NOAA Soil Moisture Operational Product System (SMOPS): Version 2
|
|
|
- Merry Norton
- 5 years ago
- Views:
Transcription
1 CICS-MD Science Meeting (November 12-13, 2014) NOAA Soil Moisture Operational Product System (SMOPS): Version 2 Jicheng Liu 1, 2, Xiwu Zhan 2, Limin Zhao 3, Christopher R. Hain 1, 2, Li Fang 1,2, Jifu Yin 1,2, Zhenpeng Li 1,2, Weizhong Zheng 4,5, and Micheal Ek 4 1 Earth System Science Interdisciplinary Center/Cooperative Institute for Climate and Satellites-Maryland, University of Maryland, College Park, MD 20740, USA. 2 Center for Satellite Applications and Research, NOAA/NESIDS, College Park, MD 20740, USA. 3 Office of Satellite an Product Operations, NOAA/NESIDS, College Park, MD 20740, USA. 4 Environmental Modeling Center, NOAA/NCEP, College Park, MD 20740, USA. 5 I.M. Systems Group, Rockville, MD 20852, USA. 11/13/2014 CICS-MD Science Meeting 1
2 Outline What s SMOPS Why How When Who cares What else 11/13/2014 CICS-MD Science Meeting 2
3 What s SMOPS The Soil Moisture Operational Products System (SMOPS) combines soil moisture retrievals from multi-satellites/sensors to provide a global soil moisture map with more spatial and temporal coverage. The SMOPS first retrieves soil moisture from WindSat onboard the Coriolis satellite and then combines its baseline retrievals with those from other available satellites/sensors, including ASCAT and SMOS, to improve the spatial and temporal coverage of the WindSat observations. 11/13/2014 CICS-MD Science Meeting 3
4 What s SMOPS Developed by NOAA/NESDIS/STAR Operationally running at NOAA/NESDIS/OSPO Operational data access contact: Limin.Zhao@noaa.gov Historical data contact: Xiwu.Zhan@noaa.gov, Jicheng.Liu@noaa.gov 11/13/2014 CICS-MD Science Meeting 4
5 Why SMOPS Global soil moisture is one of the critical land surface initial conditions for numerical weather, climate, and hydrological predictions. Land surface soil moisture remote sensing provides a practical tool. A number of soil moisture products have been produced from different satellite sensors (AMSR-E, SMOS, ASCAT, WindSat, AMSR2, etc). Different data formats, projection and insufficient spatial and temporal coverage of soil moisture products from individual sensors. SMOPS is to: 1. Provide a one-stop for most of the available operational soil moisture products. 2. Provide a blended soil moisture layer that is merged form soil moisture retrievals from all available products. 11/13/2014 CICS-MD Science Meeting 5
6 How SMOPS Works WindSat TB AVHRR NDVI SMOPS 6 Hour Product NDVI Climatology 6 Hour Product Status Sand Map Clay Map SMOPS Daily Product External Input Porosity Map Land Cover Map SMOPS Daily Product Status Archive Product Land Cover Parameters SMOS Soil Moisture Archive Product Status ASCAT(A/B) Soil Moisture Validation Results Merging LU Table In-Situ Data External Output 11/13/2014 CICS-MD Science Meeting 6
7 SMOPS Process Flow Branch 1 is the Pre- Processing function Branch 2 is the Retrieval function Branch 3 is the Merging function Branch 4 is a reprocessing step for the Archive Product 11/13/2014 CICS-MD Science Meeting 7
8 How SMOPS Works Major SMOPS External Output Description Item Description Format Projection Spatial Coverage Spatial Resolution Main Purpose SMOPS 6 Hour Product SMOPS 6 hour Gridded Soil Moisture GRIB2 Lat/Long Global 0.25 degree (720x1440) For NCEP SMOPS Daily Product SMOPS Daily Gridded Soil Moisture GRIB2 Lat/Long Global 0.25 degree (720x1440) For NCEP SMOPS Archive Product SMOPS Daily Gridded Soil Moisture netcdf4 Lat/Long Global 0.25 degree (720x1440) For CLASS 11/13/2014 CICS-MD Science Meeting 8
9 How SMOPS Works SMOPS Product Data Layers Layer # Data Description Data Type Fill Value Valid Range Scale Factor 1 Blended Soil Moisture 2-byte signed integer AMSR-E/2 Soil Moisture 2-byte signed integer SMOS Soil Moisture 2-byte signed integer ASCAT-A Soil Moisture 2-byte signed integer ASCAT-B Soil Moisture 2-byte signed integer WindSat Soil Moisture 2-byte signed integer NOAA SMOS Soil Moisture 2-byte signed integer Observation Time Layers 1-byte signed integer Bit-packed Quality Assessments byte signed integer -999 N/A N/A 1 11/13/2014 CICS-MD Science Meeting 9
10 How SMOPS Works WindSat SMOS ASCAT Blended 11/13/2014 CICS-MD Science Meeting 10
11 SMOPS Project Milestones Gate 3 Review Dec 14, 2009 Preliminary Design Review May 26, 2010 Critical Design Review Oct 28, 2010 System Readiness Review Sep 13, 2011 Delivery to Operations Oct 7, 2011 Version 2 Update Early /13/2014 CICS-MD Science Meeting 11
