CEOP-AEGIS (GA n° 212921)                                                                                            Perio...
CEOP-AEGIS (GA n° 212921)                                                                                          Periodi...
CEOP-AEGIS (GA n° 212921)                                                                         Periodic Report no. 1   ...
CEOP-AEGIS (GA n° 212921)                                                               Periodic Report no. 1Coordinator c...
CEOP-AEGIS (GA n° 212921)                                                                    Periodic Report no. 1CO = Coo...
CEOP-AEGIS (GA n° 212921)                                                                      Periodic Report no. 1“Main ...
CEOP-AEGIS (GA n° 212921)                                                                   Periodic Report no. 1fields ge...
CEOP-AEGIS (GA n° 212921)                                                                   Periodic Report no. 1-       P...
CEOP-AEGIS (GA n° 212921)                                                                   Periodic Report no. 12nd perio...
CEOP-AEGIS (GA n° 212921)                                                                      Periodic Report no. 1      ...
CEOP-AEGIS (GA n° 212921)                                                                              Periodic Report no....
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CEOP-AEGIS (GA n° 212921)                                                                              Periodic Report no....
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CEOP-AEGIS (GA n° 212921)                                                                        Periodic Report no. 1    ...
CEOP-AEGIS (GA n° 212921)                                                                      Periodic Report no. 1   •  ...
CEOP-AEGIS (GA n° 212921)                                                                        Periodic Report no. 1    ...
CEOP-AEGIS (GA n° 212921)                                                                            Periodic Report no. 1...
CEOP-AEGIS (GA n° 212921)                                                                  Periodic Report no. 1representa...
CEOP-AEGIS (GA n° 212921)                                                                               Periodic Report no...
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CEOP-AEGIS (GA n° 212921)                                                                   Periodic Report no. 1   3.2 Wo...
CEOP-AEGIS (GA n° 212921)                                                                    Periodic Report no. 1  model)...
CEOP-AEGIS (GA n° 212921)                                                                              Periodic Report no....
CEOP-AEGIS (GA n° 212921)                                                                   Periodic Report no. 1   Jie Zo...
CEOP-AEGIS (GA n° 212921)                                                                     Periodic Report no. 1  The d...
CEOP-AEGIS (GA n° 212921)                                                                        Periodic Report no. 1  co...
CEOP-AEGIS (GA n° 212921)                                                                Periodic Report no. 1  A quantita...
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CEOP-AEGIS (GA n° 212921)                                                                  Periodic Report no. 1   3.3 Wor...
CEOP-AEGIS (GA n° 212921)                                                                     Periodic Report no. 1      •...
CEOP-AEGIS (GA n° 212921)                                                                  Periodic Report no. 1Figure 1: ...
CEOP-AEGIS (GA n° 212921)                                                                          Periodic Report no. 1  ...
CEOP-AEGIS (GA n° 212921)                                                                     Periodic Report no. 1    •  ...
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CEOP-AEGIS (GA n° 212921)                                                                     Periodic Report no. 1In orde...
CEOP-AEGIS (GA n° 212921)                                                                     Periodic Report no. 1From th...
CEOP-AEGIS (GA n° 212921)                                                                       Periodic Report no. 13.4.2...
CEOP-AEGIS (GA n° 212921)                                                                      Periodic Report no. 1Figure...
CEOP-AEGIS (GA n° 212921)                                                                   Periodic Report no. 1In conclu...
CEOP-AEGIS (GA n° 212921)                                                                      Periodic Report no. 13.5 Wo...
CEOP-AEGIS (GA n° 212921)                                                                       Periodic Report no. 1Task ...
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CEOP-AEGIS Periodic Report #1

  1. 1. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 PROJECT PERIODIC REPORTGrant Agreement number: 212921Project acronym: CEOP-AEGISProject title: Coordinated Asia-European long-term Observing system of Qinghai – TibetPlateau hydro-meteorological processes and the Asian-monsoon systEm with Ground satelliteImage data and numerical SimulationsFunding Scheme: CP-SICADate of latest version of Annex I against which the assessment will be made: 25/08/2009Periodic report: 1st x 2nd ! 3rd ! 4th !Period covered: from 1/5/2008 to 31/10/2009Name, title and organisation of the scientific representative of the projects coordinator1:Prof.dr. Massimo Menenti Faculty of Aerospace Engineering, TU Delft, Delft, The NetherlandsTel: +31 15 2784244Fax: +31 15 278348E-mail: M.Menenti@tudelft.nlProject website2 address: http://www.ceop-aegis.org/1 Usually the contact person of the coordinator as specified in Art. 8.1. of the grant agreement2 The home page of the website should contain the generic European flag and the FP7 logo which are available in electronicformat at the Europa website (logo of the European flag: http://europa.eu/abc/symbols/emblem/index_en.htm ; logo of the 7thFP: http://ec.europa.eu/research/fp7/index_en.cfm?pg=logos). The area of activity of the project should also be mentioned. Page 1 of 98
  2. 2. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Declaration by the scientific representative of the project coordinator1 1 I, as scientific representative of the coordinator of this project and in line with the obligations as stated in Article II.2.3 of the Grant Agreement declare that: ! The attached periodic report represents an accurate description of the work carried out in this project for this reporting period; ! The project (tick as appropriate): x has fully achieved its objectives and technical goals for the period; ! has achieved most of its objectives and technical goals for the period with relatively minor 3 deviations ; ! has failed to achieve critical objectives and/or is not at all on schedule . 4 ! The public website is up to date, if applicable. ! To my best knowledge, the financial statements which are being submitted as part of this report are in line with the actual work carried out and are consistent with the report on the resources used for the project (section 6) and if applicable with the certificate on financial statement. ! All beneficiaries, in particular non-profit public bodies, secondary and higher education establishments, research organisations and SMEs, have declared to have verified their legal status. Any changes have been reported under section 5 (Project Management) in accordance with Article II.3.f of the Grant Agreement. Name of scientific representative of the Coordinator1: .Prof. Dr. Massimo Menenti. Date: ....21..../ ...12......./ 2009...... Signature of scientific representative of the Coordinator1:3 If either of these boxes is ticked, the report should reflect these and any remedial actions taken.4 If either of these boxes is ticked, the report should reflect these and any remedial actions taken. Page 2 of 98
  3. 3. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Project Title Coordinated Asia-European long-term Observing system of Qinghai – Tibet Plateau hydro-meteorological processes and the Asian-monsoon systEm with Ground satellite Image data and numerical Simulations CEOP AEGISThematic Priority: ENV.2007.4.1.4.2. Improving observing systems for water resource managementStart Date of the Project: 1 – May – 2008 Duration: 48 months Report Title 1st Periodic Report May 1st 2008 – October 31st 2009 Massimo Menenti1, Li Jia2 and Jerome Colin3 1 Faculty of Aerospace Engineering, TU Delft, Delft, The Netherlands, 2 Alterra, Wageningen University and Research Centre, Wageningen, The Netherlands 3 Image Sciences, Computing Sciences and Remote Sensing Laboratory, University of Strasbourg, Illkirch, FranceDate: December 21st 2009Version: 1.0 Page 3 of 98
  4. 4. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Coordinator contact detailsProf.dr. Massimo MenentiE-mail: M.Menenti@tudelft.nlWeb site: www.lr.tudelft.nl/olrsTelephone: +31 15 2784244 Fax: +31 15 278348Deputy coordinator details:Dr. Li JiaE-mail: li.jia@wur.nlWeb site: http://www.alterra.wur.nl/UK/Telephone: +31 317 481610 Fax: +31 317 419000Contractors involvedBENEFICIARY BENEFICIARY NAME BENEFICIARY COUNTRY DATE DATE EXITNUMBER SHORT NAME ENTER PROJECT PROJECT1 CO Université de Strasbour LSIIT UDS France 1 482 CR International Institute for Geo- ITC The 1 48 information science and Earth Netherlands Observation3 CR ARIES Space ARIES Italy 1 484 CR University of Bayreuth UBT Germany 1 485 CR Alterra - Wageningen University ALTERRA The 1 48 and Research Centre Netherlands6 CR University of Valencia UVEG Spain 1 487 CR Institute for Tibetan Plateau ITP China 1 48 Research – Lhasa, Tibet8 CR China Meteorological CAMS China 1 48 Administration – Beijing9 CR Beijing Normal University– BNU China 1 48 Beijing11CR University of Tsukuba – UNITSUK Japan 1 4812 CR WaterWatch WAWATCH The 1 48 Netherlands13 CR Cold and Arid Regions CAREERI China 1 48 Environmental and Engineering Research Institute – Lanzhou, Gansu14 CR University of Ferrara UNIFE Italy 1 4815 CR Institute of Geographical IGSNRR China 1 48 Sciences and Natural Resources Research CAS – Beijing16 CR Institute for Remote Sensing IRSA China 1 48 Applications CAS – Beijing17 CR Future Water FUWATER The 1 48 Netherlands18 CR Delft University of Technology TUD The 12 48 Netherlands19 CR National Institute of Technology NIT India 12 48 Page 4 of 98
