Aquarius SSS space/time biases with respect to Argo data

Slides:



Advertisements
Similar presentations
OSE meeting GODAE, Toulouse 4-5 June 2009 Interest of assimilating future Sea Surface Salinity measurements.
Advertisements

Tim Smyth and Jamie Shutler Assessment of analysis and forecast skill Assessment using satellite data.
Clima en España: Pasado, presente y futuro Madrid, Spain, 11 – 13 February 1 IMEDEA (UIB - CSIC), Mallorca, SPAIN. 2 National Oceanography Centre, Southampton,
1 Boutin et al., Avril 2015, SMOS-OCEAN TOSCA SMOS Salinity anomalies – Towards the correction of SMOS SSS systematic biases - J. Boutin 1, N. Martin 1,
1 Boutin et al., 2014 SMOS Salinity anomalies: new insights into SMOS capability at sensing SSS variability and into the improvements to be made in the.
Sea water dielectric constant, temperature and remote sensing of Sea Surface Salinity E. P. Dinnat 1,2, D. M. Le Vine 1, J. Boutin 3, X. Yin 3, 1 Cryospheric.
INTRODUCTION Although the forecast skill of the tropical Pacific SST is moderate due to the largest interannual signal associated with ENSO, the forecast.
T(z) and S(z) in the Bay of Bengal under the fresh water plume P. Hacker (SISS, 26 Feb 2014) WOCE I9N Feb-Mar 1995 Plume SSS range psu Plume SST.
Semyon A. Grodsky and James A. Carton, University of Maryland, College Park, MD The PIRATA (PIlot Research Array moored in the Tropical Atlantic) project.
Argo Products at the Asia-Pacific Data-Research Center Konstantin Lebedev, Sharon DeCarlo, Peter Hacker, Nikolai Maximenko, James Potemra, Yingshuo Shen.
GOES-R 3 : Coastal CO 2 fluxes Pete Strutton, Burke Hales & Ricardo Letelier College of Oceanic and Atmospheric Sciences Oregon State University 1. The.
1 High Resolution Daily Sea Surface Temperature Analysis Errors Richard W. Reynolds (NOAA, CICS) Dudley B. Chelton (Oregon State University)
Interannual Caribbean salinity in satellite data and model simulations Semyon Grodsky 1, Benjamin Johnson 1, James Carton 1, Frank Bryan 2 1 Department.
Linking Aquarius Salinity Measurements to River Discharges and Ocean Surface Carbon Dioxide Fugacity Xiaosu Xie and W. Timothy Liu Jet Propulsion Laboratory.
Aquarius optimum interpolation analysis for global and regional studies O. Melnichenko, P. Hacker, N. Maximenko, G. Lagerloef, and J. Potemra 2014 Aquarius/SAC-D.
NOAA Climate Obs 4th Annual Review Silver Spring, MD May 10-12, NOAA’s National Climatic Data Center 1.SSTs for Daily SST OI NOAA’s National.
1 Improved Sea Surface Temperature (SST) Analyses for Climate NOAA’s National Climatic Data Center Asheville, NC Thomas M. Smith Richard W. Reynolds Kenneth.
1 NOAA’s National Climatic Data Center April 2005 Climate Observation Program Blended SST Analysis Changes and Implications for the Buoy Network 1.Plans.
1 Assessment of the CFSv2 real-time seasonal forecasts for Wanqiu Wang, Mingyue Chen, and Arun Kumar CPC/NCEP/NOAA.
Impact of common SST anomalies on global drought and pluvial frequency Kirsten Findell and Tom Delworth Geophysical Fluid Dynamics Laboratory Princeton,
SST Diurnal Cycle over the Western Hemisphere: Preliminary Results from the New High-Resolution MPM Analysis Wanqiu Wang, Pingping Xie, and Chenjie Huang.
“ New Ocean Circulation Patterns from Combined Drifter and Satellite Data ” Peter Niiler Scripps Institution of Oceanography with original material from.
Atlantic Multidecadal Variability and Its Climate Impacts in CMIP3 Models and Observations Mingfang Ting With Yochanan Kushnir, Richard Seager, Cuihua.
Aquarius Ocean Salinity Open Discussion Yi Chao and Peter Hacker  Surface Stratification Advanced Argo floats Flag in situ data for expected mixed layer.
Cambiamento attuale: Ghiaccio e mare CLIMATOLOGIA Prof. Carlo Bisci.
Satellite Sea-surface Salinity: Data and Product Biases and Differences Eric Bayler and Li Ren NOAA/NESDIS Center for Satellite Applications and Research.
Ocean Salinity validation of mission requirements review / improvements: Points of Reflexion ESL teams Mission Requirements: The so-called GODAE requirements:
SMOS QWG-6, ESRIN October 2011 OTT generation strategy and associated issues 1 The SMOS L2 OS Team.