12 Who Cares about SMOPS Does Global Forecast System (GFS) Care? Experiment: Control Run: GFS run without SM assimilation EnKF run: GFS run with SMOPS Blended SM assimilated using Ensemble Kalman Filter. 11/13/2014 CICS-MD Science Meeting 12
13 Comparison of soil moisture maps (18Z, 1-30 April 2012) SMOPS Blended SM GFS_CTL GFS_EnKF EnKF-CTL 11/13/2014 CICS-MD Science Meeting 13
14 Who Cares about SMOPS GFS Cares: ( Assimilation of Blended Soil Moisture Products from SMOPS in the NCEP GFS, W. Zheng et al., JCSDA Quarterly, Dec. 2013) The satellite soil moisture blended products from SMOPS were assimilated in NCEP GFS and the result shows 1) Improved GFS deeper layer soil moisture estimates comparing with in situ measurements; 2) Improved GFS forecast scores and reduced its bias and root-mean-square errors; and, 3) Showed some positive impact on precipitation on CONUS but not for heavy precipitation. 12h 60h 11/13/2014 CICS-MD Science Meeting 14
15 Who Cares about SMOPS GFS Cares: (W. Zheng et al.: JCSDA Quarterly, Dec. 2013) Validation using USDA-SCAN ground observations. East CONUS (26 sites) West CONUS (25 sites) Whole CONUS RMSE Bias Corr- Coef RMSE Bias Corr- Coef RMSE Bias Corr- Coef CTL EnKF Improved GFS SM product quality with SMOPS SM assimilated. 11/13/2014 CICS-MD Science Meeting 15
16 Who Cares about SMOPS GFS Cares: ( Enhancing Model Skill by Assimilating SMOPS-Blended Soil Moisture Product into Noah Land Surface Model, J. Yin et al., Journal of Hydrometeorology, 2014) The improvements of assimilating SMOPS-Blended data on model soil moisture and soil temperature can be seen not only in low and middle GVF areas, but also in high GVF areas, and the best performance is shown in middle GVF areas. Temporal correlations between in-situ observations and SWnet/LWnet are stronger with assimilating SMOPS- Blended product than without the benefit of data assimilation. Negative (Blue): Increased model performance. 11/13/2014 CICS-MD Science Meeting 16
17 What else we need to do about SMOPS? Soil Moisture Product SMOPS Version 1 SMOPS Version 2 SMOPS Blended NOAA AMSR-E ESA SMOS EUMETSAT ASCAT-A EUMETSAT ASCAT-B NOAA WindSat NOAA AMSR2 NOAA SMOS SMAP Other Products 11/13/2014 CICS-MD Science Meeting 17
18 What else we need to do about SMOPS? To include more soil moisture products To update CDFs for blended product regularly To improve retrieval algorithm To do more DA experiments 11/13/2014 CICS-MD Science Meeting 18
19 Go SMOPS! Thank you! 11/13/2014 CICS-MD Science Meeting 19
Enhancing Weather Forecasts via Assimilating SMAP Soil Moisture and NRT GVF
CICS Science Meeting, ESSIC, UMD, 2016 Enhancing Weather Forecasts via Assimilating SMAP Soil Moisture and NRT GVF Li Fang 1,2, Christopher Hain 1,2, Xiwu Zhan 2, Min Huang 1,2 Jifu Yin 1,2, Weizhong Zheng
Satellite Surface Data Products from NESDIS for Numerical Weather Prediction
Satellite Surface Data Products from NESDIS for Numerical Weather Prediction Xiwu Zhan 1, Jicheng Liu 1,2, Jifu Yin 1,2, Li Fang 1,2, Min Huang 1,2, Chris Hain 3, Weizhong Zheng 4, Jiarui Dong 4, Michael
Multivariate assimilation of satellite-derived remote sensing datasets in the North American Land Data Assimilation System (NLDAS)
Multivariate assimilation of satellite-derived remote sensing datasets in the North American Land Data Assimilation System (NLDAS) Sujay V. Kumar 1,2, Christa D. Peters-Lidard 1, David Mocko 1,2,3, Rolf
Assimilation of satellite derived soil moisture for weather forecasting
Assimilation of satellite derived soil moisture for weather forecasting www.cawcr.gov.au Imtiaz Dharssi and Peter Steinle February 2011 SMOS/SMAP workshop, Monash University Summary In preparation of the
Assimilation of land surface satellite data for Numerical Weather Prediction at ECMWF
4 th workshop on Remote Sensing and Modelling of Surface properties Saint Martin d Hères, 14-16 March 2016 Assimilation of land surface satellite data for Numerical Weather Prediction at ECMWF P. de Rosnay,
Global Satellite Products & Services for Agricultural and Vegetation Health
Global Satellite Products & Services for Agricultural and Vegetation Health Presented at the WMO Expert Meeting on National Early Warning System for Agricultural Weather Management George Mason University
Climate Models and Snow: Projections and Predictions, Decades to Days
