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The South America Land Data Assimilation System (SALDAS) Luis Gustavo G. De Goncalves ESSIC University of Maryland NASA Goddard.

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Presentation on theme: "The South America Land Data Assimilation System (SALDAS) Luis Gustavo G. De Goncalves ESSIC University of Maryland NASA Goddard."— Presentation transcript:

1 The South America Land Data Assimilation System (SALDAS) Luis Gustavo G. De Goncalves ESSIC University of Maryland NASA Goddard Space Flight Center LIMA Water Cycle Capacity Building workshop November 30 – December 4, 2009

2 You are going in the right general direction… …but when you open your eyes, you correct towards the center of the road (Land surface model) Observations (Observations) Data Assimilation: Sleepy Driver Analogy Courtesy Matt Rodell

3 Weight matrix K depends on model uncertainty. Extended KF: Linearized dynamic matrix equation for P. Horizontal error correlations too expensive. EnKF: Use sample covariance from ensemble. Nonlinear propagation of uncertainty. Can account for horizontal error correlations. Update: x i (+) = x i (-) + K [ Z - H x i (-) ] K = P H t [ H P H t + R] -1 X = soil moisture state (i-th ensemble member) (-) before update (+) after update Z = remote sensing data H = measurement operator K = weight matrix P = state error covariance (model uncertainty) R = measurement error cov (meas. uncertainty) Ensemble Kalman Filter Slide credit: Paul Houser Courtesy Matt Rodell

4 Goal: combine local observations and parameters with NASA advanced hydrological modeling expertise and capabilities to improve Global and SA NWP, climate and water management through collaboration with various centers (government, universities and research institutes) Missions Land Information System NASA/GSFC Local resources (observations) Human Resources Central to South America CPTEC/INPE SALDAS REGIONAL Weather and Climate Water Management Global Hydromet Databanks Capacity Building South American Land Data Assimilation System (SALDAS)

5 Background SALDAS project seeks to provide accurate, near-real-time and retrospective land surface states over South America Quality of land surface model (LSM) output is closely tied to the quality of the meteorological forcing data used to drive the model Model and observation-based data used to create high-quality forcing data used by Noah, SSiB, SiB2, CLM2, MOSAIC, and VIC (to be tested) LSMs – Retrospective ( , CPTEC) – Real-time (2002-Present, CPTEC)

6 GRACE Data Assimilation three snow layers surface excess root zone excess “catchment deficit” Catchment LSM (Koster et al., 2000): z The Gravity Recovery and Climate Experiment (GRACE) satellite mission produces monthly maps of Earth’s gravity field with enough precision to infer changes in total terrestrial water storage (groundwater + soil moisture + surface water + snow) over large areas (> 160,000 km 2 ) Catchment land surface model Ensemble Kalman smoother data assimilation Courtesy Matt Rodell

7 Results have higher resolution than GRACE alone, better accuracy than model alone. GRACE TWS anomaly January 2003 – June 2006 GRACE Assimilating Catchment LSM TWS anomaly, mm January 2003 – June 2006 From scales useful for water cycle and climate studies… To scales needed for water resources and agricultural applications GRACE Data Assimilation Zaitchik, Rodell, and Reichle, J. Hydromet., 2008.

8 Upper Mississippi column water (mm) Ohio-Tennessee column water (mm) Missouri column water (mm) Lower Miss-Red-Arkansas column water (mm) Modeled Water Storage Model-GRACE Assimilation GRACE Water Storage LDAS models produce continuous time series; near-real time capable. Mississippi River sub-basins Daily estimates are critical for operational applications Monthly GRACE data anchor model results in reality GRACE Data Assimilation

9 LDAS models produce continuous time series; near-real time capable. GRACE Data Assimilation LPB Implementation plan 2005

10 Schematic representation of the land surface modeling into the NASA Land Information System (LIS), a multi-LSM distributed framework where SALDAS in built upon. (S.V. Kumar, et al, 2006) South American Land Data Assimilation System (SALDAS) NASA Land Information System (LIS)

11 3-Hourly files 1/8 th and 1/10 th Degree over Equator Quality controlled, adjusted for terrain height Modeled and observation-based fields Spatial Domain South American Land Data Assimilation System (SALDAS) Forcing Specification