© Crown copyright Met Office The EN4 dataset of quality controlled ocean temperature and salinity profiles and monthly objective analyses Simon Good.
Simulator Wish-List Gary Lagerloef Aquarius Principal Investigator Cal/Val/Algorithm Workshop March GSFC.
SMOS QWG-9, ESRIN October 2012 L2OS: Product performance summary v550 highlights 1 The SMOS L2 OS Team.
T. Meissner and F. Wentz Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014 Seattle. Washington,
A high-resolution Aquarius OI SSS L4 analysis: 3-year, near-global, weekly, 0.5 degree grid Oleg Melnichenko, Peter Hacker, Nikolai Maximenko, and James.
Extratropical Sensitivity to Tropical SST Prashant Sardeshmukh, Joe Barsugli, and Sang-Ik Shin Climate Diagnostics Center.
Assimilating Satellite Sea-Surface Salinity in NOAA Eric Bayler, NESDIS/STAR Dave Behringer, NWS/NCEP/EMC Avichal Mehra, NWS/NCEP/EMC Sudhir Nadiga, IMSG.
Madden/Julian Oscillation: Recent Evolution, Current Status and Forecasts Update prepared by Climate Prediction Center / NCEP April 3, 2006.
Combining Altimeter-derived Currents With Aquarius Salinity To Study The Marine Freshwater Budget Gary Lagerloef Aquarius Principal Investigator Yi Chao.
Meeting Dates Location Title Author. 2 of 12 Dates Meeting Info Presentation info NASA Aquarius Mission Timeline May SPURS-2 Workshop EOPM OSST Seattle.
Errors on SMOS retrieved SSS and their dependency to a priori wind speed X. Yin 1, J. Boutin 1, J. Vergely 2, P. Spurgeon 3, and F. Gaillard 4 1. LOCEAN.
Dependence of SMOS/MIRAS brightness temperatures on wind speed: sea surface effect and latitudinal biases Xiaobin Yin, Jacqueline Boutin LOCEAN.
MODIS Atmosphere Products: The Importance of Record Quality and Length in Quantifying Trends and Correlations S. Platnick 1, N. Amarasinghe 1,2, P. Hubanks.
Towards the utilization of GHRSST data for improving estimates of the global ocean circulation Dimitris Menemenlis 1, Hong Zhang 1, Gael Forget 2, Patrick.
ESA Climate Change Initiative Sea-level-CCI project A.Cazenave (Science Leader), G.Larnicol /Y.Faugere(Project Leader), M.Ablain (EO) MARCDAT-III meeting.
T. Meissner, F. Wentz, J. Scott, K. Hilburn Remote Sensing Systems 2014 Aquarius / SAC-D Science Team Meeting November , 2014.
Validating SMAP SSS with in situ measurements
Climate Change Climate change scenarios of the
Spatial Modes of Salinity and Temperature Comparison with PDO index
Plans for Met Office contribution to SMOS+STORM Evolution
Challenges of Seasonal Forecasting: El Niño, La Niña, and La Nada
2016 ROMS Asia-Pacific Workshop, Hobart, Australia
Wet tropospheric correction comparison for Jason-2 mission between
Instrumental Surface Temperature Record
Intraseasonal latent heat flux based on satellite observations
Roughness Correction for Aquarius (AQ) Sea Surface Salinity (SSS) Algorithm using MicroWave Radiometer (MWR) W. Linwood Jones, Yazan Hejazin Central FL.
Coastal CO2 fluxes from satellite ocean color, SST and winds
Extreme sea level variability due to El Niño Taimasa
‘Aquarius’ Maps Ocean Salinity Fine-scale Structure
Y. Xue1, C. Wen1, X. Yang2 , D. Behringer1, A. Kumar1,
Instrumental Surface Temperature Record
Haline hurricane wake in the Amazon/Orinoco plume: AQUARIUS/SACD and SMOS observations S. A. Grodsky1 N. Reul2, G. Lagerloef3, G. Reverdin4, J. A. Carton1,
Fig. 1 The temporal correlation coefficient (TCC) skill for one-month lead DJF prediction of 2m air temperature obtained from 13 coupled models and.
Atlantic Ocean Forcing of North American and European Summer Climate
20th Century Sahel Rainfall Variability in IPCC Model Simulations and Future Projection Mingfang Ting With Yochanan Kushnir, Richard Seager, Cuihua Li,
NOAA Objective Sea Surface Salinity Analysis P. Xie, Y. Xue, and A
Instrumental Surface Temperature Record
Korea Ocean Research & Development Institute, Ansan, Republic of Korea
Figure 2 Atlantic sector of the first rotated EOF of non-ENSO global SST variability for 1870–2000 referred to as the “Atlantic multidecadal mode” (38,
Thomas Smith1 Phillip A. Arkin2 George J. Huffman3 John J. Bates1
by Stanley B. Goldenberg, Christopher W. Landsea, Alberto M
Presentation transcript:

