scholarly journals Improving Near-Surface Short-Range Weather Forecasts Using Strongly Coupled Land–Atmosphere Data Assimilation with GSI-EnKF

2020 ◽  
Vol 148 (7) ◽  
pp. 2863-2888
Author(s):  
Liao-Fan Lin ◽  
Zhaoxia Pu
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Strongly Coupled ◽  
Soil Moisture Data ◽  
Control State

Abstract Strongly coupled land–atmosphere data assimilation has not yet been implemented into operational numerical weather prediction (NWP) systems. Up to now, upper-air measurements have been assimilated mainly in atmospheric analyses, while land and near-surface data have been assimilated mainly into land surface models. Thus, this study aims to explore the benefits of assimilating atmospheric and land surface observations within the framework of strongly coupled data assimilation. Specifically, we added soil moisture as a control state within the ensemble Kalman filter (EnKF)-based Gridpoint Statistical Interpolation (GSI) and conducted a series of numerical experiments through the assimilation of 2-m temperature/humidity and in situ surface soil moisture data along with conventional atmospheric measurements such as radiosondes into the Weather Research and Forecasting (WRF) Model with the Noah land surface model. The verification against in situ measurements and analyses show that compared to the assimilation of conventional data, adding soil moisture as a control state and assimilating 2-m humidity can bring additional benefits to analyses and forecasts. The impact of assimilating 2-m temperature (surface soil moisture) data is positive mainly on the temperature (soil moisture) analyses but on average marginal for other variables. On average, below 750 hPa, verification against the NCEP analysis indicates that the respective RMSE reduction in the forecasts of temperature and humidity is 5% and 2% for assimilating conventional data; 10% and 5% for including soil moisture as a control state; and 16% and 11% for simultaneously adding soil moisture as a control state and assimilating 2-m humidity data.

scholarly journals From Near-Surface to Root-Zone Soil Moisture Using Different Assimilation Techniques

2007 ◽  
Vol 8 (2) ◽  
pp. 194-206 ◽  
Author(s):  
Joaquín Muñoz Sabater ◽  
Lionel Jarlan ◽  
Jean-Christophe Calvet ◽  
François Bouyssel ◽  
Patricia De Rosnay
Keyword(s):  
Soil Moisture ◽  
Data Assimilation ◽  
Land Surface ◽  
Surface Soil ◽  
Diffusion Processes ◽  
Root Zone ◽  
Computational Cost ◽  
Climatic Conditions ◽  
Surface Soil Moisture ◽  
Near Surface

Abstract Root-zone soil moisture constitutes an important variable for hydrological and weather forecast models. Microwave radiometers like the L-band instrument on board the European Space Agency’s (ESA) future Soil Moisture and Ocean Salinity (SMOS) mission are being designed to provide estimates of near-surface soil moisture (0–5 cm). This quantity is physically related to root-zone soil moisture through diffusion processes, and both surface and root-zone soil layers are commonly simulated by land surface models (LSMs). Observed time series of surface soil moisture may be used to analyze the root-zone soil moisture using data assimilation systems. In this paper, various assimilation techniques derived from Kalman filters (KFs) and variational methods (VAR) are implemented and tested. The objective is to correct the modeled root-zone soil moisture deficiencies of the newest version of the Interaction between Soil, Biosphere, and Atmosphere scheme (ISBA) LSM, using the observations of the surface soil moisture of the Surface Monitoring of the Soil Reservoir Experiment (SMOSREX) over a 4-yr period (2001–04). This time period includes contrasting climatic conditions. Among the different algorithms, the ensemble Kalman filter (EnKF) and a simplified one-dimensional variational data assimilation (1DVAR) show the best performances. The lower computational cost of the 1DVAR is an advantage for operational root-zone soil moisture analysis based on remotely sensed surface soil moisture observations at a global scale.

scholarly journals Developing a drought-monitoring index for the contiguous US using SMAP

2018 ◽  
Vol 22 (12) ◽  
pp. 6611-6626 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Drought Index ◽  
Surface Soil ◽  
Root Zone ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Short Term ◽  
Near Surface ◽  
Index Maps

