Sea Surface Heat and Water Fluxes Estimates

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INTAS-ESA Project 99-684

Satellite Active and Passive Microwave Remote Sensing as a Tool for Improvement of the Sea Surface Heat and Water Fluxes Estimates

First Year Annual Report Period: April, 2000 - May, 2001

Partners: Nansen Environmental and Remote Sensing Center

Bergen, Norway

Catholic University of Louvain, Faculty of Applied Sciences

Louvain-Ia-Neuve, Belgium

Nansen International Environmental and Remote Sensing Centre

St. Petersburg, Russia

Pacific Oceanological Institute, Far Eastern Branch RAS

Vladivostok, Russia

July 2001


CONTENTS ...................................................................................................

1.

RESEARCH

1.1.

Overview of Research Activities / Conformance With the Work Program

3

1.2.

Scientific Results

6

3

.............................................................................................. .

1.3.

Impact and Applications

2.

MANAGEMENT

7

2.1.

Meetings and Visits

7

7

....................................................... .... ......... ..... ............. .

2.2.

Collaboration

8

2.3.

Time Schedule

8

2.4.

Problems Encountered

9

............................................................................................. .

9

.................................................................................................... .

........................................................... .

10 10 10 10 10

..................................................................................................... .

11

2.5.

Actions Required

3.

FINANCES

3.1.

Cost Breakdown

3.2.

Accordance With the Work Program

3.3.

Expenditures on Equipment, Consumables, Other Costs

3.4.

Expected Deviations From the Work Program

4.

ANNEXES

4.1.

Summary Reports From Each Team

11

4.2.

Reprints of Key Publications

14

INTAS-ESA Project 99-684 First Annual Report

2114


1. RESEARCH

1.1. Overview of ~esearch Activities / Conformance With the Work Program 1. 1. 1. Work carried out up to now Task 1: Databases buildup Planned: Creation of the theoretical and experimental databases for algorithm development. Achieved: Buildup' of the theoretical database for the project. Sub-Task 1.1: Creation of database for the numerical simulations. Planned: Creation of the theoretical database consisting of the simultaneous meteorological and hydrological measurements. Achieved: Database consisting of 1620 ship radiosonde profiles, meteorological and hydrological measurements, calculated total water vapor content (TWVC) and total cloud liquid water content (TL WC) has been submitted. The results were presented at the St. Petersburg meeting and included in Maia Mitnik's PhD Thesis. In 200 I additional radiosondes obtained mainly at the moderate and high latitudes were included in the database. Now the database consists of 2049 radiosondes and ancillary parameters. No problems or delays encounted. Sub-Task 1.2: Buildup of the experimental databases. Planned: Collect collocated quasi synchronous microwave (from SSM/I and TMI radiometers) and scatterometer (from ERS-1I2 and NSCAT) to buildup the experimental databases. Achieved: POI has received the SSM/I data from MSFC on 8 mm exabyte magnetic tapes and ERS-1I2 scatterometer data from IFREMER, France on CD-ROMS. Not much specific work has been done so far to create the databases, due to the absence of an Exabyte driver both at POI and NIERSC. (The drive is too expensive to buy within for project budget, but NERSC will now give wayan old drive for use in the project. Additionally, continuation of cooperation between NASDA and POI is now under discussion. NASDA did not provide the ADEOS-I NSCAT measurements to collocate with SSM/I and TMI. At last the AMSR observations will be available in 6 months after launch of ADEOS-II satellite (February 2002). That is why there is a delay in buildup of our experimental databases. To carry out our research we began to download the data collected at CERSA T (http://www.ifremer.fr/cersat) such as ERS-1I2, NSCAT and QuikSCAT scatterometer data and the SSM/I brightness temperatures and plan to use these data to buildup the databases needed for our research. It is time consuming taking into account both the large data volumes and not reliable Internet communication.

Task 2: Passive microwave measurements simulations: Forward problem solution Planned: After the buildup of the theoretical database, choose the microwave radiative transfer model and its components (absorption spectra of atmospheric gases, spectra of dielectric permittivity of water, etc.) and compute the brightness temperatures at the SSM/I, TMI and AMSR frequencies at different polarizations. Achieved: All numerical computations were carried out. Databases consisting of the brightness temperatures at the SSM/I, TMI and AMSR frequencies, meteorological and hydrological parameters are available for algorithm development. No problems, no delays. Sub- Task 2.1: The choice of physical models for the forward problem algorithm development Planned: Select models describing absorption by water vapor, oxygen, water and ice clouds, dielectric permittivity of fresh and saline water and dependencies of sea surface emissivity on wind speed and direction.


3/14


Achieved: All above-mentioned models were selected and upgraded with using of recently published scientific papers. It is supposed to continue improvement of the models on the basis of new experimental findings. No problems, no delays.