Aquarius SSS space/time biases with respect to Argo data P. Hacker, O. Melnichenko, N. Maximenko, and J. Potemra Motivation- Significant mean and time-varying Aq-Argo SSS biases persist in version 3.0; Quantify biases for researchers; Quantify and provide input for algorithm improvements; Suggest methods for improving L-3 and L-4 products. References on Aquarius Validation- Lagerloef et al., 2013; Aquarius Project Document, AQ-014-PS-0016. Drucker and Riser, 2014; JGR-Oceans, 119, 4626-4637. Tang et al., 2014; JGR-Oceans, 119, 6171-6189. Vinogradova et al., 2014; JGR-Oceans, 119, 4732-4744. Presentations at this meeting. 2014 Aquarius/SAC-D Science Team Meeting 11-14 November 2014, Seattle, Washington

Aquarius-Argo Static (Time-Mean) Biases Beam 2 Beam 3 Beam 1 Ascending Descending psu Note- Generally similar spatial patterns for asc and dsc (not everywhere). Amplitudes differ. Potential RFI regions are likely correctable. Mean spatial bias correction fields for Aquarius ascending (left) and descending (right) data and for each of the three beams, version 3.0, standard product.

Static Bias (3-year mean) Ascending Descending Standard Aquarius SSS SST-adjusted SSS psu SST-based adjustment improved static bias in many areas, but not everywhere. Note: Central Pacific; Indian Ocean; N Atlantic; SE Atlantic; asc/dsc differences.

Time-Varying Bias (example for April 2013) Ascending Standard Aquarius SSS SST-adjusted SSS psu Descending Standard Aquarius SSS SST-adjusted SSS SST-based adjustment provides no significant improvements in time-varying bias. Asc/dsc differences persist. Some regions are somewhat better, others somewhat worse.

EOF Decomposition of Time-Varying Bias Standard Aquarius SSS SST-adjusted SSS 3 pcs: ~50% of variance 1 and 2: ~43% (annual) 3: ~7% (semi-annual) Asc Desc % of variance explained annual cycle 3-month phase shift PC1 Asc PC1 Desc PC2 Asc PC2 Desc semi-annual cycle PC3 Asc PC3 Desc

EOF Spatial Structure Ascending Descending 0.5 psu -0.5 Original Bias_adj EOF 1 EOF 2 EOF 3 Descending 0.5 psu -0.5 Add these spatial patterns with time-series weights to produce smoothed fields. Note non-zonal structures in EOF 2 and 3 in tropics and mid-latitudes.

Amplitude of the Annual Cycle in the Bias Field psu Standard Aquarius SSS (A+D) SST-adjusted SSS (A+D) median= 0.073 psu median= 0.074 psu Amplitude of the annual cycle in Argo gridded SSS Note- Very similar patterns for standard and SST-adjusted fields; Modest differences in structures and amplitudes (ie NW Pacific).