Climate Models and Snow: Projections and Predictions, Decades to Days Outline Three Snow Lectures: 1. Why you should care about snow 2. How we measure snow 3. Snow and climate modeling The observational
The NOAA/NESDIS/STAR IASI Near Real-Time Product Processing and Distribution System
The NOAA/NESDIS/STAR Near Real-Time Product Processing and Distribution System W. Wolf 2, T. King 1, Z. Cheng 1, W. Zhou 1, H. Sun 1, P. Keehn 1, L. Zhou 1, C. Barnet 2, and M. Goldberg 2 1 QSS Group Inc,
Incorporation of SMOS Soil Moisture Data on Gridded Flash Flood Guidance for Arkansas Red River Basin
Incorporation of SMOS Soil Moisture Data on Gridded Flash Flood Guidance for Arkansas Red River Basin Department of Civil and Environmental Engineering, The City College of New York, NOAA CREST Dugwon
Acknowledging: Paul R. Houser (CREW & GMU)
Recent Advances in Land Surace Data Assimilation Acknowledging: Paul R. Houser (CREW & GMU) Yan Luo, Xiwu Zhan, Je Walker, Brian Cosgrove, Jared Entin, Jiarui Dong, Alok Sahoo, Gabrielle de Lannoy Water
Land Data Assimilation and the (Coordinated) National Soil Moisture Network
Land Data Assimilation and the (Coordinated) National Soil Moisture Network Wade Crow USDA ARS Hydrology and Remote Sensing Laboratory Alexander Gruber, Wouter Dorigo TU-Wien, Department of Geodesy and
Land data assimilation in the NASA GEOS-5 system: Status and challenges
Blueprints for Next-Generation Data Assimilation Systems Boulder, CO, USA 8-10 March 2016 Land data assimilation in the NASA GEOS-5 system: Status and challenges Rolf Reichle Clara Draper, Ricardo Todling,
Improved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter
Improved analyses and forecasts with AIRS retrievals using the Local Ensemble Transform Kalman Filter Hong Li, Junjie Liu, and Elana Fertig E. Kalnay I. Szunyogh, E. J. Kostelich Weather and Chaos Group
Bringing Consistency into High Wind Measurements with Spaceborne Microwave Radiometers and Scatterometers
International Ocean Vector Wind Science Team Meeting May 2-4, 2017, Scripps Bringing Consistency into High Wind Measurements with Spaceborne Microwave Radiometers and Scatterometers Thomas Meissner, Lucrezia
ECMWF. ECMWF Land Surface Analysis: Current status and developments. P. de Rosnay M. Drusch, K. Scipal, D. Vasiljevic G. Balsamo, J.
Land Surface Analysis: Current status and developments P. de Rosnay M. Drusch, K. Scipal, D. Vasiljevic G. Balsamo, J. Muñoz Sabater 2 nd Workshop on Remote Sensing and Modeling of Surface Properties,
Land Data Assimilation at NCEP NLDAS Project Overview, ECMWF HEPEX 2004
Dag.Lohmann@noaa.gov, Land Data Assimilation at NCEP NLDAS Project Overview, ECMWF HEPEX 2004 Land Data Assimilation at NCEP: Strategic Lessons Learned from the North American Land Data Assimilation System
Climate Hazards Group, Department of Geography, University of California, Santa Barbara, CA, USA. 2
Forecasting seasonal agricultural droughts in East Africa using satellite based observations, land surface models and dynamical weather/climate forecasts Shraddhanand Shukla 1, Amy McNally 3,4, Greg Husak
Land Data Assimilation for operational weather forecasting
Land Data Assimilation for operational weather forecasting Brett Candy Richard Renshaw, JuHyoung Lee & Imtiaz Dharssi * *Centre Australian Weather and Climate Research Contents An overview of the Current
Soil moisture Product and science update
Soil moisture Product and science update Wouter Dorigo and colleagues Department of Geodesy and Geo-information, Vienna University of Technology, Vienna, Austria 2 June 2013 Soil moisture is getting mature
Estimation via Data Assimilation Using. Mississippi State University GeoResources Institute
High Resolution Soil Moisture Estimation via Data Assimilation Using NASA Land Information System Mississippi State University GeoResources Institute LIS Evaluation Team & Collaborators RPC Team Valentine
Toward improving the representation of the water cycle at High Northern Latitudes
Toward improving the representation of the water cycle at High Northern Latitudes W. A. Lahoz a, T. M. Svendby a, A. Griesfeller a, J. Kristiansen b a NILU, Kjeller, Norway b Met Norway, Oslo, Norway wal@nilu.no
Assimilation of ASCAT soil wetness
EWGLAM, October 2010 Assimilation of ASCAT soil wetness Bruce Macpherson, on behalf of Imtiaz Dharssi, Keir Bovis and Clive Jones Contents This presentation covers the following areas ASCAT soil wetness
5.2 PRE-PROCESSING OF ATMOSPHERIC FORCING FOR ENSEMBLE STREAMFLOW PREDICTION