  5. 5. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1CO = Coordinator CR = Contractor 1. Publishable summaryCEOP AEGIS 1st Periodic Report: May 1st 2008 – October 31st 2009Summaryhttp://www.ceop-aegis.org/ObjectivesThe goal of this project is to:1. Construct out of existing ground measurements and current / future satellites an observingsystem to determine and monitor the water yield of the Plateau, i.e. how much water is finally goinginto the seven major rivers of SE Asia; this requires estimating snowfall, rainfall, evapotranspirationand changes in soil moisture;2. Monitor the evolution of snow, vegetation cover, surface wetness and surface fluxes andanalyze the linkage with convective activity, (extreme) precipitation events and the Asian Monsoon;this aims at using monitoring of snow, vegetation and surface fluxes as a precursor of intenseprecipitation towards improving forecasts of (extreme) precipitations in SE Asia.Work PerformedThe project started with a kick-off meeting held in Beijing on May 1st – 5th 2008 attended by 65participants. In preparation of the meeting all partners were requested to define more precisely theircontribution and roles. This material provided a good basis for a productive meeting. A project mailinglist system was established to handle internal communication, given the complexity of the consortium.The 1st Annual Progress Meeting was held in Milano, Italy on June 29th through July 3rd, including ajoint workshop with the CEOP High Elevation Initiative (HE) and an internal businness meetingdedicated to a review of progress and to the preparation of the 1st Periodic Report. The meeting wasattended by 30 participants. In preparation of the meeting all partners were requested to prepare anoverview presentation for each Work Package.The material prepared for the meetings is available on the project web site. To date there are 112registered Team Members.During the 1st six months period work focused on three main objectives:1. Define the work plan and detailed contributions of partners;2. Perform local experiments and collect first data for validation of algorithms and models;3. Review and improvements of algorithms and models.Ad.1. In order to identify more precisely roles and responsibilities all partners were requested toelaborate further the work plan now included in the Description of Work. This includes now a moreprecise description of (sub)-tasks and of elements of contractual deliverables with individualresponsibilities clearly identified.Ad.2. Field experiments were carried out during the reporting period as described under “MainResults” belowAd.3 Work towards improvement of retrieval algorithms, process models and land-atmospheric modelsadvanced in several directions. This included collection and preparation of data sets acquired by space-and airborne platforms to test algorithms and models, numerical experiments to document theperformance of algorithms and process models and improvement of algorithms and models in thosecases where the causes of poor performances was known already. More details are provided under Page 5 of 98
  6. 6. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1“Main Results” below.During the 2nd six-months period work focused on five main objectives:1. Finalize and implement Grant Agreement, including accession of partners;2. Perform local experiments and collect first data for validation of algorithms and models;3. Review and improvements of algorithms and models.4. Design, development and use of atmospheric and water balance models;5. First analyses of time series of drought and flood indicatorsAd.1. The Grant Agreement was completed and signed on December 4th 2008. Accession forms weresigned by all partners except Partner NIH. As explained below, the National Institute of Technology,Rourkela, will replace NIH and carry out all planned tasks.Ad.2. Field experiments were carried out during the reporting period and data analysis started asdescribed under “Main Results” below. Work concentrated on the analysis of ground measurements onland – atmosphere interactions collected at the permanent observatories on the Plateau.Ad.3 Work towards improvement of retrieval algorithms, process models and land-atmospheric modelsadvanced towards the implementation of specific improvements emerged in the previous period. Thisincluded development of new procedures to deal with complex terrain in radiative transfer models andretrieval algorithms, new algorithms for the retrieval of land surface temperature and radiative fluxes atthe surface and preparation of data sets on precipitation measurements with rain radars.Ad.4 Work advanced both on the assessment of connections between land surface conditions withconvective activity and precipitation events and on the design of the regional water balance model tointegrate all observations for the entire Plateau.Ad. 5 Work was also initiated on the analysis of time series of satellite data towards the early detectionof anomalies in land surface conditions and early warning on droughts and floods. Because of the needfor extended data records, this element of the project relies on existing data sets, besides the onesgenerated by the project. During this 6-months period work concentrated on development ofprocedures for the detection of anomalies, based on a moving window analysis and comparison withthe climatology of the land surface variables under consideration. Different indicators were evaluated.During the 3rd six-months period work focused on the same five main objectives as in the 2nd six-months period:1. Finalize documents for the amendments of the Grant Agreement, including accession of newpartners;2. Perform local experiments and collect first data for validation of algorithms and models;3. Improvements of algorithms and models.4. Development and use of atmospheric and water balance models;5. First analyses of time series of drought and flood indicators.Ad.1.The access of two new partners, i.e. NIT and TUD required a significant amount of time andwork. Progress of the project was monitored through a series of Skype conferences, the 1st AnnualProgress Meeting and additional working meetings in 2009: Beijing August and October, Lanzhou inAugust and Roorkee in September.Ad.2. Field work intensified during this period. In addition to the normal operation of theobservatories, new instruments were installed to improve observations of radiative and turbulent heatfluxes and to characterize the size distribution of rain droplets, necessary to improve accuracy ofretrievals by rain radars (see Main Results below).Ad.3 Work towards improvement of retrieval algorithms was focused on atmospheric correction ofsatellite measurements in the VNIR-SWIR, TIR and microwave regions. This included dealing withretrieval of Land Surface Variables using data acquired by the new satellites HJ-1B (China) and IRS(India). The new algorithms developed in the previous period were applied to generate time series ofSnow Covered Area and Snow Water Equivalent. The development of a new data processing systemfor Surface Energy Balance analyses based on the combination of satellite measurements with PBL Page 6 of 98
  7. 7. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1fields generated by the GRAPES NWF model was completed.Ad.4 Significant advances have been achieved towards analysis of land – atmosphere interactions withatmospheric models and towards regional modeling of the Plateau water balance. A full forecast runwas performed for the entire year 2008 with the system GRAPES. A study of the sensitivity MonsoonConvective Systems (MCS) to land surface conditions was carried out with the model WRF at theUniversity of Tsukuba and the prototype water balance model of the Qinghai –Tibet Plateau wasimplemented at a 5 km x 5 km spatial resolution and applied to obtain daily rainfall excess and riverflow over the entire domainAd.5 Several results became available on different indicators relevant to drought and flood earlywarning. Work focused on two parallel streams: improving algorithms and analyzing available timeseries of satellite data. A new version of the HANTS algorithm was released and a new model tocompute daily EvapoTranspiration (ET) was developed and applied. Time series of satellite data onLand Surface Temperature (LST), photosynthetic activity (EVI, fAPAR) and soil wetness wereanalyzed to document inter-annual variability, detect anomalies and evaluate them as precursorindicators for drought and flood early warning.Main ResultsField experiments During the 1st Reporting Period the existing system of Plateau observatories wasimproved by adding several instruments: gauges to measure total precipitation above 6000 m, twoLong Path Scintillometers, three disidrometers to measure the size distribution of water droplets, foursets of radiometers to measure the four components of the radiative balance and one suntracker tomeasure direct irradiance.Several Co-Investigators participated in a major RS experiment covering an entire watershed on thenorthern rime of the Plateau: the WATER project provided invaluable detailed data to improve andvalidate several algorithms to be used within CEOP-AEGIS. Collection of soil moisture andtemperature measurements at the Maqu site for the validation of algorithms to retrieve soil moisturecontinued. An expedition to the the Yamdruk-tso lake basin and Qiangyong Glacier was carried out.The NaimonaNyi ice core was processed in cold room.The first eddy-covariance measurements of turbulent flux densities became available after qualitycharacterization and gap filling. The analysis of the data collected at the NamCo observatory revealeda significantly higher number of free convection events in the monsoon period. The results have beenpublished in JGR. An approach to upscale flux measurements to the grid scale of meso-scale modelsand remote sensing data was developed.Work towards improvement of retrieval algorithms, process models and land-atmospheric modelsadvanced in several directions:- Collection and preparation of several data sets comprising multi-spectral, multi-angularradiometric data;- Evaluation of land – atmosphere models- Review and preparation of codes of radiative transfer models of the soil-vegetation system- Improvement and generalization of multi-scale model of land surface energy balance;- Estimation and mapping of land – atmosphere heat and water exchanges with ASTER multi-spectral radiometric data for the areas surrounding the ITP observatories on the Plateau;- Preparation of microwave radiometer data (AMSR-E) for the evaluation of soil moistureretrieval algorithms;- Improvement of model to characterize the diurnal cycle of Land Surface Temperature usingFeng Yun infrared data and use of the CLM to relate the diurnal LST cycle to soil moisture- Improvements in the meso-scale land-atmospheric model GRAPES of CMA; preliminary casestudies performed and hypotheses identified;- Preparation of data sets for the evaluation of candidate water balance models; evaluation ofsnow-melt-runoff models using MODIS and AMSR-E satellite data; Page 7 of 98