Sub-Task 2.2: Numerical calculations Planned: Write a program for brightness temperature computations using the chosen models of atmospheric absorption and sea surface emissivity. Compute the brightness temperatures at the SSM/I, TMI and AMSR frequencies for the theoretical database (sub - task 1.1). Estimate statistical relations of the atmospheric doubled attenuation (ADA) at the scatterometer frequencies upon the brightness temperatures. Achieved:

1) 2)

A program for brightness temperature computations was written at the POI and NIERSC. Arrays of simulated SSM/I, TMI and AMSR brightness temperatures, optical depths at ERS-l/2, ADEOS-I NSCAT and Envisat ASAR frequencies, were computed. The arrays of the brightness temperatures with the added radiometer noises and with the corresponding ground-truth values of SST, SSWS, SWD, TWVC and TLWC are available for algorithm development. 3) Statistical relations of the atmospheric doubled attenuation (ADA) at the frequencies of 5.3 and 13.4 GHz upon the brightness temperatures and atmospheric absorption at 10.6 GHz were determined. No problems, no delays.

Sub- Task 2.3: Investigation of the real sensors sensitivity to the parameters to be retrieved Planned: Estimate the sensitivity of the brightness temperatures at the SSM/I, TMI and AMSR frequencies to the environmental parameters. Achieved: The partial derivatives of the brightness temperatures with the respect to the atmospheric and oceanic parameters were computed for different combinations of the parameters. Analysis of the partial derivatives behavior has allowed to formulate the preliminary recommendations for the Neural Networks models configuration. No problems, no delays.

Task 3: Active microwave measurements simulations: Forward problem solution Planned: Write a program to calculate the normalized radar cross-section (NRCS) and use the program to simulate ERS Wind scatterometer, NSCAT and QuikSCAT data. Achieved: A program for the NRCS calculation was written and tested. The NRCS calculations have been performed for ERS and ADEOS-I scatterometers using the data on wind speed and direction from the theoretical database (Sub-Task 1.1). Arrays of the simulated ERS Wind scatterometer and NSCAT NRCS were obtained. These arrays with corresponding ground-truth values of SST, SSWS, SWD and other parameters are available for retrieval algorithm development. Estimates of atmospheric attenuation influence on the NRCS of sea surface were obtained. No problems, no significant delays.

Task 4: Passive microwave measurements: Inverse problem solution Planned: Build the retrieval algorithms for passive microwave using Neural Networks (NNs) approach, basing on simulated and experimental brightness temperatures of real passive sensors. Compare and analyze the resulting retrieval errors for existing traditional and NNs algorithms. Achieved: Physical-based and statistical retrieval algorithms were developed with usage of the theoretical databases to compare the errors of parameters which were retrieved with those of NN-based algorithms. No problems, no sigpificant delays.

Sub-Task 4.1: Neural Networks model development


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Planned: Determine the specific type of the optimal Neural Network model including the number of hidden layers and nodes for parameter retrievals. Achieved: Standard feedforward backpropagation Neural Network was used for parameter retrievals. Different Neural Networks configurations were explored. The optimal NNs model for parameter retrievals for different states of the atmosphere-ocean system was found. No problems, no significant delays.

Sub-Task 4.2: Neural Networks training and testing for different predetermined physical limitations Planned: Form the data subsets for polar (SST ~ 10°C) and tropical (SST ~ 25°C) latitudes correspondingly. Train different Neural Networks for the whole data set, and polar and tropics subsets, using the value of atmospheric absorption as an additional parameter influencing the retrieval error. Give the error estimation. Achieved: Neural Networks algorithms for geophysical parameter retrievals (SSWS, SST, TWVC and TLWC for AMSR; SSWS, TWVC and TLWC - for SSM/I) were trained and tested for different physical limitations. Retrieval errors were estimated for different physical conditions applying both Neural Network and traditional algorithms. No problems, no serious delays.

Task 5: Active microwave measurements: Inverse problem solution Planned: Develop Neural Networks algorithms for scatterometer measurements using theoretical and experimental matched up databases. Achieved: The research has been started but no results are received yet. According to the project schedule the Task 5 is going to be fulfilled during the second project year.

Task 6: Combined (active and passive) microwave measurements: recommendation for further satellite systems development Planned: Develop new approach for wind vector retrieval based on the combination of the passive and active

measurements. Achieved: No research has been started for this task. According to the project schedule the Task 6 is going to be fulfilled during the second project year.

Task 7: Case study Planned: To demonstrate the possibilities of the new approach as applied to North-Atlantic polar cyclone. Achieved: No research has been started for this task. According to the project schedule the Task 7 is going to be fulfilled during the second project year.

Task 8: Project management Up to now each of the Contractors have carried out the planned work specified in the Work Programme. Up to date, the research in this project is mainly in accordance with the Work Program. A delay in buildup of experimental database is supposed to be eliminated till October 2001. Expected Deviations from the Work Program Based on the good experiences from the first project year, we don't expect serious deviations from the Work Program for the remaining project period.