Phase of the Annual Cycle in the Bias Field Standard Aquarius SSS (A+D) SST-adjusted SSS (A+D) D N O S A J M F 3-month lag for PC-1 and PC-2 implies a propagating pattern, which can confuse analysis of propagating ocean signals. Phase of the annual cycle in Argo gridded SSS D N O S A J M F

Regional Time-Series (Aq. L2, v2.7.1, Argo) Hacker et al., 2014, OSM Examples show the quantification of the steady state bias and the annual cycle bias for regional studies. Aquarius is saltier than Argo and has larger magnitude annual cycle. Aquarius is fresher than Argo and has a somewhat larger magnitude annual cycle during 2012-2013. Box 1 in NP [158oE-140oW, 28-40oN] SSS (psu) Box 2 in NP [158oE-140oW, 2-14oN] SSS (psu) Time-series of weekly box-averaged Aquarius and Argo data. Light green line shows weekly Argo mean with shading indicating the standard error. Heavy green line shows the weekly Argo mean calculated from the gridded APDRC Argo product available at http://apdrc.soest.hawaii.edu/. Solid red and blue lines show weekly Aquarius mean SSS from ascending and descending tracks. Dashed/dotted green, red and blue lines show the individual beams.

Conclusions For the present version 3.0 data from September 2011 to August 2014: Time-mean and annually varying Aquarius-Argo biases are significant. Compared to Argo, ascending and descending biases for each of the three beams, the 3-year mean, and time-varying biases for both the standard Aquarius and SST- adjusted SSS products have typical ranges of spatial variability of +/- 0.25 psu globally. The amplitude of the annual cycle in the bias field can be a significant bias compared to the Argo-derived annual cycle regionally. An EOF analysis provides the amplitude and spatial structure for the first three components, which account for ~50% of the time-varying Aquarius-Argo SSS bias. The first two components are annually varying with a 3-month lag. The third component is primarily semi-annual. The quantified biases can be used to improve L-2, L-3 and L-4 products (currently) and for version 4.0 in the future (Melnichenko et al.,2014).

Static bias (3-year mean) Ascending Aquarius - APDRC Aquarius – Met Office Descending Aquarius - APDRC Aquarius – Met Office APDRC and Met Office Argo products show very similar large-scale patterns except in the NW Atlantic along the North American boundary currents.

Issues and Ideas Towards v4.0 The need for one or more improved, reference data sets for evaluation of Aquarius SSS. Argo, moorings, other in situ, model, climatologies, L-4 products etc. all have limitations and potential utility. The need to harmonize Aquarius and Argo products on defined time/space scales. OSTST uses both tide stations and satellite data to optimize and harmonize products. OSST could do the same. The need for a coastal focus group to optimize Aquarius near land and islands. Both OSTST and OVWST have such very productive groups addressing a host of issues. The need and opportunity for improved L-3 and L-4 products for research and applications.

Aquarius SSS space/time biases with respect to Argo data  Peter Hacker, Oleg Melnichenko, Nikolai Maximenko and James Potemra The Aquarius/SAC-D satellite provides an opportunity to observe near-global sea surface salinity (SSS) with unprecedented space and time resolution not available by other components of the Global Ocean Observing System. In order to evaluate and quantify the potential utility of the SSS data for global and regional studies of SSS variability, our research group has been using the Level-2, three-beam swath data and Argo data to characterize and quantify systematic space/time biases and random errors on a global grid. Despite continuing Level-2 product improvement of Aquarius data over the past three years, significant ascending/descending and inter-beam space/time biases with respect to Argo data persist. Time-mean and annual biases are particularly significant. For the present version 3.0 data, our analyses include quantifying the mean spatial biases for ascending and descending data for each of the three beams (typical range of +/- 0.25 psu), and 3-year mean and time-varying biases for the standard Aquarius and SST-adjusted SSS products from September 2011 to August 2014 (typical range of +/- 0.25 psu). An EOF analysis provides the amplitude and spatial structure for the first three significant components, which account for ~50% of the time-varying Aquarius minus Argo SSS bias. The first two components are annually varying with a 3-month lag; the third component is primarily semi-annual. The amplitude of the annual cycle in the bias field varies spatially from 0-0.25 psu and can be a significant bias compared to the Argo-derived annual cycle regionally. 2014 Aquarius/SAC-D Science Team Meeting 11-14 November 2014, Seattle, Washington