Validation of AATSR using in situ radiometers in the English channel and Bay of Biscay

Transcription

1 Validation of AATSR using in situ radiometers in the English channel and Bay of Biscay Craig Donlon, Ian Robinson, Werenfrid Wimmer and Dave Poulter AATSR & MERIS Meeting, ESA/ESRIN, Frascati, Italy, September 2005

2 Outline Data collection Matchup Methodology Results Summary AATSR validation results Further work Why do we need an in situ radiometer?

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4 ISAR installation: Pride of Bilbao Solar short-wave radiation Long-wave radiation Met Office Automatic Weather station In Situ SST SAHFOS Towed CPR June In-water temperature SeaBird hull-mounted thermometer Kipp&Zonen CM11 pyranometer Deployed on a routine ferry Humidity service between Portsmouth and Bilbao, crossing the Vaisala wet-dry Bay of Biscay and English humidity sensor Channel Eppley PIR pyrgeometer Vector wind Gill Wind-master 3 axis sonic anemometer 1 sec)

5 PoB Ship track Portsmouth Bilbao Cherbourg 2 legs to ship track: Portsmouth to Cherbourg (booze cruise) Portsmouth to Bilbao 20,000+ SSTskin observations ~ 1 every 2 minutes Many breaks due to bad weather

6 Historical & current Status Proven autonomous system 4 instruments now in operations (1 on, 1 off configuration) 2 in EU, 2 in USA. Data collected since 16 th March 2004 Present (19 months in operation!) Fully flexible on-board data logger system Moving towards real-time system with irridium link Relational database to manage ISAR deployments Project is a major success Many data for AATSR in cool ocean regions Careful calibration and data processing Accurate results

7 Matchups the basis of validation The concept of validation is easy Compare the product temperature with the true temperature The practice of validation is difficult but very rarely are we able to compare like with like Satellite represents the instantaneous average (sub)skin temperature over the sensor field of view In situ measurement represents point samples, at a specific depth, typically at a time and location different from the satellite measurement We risk undermining product quality by using inadequate in situ validation data and too simplistic a comparison method

8 Matchup uncertainty estimations - 1 The error E s of a satellite measurement of SST is E S = V S - V (1) where V S is the satellite-estimated value and V is the true value of the ocean variable represented by the satellite view, i.e. the average over the satellite pixel area at the instant of the overpass In practice V is not known precisely and instead, the in situ measured value, V W, is used.

9 Thinking about matchups - 2 The error associated with an in situ sample is given by E W where: E W = V W - V (2) The matchup deviation (what we typically quote) is based on an analysis of a MDB This yields a match-up difference, MDB That is, for a given sample pair (or average of many MDB records) MDB = V S - V W (3) But, MDB E s E S and E W errors (associated with using the in situ measurement as a proxy for the true temperature, V) must be included in the validation analysis

10 In situ measurement errors In order to estimate E S it is necessary to estimate E w and if possible to minimise it. It can be broken down into several different types of error: E W = E Wt + E Wr + E Wm + E Ws + E Wz. (5) Time mismatch error Sampling depth error Location mismatch error In situ Instrument measurement error Point-in-area sampling error

11 Sources of error: 1 E Wm = inherent instrument measurement error (±0.1K) Determine independently. Apply bias correction. Reduce random error by an increased number of independent observations E Ws = point-within-area sampling error (<0.1K in most areas) it arises from using a point sample of V to represent the spatial average of V over the pixel field of view. Its magnitude depends on the degree of sub-pixel variability of V. This is not readily measurable (look at SST gradients), but might be minimised by choosing High Wind speed regimes > 6 m/s E Wm = Sampling depth error (<0.1 4K depending on situation) This arises when we use in situ SST depth (SST at depth) measurements to compare with skin, subskin or foundation temperatures.

12 Sources of error: 2 E Wt = time displacement error (variable, depends on SST gradient & FoV) caused by the mismatch in time, t dif, between the in situ sample and the satellite overpass. It can be estimated as E Wt =t dif. V/ t t dif could be due to timing error (small) but normally arises from trying to maximise the number of matchups when satellite sampling is sparse and prevented by cloud E Wr = spatial displacement error (variable, depends on SST gradient & FoV) caused when the in situ sample is displaced from the matched image pixel by a distance r. It can be estimated as E Wr = r. V/ r r could be due to location error (small) but normally arises from trying to maximise the number of matchups when satellite sampling is sparse and prevented by cloud E Ws, E Wm (and to a certain extent, E Wt and E Wr) can be minimised by choosing High Wind speed regimes > 6 m/s in low SST gradient regions

13 Results - typical matchups AATSR data delivered on CD from UK-PAC (Infoterra) Delays of ~ 2 months Recently switched to use the NRT product from ESA T dif =2hr r=20km

14 D1-D4 deployment PoB (AATSR-ISAR) D1-D4 Results 1.2 dt (K) Dual view Day dual view Night dual view Nadir view Day nadir view Night nadir view 0 1 2a 2b Grade (1,2a,2b,3)

15 D5 nil point (1 st Feb March-15) January 2005 the ship was in re-fit and not at sea No matchups in Feb-March-15 (cloudy, bad weather)

16 D6 16 th March 22 nd June (AATSR-ISAR) Changed to match against AATSR R/T system via ftp from GHRSST-PP Medspiration Biases are lower are the real time and offline processors the same?? Geophysical issue? Diagnostics to monitor performance