  8. 8. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1- Preparation of MODIS time series (LAI/fAPAR, Vis, and LST) for entire China;- Improvements in the algorithms to detect and predict anomalies in vegetation development;- Case studies on drought events combining ground and satellite data;2nd period- Analysis of sample data set HeiHe basin with simultaneous multi-angular, multi-spectral andlidar observations of vegetation canopies- Topography correction inserted in the RT modeling system for the VNIR, SWIR and TIRspectral ranges.- Development of a simple model to describe the thermal directional radiation in rugged terrain;- A topographic correction algorithm for albedo retrieval in rugged terrain was developed.- Development of a preliminary algorithm to calculate land surface temperature from AMSR-Edata;- Developing the concept of a new radiative transfer model, capable of simulating the seasonalchanges of canopy structure;- Development of new version of MSSEBS (vers. 2.0.2) SEB algorithm;- Development of algorithm for regional estimation of net radiation flux;- Determination the surface albedo, surface temperature, vegetation fractional cover, NDVI,LAI and MSAVI over whole Tibetan Plateau;- Implementation of a radiative transfer soil moisture retrieval method using ASCAT data- Comparison of in situ data collected by Maqu soil moisture monitoring network with AMSR-EVUA-NASA satellite soil moisture products;- Collected the soil moisture and temperature data of 20 SMTMS, and replaced 4 temperatureand moisture probes;- Processing the raw precipitation radar data in the Tibetan Plateau and provide the griddedprecipitation data for case studies;- Final revision of paper on the nighttime monsoon precipitation over the TP was submitted toJMSJ and accepted in March- Simulation of daily snow cover using daily and eight-day MODIS snow cover products andmeteorological observation;- Analysis of glacier and lake changes using observed data and RS data in the Nam Co Basin;.3rd periodDevelopment of algorithms and retrieval of canopy structure from airborne LIDAR;- Development of algorithm for atmospheric corrections of AMSR-E (microwave);- Generalized procedure for atmospheric correction based on an ensemble of MODTRANsimulations;- Automation of procedures to generate LST from MODIS data;- Implementation and first tests on generic algorithm for retrieval of LAI and fCover;- Development of new algorithm to retrieve LST from HJ-1B (China) and IRS (India) data;- Development of new Angular & Spectral Kernel based BRDF model for the normalization ofdata acquired with different angular and spectral configurations;- First test of SEB algorithm combining satellite data for land surface observations and PBLfields generated with high resolution atmospheric model (GRAPES);- Evaluation against turbulent heat flux measurements of SEB estimates based on ASTER data;- Mosaic of rain-rates observed with rain radars over the Plateau have been generated anddelivered to other investigators for calibration of algorithms based on satellite data;- Improved algorithm for retrieval of snow covered area from MODIShas been developed and evaluated against observations at higher spatial resolution ( TM);Design, development and use of atmospheric and water balance models. Page 8 of 98
  9. 9. CEOP-AEGIS (GA n° 212921) Periodic Report no. 12nd period- The first numerical experiments with the GRAPES land – atmosphere modeling and data assimilationsystem were performed and evaluated:- Sensitivity experiments of different soil initial conditions on the development of convectionsby using 2-km resolution of GRAPES_Meso- Detection of Meso-scale Convective System (MCS) on the TP was done for the passed sixyears using METEOSAT-IR data- Preparing GIS files for hydrological modeling, including boundary, DEM. Slope, aspect,stream network.-Model selection and algorithm comparison report for Plateau water balance monitoring tool wascompleted3rd period.- The system GRAPES of CMA has been applied to generate forecasts for the entire year 2008;- A study on the sensitivity of MCS to land surface heating has started using the WRF numericalmodel at the Univ. Tsukuba;- Gridded climate data have been used to compute the water balance of the Headwaters of theYellow River Basin and to compute potential ET;- The prototype of the Qinghai – Tibet Plateau distributed water balance model has beenimplemented and applied to compute for the year 2000 daily water balance for each 5 km x 5 km gridand water routing; model riverflow at seven selected sections is being compared with observations;-Model parameterization of glaciers mass balance is being applied to the Zhadang glacier; in- depthcase – study including the use of satellite data is in progress;Analyses of time series of drought and flood indicators2nd period-Available satellite data were retrieved, time series were constructed and first analyses wereperformed:-Algorithm development on drought monitoring by time series analysis of anomalies in several landsurface parameters;-Using time series of VTCI AVI, VCI and TCI as indicators for the estimation of the droughtimpacts;-Analysis of time series meteorological data (air temperature and precipitation, wind speed, airhumidity, solar radiation, etc)-Development of soft computing techniques based on ANN and Fuzzy logic model for real time floodforecasting3rd period- A new version of the HANTS algorithm for time series analysis of satellite data has beenreleased;- A multi-annual MODIS data set covering the Plateau and surrounding regions has been createdafter improved cloud screening and used to compute at-surface net radiation in addition to LST, EVIand fAPAR;- Analysis of a 25 years climatology of AVHRR LST and NDVI has been completed;- Time series of SPI and VTCI have been generated and used as an indicator for droughtforecasting;- A new ET model has been applied to evaluate potential yield loss in the winter 2008;- A first evaluation of AMSR-E time series as an indicator of soil wetness and to detect(positive) anomalies has been completed for the Plateau and Northern India;Expected Final Results Page 9 of 98
  10. 10. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Data base containing ground observations, satellite data and higher level products, hydrologic and atmospheric model fields for the period 2008 – 2010 over the Qinghai – Tibet Plateau. System to generate daily streamflow in the upper catchment of all major river in SE Asia gridded to 5 km x 5 km.Potential Impact and Use of Results Implementation and demonstration of an observing system of water balance and water flow on and around the Qinghai – Tibet Plateau will provide to all countries information on water resources and the role of the Plateau in determining weather and climate in the region. 2. Project objectives for the periodThe goal of this project is to:1. Construct out of existing ground measurements and current / future satellites an observing system todetermine and monitor the water yield of the Plateau, i.e. how much water is finally going into the seven majorrivers of SE Asia; this requires estimating snowfall, rainfall, evapotranspiration and changes in soil moisture;2. Monitor the evolution of snow, vegetation cover, surface wetness and surface fluxes and analyze thelinkage with convective activity, (extreme) precipitation events and the Asian Monsoon; this aims at usingmonitoring of snow, vegetation and surface fluxes as a precursor of intense precipitation towards improvingforecasts of (extreme) precipitations in SE Asia.During the first year of the project, emphasis in all WP-s will be on review tools, experimental protocols,algorithms and models. On this basis, the elements of the investigations next step will be identified in detail: thefirst detailed description of new retrieval algorithms will be available, data analysis protocols will be agreed,modelling experiments will be designed and the organization of data base will be consolidated.During the second year of the project, work will be focused on the Algorithms Theoretical Basis Documents andpotential progresses towards community model to determine land-atmosphere energy and water fluxes withmulti-spectral satellite images. First analysis of datasets with candidate algorithms and models will be presented,with preliminary results on time series analysis of Plateau water balance, droughts and floods indicators. Page 10 of 98
  11. 11. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3. Work progress and achievements during the periodPlease provide a concise overview of the progress of the work in line with the structure of Annex I of the Grant Agreement.For each work package -- except project management, which will be reported in section 3.5--please provide thefollowing information: • A summary of progress towards objectives and details for each task; • Highlight clearly significant results; • If applicable, explain the reasons for deviations from Annex I and their impact on other tasks as well as on available resources and planning; • If applicable, explain the reasons for failing to achieve critical objectives and/or not being on schedule and explain the impact on other tasks as well as on available resources and planning (the explanations should be coherent with the declaration by the project coordinator) ; • a statement on the use of resources, in particular highlighting and explaining deviations between actual and planned person-months per work package and per beneficiary in Annex 1 (Description of Work) • If applicable, propose corrective actions. Page 11 of 98
  12. 12. CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.1 Work progress in WP 1 and achievements during the period " A summary of progress towards objectives and details for each task Task 1.1 The in-situ data has been collected in the observation network of the GAME/Tibet and CAMP/Tibet and the Mt. Everest station(QOMS), the Nam Co station(NAMOR) and the Linzhi Station(SETS) of the TORP(Tibetan Observation and Research Platform) and Namco site of Tip( formally KEMA Station of TiP). Four components radiation system were set up at the sites of D110, MS3608, Namco area, and Lhasa branch of ITP (formally Yakou of Namco). Field trip to the Yamdruk-tso lake basin and Qiangyong Glacier was performed. Precipitation, lake water and river water samples has been collected at 3 stations in this basin for isotope analysis in the laboratory in Beijing. Glacier shallow ice cores were drilled at 6100m of the glacier to rebuild the annual precipitation data in high elevation region. Daily atmospheric vapor samples were collected at Lhasa and are still on going. Fig.1 to Fig.4 are the sites layout and the stations of this WP. (a) (b) Fig.1.1 The geographic map and the sites layout during the GAME/Tibet and the CAMP/Tibet. (a) GAME/Tibet; (b) CAMP/Tibet. Fig.1.2 The instruments in Mt.Everest station, Namco station and Linzhi station of ITP/CAS Page 12 of 98
  13. 13. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Fig.1.3. Sites of the four components radiation system over the Tibetan Plateau. The seasonal and inter-annual time scale of the exchange of surface heat flux, momentum flux, watervapour flux, surface and soil moisture over the different land surfaces of the Tibetan Plateau, and the structurecharacteristics of the Surface Layer (SL) and Atmospheric Boundary Layer (ABL) were analyzed in the last oneand half year. The aerodynamic and thermodynamic variables were determined over the different land surfacesof the Tibetan Plateau. The characteristics of precipitation and atmospheric water vapour transport over andsurrounding the Tibetan Plateau area were analyzed.Task 1.2: A technical report was prepared for the documentation of the flux calculation procedure in order to provideall users of flux data the necessary information. Furthermore, within an UBT field trip to the Tibetan Plateau(June-August 2009) a workshop was held from June 29th to July 1st for participants of ITP and CAREERI aboutthe usage of the UBT software packages for EC data post processing, footprint and QA/QC techniques. Thisensures a uniform data processing for all ground truth EC stations related to CEOP AEGIS, which is the task ofITP and CAREERI according to the data policy rules.Task 1.3:In order to apply detailed footprint analysis for the EC stations, all necessary site information to prepare therequired land use maps were collected for Bj, Namco and Qomolangma site during the UBT field trip in summer2009. Detailed footprint analysis already exists for Namco in late 2005 and from Oct 2005 up to Sept 2006, but Page 13 of 98