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1.2. Scientific Results 1.2.1. Main Resulfs Achieved up to now and their Scientific Significance Main scientific results until now are the research on the applications of Neural Network-based algorithms to the microwave radiometer measurements and its potential to improve accuracy of the retrieved geophysical parameters comparing to most commonly used current statistical and physically-based retrieval algorithms. The NN-based algorithms have a simple architecture and consist of 1 hidden layer of 2-6 processing neurons. A separate NN algorithm for each retrieved parameter ensures the best results. • POI and NIERSC are working on creation of the regional databases, which are supposed to be used to check efficiency of regional retrieval algorithms comparing to global ones. • POI and NIERSC has started to develop an improved radiative transfer model using the new model of sea surface emissivity in particular the dependencies of emissivity on SST and wind speed. The model will be used for new calculations of the brightness temperatures at the SSM/I, TMI and AMSR frequencies. VCL team experience in developing • POI and UCL are working on a comparison of different satellite scatterometer model functions to estimate how choosing of a model function influences on the calculated NRCS values and retrieval errors of wind speed and direction. • NIERSC and POI are working on Neural Network-based and physical-based algorithms development for retrieval of SSWS, SSWD, SST, TWVC and TLWC from the simulated SSM/I, TMI and AMSR brightness temperatures. • NERSC is carrying out the general coordination of the project. NIERSC is carrying out the coordination of the Russian partners.

1.2.2. Publications Joint publications of INTAS and NIS project teams 1.

Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Development and validation of sea surface wind speed retrieval algorithms using SSM/I data, Neural Networks and some physical limitations. Earth Observation and Remote Sensing, 2000, V. ,N2, P. 62-71.

2.

Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Ocean winds retrieved from SSM/I data using Neutral Network-based algorithms. In: Ocean Winds, 19-22 June 2000, Brest, France, p. 101.

3.

Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Neural networks algorithms for passive microwave atmosphere-ocean system parameters retrieval. In: Proc. 29th Int. Symp. Remote Sensing Environment, Cape Town, South Africa, 2000.

4.

Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Retrievals of Atmospheric Integrated Parameters over Oceans with SSM/I Data Using Neural Networks Approach. Proc. International Radiation Symposium 2000, St. Petersburg, Russia.

Publications without INT AS-NIS co-authorship of the project teams Books, monographs, internal reports, theses, patents 1.

PhD thesis (defended) "Microwave Sensing of Ocean-Atmosphere System in Tropics" by Maia Mitnik (POI).

2.

PhD thesis (submitted) "Neural Network retrievals of atmospheric water and sea surface wind speed from satellite microwave data" by Elizaveta Zabolotskikh (NIERSC).


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Summary table ONLY: Jointly by INT AS and NIS Project teams

ALL PUBLICATIONS Scientific Output Paper in an International Journal Paper in a National Journal *) Abstract in proceedings (conferences, workshops) Book, Monograph *) Internal Report **) Thesis (MSc, PhD, etc.) *)

Published 1 1 (Russian) 3

in press/accepted

0

0 1 (POI) 1 PhD (NIERSC) (Russian) 0

1 PhD (POI) (Russian) 0

submitted 1 (Ocean Winds) 3 (POI, NIERSC + POI) 0 0

1 1 3

0

Patents 0 *) Indicate the language **) Indicate if a report has not been published purely in order to protect intellectual property rights.

1.3. Impact and Applications 1.3.1. Impact of Results Achieved to Date The results obtained to date indicate that existing retrieval algorithms need to be improved in order to decrease errors of the retrievals of geophysical parameter important for heat and water flux estimation. If the improved algorithms, which are currently being developed, posses significantly better accuracy than the existing algorithms, this will have a significant impact on many activities dealing with the study of air/sea interaction and global climate research.

1.3.2. Patents / Other Protection of Intellectual Property So far, we have not been seeking patents or other protection of intellectual properties.

2. MANAGEMENT 2.1. Meetings and Visits 2.1.1. Meetings and Visits up to Date Up to date, the following meetings and visits were performed: •

Kick-off meeting at NIERSC, St. Petersburg, 27-28 July 2000; partIclpants: Johannessen (NERSC); Guissiard (DCL), Bobylev (NIERSC), Zabolotskikh (NIERSC), and Maia Mitnik (POI).

• •

Trip of Elizaveta Zabolotskikh (NIERSC) to Ocean Winds Symposium (Brest, France), 17 - 23 June, 2000. Business trip by Leonid Mitnik (POI) to NIERSC, St. Petersburg, 1-11 July 2000 on the way from Ocean Winds Symposium (Brest, France). Purpose: Selection of components of radiative transfer model, discussions on database buildup, etc.

•

Business trip by Maia Mitnik (POI) to NIERSC, St. Petersburg, 3 July - 11 August 2000. Purpose: Collaboration on buildup up and checking of databases, selection of radiative transfer model, discussions of data formats, etc.

•

Business trip by Leonid Mitnik (POI) to NIERSC, St. Petersburg, 6-15 and 21-25 October 2000 on the way to and from the ESA-Envisat Symposium (16-20 Oct 2000, Goteborg, Sweden). Purpose: Collaboration on numerical simulations of the brightness temperatures and radar cross-section. Discussions ofthe results.

