How Does Precipitation Data Influence the Land Surface Data Assimilation Drought Monitoring?

2021 ◽  
Author(s):  
Keyhan Gavahi ◽  
Peyman Abbaszadeh ◽  
Hamid Moradkhani
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Drought Monitoring ◽  
Precipitation Data ◽  
Surface Data

scholarly journals Hydrologic Remote Sensing and Land Surface Data Assimilation

Sensors ◽  
2008 ◽  
Vol 8 (5) ◽  
pp. 2986-3004 ◽  
Author(s):  
Hamid Moradkhani
Keyword(s):  
Remote Sensing ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Data

scholarly journals Combined Assimilation of Satellite Precipitation and Soil Moisture: A Case Study Using TRMM and SMOS Data

2017 ◽  
Vol 145 (12) ◽  
pp. 4997-5014 ◽  
Author(s):  
Liao-Fan Lin ◽  
Ardeshir M. Ebtehaj ◽  
Alejandro N. Flores ◽  
Satish Bastola ◽  
Rafael L. Bras
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Specific Humidity ◽  
Surface Model ◽  
Precipitation Data ◽  
Near Surface ◽  
Satellite Precipitation ◽  
Soil Moisture Data ◽  
Mean Square Errors

This paper presents a framework that enables simultaneous assimilation of satellite precipitation and soil moisture observations into the coupled Weather Research and Forecasting (WRF) and Noah land surface model through variational approaches. The authors tested the framework by assimilating precipitation data from the Tropical Rainfall Measuring Mission (TRMM) and soil moisture data from the Soil Moisture Ocean Salinity (SMOS) satellite. The results show that assimilation of both TRMM and SMOS data can effectively improve the forecast skills of precipitation, top 10-cm soil moisture, and 2-m temperature and specific humidity. Within a 2-day time window, impacts of precipitation data assimilation on the forecasts remain relatively constant for forecast lead times greater than 6 h, while the influence of soil moisture data assimilation increases with lead time. The study also demonstrates that the forecast skill of precipitation, soil moisture, and near-surface temperature and humidity are further improved when both the TRMM and SMOS data are assimilated. In particular, the combined data assimilation reduces the prediction biases and root-mean-square errors, respectively, by 57% and 6% (for precipitation); 73% and 27% (for soil moisture); 17% and 9% (for 2-m temperature); and 33% and 11% (for 2-m specific humidity).

Computational Issues for Large-Scale Land Surface Data Assimilation Problems

2006 ◽  
Vol 7 (3) ◽  
pp. 494-510 ◽  
Author(s):  
Dennis McLaughlin ◽  
Yuhua Zhou ◽  
Dara Entekhabi ◽  
Virat Chatdarong
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Spatial Structure ◽  
Computational Efficiency ◽  
Land Surface ◽  
Kalman Filters ◽  
Large Scale ◽  
Singular Value ◽  
High Dimensionality ◽  
Surface Data

Abstract Land surface data assimilation problems are often limited by the high dimensionality of states created by spatial discretization over large high-resolution computational grids. Yet field observations and simulation both confirm that soil moisture can have pronounced spatial structure, especially after extensive rainfall. This suggests that the high dimensionality of the problem could be reduced during wet periods if spatial patterns could be more efficiently represented. After prolonged drydown, when spatial structure is determined primarily by small-scale soil and vegetation variability rather than rainfall, the original high-dimensional problem can be effectively replaced by many independent low-dimensional problems that can be solved in parallel with relatively little effort. In reality, conditions are continually varying between these two extremes. This is confirmed by a singular value decomposition of the replicate matrix (covariance square root) produced in an ensemble forecasting simulation experiment. The singular value spectrum drops off quickly after rainfall events, when a few leading modes dominate the spatial structure of soil moisture. The spectrum is much flatter after a prolonged drydown period, when spatial structure is less significant. Deterministic reduced-rank Kalman filters can achieve significant computational efficiency by focusing on the leading modes of a system with large-scale spatial structure. But these methods are not well suited for land surface problems with complex uncertain inputs and rapidly changing spectra. Local ensemble Kalman filters are suitable for such problems during dry periods but give less accurate results after rainfall. The most promising option for achieving computational efficiency and accuracy is to develop generalized localization methods that dynamically aggregate states, reflecting structural changes in the ensemble.