  14. 14. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1has to be refined with actual flux data. Missing site information for Linzhi station will be gathered during a postworkshop excursion in July 2010 right after the CEOP workshop in Lhasa and the calculation of the footprintanalysis starts as soon as the flux data is available.Task 1.4:The gap filling will be processed following a procedure developed by Ruppert et al., but an extension to thelatent heat flux has to be made, for which data from Tibetan Plateau are necessary. The procedure starts as soonas the flux data is available.Task 1.5:In order to find an adequate path for LAS measurements at Qomolangma site possible solutions wereinvestigated during the UBT field survey in summer 2009. The LAS system was set up in Mt.Everest(Mt.Qomolangma) station in November, 2009 (Fig.4).Afterwards a preliminary footprint report was elaboratedexamining the possible paths and hinting at the optimal solution. The results were documented within a specialreport, the selected path and its respective footprint is shown in Fig.5. Fig.4 The LAS system in Mt.Qomolangma(Mt.Everest ) StationFig.5: Selected path (solid red line) for the LAS measurements at Qomolangma site with source contributions for a footprint “climatology” of the expected wind distribution, unstable stratification, zm = 20m.A set of LAS was installed and aligned in Naqu BJ station (31°227.18"N, E91°5355.36"E) in July, 2009, Naquarea of Tibet. The underlying surface of observation site is alpine meadow. The effective height and path lengthis 8.63 m and 1560m, respectively. Combined with the measurements of Eddy Covariance system (EC) andAutomatic Weather Station (AWS), the performance of LAS under Tibetan plateau environment has beenchecked. Page 14 of 98
  15. 15. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Fig.6 The LAS system in Naqu BJ stationTask 1.6:A first error analysis of flux data was given in a technical report. This will be updated as soon as the flux data isavailable.Task 1.7:For tasks 1.6 and 1.7 a footprint scheme is currently developed by UBT and will soon be published in a peerreviewed journal. A foundation for this scheme was elaborated within a Master thesis, for a description seesection results. Furthermore, a experiment was performed nearby the Namco Station (Fig.7). The investigationscover EC, energy balance and soil moisture measurements for a period from June 26th to August 8th and was setup directly at the shoreline of a small lake, pre-located to the Namco lake. This measurements will be used tovalidate the footprint related upscaling scheme and serve for parameterization of fluxes above lake surface andKobresia mats. A documentation of the experiment is now available. Fig.7. Turbulence measurements at Namco lake Page 15 of 98
  16. 16. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 • Highlight clearly significant results1. Underlying surface roughness lengths under the quality control of observation were determined Eddy covariance flux data collected from ITP/CAS three research stations (Qomolangma station, Namcostation and Southeast Tibet station-Linzhi station) on the Tibetan Plateau are used to analyze the variation ofmomentum transfer coefficient (CD), heat transfer coefficient (CH), aerodynamic roughness length (z0m), thermalroughness length (z0h) and excess resistance to heat transfer (kB-1). All the data was checked under thequality control firstly. The monthly average surface roughness, bulk transfer coefficient and excess resistance toheat transfer at all three sites are obtained. Momentum transfer coefficient (CD) is quite changeable during theday but relatively stable and lower in the night. The parameter kB-1 exhibits clear diurnal variations with lowervalues in the night and higher values in the daytime, especially in the afternoon. Negative values of kB-1 are oftenobserved in the night for relatively smooth surfaces on the Tibetan Plateau. Page 16 of 98
  17. 17. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 (a) (b) Fig. 8 Frequency distribution of ln(z0m) at Nam Co station in September(a) and October(b) Page 17 of 98
  18. 18. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 (a) (b) (c)Fig. 9 The diurnal variations of observed excess resistance to heat transfer (kB-1)at Qomolangma station(a), Namco station(b) and Southeast Tibet station(c) in March2 Variation characteristics of radiation of the wetland surface in the Northern Tibetan Plateau Based on the observed data at Automatic Weather Site(AWS) of MS3478 in the typical wetland of northernTibetan Plateau from March 2007 to February 2008. The seasonal mean diurnal, seasonal and annual variationfeatures of the radiation budget components were analyzed in this paper. The results indicated that in springdiurnal variations of both global solar radiation and the reflective radiation were larger than in other seasons, andtheir annual variations were double-peak-shaped, but the phases were different. The distributions of both thediurnal variation and the annual variation of the earth surface long-wave radiation were unsymmetrical. Annualvariation of the earth effective radiation was of bimodal pattern. One peak corresponded to March and April,when frozen soil melted, while the other to October, when froze soil froze. Net radiation mainly concentrated inMay, June and July, accounting for 40.14% of the total, indicating that in late spring and early summer theregions surface had obtained the largest net energy, which played a decisive role for the formation of terrestrialheat and the heating of the atmosphere.3. Analysis onpotential evapo-transpiration and dry-wet condition in the seasonal frozen soil region ofnorthern Tibetan Plateau This study was based on the observed data at Automatic Weather Site(AWS) of MS3478 in the seasonalfrozen soil region of northern Tibetan plateau from March 2007 to February 2008.The variation characteristics ofpotential evapotranspiration (PE) was analyzed based on Penman-Monteith method recommended by FAO. Thecontributions of dynamic, thermal and water factors to PE were discussed. Meanwhile, the wet-dry condition ofthat region was further studied. The results indicated that daily PE was between 0.52mm and 6.46mm in thewhole year. In summer evaporation was the most intensive, and from May to September monthly PE was over100mm. In November, there was a clear mutant. Annual potential evapotranspiration was 1037.83mm. Insummer, thermal evapotranspiration was much more significantly than dynamic evapotranspiration; in winter itwas on the contrary. In addition, drought and semi-drought climate lasted for a long time while semi-humidclimate short. The effect of water and dynamic factors on PE varied considerably with the season. Soil moisturewas not the main factor affected PE.4.Up-scaling scheme was developedThe location of the footprint function varies in time due to changing wind direction and atmospheric stability.Therefore the footprint of atmospheric measurements does not only affect data quality but also Page 18 of 98
  19. 19. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1representativeness of the observed data for the grid level. A scheme to overcome this drawback is indevelopment and will work in principle as shown in figure 3. Fig.10. Upscaling scheme for turbulent flux data from heterogeneous landscapes5.Free convection events at Nam Co site of the Tibetan Plateau were found and analyzed The spatial and temporal structure in the quality of eddy covariance (EC) measurements at Nam Co site isanalyzed, by using the comprehensive software package TK2 together with a footprint model, and the highquality turbulent flux data have been obtained for the investigation of free convection events (FCEs). Theresearch of FCEs at Nam Co site indicates that the generation of FCEs not only can be detected in the morninghours, when the diurnal circulation system changes its previously prevailed wind direction, but also can betriggered by the quick variation of heating difference between different types of land use during the daytimewhen clouds cover the underlying surface or move away. FCEs at Nam Co site are found to occur frequently,which can lead to the effective convective release of near ground air masses into the atmosphere boundary layer(ABL) and may strongly influence its local moisture and temperature profiles and its structure. Page 19 of 98
  20. 20. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Fig. 11. The distribution (a) and frequency statistics (b) of free convection events (FCEs) times at Nam Co site.6. Diurnal variation of sensible heat flux were very clear Careful data processing and quality control of LAS has been performed in Naqu BJ station. The comparisonof sensible heat flux measurement by LAS and EC are plotted in Fig12, which shows the similar variationbetween LAS and EC measurement. Page 20 of 98
  21. 21. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Fig 12 Comparison of sensible heat flux measurement by LAS and EC (2009.08.01-2009.08.28) Page 21 of 98
  22. 22. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Page 22 of 98
  23. 23. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3.2 Work progress in WP2 and achievements during the period WP2 aims to develop algorithms to retrieve surface parameters from a broad family of multi-spectral and/or multi-angular radiometric data and produce a consistent data set over the region of Tibetan Plateau. # Instrument and validation A multispectral canopy imager (MCI) was developed for the field measurements of forestry canopy LAI. It can capture image pairs in three different wavelength bands at arbitrary zenithal and horizontal directions. The MCI image pairs can be used to discriminate the sky, leaves, cloud and woody components. As a result, this instrument is capable of measuring the woody area index which is very important in field LAI measurements. In the Heihe river field campaign which was taken in June 2008, MCI was used to get the directional clumping index and woody-to-total area ratio. Finally, the LAI values were obtained in several locations after consider the correcting of the clumping effects and woody components. # Model development A Whole Growth Stages (WGS) model was developed for simulating the directional reflectance of the row planted canopy across the whole growth stages. Based on a series of simplifications and assumptions, we gave out an analytical expression to describe the spatial regular fluctuation of LAVD of row planted wheat canopy. We found that the LAVD of the vegetal row is approximately negative correlation to the distance from the centre of the row. Then we put forward a suit of calculation scheme to estimate the directional gap fraction which well considering the spatial regular fluctuation of LAVD within row-planted wheat canopy. In our new model, only 4 input parameters are needed, including LAI, the ratio of row width to height, the ratio of row space to height, row direction. A new angular & spectral kernel model was developed to describe the BRDF characteristics for most of the land covers. Compared with the semi-empirical kernel-driven model used by AMBRALS (Algorithm for Model Bidirectional Reflectance Anisotropies of the Land Surface) which was employed in the MODIS (Moderate Resolution Imaging Spectra Radiometer) albedo/BRDF product, the component spectra were combined into the kernel functions instead of kernel coefficients. Then the kernels were expressed as function of both the observed geometry and wavelength. As a result, the kernel coefficients are independent of wavelength in this new model. That characteristic enables the broad band conversion to be a linear combination of the new integral kernels which is much simple and efficient. A model describing thermal directional radiation was established for the rugged terrain. By parameterization of sky-view factor and terrain configuration factor, the emitted radiance was parameterized as a linear composition of the contributions of radiance from vegetation and soil, taking into account the coupling between vegetation-soil, vegetation-vegetation and soil-vegetation interactive processes. # A generic inversion algorithm In order to enable the application of the method to several satellite sensors, the observation model SLC (soil- leaf-canopy) was extended for applications in the thermal domain, and the MODTRAN interrogation technique was extended to this domain as well. In addition, look-up table (LUT) techniques were optimized in order to allow for efficient image simulations under various conditions. This means that for angular interpolations of the sun-target-sensor geometry only a limited size of the LUTs is required. Topographic effects were included by considering slope and aspect angles to be obtained from a DEM (digital elevation Page 23 of 98