12 Atmospheric Forcing Fields Model-Based Estimates of Standard Climate Station Data – Temperature( 2 m assuming grass) – Specific Humidity( 2 m assuming grass) – U East-West Wind Component(10 m assuming grass) – V North-South Wind Component (10 m assuming grass) – Surface Pressure( 0 m assuming grass) Observation-Based Data –Downward Shortwave Radiation –Precipitation South American Land Data Assimilation System (SALDAS)

13 Terrain Height Adjustment South American Land Data Assimilation System (SALDAS) ETA temperature, pressure, humidity and longwave radiation adjusted for differences in ETA versus LDAS terrain height Temperature and pressure corrected using standard lapse rate Specific humidity and longwave radiation corrected by holding relative humidity constant Corrections of up to 3.7K, 60hPa, 2.24W/m 2, 0.06 Kg/kg

14 South American Land Data Assimilation System (SALDAS) Forcing File Creation Observations not always available, so CPTEC/SARR and CPTEC/ODAS data used as base – Current stage: – SARR, 6 hourly, 40km, (planned ) – Next step: – ODAS, 3 and 6 hourly, 20km, 2002-present (planned 20Km) Spatially interpolated to 1/10th degree Temporally interpolated to 3-hourly data To be quality controlled using ALMA ranges CPTEC ETA South American Regional Reanalysis SARR CPTEC ETA Operational Data Assimilation System ODAS

15 Precipitation

16 Automated Surface Stations Courtesy: INMET/INPE

17 Observation-based forcing Model-based data subject to model error, so observations used when possible Radiation – GOES-CPTEC downward shortwave – GOES-CPTEC PAR (not implemented yet) – GOES-CPTEC skin temperature (not implemented yet) Precipitation – CPC daily gauge data – Combined TRMM-raingauge – Sub daily automated surface stations network – Rain gauges from various SA agencies (~5x > GTS) South American Land Data Assimilation System (SALDAS)

18 GOES data processed at CPTEC/DSA (Divisao de Satelites Ambientais: Environmental Satellites Division) to create 1/25 degree, hourly, instantaneous surface downward shortwave radiation, PAR and skin temperature fields – Interpolated to 1/10th degree GL 1.2 GOES downward shortwave radiation (W/m 2 ) SARR downward shortwave radiation (W/m 2 ) Merged SALDAS downward shortwave radiation (W/m 2 ) South American Land Data Assimilation System (SALDAS)

19 Radiation Forcing Processing Statistics South American Land Data Assimilation System (SALDAS)

20 Observed Radiation Mean difference SARR - GL1.2 (W/m 2 ) 15Z - January 2004 Mean GL1.2 (W/m 2 ) 15Z - January 2004

21 Mean difference SARR - GL1.2 (W/m 2 ) 15Z - June 2004 Mean GL1.2 (W/m 2 ) 15Z - June 2004 Observed Radiation

22 Precipitation Make use of TRMM and raingauges analysis data to form best available product—a temporally disaggregated 3-hourly value Temporal Disaggregation Process  Description: Use of rain gauge to correct satellite bias precipitation  Spatial Resolution: 0.25 degree  Temporal Resolution: total daily precipitation  Domain: South America  Methodology: Use TRMM sub-daily precipitation pulses to dissagregate the total daily amounts. Note: 3B42RT data used to derive temporal disaggregation weights Sum of hourly data values equals original total daily TRMM/gauge Additive/Ratio Bias correction (Vila et al, 2009) JHM

23 South American Land Data Assimilation System (SALDAS) Implement data assimilation techniques (EKF, EnKF) for low data density regions Participants: CPTEC, NASA, COLA, GMU, University of Arizona, Utah University, IPH, Universidade de Vicosa, Universidade de Santa Maria Streamflow data assimilation (use of ANA monitoring capabilities for determining average soil moisture over low data density basins) Soil moisture data assimilation (combine AMSR- E Aqua satellite estimates with ground based observations over areas with low vegetation cover) PCD’s Skin temperature data assimilation (remote sensed skin temperature, DSA derived, combined with surface observations) PCD’s SENAMHI (Peru)?