5.2 PRE-PROCESSING OF ATMOSPHERIC FORCING FOR ENSEMBLE STREAMFLOW PREDICTION John Schaake*, Sanja Perica, Mary Mullusky, Julie Demargne, Edwin Welles and Limin Wu Hydrology Laboratory, Office of Hydrologic
Accuracy Issues Associated with Satellite Remote Sensing Soil Moisture Data and Their Assimilation
Proceedings of the 8th International Symposium on Spatial Accuracy Assessment in Natural Resources and Environmental Sciences Shanghai, P. R. China, June 25-27, 2008, pp. 213-220 Accuracy Issues Associated
J11.5 HYDROLOGIC APPLICATIONS OF SHORT AND MEDIUM RANGE ENSEMBLE FORECASTS IN THE NWS ADVANCED HYDROLOGIC PREDICTION SERVICES (AHPS)
J11.5 HYDROLOGIC APPLICATIONS OF SHORT AND MEDIUM RANGE ENSEMBLE FORECASTS IN THE NWS ADVANCED HYDROLOGIC PREDICTION SERVICES (AHPS) Mary Mullusky*, Julie Demargne, Edwin Welles, Limin Wu and John Schaake
Assimilation of passive and active microwave soil moisture retrievals
GEOPHYSICAL RESEARCH LETTERS, VOL. 39,, doi:10.1029/2011gl050655, 2012 Assimilation of passive and active microwave soil moisture retrievals C. S. Draper, 1,2 R. H. Reichle, 1 G. J. M. De Lannoy, 1,2,3
Retrieving Snowfall Rate with Satellite Passive Microwave Measurements
Retrieving Snowfall Rate with Satellite Passive Microwave Measurements Huan Meng 1, Ralph Ferraro 1, Banghua Yan 1, Cezar Kongoli 2, Nai-Yu Wang 2, Jun Dong 2, Limin Zhao 1 1 NOAA/NESDIS, USA 2 Earth System
Evaluation strategies of coarse resolution SM products for monitoring deficit/excess conditions in the Pampas Plains, Argentina
Evaluation strategies of coarse resolution SM products for monitoring deficit/excess conditions in the Pampas Plains, Argentina Francisco Grings, Cintia Bruscantini, Federico Carballo, Ezequiel Smucler,
Satellite Activities and Products for Hydrological Applications: NOAA & CEOS
GEO UNESCO Workshop: AfWCCI Demo Project Session Satellite Activities and Products for Hydrological Applications: NOAA & CEOS Jacob Sutherlun National Oceanic and Atmospheric Administration Vice-Chair
Recent Data Assimilation Activities at Environment Canada
Recent Data Assimilation Activities at Environment Canada Major upgrade to global and regional deterministic prediction systems (now in parallel run) Sea ice data assimilation Mark Buehner Data Assimilation
Combining sensor and forecast information to aid decision making: real-time determination of hydrological peat fire risk in Kalimantan
Combining sensor and forecast information to aid decision making: real-time determination of hydrological peat fire risk in Kalimantan Jaap Schellekens Based on work by: Aljosja Hooijer, Ronald Vernimmen,
SMAP and SMOS Integrated Soil Moisture Validation. T. J. Jackson USDA ARS
SMAP and SMOS Integrated Soil Moisture Validation T. J. Jackson USDA ARS Perspective Linkage of SMOS and SMAP soil moisture calibration and validation will have short and long term benefits for both missions.
2009 Progress Report To The National Aeronautics and Space Administration NASA Energy and Water Cycle Study (NEWS) Program
2009 Progress Report To The National Aeronautics and Space Administration NASA Energy and Water Cycle Study (NEWS) Program Proposal Title: Grant Number: PI: The Challenges of Utilizing Satellite Precipitation
3850 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 10, NO. 9, SEPTEMBER 2017
3850 IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING, VOL. 10, NO. 9, SEPTEMBER 2017 Comparison of X-Band and L-Band Soil Moisture Retrievals for Land Data Assimilation
A two-season impact study of the Navy s WindSat surface wind retrievals in the NCEP global data assimilation system
A two-season impact study of the Navy s WindSat surface wind retrievals in the NCEP global data assimilation system Li Bi James Jung John Le Marshall 16 April 2008 Outline WindSat overview and working
Land surface data assimilation for Numerical Weather Prediction
Sixth WMO Symposium on Data Assimilation, University of Maryland, 7-11 October 2013 Land surface data assimilation for Numerical Weather Prediction P. de Rosnay, J. Muñoz Sabater, C. Albergel, G. Balsamo,
The Application of Satellite Data i n the Global Surface Data Assimil ation System at KMA
The Application of Satellite Data i n the Global Surface Data Assimil ation System at KMA Mee-Ja Kim, Hae-Mi Noh, SeiYoung Park, Sangwon Joo KMA/NIMS kimmee74@korea.kr 14 March, 2016 The 4th Workshop on
H SAF SATELLITE APPLICATION FACILITY ON SUPPORT TO OPERATIONAL HYDROLOGY AND WATER MANAGEMENT EUMETSAT NETWORK OF SATELLITE APPLICATION FACILITIES
