Articles | Volume 21, issue 6
https://doi.org/10.5194/hess-21-2637-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
https://doi.org/10.5194/hess-21-2637-2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.
the Creative Commons Attribution 3.0 License.
Research article
| 
02 Jun 2017
Research article |
| 02 Jun 2017
Role of forcing uncertainty and background model error characterization in snow data assimilation
Sujay V. Kumar
CORRESPONDING AUTHOR
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Jiarui Dong
I.M. Systems Group Inc., Environmental Modeling Center, NOAA NCEP, College Park, MD, USA
Christa D. Peters-Lidard
Hydrosphere, Biosphere and Geophysics, Earth Science Division, NASA Goddard Space Flight Center, Greenbelt, MD, USA
David Mocko
Science Applications International Corporation, McLean, VA, USA
Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA
Breogán Gómez
Research to Operations, Met Office, Exeter, UK
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Cited
17 citations as recorded by crossref.
- Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
- Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation S. Pathiraja et al. 10.1002/2018WR022627
- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto 10.3389/frwa.2022.948832
- Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment C. Garnaud et al. 10.1175/JHM-D-17-0241.1
- Assimilating snow observations to snow interception process simulations Z. Lv & J. Pomeroy 10.1002/hyp.13720
- Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System T. Lahmers et al. 10.1029/2021WR029867
- Assimilation of Passive L-band Microwave Brightness Temperatures in the Canadian Land Data Assimilation System: Impacts on Short-Range Warm Season Numerical Weather Prediction M. Carrera et al. 10.1175/JHM-D-18-0133.1
- Sequential data assimilation for real-time probabilistic flood inundation mapping K. Jafarzadegan et al. 10.5194/hess-25-4995-2021
- Estimating Terrestrial Snow Mass via Multi‐Sensor Assimilation of Synthetic AMSR‐E Brightness Temperature Spectral Differences and Synthetic GRACE Terrestrial Water Storage Retrievals J. Wang et al. 10.1029/2021WR029880
- Reanalysis Surface Mass Balance of the Greenland Ice Sheet Along K‐Transect (2000–2014) M. Navari et al. 10.1029/2021GL094602
- Impact of Soil Moisture Data Assimilation on Analysis and Medium-Range Forecasts in an Operational Global Data Assimilation and Prediction System S. Jun et al. 10.3390/atmos12091089
- Uncertainties in Evapotranspiration Estimates over West Africa H. Jung et al. 10.3390/rs11080892
- Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions S. Tanniru & R. Ramsankaran 10.3390/rs15041052
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Exploration of Synthetic Terrestrial Snow Mass Estimation via Assimilation of AMSR‐E Brightness Temperature Spectral Differences Using the Catchment Land Surface Model and Support Vector Machine Regression J. Wang et al. 10.1029/2020WR027490
- Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
17 citations as recorded by crossref.
- Reanalysis in Earth System Science: Toward Terrestrial Ecosystem Reanalysis R. Baatz et al. 10.1029/2020RG000715
- A Digital Twin of the terrestrial water cycle: a glimpse into the future through high-resolution Earth observations L. Brocca et al. 10.3389/fsci.2023.1190191
- Data‐Driven Model Uncertainty Estimation in Hydrologic Data Assimilation S. Pathiraja et al. 10.1002/2018WR022627
- Recent advances and opportunities in data assimilation for physics-based hydrological modeling M. Camporese & M. Girotto 10.3389/frwa.2022.948832
- Quantifying Snow Mass Mission Concept Trade-Offs Using an Observing System Simulation Experiment C. Garnaud et al. 10.1175/JHM-D-17-0241.1
- Assimilating snow observations to snow interception process simulations Z. Lv & J. Pomeroy 10.1002/hyp.13720
- Assimilation of NASA's Airborne Snow Observatory Snow Measurements for Improved Hydrological Modeling: A Case Study Enabled by the Coupled LIS/WRF‐Hydro System T. Lahmers et al. 10.1029/2021WR029867
- Assimilation of Passive L-band Microwave Brightness Temperatures in the Canadian Land Data Assimilation System: Impacts on Short-Range Warm Season Numerical Weather Prediction M. Carrera et al. 10.1175/JHM-D-18-0133.1
- Sequential data assimilation for real-time probabilistic flood inundation mapping K. Jafarzadegan et al. 10.5194/hess-25-4995-2021
- Estimating Terrestrial Snow Mass via Multi‐Sensor Assimilation of Synthetic AMSR‐E Brightness Temperature Spectral Differences and Synthetic GRACE Terrestrial Water Storage Retrievals J. Wang et al. 10.1029/2021WR029880
- Reanalysis Surface Mass Balance of the Greenland Ice Sheet Along K‐Transect (2000–2014) M. Navari et al. 10.1029/2021GL094602
- Impact of Soil Moisture Data Assimilation on Analysis and Medium-Range Forecasts in an Operational Global Data Assimilation and Prediction System S. Jun et al. 10.3390/atmos12091089
- Uncertainties in Evapotranspiration Estimates over West Africa H. Jung et al. 10.3390/rs11080892
- Passive Microwave Remote Sensing of Snow Depth: Techniques, Challenges and Future Directions S. Tanniru & R. Ramsankaran 10.3390/rs15041052
- An Agenda for Land Data Assimilation Priorities: Realizing the Promise of Terrestrial Water, Energy, and Vegetation Observations From Space S. Kumar et al. 10.1029/2022MS003259
- Exploration of Synthetic Terrestrial Snow Mass Estimation via Assimilation of AMSR‐E Brightness Temperature Spectral Differences Using the Catchment Land Surface Model and Support Vector Machine Regression J. Wang et al. 10.1029/2020WR027490
- Snow Water Equivalent Monitoring—A Review of Large-Scale Remote Sensing Applications S. Schilling et al. 10.3390/rs16061085
Latest update: 23 Nov 2024
Short summary
Data assimilation deals with the blending of model forecasts and observations based on their relative errors. This paper addresses the importance of accurately representing the errors in the model forecasts for skillful data assimilation performance.
Data assimilation deals with the blending of model forecasts and observations based on their...