  24. 24. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 model) of the area. Slope and aspect are used to estimate the fractions of solar and sky spectral irradiance in the optical and thermal domains. A unified equation was derived to describe the TOA radiance as a function of surface and atmospheric parameters in the optical and thermal domains with the incorporation of topographic effects. The MODTRAN interrogation technique was extended into the thermal domain as well, and several MODTRAN outputs were identified with physical quantities of four-stream radiative transfer theory. # Topography and scale effect correction for albedo products One coarse scale pixel includes many tilted micro-areas, which have different slopes and aspects. Its directional reflectance is affected by these micro-areas and their shadows. An equivalent smooth surface directional reflectance was introduced for a virtual surface of the coarse scale pixel, which was assumed to be smooth so that there were no micro-area topography effects. A scale effect correction factor was defined to correct the topography and scale effect. This factor is only dependent on DEM and the geometry of sun and sensor. The topography and scale effect correction algorithm includes three steps: (1) Setting up a database for pixel-average slope and aspect angle for each pixel of 500m grid and 5km grid, and scale effect correction factor for each 5km pixel; (2) Correcting the pixel level topography effect for 500m directional reflectance, using slope and aspect angles; (3) Correcting the pixel level, as well as subpixel level, topography effect for 5km directional reflectance, using slope, aspect angles and the scale effect correction factor. # A priori knowledge based LAI inversion The a priori knowledge of LAI was obtained by three ways: (1) Getting the relationship between a multidirectional averaged NDVI and LAI by simulation using a BRDF model (eg. SAILH model); (2) Developing the empirical crop growth model by the regression of a LOGISTIC equation and the field measured LAI data sets; (3) Developing a priori LAI trend from several years’ MODIS LAI product. All of this a priori information was used in the inversion of radiative transfer models to get the temporal continuous and robust LAI. Both of the MODIS and MISR data were used in the inversion to improve LAI product. # Angular effect correction of fractional vegetation cover Under the assumption of that a remote sensing pixel is mixed by vegetation and background, a simple directional fractional vegetation cover (FVC) model was developed based on Beer-Lambert law. The variables in this model can be got by using the MODIS images in 16 days and high resolution HJ-1 images The Scaled Trust-Region Solver for Constrained Nonlinear Equations (STRSCNE) algorithm was used to retrieve the variables. A vegetation growth model was introduced to constrain the relative worse quality of HJ data in a temporal scale. The different spectral responds of MODIS and HJ were also compared with spectrums of typical surface class. Uncertainty was assessed by error propagation theory and field experiments. # LST inversion using polar satellite data A review of existing algorithms to retrieve land surface emissivities (LSE) and land surface temperatures (LST) has been carried out. This review has allowed the selection of the needed algorithms to retrieve LSE and LST, which includes the preliminary determination of several parameters such as NDVI (Normalized Difference Vegetation Index), FVC (Fraction of Vegetation Cover), total atmospheric water vapour content, as well as carrying out cloud tests, image atmospheric and geometric correction. In the absence of the MODIS – CEOP-AEGIS dataset, these algorithms are being implemented on the data acquired by the Global Change Unit at the University of Valencia (Spain), in order to obtain a near-real estimation of LSE and LST. The completion of this process is expected during the next reporting period. In a second step, this processing chain will be adapted to the Tibet area in order to process the MODIS – CEOP-AEGIS dataset. Page 24 of 98
  25. 25. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 The algorithm of daytime 150m LST product was proposed by using the HJ-1 dataset over the Tibet Plateau. A view angle dependent single channel LST algorithm has been developed for correcting atmospheric and emissivity effects for all land cover types. • Highlight clearly significant results (3 pages) # Multispectral canopy imager (MCI) and its use in woody-to-total area ratio determination The MCI, which mainly comprises a near-infrared band camera, two visible band cameras, filters and a pan tilt, was developed to measure clumping index, woody-to-total area ratio and geometrical parameters of isolated trees (figure 1). Two typical sampling plots (Plots 1 and 5) which were covered by Picea crassifolia were selected for the estimation of woody-to-total area ratio and its directional change in Heihe river basin, China. The clumping index and woody-to-total area ratio values of the forest canopy were got at eight zenith angles (from 0 to 70° in increments of 10°) using MCI images based on gap size distribution theory (figure 2,3). Figure 1. Illustration of the multispectral canopy imager (MCI).Erreur ! Des objets ne peuvent pas être créés à partir des codes de champs de mise en forme.Erreur ! Des objets nepeuvent pas être créés à partir des codes de champs de mise en forme. Figure2. Clumping indices at Plot 1 (a) and Plot 5 (b).Erreur ! Des objets ne peuvent pas être créés à partir des codes de champs de mise en forme.Erreur ! Des objets nepeuvent pas être créés à partir des codes de champs de mise en forme. Figure3. The woody-to-total area ratio of Plot 1 (a) and Plot 5 (b). The detailed description of the equipment and the method can be found in the following paper: Page 25 of 98
  26. 26. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Jie Zou, Guangjian Yan, Lin Zhu and Wuming Zhang, Woody-to-total area ratio determination with a multispectral canopy imager (MCI), Tree Physiology, 2009; doi: 10.1093/treephys/tpp042. # Unified modelling of TOA radiance for the generic inversion algorithm A unified equation was derived to describe the TOA radiance as a function of surface and atmospheric parameters in the optical and thermal domains with the incorporation of topographic effects. This equation reads:where and are the viewing factors associated with illumination from the sun and the sky, respectively.They are given by ,where and are terrain slope and aspect, respectively.The four terms in square brackets are the ones associated with: • Atmospheric path radiance in both domains • Adjacency effects in both domains • Sky irradiance contributions in both domains for the target • Direct solar bi-directional and thermal direct target contributionsNote, that emissivities are represented here by their associate reflectance equivalents and(hemispherical and directional emissivity). # Time series LAI mapping over Heihe river basin Page 26 of 98
  27. 27. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 The developed variational assimilation method was implemented and some results on LAI values for the whole year of 2008 over Heihe River Basin are presented in Figure 4. It shows the regional LAI mapping results from the time series MODIS reflectance data acquired over this area in 2008 with the spatial resolution of 500m. As seen, temporal variation of the LAI values in this region is reasonable. And the spatial variability is consistent with the vegetation cover map in this area. Figure 4 LAI inversion results in the middle of Heihe River area. # Emissivity measurements and data preparation Several papers have been published regarding different topics of LST from polar satellites such as: (1) José A. Sobrino, Cristian Mattar, Pablo Pardo, Juan C. Jiménez-Muñoz, Simon J. Hook, Alice Baldridge, and Rafael Ibañez. 2009. Soil emissivity and reflectance spectra measurements. Applied Optics, Vol. 48, Issue 19, pp. 3664-3670. This work present a laboratory procedure to characterize the emissivity spectra about several soil samples collected in diverse suite of test sites in Europe, North Africa, and South America from 2002 to 2008. Here, we presented a cross calibration with in-situ measurements and further application to thermal remote sensing. This work presents a methodology to characterise the emissivity values of a given soil sample, additionally, the soil emissivity values analyzed here were presented for all polar satellites which have thermal sensors. (2) C. Mattar, J.A. Sobrino, Y.Julien, J.C. Jiménez-Muñoz, G. Soriá, J. Cuenca, M. Romaguera, V. Hidalgo, B. Franch, R. Oltra. 2009. Database of atmospheric profiles over Europe for correction of Landsat thermal data. Proceedings of the 33rd International Symposium on Remote Sensing of Environment. (in press) This work presents a new vertical profile data base for correct thermal remote sensing images. In this case we focused our work to provide useful information to correct Landsat thermal images. However, the data base could be used for other remote sensing sensors. # Spectra normalization of HJ and MODIS data Difference of spectral responds of HJ and MODIS sensors should be considered in FVC retrieval, though MODIS and HJ sensors have overlapped region in spectral respond functions (figure 5). Many reflectance spectrums of leaves and soils were selected from spectrum library of ENVI software. The mean values were Page 27 of 98