H SAF SATELLITE APPLICATION FACILITY ON SUPPORT TO OPERATIONAL HYDROLOGY AND WATER MANAGEMENT EUMETSAT NETWORK OF SATELLITE APPLICATION FACILITIES H-SAF: SATELLITE PRODUCTS FOR OPERATIONAL HYDROLOGY H-SAF
ASSESSMENT OF NORTHERN HEMISPHERE SWE DATASETS IN THE ESA SNOWPEX INITIATIVE
ASSESSMENT OF NORTHERN HEMISPHERE SWE DATASETS IN THE ESA SNOWPEX INITIATIVE Kari Luojus 1), Jouni Pulliainen 1), Matias Takala 1), Juha Lemmetyinen 1), Chris Derksen 2), Lawrence Mudryk 2), Michael Kern
High resolution land reanalysis
Regional Reanalysis Workshop 19-20 May 2016, Reading High resolution land reanalysis P. de Rosnay, G. Balsamo, J. Muñoz Sabater, E. Dutra, C. Albergel, N. Rodríguez-Fernández, H. Hersbach Introduction:
Assimilation of Snow and Ice Data (Incomplete list)
Assimilation of Snow and Ice Data (Incomplete list) Snow/ice Sea ice motion (sat): experimental, climate model Sea ice extent (sat): operational, U.S. Navy PIPs model; Canada; others? Sea ice concentration
Modelling and Data Assimilation Needs for improving the representation of Cold Processes at ECMWF
Modelling and Data Assimilation Needs for improving the representation of Cold Processes at ECMWF presented by Gianpaolo Balsamo with contributions from Patricia de Rosnay, Richard Forbes, Anton Beljaars,
Parameter Estimation in EnKF: Surface Fluxes of Carbon, Heat, Moisture and Momentum
Parameter Estimation in EnKF: Surface Fluxes of Carbon, Heat, Moisture and Momentum *Ji-Sun Kang, *Eugenia Kalnay, *Takemasa Miyoshi, + Junjie Liu, # Inez Fung, *Kayo Ide *University of Maryland, College
ECMWF snow data assimilation: Use of snow cover products and In situ snow depth data for NWP
snow data assimilation: Use of snow cover products and In situ snow depth data for NWP Patricia de Rosnay Thanks to: Ioannis Mallas, Gianpaolo Balsamo, Philippe Lopez, Anne Fouilloux, Mohamed Dahoui, Lars
Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations
Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations C. Albergel (1), P. de Rosnay (1), G. Balsamo (1),J. Muñoz-Sabater(1 ), C. Gruhier (2),
GLOBAL LAND DATA ASSIMILATION SYSTEM (GLDAS) PRODUCTS FROM NASA HYDROLOGY DATA AND INFORMATION SERVICES CENTER (HDISC) INTRODUCTION
GLOBAL LAND DATA ASSIMILATION SYSTEM (GLDAS) PRODUCTS FROM NASA HYDROLOGY DATA AND INFORMATION SERVICES CENTER (HDISC) Hongliang Fang, Patricia L. Hrubiak, Hiroko Kato, Matthew Rodell, William L. Teng,
GOES-R. Getting ready for the next generation earth observing system. Yuguang He AER April 10, 2015
GOES-R Getting ready for the next generation earth observing system Yuguang He AER April 10, 2015 Outline! AER in research and applica0ons! AER on GOES- R! GOES- R: the next genera0on! Ge:ng ready for
ECMWF. ECMWF Land Surface modelling and land surface analysis. P. de Rosnay G. Balsamo S. Boussetta, J. Munoz Sabater D.
Land Surface modelling and land surface analysis P. de Rosnay G. Balsamo S. Boussetta, J. Munoz Sabater D. Vasiljevic M. Drusch, K. Scipal SRNWP 12 June 2009 Slide 1 Surface modelling (G. Balsamo) HTESSEL,
Analyzing and Visualizing Precipitation and Soil Moisture in ArcGIS
Analyzing and Visualizing Precipitation and Soil Moisture in ArcGIS Wenli Yang, Pham Long, Peisheng Zhao, Steve Kempler, and Jennifer Wei * NASA Goddard Earth Science Data and Information Services Center
Real-Time Meteorological Gridded Data: What s New With HEC-RAS
Real-Time Meteorological Gridded Data: What s New With HEC-RAS Acquisition and Application of Gridded Meteorological Data in Support of the USACE s Real-Time Water Management Mission Fauwaz Hanbali, Tom
Mapping Evapotranspiration and Drought at Local to Continental Scales Using Thermal Remote Sensing
Mapping Evapotranspiration and Drought at Local to Continental Scales Using Thermal Remote Sensing M.C. Anderson, W.P. Kustas USDA-ARS, Hydrology and Remote Sensing Laboratory J.M Norman University of
Comparing the SEKF with the DEnKF on a land surface model
Comparing the SEKF with the DEnKF on a land surface model David Fairbairn, Alina Barbu, Emiliano Gelati, Jean-Francois Mahfouf and Jean-Christophe Caret CNRM - Meteo France Partly funded by European Union
Ting Lei, Xuguang Wang University of Oklahoma, Norman, OK, USA. Wang and Lei, MWR, Daryl Kleist (NCEP): dual resolution 4DEnsVar
GSI-based four dimensional ensemble-variational (4DEnsVar) data assimilation: formulation and single resolution experiments with real data for NCEP GFS Ting Lei, Xuguang Wang University of Oklahoma, Norman,
First International Surface Working Group (ISWG)