•

Business trip by Maia Mitnik (POI) to NIERSC, St. Petersburg, 17-22 December 2000. Purpose: Discussions of the physical-based and Neural Networks-based retrieval algorithms and the results of numerical simulations of the radar cross-section.


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Summary table Visits West -7 East East -7 West West-7 West East -7 East

Number of person days 15 5

Number of scientists 3 1 0 2

70

2.1.2. Meetings and Visits Planned for the Remaining Duration •

Leonid Mitnik (POI) to NIERSC, St. Petersburg in July 2001. Purpose: Collaboration on buildup of the experimental databases modeling, comparison of retrieval algorithms.

•

Maia Mitnik (POI) to NIERSC, St. Petersburg in July 2001. Purpose: Collaboration on development of retrieval algorithms for passive microwave and scatterometer measurements.

•

First Progress Meeting at NIERSC, St. Petersburg in August 2001.

•

Leonid Mitnik (POI) to NIERSC, St. Petersburg in September 2001 on the way to the international symposium "Remote Sensing of the Ocean - Achievements and Perspectives".

•

Second Progress Meeting at NIERSC, St. Petersburg in late 2001.

•

Workshop: Discussion of results of combined algorithm for active and passive techniques.

•

Final meeting near the end of the project.

• 2.2. Collaboration 2.2.1. Intensity of Collaboration Among the Contractors Intensity of Collaboration West East West West East East

high

rather high

• •

rather low

low

•

2.2.2. Co-operation With Additional Organizations and Institutions For buildup of the scatterometer database and modelling of scatterometer transfer function POI have initiated a collaboration with Dr. Ad Stoffelen, The Royal Dutch Meteorological Institute (KNMI), The Netherlands and Cersat (France). This collaboration includes an exchange of information on scatterometer transfer functions, scatterometer and SSM/I data, discussions on model results, etc.

2.3. Time Schedule 2.3.1. Accordance with the Work Program Up to date, the time planning has been well in accordance with the Work Program.

2.3.2. Expected Deviations From the Work Program Based on the good experiences from the first year, we don't expect deviations from the Work Program in the future.


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2.4. Problems Encountered 2.4.1. Major Problems The project is running well- quality and quantity of scientific contributions, collaboration, communications, transfer of funds and goods are okay; only one significant problems encountered up to date.

Summary table Problems encountered Co-operation of team members Transfer of funds Telecommunication

major

Minor

.

none

. .

not applicable

Periodically slow Internet communication to NIS partners.

2.5. Actions Required At present, we do not have or foresee any problems that require action from INTAS.


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3. FINANCES 3.1. Cost Breakdown Contractor # *)

Name of Contractor *)

I 2

NERSC

3 4

Cost Category Individ. Grants Labour Costs

UCL

Overheads

0

Travel and Subsistence

0 0

NIERSC

0

POI

3450

300

1970

10400

300

6818

TOT AL (Euro)

.,. '"'"

2000l"

0 6950

Consumables

Equipment

TOTAL (Euro)

Other Costs

250

2250 I 800

0

0 148

0

8 146

1000

350

800

7870

1000

748

800

20066

I 800 l" 1048

*) List the Contractors in the same order and with the same numbers as in the Work Programme. **) Contractors from INTAS Member States are not allowed to spend money of this grant on equipment in projects up until calls 1999.

3.2. Accordance With the Work Program All expenses are in accordance with the Work Program.

3.3. Expenditures on Equipment. Consumables, Other Costs POI-RAS: 1. PC Pentium III -1000 2. . Monitor 17" Samsung

3.4. Expected Deviations From the Work Program In general, we don't expect deviations from the Work Program in the future.


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4. ANNEXES 4.1. Summary Rep?rts From Each Team 4.1.1. Nansen Environmental and Remote Sensing Centre (NERSC) The NERSC team took part in the execution of the following project tasks: •

Task 8: Project management.

Publications: Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Development and validation of sea surface wind speed retrieval algorithms using SSMII data, Neural Networks and some physical limitations. Earth Observation and Remote Sensing, 2000, V. ,N2, P. 62-7l. Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Ocean winds retrieved from SSM/I data using Neutral Network-based algorithms. In: Ocean Winds, 19-22 June 2000, Brest, France, P. 10l. Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Neural networks algorithms for passive microwave atmosphere-ocean system parameters retrieval. In: Proc. 29th ISRSE, Cape Town, South Africa, 2000. P. ? Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Retrievals of Atmospheric Integrated Parameters over Oceans with SSMII Data Using Neural Networks Approach. Proc. International Radiation Symposium 2000, St. Petersburg, Russia. P?

Team members: Prof. Ola M. Johannessen and Lasse H. Pettersson Team leader and scientist; • Discussion of the results and project methodology, participating in all active Tasks. • Making data material available to the project. • International integration and evaluation of project methodology. • Overall coordination of the project.