24 South American Land Data Assimilation System (SALDAS) Eta CPTEC RPSAS Observations Error Stats LSM 4DDA EKF/EnKF Observations Error Stats Eta LSM An operational implementation of a land surface data assimilation into the CPTEC- INPE ETA/PSAS/LTEKF atmospheric data assimilation cycle … Timestep 1 Timestep 2 Timestep … 3 SALDAS

25 Modeling Activities Summary LDASApplicationsLDASODAS Uncoupled Land Data Assimilation Coupled Earth System Model with Atmospheric, Land and Ocean Data Assimilation ADAS Current Future

26 South American Land Data Assimilation System (SALDAS) La Plata Basin: high spatial and temporal resolution runs help to improve the understanding of the hydrological and meteorological processes over the region. Under the LIS framework, SALDAS can be set to run at up to 1Km spatial resolution with hourly output. La Plata basin volumetric soil moisture at 1Km resolution (January 2000) La Plata basin integrated total runoff (Kg/m 2 ) at 1Km resolution (January 2000)

27 South American Land Data Assimilation System (SALDAS) High resolution satellite-based 1Km crop mapping over the LPB Courtesy: Mutlu Ozdogan

28  Four LSM’s (NOAH, CLM, MOSAIC and VIC) were run from using GDAS and NASA’s MERRA as atmospheric forcing.  We are interested on seeing how different models under the same conditions simulate the same period of time.  The focus is on the models spread and mean for simulated water balance variables: surface and subsurface runoff and evaporation and how they compare with input precipitation.  We concentrate on three major basins in South America: Amazonas, Tocantins and La Plata, each with its unique characteristics. South American Land Data Assimilation System (SALDAS) Multi-model ensemble

29 South American Land Data Assimilation System (SALDAS) Multi-model ensemble Three major South American hydrological basins analyzed: Amazonas (blue), Tocantins (green) and La Plata (purple). High resolution simulations (1Km) are being performed at the Parana sub-basin for the La Plata Basin as shown in the figure details.

30 South American Land Data Assimilation System (SALDAS) Multi-model ensemble

31 South American Land Data Assimilation System (SALDAS) Summary Model and observation based data merged to create robust, accurate 1/10th degree 3-hourly forcing data set – CPTEC-SARR/ODAS/ETA data serves as base – CPTEC-DSA/GOES, TRMM/raingauge data used to augment data set Common set of forcing integral to SALDAS LSM intercomparisons Five years archived, with continuing production Validation effort proceeding

32 South American Land Data Assimilation System (SALDAS) Summary High resolution simulations up to 1Km over selected regions (e.g. La Plata basin) with soil moisture and soil temperature DA Improvement of ancillary data over regions outside Brazil based on in situ and remote sensing observations (SENAMHI?)

33 Gracias Luis Gustavo G. De Goncalves ESSIC University of Maryland NASA Goddard Space Flight Center LIMA Water Cycle Capacity Building workshop November 30 – December 4, ftp://ftp1.cptec.inpe.br/jgerd/SALDAS/Assimila.html

34 Integrating NASA Earth Sciences Research results into Decision Support Systems for Agriculture and Water Management in South America Ernesto H. Berbery 3, Eric F. Wood 4, Maria Assuncao F. Silva Dias 5, Osvaldo Luiz L. De Moraes 6 and Jurandir Zullo 7 & Latin America Water Resources and Capacity Building David L. Toll 2, Ted Engman 2 Luis Gustavo G. de Goncalves 1,2 1 Earth System Science Interdisciplinary Center, University of Maryland 2 Hydrological Sciences Branch, NASA Goddard Space Flight Center 3 Department of Atmospheric and Oceanic Science, University of Maryland 4 Princeton University 5 Centro de Previsao do Tempo e Estudos Climaticos/Instituto Nacional de Pesquisas Espaciais 6 Universidade Federal de Santa Maria 7 University of Campinas

35 Paul Simon Water Act for the Poor legislation that requires special attention to be given to semi-arid regions such as Northeast Brazil, western Argentina and Atacama Integrating NASA Earth Sciences Research results into Decision Support Systems for Agriculture and Water Management in South America  NASA Applied Sciences starting FY10  Use of NASA remote sensing and modeling products combined with surface observations at various scales to improve decisions support systems in agriculture, drought and water resources management for South America (SA)  Build upon a partnership between NASA and various U.S. and international agencies and universities to contribute to the dissemination of NASA Earth Sciences research results within that continent  Provide valuable information based on NASA Earth Sciences products to South American national agencies and other end-users OUTLINE