First International Surface Working Group (ISWG) LIS-based operational land data assimilation at the 557 th weather wing Mahdi Navari 6,1,Christa Peters-Lidard 2, Sujay Kumar 1, Jerry Wegiel 3,1, Eric
3.3 SMOS SOIL MOISTURE DATA ASSIMILATION IN THE NASA LAND INFORMATION SYSTEM: IMPACT ON LSM INITIALIZATION AND NWP FORECASTS
3.3 SMOS SOIL MOISTURE DATA ASSIMILATION IN THE NASA LAND INFORMATION SYSTEM: IMPACT ON LSM INITIALIZATION AND NWP FORECASTS Clay B. Blankenship, USRA, Huntsville, Alabama* Jonathan L. Case, ENSCO, Inc.,
Validation of satellite derived snow cover data records with surface networks and m ulti-dataset inter-comparisons
Validation of satellite derived snow cover data records with surface networks and m ulti-dataset inter-comparisons Chris Derksen Climate Research Division Environment Canada Thanks to our data providers:
CONDUIT Update Cooperative Opportunity for NCEP Data using IDD Technology Rebecca Cosgrove NCEP/NCO/Production Management Branch September 15, 2014
CONDUIT Update Cooperative Opportunity for NCEP Data using IDD Technology Rebecca Cosgrove NCEP/NCO/Production Management Branch September 15, 2014 Agenda Technology Refresh Data available today NOAAPORT/SBN
Improving Streamflow Prediction in Snow- fed River Basins via Satellite Snow Assimilation
Improving Streamflow Prediction in Snow- fed River Basins via Satellite Snow Assimilation Yuqiong Liu NASA GSFC & University of Maryland, College Park Co- authors: Christa Peters- Lidard, Sujay Kumar,
Extended Kalman Filter soil-moisture analysis in the IFS
from Newsletter Number 127 Spring 211 METEOROLOGY Extended Kalman Filter soil-moisture analysis in the IFS doi:1.21957/ik7co53s This article appeared in the Meteorology section of ECMWF Newsletter No.
Joint International Surface Working Group and Satellite Applications Facility on Land Surface Analysis Workshop, IPMA, Lisboa, June 2018
Joint International Surface Working Group and Satellite Applications Facility on Land Surface Analysis Workshop, IPMA, Lisboa, 26-28 June 2018 Introduction Soil moisture Evapotranspiration Future plan
The Canadian Land Data Assimilation System (CaLDAS)
The Canadian Land Data Assimilation System (CaLDAS) Marco L. Carrera, Stéphane Bélair, Bernard Bilodeau and Sheena Solomon Meteorological Research Division, Environment Canada Dorval, QC, Canada 2 nd Workshop
Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS)
Development of the Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS) Marco L. Carrera, Vincent Fortin and Stéphane Bélair Meteorological Research Division Environment
Updates to Land DA at the Met Office
Updates to Land DA at the Met Office Brett Candy & Keir Bovis Satellite Soil Moisture Workshop, Amsterdam, 10th July 2014 Land DA - The Story So Far - Kalman Filter 18 months in operations - Analyses of
The National Weather Service of the Future: Building a Weather-Ready Nation
The of the Future: Building a Weather-Ready Nation Dr. Louis W. Uccellini Director, NOAA Assistant Administrator for Weather Services NCEP Production Suite Review December 2, 2014 Outline Building a Weather-Ready
Bayesian Hierarchical Modelling: Incorporating spatial information in water resources assessment and accounting
Bayesian Hierarchical Modelling: Incorporating spatial information in water resources assessment and accounting Grace Chiu & Eric Lehmann (CSIRO Mathematics, Informatics and Statistics) A water information
Aquarius/SAC-D Soil Moisture Product using V3.0 Observations
Aquarius/SAC-D Soil Moisture Product using V3. Observations R. Bindlish, T. Jackson, M. Cosh November 214 Overview Soil moisture algorithm Soil moisture product Validation Linkage between Soil Moisture
Scatterometer winds in rapidly developing storms (SCARASTO) First experiments on data assimilation of scatterometer winds
Scatterometer winds in rapidly developing storms (SCARASTO) First experiments on data assimilation of scatterometer winds Teresa Valkonen, EUMETSAT fellow Norwegian Meteorological Institute MET Norway,Oslo
Some NOAA Products that Address PSTG Satellite Observing Requirements. Jeff Key NOAA/NESDIS Madison, Wisconsin USA
Some NOAA Products that Address PSTG Satellite Observing Requirements Jeff Key NOAA/NESDIS Madison, Wisconsin USA WMO Polar Space Task Group, 4 th meeting, Greenbelt, 30 September 2014 Relevant Missions
NESDIS Global Automated Satellite Snow Product: Current Status and Recent Results Peter Romanov