17 D7 results: June-September (AATSR-ISAR) Hot months this year what is ISAR doing?

18 Future work Full use of ancillary data for refinement of match up process (quantify errors) Continued deployments (PoB available until at least end of 2007) GHRSST-PP validations (L2P & L4) Possible trans-atlantic lines in the next 6 months in collaboration with USA team

19 ISAR forms a core component of the GHRSST-PP HR-DDS system Independent, high accuracy, regular (pre-operational) and near complete measurement suite

20 ISAR Activities in the USA Collaboration with University of Miami under the NOPP ISAR aboard the M/W Jingumaru & Explorer of the seas Deployments aboard the R/V Ron Brown in the intra-american seas and Easter Pacific Jingumaru has a real-time reporting system Inter-comparisons with other radiometers next year ISAR

21 Conclusions Data Collection Mature and regular program of data collection Calibration of instruments and diagnostics ensure accuracy Moving toward a NRT system ISAR data are consistent across the record Unique location in cool varied waters Validation of AATSR: Robust reproducible procedure-based validation AATSR is performing exceptionally well Summary of results March 2004 September 20 th 2005 Nadir worse in the day (Day=0.96 ±0.27K, Night=0.33 ±0.25K) So: (a) processing centre bias? (b) Geophysical issue in 2004? (ISAR same as MAERI results)

22 AATSR ISAR summary Set Date Dual Bias (K) Dual SD (K) Nadir bias (K) Nadir SD (K) D1-4 D6 D7 Mar-Dec 2004 Mar-Jun 2005 Jun Sep

23 ISAR Deployment history Deployments Calibrations

24 ISAR data management Stores ALL L0 engineering data ALL L2 data ALL AATSR data for validations ALL calibration data (pre-post dep) ALL configuration data

25 CASOTS-II reference black body

26 ISAR-3: Post deployment calibration after 3-months at sea

27 Comparison of ISAR to other instruments Data obtained in N Atlantic over a 4 week deployment Erica Key, Peter Minnett and Mike Reynolds, NOPP ISAR project

28 The regional SST characteristics Surface 7 m depth Met Office POLCOMS ocean model analysis of SST, 7 th August 2004

29 Ocean Variability using Atlantic Margins Model AMM SST 2 AMM SST 5.1 C C km -1 1

30 GHRSST-PP: Regional/Global Task Sharing (R/GTS) A working demonstration of GEOSS Regional Data Assembly Centres (RDAC) RDAC RDAC RDAC RDAC RDAC SST data products (L2P, L4) Ingestion, Dissemination and Processing Service (IDPS) Global Data Analysis Centre (GDAC) Product Rolling Archive Metadata repository (MMR) HR-DDS archive USA EU Matchup database (MDB) Global L4 analysis systems Global coverage L4 SST products GHRSST-PP Long Term Reanalysis and data Stewardship Facility (LTSRF) Historical time series SST CDR products

31 Definitions of SST 10µm 1 mm 1 m SSTskin SST10m (K) Tskin ~0-0.5K 10µm 1 mm 1 m SST skin SST 10m (K) Tdiurnal ~0 4.0K SSTint Depth Depth Model SSTskin SSTsubskin SSTdepth 10 m 10 m SSTfnd (a) Night time situation, light wind (b) Day time time situation, strong solar radiation and light winds

32 What is Truth? Measurements of SST SST skin SST 10m (K) 10µm 1 mm Can t measure (δt <1s) Infrared sensors (δt ~10s) Microwave Sensors (δt ~10+ s) Depth 1 m Contact thermometers Ships/Buoys (δt ~minutes) SSTint SSTskin 10 m Day time time situation, strong solar radiation and light winds Contact thermometers Ships/Buoys (δt ~hours) SSTsubskin SSTdepth SSTfnd

33 Arabian Sea WHOI Mooring Data - Spring 1995 (1mm data estimated using Fairall et al. (1996)) (From the work of A. Stuart-Menteth) Temperature ( o C) SSTfnd Temperatures at all depths collapse to the same value before local sunrise Year Day

34 E Wt = time displacement error E Wr =spatial displacement error r = 0 T diff =0 E Wt =0 E Wr =0 r 0 T diff =0 E Wt =0 E Wr >0 E Wt = time displacement error, mismatch in time, t dif between the in situ sample and the satellite overpass. It can be estimated as E Wt =t dif. V/ t r = 0 T diff 0 E Wt >0 E Wr =0 r 0 T diff 0 E Wt >0 E Wr >0 E Wr = spatial displacement error when the in situ sample is displaced from the matched image pixel by a distance r. It can be estimated as E Wr = r. V/ r

35 Validation grades Grade-1 Coincidence of ISAR and AATSR sample within ± 2000 s time window and 1 km search radius in space. Grade-2A Temporal match within ± 2000 s and spatial match within ± 20 km Grade-2B Temporal match within ± 2 hrsand spatial match within ± 1 km Grade-3 Temporal match within ± 2 hrsand spatial match within ± 20 km.

36 Resolving SSTskin & SSTdepth Differences Donlon, C. J., P. Minnett, C. Gentemann, T. J. Nightingale, I. J. Barton, B. Ward and, J. Murray, Towards Improved Validation of Satellite Sea Surface Skin Temperature Measurements for Climate Research, J. Climate, Vol. 15, No. 4, , 2002 (Peter Minnett, RSMAS) High winds: minimise Ews & Ewz

37 Ambient temperatures ISAR ambient temperature range is up to 20K above SST!! 91% < 7.5 K

38 Diagnostic monitoring (B2-B1sig)/(B2-B1)radance Normal conditions ratios are constant Problem conditions ratios are highly variable Failure on 18 th May direct hit by wave! soaking the instrument no data are usable beyond Day 139