  28. 28. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 computed for the two sensors (table 1). Scattering plot of 4 bands in figure 6 didn’t exhibit much difference for HJ and MODIS. Figure 5. Relative spectral respond function of MODIS and HJ-1 bands used in FVC retrieval Table 1. Mean reflectance of typical land covers with HJ and MODIS relative spectral response Reflectance of typical leaves and soils conifer deciduous Grass and soil arbre Blue HJ-1 0.0704562 0.07849 0.08478 0.077605 MODIS 0.0621984 0.065187 0.071822 0.064877 Green HJ-1 0.100901 0.132595 0.135229 0.139566 MODIS 0.114949 0.149223 0.14475 0.12815 Red HJ-1 0.075 0.119595 0.129705 0.204328 MODIS 0.071389 0.110964 0.12425 0.195855 Near- HJ-1 0.51273 0.683053 0.517343 0.281649 infrared MODIS 0.525689 0.692068 0.534383 0.300353 Scattering plot of reflectances Blue Green Red NIR Figure 6. Reflectances of HJ-1 and MODIS signal corresponding to typical land cover types # Development of a quantitative remote sensing products inversion system Page 28 of 98
  29. 29. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 A quantitative remote sensing products inversion system is being developed for the parameters products generation. It is composed of 5 sub-systems, including database, data pre-processing, products inversion, validation, and visualization. (1) Database subsystem takes charge of the data management and data flow of the whole system. All the other sub-systems will be connected together by database without physical connection between the 4 sub-systems; (2) Data pre-processing subsystem will process all the incoming remotely sensed data into standard data products. The pre-processing procedures include cross radiometric calibration, geometric correction, projection transferring, gridding, and cloud screening; (3) Products inversion subsystem is a products “pool” which is composed of 22 geo and bio parameters and system users will make their own product producing workflow. The subsystem will be producing products through the workflow instantaneously or routinely; (4) Validation subsystem will validate the inversion products based on the predefined methods routinely or by users’ convenience; (5) Visualization subsystem is a visual interface which provides users with data management, image display environment, image and graphic processing, terrain analysis, statistics analysis, and annotating. Page 29 of 98
  30. 30. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Page 30 of 98
  31. 31. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3.3 Work progress in WP 3 and achievements during the period Summary of progress towards objectives, per task: Task 3.1 (ALTERRA, ITC, BNU, CAREERI, TUD): local validation of algorithms with ground eddy covariance measurements at footprint scale and cross-comparison of approaches to turbulent flux partitioning. The remote sensing based algorithm for flux calculation to be evaluated in this task can be applied at local scale (S-SEBI, SEBS) or at a larger (meso) scale (SEBS, MSSEBS). They all follow the approach proposed by Menenti and Choudhury (1993) stating that for a given net radiation value, and for homogeneous atmospheric conditions, the surface temperature is related to the ratio between actual and potential evaporation. Both methods require physical properties of the surface extracted from remote sensing to characterize the surface radiative balance (albedo, surface temperature, emissivity) and vegetation structure (fractional cover, Leaf Area Index). Also they differ in the way to define wet and dry boundaries in terms of normalized surface to air temperature gradient, they all require some basic meteorological information. Therefore the contribution of UDS in this task consisted in: i. identify remote sensing products available to conduct SEB calculation for areas and periods of time where reliable ground measurement data were available; ii. gather and post-process meteorological data to be used as forcing conditions in the SEB schemes The remote sensing products used to conduct the algorithm comparisons are Modis images acquired by Terra. The reasons are: i. the adequate spatial and temporal resolution of the sensor; ii. the panel of adequate products; iii. ad hoc products from WP2 are not available at this stage of the project. The products and dates are summarized in the tables bellow. The candidate dates were selected on the basis of global cloudiness on the Plateau.April 2003 15th and 25thMay 2003 28thOctober 2003 17th and 23rdNovember 2003 8th and 11thProduct Variable Spatial resolution Temporal resolutionMOD11A1 LST/Emissivity 1km DailyMCD43B3 Albedo 1km 16 daysMOD13A2 Vegetation index NDVI 1km 16 daysMOD15A2 LAI 1km 8 days The characterization of the state of the Planetary Boundary Layer is based on the output from the Meso-scale Numerical Weather Prediction Model GRAPES developed by the Chinese Academy of Meteorological Sciences, partner in this project. The following variables were extracted from GRAPES simulations covering the entire Plateau at a resolution of 30 km and 30’ time step: Variables extracted at the height of the Atmospheric Boundary Layer: • ABL height • Air temperature • Specific humidity • Wind speed • Air pressure Variables needed at 2 meters: • Specific humidity Page 31 of 98
  32. 32. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 • Air pressure UDS prepared two set of inputs centered on the validation site called BJ, with either 400x400 km or 100x100 km extent. The processing consisted in: • extraction of MODIS products, re-projection and spatial re-sampling of albedo, LST with corresponding acquisition time layer, NDVI, LAI • extraction of GRAPES outputs from GrADS raw files, geo-processing of layer variables to the same resolution and coverage as MODIS products • creation of time-composite PBL layers to associate adequate GRAPES field to MODIS LST following MODIS LST acquisition time • extraction of SRTM Digital Elevation Data for the selected scene to calculate PBL elevation This dataset was used to perform S-SEBI, SEBS and MSSEBS calculations, tests and comparisons (see next section). Task 3.2 (UDS, ALTERRA, ITC, ITP, BNU): generalize SEB calculation at a high spatial resolution and on a regional extent. On such an extent, local towers cannot be used to define boundary conditions. The MSSEBS (Colin, 2006) approach enables to link ground variables at a high spatial resolution (typically 30 meters) with Atmospheric Boundary Layer (ABL) state at a proper resolution related to the typical ABL length scale. Generalize SEB calculation on the entire Plateau lead to several conceptual and technical challenges: • the combination of high resolution remote sensing products with medium (meso) resolution NWPM outputs in a single calculation scheme, combining physical variables whose meaning is closely related to their inherent scale, as to be taken into account in the algorithm implementation • the use of high (1km) resolution remote sensing products over the Plateau lead to significant amount of data (e.g. 1,400 x 1,700 km grid means 2.4E6 calculation nodes, for n variables and j time steps with n > 25 and j >> 100). • the use of NWPM with different spatial and temporal resolution, geo projection, supposes to have a powerful pre-processing procedure to mix various data sources in a single model input set of layers • the probable occurrence of data unavailability (clouds…), data inconsistency (NaN, error code) supposes to have a flexible enough implementation to manage with various situations with a minimum of manual work These considerations lead to the prototyping and current development of a new SEB framework, with the following characteristics: • core algorithms are separated from I/O procedures; external I/O procedures can be extended without any modification of the algorithms to allow the use of new data sources • efficient object oriented python coding based on Numpy and SciPy math libraries for fast processing of numerical arrays; multi-core computation capability; fully open source based and cross-plateform • XML based configuration, with HTML/PhP user interface (under development) • powerful geo-processing library GDAL embedded • self-diagnosis capability for fast analysis of mass of log files At this stage of the project, this code is under development, with evaluation of a beta version. The first stable version will be described in details in the Algorithm Theoretical Basis Document to be delivered on milestone M2. The resulting products will be made available to WP8 partners, and as a new product in the database of the project to be registered to GEOSS. Page 32 of 98
  33. 33. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Figure 1: SEB framework chart Task 3.3 (UDS, ALTERRA, ITC, ITP) : The same MSSEBS approach is used with low resolution satellite images (Feng Yun-2) and NWP model outputs over the entire plateau. These low resolution fluxes maps can be validated from spatially integrated maps obtained in Task 3.2. (nothing at this stage of the project) Significant results The aim of the calculations performed with the 2003 dataset is to perform a cross-comparison of algorithms and a validation with ground measurements. The candidate algorithm of UDS is the Multi-Scale Surface Energy Balance System (Colin, 2006). This is a single source SEBI based scheme designed to process radiative balance, PBL stability and external resistances at appropriate scales as regards the physical meaning of key variables (e.g. roughness length for momentum and heat, stability functions in the atmospheric boundary layer…), to produce evaporative fraction maps. The soil heat flux is computed following vegetation fraction data, and the total diurnal evaporation is computed with a locally fitted model of net available energy for turbulent flux. The sensible heat flux is calculated as the residual of the energy balance. Page 33 of 98
  34. 34. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 Figure 2: example of results for Nov 11th 2003: (top left) PBL forcing from GRAPES, values are allocated following the acquisition time of the LST; (top right) MODIS products; (bottom left) Sensible heat flux map from MSSEBS; (top right) Latent heat flux map from MSSEBS.For the 1x1 km pixel where the Bijie site is located is, for Nov. 11th 2003 at 11:06, the latent heat flux calculatedwith MSSEBS is 7.6 W.m-2 , and the sensible heat flux is 143.1 W.m-2, while ground values of latent heat fluxmeasured at respectively 10:30 and 11:30 range from -14.3 W.m-2 to -55.5 W.m-2, and the corresponding sensibleheat flux ranges from 91.4 W.m-2 to 200.0 W.m-2.Since the latent heat flux from MSSEBS is of the order of magnitude of the model uncertainty (Colin 2006), theevaporation can be considered as almost negligible. Moreover, as the ground measurement values used here aresensor values, a comparison with a 1 km resolution pixel would require further analysis of the spatial meaning ofthe measures.This first experiment gives important information for the preparation of the next phase of the project: • whatever the date of the year, even a limited scene is affected by clouds. The SEB framework has to be able to deal with missing values in mathematical processing, and gap filling technics to be implemented in WP2 will probably be critical to provide a continuous flow of inputs for the time series processing phase to come Page 34 of 98