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe (between 85.044∘ N/S) using an L-band (1.4 GHz) microwave radiometer in 2–3 days depending on location. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. However, the short SMAP record length poses a statistical challenge for meaningful assessment of its indices. This study presents initial insights about using SMAP for monitoring drought and pluvial regions with a first application over the contiguous United States (CONUS). SMAP soil moisture data from April 2015 to December 2017 at both near-surface (5 cm) SPL3SMP, or Level 3, at ∼36 km resolution, and root-zone SPL4SMAU, or Level 4, at ∼9 km resolution, were fitted to beta distributions and were used to construct probability distributions for warm (May–October) and cold (November–April) seasons. To assess the data adequacy and have confidence in using short-term SMAP for a drought index estimate, we analyzed individual grids by defining two filters and a combination of them, which could separate the 5815 grids covering CONUS into passed and failed grids. The two filters were (1) the Kolmogorov–Smirnov (KS) test for beta-fitted long-term and the short-term variable infiltration capacity (VIC) land surface model (LSM) with 95 % confidence and (2) good correlation (≥0.4) between beta-fitted VIC and beta-fitted SPL3SMP. To evaluate which filter is the best, we defined a mean distance (MD) metric, assuming a VIC index at 36 km resolution as the ground truth. For both warm and cold seasons, the union of the filters – which also gives the best coverage of the grids throughout CONUS – was chosen to be the most reliable filter. We visually compared our SMAP-based drought index maps with metrics such as the U.S. Drought Monitor (from D0–D4), 1-month Standard Precipitation Index (SPI) and near-surface VIC from Princeton University. The root-zone drought index maps were shown to be similar to those produced by the root-zone VIC, 3-month SPI, and the Gravity Recovery and Climate Experiment (GRACE). This study is a step forward towards building a national and international soil moisture monitoring system without which quantitative measures of drought and pluvial conditions will remain difficult to judge.

Vegetation parameter retrieval from SAR data using near-surface soil moisture estimates derived from a hydrological model

2005 ◽  
Author(s):  
Cozmin Lucau-Danila ◽  
Moira Callens ◽  
Pierre Defourny ◽  
Niko E. C. Verhoest ◽  
Valentijn R. N. Pauwels
Keyword(s):  
Soil Moisture ◽  
Hydrological Model ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Vegetation Parameter ◽  
Sar Data ◽  
Parameter Retrieval

scholarly journals A SMAP-Based Drought Monitoring Index for the United States

2018 ◽  
Author(s):  
Sara Sadri ◽  
Eric F. Wood ◽  
Ming Pan
Keyword(s):  
United States ◽  
Soil Moisture ◽  
Land Surface ◽  
Surface Soil ◽  
Root Zone ◽  
Warm Season ◽  
The United States ◽  
Surface Model ◽  
Surface Soil Moisture ◽  
Near Surface

Abstract. Since April 2015, NASA's Soil Moisture Active Passive (SMAP) mission has monitored near-surface soil moisture, mapping the globe between the latitude bands of 85.044° N/S in 2–3 days depending on location. SMAP Level 3 passive radiometer product (SPL3SMP) measures the amount of water in the top 5 cm of soil except for regions of heavy vegetation (vegetation water content >4.5 kg/m2) and frozen or snow covered locations. SPL3SMP retrievals are spatially and temporally discontinuous, so the 33 months offers a short SMAP record length and poses a statistical challenge for meaningful assessment of its indices. The SMAP SPL4SMAU data product provides global surface and root zone soil moisture at 9-km resolution based on assimilating the SPL3SMP product into the NASA Catchment land surface model. Of particular interest to SMAP-based agricultural applications is a monitoring product that assesses the SMAP near-surface soil moisture in terms of probability percentiles for dry and wet conditions. We describe here SMAP-based indices over the continental United States (CONUS) based on both near-surface and root zone soil moisture percentiles. The percentiles are based on fitting a Beta distribution to the retrieved moisture values. To assess the data adequacy, a statistical comparison is made between fitting the distribution to VIC soil moisture values for the days when SPL3SMP are available, versus fitting to a 1979–2017 VIC data record. For the cold season (November–April), 57 % of grids were deemed to be consistent between the periods, and 68 % in the warm season (May–October), based on a Kolmogorov–Smirnov statistical test. It is assumed that if grids passed the consistency test using VIC data, then the grid had sufficient SMAP data. Our near-surface and root zone drought index on maps are shown to be similar to those produced by the U.S. Drought Monitor (from D0-D4) and GRACE. In a similar manner, we extend the index to include pluvial conditions using indices W0-W4. This study is a step forward towards building a national and international soil moisture monitoring system, without which, quantitative measures of drought and pluvial conditions will remain difficult to judge.

An analytical model for estimation of land surface net water flux from near-surface soil moisture observations

2019 ◽  
Vol 570 ◽  
pp. 26-37 ◽  
Author(s):  
Morteza Sadeghi ◽  
Markus Tuller ◽  
Arthur W. Warrick ◽  
Ebrahim Babaeian ◽  
Kshitij Parajuli ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Analytical Model ◽  
Land Surface ◽  
Surface Soil ◽  
Water Flux ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Net Water Flux
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