4.1.2. Laboratoire de Telecommunicetions et Teleditection, Universite Catholique de Louvain (UCL) The VCL team took part in the execution of the following project tasks: • •

Sub - Task 2.1: The choice of physical models for the forward problem algorithm development. Task 3: Active microwave measurement simulations

Team members: Prof. Albert Guissard Team leader; • •

Discussion of the results. The choice of physical models for the forward problem algorithm development.

Dr. Piotr Sobieski •

The choice of physical models for the forward problem algorithm development.

4.1.3. Nansen International Environmental and Remote Sensing Centre (NIERSC) The NIERSC team took part in the execution of the following project tasks: •

Sub - Task 1.1: Creation of database for the numerical simulations


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• • • • •

Sub - Task 1.2: Sub - Task 2.1: Sub - Task 2.2: Sub - Task 4.1: Sub - Task 4.2:

Buildup of the experimental databases The choice of physical models for the forward problem algorithm development. Numerical calculations. Neural Network model development Ne~ral Networks training and testing for different predetermined physical limitations

The research has been in accordance with the Work Program. We do not foresee any deviations from the Work Program for the future. Scientific Results: Neural Networks-based algorithms for parameter retrievals.

Publications: Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Development and validation of sea surface wind speed retrieval algorithms using SSM/I data, Neural Networks and some physical limitations. Earth Observation and Remote Sensing, 2000, V. ,N2, P. 62-71. Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Ocean winds retrieved from SSM/I data using Neutral Network-based algorithms. In: Ocean Winds, 19-22 June 2000, Brest, France, P. 101. Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Neural networks algorithms for passive microwave atmosphere-ocean system parameters retrieval. In: Proc. 29th ISRSE, Cape Town, South Africa, 2000. P. ? Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Retrievals of Atmospheric Integrated Parameters over Oceans with SSM/I Data Using Neural Networks Approach. Proc. International Radiation Symposium 2000, St. Petersburg, Russia. P?

Team members: Dr. Leonid Bobylev Team leader; • • •

Advice on the construction of the numerical experiment scheme. Discussion of results. NIS teams coordination

Elizaveta Zabolotskikh • •

Choice of the optimal NN models for the predetermined physical limitations. Development of the NNs-based retrieval algorithms.

Igor Samsonov •

Buildup of SSM/I, TMI and AMSR databases.

Dmitry Akimov •

Buildup of scatterometer database.

Svetlana Kuzmina •

Consulting on the NN optimal model choice.

Lev Zaitsev •

Computer program, software and hardware management and support.

4.1.4. Pacific Oceanological Institute, Russian Academy of Sciences (POI-RAS) The POI team took part in the execution of the following project tasks: •

Sub - Task 1.1: Creation of database for the numerical simulations


12114


• • • • •

Sub - Task 1.2: Buildup of the experimental databases Sub - Task 2.1: The choice of physical models for the fOlWard problem algorithm development. Sub - Task 2.2: Numerical calculations. Sub - Task 2.3: In,.vestigation of the real sensor sensitivity Task 3: Active microwave measurement simulations

The research has been in accordance with the Work Program. We do not foresee any deviations from the Work Program for the future. Scientific Results: Databases.

Publications: Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Development and validation of sea surface wind speed retrieval algorithms using SSMII data, Neural Networks and some physical limitations. Earth Observation and Remote Sensing, 2000, V. ,N2, P. 62-71. Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Ocean winds retrieved from SSM/I data using Neutral Network-based algorithms. In: Ocean Winds, 19-22 June 2000, Brest, France, P. 101. Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Neural networks algorithms for passive microwave atmosphere-ocean system parameters retrieval. In: Proc. 29th ISRSE, Cape Town, South Africa, 2000. P. ? Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Retrievals of Atmospheric Integrated Parameters over Oceans with SSM/I Data Using Neural Networks Approach. Proc. International Radiation Symposium 2000, St. Petersburg, Russia. P?

Team members: Dr. Leonid Mitnik Team leader; • •

Advice on selection of microwave radiative transfer model and scatterometer transfer function; Discussion of results.

MaiaMitnik • buildup of radiosonde, SSM/I, TMI and AMSR databases; • Modeling of brightness temperatures for SSM/I, TMI and AMSR • Build-up of SSM/I, TMI and AMSR databases. Aleksey Bychenkov • Selection of scatterometer transfer function and build-up of scatterometer database Dmitrii Kaplunenko • Statistical processing of the results of numerical simulations • Buildup of scatterometer database Eugeniya Artem 'eva • radiosonde data checking • scatterometer data processing Yuliya Sofienko • radiosonde data collection, digitizing and checking;


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4.2. Reprints of Key Publications 1.

Zabolotskikh E.V." L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Development and validation of sea surface wind speed retrieval algorithms using SSM/I data, Neural Networks and some physical limitations. Earth Observation and Remote Sensing, 2000, N2, P. 62-71 . (in Russian, English abstract is attached)

2.

Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, and O.M. Johannessen. Ocean winds retrieved from SSM/I data using Neutral Network-based algorithms. In: Ocean Winds, 19-22 June 2000, Brest, France, P. 101.

3.

