses were made without satellite bias correction. The average difference with respect to the AMSR+AVHRR was computed for this period for the OI.v2, the AVHRR-only daily OI with and without bias correction and the AMSR+AVHRR daily OI without bias correction. For this comparison the OI.v2 was linearly interpolated to the daily OI grid. The average difference is shown in Fig. 4 where "NO" indicates an analysis without bias correction. The OI.v2 (top left in Fig. 4) shows that the biggest difference with respect to the AMSR+AVHRR daily OI occurs between 60 S and 40 S, with largest values in the Pacific east of the dateline. This is the region with sparse in situ data and thus, the true bias is not well known. Many of the other differences in the western boundary current regions (e.g., in the Gulf Stream and Kuroshio) and the eastern Pacific equatorial region are due to the increased resolution of the daily OI. The AVHRR-only OI with bias correction (top right) shows some residual biases with respect to the AMSR+AVHRR daily OI primarily along the ITCZ and SPCZ, a reminder that residual biases can survive the bias correction step if the biases persist. The bottom row of Fig. 4 shows the biases in the OI analyses without satellite bias correction. The AVHRR-only OI analysis without bias correction (bottom left) shows the 3

largest biases with respect to the AMSR+AVHRR analysis with bias correction. The biases are especially in evident tropical oceans. Comparison with the AVHRR-only OI analysis with bias correction (top right) shows the necessity of the bias correction. The AMSR+AVHRR OI analysis without bias correction (bottom right) with respect to the AMSR+AVHRR analysis with bias correction shows smaller long-term biases although some biases remain. 3. Plans for Additional Work Further work is needed and will continue. One of the most important steps is to develop a method to improve the bias correction and to correct any ship and buoy biases. As improved satellite AMSR and AVHRR data sets become available, the analyses will be reprocessed. One of the most important potential improvements would occur due to the addition of new satellite data sets. The next daily OI product will include the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) (which samples between 38 S and 38 N) and the global Along Track Scanning Radiometer (ATSR) series of instruments. Each of these satellite data sets will first be examined separately using an independent analysis. It is hoped that number of final products would not have to be expanded. New products will only be added if the SST analyses show a significant improvement with the addition of new satellite. Improvements with updated documentation will be added as needed. 4. References Chelton, D. B., and F. J. Wentz, 2005: Global microwave satellite observations of seasurface temperature for numerical weather prediction and climate research. Bull. Amer. Meteor. Soc., 86, 1097-1115. Kilpatrick, K. A., G. P. Podesta, R. Evans, 2001: Overview of the NOAA/NASA advanced very high resolution radiometer Pathfinder algorithm for sea surface temperature and associated matchup database. J. Geophys. Res., 106, 9179-9198. May, D.A., M. M. Parmeter, D. S. Olszewski and B. D. McKenzie, 1998: Operational processing of satellite sea surface temperature retrievals at the Naval Oceanographic Office, Bull. Amer. Met. Soc., 79, 397 407. Reynolds, R. W., N. A. Rayner, T. M. Smith, D. C. Stokes and W. Wang, 2002: An improved in situ and satellite SST analysis for climate. J. Climate, 15, 1609-1625. Thiébaux, J., E. Rogers, W. Wang and B. Katz, 2003: A new high-resolution blended real-time global sea surface temperature analysis. J. Climate, 15, 645-656. 4

Figure 1. Three-day averages of SST gradient magnitudes for analyses and data centered on 1 October 2003 for the Gulf Stream region. The data products are AVHRR and AMSR. The analyses are: OI.v2, RTG_SST and the daily OI for AVHRR-only and AMSR+AVHRR. 5

Figure 2. Daily percentage of 1/4 ocean grid boxes with day and nighttime satellite data. The types of data are Pathfinder AVHRR, operational AVHRR, and AMSR. 6

Figure 3. Analysis SST gradient index (see text) for the Gulf Stream region for 1 June 2002-31 December 2004. The analyses are the OI.v2, the RTG_SST and the daily OI using AVHRR-only and AMSR+AVHRR. 7

Figure 4. Average analysis differences for 2003-2005 with respect to the daily OI AMSR+AVHRR with bias correction. The analyses compared with bias correction are the AVHRR-only daily OI and the OI.v2 (top row). The analyses without bias correction are the AVHRR-only and the AMSR+AVHRR daily OI (bottom row). "No" in the title indicates no bias correction. 8