  35. 35. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 • also these experiments are based of GRAPES simulations, GRAPES usually provide analysis data, ie. at a fixed 6 hour time step. This is of consequence as regard the acquisition time of LST products. An additional step may be required to derive LST at a GRAPES time step from the remote sensing products.This first experiment has several significant limitations: • no data were available to conduct a dual-source calculation • validation data were only available for one point, and local meteorological conditions only allowed to use one of the selected dates • ground measurement data used for validation didn’t passed through detailed quality and footprint analysisTherefore a new validation experiment was initiated with a selection of 3 different sites located in very differentparts of the Plateau, using 4 sets of 10 days of data in January, April, July and October 2008. This set ofvalidation data was made available late September 2009 by WP1 partners. MODIS products were collected, andGRAPES simulations still have to be performed at the time of writing this report. Therefore it is asked that thetarget delivery time of deliverable de 3.1 “Review of selected existing algorithms and models on local, regionaland Plateau scales data sets” is set to December 20th to allow for the completition of this analysis. Page 35 of 98
  36. 36. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1 3.4 Work progress in WP 4 and achievements during the periodSummary of progressTask 4.1: Review and inter-comparison of available algorithms and products (microwave backscatteringcoefficient, microwave emissivity and land surface temperature diurnal cycle) (ITC, CAREERI, BNU,IGSNRR) This task is completed and report is writtenTask 4.2: Collection of consistent continuous in-situ soil moisture measurements at regional scale ofselected sites on the Tibetan plateau measurements which will include soil moisture (including soiltemperature, vegetation parameter, soil texture and land surface roughness) at two sites (Maqu-grassland,and Naqu tentatively) (CAREERI, ITC) Task 4.2 has been completed and Deliverable 4.1 has been distributed. CARRERI and ITC have installed in May-July 2008 an extensive soil moisture and soil temperature monitoring network in the water source region of the Yellow River to the South of Maqu city, on the border between Gansu and Sichuan province, in China (33°30’-34°15’N, 101°38’-102°45’E). The network consists of 20 stations monitoring the soil moisture and temperature at different depths (from 5 to 80 cm deep) every 15 minutes. The network covers an area of approximately 40 km*80 km, where the elevation ranges between 3430 m and 3750 m a.s.l (north-eastern edge of the Tibetan Plateau). To ensure complete data continuity, the data are downloaded twice per year by CAREERI: at the beginning of the monsoon season (in May) and at the end (in October). A specific calibration of the probes has been carried out for the soil type of Maqu area, increasing the accuracy of the soil moisture measurements from 6% to 2%. The quality of the data downloaded from Maqu monitoring network has been checked by evaluating their consistency in time and space and by comparing their trends with meteorological data and with soil moisture satellite products. A clear consistency and a good agreement have been found. The calibrated data collected at all the stations and at all available depths are reported in an Excel file and a detailed technical report has been attached to the data. Both of them have been delivered to the project teams. Task 4.3: Development of a satellite sensor independent system for the soil moisture combined retrieval algorithms (ITC, CAREERI, BNU) This task is in progress. A retrieval model is developed for ASCAT data which will be combined with passive microwave data in the course of the project.Task 4.4: Estimation of soil moisture from Geostationary Satellite (GS) data (optical remotely senseddata) (IGSNRR)In order to develop method of estimate soil moisture based on geostationary satellite data using the diurnalvariation of LST derived and global radiation (shortwave). Following investigations were conducted during thistime: 1. Construction of land surface diurnal temperature cycle model and the ellipse relationship between LST and solar shortwave radiance.In geostationary satellite observation system, there are adequate images to describe land surface temperaturevariation under clear sky condition. In generally, land surface temperature diurnal variation can be expressed as aharmonic term in daytime and an exponential term during the nighttime. This two-part semi-empirical diurnaltemperature cycle (DTC) model has used by Göttsche and Olesen (2001), Schädlich et al. (2001) and Jiang et al.(2006). In our work, we chose the model applied in Jiang (2006). 2. Land surface temperature simulation with land surface model (i.e. Common Land Model ) Page 36 of 98
  37. 37. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1In order to validate some assumption and analyze the method mentioned above, simulation data is an easy andfast way. In our simulation, Common Land Model was selected to simulate land surface temperature underdifferent environment conditions in clear air condition. During the simulation, soil type and land cover type wereusually set to be constant. Then we modeled the land surface temperature variation under different percentvegetation cover varying from 0%- 100% with a step of 10% and soil volumetric water content varying from0%-50% with a step of 5%.Several parameters were extracted from the land surface temperature daily cycle like maximum temperature,minimum temperature, daily temperature amplitude, temperature morning raising rate and so on. Correlationanalysis was conducted here to analyze the relationship of there parameters with soil water content and percentvegetation cover. The results showed that land surface temperature is a complex variable. It is influenced notonly by soil water content, but also is greatly influenced by surface land cover type and percent vegetation cover.As an interface between land and air, Land surface has strong energy and material exchange processes. In orderto understand the degree of soil water content’s influence on land surface temperature, the other factors shouldbe eliminated firstly. 3. Organization and implement field experiment in Lang fang experimental base.Beside land surface model simulation, we also organized a field experiment in Lang fang experimental base inHe bei province, China. In order to measure the atmosphere and soil data, such as air temperature, wind velocity,soil volumetric water content, we purchased an Automatic Weather Station and Time Domain Relectometers(TDR). Meanwhile, land surface temperature was measured by infrared thermometer. Down-welling globeradiation and net radiation were also recorded using Solar Radiometer.The experiment was implemented from 17th Oct. to 5th Nov. 2008 for 20 days. Three sites were executedsimultaneously with three soil types (sand, watered local soil and non-watered local soil). 4. In-situ measurement data analysisFrom the experiment, many data was collected. Fig. 3.4.1 shows the observed records of soil surfacetemperature, wind speed and air temperature at 2 Meter height of 5 days.Fig.3.4.1 Sample of observed data during the experiment Page 37 of 98
  38. 38. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1From the observed data, we analyzed the soil temperature raising rate related to the Net Surface ShortwaveRadiation (NSSR) during the morning time, and the temperature falling rate related to NSSR or Net SurfaceRadiation (NSR) during the afternoon time. 5. Abnormal surface nocturnal cooling effect analysisFrom the in-situ measurements and satellite data of MSG SEVIRI, we found that there exists an abnormal risingof the change of the soil temperature in the nocturnal cooling process. Nocturnal surface intense cooling mayresult in the inversion of the atmospheric temperature and water vapor. In order to study the abnormalphenomenon, we analyze and simulate the changes of surface temperature under different atmosphericconditionsTask 4.5: A data product of the plateau using different sensors simultaneously (AMSR-E, ASCAT,SMOS) (BNU, ITC) Up to October 2009, we had collected all of the satellite observation data and ancillary data used for retrieval, including AMSR-E Level 2A, Level 3 brightness temperature data, SRTM 90m DEM data, MODIS IGBP land cover map, and surface freeze/thaw status data, etc. Available ground surface emission models were evaluated and compared in detail, on this basis, a forward simulation system was established. It uses Qp model to calculate the emission of rough soil surface, and !-" model to consider the vegetation effects. Through simulation analysis, the crucial inversion methods were determined. A multi-channel temperature estimation algorithm using AMSR-E was selected to obtain the surface temperature. The new developed microwave vegetation Indices (MVIs) was used to eliminate the vegetation effects. And a soil moisture index developed from Qp model was put forward to minimize the effects of surface roughness. When the above methods were used in the soil moisture retrieval, some good results were achieved, and further results are still in progress.Task 4.6: Validation results and documentation of uncertainties (CAREERI, BNU) There is no progress made so far and is in accordance with project plan.Significant resultsCollection of consistent continuous in-situ soil moisture measurements at regional scaleOne of the objectives of the CEOP-AEGIS project is to develop a soil moisture retrieval algorithm based on thesimultaneous use of active and passive microwave satellite data. The developed algorithm is sensorconfiguration independent and is able to incorporate data of present and future satellite data, such as AMSR-E,ASCAT and SMOS. The long term and large scale products obtained applying the developed algorithm over theTibetan Plateau, will be extremely important to understand the links between Monsoon system, precipitationpatterns and soil moisture.For this reason, extensive soil moisture monitoring networks are required to obtain ground information whichcan be compared to the retrieved soil moisture products, in order to evaluate their consistency.To tackle this validation problem, CARRERI and ITC have installed in July 2008 an extensive soil moisture andsoil temperature monitoring network in the water source region of the Yellow River to the South of Maqu city(Gansu province, China). The network consists of 20 stations monitoring the soil moisture and temperature atdifferent depths (from 5 to 80 cm deep) every 15 minutes. The network covers an area of approximately 40km*80 km.The area selected for the installation of an extensive soil moisture monitoring network is located to the South ofMaqu city, on the border between Gansu and Sichuan province, in China. The network is at the north-easternedge of the Tibetan Plateau (33°30 -34°15’N, 101°38’-102°45’E) and at the first major meander of the YellowRiver, where it meets the Black river. It covers the large valley of the river and the surrounding hills (Figure Page 38 of 98