Zabolotskikh E.V., L.M. Mitnik, L.P. Bobylev, Yu.M. Timofeev, and O.M. Johannessen. Neural networks algorithms for passive microwave atmosphere-ocean system parameters retrieval. In: Proc. 29th ISRSE, Cape Town, South Africa, 2000. (on CD)


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DEVELOPMENT AND VALIDATION OF SEA SURFACE WIND SPEED RETRIEVAL ALGORITHMS USING SSM/I DATA, NEURAL NETWORKS AND SOME PHYSICAL LIMITATIONS Elizaveta

v. ZABOLOTSKlKH1, Leonid M. MITNIK2I1 , Leonid P. BOBYLEV1, Ola M. JOHANNESSEN3 Nansen International Environmental and Remote Sensing Centre, Sf. Petersburg, Russia 2 Pacific Oceanological Institute, Far Eastern Branch, Russian Academy ofSciences, Vladivostok, Russia 3 Nansen Environmental and Remote Sensing Centre, Bergen, Norway I

Abstract.

The algorithms for Sea Surface Wind Speed retrieval from Special Sensor Microwave/Imager data based on Neural Networks approach and some physical limitations are considered in the paper. Different physical limitations, based on the values of atmospheric absorption and applied to training and testing data sets, are used. It is shown that the best results (root mean square difference

=

1.05

m/s) were obtained for the limitations of horizontal uniformity of the atmosphere with the absence of large absorption. Comparison with algorithm of Goodberlet et aI., 1992, is done. The comparison showed the advantages of the Neural Networks based algorithms for all physical limitations.

Published in: Earth Observation and Remote Sensing, 2000, N2, P. 62-71. (Paper in Russian, English abstract is attached)

ISSN 0205-9614

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face wind speed meaurements of the Special Sensor Mi- .' crowave/lmager (SSMII) II IEEE Trans. Geosci . Remote. Sensing. 1990. V. 28. Nt? 5. P. R2l - X29. 3. Goodherlet M.A., Swift CT. Improves retrievals from

the OMSP wind speed algorithm under adv erse weather conditions II IEEE Trans. Geosci. Relllot e. Sensing. 1992. V. GE-30. P. 1()7()- I077 . 4 . Petty C.W ., Katsaros KB. New geophysiclIl algorithms

for th e Special Sensor Microwav e/ ll1la ~ cr II Proc. 5th Conf. on Satellite Meteorology lind (k ~'an()!2raphy. L.: Amer. Mcteorol. Soc .. 1990. P 24 7· 25 I . 5. Wellt .:: F..! Measurement of occal1ic \\ il1d \ l '\: !()1 Llsing satellite microwave radiol11L~ tcrs II 11 ·f -TI, :1I1 \ (fcosci:

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7. Srogryn A.P .. Butler Cr.. Bartolac T.J. Ocean surface wind retrievals from Special Sensor Microwave Imager ,r data with Neural Networks II J. Geophys. Res. 1994. V. 90. P. 9X 1- 9X4 . i

v. (8. Krasllopolsky VM .. Breaker L.c., Gemmill W.H. II !) A Neural Network as a nonlinear transfer function model for retrieving surface wind speeds from the Special Sensor Microwave Imager II J. Geophys. Res. 1995. V. 100. II033-l1045.

1

e

~9. 11 1

13. Rosnkranz P.W. Rough-sea microwave emissivities

14. 15.

16.

Krasl1opolsy VM .. Breaker L.C, Gemmill W.H. Improved SSM/I wind speed retrievals at high wind speeds II Technical Note. OMB contribution. Ill. Wash.: Environ. Modeling Center, 1995. P. 45.

17.

1)10. Krasnopolsy VM., Breaker L.C, Gemmill W.H. A Neu-

18.

ral Network multi-parameter algorithm for SSMfI ocean retrievals: Comparisons and validations II Proc. 5th lnt. Conf. Remote Sending and Coastal Environment. San Diego, California. October 5-7. 1998.

19.

11. Schluessel P., Luthardt H. Surface wind speeds over the North Sea from Special Sensor Microwave Imager obsera vationsll J. Geophys. Res. 1991. V. 96. P. 4845-4853. H ,

L.M. Mitnik2), L.P. Bobylevl), and O.M. Johannessen 3)

1 Nansen

International Environmental and Remote Sensing Centre, St. Petersburg, Russia 2 Pacific Oceanological Institute, Far Eastern Branch, 3 Rus~ian Academy of Sciences, Vladivostok, Russia Nansen Envlronmental and Remote Sensing Centre, Bergen, Norway