NESDIS Global Automated Satellite Snow Product: Current Status and Recent Results Peter Romanov NOAA-CREST, City University of New York (CUNY) Center for Satellite Applications and Research (STAR), NOAA/NESDIS
Soil frost from microwave data. Kimmo Rautiainen, Jouni Pulliainen, Juha Lemmetyinen, Jaakko Ikonen, Mika Aurela
Soil frost from microwave data Kimmo Rautiainen, Jouni Pulliainen, Juha Lemmetyinen, Jaakko Ikonen, Mika Aurela Why landscape freeze/thaw? Latitudinal variation in mean correlations (r) between annual
Transitioning NCAR s Aviation Algorithms into NCEP s Operations
Transitioning NCAR s Aviation Algorithms into NCEP s Operations Hui-Ya Chuang 1, Yali Mao 1,2, and Binbin Zhou 1,2 1. EMC/NCEP/NOAA, College Park MD 2. I.M. System Group, Inc. AMS 18th Conference on Aviation,
Regional Drought and Crop Yield Information System to enhance drought monitoring and forecasting in Lower Mekong region
Regional Drought and Crop Yield Information System to enhance drought monitoring and forecasting in Lower Mekong region Asian Disaster Preparedness Center/SERVIR-Mekong 2 Anticipated Results Improved capacity
Central Asia Regional Flash Flood Guidance System 4-6 October Hydrologic Research Center A Nonprofit, Public-Benefit Corporation
http://www.hrcwater.org Central Asia Regional Flash Flood Guidance System 4-6 October 2016 Hydrologic Research Center A Nonprofit, Public-Benefit Corporation FFGS Snow Components Snow Accumulation and
Validating a Satellite Microwave Remote Sensing Based Global Record of Daily Landscape Freeze- Thaw Dynamics
University of Montana ScholarWorks at University of Montana Numerical Terradynamic Simulation Group Publications Numerical Terradynamic Simulation Group 2012 Validating a Satellite Microwave Remote Sensing
An Overview of Atmospheric Analyses and Reanalyses for Climate
An Overview of Atmospheric Analyses and Reanalyses for Climate Kevin E. Trenberth NCAR Boulder CO Analysis Data Assimilation merges observations & model predictions to provide a superior state estimate.
LACE Data assimilation activities EWGLAM meeting 2016
LACE Data assimilation activities EWGLAM meeting 2016 Mate Mile and many LACE DA colleagues LACE data assimilation systems dx OI+ 1.2 2.5 OI+OI+ 4 OI+ Blend OI+ VAR OI VAR CAN PACK OI+ OI+ OI Blending
Assessment of AHI Level-1 Data for HWRF Assimilation
Assessment of AHI Level-1 Data for HWRF Assimilation Xiaolei Zou 1 and Fuzhong Weng 2 1 Earth System Science Interdisciplinary Center, University of Maryland, College Park, Maryland 2 Satellite Meteorology
Use of satellite soil moisture information for NowcastingShort Range NWP forecasts
Use of satellite soil moisture information for NowcastingShort Range NWP forecasts Francesca Marcucci1, Valerio Cardinali/Paride Ferrante1,2, Lucio Torrisi1 1 COMET, Italian AirForce Operational Center
NCEP Aerosol Data Assimilation Update: Improving NCEP global aerosol forecasts using JPSS-NPP VIIRS aerosol products
NCEP Aerosol Data Assimilation Update: Improving NCEP global aerosol forecasts using JPSS-NPP VIIRS aerosol products Sarah Lu, Shih-Wei Wei (SUNYA) Shobha Kondragunta, Qiang Zhao (NESDIS/STAR) Jeff McQueen,
Multi-Sensor Satellite Retrievals of Sea Surface Temperature
Multi-Sensor Satellite Retrievals of Sea Surface Temperature NOAA Earth System Research Laboratory With contributions from many Outline Motivation/Background Input Products Issues in Merging SST Products
Research applications with Click to edit Master title style NOAA s Climate Data Record Program
Research applications with Click to edit Master title style NOAA s Climate Data Record Program Click to edit Master subtitle style Jessica Matthews November 29, 2016 1 NOAA s CDR Program Mission: To develop
The NASA Soil Moisture Active Passive Mission (SMAP) Status and Early Results
20154 California Institute of Technology. Government sponsorship acknowledged. National Aeronautics and Space Administration The NASA Soil Moisture Active Passive Mission (SMAP) Status and Early Results
An Ensemble Land Surface Modeling and Assimilation Testbed for HEPEX
GSFC s Land Data Assimilation Systems: An Ensemble Land Surface Modeling and Assimilation Testbed for HEPEX Christa Peters-Lidard Lidard,, Paul Houser, Matthew Rodell, Brian Cosgrove NASA Goddard Space
The Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS)
The Canadian Precipitation Analysis (CaPA) and the Canadian Land Data Assimilation System (CaLDAS) Vincent Fortin (and many collaborators) Division de la recherche en météorologie Environnement en Changement