36 Integrating NASA Earth Sciences Research results into Decision Support Systems for Agriculture and Water Management in South America  Development of methods to estimate climatic risks of regional individual crops, intercropping and cattle-farming integration systems The “Agricultural Zoning for Climatic Risks” (AZCR) is a program developed in Brazil since 1995, to provide guidance information for agricultural practices. THE AZCR  Cattle farming systems are adding up quality to the grain production and yield of pastures, mitigating the environmental impacts and reducing the pressure on the Amazonian forest

37 Integrating NASA Earth Sciences Research results into Decision Support Systems for Agriculture and Water Management in South America “Incorporating high quality observations of surface conditions from NASA satellites and other observing systems to quantify continental scale daily ET at 5~20Km spatial resolution and water storages and fluxes at basin scale will enable enhancement of decision support tools for agriculture, human use and disaster mitigation over South America.” EOS multi-sensor (AIRS, CERES and MODIS) based evapotranspiration The Gravity Recovery and Climate Experiment (GRACE) estimates of monthly variations in terrestrial water storage (TWS) The Tropical Rainfall Mission Measurement (TRMM) derived precipitation AMSR-E and TRMM Microwave Imager (TMI) soil moisture NASA’s Land Information System (LIS) and the South American Land Data Assimilation System (SALDAS) Land Surface Modeling Framework CPTEC/INPE atmospheric models INMET surface observations network

38 Integrating NASA Earth Sciences Research results into Decision Support Systems for Agriculture and Water Management in South America PRINCETON UNIVERSITY Remote Sensed ET Estimation

39 NASA Water Resources Program Project Support for Activities in Latin America “Initiate capacity building {and end to end projects} programs to develop tools for using remote sensing data in support of water management, and to show the value of Earth observations generally in water resource management. The program will be initiated in Latin America” {GEO Tasks WA & DI } The NASA Water Resources Program may assist: 1) Assist with workshop support including the training of students and travel for US visits. 2) Support graduate students & post-docs 3) End to End Projects with Decision Support Systems

40 South American Land Data Assimilation System (SALDAS) Collaborative work for the past 4 years (mostly unfunded) with the Brazilian Center for Weather Forecast and Climate Studies (CPTEC - an equivalent no NOAA/NCEP in SA) a division from the Brazilian National Institute for Space Research (INPE - an equivalent to NASA in SA). CPTEC/INPE is a lead and reference institution in Latin America for operational and research modeling. This collaborative work includes promoting interaction between Latin America students and researchers and US institutions as well as Capacity Building Funded Activities Recently Funded La Plata basin integrated total runoff (Kg/m 2 ) at 1Km resolution (January 2000) NASA THP - La Plata Basin combine NASA products with local observations to improve understanding of the hydrological and meteorological processes over the region NDVI trends: surrogate for primary production from NOAA-AVHRR images. Red: decrease Blue: increase. [Courtesy of Jobbagy.] IAI - La Plata Basin Land Use/Land Change due to natural and anthropogenic causes NASA LBA Ecology – LSM Intercomparison Project leverage on 8 flux sites in the Amazon region. SALDAS forcing used for wall-to-wall intercomparison. NASA Applied Sciences Program proposal to improve decisions support systems in agriculture, drought and water resources management for South America Amazon Observational Network of eddy flux tower sites (red dots) on a map of vegetation types in Amazônia and Brazil. Regions with significant impacts on latent heat flux when comparing initial conditions for operational NWP models models with well-balanced SALDAS fields NASA Water Resources Program Project Support for Activities in Latin America

41 2006 Enrique Rosero (Ecuador) Amazon LSM parameter calibration 2007 Rafael Rosolem (Brazil) Amazon Hydrology & Calibration 2009 Mario Quadro (Brazil) La Plata Precipitation Recycling Debora Roberti (Brazil) Wet Lab for regional soil moisture and ET In support to agriculture 2008 Joao Mattos (Brazil) Operational Land-Surface DA 2009 Claudia Ramos (Brazil) AMSR-E Soil Moisture DA Visiting Researchers and Students to NASA/GSFC/HSB

42 NASA Water Resources Program Project Support for Activities in Latin America Capacity Building Meetings and Workshops Public: graduate students and young scientists Two weeks hands-on training in hydrology, meteorology, ecological modeling and data assimilation More than 100 registrations from North and South America, Africa, Europe and few from Asia… To be held at the ITAIPU hydropower plant at the border of Brazil, Argentina and Paraguay Inter-American Institute PI: Hugo Berbery (UMD)


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