During the last two decades satellite remote sensing of the Ocean - Atmosphere System (OAS) has already been proved to be an efficient way of getting reasonable estimates of a number of geophysical parameters. Satellite radiometric measurements are of special importance in oceanographic and atmospheric sciences since they provide a global survey of simultaneously measured environmental parameters both in space and time. Sea surface wind speed (SSWS) is one of these parameters retrieved from remotely sensed radiometric data. A number of different SSWS retrieval algorithms exist using Special Sensor Microwave Imager (SSM/I) data and a large variety of approaches and methods. The necessity of the accurate allweather retrieval methods demands new effective approach to be developed. Neural Networks (NN s) has already proved to be one of the most effective approaches for the solution of remote sensing inverse problems. A number of algorithms based on NN s approach have been developed during the last years and efficiency of such algorithms as applied to geophysical parameters retrieval was demonstrated. It was shown that retrieval accuracy of developed algorithms met the ±2 mls accuracy specification under predetermined weather conditions that were different for different algorithms. The choice of these conditions seems to be not always necessarily physical-valid. These factors hamper a comparison of efficiency of the algorithms and analysis of SSWS retrieval errors as a function of characteristics of the ocean-atmosphere system. So the necessity of the development of new effective algorithms giving high retrieval accuracy in a broader range of environmental parameter variability remains actual. The algorithms for SSWS retrieval from SSM/I data based on NNs approach and some physical limitations were considered in this study. An investigation was done concerning the optimal NN s model used for the algorithm development. It was shown that a simple NN s model, consisting of 5 SSM/I inputs, one hidden layer with 2-5 neurons, and SSWS as output ensures the best results of the algorithm performance. The whole matched up database, used in the study, was made available by National Space Development Agency of Japan (NASDA). It was divided into two subsets: the data of 1992 and 1993 years were used for training NNsbased algorithms, the data of 1994 and 1995 years were used for the validation of developed algorithms. Different physical limitations, based on the values of atmospheric absorption and sea surface temperature, applied to training and testing data sets, were considered. For each limitation comparison with the algorithm of Goodberlet and Swift, 1992 (GS) algorithm performance was carried out. It was shown that in each case root mean square (rms) difference for NNs-based algorithm was significantly smaller than for GS algorithm. The best results (rms = l.05 m/s) were obtained for the limitations of spatial uniformity of the atmosphere with the absence of large absorption. This error is the same as reported for Wentz's and Petty's algorithms. However, the problem of data rejected by quality control (erroneous brightness temperatures) and the presence of precipitation for algorithm comparison remains open. It can be concluded from the results of algorithm performance that the retrieval error increases with large atmospheric absorption and mesoscale variations of both atmospheric and oceanic parameters.

Ocean winds: present and emerging remote sensing methods 19 - 22 June 2000, Brest, France

RETRIEVALS OF ATMOSPHERIC INTEGRATED PARAMETERS OVER OCEANS WITH SSMII DATA USING NEURAL NETWORKS APPROACH Elizaveta V. Zabolotskikh l , Leonid P. Bobylev Il4 , Leonid M. Mitnik2/1 , Yuri M. Timofeev 31I , Ola M. Johannessen4 I

2

Nansen International Environmental and Remote Sensing Centre (NIERSC) 18 Korpusnaya St., 197110 St. Petersburg, Russia [email protected]

Pacific Oceanological Institute, Far Eastern Branch, Russian Academy of Sciences 43 Baltiyskaya St., 690041 Vladivostok, Russia [email protected] 3 Research Institute of Physics, St. Petersburg State University 1 Ulyanovskaya St., Petrodvorets, 198904 St. Petersburg, Russia [email protected]

4

Nansen Environmental and Remote Sensing Centre (NERSC) Edvard Griegsvei 3a, N - 5059, Bergen, Norway [email protected]

1. INTRODUCTION Knowledge of such atmospheric parameters as atmospheric Total Water Vapour Content (Q) and Total Liquid Water Content (W) is of great importance for a number of applications. Previous studies showed that satellite microwave radiometers are capable to provide reasonable estimates of these parameters over the oceans, where in-situ observations are scarce or unavailable. Existing retrieval methods often perform with quite large errors since the parameters to be retrieved are non-linear functions of measured brightness temperatures and the specific type of non-linearity is not known in advance. Thus, new easily adaptable non-linear algorithms are needed. They should be self-tuneable depending on the values of measured microwave and in situ data. The results of numerical experiments showed a potential efficiency of such algorithms for retrieval of geophysical parameters [Bobylev et al., 1998]. In the present study new algorithms for Q and W retrievals based on Neural Networks (NNs) and some physical restrictions are suggested. A new approach for modelling W values, based on NN, the results of a numerical experiment, and collocated SSM/I and radiosonde (rls) data has been developed. Simulated values of Wand rls data on Q are used for developing the algorithms. Different physical limitations, based on the atmospheric absorption, sea surface water temperature and horizontal uniformity of the atmosphere are used and

applied to trammg and testing data sets. An extensive comparison of NNs algorithm performance with existing traditional algorithms has shown the advantages of the NNs-algorithms

2. DATA

The entire matched up database that has been made available by National Space Development Agency of Japan (NASDA) consists of 29000 radiosonde and SSM/I collocated data. The SSMII-rls data set contains 5 SSMII-pixels located off a small island radiosonde station at a distance ranging between 0 to 50 km along the line of view to the center of the SSM/I field. The time difference between the SSM/I and rls measurements was within 12 hours for the period from January 1992 to December 1995 (Fig. 1). SSM/I -pixel

rls station

SSM/I -scans

5 nearest to rls station SSM/I - pixels

Fig. 1. The scheme of the SSM/I-radiosonde data collocation.