scholarly journals The Land surface Data Toolkit (LDT v7.2) – a data fusion environment for land data assimilation systems

2018 ◽  
Vol 11 (9) ◽  
pp. 3605-3621 ◽  
Author(s):  
Kristi R. Arsenault ◽  
Sujay V. Kumar ◽  
James V. Geiger ◽  
Shugong Wang ◽  
Eric Kemp ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Land Surface ◽  
Data Science ◽  
Initial Conditions ◽  
Integrated Framework ◽  
Framework Design ◽  
Data Formats ◽  
Surface Data ◽  
Land Data Assimilation ◽  
Land Information System

Abstract. The effective applications of land surface models (LSMs) and hydrologic models pose a varied set of data input and processing needs, ranging from ensuring consistency checks to more derived data processing and analytics. This article describes the development of the Land surface Data Toolkit (LDT), which is an integrated framework designed specifically for processing input data to execute LSMs and hydrological models. LDT not only serves as a preprocessor to the NASA Land Information System (LIS), which is an integrated framework designed for multi-model LSM simulations and data assimilation (DA) integrations, but also as a land-surface-based observation and DA input processor. It offers a variety of user options and inputs to processing datasets for use within LIS and stand-alone models. The LDT design facilitates the use of common data formats and conventions. LDT is also capable of processing LSM initial conditions and meteorological boundary conditions and ensuring data quality for inputs to LSMs and DA routines. The machine learning layer in LDT facilitates the use of modern data science algorithms for developing data-driven predictive models. Through the use of an object-oriented framework design, LDT provides extensible features for the continued development of support for different types of observational datasets and data analytics algorithms to aid land surface modeling and data assimilation.

scholarly journals Impacts of High-Resolution Land Surface Initialization on Regional Sensible Weather Forecasts from the WRF Model

2008 ◽  
Vol 9 (6) ◽  
pp. 1249-1266 ◽  
Author(s):  
Jonathan L. Case ◽  
William L. Crosson ◽  
Sujay V. Kumar ◽  
William M. Lapenta ◽  
Christa D. Peters-Lidard
Keyword(s):  
High Resolution ◽  
Data Assimilation ◽  
Land Surface ◽  
Initial Conditions ◽  
Surface Model ◽  
Surface Data ◽  
Initial Soil ◽  
Eta Model ◽  
Data Assimilation System ◽  
Assimilation System

Abstract This manuscript presents an assessment of daily regional simulations of the Weather Research and Forecasting (WRF) numerical weather prediction (NWP) model initialized with high-resolution land surface data from the NASA Land Information System (LIS) software versus a control WRF configuration that uses land surface data from the National Centers for Environmental Prediction (NCEP) Eta Model. The goal of this study is to investigate the potential benefits of using the LIS software to improve land surface initialization for regional NWP. Fifty-eight individual nested simulations were integrated for 24 h for both the control and experimental (LISWRF) configurations during May 2004 over Florida and the surrounding areas: 29 initialized at 0000 UTC and 29 initialized at 1200 UTC. The land surface initial conditions for the LISWRF runs came from an offline integration of the Noah land surface model (LSM) within LIS for two years prior to the beginning of the month-long study on an identical grid domain to the subsequent WRF simulations. Atmospheric variables used to force the offline Noah LSM integration were provided by the North American Land Data Assimilation System and Global Data Assimilation System gridded analyses. The LISWRF soil states were generally cooler and drier than the NCEP Eta Model soil states during May 2004. Comparisons between the control and LISWRF runs for one event suggested that the LIS land surface initial conditions led to an improvement in the timing and evolution of a sea-breeze circulation over portions of northwestern Florida. Surface verification statistics for the entire month indicated that the LISWRF runs produced a more enhanced and accurate diurnal range in 2-m temperatures compared to the control as a result of the overall drier initial soil states, which resulted from a reduction in the nocturnal warm bias in conjunction with a reduction in the daytime cold bias. Daytime LISWRF 2-m dewpoints were correspondingly drier than the control dewpoints, again a manifestation of the drier initial soil states in LISWRF. The positive results of the LISWRF experiments help to illustrate the importance of initializing regional NWP models with high-quality land surface data generated at the same grid resolution.

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