  39. 39. CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.4.2), characterised by a uniform land cover of short grassland used for grazing by sheep and yaks. In this areathe elevation ranges between 3430 m and 3750 m a.s.l.The installation of the soil moisture and soil temperature monitoring stations started in May 2008 with thestations CST_01-05 and was concluded at the end of June 2008 with all the other stations. Therefore since July2008 the complete network is operative.The network covers an area of approximately 80 km*40 km and the locations have been selected in order tomonitor the area extensively at different altitudes and for different soil characteristics.During the installation, soil samples were collected in order to analyse bulk density, particle size distribution andorganic matter content. The samples for particle size and organic matter were collected at a depth between 5 and15 cm. A laser scanner (Mastersizer S Ver. 2.18 by Malvern Instruments Ltd.) was employed to estimate the soilparticle size distribution and the standard method for the organic matter content. Soil sample rings (aluminiumcylinders of known volume) were collected at 5 cm depth and oven dried at 105°C to estimate the bulk density(i.e. dry soil mass in a known volume). When the soil profile showed a variation at deeper layers, the samplecollection and the analyses were repeated for the second horizon as well.Figure 3.4.2 Maqu area, Yellow River valley and location of the 20 soil moisture and soil temperature stations of thenetwork.Each network station consists of one Em50 ECH2O datalogger (by Decagon), which is recording the datacollected by two to five EC-TM ECH2O probes (by Decagon) able to measure both soil moisture and soiltemperature.EC-TM ECH2O probe consists of 3 flat pins 5.2 cm long. It is a capacitance sensor measuring the dielectricpermittivity of the soil surrounding the pins. The dielectric permittivity is then converted in volumetric soilmoisture according to a standard calibration equation. The soil temperature is measured using a thermistorlocated on the same probe. Page 39 of 98
  40. 40. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Figure 3.4.3 Installation procedureA specific calibration of the probes was needed for the soil type of Maqu area. Therefore soil samples werecollected and laboratory calibrations were carried out (see following paragraph).For the installation, a deep hole in the soil was dug and the probes were installed on one of the hole walls, atdifferent depths and with the pins in horizontal direction. Then probes and datalogger (closed in a box) werecompletely buried (see Figure 3.4.3).EC-TM ECH2O probes estimate the volumetric water content of the soil by measuring the dielectric constant ofthe soil. However the dielectric properties of the soils depend on soil texture and salinity. Decagon hasdetermined a generic calibration equation (applied by default by the datalogger), which is valid for all finetextured mineral soils with an accuracy of approximately ± 3%. This accuracy can be increased to 1-2%,performing a soil-specific calibration. For this reason about 5-6 kg of soil were collected in each location at adepth of about 5-15 cm (as well as at deeper layers, in case the soil profile was different) in order to carry out alaboratory specific calibration, following the instruction guide provided by Decagon.Figure 3.4.4 Results of the soil specific calibration of ECH2O probes Page 40 of 98
  41. 41. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1In conclusion, the calibration (Fig.3.4.4) has led to a decrease of the rmse between the volumetric soil moisturemeasured by the rings and that measured by the probes from 0.06 to 0.02 m3/m3. Page 41 of 98
  42. 42. CEOP-AEGIS (GA n° 212921) Periodic Report no. 13.5 Work progress in WP 5 and achievements during the periodEstimation of precipitation over the Plateau and surrounding zones with optical and microwaveobservationsThe objective of this WP was twofold: to provide multisensor and multiplatform observation of precipitationover the Plateau, and to get a deeper understanding of cloud and precipitation processes ongoing over this area.The temporal development of the activities identified as the first step the set-up of a reliable strategy to providequantitative precipitation measurement. This was achieved during the first reporting period of the project: theweather radar data have been pre-processed to provide the project with a quality controlled 3D precipitationdataset over the project target area.On the other side, two studies were completed indicating that prevailing synoptic scale trough is one of a keyindicator to establish unique precipitation system over the Tibetan Plateau. Other activities are in their firstdeveloping phase, and did not yet achieved significant results, as planned in the DoW document.In the next pages a more detailed description of the activities is presented task by task.Summary of progress.Task 5.1: To observe the cloud and precipitation microphysics processes in Tibetan Plateau andsouthwestern China by cloud Doppler radar, movable X and C band dual linear polarization radar. Ahydrometeors classification algorithm will be applied to retrieve the 3D microphysical cloud structure.The radar observation has started in the sites operated by CAMS: the radar network and rain gauge information,analyze the ground blockage for radar in Tibetan and Qinghai Province. The results show that the radars inTibetan are blockage by around mountain severely, the radar coverage is limited. The radar in Qinghai provincecan be used to precipitation estimation with rain gauges. A fuzzy-logic based algorithm for hydrometeor phaseclassification with polarimetric radar has been developed by CAMS. A small network of three X-banddisdrometers (PLUDIX) is planned by UNIFE (with the assistance of ITP-CAS) and the installation will becompleted in November 2009.Task 5.2: To develop the QC and mosaic algorithms for operational Doppler radar network. Thedisdrometric data will be used in radar QC and for radar calibration if disdrometers instruments areavailable.Research work on radar data quality and reflectivity remap and mosaic has been carried on by CAMS, and thealgorithm for 3 D mosaic. A the fuzzy logic based algorithm is used to detect the anomalous propagation andground clutter; four interpolation approaches are used to remap raw radar reflectivity fields onto a 3D Cartesiangrid with high resolution, and three approaches of combining multiple-radar reflectivity fields are used. Thealgorithm has been used to process the radar data and provide 3D data to the other partners of WP5.In particular, the raw precipitation data in Tibetan and the gridded precipitation data were provided by CAMS toUNIFE for two case studies. for period of 18 June 2008-19 June 2008 and 18 July 2008-20 July 2008, withspatial and temporal resolution (0.01°#0.01°#0.5km#5min)Finally, CAMS processed radar data and provided 3D reflectivity data to WP5. Grid Reflectivity in Qinghaifrom 18 July 2008 -21 July 2008 were product, the radar data in Tibetan from 18 June 2008 to19 June 2008, 18July 2008 to 20 July 2008 were provide.The data of three X-band disdrometers will made available by UNIFE for the period 1 November 2009 – 30October 2010, to improve the quantitative radar rainfall products. Page 42 of 98
  43. 43. CEOP-AEGIS (GA n° 212921) Periodic Report no. 1Task 5.3: To analyze the meso-scale structures and processes of precipitation systems in Tibetan Plateau andsouthwestern China by operational Doppler radar network in China and satellite (e.g. cloud products of MODIS).The precipitation distributions with different algorithms will be compared in case studies.UNIFE carried out an inventory of satellite precipitation estimation techniques, including both physical andstatistical approach and considering microwave (AMSR-E, SSM/I-SSMIS and AMSU), visible-infrared (MODIS,AVHRR, Meteosat, FY-2C), and blended techniques. The characteristics of different techniques were analyzedto select the more suitable ones for application over the Tibetan Plateau. The events proposed by CAMS wereselected as case study for the early application of selected techniques.UNITSUK completed an analysis of the meso-scale structures and processes of precipitation systems andidentification of the indicators for the rainfall processes in Tibetan Plateau (TP) and southwestern China, and theresults will be summarized in the next section.Task 5.4: To use the rain maps obtained by the ANN technique along two main lines: improve the performanceof floods and drought warning systems, and analyze long term (seasonal) rainfall pattern.IGSNRR performed an inventory of Satellite Rainfall Estimation approaches and studied the theory of ArtificialNeural Network (ANN) and application in satellite rainfall estimation. The MATLAB software is considered forANN implementation. A first satellite dataset (June 2007 to September 2007) has collected and processed: FY-2C satellite images (provided by the National Satellite Meteorological Center of China at 5Km spatial resolutionand hourly temporal resolution) and Gauge data (purchasing from the National Satellite Meteorological Centerof China) at hourly temporal resolution as well.An ANN technique is implemented and tested with gauges data by IGSNRR, and the preliminary results will besummarized in the next section.UNIFE started to apply an ANN technique developed for MODIS data and focused on mid-latitude, to the casestudies over the Tibetan Plateau.Task 5.5: To retrieve the precipitation with Doppler radar, satellite data and rain gauges in mountain region.The retrieval of precipitation fields from radar and rain gauges has started (see task 5.2), while the satelliteapproach is still in its preliminary phase (see also Tasks 5.3, 5.4 and 5.8).Task 5.6: To obtain the distribution of Precipitable Water Vapor (PWV) in Tibetan Plateau and itsadjoining area by GPS receiver.This task is not yet started by CAMS.Task 5.7: To obtain the indicators of the rainfall process in Tibetan Plateau and southwestern China by analyzingthe change of PWV.UNITSUK carried on a study on the relevance of water vapor transportation processes, using reanalysis data andnumerical weather prediction output. Results of this study will be summarized in the next section.Task 5.8: To improve the current combined precipitation estimation technique with the radiometer(TMI) and PR with the simulation database developed above and inclusion of the effects of topography over thePlateau; Also here we will correct the satellite estimation of precipitation using the ground rain gauge data in thealgorithm, and validate the inversion scheme with ground observation.For this task UNIFE planned to apply a rainfall retrieval scheme that works on conical scanner data (SSM/I-SSMIS, AMSR-E, TMI). The algorithm is based on a cloud radiation database constructed as follows. A cloudprofile data set is assembled by means of cloud resolving model outputs (the Non-hydrostatic Modeling Systemof the University of Wisconsin is used to this end), then a radiative transfer algorithm is applied to simulate theradiances upwelling from the modeled cloud profiles. When a set of satellite radiances is measured from a givensensor, the database is searched for the cloud profile whose simulated radiance better match the observed ones.This algorithm is currently applied in different regions with encouraging results.Significant results Page 43 of 98

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