For the algorithm development a database of collocated measurements was made up. About 5 000 data of poor quality were discarded on various physical grounds. For each physical limitation all data were divided into two subsets: the 1992 and 1993 data were used for training NNs-based algorithms, the 1994 and 1995 data were used for the validation of the developed algorithms. The ground-truth values of Q were calculated using radiosonde absolute humidity data. The ground-truth W values were calculated by means of the Neural Networks technique, developed on the basis of numerical modelling and SSM/I data. Based on a radiation transfer forward problem solution, an algorithm was developed for :'precipitation-free conditions. This algorithm calculates upwelling brightness temperatures at 37 GHz (both polarizations) - T37V and T37H for the known values of sea surface wind speed V, Q and W [Bobylev et ai., 1998]. Then the NN-function was trained on the known data of V, Q, Wand DT37 = T37V - T37H: W = W(V, Q, DT37). The NN, trained on simulated data, was applied to the data of the collocated matched up database to retrieve the values of W that later were used as ground-truth values of this parameter.

3. PHYSICALLY-BASED LIMITATIONS 1. Water temperature Ts affects the atmospheric parameters not only near the sea surface but also in the whole troposphere and thus controls the total atmospheric absorption. The average and maximum values of Q and W over the Polar Ocean are significantly lower than those of ovec the Tropical Oceans. Dependencies of the sea surface emissivity Xv,h on Ts are essentially non-linear. The highest values of negative derivatives dXv,h/dTs are observed at low Ts and frequencies larger than 1518 GHz. Thus, depending on the criteria Ts < 10 0 e and T s > 25°e the data subsets for polar and tropical latitudes were formed from the main data set of collocated data. Then, when the number of collocated data was large enough for training NNs, different algorithms have been developed for both the entire data set (global algorithm G) and local subsets (polar algorithm - N, tropical algorithm - T) 2. The value of total atmospheric absorption was used as an additional parameter influencing the performance of the algorithms. In this study DT37 criterion [Stogryn et aI, 1994] was used to subdivide each of the data sets onto two subsets and specialized algorithms were developed for the conditions defined by DT37 > 50 K: algorithms Gl, Nl and Tl.

3. The last limitation was defined in terms of the definition of horizontally uniform atmosphere. This uniformity criterion defines dissimilarity between the atmospheric conditions that are integrated by the SSMII antenna (Fig. 2).

Fig. 2. Horizontal non-uniformity of the atmosphere. Within the framework of this limitation only those data were considered where the difference between the measurements at 37 GHz, horizontal polarization (T37H) for each two of 5 collocated SSMII pixels of a data match up was lower than the criterion chosen: T37HiT37Hj < 10 K; i = 1, ... , 5; j = 1, ... ,5. These measurements are believed to be the most sensitive to the presence of liquid water content. Then the SSMII data with minimal T37H were selected for the collocation (to exclude strong atmosphere interference).

4. NEURAL NETWORK MODEL The following feed forward Neural Network architecture was used in the study: 5 input nodes (T19V, TI9H, T22, T37V, T37H) in the input layer, 1 hidden layer and one output parameter as an output node were used (Fig. 3).

T19V T19H T37V T37H T22V

• Q

Fig. 3. En example of Neural Network model used in the study.

Different NN s were trained for Q and W retrievals since simultaneous retrievals with a single NN showed worse results. Different options of the number of hidden nodes were tested. It was found that different numbers of hidden nodes for various p'hysical limitations ensured the best retrievals.

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50 K. So all the further algorithms, based on the other physical limitations (atmospheric absorption and sea surface temperature), were tuned under the conditions of horizontally uniform atmosphere. The results of NNs and traditional algorithm performance are presented in Table I.

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Table I RETRIEV AL ERRORS FOR NN AND TRADITIONAL ALGORITHMS The number of data in testing data set 9630

CJQ,

CJQ,

kg/m2 NN 4.67

kg/m2 PK 5.56

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Bobylev L.P., L.M. Mitnik, M.L. Mitnik, E.V. Zabolotskikh, Yu. M. Timofeev, O.M. Johannessen, 1998: Microwave Remote Sounding Of AtmosphereOcean System Using Neural Network Approach. In Proceedings on the 27th Intern. Symp. On Rem. Sens. of the Environment, Tromso, Norway, pp.312-315. Stogryn A.P., C.T. Butler and T.J. Bartolac, 1994: Ocean surface wind retrievals from special sensor microwave imager (SSM/I). In 1. Geophys. Res., 99, pp.25 53525551. Petty G.W., Katsaros K.B., 1990: New geophysical algorithms for the Special Sensor Microwave/Imager. In Fifth Conference on Satellite Metorology and Oceanography, London, American Metorological Society, pp. 247 - 251.