Implementation of the NCEP operational GLDAS for the CFS land initialization
Implementation of the NCEP operational GLDAS for the CFS land initialization Jesse Meng, Mickael Ek, Rongqian Yang NOAA/NCEP/EMC July 2012 1 Improving the Global Land Surface Climatology via improved Global
Summary of activities with SURFEX data assimilation at Météo-France. Jean-François MAHFOUF CNRM/GMAP/OBS
Summary of activities with SURFEX data assimilation at Météo-France Jean-François MAHFOUF CNRM/GMAP/OBS Outline Status at Météo-France in 2008 Developments undertaken during 2009-2011 : Extended Kalman
Improved ensemble representation of soil moisture in SWAT for data assimilation applications
Improved ensemble representation of soil moisture in SWAT for data assimilation applications Amol Patil and RAAJ Ramsankaran Hydro-Remote Sensing Applications (H-RSA) Group, Department of Civil Engineering
Improvements to the NCEP Global and Regional Data Assimilation Systems
Improvements to the NCEP Global and Regional Data Assimilation Systems Stephen J. Lord Director NCEP Environmental Modeling Center EMC Staff NCEP: where America s climate, weather, and ocean services begin
ERA5 and the use of ERA data
ERA5 and the use of ERA data Hans Hersbach, and many colleagues European Centre for Medium-Range Weather Forecasts Overview Overview of Reanalysis products at ECMWF ERA5, the follow up of ERA-Interim,
Using VIIRS Land Surface Temperature to Evaluate NCEP North American Mesoscale Model (NAM) Forecast
Using VIIRS Land Surface Temperature to Evaluate NCEP North American Mesoscale Model (NAM) Forecast Zhuo Wang (University of Maryland) Yunyue Yu (NOAA/NESDIS/STAR) Peng Yu (University of Maryland) Yuling
Spaceborne and Ground-based Global and Regional Precipitation Estimation: Multi-Sensor Synergy
Hydrometeorology and Remote Sensing Lab (hydro.ou.edu) at The University of Oklahoma Spaceborne and Ground-based Global and Regional Precipitation Estimation: Multi-Sensor Synergy Presented by: 温逸馨 (Berry)
Development toward global aerosol DA system at NCEP
Development toward global aerosol DA system at NCEP Jun Wang, Jeff Mcqueen (NOAA/NWS/NCEP/EMC) Sarah Lu (SUNY at Albany) Shobha Kondragunta, Qiang Zhao (NESDIS) Arlindo da Silva (GSFC) EMC GSI-EnKF group
Climate Prediction Center National Centers for Environmental Prediction
NOAA s Climate Prediction Center Climate Monitoring Tool Wassila M. Thiaw and CPC International Team Climate Prediction Center National Centers for Environmental Prediction CPC International Team Vadlamani
OSI SAF Sea Ice products
OSI SAF Sea Ice products Lars-Anders Brevik, Gorm Dybkjær, Steinar Eastwood, Øystein Godøy, Mari Anne Killie, Thomas Lavergne, Rasmus Tonboe, Signe Aaboe Norwegian Meteorological Institute Danish Meteorological
Arctic Regional Ocean Observing System Arctic ROOS Report from 2012
Arctic Regional Ocean Observing System Arctic ROOS Report from 2012 By Stein Sandven Nansen Environmental and Remote Sensing Center (www.arctic-roos.org) Focus in 2012 1. Arctic Marine Forecasting Center
European High-Resolution Soil Moisture Analysis (EHRSOMA)
European High-Resolution Soil Moisture Analysis (EHRSOMA) Jasmin Vural EUMETSAT Fellow Day, 05.03.2018 European High-Resolution Soil Moisture Analysis (EHRSOMA) Jasmin Vural EUMETSAT Fellow Day, 05.03.2018
REQUIREMENTS FOR WEATHER RADAR DATA. Review of the current and likely future hydrological requirements for Weather Radar data
WORLD METEOROLOGICAL ORGANIZATION COMMISSION FOR BASIC SYSTEMS OPEN PROGRAMME AREA GROUP ON INTEGRATED OBSERVING SYSTEMS WORKSHOP ON RADAR DATA EXCHANGE EXETER, UK, 24-26 APRIL 2013 CBS/OPAG-IOS/WxR_EXCHANGE/2.3
Observing System Simulation Experiments (OSSEs) with Radio Occultation observations
Observing System Simulation Experiments (OSSEs) with Radio Occultation observations Lidia Cucurull Deputy Director, NOAA Quantitative Observing System Assessment Program (QOSAP) NOAA OAR Principal Investigator
Impact of assimilating the VIIRS-based CrIS cloudcleared radiances on hurricane forecasts
Impact of assimilating the VIIRS-based CrIS cloudcleared radiances on hurricane forecasts Jun Li @, Pei Wang @, Jinlong Li @, Zhenglong Li @, Jung-Rim Lee &, Agnes Lim @, Timothy J. Schmit #, and Mitch
CERA-SAT: A coupled reanalysis at higher resolution (WP1)
CERA-SAT: A coupled reanalysis at higher resolution (WP1) ERA-CLIM2 General assembly Dinand Schepers 16 Jan 2017 Contributors: Eric de Boisseson, Per Dahlgren, Patrick Lalolyaux, Iain Miller and many others